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Optimization Control for Mean Square Synchronization of Stochastic Semi-Markov Jump Neural Networks with Non-Fragile Hidden Information and Actuator Saturation

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SSRN 2024年

作者： Yang, Zou Wang, Jun Shi, Kaibo Cai, Xiao Han, Sheng College of Electrical and Information Engineering Southwest Minzu University Chengdu610041 China School of Electronic Information and Electrical Engineering Chengdu University Sichuan Chengdu610106 China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China

This paper studies the asynchronous output feedback control and H∞ synchronization problems for a class of continuous-time stochastic hidden semi-Markov jump neural networks (SMJNNs) affected by actuator saturation. Initially, a novel neural networks (NNs) model is constructed, incorporating semi-Markov process (SMP), hidden information, and Brownian motion to accurately simulate the complexity and uncertainty of real-world environments. Secondly, acknowledging system mode mismatches and the need for robust anti-interference capabilities, a non-fragile controller based on hidden information is proposed. The designed controller effectively mitigates the impact of uncertainties, enhancing system reliability. Furthermore, we provide sufficient conditions for stochastic mean square synchronization (MSS) within the domain of attraction, achieving optimal control through the construction of a Lyapunov function based on SMP. Finally, the feasibility of the proposed method is verified through numerical examples. © 2024, The Authors. All rights reserved.

关键词： Continuous time systems

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Application of PSO-LSTM for Forecasting of Ship Traffic Flow

Application of PSO-LSTM for Forecasting of Ship Traffic Flow

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2021 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2021

作者： Zhou, Haoxuan Zuo, Yi Li, Tieshan Li, Shanshan Navigation College Dalian Maritime University Dalian China School of Automation Engineering University of Electtronic Science and Technology of China Chengdu China School of Automation Engineering University of Electtronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665443227

Ship traffic flow prediction is facing many problems at present, such as high randomness, many influencing factors and low accuracy. In this paper, a long short-term memory (LSTM) prediction model based on particle swarm optimization (PSO) is proposed to improve the accuracy of ship traffic flow prediction. Aiming at the problem that the parameters of LSTM model are difficult to select, PSO is used to optimize the parameters of LSTM model. The PSOLSTM model was constructed based on the data of the intersections of inland waterways in Huaian section of the Beijing-Hangzhou Grand Canal. The prediction results of this model are compared with the LSTM model and Support Vector Regression (SVR) model. The experimental results show that the root mean square error (RMSE) of the model is 2.211, the mean absolute error (MAE) is 2.068. Compared with other prediction models, the prediction accuracy is improved obviously. © 2021 IEEE.

关键词： Long short-term memory

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A Systemic Method of Traffic Flow Velocity Prediction in Narrow Waterways Using AIS Data

A Systemic Method of Traffic Flow Velocity Prediction in Nar...

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2021 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2021

作者： Bai, Xiaoyu Zuo, Yi Li, Tieshan Wang, Haocheng Dalian Maritime University Navigation College Dalian China University of Electronic Science and Technology of China School of Automation Engineering Chengdu China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665443227

The vessel traffic flow refers to the overall movement of continuously operating vessels and other objects which exhibit similar characteristics in water transportation. There are five parameters that quantitatively describe the vessel traffic flow: traffic flow direction, traffic flow, traffic flow width, traffic flow density and traffic flow velocity. The current researches of vessel traffic flow mainly focus on flow forecasting. In order to solve the problem of traffic flow velocity prediction, a time series models which based on automatic identification system (AIS) data are used to predict the traffic flow velocity of narrow waterway in Beijing-Hangzhou Grand Canal. The Prophet Model, which is an open source time series analysis model developed by Facebook, is based on the Additive Model, which uses year, month, day, etc., plus holiday effects to fit non-linear trends. This model is most suitable for fitting data with strong periodicity and several cycles, and has good support for missing values, trend offsets and outliers. The results show that the Prophet model is superior to the Long Short-term Memory (LSTM) model in terms of ship traffic flow velocity prediction accuracy and training time. It has stronger applicability and higher accuracy. The Prophet time series prediction model can provide significant scientific basis for maritime administration authorities in their daily management and program of jurisdiction waters. © 2021 IEEE.

关键词： Long short-term memory

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Nonlinear-Drift-Driven Wiener Process–Markov Chain Switching Model for Predicting Lithium-Ion Battery Remaining Useful Life 1

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5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022

作者： Zhang, Yixing Feng, Fei Wang, Shunli Meng, Jinhao Xie, Jiale Yin, Hongpeng Chai, Yi School of Automation Chongqing University Chongqing400044 China Key Laboratory of Complex System Safety and Control Chongqing University Chongqing400044 China School of Information Engineering Southwest University of Science and Technology Mianyang621010 China College of Electrical Engineering Sichuan University Chengdu610065 China School of Automation North China Electric Power University Baoding071003 China

ISBN: (数字)9789819910274

ISBN: (纸本)9789819910267

The accurate prediction of remaining useful life (RUL) is vital to improve the safety and reliability of lithium-ion battery power systems. However, owing to the effects of state (work and storage) switching and retention time randomness, it is difficult to accurately predict the RUL of lithium-ion batteries in real time. This study uses a nonlinear-drift-driven Wiener process to describe the dynamic degradation paths of lithium-ion batteries, and utilizes the Markov chain to establish a switching model and to predict the future state-switching probability. The analytical distribution of the first arrival time of the lithium-ion battery failure threshold is derived, and the model posterior parameters are updated using the Bayesian strategy. Finally, the model is verified using a lithium-ion battery dataset from the National Aeronautics and Space Administration. The RUL prediction model that considers state switching is superior to models that do not consider state switching. Moreover, the model shows a relative error of only 5.9388%. Overall, this study provides a theoretical basis for the research and development of lithium-ion battery RUL prediction and health management systems. © 2023, Beijing Paike Culture Commu. Co., Ltd.

关键词： Lithium-ion batteries

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Correction to:Fast nuclide identification based on a sequential Bayesian method

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nuclear Science and Techniques 2022年第3期33卷 29-29页

作者： Xiao-Zhe Li Qing-Xian Zhang He-Yi Tan Zhi-Qiang Cheng Liang-Quan Ge Guo-Qiang Zeng Wan-Chang Lai Chengdu University of Technology The College of Nuclear Technology and Automation EngineeringChengdu610059China

Correction to:NUCL SCI TECH(2021)32:143 https://***/10.1007/s41365-021-00982-z In the original article,the funding information were missing and in this correction the funding information is given below:Funding This work was supported by the National Natural science Foundation of China(No.41774190),the Special-funded program on national key scientific instruments and equipment development(No.2017YFC0602105),and the Department of Science and technology of Sichuan Province(No.2020JDRC0109).

关键词： funded correction funding

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Design of Shocks & Vibrations Monitoring System for Long Distance Transportation in Confined Spaces

Design of Shocks & Vibrations Monitoring System for Long Dis...

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Chinese automation Congress (CAC)

作者： Shiyong Huang Yuru Geng Yong Han Mingfei Gu Chengdu China Nuclear Haichuan Nuclear Technology Co. Ltd Nuclear Power Institute of China Chengdu China School of Network and Communication Engineering Chengdu Technological University Chengdu China Chengdu Vocational & Technical College of Industry Chengdu China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China

In real life, many valuable products and highvalue important products are transported in long distances. Shocks and vibrations are caused during transportation, loading and unloading for various reasons. Any shock and vibration exceeding the regulations may cause damage to the transported products. So, it is necessary to monitor and analyze these data of shocks and vibrations scientifically, while considering that some transportation conditions have special requirements for installation space and transportation time. Therefore, an ultra-thin and long-lasting monitoring system has been developed based on MEMS accelerometer technology to measure shocks and vibrations. After series of independent tests and calibration tests conducted by a third party on the product, the results show that it can record and store the threeaxis acceleration, temperature, humidity, pressure, time and other parameters of the transportation process in real time, and complete the preliminary assessment of the transportation status of high-value products. Then engineers can conduct professional analysis of the data to obtain scientific transportation monitoring reports, and provide scientific basis for taking safety prevention and treatment measures for subsequent transportation.

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Impact Increment Based Hybrid Reliability Assessment Method for Transmission Systems

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CSEE Journal of Power and Energy Systems 2022年第1期8卷 317-328页

作者： Yunkai Lei Yeguang Sun Kai Hou Pei Zhang Lewei Zhu Xiaoxi Yang Xiaonan Liu State Grid Sichuan Economic Research Institute Chengdu610000 China School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China State Grid Customer Service Center Tianjin 300072China School of Electrical and Automation Engineering East China Jiaotong UnviersityNanchang 330052China Maritime College Tianjin University of TechnologyTianjin 300384China College of Economics and Management Southwest Petroleum UniversityChengdu 610000China State Grid Tianjin Electric Power Research Institute Tianjin 300072China

This paper proposes an impact-increment-based hybrid(IIHybrid)reliability assessment approach for power transmission *** proposed approach integrates the advantages of the impact-increment-based state enumeration method(IISE)and impact-increment-based Monte Carlo simulation(IIMC)to improve computational efficiency and *** IISE can efficiently assess the impacts of low-order *** accuracy is,however,sacrificed as highorder contingencies are usually *** IIMC is more suitable for large-scale contingency spaces compared with IISE,although the calculation process is *** this paper,the proposed IIHybrid takes advantage of its strengths while avoiding its *** IISE and the IIMC are applied to lower and higher contingency spaces *** high-order contingencies elimination technique proposed in our previous studies is still applicable to the *** addition,efficiency can be controlled by modifying the preset parameters to adapt to various *** studies are performed on the IEEE 118-bus test system and PEGASE *** results show that the proposed approach is more efficient and practicable than traditional methods.

关键词： Hybrid method impact-increment reliability assessment transmission system

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Temperature-dependent degradation of Al2O3—CrAl-O—Cr-O—Al-O multilayer coatings on ferritic-martensitic steels in oxygen-saturated lead–bismuth eutectic

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Materials and Design 2025年 254卷

作者： Ma, Jiaojiao Xing, Lili Tan, Jie Wang, Hui He, Xiujie Yin, Xing Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen University Guangdong Zhuhai519082 China Cancer Center of the Fifth Affiliated Hospital of Sun Yat-sen University Guangdong Zhuhai519000 China College of Materials Science and Engineering Sichuan University Chengdu610065 China National Key Laboratory of Nuclear Reactor Technology Nuclear Power Institute of China Chengdu610041 China State Key Laboratory of Advanced Nuclear Energy Technology Nuclear Power Institute of China Chengdu610041 China

The strong corrosiveness of lead–bismuth eutectic (LBE) to alloys significantly limits the development of lead-cooled fast reactors. Herein, a multilayer coating containing Al-O, Cr-O, CrAl-O, and Al2O3 layers were deposited on ferritic–martensitic steel using magnetron sputtering and tested in oxygen-saturated LBE at 450, 550, and 650 °C for 2000 h. The phase transformation and microstructural evolution of the coating, owing to temperature and element diffusion, were characterized in detail at the microscale. At 450 °C, the Al2O3 layer partially transformed to δ*-Al2O3 and PbAl2O4, while the Cr-O layer crystallized into Cr2O3. At 650 °C, γ-Al2O3 transitioned to α-Al2O3, and a dense α-(Cr,Al)2O3 phase formed at the CrAl-O/Al2O3 interface, effectively blocking LBE penetration. Despite full infiltration of the Al2O3 layer at 650 °C, the CrAl-O interlayer suppressed elemental interdiffusion, maintaining substrate integrity. Temperature-dependent degradation revealed critical failure at the Cr-O layer due to MnCr2O4-induced porosity. The multilayer coating maintained a relatively good integrity at high temperatures and protected the substrate from LBE corrosion. © 2025 The Author(s)

关键词： Steel corrosion

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Integrated Sliding Mode Fault- Tolerant Control for Dynamic Positioning T-S Fuzzy Models of Unmanned Marine Vehicles with Thruster Saturation

Integrated Sliding Mode Fault- Tolerant Control for Dynamic ...

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Asian Control Conference

作者： Yang Wang Li–Ying Hao Tieshan Li C.L. Philip Chen College of Navigation Dalian Maritime University Dalian China Marine Electrical Engineering College Dalian Maritime University Dalian China College of Automation Engineering University of Electronic Science and Technology of China Chengdu China College of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This research focuses on addressing the fault-tolerant control (FTC) issue in the context of Takagi-Sugeno (T-S) fuzzy models applied to unmanned marine vehicles (UMVs) with saturation phenomenon. Firstly, considering the saturation and faults of the thrusters, the corresponding T-S fuzzy models for UMV s are established. a novel strategy for integral sliding mode control (ISM C) is developed to address the impact of thruster faults, saturation and ocean disturbances. In contrast to conventional integral sliding mode approaches, this study simultaneously integrates thruster saturation and fault estimation information to construct the sliding surface, thereby reducing conservatism. Ultimately, the comparative simulations have show-cased the effectiveness of the proposed scheme.

关键词： Adaptation models Sea surface Attitude control Prevention and mitigation Estimation Takagi-Sugeno model Nonlinear dynamical systems

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Data Enhancement technology Based on I-GOS Algorithm

Data Enhancement Technology Based on I-GOS Algorithm

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2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021

作者： Li, Fei Deng, Xiaotong Shi, Feifei Zhou, Xiaoxue Xia, Kaijun Hu, Guangdi Mechanical Engineering Southwest Jiaotong University Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China School of Nuclear Science and Technology Lanzhou University Lanzhou China Jiangsu Xintongda Electronic Technology Co. Ltd Jiangsu China Zhejiang Geely New Energy Commercial Vehicle Group Co. Ltd Zhejiang China

ISBN: (纸本)9781665437554

Although deep learning is developing rapidly and being applied on a large scale, its essence is still a black box model. When evaluating the credibility of the model, the judgment for the prediction of the model is important. Based on the I-GOS algorithm in the field of visual interpretation, we effectively extract the effective information of the sample and then use the sample to retrain a new model to improve the accuracy of the model. Thus, a new data enhancement method is proposed to train the algorithm by using the ImageNet dataset and retrain it by using the I-GOS algorithm to extract effective information. The experiment result shows that compared with the original model, the accuracy of the new model is improved by 5% in the top 1 and 3% in the top 5, which fully proves the effectiveness of this data enhancement method. © 2021 IEEE.

关键词： Image enhancement

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