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Chen L, Liu X, Zeng C, He X, Chen F, Zhu B. Temperature Prediction of Seasonal Frozen Subgrades Based on CEEMDAN-LSTM Hybrid Model. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22155742. [PMID: 35957299 PMCID: PMC9370898 DOI: 10.3390/s22155742] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 05/27/2023]
Abstract
Improving the temperature prediction accuracy for subgrades in seasonally frozen regions will greatly help improve the understanding of subgrades' thermal states. Due to the nonlinearity and non-stationarity of the temperature time series of subgrades, it is difficult for a single general neural network to accurately capture these two characteristics. Many hybrid models have been proposed to more accurately forecast the temperature time series. Among these hybrid models, the CEEMDAN-LSTM model is promising, thanks to the advantages of the long short-term memory (LSTM) artificial neural network, which is good at handling complex time series data, and its combination with the broad applicability of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) in the field of signal decomposition. In this study, by performing empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and CEEMDAN on temperature time series, respectively, a hybrid dataset is formed with the corresponding time series of volumetric water content and frost heave, and finally, the CEEMDAN-LSTM model is created for prediction purposes. The results of the performance comparisons between multiple models show that the CEEMDAN-LSTM model has the best prediction performance compared to other decomposed LSTM models because the composition of the hybrid dataset improves predictive ability, and thus, it can better handle the nonlinearity and non-stationarity of the temperature time series data.
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Affiliation(s)
- Liyue Chen
- Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China; (L.C.); (B.Z.)
- China Communications Construction Company Second Highway Consultants Co., Ltd., Wuhan 430056, China; (C.Z.); (X.H.); (F.C.)
| | - Xiao Liu
- Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China; (L.C.); (B.Z.)
| | - Chao Zeng
- China Communications Construction Company Second Highway Consultants Co., Ltd., Wuhan 430056, China; (C.Z.); (X.H.); (F.C.)
| | - Xianzhi He
- China Communications Construction Company Second Highway Consultants Co., Ltd., Wuhan 430056, China; (C.Z.); (X.H.); (F.C.)
| | - Fengguang Chen
- China Communications Construction Company Second Highway Consultants Co., Ltd., Wuhan 430056, China; (C.Z.); (X.H.); (F.C.)
| | - Baoshan Zhu
- Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China; (L.C.); (B.Z.)
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From Intelligent Energy Management to Value Economy through a Digital Energy Currency: Bahrain City Case Study. SENSORS 2020; 20:s20051456. [PMID: 32155853 PMCID: PMC7085701 DOI: 10.3390/s20051456] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 03/03/2020] [Accepted: 03/04/2020] [Indexed: 11/29/2022]
Abstract
The transition of the energy system into a more efficient state requires innovative ideas to finance new schemes and engage people into adjusting their behavioural patterns concerning consumption. Effective energy management combined with Information and Communication Technologies (ICTs) open new opportunities for local and regional authorities, but also for energy suppliers, utilities and other obligated parties, or even energy cooperatives, to implement mechanisms that allow people to become more efficient either by producing and trading energy or by reducing their energy consumption. In this paper, a novel framework is proposed connecting energy savings with a digital energy currency. This framework builds reward schemes where the energy end-users could benefit financially from saving energy, by receiving coins according to their real consumption compared to the predicted consumption if no actions were to take place. A pilot appraisal of such a scheme is presented for the case of Bahrain, so as to simulate the behaviour of the proposed framework in order for it to become a viable choice for intelligent energy management in future action plans.
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Dong Y, Wang J, Guo Z. Research and application of local perceptron neural network in highway rectifier for time series forecasting. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2017.12.022] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Ma J, Meng F, Zhou Y, Wang Y, Shi P. Distributed Water Pollution Source Localization with Mobile UV-Visible Spectrometer Probes in Wireless Sensor Networks. SENSORS (BASEL, SWITZERLAND) 2018; 18:E606. [PMID: 29462929 PMCID: PMC5855095 DOI: 10.3390/s18020606] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 02/10/2018] [Accepted: 02/13/2018] [Indexed: 11/16/2022]
Abstract
Pollution accidents that occur in surface waters, especially in drinking water source areas, greatly threaten the urban water supply system. During water pollution source localization, there are complicated pollutant spreading conditions and pollutant concentrations vary in a wide range. This paper provides a scalable total solution, investigating a distributed localization method in wireless sensor networks equipped with mobile ultraviolet-visible (UV-visible) spectrometer probes. A wireless sensor network is defined for water quality monitoring, where unmanned surface vehicles and buoys serve as mobile and stationary nodes, respectively. Both types of nodes carry UV-visible spectrometer probes to acquire in-situ multiple water quality parameter measurements, in which a self-adaptive optical path mechanism is designed to flexibly adjust the measurement range. A novel distributed algorithm, called Dual-PSO, is proposed to search for the water pollution source, where one particle swarm optimization (PSO) procedure computes the water quality multi-parameter measurements on each node, utilizing UV-visible absorption spectra, and another one finds the global solution of the pollution source position, regarding mobile nodes as particles. Besides, this algorithm uses entropy to dynamically recognize the most sensitive parameter during searching. Experimental results demonstrate that online multi-parameter monitoring of a drinking water source area with a wide dynamic range is achieved by this wireless sensor network and water pollution sources are localized efficiently with low-cost mobile node paths.
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Affiliation(s)
- Junjie Ma
- Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
| | - Fansheng Meng
- Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
| | - Yuexi Zhou
- Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
| | - Yeyao Wang
- China National Environmental Monitoring Centre, Beijing 100012, China.
| | - Ping Shi
- Yiwen Environmental Science Technology Co., Ltd., Guangzhou 510663, China.
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Zhou R, Yang C, Wan J, Zhang W, Guan B, Xiong N. Measuring Complexity and Predictability of Time Series with Flexible Multiscale Entropy for Sensor Networks. SENSORS 2017; 17:s17040787. [PMID: 28383496 PMCID: PMC5422060 DOI: 10.3390/s17040787] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Revised: 03/15/2017] [Accepted: 03/24/2017] [Indexed: 11/16/2022]
Abstract
Measurement of time series complexity and predictability is sometimes the cornerstone for proposing solutions to topology and congestion control problems in sensor networks. As a method of measuring time series complexity and predictability, multiscale entropy (MSE) has been widely applied in many fields. However, sample entropy, which is the fundamental component of MSE, measures the similarity of two subsequences of a time series with either zero or one, but without in-between values, which causes sudden changes of entropy values even if the time series embraces small changes. This problem becomes especially severe when the length of time series is getting short. For solving such the problem, we propose flexible multiscale entropy (FMSE), which introduces a novel similarity function measuring the similarity of two subsequences with full-range values from zero to one, and thus increases the reliability and stability of measuring time series complexity. The proposed method is evaluated on both synthetic and real time series, including white noise, 1/f noise and real vibration signals. The evaluation results demonstrate that FMSE has a significant improvement in reliability and stability of measuring complexity of time series, especially when the length of time series is short, compared to MSE and composite multiscale entropy (CMSE). The proposed method FMSE is capable of improving the performance of time series analysis based topology and traffic congestion control techniques.
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Affiliation(s)
- Renjie Zhou
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
- Key Laboratory of Complex Systems Modeling and Simulation of Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China.
| | - Chen Yang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
- Key Laboratory of Complex Systems Modeling and Simulation of Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China.
| | - Jian Wan
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
- Key Laboratory of Complex Systems Modeling and Simulation of Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China.
| | - Wei Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
- Key Laboratory of Complex Systems Modeling and Simulation of Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China.
| | - Bo Guan
- School of Electronic and Information Engineer, Ningbo University of Technology, Ningbo 315211, China.
| | - Naixue Xiong
- Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK 74464, USA.
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Intelligent simultaneous quantitative online analysis of environmental trace heavy metals with total-reflection X-ray fluorescence. SENSORS 2015; 15:10650-75. [PMID: 25954949 PMCID: PMC4481981 DOI: 10.3390/s150510650] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Revised: 04/20/2015] [Accepted: 04/30/2015] [Indexed: 12/02/2022]
Abstract
Total-reflection X-ray fluorescence (TXRF) has achieved remarkable success with the advantages of simultaneous multi-element analysis capability, decreased background noise, no matrix effects, wide dynamic range, ease of operation, and potential of trace analysis. Simultaneous quantitative online analysis of trace heavy metals is urgently required by dynamic environmental monitoring and management, and TXRF has potential in this application domain. However, it calls for an online analysis scheme based on TXRF as well as a robust and rapid quantification method, which have not been well explored yet. Besides, spectral overlapping and background effects may lead to loss of accuracy or even faulty results during practical quantitative TXRF analysis. This paper proposes an intelligent, multi-element quantification method according to the established online TXRF analysis platform. In the intelligent quantification method, collected characteristic curves of all existing elements and a pre-estimated background curve in the whole spectrum scope are used to approximate the measured spectrum. A novel hybrid algorithm, PSO-RBFN-SA, is designed to solve the curve-fitting problem, with offline global optimization and fast online computing. Experimental results verify that simultaneous quantification of trace heavy metals, including Cr, Mn, Fe, Co, Ni, Cu and Zn, is realized on the online TXRF analysis platform, and both high measurement precision and computational efficiency are obtained.
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Cheng C, Sa-Ngasoongsong A, Beyca O, Le T, Yang H, Kong Z(J, Bukkapatnam ST. Time series forecasting for nonlinear and non-stationary processes: a review and comparative study. ACTA ACUST UNITED AC 2015. [DOI: 10.1080/0740817x.2014.999180] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Power Conservation through Energy Efficient Routing in Wireless Sensor Networks. SENSORS 2009; 9:7320-42. [PMID: 22399998 PMCID: PMC3290468 DOI: 10.3390/s90907320] [Citation(s) in RCA: 78] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2009] [Revised: 09/09/2009] [Accepted: 09/10/2009] [Indexed: 11/17/2022]
Abstract
The power awareness issue is the primary concern within the domain of Wireless Sensor Networks (WSNs). Most power dissipation ocurrs during communication, thus routing protocols in WSNs mainly aim at power conservation. Moreover, a routing protocol should be scalable, so that its effectiveness does not degrade as the network size increases. In response to these issues, this work describes the development of an efficient routing protocol, named SHPER (Scaling Hierarchical Power Efficient Routing).
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Niu J, Deng Z. Collaborative distributed scheduling approaches for wireless sensor network. SENSORS 2009; 9:8007-30. [PMID: 22408491 PMCID: PMC3292093 DOI: 10.3390/s91008007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2009] [Revised: 09/04/2009] [Accepted: 09/29/2009] [Indexed: 11/21/2022]
Abstract
Energy constraints restrict the lifetime of wireless sensor networks (WSNs) with battery-powered nodes, which poses great challenges for their large scale application. In this paper, we propose a family of collaborative distributed scheduling approaches (CDSAs) based on the Markov process to reduce the energy consumption of a WSN. The family of CDSAs comprises of two approaches: a one-step collaborative distributed approach and a two-step collaborative distributed approach. The approaches enable nodes to learn the behavior information of its environment collaboratively and integrate sleep scheduling with transmission scheduling to reduce the energy consumption. We analyze the adaptability and practicality features of the CDSAs. The simulation results show that the two proposed approaches can effectively reduce nodes' energy consumption. Some other characteristics of the CDSAs like buffer occupation and packet delay are also analyzed in this paper. We evaluate CDSAs extensively on a 15-node WSN testbed. The test results show that the CDSAs conserve the energy effectively and are feasible for real WSNs.
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Affiliation(s)
- Jianjun Niu
- State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
- Beijing Special Engineering Design and Research Institute, Beijing 100028, China; E-Mail:
| | - Zhidong Deng
- State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
- Author to whom correspondence should be addressed; E-Mail: ; Tel.: +86-10-62796830; Fax: +86-10-62796830
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Xia F, Tian YC, Li Y, Sun Y. Wireless Sensor/Actuator Network Design for Mobile Control Applications. SENSORS 2007; 7:2157-2173. [PMID: 28903220 PMCID: PMC3864515 DOI: 10.3390/s7102157] [Citation(s) in RCA: 111] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2007] [Accepted: 10/08/2007] [Indexed: 12/02/2022]
Abstract
Wireless sensor/actuator networks (WSANs) are emerging as a new generation of sensor networks. Serving as the backbone of control applications, WSANs will enable an unprecedented degree of distributed and mobile control. However, the unreliability of wireless communications and the real-time requirements of control applications raise great challenges for WSAN design. With emphasis on the reliability issue, this paper presents an application-level design methodology for WSANs in mobile control applications. The solution is generic in that it is independent of the underlying platforms, environment, control system models, and controller design. To capture the link quality characteristics in terms of packet loss rate, experiments are conducted on a real WSAN system. From the experimental observations, a simple yet efficient method is proposed to deal with unpredictable packet loss on actuator nodes. Trace-based simulations give promising results, which demonstrate the effectiveness of the proposed approach.
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Affiliation(s)
- Feng Xia
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China E-mail:
- Faculty of Information Technology, Queensland University of Technology, GPO Box 2434, Brisbane QLD 4001, Australia E-mail:
| | - Yu-Chu Tian
- Faculty of Information Technology, Queensland University of Technology, GPO Box 2434, Brisbane QLD 4001, Australia E-mail:
- Authors to whom correspondence should be addressed. E-mail:
| | - Yanjun Li
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China E-mail:
| | - Youxian Sun
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China E-mail:
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