1
|
Zhao P, Jiang L, Tang T, Wu Z, Wang D. Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares. Sensors (Basel) 2024; 24:2628. [PMID: 38676244 PMCID: PMC11055072 DOI: 10.3390/s24082628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/01/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024]
Abstract
Combining multiple devices for localization has important applications in the military field. This paper exploits the land-based short-wave platforms and satellites for fusion localization. The ionospheric reflection height error and satellite position errors have a great impact on the short-wave localization and satellite localization accuracy, respectively. In this paper, an iterative constrained weighted least squares (ICWLS) algorithm is proposed for these two kinds of errors. The algorithm converts the nonconvex equation constraints to linear constraints using the results of the previous iteration, thus ensuring convergence to the globally optimal solution. Simulation results show that the localization accuracy of the algorithm can reach the corresponding Constrained Cramér-Rao Lower Bound (CCRLB). Finally, the localization results of the two methods are fused using Kalman filtering. Simulations show that the fused localization accuracy is improved compared to the single-means localization.
Collapse
Affiliation(s)
| | - Linqiang Jiang
- Institute of Information Engineering, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China; (P.Z.); (D.W.)
| | | | | | | |
Collapse
|
2
|
Zhang X, Fu Y, Li J, Wei Y, Li Y, Zheng L. Research on Combined Localization Algorithm Based on Active Screening- Kalman Filtering. Sensors (Basel) 2024; 24:2372. [PMID: 38610581 PMCID: PMC11014096 DOI: 10.3390/s24072372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 04/01/2024] [Accepted: 04/05/2024] [Indexed: 04/14/2024]
Abstract
Real-time acquisition of location information for agricultural robotic systems is a prerequisite for achieving high-precision intelligent navigation. This paper proposes a data filtering and combined positioning method, and establishes an active screening model. The dynamic and static positioning drift points of the carrier are eliminated or replaced, reducing the complexity of the original Global Navigation Satellite System (GNSS) output data in the positioning system. Compared with the traditional Kalman filter combined positioning method, the proposed active filtering-Kalman filter algorithm can reduce the maximum distance deviation of the carrier along a straight line from 0.145 m to 0.055 m and along a curve from 0.184 m to 0.0640 m. This study focuses on agricultural robot positioning technology, which has an important influence on the development of smart agriculture.
Collapse
Affiliation(s)
- Xiao Zhang
- College of Engineering, China Agricultural University, Beijing 100083, China; (X.Z.); (Y.F.); (J.L.); (Y.L.)
| | - Yuting Fu
- College of Engineering, China Agricultural University, Beijing 100083, China; (X.Z.); (Y.F.); (J.L.); (Y.L.)
| | - Jie Li
- College of Engineering, China Agricultural University, Beijing 100083, China; (X.Z.); (Y.F.); (J.L.); (Y.L.)
| | - Yandong Wei
- Industrial Technology Centre, Hebei Petroleum University of Technology, Chengde 067000, China;
| | - Yu Li
- College of Engineering, China Agricultural University, Beijing 100083, China; (X.Z.); (Y.F.); (J.L.); (Y.L.)
| | - Lu Zheng
- College of Engineering, China Agricultural University, Beijing 100083, China; (X.Z.); (Y.F.); (J.L.); (Y.L.)
| |
Collapse
|
3
|
Zhong C, Darbandi M, Nassr M, Latifian A, Hosseinzadeh M, Jafari Navimipour N. A new cloud-based method for composition of healthcare services using deep reinforcement learning and Kalman filtering. Comput Biol Med 2024; 172:108152. [PMID: 38452470 DOI: 10.1016/j.compbiomed.2024.108152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 01/06/2024] [Accepted: 02/12/2024] [Indexed: 03/09/2024]
Abstract
Healthcare has significantly contributed to the well-being of individuals around the globe; nevertheless, further benefits could be derived from a more streamlined healthcare system without incurring additional costs. Recently, the main attributes of cloud computing, such as on-demand service, high scalability, and virtualization, have brought many benefits across many areas, especially in medical services. It is considered an important element in healthcare services, enhancing the performance and efficacy of the services. The current state of the healthcare industry requires the supply of healthcare products and services, increasing its viability for everyone involved. Developing new approaches for discovering and selecting healthcare services in the cloud has become more critical due to the rising popularity of these kinds of services. As a result of the diverse array of healthcare services, service composition enables the execution of intricate operations by integrating multiple services' functionalities into a single procedure. However, many methods in this field encounter several issues, such as high energy consumption, cost, and response time. This article introduces a novel layered method for selecting and evaluating healthcare services to find optimal service selection and composition solutions based on Deep Reinforcement Learning (Deep RL), Kalman filtering, and repeated training, addressing the aforementioned issues. The results revealed that the proposed method has achieved acceptable results in terms of availability, reliability, energy consumption, and response time when compared to other methods.
Collapse
Affiliation(s)
- Chongzhou Zhong
- School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | | | - Mohammad Nassr
- Communication Technology Engineering Department, Tartous University, Syria; Department of Mathematics and Natural Sciences, Gulf University for Science and Technology, Mishref Campus, Kuwait.
| | - Ahmad Latifian
- Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Iran.
| | - Mehdi Hosseinzadeh
- Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam; School of Medicine and Pharmacy, Duy Tan University, Da Nang, Viet Nam.
| | - Nima Jafari Navimipour
- Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Kadir Has University, Istanbul, Turkey; Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin, 64002, Taiwan.
| |
Collapse
|
4
|
Santos CAG, do Nascimento GR, Freitas LMT, Batista LV, Zerouali B, Mishra M, Silva RMD. Coastal evolution and future projections in Conde County, Brazil: A multi-decadal assessment via remote sensing and sea-level rise scenarios. Sci Total Environ 2024; 915:169829. [PMID: 38211851 DOI: 10.1016/j.scitotenv.2023.169829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 12/20/2023] [Accepted: 12/30/2023] [Indexed: 01/13/2024]
Abstract
Global sea levels, having risen by approximately 20 cm since the mid-19th century, necessitate a critical examination of their impacts on shoreline dynamics. This research evaluates the historical (1985-2022) and future shoreline changes in Conde County, Paraíba State, Brazil, an area of significant touristic interest. Employing Landsat satellite imagery, the study utilized the Digital Shoreline Analysis System (DSAS) and a Kalman filter algorithm for cloud removal, while also assessing land use and land cover changes using data from the MapBiomas Project for 2000, 2010, and 2020. These analyses informed projections of potential inundation under various sea-level rise (SLR) scenarios: 1, 2, 5, and 10 m. Key findings revealed a negative average coastline change rate of -0.27 m/year from 1985 to 2022, indicative of erosive trends likely accelerated by human activities. Long-term projections for 2032 and 2042 anticipate continued erosion in areas identified as highly vulnerable. The SLR scenario analysis underscores the urgent need for adaptive climate measures; while a 1- or 2-meter SLR presents limited immediate effects, a 5-meter rise could lead to significant inundation across key sectors, including urban and agricultural landscapes. The projected severity of a 10-meter SLR necessitates immediate, comprehensive interventions to safeguard both natural and human systems.
Collapse
Affiliation(s)
- Celso Augusto Guimarães Santos
- Department of Civil and Environmental Engineering, Federal University of Paraíba, João Pessoa 58051-900, Paraíba, Brazil.
| | | | | | - Leonardo Vidal Batista
- Department of Computer Systems, Federal University of Paraíba, João Pessoa 58051-900, Paraíba, Brazil
| | - Bilel Zerouali
- Vegetal Chemistry-Water-Energy Laboratory, Department of Hydraulic, Faculty of Civil Engineering and Architecture, Hassiba Benbouali, University of Chlef, B.P. 78C, Ouled Fares 02180, Chlef, Algeria
| | - Manoranjan Mishra
- Department of Geography, Fakir Mohan University, Vyasa Vihar, Nuapadhi, Balasore 756089, Odisha, India
| | | |
Collapse
|
5
|
Bourquin C, Porée J, Rauby B, Perrot V, Ghigo N, Belgharbi H, Bélanger S, Ramos-Palacios G, Cortes N, Ladret H, Ikan L, Casanova C, Lesage F, Provost J. Quantitative pulsatility measurements using 3D dynamic ultrasound localization microscopy. Phys Med Biol 2024; 69:045017. [PMID: 38181421 DOI: 10.1088/1361-6560/ad1b68] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 01/05/2024] [Indexed: 01/07/2024]
Abstract
A rise in blood flow velocity variations (i.e. pulsatility) in the brain, caused by the stiffening of upstream arteries, is associated with cognitive impairment and neurodegenerative diseases. The study of this phenomenon requires brain-wide pulsatility measurements, with large penetration depth and high spatiotemporal resolution. The development of dynamic ultrasound localization microscopy (DULM), based on ULM, has enabled pulsatility measurements in the rodent brain in 2D. However, 2D imaging accesses only one slice of the brain and measures only 2D-projected and hence biased velocities . Herein, we present 3D DULM: using a single ultrasound scanner at high frame rate (1000-2000 Hz), this method can produce dynamic maps of microbubbles flowing in the bloodstream and extract quantitative pulsatility measurements in the cat brain with craniotomy and in the mouse brain through the skull, showing a wide range of flow hemodynamics in both large and small vessels. We highlighted a decrease in pulsatility along the vascular tree in the cat brain, which could be mapped with ultrasound down to a few tens of micrometers for the first time. We also performed an intra-animal validation of the method by showing consistent measurements between the two sides of the Willis circle in the mouse brain. Our study provides the first step towards a new biomarker that would allow the detection of dynamic abnormalities in microvessels in the brain, which could be linked to early signs of neurodegenerative diseases.
Collapse
Affiliation(s)
- Chloé Bourquin
- Department of Engineering Physics, Polytechnique Montréal, Montréal, QC H3T 1J4, Canada
| | - Jonathan Porée
- Department of Engineering Physics, Polytechnique Montréal, Montréal, QC H3T 1J4, Canada
| | - Brice Rauby
- Department of Engineering Physics, Polytechnique Montréal, Montréal, QC H3T 1J4, Canada
| | - Vincent Perrot
- Department of Engineering Physics, Polytechnique Montréal, Montréal, QC H3T 1J4, Canada
| | - Nin Ghigo
- Department of Engineering Physics, Polytechnique Montréal, Montréal, QC H3T 1J4, Canada
| | - Hatim Belgharbi
- Department of Engineering Physics, Polytechnique Montréal, Montréal, QC H3T 1J4, Canada
- Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC 27599, United States of America
| | | | | | - Nelson Cortes
- School of Optometry, University of Montreal, Montréal, QC H3T 1P1, Canada
| | - Hugo Ladret
- School of Optometry, University of Montreal, Montréal, QC H3T 1P1, Canada
- Institut de Neurosciences de la Timone, UMR 7289, CNRS and Aix-Marseille Université, Marseille, F-13005, France
| | - Lamyae Ikan
- School of Optometry, University of Montreal, Montréal, QC H3T 1P1, Canada
| | - Christian Casanova
- School of Optometry, University of Montreal, Montréal, QC H3T 1P1, Canada
| | - Frédéric Lesage
- Department of Electrical Engineering, Polytechnique Montréal, Montréal, QC H3T 1J4, Canada
- Montreal Heart Institute, Montréal, QC H1T 1C8, Canada
| | - Jean Provost
- Department of Engineering Physics, Polytechnique Montréal, Montréal, QC H3T 1J4, Canada
- Montreal Heart Institute, Montréal, QC H1T 1C8, Canada
| |
Collapse
|
6
|
Rhudy MB, Mahoney JM, Altman-Singles AR. Knee Angle Estimation with Dynamic Calibration Using Inertial Measurement Units for Running. Sensors (Basel) 2024; 24:695. [PMID: 38276387 PMCID: PMC10819858 DOI: 10.3390/s24020695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/17/2024] [Accepted: 01/19/2024] [Indexed: 01/27/2024]
Abstract
The knee flexion angle is an important measurement for studies of the human gait. Running is a common activity with a high risk of knee injury. Studying the running gait in realistic situations is challenging because accurate joint angle measurements typically come from optical motion-capture systems constrained to laboratory settings. This study considers the use of shank and thigh inertial sensors within three different filtering algorithms to estimate the knee flexion angle for running without requiring sensor-to-segment mounting assumptions, body measurements, specific calibration poses, or magnetometers. The objective of this study is to determine the knee flexion angle within running applications using accelerometer and gyroscope information only. Data were collected for a single test participant (21-year-old female) at four different treadmill speeds and used to validate the estimation results for three filter variations with respect to a Vicon optical motion-capture system. The knee flexion angle filtering algorithms resulted in root-mean-square errors of approximately three degrees. The results of this study indicate estimation results that are within acceptable limits of five degrees for clinical gait analysis. Specifically, a complementary filter approach is effective for knee flexion angle estimation in running applications.
Collapse
Affiliation(s)
- Matthew B. Rhudy
- Mechanical Engineering, The Pennsylvania State University, Berks College, Reading, PA 19610, USA
| | - Joseph M. Mahoney
- Mechanical Engineering, Alvernia University, Reading, PA 19607, USA;
| | - Allison R. Altman-Singles
- Mechanical Engineering, The Pennsylvania State University, Berks College, Reading, PA 19610, USA
- Kinesiology, The Pennsylvania State University, Berks College, Reading, PA 19610, USA;
| |
Collapse
|
7
|
Marin R, Runvik H, Medvedev A, Engblom S. Bayesian monitoring of COVID-19 in Sweden. Epidemics 2023; 45:100715. [PMID: 37703786 DOI: 10.1016/j.epidem.2023.100715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/28/2023] [Accepted: 08/16/2023] [Indexed: 09/15/2023] Open
Abstract
In an effort to provide regional decision support for the public healthcare, we design a data-driven compartment-based model of COVID-19 in Sweden. From national hospital statistics we derive parameter priors, and we develop linear filtering techniques to drive the simulations given data in the form of daily healthcare demands. We additionally propose a posterior marginal estimator which provides for an improved temporal resolution of the reproduction number estimate as well as supports robustness checks via a parametric bootstrap procedure. From our computational approach we obtain a Bayesian model of predictive value which provides important insight into the progression of the disease, including estimates of the effective reproduction number, the infection fatality rate, and the regional-level immunity. We successfully validate our posterior model against several different sources, including outputs from extensive screening programs. Since our required data in comparison is easy and non-sensitive to collect, we argue that our approach is particularly promising as a tool to support monitoring and decisions within public health. Significance: Using public data from Swedish patient registries we develop a national-scale computational model of COVID-19. The parametrized model produces valuable weekly predictions of healthcare demands at the regional level and validates well against several different sources. We also obtain critical epidemiological insights into the disease progression, including, e.g., reproduction number, immunity and disease fatality estimates. The success of the model hinges on our novel use of filtering techniques which allows us to design an accurate data-driven procedure using data exclusively from healthcare demands, i.e., our approach does not rely on public testing and is therefore very cost-effective.
Collapse
Affiliation(s)
- Robin Marin
- Division of Scientific Computing, Department of Information Technology, Uppsala University, SE-751 05, Uppsala, Sweden.
| | - Håkan Runvik
- Division of Systems and Control, Department of Information Technology, Uppsala University, SE-751 05, Uppsala, Sweden.
| | - Alexander Medvedev
- Division of Systems and Control, Department of Information Technology, Uppsala University, SE-751 05, Uppsala, Sweden.
| | - Stefan Engblom
- Division of Scientific Computing, Department of Information Technology, Uppsala University, SE-751 05, Uppsala, Sweden.
| |
Collapse
|
8
|
Ni Y, Liu X, Yang C. Sensor Scheduling for Remote State Estimation with Limited Communication Resources: A Time- and Event-Triggered Hybrid Approach. Sensors (Basel) 2023; 23:8667. [PMID: 37960367 PMCID: PMC10647928 DOI: 10.3390/s23218667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 10/21/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023]
Abstract
This paper proposes a time- and event-triggered hybrid scheduling for remote state estimation with limited communication resources. A smart sensor observes a physical process and decides whether to send the local state estimate to a remote estimator via a wireless communication channel; the estimator computes the state estimate of the process according to the received data packets and the known scheduling mechanism. Based on the existing optimal time-triggered scheduling, we employ a stochastic event trigger to save precious communication chances and further improve the estimation performance. The minimum mean-squared error (MMSE) state estimate is derived since the Gaussian property is preserved. The estimation performance upper bound and communication rate are analyzed. The main results are illustrated by numerical examples.
Collapse
Affiliation(s)
- Yuqing Ni
- The Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
| | - Xiaochen Liu
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA;
| | - Chao Yang
- The Key Laboratory of Smart Manufacturing in Energy Chemical Process (Ministry of Education), Department of Automation, East China University of Science and Technology, Shanghai 200237, China
| |
Collapse
|
9
|
Jia L, Wang Y, Ma L, He Z, Li Z, Cui Y. Integrated Positioning System of Kiwifruit Orchard Mobile Robot Based on UWB/LiDAR/ODOM. Sensors (Basel) 2023; 23:7570. [PMID: 37688027 PMCID: PMC10490773 DOI: 10.3390/s23177570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/24/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023]
Abstract
To address the issue of low positioning accuracy of mobile robots in trellis kiwifruit orchards with weak signal environments, this study investigated an outdoor integrated positioning method based on ultra-wideband (UWB), light detection and ranging (LiDAR), and odometry (ODOM). Firstly, a dynamic error correction strategy using the Kalman filter (KF) was proposed to enhance the dynamic positioning accuracy of UWB. Secondly, the particle filter algorithm (PF) was employed to fuse UWB/ODOM/LiDAR measurements, resulting in an extended Kalman filter (EKF) measurement value. Meanwhile, the odometry value served as the predicted value in the EKF. Finally, the predicted and measured values were fused through the EKF to estimate the robot's pose. Simulation results demonstrated that the UWB/ODOM/LiDAR integrated positioning method achieved a mean lateral error of 0.076 m and a root mean square error (RMSE) of 0.098 m. Field tests revealed that compared to standalone UWB positioning, UWB-based KF positioning, and LiDAR/ODOM integrated positioning methods, the proposed approach improved the positioning accuracy by 64.8%, 13.8%, and 38.3%, respectively. Therefore, the proposed integrated positioning method exhibits promising positioning performance in trellis kiwifruit orchards with potential applicability to other orchard environments.
Collapse
Affiliation(s)
- Liangsheng Jia
- College of Mechanical and Electrical Engineering, Northwest A&F University, Xianyang 712100, China
| | - Yinchu Wang
- College of Mechanical and Electrical Engineering, Northwest A&F University, Xianyang 712100, China
| | - Li Ma
- College of Mechanical and Electrical Engineering, Northwest A&F University, Xianyang 712100, China
| | - Zhi He
- College of Mechanical and Electrical Engineering, Northwest A&F University, Xianyang 712100, China
| | - Zixu Li
- College of Mechanical and Electrical Engineering, Northwest A&F University, Xianyang 712100, China
| | - Yongjie Cui
- College of Mechanical and Electrical Engineering, Northwest A&F University, Xianyang 712100, China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Xianyang 712100, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Xianyang 712100, China
| |
Collapse
|
10
|
Kim DW, Mayer C, Lee MP, Choi SW, Tewari M, Forger DB. Efficient assessment of real-world dynamics of circadian rhythms in heart rate and body temperature from wearable data. J R Soc Interface 2023; 20:20230030. [PMID: 37608712 PMCID: PMC10445022 DOI: 10.1098/rsif.2023.0030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 07/31/2023] [Indexed: 08/24/2023] Open
Abstract
Laboratory studies have made unprecedented progress in understanding circadian physiology. Quantifying circadian rhythms outside of laboratory settings is necessary to translate these findings into real-world clinical practice. Wearables have been considered promising way to measure these rhythms. However, their limited validation remains an open problem. One major barrier to implementing large-scale validation studies is the lack of reliable and efficient methods for circadian assessment from wearable data. Here, we propose an approximation-based least-squares method to extract underlying circadian rhythms from wearable measurements. Its computational cost is ∼ 300-fold lower than that of previous work, enabling its implementation in smartphones with low computing power. We test it on two large-scale real-world wearable datasets: [Formula: see text] of body temperature data from cancer patients and ∼ 184 000 days of heart rate and activity data collected from the 'Social Rhythms' mobile application. This shows successful extraction of real-world dynamics of circadian rhythms. We also identify a reasonable harmonic model to analyse wearable data. Lastly, we show our method has broad applicability in circadian studies by embedding it into a Kalman filter that infers the state space of the molecular clocks in tissues. Our approach facilitates the translation of scientific advances in circadian fields into actual improvements in health.
Collapse
Affiliation(s)
- Dae Wook Kim
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Caleb Mayer
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Minki P. Lee
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sung Won Choi
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA
- Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA
| | - Muneesh Tewari
- Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA
- Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Daniel B. Forger
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| |
Collapse
|
11
|
Tseng CH, Chen JH, Hsu SM. The Effect of the Peristimulus α Phase on Visual Perception through Real-Time Phase-Locked Stimulus Presentation. eNeuro 2023; 10:ENEURO.0128-23.2023. [PMID: 37507226 PMCID: PMC10436686 DOI: 10.1523/eneuro.0128-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 06/17/2023] [Accepted: 07/23/2023] [Indexed: 07/30/2023] Open
Abstract
The α phase has been theorized to reflect fluctuations in cortical excitability and thereby impose a cyclic influence on visual perception. Despite its appeal, this notion is not fully substantiated, as both supporting and opposing evidence has been recently reported. In contrast to previous research, this study examined the effect of the peristimulus instead of prestimulus phase on visual detection through a real-time phase-locked stimulus presentation (PLSP) approach. Specifically, we monitored phase data from magnetoencephalography (MEG) recordings over time, with a newly developed algorithm based on adaptive Kalman filtering (AKF). This information guided online presentations of masked stimuli that were phased-locked to different stages of the α cycle while healthy humans concurrently performed detection tasks. Behavioral evidence showed that the overall detection rate did not significantly vary according to the four predetermined peristimulus α phases. Nevertheless, the follow-up analyses highlighted that the phase at 90° relative to 180° likely enhanced detection. Corroborating neural parietal activity showed that early interaction between α phases and incoming stimuli orchestrated the neural representation of the hits and misses of the stimuli. This neural representation varied according to the phase and in turn shaped the behavioral outcomes. In addition to directly investigating to what extent fluctuations in perception can be ascribed to the α phases, this study suggests that phase-dependent perception is not as robust as previously presumed, and might also depend on how the stimuli are differentially processed as a result of a stimulus-phase interaction, in addition to reflecting alternations of the perceptual states between phases.
Collapse
Affiliation(s)
- Chih-Hsin Tseng
- Graduate Institute of Biomedical Electronic and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan (Republic of China)
| | - Jyh-Horng Chen
- Graduate Institute of Biomedical Electronic and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan (Republic of China)
| | - Shen-Mou Hsu
- Imaging Center for Integrated Body, Mind and Culture Research, National Taiwan University, Taipei 10617, Taiwan (Republic of China)
- MOST AI Biomedical Research Center, Tainan City 701, Taiwan (Republic of China)
| |
Collapse
|
12
|
Yang L, Tao J, Liu YH, Xu Y, Su CY. Energy scheduling for DoS attack over multi-hop networks: Deep reinforcement learning approach. Neural Netw 2023; 161:735-745. [PMID: 36848827 DOI: 10.1016/j.neunet.2023.02.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 12/01/2022] [Accepted: 02/15/2023] [Indexed: 02/19/2023]
Abstract
This paper studies the energy scheduling for Denial-of-Service (DoS) attack against remote state estimation over multi-hop networks. A smart sensor observes a dynamic system, and transmits its local state estimate to a remote estimator. Due to the limited communication range of the sensor, some relay nodes are employed to deliver data packets from the sensor to the remote estimator, which constitutes a multi-hop network. To maximize the estimation error covariance with energy constraint, a DoS attacker needs to determine the energy level implemented on each channel. This problem is formulated as an associated Markov decision process (MDP), and the existence of an optimal deterministic and stationary policy (DSP) is proved for the attacker. Besides, a simple threshold structure of the optimal policy is obtained, which significantly reduces the computational complexity. Furthermore, an up-to-date deep reinforcement learning (DRL) algorithm, dueling double Q-network (D3QN), is introduced to approximate the optimal policy. Finally, a simulation example illustrates the developed results and verifies the effectiveness of D3QN for optimal DoS attack energy scheduling.
Collapse
Affiliation(s)
- Lixin Yang
- Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
| | - Jie Tao
- Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
| | - Yong-Hua Liu
- Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
| | - Yong Xu
- Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
| | - Chun-Yi Su
- Department of Mechanical and Industrial Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.
| |
Collapse
|
13
|
Krayden A, Shlenkevitch D, Blank T, Stolyarova S, Nemirovsky Y. Selective Sensing of Mixtures of Gases with CMOS-SOI-MEMS Sensor Dubbed GMOS. Micromachines (Basel) 2023; 14:mi14020390. [PMID: 36838090 PMCID: PMC9962487 DOI: 10.3390/mi14020390] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/23/2023] [Accepted: 02/02/2023] [Indexed: 06/01/2023]
Abstract
The need to achieve digital gas sensing technology, namely, a technology to sense and transmit gas-enabled digital media, has been recognized as highly challenging. This challenge has motivated the authors to focus on complementary metal oxide semiconductor silicon on insulator micro electro-mechanical system (CMOS-SOI-MEMS) technologies, and the result is a new pellistor-like sensor, dubbed GMOS, with integrated signal processing. In this study, we describe the performance of such sensors for the selective detection of mixtures of gases. The novel key ideas of this study are: (i) the use of the GMOS for gas sensing; (ii) applying the Kalman filter to improve the signal-to-noise ratio; (iii) adding artificial intelligence (AI) with tiny edge approach.
Collapse
Affiliation(s)
- Adir Krayden
- Electrical and Computer Engineering Department, Technion—Israel Institute of Technology, Haifa 3200003, Israel
| | - Dima Shlenkevitch
- Todos Technologies, Kinneret 12 Street, Airport City 7019900, Israel
| | - Tanya Blank
- Electrical and Computer Engineering Department, Technion—Israel Institute of Technology, Haifa 3200003, Israel
| | - Sara Stolyarova
- Electrical and Computer Engineering Department, Technion—Israel Institute of Technology, Haifa 3200003, Israel
| | - Yael Nemirovsky
- Electrical and Computer Engineering Department, Technion—Israel Institute of Technology, Haifa 3200003, Israel
| |
Collapse
|
14
|
He Q, Hu T, Zhong Y, Li W, Sun R. Development and Practice of Sports-Related Public Welfare Platform Based on Multi-Sensor Technology. Sensors (Basel) 2023; 23:713. [PMID: 36679512 PMCID: PMC9865330 DOI: 10.3390/s23020713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/29/2022] [Accepted: 01/05/2023] [Indexed: 06/17/2023]
Abstract
Today, more and more Internet public media platforms allowing people to make donations or seek help are being founded in China. However, there are few specialized sports-related public welfare platforms. In this paper, a sports-related public welfare platform that aims to help people who were disabled due to participation in sports and those who are disabled but want to participate in sports was developed based on multi-sensor technology. A multi-sensor data fusion algorithm was developed, and its estimation performance was verified by comparing it with the existing Kalman consistent filtering algorithm in terms of average estimation and average consistency errors. Experimental results prove that the speed of the data collection and analysis of the sports-related public welfare platform using the algorithm established in this paper was greatly improved. Relevant data on how users used this platform showed that various factors affected users' practical satisfaction with sports-related public welfare media platforms. It is suggested that a sports-related public welfare media platform should pay attention to the aid effect, and specific efforts should be devoted to improving the reliability and timeliness of public welfare aid information, and ensuring the stability of the platform system.
Collapse
Affiliation(s)
- Quantao He
- Sport School, Shenzhen University, Shenzhen 518000, China
| | - Tongchang Hu
- Football School, Wuhan Sports University, Wuhan 430079, China
| | - Yong Zhong
- School of Sports Leisure, Beijing Normal University at Zhuhai, Zhuhai 519000, China
| | - Wenjuan Li
- Sport School, Shenzhen University, Shenzhen 518000, China
| | - Ren Sun
- Faculty of Physical Education, Beijing Institute of Technology at Zhuhai, Zhuhai 519000, China
| |
Collapse
|
15
|
Feng L, Du L, Guo J, Cui J, Lu J, Zhu Z, Wang L. A Bias Drift Suppression Method Based on ICELMD and ARMA-KF for MEMS Gyros. Micromachines (Basel) 2022; 14:109. [PMID: 36677170 PMCID: PMC9863515 DOI: 10.3390/mi14010109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 12/23/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
The applications of Micro-Electro-Mechanical-System (MEMS) gyros in inertial navigation system is gradually increasing. However, the random drift of gyro deteriorates the system performance which restricting the applications of high precision. We propose a bias drift compensation model based on two-fold Interpolated Complementary Ensemble Local Mean Decomposition (ICELMD) and autoregressive moving average-Kalman filtering (ARMA-KF). We modify CELMD into ICELMD, which is less complicated and overcomes the endpoint effect. Further, the ICELMD is combined with ARMA-KF to separate and simplify the preprocessed signal, resulting improved denoising performance. In the model, the abnormal noise is removed in preprocess by 2σ criterion with ICELMD. Then, continuous mean square error (CMSE) and Permutation Entropy (PE) are both applied to categorize the preprocessed signal into noise, mixed and useful components. After abandon the noise components and denoise the mixed components by ARMA-KF, we rebuild the noise suppression signal of MEMS gyro. Experiments are carried out to validate the proposed algorithm. The angle random walk of gyro decreases from 2.4156∘/h to 0.0487∘/h, the zero bias instability lowered from 0.3753∘/h to 0.0509∘/h. Further, the standard deviation and the variance are greatly reduced, indicating that the proposed method has better suppression effect, stability and adaptability.
Collapse
Affiliation(s)
- Lihui Feng
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonics Information Technology, Ministry of Industry and Information, Beijing 100081, China
| | - Le Du
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonics Information Technology, Ministry of Industry and Information, Beijing 100081, China
| | - Junqiang Guo
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonics Information Technology, Ministry of Industry and Information, Beijing 100081, China
| | - Jianmin Cui
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonics Information Technology, Ministry of Industry and Information, Beijing 100081, China
| | - Jihua Lu
- School of Integrated Circuits and Electronics, Beijing Institution of Technology, Beijing 100081, China
- Science and Technology on Communication Networks Laboratory, Shijiazhuang 050050, China
| | - Zhengqiang Zhu
- Beijing Institute of Aerospace Control Devices, Beijing 100039, China
| | - Lijuan Wang
- Beijing Institute of Aerospace Control Devices, Beijing 100039, China
| |
Collapse
|
16
|
Amitrano D, Cicala L, Cuciniello G, De Mizio M, Poderico M, Tufano F. Near Real-Time Volumetric Estimates Using Unmanned Aerial Platforms Equipped with Depth and Tracking Sensors. Sensors (Basel) 2022; 22:9462. [PMID: 36502163 PMCID: PMC9737846 DOI: 10.3390/s22239462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/29/2022] [Accepted: 12/01/2022] [Indexed: 06/17/2023]
Abstract
Volume estimation of specific objects via close-range remote sensing is a complex task requiring expensive hardware and/or significant computational burden, often discouraging users potentially interested in the technology. This paper presents an innovative system for cost-effective near real-time volume estimation based on a custom platform equipped with depth and tracking cameras. Its performance has been tested in different application-oriented scenarios and compared against measurements and state-of-the-art photogrammetry. The comparison showed that the developed architecture is able to provide estimates fully comparable with the benchmark, resulting in a quick, reliable and cost-effective solution to the problem of volumetric estimates within the functioning range of the exploited sensors.
Collapse
|
17
|
An Q, Cai Y, Zhu J, Wang S, Han F. Multi-Target Tracking Algorithm Combined with High-Precision Map. Sensors (Basel) 2022; 22:9371. [PMID: 36502085 PMCID: PMC9737284 DOI: 10.3390/s22239371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/24/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
On high-speed roads, there are certain blind areas within the radar coverage, especially when the blind zone is in curved road sections; because the radar does not have the measurement point information in multiple frames, it is easy to have a large deviation between the real trajectory and the filtered trajectory. In this paper, we propose a track prediction method combined with a high-precision map to solve the problem of scattered tracks when the targets are in the blind area. First, the lane centerline is fitted to the upstream and downstream lane edges obtained from the high-precision map in certain steps, and the off-north angle at each fitted point is obtained. Secondly, the normal trajectory is predicted according to the conventional method; for the extrapolated trajectory, the northerly angle of the lane centerline at the current position of the trajectory is obtained, the current coordinate system is converted from the north-east-up (ENU) coordinate system to the vehicle coordinate system, and the lateral velocity of the target is set to zero in the vehicle coordinate system to reduce the error caused by the lateral velocity drag of the target. Finally, the normal trajectory is updated and corrected, and the normal and extrapolated trajectories are managed and reported. The experimental results show that the accuracy and convergence effect of the proposed methods are much better than the traditional methods.
Collapse
Affiliation(s)
- Qingru An
- Beijing Rxbit Electronic Technology Co., Ltd., Beijing 100081, China
| | - Yawen Cai
- School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Juan Zhu
- School of Physics & Electronic Engineering, Hubei University of Arts and Science, Xiangyang 441053, China
| | - Sijia Wang
- The Trade Desk Inc., 42 N Chestnut St., Ventura, CA 93001, USA
| | - Fengxia Han
- School of Software Engineering, Tongji University, Shanghai 201804, China
| |
Collapse
|
18
|
Zhang W, Yang Y, Zhao J, Huang R, Cheng K, He M. Research on a Non-Contact Multi-Electrode Voltage Sensor and Signal Processing Algorithm. Sensors (Basel) 2022; 22:8573. [PMID: 36366268 PMCID: PMC9659014 DOI: 10.3390/s22218573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 10/24/2022] [Accepted: 10/30/2022] [Indexed: 06/16/2023]
Abstract
Traditional contact voltage measurement requires a direct electrical connection to the system, which is not easy to install and maintain. The voltage measurement based on the electric field coupling plate capacitance structure does not need to be in contact with the measured object or the ground, which can avoid the above problems. However, most of the existing flat-plate structure voltage measurement sensors are not only expensive to manufacture, but also bulky, and when the relative position between the wire under test and the sensor changes, it will bring great measurement errors, making it difficult to meet actual needs. Aiming to address the above problems, this paper proposes a multi-electrode array structure non-contact voltage sensor and signal processing algorithm. The sensor is manufactured by the PCB process, which effectively reduces the manufacturing cost and process difficulty. The experimental and simulation results show that, when the relative position of the wire and the sensor is offset by 10 mm in the 45° direction, the relative error of the traditional single-electrode voltage sensor is 17.62%, while the relative error of the multi-electrode voltage sensor designed in this paper is only 0.38%. In addition, the ratio error of the sensor under the condition of power frequency of 50 Hz is less than ±1% and the phase difference is less than 4°. The experimental results show that the sensor has good accuracy and linearity.
Collapse
Affiliation(s)
- Wenbin Zhang
- College of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650504, China
| | - Yonglong Yang
- College of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650504, China
| | - Jingjing Zhao
- College of Science, Kunming University of Science and Technology, Kunming 650504, China
| | - Rujin Huang
- College of Science, Kunming University of Science and Technology, Kunming 650504, China
| | - Kang Cheng
- College of Science, Kunming University of Science and Technology, Kunming 650504, China
| | - Mingxing He
- College of Science, Kunming University of Science and Technology, Kunming 650504, China
| |
Collapse
|
19
|
Zhao Y, Boley M, Pelentritou A, Karoly PJ, Freestone DR, Liu Y, Muthukumaraswamy S, Woods W, Liley D, Kuhlmann L. Space-time resolved inference-based neurophysiological process imaging: application to resting-state alpha rhythm. Neuroimage 2022; 263:119592. [PMID: 36031185 DOI: 10.1016/j.neuroimage.2022.119592] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 07/28/2022] [Accepted: 08/24/2022] [Indexed: 11/28/2022] Open
Abstract
Neural processes are complex and difficult to image. This paper presents a new space-time resolved brain imaging framework, called Neurophysiological Process Imaging (NPI), that identifies neurophysiological processes within cerebral cortex at the macroscopic scale. By fitting uncoupled neural mass models to each electromagnetic source time-series using a novel nonlinear inference method, population averaged membrane potentials and synaptic connection strengths are efficiently and accurately inferred and imaged across the whole cerebral cortex at a resolution afforded by source imaging. The efficiency of the framework enables return of the augmented source imaging results overnight using high performance computing. This suggests it can be used as a practical and novel imaging tool. To demonstrate the framework, it has been applied to resting-state magnetoencephalographic source estimates. The results suggest that endogenous inputs to cingulate, occipital, and inferior frontal cortex are essential modulators of resting-state alpha power. Moreover, endogenous input and inhibitory and excitatory neural populations play varied roles in mediating alpha power in different resting-state sub-networks. The framework can be applied to arbitrary neural mass models and has broad applicability to image neural processes in different brain states.
Collapse
Affiliation(s)
- Yun Zhao
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Mario Boley
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Andria Pelentritou
- Swinburne University of Technology, Hawthorn, Australia; Laboratoire de Recherche en Neuroimagerie (LREN), University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Philippa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia
| | - Dean R Freestone
- Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia; Seer Medical Pty Ltd, Melbourne, Australia
| | - Yueyang Liu
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | | | - William Woods
- School of Health Sciences, Swinburne University of Technology, Hawthorn, Australia
| | - David Liley
- Swinburne University of Technology, Hawthorn, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia; School of Health Sciences, Swinburne University of Technology, Hawthorn, Australia
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia.
| |
Collapse
|
20
|
Han J, Liu J, Chang H. The Key Technologies of Road Elevation Detection Based on Sensor Fusion. Sensors (Basel) 2022; 22:5756. [PMID: 35957312 PMCID: PMC9370868 DOI: 10.3390/s22155756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/28/2022] [Accepted: 07/28/2022] [Indexed: 06/15/2023]
Abstract
The detection of long-distance pavement elevation undulation is the main data basis for pavement slope detection and flatness detection, and is also the data source for 3D modeling and quality evaluation of pavement surfaces. The traditional detection method is to use a level and manual coordination to measure; however, the detection accuracy is low and the detection speed is slow. In this paper, the high-speed non-contact vehicle-mounted road undulationelevation detection method is adopted, combined with the advantages of each sensor measurement; three methods are proposed to detect the road undulation elevation: rotary encoders, accelerometers, attitude sensor data fusion detection; GPS RTK detection; and Kalman filtering detection. Through modeling and experimental comparison, Kalman filter detection is not disturbed by the environment, and the detection accuracy is higher than the current international standard.
Collapse
Affiliation(s)
- Jin Han
- Correspondence: ; Tel.: +86-138-9295-1675
| | | | | |
Collapse
|
21
|
Hunter MD, Fatimah H, Bornovalova MA. Two Filtering Methods of Forecasting Linear and Nonlinear Dynamics of Intensive Longitudinal Data. Psychometrika 2022; 87:477-505. [PMID: 35064891 PMCID: PMC9177592 DOI: 10.1007/s11336-021-09827-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 10/22/2021] [Indexed: 06/14/2023]
Abstract
With the advent of new data collection technologies, intensive longitudinal data (ILD) are collected more frequently than ever. Along with the increased prevalence of ILD, more methods are being developed to analyze these data. However, relatively few methods have yet been applied for making long- or even short-term predictions from ILD in behavioral settings. Applications of forecasting methods to behavioral ILD are still scant. We first establish a general framework for modeling ILD and then extend that frame to two previously existing forecasting methods: these methods are Kalman prediction and ensemble prediction. After implementing Kalman and ensemble forecasts in free and open-source software, we apply these methods to daily drug and alcohol use data. In doing so, we create a simple, but nonlinear dynamical system model of daily drug and alcohol use and illustrate important differences between the forecasting methods. We further compare the Kalman and ensemble forecasting methods to several simpler forecasts of daily drug and alcohol use. Ensemble forecasts may be more appropriate than Kalman forecasts for nonlinear dynamical systems models, but further forecasting evaluation methods must be put into practice.
Collapse
Affiliation(s)
- Michael D Hunter
- Department of Human Development and Family Studies, Pennsylvania State University, 119 Health and Human Development Building, University Park, PA, 16802, USA
| | - Haya Fatimah
- Department of Psychology, University of South Florida, Tampa, FL, 33620, USA
| | | |
Collapse
|
22
|
Wang L, Li H, Lu X, Li X, Zhang J, Wang X, Chen C. Design of Intelligent Monitoring System in Galloping Power Transmission Line. Sensors (Basel) 2022; 22:4197. [PMID: 35684822 DOI: 10.3390/s22114197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/28/2022] [Accepted: 05/29/2022] [Indexed: 02/01/2023]
Abstract
To prevent the frequent occurrence of transmission line galloping accidents, many scholars have carried out studies. However, there are still many difficulties that have not been solved. To address the issues that have arisen during the installation of the monitoring system, a new installation technique for the galloping monitoring terminal structure has been developed, and structural design and transmission line impact have been taken into account. A method combining Kalman and Mahony complementary filtering has been shown to solve the problem of wire twisting when galloping is taken into account. The displacement is derived by double-integrating the acceleration, although the trend term has a significant impact on the integration result. To handle the trend term issue and other error effects, a method combining the least-squares method, the adaptive smoothing method, and the time-frequency domain hybrid integration approach is used. Finally, the monitoring terminal’s structural design is simulated and evaluated, and the measured amplitude is assessed on a galloping standard test bench. The difference between the measured amplitude and the laboratory standard value is less than 10%, meeting the engineering design criteria. And the galloping trajectory is identical to the test bench trajectory, which is critical for user end monitoring.
Collapse
|
23
|
Song L, Zhou L, Xu P, Zhao W, Li S, Li Z. Research on the Error of Global Positioning System Based on Time Series Analysis. Sensors (Basel) 2022; 22:3614. [PMID: 35632023 DOI: 10.3390/s22103614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/09/2022] [Accepted: 04/15/2022] [Indexed: 02/04/2023]
Abstract
Due to the poor dynamic positioning precision of the Global Positioning System (GPS), Time Series Analysis (TSA) and Kalman filter technology are used to construct the positioning error of GPS. According to the statistical characteristics of the autocorrelation function and partial autocorrelation function of sample data, the Autoregressive (AR) model which is based on a Kalman filter is determined, and the error model of GPS is combined with a Kalman filter to eliminate the random error in GPS dynamic positioning data. The least square method is used for model parameter estimation and adaptability tests, and the experimental results show that the absolute value of the maximum error of longitude and latitude, the mean square error of longitude and latitude and average absolute error of longitude and latitude are all reduced, and the dynamic positioning precision after correction has been significantly improved.
Collapse
|
24
|
Dos Santos Gomes DC, de Oliveira Serra GL. Interval type-2 fuzzy computational model for real time Kalman filtering and forecasting of the dynamic spreading behavior of novel Coronavirus 2019. ISA Trans 2022; 124:57-68. [PMID: 35450726 PMCID: PMC8992003 DOI: 10.1016/j.isatra.2022.03.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 03/30/2022] [Accepted: 03/30/2022] [Indexed: 05/09/2023]
Abstract
This paper presents a computational model based on interval type-2 fuzzy systems for analysis and forecasting of COVID-19 dynamic spreading behavior. The proposed methodology is related to interval type-2 fuzzy Kalman filters design from experimental data of daily deaths reports. Initially, a recursive spectral decomposition is performed on the experimental dataset to extract relevant unobservable components for parametric estimation of the interval type-2 fuzzy Kalman filter. The antecedent propositions of fuzzy rules are obtained by formulating a type-2 fuzzy clustering algorithm. The state space submodels and the interval Kalman gains in consequent propositions of fuzzy rules are recursively updated by a proposed interval type-2 fuzzy Observer/Kalman Filter Identification (OKID) algorithm, taking into account the unobservable components obtained by recursive spectral decomposition of epidemiological experimental data of COVID-19. For validation purposes, through a comparative analysis with relevant references of literature, the proposed methodology is evaluated from the adaptive tracking and forecasting of COVID-19 dynamic spreading behavior, in Brazil, with the better results for RMSE of 1.24×10-5, MAE of 2.62×10-6, R2 of 0.99976, and MAPE of 6.33×10-6.
Collapse
|
25
|
Xiao P, Wu J, Wang Y, Chi J, Wang Z. Stable Gaze Tracking with Filtering Based on Internet of Things. Sensors (Basel) 2022; 22:3131. [PMID: 35590821 PMCID: PMC9101891 DOI: 10.3390/s22093131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 03/09/2022] [Accepted: 03/18/2022] [Indexed: 06/15/2023]
Abstract
Gaze tracking is basic research in the era of the Internet of Things. This study attempts to improve the performance of gaze tracking in an active infrared source gaze-tracking system. Owing to unavoidable noise interference, the estimated points of regard (PORs) tend to fluctuate within a certain range. To reduce the fluctuation range and obtain more stable results, we introduced a Kalman filter (KF) to filter the gaze parameters. Considering that the effect of filtering is relevant to the motion state of the gaze, we design the measurement noise that varies with the speed of the gaze. In addition, we used a correlation filter-based tracking method to quickly locate the pupil, instead of the detection method. Experiments indicated that the variance of the estimation error decreased by 73.83%, the size of the extracted pupil image decreased by 93.75%, and the extraction speed increased by 1.84 times. We also comprehensively discussed the advantages and disadvantages of the proposed method, which provides a reference for related research. It must be pointed out that the proposed algorithm can also be adopted in any eye camera-based gaze tracker.
Collapse
Affiliation(s)
- Peng Xiao
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China; (P.X.); (Z.W.)
| | - Jie Wu
- School of Automation and Electronic Engineering, University of Science and Technology Beijing, Beijing 100083, China; (J.W.); (Y.W.)
| | - Yu Wang
- School of Automation and Electronic Engineering, University of Science and Technology Beijing, Beijing 100083, China; (J.W.); (Y.W.)
| | - Jiannan Chi
- School of Automation and Electronic Engineering, University of Science and Technology Beijing, Beijing 100083, China; (J.W.); (Y.W.)
| | - Zhiliang Wang
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China; (P.X.); (Z.W.)
| |
Collapse
|
26
|
Viset F, Helmons R, Kok M. An Extended Kalman Filter for Magnetic Field SLAM Using Gaussian Process Regression. Sensors (Basel) 2022; 22:s22082833. [PMID: 35458817 PMCID: PMC9025971 DOI: 10.3390/s22082833] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 04/01/2022] [Accepted: 04/05/2022] [Indexed: 02/01/2023]
Abstract
We present a computationally efficient algorithm for using variations in the ambient magnetic field to compensate for position drift in integrated odometry measurements (dead-reckoning estimates) through simultaneous localization and mapping (SLAM). When the magnetic field map is represented with a reduced-rank Gaussian process (GP) using Laplace basis functions defined in a cubical domain, analytic expressions of the gradient of the learned magnetic field become available. An existing approach for magnetic field SLAM with reduced-rank GP regression uses a Rao-Blackwellized particle filter (RBPF). For each incoming measurement, training of the magnetic field map using an RBPF has a computational complexity per time step of O(NpNm2), where Np is the number of particles, and Nm is the number of basis functions used to approximate the Gaussian process. Contrary to the existing particle filter-based approach, we propose applying an extended Kalman filter based on the gradients of our learned magnetic field map for simultaneous localization and mapping. Our proposed algorithm only requires training a single map. It, therefore, has a computational complexity at each time step of O(Nm2). We demonstrate the workings of the extended Kalman filter for magnetic field SLAM on an open-source data set from a foot-mounted sensor and magnetic field measurements collected onboard a model ship in an indoor pool. We observe that the drift compensating abilities of our algorithm are comparable to what has previously been demonstrated for magnetic field SLAM with an RBPF.
Collapse
Affiliation(s)
- Frida Viset
- Delft Center for Systems and Control, Delft University of Technology, 2628 CD Delft, The Netherlands;
- Correspondence:
| | - Rudy Helmons
- Maritime and Transport Technology, Delft University of Technology, 2628 CD Delft, The Netherlands;
| | - Manon Kok
- Delft Center for Systems and Control, Delft University of Technology, 2628 CD Delft, The Netherlands;
| |
Collapse
|
27
|
de Alteriis G, Conte C, Caputo E, Chiariotti P, Accardo D, Cigada A, Schiano Lo Moriello R. Low-Cost and High-Performance Solution for Positioning and Monitoring of Large Structures. Sensors (Basel) 2022; 22:1788. [PMID: 35270934 PMCID: PMC8914905 DOI: 10.3390/s22051788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 02/18/2022] [Accepted: 02/22/2022] [Indexed: 06/14/2023]
Abstract
Systems for accurate attitude and position monitoring of large structures, such as bridges, tunnels, and offshore platforms are changing in recent years thanks to the exploitation of sensors based on Micro-ElectroMechanical Systems (MEMS) as an Inertial Measurement Unit (IMU). Currently adopted solutions are, in fact, mainly based on fiber optic sensors (characterized by high performance in attitude estimation to the detriment of relevant costs large volumes and heavy weights) and integrated with a Global Position System (GPS) capable of providing low-frequency or single-update information about the position. To provide a cost-effective alternative and overcome the limitations in terms of dimensions and position update frequency, a suitable solution and a corresponding prototype, exhibiting performance very close to those of the traditional solutions, are presented and described hereinafter. The solution leverages a real-time Kalman filter that, along with the proper features of the MEMS inertial sensor and Real-Time Kinematic (RTK) GPS, allows achieving performance in terms of attitude and position estimates suitable for this kind of application. The results obtained in a number of tests underline the promising reliability and effectiveness of the solution in estimating the attitude and position of large structures. In particular, several tests carried out in the laboratory highlighted high system stability; standard deviations of attitude estimates as low as 0.04° were, in fact, experienced in tests conducted in static conditions. Moreover, the prototype performance was also compared with a fiber optic sensor in tests emulating actual operating conditions; differences in the order of a few hundredths of a degree were found in the attitude measurements.
Collapse
Affiliation(s)
- Giorgio de Alteriis
- Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy; (C.C.); (E.C.); (D.A.); (R.S.L.M.)
- Department of Management, Information and Production Engineering, University of Bergamo, 24044 Dalmine, Italy
| | - Claudia Conte
- Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy; (C.C.); (E.C.); (D.A.); (R.S.L.M.)
- Department of Management, Information and Production Engineering, University of Bergamo, 24044 Dalmine, Italy
| | - Enzo Caputo
- Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy; (C.C.); (E.C.); (D.A.); (R.S.L.M.)
| | - Paolo Chiariotti
- Department of Mechanical Engineering, Politecnico di Milano, 20156 Milano, Italy; (P.C.); (A.C.)
| | - Domenico Accardo
- Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy; (C.C.); (E.C.); (D.A.); (R.S.L.M.)
| | - Alfredo Cigada
- Department of Mechanical Engineering, Politecnico di Milano, 20156 Milano, Italy; (P.C.); (A.C.)
| | - Rosario Schiano Lo Moriello
- Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy; (C.C.); (E.C.); (D.A.); (R.S.L.M.)
| |
Collapse
|
28
|
Arpaia P, Buzio M, Di Capua V, Grassini S, Parvis M, Pentella M. Drift-Free Integration in Inductive Magnetic Field Measurements Achieved by Kalman Filtering. Sensors (Basel) 2021; 22:182. [PMID: 35009722 PMCID: PMC8749566 DOI: 10.3390/s22010182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/15/2021] [Accepted: 12/23/2021] [Indexed: 06/14/2023]
Abstract
Sensing coils are inductive sensors commonly used to measure magnetic fields, such as those generated by electromagnets used in many kinds of industrial and scientific applications. Inductive sensors rely on integrating the output voltage at the coil's terminals in order to obtain flux linkage, which may suffer from the magnification of low-frequency noise resulting in a drifting integrated signal. This article presents a method for the cancellation of integrator drift. The method is based on a first-order linear Kalman filter combining the data from the coil and a second sensor. Two case studies are presented. In the first one, the second sensor is a Hall probe, which senses the magnetic field directly. In a second case study, the magnet's excitation current was used instead to provide a first-order approximation of the field. Experimental tests show that both approaches can reduce the measured field drift by three orders of magnitude. The Hall probe option guarantees, in addition, one order of magnitude better absolute accuracy than by using the excitation current.
Collapse
Affiliation(s)
- Pasquale Arpaia
- Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80100 Naples, Italy;
| | - Marco Buzio
- Technology Department, European Organization for Nuclear Research (CERN), 1211 Geneva, Switzerland; (M.B.); (M.P.)
| | - Vincenzo Di Capua
- Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80100 Naples, Italy;
- Technology Department, European Organization for Nuclear Research (CERN), 1211 Geneva, Switzerland; (M.B.); (M.P.)
| | - Sabrina Grassini
- Department of Applied Science and Technology, Polytechnic of Turin, 10129 Turin, Italy;
| | - Marco Parvis
- Department of Electronics and Telecommunications, Polytechnic of Turin, 10129 Turin, Italy;
| | - Mariano Pentella
- Technology Department, European Organization for Nuclear Research (CERN), 1211 Geneva, Switzerland; (M.B.); (M.P.)
- Department of Applied Science and Technology, Polytechnic of Turin, 10129 Turin, Italy;
| |
Collapse
|
29
|
Liao Z, Xu B, Gu J, Shi C. Sea Surface Temperature Analysis for Fengyun-3C Data Using Oriented Elliptic Correlation Scales. Sensors (Basel) 2021; 21:8067. [PMID: 34884071 PMCID: PMC8659523 DOI: 10.3390/s21238067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 11/26/2021] [Accepted: 11/30/2021] [Indexed: 11/16/2022]
Abstract
Sea surface temperature (SST) is critical for global climate change analysis and research. In this study, we used visible and infrared scanning radiometer (VIRR) sea surface temperature (SST) data from the Fengyun-3C (FY-3C) satellite for SST analysis, and applied the Kalman filtering methods with oriented elliptic correlation scales to construct SST fields. Firstly, the model for the oriented elliptic correlation scale was established for SST analysis. Secondly, observation errors from each type of SST data source were estimated using the optimal matched datasets, and background field errors were calculated using the model of oriented elliptic correlation scale. Finally, the blended SST analysis product was obtained using the Kalman filtering method, then the SST fields using the optimum interpolation (OI) method were chosen for comparison to validate results. The quality analysis for 2016 revealed that the Kalman analysis with a root-mean-square error (RMSE) of 0.3243 °C had better performance than did the OI analysis with a RMSE of 0.3911 °C, which was closer to the OISST product RMSE of 0.2897 °C. The results demonstrated that the Kalman filtering method with dynamic observation error and background error estimation was significantly superior to the OI method in SST analysis for FY-3C SST data.
Collapse
Affiliation(s)
- Zhihong Liao
- National Meteorological Information Center, Beijing 100081, China; (J.G.); (C.S.)
| | - Bin Xu
- National Meteorological Information Center, Beijing 100081, China; (J.G.); (C.S.)
| | | | | |
Collapse
|
30
|
Pei L, Zhang H, Yang B. Improved Camshift object tracking algorithm in occluded scenes based on AKAZE and Kalman. Multimed Tools Appl 2021; 81:2145-2159. [PMID: 34690530 PMCID: PMC8526530 DOI: 10.1007/s11042-021-11673-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 09/16/2021] [Accepted: 10/07/2021] [Indexed: 06/13/2023]
Abstract
Camshift algorithm tracking is susceptible to interference when a tracking object is occluded or when its hue is similar to the background. An improved Camshift object-tracking algorithm combining AKAZE (Accelerated-KAZE) feature matching and Kalman filtering is proposed. First, the video channel is converted for processing. Second, AKAZE is used to match the object feature points and Kalman filtering is used to predict the next position. Then different scenes are judged by the threshold and the Camshift and Kalman tracking algorithms are used for object tracking, respectively. Finally, the improved Camshift algorithm is used to test the moving object in a variety of situations and compared with the traditional Camshift algorithm and the Kalman filter improved Camshift algorithm. Experimental results show that the improved joint tracking algorithm can continue tracking under full occlusion. The effective frame rate of recognition is increased by about 20%, and the single-frame image processing time is less than 35 ms, which can meet the real-time tracking requirements.
Collapse
Affiliation(s)
- Lili Pei
- School of Information Engineering, Chang’an University, Xi’an, 710064 Shaanxi China
| | - He Zhang
- Xi’an Xiangteng Micro-Electronics Technology Co., Ltd., Xi’an, 710068 Shaanxi China
| | - Bo Yang
- Datang Mobile Communication Equipment Co., Ltd. Xi’an Branch, Xi’an, 710061 Shaanxi China
| |
Collapse
|
31
|
Shaukat N, Moinuddin M, Otero P. Underwater Vehicle Positioning by Correntropy-Based Fuzzy Multi-Sensor Fusion. Sensors (Basel) 2021; 21:6165. [PMID: 34577372 PMCID: PMC8470692 DOI: 10.3390/s21186165] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 09/06/2021] [Accepted: 09/10/2021] [Indexed: 11/16/2022]
Abstract
The ability of the underwater vehicle to determine its precise position is vital to completing a mission successfully. Multi-sensor fusion methods for underwater vehicle positioning are commonly based on Kalman filtering, which requires the knowledge of process and measurement noise covariance. As the underwater conditions are continuously changing, incorrect process and measurement noise covariance affect the accuracy of position estimation and sometimes cause divergence. Furthermore, the underwater multi-path effect and nonlinearity cause outliers that have a significant impact on positional accuracy. These non-Gaussian outliers are difficult to handle with conventional Kalman-based methods and their fuzzy variants. To address these issues, this paper presents a new and improved adaptive multi-sensor fusion method by using information-theoretic, learning-based fuzzy rules for Kalman filter covariance adaptation in the presence of outliers. Two novel metrics are proposed by utilizing correntropy Gaussian and Versoria kernels for matching theoretical and actual covariance. Using correntropy-based metrics and fuzzy logic together makes the algorithm robust against outliers in nonlinear dynamic underwater conditions. The performance of the proposed sensor fusion technique is compared and evaluated using Monte-Carlo simulations, and substantial improvements in underwater position estimation are obtained.
Collapse
Affiliation(s)
- Nabil Shaukat
- Institute of Oceanic Engineering Research, University of Malaga, 29010 Malaga, Spain;
| | - Muhammad Moinuddin
- Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Center of Excellence in Intelligent Engineering Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Pablo Otero
- Institute of Oceanic Engineering Research, University of Malaga, 29010 Malaga, Spain;
| |
Collapse
|
32
|
Leanza A, Reina G, Blanco-Claraco JL. A Factor-Graph-Based Approach to Vehicle Sideslip Angle Estimation. Sensors (Basel) 2021; 21:5409. [PMID: 34450851 DOI: 10.3390/s21165409] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 08/09/2021] [Accepted: 08/09/2021] [Indexed: 11/16/2022]
Abstract
Sideslip angle is an important variable for understanding and monitoring vehicle dynamics, but there is currently no inexpensive method for its direct measurement. Therefore, it is typically estimated from proprioceptive sensors onboard using filtering methods from the family of the Kalman filter. As a novel alternative, this work proposes modeling the problem directly as a graphical model (factor graph), which can then be optimized using a variety of methods, such as whole-dataset batch optimization for offline processing or fixed-lag smoothing for on-line operation. Experimental results on real vehicle datasets validate the proposal, demonstrating a good agreement between estimated and actual sideslip angle, showing similar performance to state-of-the-art methods but with a greater potential for future extensions due to the more flexible mathematical framework. An open-source implementation of the proposed framework has been made available online.
Collapse
|
33
|
Masum Bhuiyan MA, Mahmud S, Islam MR, Tasnim N. Volatility estimation for COVID-19 daily rates using Kalman filtering technique. Results Phys 2021; 26:104291. [PMID: 34026472 PMCID: PMC8130597 DOI: 10.1016/j.rinp.2021.104291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 04/30/2021] [Accepted: 05/01/2021] [Indexed: 06/12/2023]
Abstract
This paper discusses the use of stochastic modeling in the prognosis of Corona Virus-Infected Disease 2019 (COVID-19) cases. COVID-19 is a new disease that is highly infectious and dangerous. It has deeply shaken the world, claiming the lives of over a million people and bringing the world to a lockdown. So, the early detection of COVID is essential for the patients' timely treatment and preventive measures. A filtering technique with time-varying parameters is presented to predict the stochastic volatility (SV) of COVID-19 cases. The time-varying parameters are estimated using the Kalman filtering technique based on the stochastic component of data volatility. Kalman filtering is essential as it removes insignificant information from the data. We forecast one-step-ahead predicted volatility with ± 3 standard prediction errors, which is implemented by Maximum Likelihood Estimation. We conclude that Kalman filtering in conjunction with the SV model is a reliable predictive model for COVID-19 since it is less constrained by the past autoregressive information.
Collapse
|
34
|
Adduci R, Vermaut M, Naets F, Croes J, Desmet W. A Discrete-Time Extended Kalman Filter Approach Tailored for Multibody Models: State-Input Estimation. Sensors (Basel) 2021; 21:s21134495. [PMID: 34209242 PMCID: PMC8271943 DOI: 10.3390/s21134495] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 06/21/2021] [Accepted: 06/28/2021] [Indexed: 11/16/2022]
Abstract
Model-based force estimation is an emerging methodology in the mechatronic community given the possibility to exploit physically inspired high-fidelity models in tandem with ready-to-use cheap sensors. In this work, an inverse input load identification methodology is presented combining high-fidelity multibody models with a Kalman filter-based estimator and providing the means for an accurate and computationally efficient state-input estimation strategy. A particular challenge addressed in this work is the handling of the redundant state-description encountered in common multibody model descriptions. A novel linearization framework is proposed on the time-discretized equations in order to extract the required system model matrices for the Kalman filter. The presented framework is experimentally validated on a slider-crank mechanism. The nonlinear kinematics and dynamics are well represented through a rigid multibody model with lumped flexibilities to account for localized interaction phenomena among bodies. The proposed methodology is validated estimating the input torque delivered by a driver electro-motor together with the system states and comparing the experimental data with the estimated quantities. The results show the stability and accuracy of the estimation framework by only employing the angular motor velocity, measured by the motor encoder sensor and available in most of the commercial electro-motors.
Collapse
Affiliation(s)
- Rocco Adduci
- LMSD Research Group, Mechanical Engineering Department, KU Leuven University, 3000 Leuven, Belgium; (M.V.); (F.N.); (J.C.); (W.D.)
- DMMS Core Labs, Flanders Make, 3001 Heverlee, Belgium
- Correspondence:
| | - Martijn Vermaut
- LMSD Research Group, Mechanical Engineering Department, KU Leuven University, 3000 Leuven, Belgium; (M.V.); (F.N.); (J.C.); (W.D.)
- DMMS Core Labs, Flanders Make, 3001 Heverlee, Belgium
| | - Frank Naets
- LMSD Research Group, Mechanical Engineering Department, KU Leuven University, 3000 Leuven, Belgium; (M.V.); (F.N.); (J.C.); (W.D.)
- DMMS Core Labs, Flanders Make, 3001 Heverlee, Belgium
| | - Jan Croes
- LMSD Research Group, Mechanical Engineering Department, KU Leuven University, 3000 Leuven, Belgium; (M.V.); (F.N.); (J.C.); (W.D.)
- DMMS Core Labs, Flanders Make, 3001 Heverlee, Belgium
| | - Wim Desmet
- LMSD Research Group, Mechanical Engineering Department, KU Leuven University, 3000 Leuven, Belgium; (M.V.); (F.N.); (J.C.); (W.D.)
- DMMS Core Labs, Flanders Make, 3001 Heverlee, Belgium
| |
Collapse
|
35
|
Reitbauer E, Schmied C. Bridging GNSS Outages with IMU and Odometry: A Case Study for Agricultural Vehicles. Sensors (Basel) 2021; 21:4467. [PMID: 34210053 DOI: 10.3390/s21134467] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 06/21/2021] [Accepted: 06/24/2021] [Indexed: 11/17/2022]
Abstract
Nowadays, many precision farming applications rely on the use of GNSS-RTK. However, when it comes to autonomous agricultural vehicles, GNSS cannot be used as a stand-alone system for positioning. To ensure high availability and robustness of the positioning solution, GNSS-RTK must be fused with additional sensors. This paper presents a novel sensor fusion algorithm tailored to tracked agricultural vehicles. GNSS-RTK, an IMU and wheel speed sensors are fused in an error-state Kalman filter to estimate position and attitude of the vehicle. An odometry model for tracked vehicles is introduced which is used to propagate the filter state. By using both IMU and wheel speed sensors, specific motion characteristics of tracked vehicles such as slippage can be included in the dynamic model. The presented sensor fusion algorithm is tested at a composting site using a tracked compost turner. The sensor measurements are recorded using the Robot Operating System (ROS). To analyze the achievable accuracies for position and attitude of the vehicle, a precise reference trajectory is measured using two robotic total stations. The resulting trajectory of the error-state filter is then compared to the reference trajectory. To analyze how well the proposed error-state filter is suited to bridge GNSS outages, GNSS outages of 30 s are simulated in post-processing. During these outages, the vehicle's state is propagated using the wheel speed sensors, IMU, and the dynamic model for tracked vehicles. The results show that after 30 s of GNSS outage, the estimated horizontal position of the vehicle still has a sub-decimetre accuracy.
Collapse
|
36
|
Mitchell L, Arnold A. Analyzing the effects of observation function selection in ensemble Kalman filtering for epidemic models. Math Biosci 2021; 339:108655. [PMID: 34186054 DOI: 10.1016/j.mbs.2021.108655] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 06/20/2021] [Accepted: 06/22/2021] [Indexed: 11/23/2022]
Abstract
The Ensemble Kalman Filter (EnKF) is a popular sequential data assimilation method that has been increasingly used for parameter estimation and forecast prediction in epidemiological studies. The observation function plays a critical role in the EnKF framework, connecting the unknown system variables with the observed data. Key differences in observed data and modeling assumptions have led to the use of different observation functions in the epidemic modeling literature. In this work, we present a novel computational analysis demonstrating the effects of observation function selection when using the EnKF for state and parameter estimation in this setting. In examining the use of four epidemiologically-inspired observation functions of different forms in connection with the classic Susceptible-Infectious-Recovered (SIR) model, we show how incorrect observation modeling assumptions (i.e., fitting incidence data with a prevalence model, or neglecting under-reporting) can lead to inaccurate filtering estimates and forecast predictions. Results demonstrate the importance of choosing an observation function that well interprets the available data on the corresponding EnKF estimates in several filtering scenarios, including state estimation with known parameters, and combined state and parameter estimation with both constant and time-varying parameters. Numerical experiments further illustrate how modifying the observation noise covariance matrix in the filter can help to account for uncertainty in the observation function in certain cases.
Collapse
|
37
|
Ebrahimi A, Alambeigi F, Sefati S, Patel N, He C, Gehlbach P, Iordachita I. Stochastic Force-based Insertion Depth and Tip Position Estimations of Flexible FBG-Equipped Instruments in Robotic Retinal Surgery. IEEE ASME Trans Mechatron 2021; 26:1512-1523. [PMID: 34305385 PMCID: PMC8294652 DOI: 10.1109/tmech.2020.3022830] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Vitreoretinal surgery is among the most delicate surgical tasks during which surgeon hand tremor may severely attenuate surgeon performance. Robotic assistance has been demonstrated to be beneficial in diminishing hand tremor. Among the requirements for reliable assistance from the robot is to provide precise measurements of system states e.g. sclera forces, tool tip position and tool insertion depth. Providing this and other sensing information using existing technology would contribute towards development and implementation of autonomous robot-assisted tasks in retinal surgery such as laser ablation, guided suture placement/assisted needle vessel cannulation, among other applications. In the present work, we use a state-estimating Kalman filtering (KF) to improve the tool tip position and insertion depth estimates, which used to be purely obtained by robot forward kinematics (FWK) and direct sensor measurements, respectively. To improve tool tip localization, in addition to robot FWK, we also use sclera force measurements along with beam theory to account for tool deflection. For insertion depth, the robot FWK is combined with sensor measurements for the cases where sensor measurements are not reliable enough. The improved tool tip position and insertion depth measurements are validated using a stereo camera system through preliminary experiments and a case study. The results indicate that the tool tip position and insertion depth measurements are significantly improved by 77% and 94% after applying KF, respectively.
Collapse
Affiliation(s)
- Ali Ebrahimi
- Laboratory for Computational Sensing and Robotics (LCSR) at the Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Farshid Alambeigi
- Walker Department of Mechanical Engineering at the University of Texas at Austin, Austin, TX, 78712, USA
| | - Shahriar Sefati
- Laboratory for Computational Sensing and Robotics (LCSR) at the Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Niravkumar Patel
- Laboratory for Computational Sensing and Robotics (LCSR) at the Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Changyan He
- School of Mechanical Engineering and Automation at Beihang University, Beijing, 100191 China
| | - Peter Gehlbach
- Wilmer Eye Institute, Johns Hopkins Hospital, Baltimore, MD, 21287, USA
| | - Iulian Iordachita
- Laboratory for Computational Sensing and Robotics (LCSR) at the Johns Hopkins University, Baltimore, MD, 21218, USA
| |
Collapse
|
38
|
Remy C, Ahumada D, Labine A, Côté JC, Lachaine M, Bouchard H. Potential of a probabilistic framework for target prediction from surrogate respiratory motion during lung radiotherapy. Phys Med Biol 2021; 66. [PMID: 33761479 DOI: 10.1088/1361-6560/abf1b8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 03/23/2021] [Indexed: 12/25/2022]
Abstract
Purpose.Respiration-induced motion introduces significant positioning uncertainties in radiotherapy treatments for thoracic sites. Accounting for this motion is a non-trivial task commonly addressed with surrogate-based strategies and latency compensating techniques. This study investigates the potential of a new unified probabilistic framework to predict both future target motion in real-time from a surrogate signal and associated uncertainty.Method.A Bayesian approach is developed, based on a Kalman filter theory adapted specifically for surrogate measurements. Breathing motions are collected simultaneously from a lung target, two external surrogates (abdominal and thoracic markers) and an internal surrogate (liver structure) for 9 volunteers during 4 min, in which severe breathing changes occur to assess the robustness of the method. A comparison with an artificial non-linear neural network (NN) is performed, although no confidence interval prediction is provided. A static worst-case scenario and a simple static design are investigated.Results.Although the NN can reduce the prediction errors from thoracic surrogate in some cases, the Bayesian framework outperforms in most cases the NN when using the other surrogates: bias on predictions is reduced by 38% and 16% on average when using respectively the liver and the abdomen for the simple scenario, and by respectively 40% and 31% for the worst-case scenario. The standard deviation of residuals is reduced on average by up to 42%. The Bayesian method is also found to be more robust to increasing latencies. The thoracic marker appears to be less reliable to predict the target position, while the liver shows to be a better surrogate. A statistical test confirms the significance of both observations.Conclusion.The proposed framework predicts both the future target position and the associated uncertainty, which can be valuably used to further assist motion management decisions. Further investigation is required to improve the predictions by using an adaptive version of the proposed framework.
Collapse
Affiliation(s)
- Charlotte Remy
- Département de physique, Université de Montréal, Complexe des sciences, 1375 Avenue Thérèse-Lavoie-Roux, Montréal, Québec H2V 0B3, Canada.,Centre de recherche du Centre hospitalier de l'Université de Montréal, 900 Rue Saint-Denis, Montréal, Québec, H2X 0A9, Canada
| | - Daniel Ahumada
- Département de physique, Université de Montréal, Complexe des sciences, 1375 Avenue Thérèse-Lavoie-Roux, Montréal, Québec H2V 0B3, Canada.,Centre de recherche du Centre hospitalier de l'Université de Montréal, 900 Rue Saint-Denis, Montréal, Québec, H2X 0A9, Canada
| | - Alexandre Labine
- Département de physique, Université de Montréal, Complexe des sciences, 1375 Avenue Thérèse-Lavoie-Roux, Montréal, Québec H2V 0B3, Canada.,Centre de recherche du Centre hospitalier de l'Université de Montréal, 900 Rue Saint-Denis, Montréal, Québec, H2X 0A9, Canada
| | - Jean-Charles Côté
- Département de radio-oncologie, Centre hospitalier de l'Université de Montréal (CHUM), 1560 rue Sherbrooke est, Montréal, Québec H2L 4M1, Canada
| | - Martin Lachaine
- Elekta Ltd., 2050 de Bleury, Suite 200, Montréal, Québec H3A2J5, Canada
| | - Hugo Bouchard
- Département de physique, Université de Montréal, Complexe des sciences, 1375 Avenue Thérèse-Lavoie-Roux, Montréal, Québec H2V 0B3, Canada.,Centre de recherche du Centre hospitalier de l'Université de Montréal, 900 Rue Saint-Denis, Montréal, Québec, H2X 0A9, Canada.,Département de radio-oncologie, Centre hospitalier de l'Université de Montréal (CHUM), 1560 rue Sherbrooke est, Montréal, Québec H2L 4M1, Canada
| |
Collapse
|
39
|
Gomes DCDS, de Oliveira Serra GL. A novel interval type-2 fuzzy Kalman filtering and tracking of experimental data. Evol Syst (Berl) 2021; 13:243-264. [PMID: 38624867 PMCID: PMC8080208 DOI: 10.1007/s12530-021-09381-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 04/09/2021] [Indexed: 11/17/2022]
Abstract
In this paper, a methodology for design of fuzzy Kalman filter, using interval type-2 fuzzy models, in discrete time domain, via spectral decomposition of experimental data, is proposed. The adopted methodology consists of recursive parametric estimation of local state space linear submodels of interval type-2 fuzzy Kalman filter for tracking and forecasting of the dynamics inherited to experimental data, using an interval type-2 fuzzy version of Observer/Kalman Filter Identification (OKID) algorithm. The partitioning of the experimental data is performed by interval type-2 fuzzy Gustafson-Kessel clustering algorithm. The interval Kalman gains in the consequent proposition of interval type-2 fuzzy Kalman filter are updated according to unobservable components computed by recursive spectral decomposition of experimental data. Computational results illustrate the efficiency of proposed methodology for filtering and tracking the time delayed state variables of Chen's chaotic attractor in a noisy environment, and experimental results illustrate its applicability for adaptive and real time forecasting the dynamic spread behavior of novel Coronavirus 2019 (COVID-19) outbreak in Brazil.
Collapse
Affiliation(s)
| | - Ginalber Luiz de Oliveira Serra
- Electrical Electronics Department, Federal Institute of Education, Science and Technology of Maranhão, Av. Getúlio Vargas, 04, Monte Castelo, São Luís, Maranhão CEP: 65030-005 Brazil
| |
Collapse
|
40
|
Ebeigbe D, Berry T, Norton MM, Whalen AJ, Simon D, Sauer T, Schiff SJ. A Generalized Unscented Transformation for Probability Distributions. ArXiv 2021:arXiv:2104.01958v2. [PMID: 33850954 PMCID: PMC8043458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 11/15/2021] [Indexed: 11/07/2022]
Abstract
The unscented transform uses a weighted set of samples called sigma points to propagate the means and covariances of nonlinear transformations of random variables. However, unscented transforms developed using either the Gaussian assumption or a minimum set of sigma points typically fall short when the random variable is not Gaussian distributed and the nonlinearities are substantial. In this paper, we develop the generalized unscented transform (GenUT), which uses 2n+1 sigma points to accurately capture up to the diagonal components of the skewness and kurtosis tensors of most probability distributions. Constraints can be analytically enforced on the sigma points while guaranteeing at least second-order accuracy. The GenUT uses the same number of sigma points as the original unscented transform while also being applicable to non-Gaussian distributions, including the assimilation of observations in the modeling of infectious diseases such as coronavirus (SARS-CoV-2) causing COVID-19.
Collapse
Affiliation(s)
- Donald Ebeigbe
- Center for Neural Engineering, Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA
| | - Tyrus Berry
- Department of Mathematical Sciences, George Mason University, Fairfax, VA, USA
| | - Michael M Norton
- Center for Neural Engineering, Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA
| | - Andrew J Whalen
- Center for Neural Engineering, Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Dan Simon
- Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH, USA
| | - Timothy Sauer
- Department of Mathematical Sciences, George Mason University, Fairfax, VA, USA
| | - Steven J Schiff
- Center for Neural Engineering and Center for Infectious Disease Dynamics, Departments of Engineering Science and Mechanics, Neurosurgery, and Physics, Pennsylvania State University, University Park, PA, USA
| |
Collapse
|
41
|
Medina D, Li H, Vilà-Valls J, Closas P. Robust Filtering Techniques for RTK Positioning in Harsh Propagation Environments. Sensors (Basel) 2021; 21:s21041250. [PMID: 33578725 PMCID: PMC7916509 DOI: 10.3390/s21041250] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 02/05/2021] [Accepted: 02/07/2021] [Indexed: 11/18/2022]
Abstract
Global navigation satellite systems (GNSSs) play a key role in intelligent transportation systems such as autonomous driving or unmanned systems navigation. In such applications, it is fundamental to ensure a reliable precise positioning solution able to operate in harsh propagation conditions such as urban environments and under multipath and other disturbances. Exploiting carrier phase observations allows for precise positioning solutions at the complexity cost of resolving integer phase ambiguities, a procedure that is particularly affected by non-nominal conditions. This limits the applicability of conventional filtering techniques in challenging scenarios, and new robust solutions must be accounted for. This contribution deals with real-time kinematic (RTK) positioning and the design of robust filtering solutions for the associated mixed integer- and real-valued estimation problem. Families of Kalman filter (KF) approaches based on robust statistics and variational inference are explored, such as the generalized M-based KF or the variational-based KF, aiming to mitigate the impact of outliers or non-nominal measurement behaviors. The performance assessment under harsh propagation conditions is realized using a simulated scenario and real data from a measurement campaign. The proposed robust filtering solutions are shown to offer excellent resilience against outlying observations, with the variational-based KF showcasing the overall best performance in terms of Gaussian efficiency and robustness.
Collapse
Affiliation(s)
- Daniel Medina
- Institute of Communications and Navigation, German Aerospace Center (DLR), 17235 Neustrelitz, Germany
- Correspondence:
| | - Haoqing Li
- Electrical and Computer Engineering Department, Northeastern University, Boston, MA 02115, USA; (H.L.); (P.C.)
| | - Jordi Vilà-Valls
- Institut Supérieur de l’Aéronautique et de l’Espace (ISAE-SUPAERO), University of Toulouse, 31055 Toulouse, France;
| | - Pau Closas
- Electrical and Computer Engineering Department, Northeastern University, Boston, MA 02115, USA; (H.L.); (P.C.)
| |
Collapse
|
42
|
Arthurs CJ, Xiao N, Moireau P, Schaeffter T, Figueroa CA. A flexible framework for sequential estimation of model parameters in computational hemodynamics. Adv Model Simul Eng Sci 2020; 7:48. [PMID: 33282681 PMCID: PMC7717067 DOI: 10.1186/s40323-020-00186-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 11/06/2020] [Indexed: 06/02/2023]
Abstract
A major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc. acquired in the clinic. Current workflows are manual and time-consuming. This work presents a flexible computational framework for model parameter estimation in cardiovascular flows that relies on the following fundamental contributions. (i) A Reduced-Order Unscented Kalman Filter (ROUKF) model for data assimilation for wall material and simple lumped parameter network (LPN) boundary condition model parameters. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (iii) A "Netlist" implementation, supporting easy filtering of parameters in such complex LPNs. The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN. The methods described in this paper have been implemented as part of the CRIMSON hemodynamics software package.
Collapse
Affiliation(s)
| | - Nan Xiao
- Dept. of Biomedical Engineering, King’s College London, London, UK
| | - Philippe Moireau
- Inria, Inria Saclay-Ile de France, 91128 Palaiseau, France
- LMS, Ecole Polytechnique, CNRS, Institut Polytechnique de Paris, 91128 Palaiseau, France
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt, Berlin, Germany
- Technical University Berlin, Berlin, Germany
| | - C. Alberto Figueroa
- Depts. of Surgery and Biomedical Engineering, University of Michigan, 2800 Plymouth Rd, Ann Arbor, MI 48109 USA
| |
Collapse
|
43
|
DeRoos L, Nitta K, Lavieri MS, Van Oyen MP, Kazemian P, Andrews CA, Sugiyama K, Stein JD. Comparing Perimetric Loss at Different Target Intraocular Pressures for Patients with High-Tension and Normal-Tension Glaucoma. Ophthalmol Glaucoma 2020; 4:251-259. [PMID: 32950753 DOI: 10.1016/j.ogla.2020.09.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 09/13/2020] [Accepted: 09/14/2020] [Indexed: 11/26/2022]
Abstract
PURPOSE To compare forecasted changes in mean deviation (MD) for patients with normal-tension glaucoma (NTG) and high-tension open-angle glaucoma (HTG) at different target intraocular pressures (IOPs) using Kalman filtering, a machine learning technique. DESIGN Retrospective cohort study. PARTICIPANTS From the Collaborative Initial Glaucoma Treatment Study or Advanced Glaucoma Intervention Study, 496 patients with HTG; from Japan, 262 patients with NTG. METHODS Using the first 5 sets of tonometry and perimetry measurements, each patient was classified as a fast progressor, slow progressor, or nonprogressor. Using Kalman filtering, personalized forecasts of MD changes over 2.5 years' follow-up were generated for fast and slow progressors with HTG and NTG with IOPs maintained at hypothetical IOP targets of 9 to 21 mmHg. Future MD loss with different percentage IOP reductions from baseline (0%-50%) were also assessed for the groups. MAIN OUTCOME MEASURES Mean forecasted MD change at different target IOPs. RESULTS The mean (± standard deviation) patient age was 63.5 ± 10.5 years for NTG and 66.5 ± 10.9 years for HTG. Over the 2.5-year follow-up, at target IOPs of 9, 15, and 21 mmHg, respectively, the mean forecasted MD losses for fast progressors with NTG were 2.3 ± 0.2, 4.0 ± 0.2, and 5.7 ± 0.2 dB; for slow progressors with NTG, losses were 0.63 ± 0.02, 1.02 ± 0.03, and 1.49 ± 0.07 dB; for fast progressors with HTG, losses were 1.8 ± 0.1, 3.4 ± 0.1, and 5.1 ± 0.1 dB; and for slow progressors with HTG, losses were 0.55 ± 0.06, 1.04 ± 0.08, and 1.59 ± 0.10 dB. Fast progressors with NTG had greater MD decline than fast progressors with HTG at each target IOP (P ≤ 0.007 for all). The MD decline for slow progressors with HTG and NTG were similar (P ≥ 0.24 for all target IOPs). Fast progressors with HTG had greater MD loss than those with NTG with 0%-10% IOP reduction since baseline (P ≤ 0.01 for all), but not 25% (P = 0.07) or 50% (P = 0.76) reduction since baseline. CONCLUSIONS Machine learning algorithms using Kalman filtering techniques demonstrate promise at forecasting future MD values at different target IOPs for patients with NTG and HTG.
Collapse
Affiliation(s)
- Luke DeRoos
- Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan
| | - Koji Nitta
- Department of Ophthalmology, Fukui-ken Saiseikai Hospital, Fukui, Japan; Department of Ophthalmology School of Medicine, Kanazawa University, Kanazawa, Japan
| | - Mariel S Lavieri
- Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan
| | - Mark P Van Oyen
- Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan
| | - Pooyan Kazemian
- Department of Operations, Weatherhead School of Management, Case Western Reserve University, Cleveland, Ohio
| | - Chris A Andrews
- Department of Ophthalmology and Visual Sciences, University of Michigan Medical School, Ann Arbor, Michigan; Center for Eye Policy and Innovation, University of Michigan, Ann Arbor, Michigan
| | - Kazuhisa Sugiyama
- Department of Ophthalmology School of Medicine, Kanazawa University, Kanazawa, Japan
| | - Joshua D Stein
- Department of Ophthalmology and Visual Sciences, University of Michigan Medical School, Ann Arbor, Michigan; Center for Eye Policy and Innovation, University of Michigan, Ann Arbor, Michigan; Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, Michigan.
| |
Collapse
|
44
|
Yang Q, Yi C, Vajdi A, Cohnstaedt LW, Wu H, Guo X, Scoglio CM. Short-term forecasts and long-term mitigation evaluations for the COVID-19 epidemic in Hubei Province, China. Infect Dis Model 2020; 5:563-574. [PMID: 32835146 PMCID: PMC7425645 DOI: 10.1016/j.idm.2020.08.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 06/17/2020] [Accepted: 08/05/2020] [Indexed: 01/08/2023] Open
Abstract
As an emerging infectious disease, the 2019 coronavirus disease (COVID-19) has developed into a global pandemic. During the initial spreading of the virus in China, we demonstrated the ensemble Kalman filter performed well as a short-term predictor of the daily cases reported in Wuhan City. Second, we used an individual-level network-based model to reconstruct the epidemic dynamics in Hubei Province and examine the effectiveness of non-pharmaceutical interventions on the epidemic spreading with various scenarios. Our simulation results show that without continued control measures, the epidemic in Hubei Province could have become persistent. Only by continuing to decrease the infection rate through 1) protective measures and 2) social distancing can the actual epidemic trajectory that happened in Hubei Province be reconstructed in simulation. Finally, we simulate the COVID-19 transmission with non-Markovian processes and show how these models produce different epidemic trajectories, compared to those obtained with Markov processes. Since recent studies show that COVID-19 epidemiological parameters do not follow exponential distributions leading to Markov processes, future works need to focus on non-Markovian models to better capture the COVID-19 spreading trajectories. In addition, shortening the infectious period via early case identification and isolation can slow the epidemic spreading significantly.
Collapse
Affiliation(s)
- Qihui Yang
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA
| | - Chunlin Yi
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA
| | - Aram Vajdi
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA
| | - Lee W. Cohnstaedt
- Arthropod-Borne Animal Diseases Research Unit, Center for Grain and Animal Health Research, USDA ARS, Manhattan, KS, USA
| | - Hongyu Wu
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA
| | - Xiaolong Guo
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA
| | - Caterina M. Scoglio
- Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA
| |
Collapse
|
45
|
Li X, Shi D, Yu Z. Nondestructive Damage Testing of Beam Structure Based on Vibration Response Signal Analysis. Materials (Basel) 2020; 13:E3301. [PMID: 32722167 DOI: 10.3390/ma13153301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 07/11/2020] [Accepted: 07/16/2020] [Indexed: 11/25/2022]
Abstract
Nondestructive damage-testing technology based on vibration signal analysis makes full use of the response characteristics of wave and energy. With the advantages of wide bandwidths of response frequency and high sensitivity, the nondestructive testing technology based on vibration signal analysis has a superiority in the application for the detection and characterization of structural defects, and has become one of the important methods for the nondestructive testing of structural material defects and damage. This paper presents a novel method of detection localization and quantitative analysis for local damage in beam structures, based on the response analysis of vibration signals. A damage-detection and -identification algorithm based on a unscented Kalman filter (UKF) was designed, which greatly reduces the computational workload in the process of damage identification over that in conventional methods. The method presented in this paper has significances to widen the application scope of the nondestructive testing method, and increase the recognition efficiency and effectiveness of this kind of method in engineering.
Collapse
|
46
|
Wang X, Liu X, Wang Z, Li R, Wu Y. SVM+KF Target Tracking Strategy Using the Signal Strength in Wireless Sensor Networks. Sensors (Basel) 2020; 20:E3832. [PMID: 32660040 PMCID: PMC7412139 DOI: 10.3390/s20143832] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 06/24/2020] [Accepted: 07/07/2020] [Indexed: 11/23/2022]
Abstract
Target Tracking (TT) is a fundamental application of wireless sensor networks. TT based on received signal strength indication (RSSI) is by far the cheapest and simplest approach, but suffers from a low stability and precision owing to multiple paths, occlusions, and decalibration effects. To address this problem, we propose an innovative TT algorithm, known as the SVM+KF method, which combines the support vector machine (SVM) and an improved Kalman filter (KF). We first use the SVM to obtain an initial estimate of the target's position based on the RSSI. This enhances the ability of our algorithm to process nonlinear data. We then apply an improved KF to modify this estimated position. Our improved KF adds the threshold value of the innovation update in the traditional KF. This value changes dynamically according to the target speed and network parameters to ensure the stability of the results. Simulations and real experiments in different scenarios demonstrate that our algorithm provides a superior tracking accuracy and stability compared to similar algorithms.
Collapse
Affiliation(s)
- Xing Wang
- Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China; (X.W.); (Z.W.); (R.L.); (Y.W.)
- State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
| | - Xuejun Liu
- Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China; (X.W.); (Z.W.); (R.L.); (Y.W.)
- State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
| | - Ziran Wang
- Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China; (X.W.); (Z.W.); (R.L.); (Y.W.)
- State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
| | - Ruichao Li
- Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China; (X.W.); (Z.W.); (R.L.); (Y.W.)
- State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
| | - Yiguang Wu
- Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China; (X.W.); (Z.W.); (R.L.); (Y.W.)
- State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
| |
Collapse
|
47
|
McKee KL, Hunter MD, Neale MC. A Method of Correcting Estimation Failure in Latent Differential Equations with Comparisons to Kalman Filtering. Multivariate Behav Res 2020; 55:405-424. [PMID: 31362529 PMCID: PMC6989395 DOI: 10.1080/00273171.2019.1642730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Studies have used the latent differential equation (LDE) model to estimate the parameters of damped oscillation in various phenomena, but it has been shown that correct, non-zero parameter estimates are only obtained when the latent series exhibits little or no process noise. Consequently, LDEs are limited to modeling deterministic processes with measurement error rather than those with random behavior in the true latent state. The reasons for these limitations are considered, and a piecewise deterministic approximation (PDA) algorithm is proposed to treat process noise outliers as functional discontinuities and obtain correct estimates of the damping parameter. Comprehensive, random-effects simulations were used to compare results with those obtained using a state-space model (SSM) based on the Kalman filter. The LDE with the PDA algorithm (LDEPDA) successfully recovered the simulated damping parameter under a variety of conditions when process noise was present in the latent state. The LDEPDA had greater precision and accuracy than the SSM when estimating parameters from data with sparse jump discontinuities, but worse performance for diffusion processes overall. All three methods were applied to a sample of postural sway data. The basic LDE estimated zero damping, while the LDEPDA and SSM estimated moderate to high damping. The SSM estimated the smallest standard errors for both frequency and damping parameter estimates.
Collapse
|
48
|
Zhao Y, Zhang J, Hu G, Zhong Y. Set-Membership Based Hybrid Kalman Filter for Nonlinear State Estimation under Systematic Uncertainty. Sensors (Basel) 2020; 20:s20030627. [PMID: 31979194 PMCID: PMC7038318 DOI: 10.3390/s20030627] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 01/20/2020] [Accepted: 01/21/2020] [Indexed: 11/26/2022]
Abstract
This paper presents a new set-membership based hybrid Kalman filter (SM-HKF) by combining the Kalman filtering (KF) framework with the set-membership concept for nonlinear state estimation under systematic uncertainty consisted of both stochastic error and unknown but bounded (UBB) error. Upon the linearization of the nonlinear system model via a Taylor series expansion, this method introduces a new UBB error term by combining the linearization error with systematic UBB error through the Minkowski sum. Subsequently, an optimal Kalman gain is derived to minimize the mean squared error of the state estimate in the KF framework by taking both stochastic and UBB errors into account. The proposed SM-HKF handles the systematic UBB error, stochastic error as well as the linearization error simultaneously, thus overcoming the limitations of the extended Kalman filter (EKF). The effectiveness and superiority of the proposed SM-HKF have been verified through simulations and comparison analysis with EKF. It is shown that the SM-HKF outperforms EKF for nonlinear state estimation with systematic UBB error and stochastic error.
Collapse
Affiliation(s)
- Yan Zhao
- Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China;
| | - Jing Zhang
- School of Public Management, Xi’an University of Finance and Economics, Xi’an 710061, China;
| | - Gaoge Hu
- School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
- Correspondence: ; Tel.: +86-29-8843-1316
| | - Yongmin Zhong
- School of Engineering, RMIT University, Bundoora, VIC 3083, Australia;
| |
Collapse
|
49
|
Yao L, Brown P, Shoaran M. Improved detection of Parkinsonian resting tremor with feature engineering and Kalman filtering. Clin Neurophysiol 2020; 131:274-284. [PMID: 31744673 PMCID: PMC6927801 DOI: 10.1016/j.clinph.2019.09.021] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 07/25/2019] [Accepted: 09/10/2019] [Indexed: 11/23/2022]
Abstract
OBJECTIVE Accurate and reliable detection of tremor onset in Parkinson's disease (PD) is critical to the success of adaptive deep brain stimulation (aDBS) therapy. Here, we investigated the potential use of feature engineering and machine learning methods for more accurate detection of rest tremor in PD. METHODS We analyzed the local field potential (LFP) recordings from the subthalamic nucleus region in 12 patients with PD (16 recordings). To explore the optimal biomarkers and the best performing classifier, the performance of state-of-the-art machine learning (ML) algorithms and various features of the subthalamic LFPs were compared. We further used a Kalman filtering technique in feature domain to reduce the false positive rate. RESULTS The Hjorth complexity showed a higher correlation with tremor, compared to other features in our study. In addition, by optimal selection of a maximum of five features with a sequential feature selection method and using the gradient boosted decision trees as the classifier, the system could achieve an average F1 score of up to 88.7% and a detection lead of 0.52 s. The use of Kalman filtering in feature space significantly improved the specificity by 17.0% (p = 0.002), thereby potentially reducing the unnecessary power dissipation of the conventional DBS system. CONCLUSION The use of relevant features combined with Kalman filtering and machine learning improves the accuracy of tremor detection during rest. SIGNIFICANCE The proposed method offers a potential solution for efficient on-demand stimulation for PD tremor.
Collapse
Affiliation(s)
- Lin Yao
- ECE Department, Cornell University, Ithaca, NY, USA.
| | - Peter Brown
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | | |
Collapse
|
50
|
Zhou S, Liu N, Shen C, Zhang L, He T, Yu B, Li J. An adaptive Kalman filtering algorithm based on back-propagation (BP) neural network applied for simultaneously detection of exhaled CO and N 2O. Spectrochim Acta A Mol Biomol Spectrosc 2019; 223:117332. [PMID: 31288168 DOI: 10.1016/j.saa.2019.117332] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Revised: 06/17/2019] [Accepted: 06/28/2019] [Indexed: 06/09/2023]
Abstract
A compact high-resolution spectroscopic sensor using a thermoelectrically (TE) cooled continuous-wave (CW) room temperature (RT) quantum cascade laser (QCL) was demonstrated for simultaneous measurements of exhaled carbon monoxide (CO) and nitrous oxide (N2O). The sampling pressure was optimized to improve the sensitivity, the optimal pressure was determined to be 150 mbar based on an optical density analysis of simulated and measured absorption spectra. An adaptive Kalman filtering algorithm based on back-propagation (BP) neural network was developed and proposed for real-time exhaled breath analysis in order to perform fast and high precision on-line measurements. The detection limits (1σ) of 1.14 ppb and 1.12 ppb were experimentally achieved for CO and N2O detection, respectively. Typical concentrations of exhaled CO and N2O from smokers and non-smokers were analyzed. The experimental results indicated that the state-of-the-art CW-QCL based sensor has a great potential for non-invasive, on-line identification and quantification of biomarkers in human breath.
Collapse
Affiliation(s)
- Sheng Zhou
- Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education, Anhui University, 230601 Hefei, China; Laser Spectroscopy and Sensing Laboratory, Anhui University, 230601 Hefei, China.
| | - Ningwu Liu
- Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education, Anhui University, 230601 Hefei, China; Laser Spectroscopy and Sensing Laboratory, Anhui University, 230601 Hefei, China
| | - Chongyang Shen
- Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education, Anhui University, 230601 Hefei, China; Laser Spectroscopy and Sensing Laboratory, Anhui University, 230601 Hefei, China
| | - Lei Zhang
- Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education, Anhui University, 230601 Hefei, China; Laser Spectroscopy and Sensing Laboratory, Anhui University, 230601 Hefei, China
| | - Tianbo He
- Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education, Anhui University, 230601 Hefei, China; Laser Spectroscopy and Sensing Laboratory, Anhui University, 230601 Hefei, China
| | - Benli Yu
- Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education, Anhui University, 230601 Hefei, China; Laser Spectroscopy and Sensing Laboratory, Anhui University, 230601 Hefei, China
| | - Jingsong Li
- Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education, Anhui University, 230601 Hefei, China; Laser Spectroscopy and Sensing Laboratory, Anhui University, 230601 Hefei, China.
| |
Collapse
|