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Gao Y, Hu J, Yu H, Du J, Jia C. Variance-Constrained Resilient $$H_{\infty }$$ State Estimation for Time-Varying Neural Networks with Random Saturation Observation Under Uncertain Occurrence Probability. Neural Process Lett 2023. [DOI: 10.1007/s11063-022-11078-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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2
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Zhang Y, Zheng CD. Stochastic synchronization for semi-Markovian complex dynamic networks with partly unknown transition rates. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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Li X, She K, Cheng J, Shi K, Peng Z, Zhong S. Dissipativity-based synthesis for semi-Markovian systems with simultaneous probabilistic sensors and actuators faults: A modified event-triggered strategy. ISA TRANSACTIONS 2022; 128:255-275. [PMID: 34666899 DOI: 10.1016/j.isatra.2021.09.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 09/17/2021] [Accepted: 09/21/2021] [Indexed: 06/13/2023]
Abstract
Aided by a modified event-triggered communication policy (ETCP), this article addresses the dissipativity-based control synthesis problem for semi-Markovian switching systems (SMSSs) with simultaneous multiplicative probabilistic faults on sensors and actuators modules. The resulting model under consideration is more extensive, which covers semi-Markovian switching coefficients, transmission delays, and randomly occurring sensors and actuators faults in a unified systematic analytical framework instead of investigating separately in some existing works. More specifically, the probabilistic faults are assumed to happen on both the sensors and actuators modules simultaneously, and the distortion probability for each sensor and actuator is irrelevant, which can be characterized by multiplicate mutually independent stochastic variables that obeys certain statistical features and probabilistic distribution delineate on the interval [0,✠](✠≥1). To reduce the bandwidth usage, a novel event-triggered strategy is designed. Additionally, in the light of this newly developed ETCP, and considering the effects of the signal transmission delays and multitudinous probabilistic failures, a generalized and more realistic faulty pattern for SMSSs is presented, which is more fit for real applications. Hereby, the principal superiority of the established new type faulty pattern lies in its practicality and generality, which contains some previous faulty models as special scenarios. By constructing an appropriate semi-Markovian Lyapunov functional (SMLF) together with mathematical analysis technique and matrix inequality decoupling operation, sojourn-time-dependent sufficient conditions for determining both the control gain matrices and triggered configuration coefficients are developed and formulated in terms of a group of feasible linear matrix inequalities (LMIs). Eventually, several practical examples are exploited to substantiate the validity and practicability of the developed control design methodology.
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Affiliation(s)
- Xiaoqing Li
- School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China; School of Electrical Engineering, Korea University, Seoul 136-701, South Korea.
| | - Kun She
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jun Cheng
- College of Mathematics and Statistics, Guangxi Normal University, Guilin 541006, China; School of Information Science and Engineering, Chengdu University, Chengdu 610106, China
| | - Kaibo Shi
- School of Information Science and Engineering, Chengdu University, Chengdu 610106, China
| | - Zhinan Peng
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Shouming Zhong
- School of Mathematics Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
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Delayed distributed impulsive synchronization of coupled neural networks with mixed couplings. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.07.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Sun B, van Kampen EJ. Event-triggered constrained control using explainable global dual heuristic programming for nonlinear discrete-time systems. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.10.046] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Rajaei H, Esmaeilzadeh F, Mowla D. Synthesis and Characterization of Nano-Sized Pt/HZSM–5 Catalyst for Application in the Xylene Isomerization Process. Catal Letters 2022. [DOI: 10.1007/s10562-021-03604-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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Guan Z, Zhao Y, Wang X. Design pragmatic method to low-carbon economy visualisation in enterprise systems based on big data. ENTERP INF SYST-UK 2021. [DOI: 10.1080/17517575.2021.1898049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Zhigui Guan
- School of Finance and Economics, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Yuanjun Zhao
- School of Business Administration, Shanghai Lixin University of Accounting and Finance, Shanghai, China
| | - Xingdong Wang
- School of Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
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Hu J, Chen H, Heidari AA, Wang M, Zhang X, Chen Y, Pan Z. Orthogonal learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and feature selection. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106684] [Citation(s) in RCA: 123] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Double adaptive weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical diagnosis. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106728] [Citation(s) in RCA: 112] [Impact Index Per Article: 37.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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Fan Y, Wang P, Mafarja M, Wang M, Zhao X, Chen H. A bioinformatic variant fruit fly optimizer for tackling optimization problems. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106704] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Ye H, Wu P, Zhu T, Xiao Z, Zhang X, Zheng L, Zheng R, Sun Y, Zhou W, Fu Q, Ye X, Chen A, Zheng S, Heidari AA, Wang M, Zhu J, Chen H, Li J. Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:17787-17802. [PMID: 34786302 PMCID: PMC8545238 DOI: 10.1109/access.2021.3052835] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 01/15/2021] [Indexed: 05/26/2023]
Abstract
This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients' necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN's optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction.
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Affiliation(s)
- Hua Ye
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Peiliang Wu
- Department of Pulmonary and Critical Care MedicineThe 1st Affiliated Hospital, Wenzhou Medical UniversityWenzhou325000China
| | - Tianru Zhu
- The Second Clinical CollegeWenzhou Medical UniversityWenzhou325000China
| | - Zhongxiang Xiao
- Department of PharmacyAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Xie Zhang
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Long Zheng
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Rongwei Zheng
- Department of UrologyAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Yangjie Sun
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Weilong Zhou
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Qinlei Fu
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Xinxin Ye
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Ali Chen
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Shuang Zheng
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of EngineeringUniversity of TehranTehran1417466191Iran
- Department of Computer ScienceSchool of ComputingNational University of SingaporeSingapore117417
| | - Mingjing Wang
- Institute of Research and Development, Duy Tan UniversityDa Nang550000Vietnam
| | - Jiandong Zhu
- Department of Surgical OncologyAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
| | - Huiling Chen
- College of Computer Science and Artificial IntelligenceWenzhou UniversityWenzhou325035China
| | - Jifa Li
- Department of Pulmonary and Critical Care MedicineAffiliated Yueqing Hospital, Wenzhou Medical UniversityYueqing325600China
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Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11020821] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Safety is an essential topic to the architecture, engineering and construction (AEC) industry. However, traditional methods for structural health monitoring (SHM) and jobsite safety management (JSM) are not only inefficient, but also costly. In the past decade, scholars have developed a wide range of deep learning (DL) applications to address automated structure inspection and on-site safety monitoring, such as the identification of structural defects, deterioration patterns, unsafe workforce behaviors and latent risk factors. Although numerous studies have examined the effectiveness of the DL methodology, there has not been one comprehensive, systematic, evidence-based review of all individual articles that investigate the effectiveness of using DL in the SHM and JSM industry to date, nor has there been an examination of this body of evidence in regard to these methodological problems. Therefore, the objective of this paper is to disclose the state of the art of current research progress and determine the relevant gaps, challenges and future work. Methodically, CiteSpace was employed to summarize the research trends, advancements and frontiers of DL applications from 2010 to 2020. Next, an application-focused literature review was conducted, which led to a summary of research gaps, recommendations and future research directions. Overall, this review gains insight into SHM and JSM and aims to help researchers formulate more types of effective DL applications which have not been addressed sufficiently for the time being.
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Leader-Following Mean Square Consensus of Stochastic Multi-agent Systems via Periodically Intermittent Event-Triggered Control. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10388-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Asynchronous $$l_{2}$$–$$l_{\infty }$$ Filtering for Discrete-Time Fuzzy Markov Jump Neural Networks with Unreliable Communication Links. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10337-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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