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11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2019

作者： Liu, Yuhao Wan, Yiming Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China

ISBN: (纸本)9781728106816

The fault detection task in lithium-ion battery management system (BMS) is critical to the safety and reliability of rechargeable and hybrid electric vehicles. To explicitly account for inevitable errors of battery model parameters, a robust fault detection method is proposed in this paper. The residual generator is established by exploiting an equivalent circuit model. The effect of uncertainties on the residual is parametrized as polynomial dependence on probabilistic uncertain parameters and noises. Then Gaussian mixtures are adopted to derive the residual distribution. To account for residual uncertainties in the detection decision, the weighted sum of distances from the generated residual to each Gaussian component of the residual distribution is proposed. Simulation results illustrate that our proposed method outperforms the conventional approach that does not consider parametric uncertainties. © 2019 IEEE.

关键词： Fault detection

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A New control System Based on An Improved PID Neural Network controller for Landslide System control

A New Control System Based on An Improved PID Neural Network...

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Chinese control Conference (CCC)

作者： Ying Zhang Shu Sun Cheng Lian Xiaoping Wang School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In this paper, a new control system based on the improved PID neural network controller (PIDNNC) is designed to control the landslide displacement. In the control system, the fuzzy comprehensive judgement model of landslide system is established to judge whether the system needs to be controlled and give the expected displacement. To solve the problem of random initialization in PIDNNC, This paper proposed an improved PIDNNC based on fuzzy PID controller (F-PIDNNC). The fuzzy PID controller is used to obtain the proportional, integral and differential coefficients k p , k i and k d . Then the obtained parameters are taken as the initial weights of PIDNN. The effectiveness of the proposed control system is simulated on Baishuihe and Shiliushubao landslides. The results show that the proposed control system based on F-PIDNNC is much accurate and faster than the traditional one-dimensional fuzzy PID controller and PIDNNC.

关键词： Terrain factors control systems Neural networks Neurons Predictive models Stability criteria

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Prox-DBRO-VR: A Unified Analysis on Byzantine-Resilient Decentralized Stochastic Composite Optimization with Variance Reduction

arXiv

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arXiv 2023年

作者： Hu, Jinhui Chen, Guo Li, Huaqing Guo, Xiaoyu Ran, Liang Huang, Tingwen School of Automation Central South University Hunan Changsha410083 China School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia Department of Mechanical Engineering City University of Hong Kong Kowloon Tong Hong Kong Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

Decentralized stochastic gradient algorithms efficiently solve large-scale finite-sum optimization problems when all agents in the network are reliable. However, most of these algorithms are not resilient to adverse conditions, such as malfunctioning agents, software bugs, and cyber attacks. This paper aims to handle a class of general composite optimization problems over multi-agent systems (MASs) in the presence of an unknown number of Byzantine agents. Building on a resilient model-aggregation mechanism and the proximal-gradient mapping method, a Byzantine-resilient decentralized stochastic proximal-gradient algorithmic framework is proposed, dubbed ProxDBRO-VR, which achieves an optimization and control goal using only local computations and communications. To asymptotically reduce the noise variance arising from local gradient estimation and accelerate the convergence, we incorporate two localized variance-reduced (VR) techniques (SAGA and LSVRG) into ProxDBRO-VR to design Prox-DBRO-SAGA and Prox-DBRO-LSVRG. By analyzing the contraction relationships among the gradient-learning error, resilient consensus condition, and convergence error in a unified theoretical framework, it is proved that both Prox-DBRO-SAGA and Prox-DBRO-LSVRG, with a well-designed constant (resp., decaying) step-size, converge linearly (resp., sub-linearly) inside an error ball around the optimal solution to the original problem under standard assumptions. A trade-off between convergence accuracy and Byzantine resilience in both linear and sub-linear cases is also characterized. In numerical experiments, the effectiveness and practicability of the proposed algorithms are manifested via resolving a decentralized sparse machine-learning problem under various Byzantine attacks. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous agents

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Adaptive Histogram Thresholding-Based Leukocyte image Segmentation 15th

Adaptive Histogram Thresholding-Based Leukocyte Image Segmen...

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15th International Conference on intelligent information Hiding and Multimedia Signal processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of information Technology, Applications and Tools, FITAT 2019

作者： Zhou, Xiaogen Wang, Chuansheng Li, Zuoyong Zhang, Fuquan College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Computer Science and Technology Harbin University of Science and Technology Harbin China

ISBN: (纸本)9789811397097

To improve the accuracy of leukocyte segmentation, this paper presents a novel method based on adaptive histogram thresholding (AHT). The proposed method first employs color component combination and AHT to extract the nucleus of leukocyte and utilizes image color features to remove the complex backgrounds such as red blood cells (RBCs) and substantial dyeing impurities. Then, Canny edge detection is performed to extract the entire leukocyte. Finally, the cytoplasm of the leukocyte is obtained by subtracting the nucleus with the entire leukocyte. Experimental results on an image dataset containing 60 leukocyte images show that the proposed method generates more accurate segmentation results than the counterparts. © Springer Nature Singapore Pte Ltd. 2020.

关键词： image segmentation

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Indicators of Learning Ability of Autonomous Vehicles

Indicators of Learning Ability of Autonomous Vehicles

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Chinese Automation Congress (CAC)

作者： Yimin Su Junjie Zhou Hongrong Huang Lin Wang Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Saie Volkswagen Automotive Co. Ltd. Shanghai China

ISBN: (纸本)9781665426480

With the increase of research on autonomous vehicles, the evaluation research of autonomous vehicles has gradually attracted attention. Effective evaluation of autonomous vehicles can ensure the safety of autonomous vehicles and improve their level of intelligence. The current related research focuses on the evaluation of vehicles in terms of safety and comfort, while there are few studies on the evaluation of the learning ability of autonomous vehicles from the time dimension. Therefore, this article proposes three learning indicators: learning time, learning speed and learning capacity. These three indicators are used to measure the learning ability of autonomous driving algorithms. By analyzing the learning curve of the vehicle algorithm, the evaluation results of three learning indicators can be obtained. Thus the learning ability of the automatic driving algorithm can be evaluated. The differences in different vehicle algorithms can also be compared. In this article, taking the parking algorithm based on reinforcement learning as an example, simulation examples and analysis of the three learning indicators proposed are carried out.

关键词： Couplings Analytical models Automation Time series analysis Reinforcement learning Benchmark testing Time measurement

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An agricultural data storage mechanism based on HBase

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International Journal of information and Communication Technology 2019年第4期14卷 456-469页

作者： Li, Changyun Zhang, Qingqing He, Pinjie Wang, Zhibing Chen, Li College of Computer and Science Hunan University of Technology Zhuzhou412007 China Intelligent Information Perception and Processing Technology Hunan Province Key Laboratory Zhuzhou412007 China Intelligent Perception and Networked Control Hunan Province Key Laboratory of College Zhuzhou412007 China

With the development of agricultural space-Time localisation, sensor network and cloud computing, the amount of agricultural data is increasing rapidly and the data structure becomes more complicated and changeable. Currently, the widely used agricultural database is the relational database. This database handles a large amount of data with very limited throughput and is not suitable for organisation and management of distributed data. HBase is a non-relational database of distributed file storage built on Hadoop platform. HBase is suitable for unstructured data storage database and it can handle large volume of data with high scalability. To better store agricultural big data in HBase, we propose a special agricultural data buffer structure which stores data based on the data category and a two-level indexing memory organisation strategy on HBase. The method proposed saves more than a quarter of the time compared to traditional buffering methods. Experimental results show the higher efficiency of the agricultural data buffer structure and memory-organisation strategy. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Big data

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Research on Ultra-wideband Signal Detection Method Based on Improved Stochastic Resonance

Research on Ultra-wideband Signal Detection Method Based on ...

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Chinese Automation Congress (CAC)

作者： Dingxin Zhang Shangbin Jiao Qing Zhang Yannan Wang Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In order to improve the detection performance of ultra-wideband signals and obtain a higher detection signal-to-noise ratio, based on the traditional bistable stochastic resonance model, an improved stochastic resonance method is proposed to detect, analyze and compare pulse-modulated ultra-wideband signals. The simulation experiment proves that the improved stochastic resonance method has obvious effect on ultra-wideband signal detection, and is superior to traditional algorithms in terms of signal-to-noise ratio and cross-correlation coefficient.

关键词： Hafnium

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A Privacy-Enhanced Mobile Crowdsensing Strategy for Blockchain Empowered Internet of Medical Things

A Privacy-Enhanced Mobile Crowdsensing Strategy for Blockcha...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Mengyao Peng Jia Hu Hui Lin Xiaoding Wang Wenzhong Lin College of Computer and Cyber Security Fujian Normal University Fuzhou Fujian China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou Fujian China University of Exeter Exeter UK

ISBN: (纸本)9781665416597

The emergence of the Internet of Medical Things (IoMT) brings a huge impact on current medical system in the detection and prevention of medical diseases, as well as the sharing and analysis of medical data. To efficiently collect medical data for disease prevention, the mobile crowdsensing (MCS) is employed. However, the exposure of sensitive information about users and crowdsensing tasks might cause serious privacy leakage in MCS. To solve this problem, in this paper, a Privacy-enhanced Mobile Crowdsensing strategy utilizing Blockchain technology, named PMCB, is proposed. Specifically, we propose to classify the users by spectral clustering based on the social network generated by the social attributes of users. In this way, both crowdsensing tasks and participating users are classified such that task receivers are restricted to receive specific crowdsensing tasks. Furthermore, the blockchain is used to store crowdsensing tasks and smart contract is used for access control. Experiment results show that PMCB can achieve efficient privacy protection in mobile crowdsensing with high system throughput and low transaction latency.

关键词： Privacy Crowdsensing Social networking (online) Smart contracts Receivers Internet of Medical Things Throughput

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Redundancy reduction penalty term of loss function in deep neural network

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Knowledge-Based Systems 2025年

作者： Xueheng Hu Shuhuan Wen H.K. Lam Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Key Laboratory of Intelligent Control and Neural Information Processing Ministry of Education Yanshan University Qinhuangdao 066004 Hebei Province China The Department of Engineering King’s College London London WC2R 2LS United Kingdom

In this paper, we propose a novel regularization algorithm that is introduced as a penalty term to the loss function. Differing from conventional L1 and L2 regularization methods, our approach does not aim to diminish the weights of individual neurons or enforce sparsity by driving certain neurons to zero. Instead, it functions by increasing the differences between neurons and enhancing the diversity of neurons within each layer. Our method incorporates ensemble learning techniques by treating the layer weight matrix as a collective learning model, where each neuron serving as a weak learner within the layer. The proposed algorithm improves the performance of DCNN by simultaneously considering the distance between multiple filters in the same layer. This algorithm reduces the redundancy of the parameter layer filters in DCNN and enhances its robustness. The penalty term proposed by our algorithm dynamically adjusts its value in a cyclical manner, compelling the neural network to navigate away from its current gradient state. In the parameter space, different weights correspond to different locations. The proposed algorithm quantifies the distance between neurons and iteratively increases the distance between neurons during thereby encouraging greater diversity within the network. Experimental evaluations demonstrate the effectiveness of our algorithm in enhancing neural network performance without requiring adjustments to other hyper-parameters.

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An Anomalous Traffic Detection Approach for the Private Network Based on Self-learning Model 1

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3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Han, Weijie Xue, Jingfeng Zhang, Fuquan Zhang, Yingfeng School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Space Information Space Engineering University Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China Equipment Development Department Beijing100071 China

ISBN: (数字)9783030622237

ISBN: (纸本)9783030622220

Although being isolated from the external network, the private network is still faced with some security threats, such as violations communications, malware attacks, and illegal operations. It is an attractive approach to recognize these security threats by discovering the underlying anomalous traffic. By studying the anomalous traffic detection technologies, an anomalous traffic detection approach is developed by capturing and analyzing the network packets, detecting the anomaly traffic that occurs in the network, and then detects anomalous behaviors of the network timely. In order to enhance its effectiveness and efficiency, a self-learning model is proposed and deployed in the detection approach. Finally, we conduct necessary evaluations about the proposed approach. The test results show that the approach can reach a good effect for detecting the unknown anomalous traffic. © 2020, Springer Nature Switzerland AG.

关键词： Learning systems

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