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2022 Chinese automation Congress, CAC 2022

作者： Wang, Kai Hou, Jie Zeng, Xianlin School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

ISBN: (纸本)9781665465335

Predicting lane-change intentions of surrounding vehicles can effectively help autonomous vehicles reduce collisions caused by lane changes and ensure driving safety. Because prediction methods based on black-box models will lead to passengers' distrust of machine prediction, the intention prediction methods used to autonomous driving need to be interpretable and trustworthy. This paper presents a method for intention prediction of surrounding vehicles by using a bidirectional long short term memory network (BiLSTM) with a conditional random field (CRF) layer above it. Compared with intention prediction methods using deep network, the proposed method can find the features that contribute most to the prediction, thereby improving the interpretability and ensuring the prediction performance. In addition, by employing the transfer characteristic of the CRF layer, traffic rules and the experience of skilled drivers can be embedded to the prediction in the form of rules. Use rules to constrain the intention prediction, thereby improving the trustworthiness of prediction results. Test results on naturalistic driving dataset show that the proposed method can predict the lane-change intention with an accuracy of 97.22%, which is higher than that of Bi-LSTM. © 2022 IEEE.

关键词： Embeddings

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Research on Eigenvalue of Excitation Signal in Nondestructive Testing of Power Transformer Core 1

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18th Annual Conference of China Electrotechnical Society, ACCES 2023

作者： Qu, Yinghui Jin, Fang Dong, Kaifeng Cai, Shengwei Gong, Yujia Fan, Fan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China China Electric Power Research Institute Co. Ltd. Wuhan430074 China

ISBN: (数字)9789819714285

ISBN: (纸本)9789819714278

Transformer is the most critical equipment in the power system. In recent years, secondary silicon steel sheets have been used in the manufacture of transformer cores, which has caused hidden dangers in the quality of transformers. Currently, the main testing method for transformer core materials is disassembly testing, and there is no effective non-destructive testing method. Because magnetic Buckhausen noise (MBN) signals can reflect the internal defects of ferromagnetic components, this paper uses MBN testing methods to conduct non-destructive testing and analysis the side of transformer core silicon steel sheets. In the study of transformer core MBN signals, it was found that the waveform, frequency, amplitude and other parameters of the excitation signal can affect the eigenvalues of the transformer core MBN signal. The effects of excitation signal waveform, frequency, and amplitude on the characteristic values of MBN signals on the side of transformer core silicon steel sheet were experimentally studied. The research results show that when the excitation signal is a square wave with an amplitude of about 5V, excellent MBN signals can be excited. © Beijing Paike Culture Commu. Co., Ltd. 2024.

关键词： Nondestructive examination

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Analysis of LoRa for Electronic Shelf Labels Based on Distributed Machine Learning 42

Analysis of LoRa for Electronic Shelf Labels Based on Distri...

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42nd Chinese control Conference, CCC 2023

作者： Khan, Malak Abid Ali Ma, Hongbin Jin, Ying Ma, Jingxiang Rehman, Zia Ur Rahman, Mizanur School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

ISBN: (纸本)9789887581543

Deforestation is the primary source of global warming;traditional shelf labels use paper to display the price of the products, and human forces play a pivotal role in updating the tags where the pandemic has strictly limited its power. Various technologies provide connectivity and a fast-updating system to eliminate the paper-based approach. LoRa is one of the contenders to design the system for electronic shelf labels (ESLs). In this paper, LoRa has been used to minify data losses and guarantee the successful decoding of the carrier signals. The data parallelism at the network server (NS) is used to distribute the data packets among the gateways (GWs) for concurrent transmissions to the end devices (EDs). The EDs are placed in different ranges using machine clustering to avoid intra-SF interference and collision. The data rate (DR) and spreading factors (SFs) have been proposed to improve the performance of pure and slotted ALOHA for the properly allocated tags. The orthogonality principles follow industrial, scientific, and medical regulations (ISM) to avoid data traffic congestion. GWs under different duty cycles (DC) and bandwidth (BW) are examined to minify the network saturation and reduce the energy harvesting of the tags. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Deforestation

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An accurate tuning model of spintronic memristor with different switching thresholds by logic judgment of domain wall motion 41

An accurate tuning model of spintronic memristor with differ...

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第41届中国控制会议

作者： Kun Wang Yajuan Hui Zhong Yang Xiangshui Miao School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan National Lab for Optoelectronics Huazhong University of Science and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Spintronic memristor possessing extraordinary advantages such as high-speed,low-power,highly scalable,and capable of constructing dynamic complex systems,which is a potential candidate for the next-generation memory *** many excellent models of spintronic memristor have been proposed,nonlinear model is not suitable for application analysis because of its complex *** this paper,an accurate tuning model of spintronic memristor with different switching thresholds by logic judgment of domain-wall motion is *** switching thresholds of current density are introduced to drive the domain-wall movement by judging the position of state value and real-time current density,which solves the general problems of boundary ***,by the simulation and verification via PSpice modeling,an accurate regulation of the resistance by voltage excitation is *** results pave the way for the applications of diversified circuit design for spintronic memristor.

关键词： Spintronic memristor Different switching thresholds SPICE model

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Unified Addressing Algorithm for Tasks in Cloud control systems 41

Unified Addressing Algorithm for Tasks in Cloud Control Syst...

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第41届中国控制会议

作者： Guan Wang Yufeng Zhan Yuanqing Xia Liping Yan Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology School of Information Science and Engineering University of ZaoZhuang

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the rapid development of cloud computing and control theory,a new paradigm of networked control systems named cloud control systems has been proposed to satisfy the requirements of large-scale and complex *** is critical to manage large-scale tasks with personalized needs on the large number of heterogeneous ***,current TCP/IPbased addressing approach does not consider the runtime constraints of tasks,resulting highly concurrent service search,and communication between tasks will become the system *** this paper,we propose a task property encoding method which takes multi-dimensional properties into *** on the proposed encoding method,we design a uniform addressing method for tasks in the ***,we experimentally demonstrate the effectiveness of task property encoding,and the unified addressing method for tasks is more effective than other methods in the case of high task concurrency.

关键词： Cloud control systems address encoding cloud-edge-device cooperation

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Detection of Stealthy False Data Injection Attacks Against Cyber-Physical systems:A Stochastic Coding Scheme

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Journal of systems Science & complexity 2022年第5期35卷 1668-1684页

作者： GUO Haibin PANG Zhonghua SUN Jian LI Jun State Key Lab of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology Chongging Innovation Center Chongqing 401120China Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of TechnologyBeijing 100144China China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China

This paper,from the view of a defender,addresses the security problem of cyber-physical systems(CPSs)subject to stealthy false data injection(FDI)attacks that cannot be detected by a residual-based anomaly detector without other defensive *** detect such a class of FDI attacks,a stochastic coding scheme,which codes the sensor measurement with a Gaussian stochastic signal at the sensor side,is proposed to assist an anomaly detector to expose the FDI *** order to ensure the system performance in the normal operational context,a decoder is adopted to decode the coded sensor measurement when received at the controller *** this detection scheme,the residual under the attack can be significantly different from that in the normal situation,and thus trigger an *** design condition of the coding signal covariance is derived to meet the constraints of false alarm rate and attack detection *** minimize the trace of the coding signal covariance,the design problem of the coding signal is converted into a constraint non-convex optimization problem,and an estimation-optimization iteration algorithm is presented to obtain a numerical solution of the coding signal covariance.A numerical example is given to verify the effectiveness of the proposed scheme.

关键词： Attack detection cyber-physical systems(CPSs) stealthy FDI attacks stochastic coding

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Bayesian Exponential Regularized Tensor Based Approach for Sparse Geomagnetic Data Completion

Bayesian Exponential Regularized Tensor Based Approach for S...

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2023 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2023

作者： Li, Guoyu Bin, Junchi Liu, Huan School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Engineering University of British Columbia OkanaganBCV1V 1V7 Canada

ISBN: (纸本)9798350318012

Geomagnetic data is vital for predicting earthquakes and magnetic storms. In this regard, a new Bayesian exponential regularized tensor completion framework for sparse geomagnetic data, i.e. BERTC, is proposed to address this problem in the study. First, the spatiotemporal geomagnetic data is reshaped into a 3D tensor with days and hours that features random missing elements. Second, a Gibbs sampling algorithm is developed to achieve probabilistic inference on matrices' factors and corresponding parameters in this model. Thus, the sparse tensor can be gradually optimized to fill the missing entries during iterations. Third, an exponential regularizer is proposed to reduce oscillations before and after iterations to enhance imputation quality further. Finally, the derived factor matrices are aggregated from Gibbs sampling to complete the sparse tensor. Numerical geomagnetic datasets from 13 cities are employed, and extensive comparison experiments are conducted to evaluate the imputation performance of the BERTC. The results show the superiority of the proposed BERTC compared to the state-of-the-art methods in terms of imputation accuracy, with an approximate improvement of the imputation accuracy as no less than 20%. © 2023 IEEE.

关键词： Matrix algebra

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Predicting microbe-disease associations via attention guided graph convolutional networks 4

Predicting microbe-disease associations via attention guided...

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4th International Conference on Biomedicine and Bioinformatics Engineering, ICBBE 2024

作者： Gong, Shiyun Zhu, Yuan School of Automation China University of Geosciences Lumo Road Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Lumo Road Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Lumo Road Hongshan District Hubei Wuhan430074 China School of Mathematics and Physics China University of Geosciences Lumo Road Wuhan430074 China

ISBN: (纸本)9781510682443

It is important to predict microbe-disease associations, as it helps to understand the cause of diseases episodes, the prevention of diseases, among other roles. Traditionally, the study of microbe-disease association is mainly based on laboratory purified culture for research and observation, which is highly scientific but has some limitations, such as some microbes needing to be cultured in a laboratory environment and costs heavily. This study proposes an attention guided graph convolutional network d approach (AGGCN) to predict possible potential associations between microbes and diseases. In the first place, AGGCN constructs four different similarity networks of microbes and diseases, which are combined with association networks to form a microbe-disease heterogeneous network. Subsequently, it is used as an input to the graph convolutional neural network to obtain each node embedding of this heterogeneous network. In this study, a three-layer network is used, so three different types of node embeddings can be obtained, and these three different types of node embedding representations are integrated via the multichannel graph attention mechanism to obtain the final node embeddings. Finally, the node embeddings obtained above are used for association prediction of microbes-diseases in heterogeneous networks. Experimentally verified, the AUC of AGGCN is 0.8972, and the AUPR is 0.5387. Comparative experimental results indicated that AGGCN shows better than the other five mainstream methods. It provides a new insight to explore network potential feature representation and predict associations between microbe and disease. © 2024 SPIE.

关键词： Graph neural networks

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A Finite-Time Posture control Strategy for the Swarm Underactuated Robots 5th

A Finite-Time Posture Control Strategy for the Swarm Under...

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5th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2021

作者： Huang, Zixin Qin, Xiangyu Wei, Shaoqi Wang, Lejun Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University Wuhan430200 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789811939976

Under the background of industrial machinery automation. The swarm robots are required to cooperate to complete complex and accurate works in finite-time and create more relative residual value. Based on the inherent advantages of underactuated robot, the research of finite time posture control strategy has higher theoretical and practical value. Firstly, the dynamic model of a single Pendubot of planar swarm underactuated robots was built. However, the robots reach to the same objective from different initial states and keep being at the same posture, so it was necessary to plan a feasible trajectory. Then, the planned trajectory parameters are optimized to make the underactuated robots relized the objectives in finite time. Finally, an unified tracking controller is designed for the underactuated robots. Simulation results of the swarm underactuated robots demonstrated the effectiveness of the proposed control strategy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Robots

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Dense Color Constraints based 6D object pose estimation from RGB-D images 41

Dense Color Constraints based 6D object pose estimation from...

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第41届中国控制会议

作者： Zilun Wang Yi Liu Chi Xu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems CRRC Zhuzhou Locomotive Co. Ltd. National Innovation Center of Advanced Rail Transit Equipment

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

A key problem for 6 D pose estimation based on RGB-D image input is how to make full use of these two different data *** previous work simply took the depth map as the input of the fourth channel of CNN,or carried out the fusion of features extracted from these two data sources with different *** their fusion did not impose the right constraints and lost some valuable *** this work,we propose that DCC(Dense Color Constraints).6 D pose estimation performance can be improved effectively by using dense corresponding color *** show the most advanced end-to-end performance in LineMod datasets.

关键词： Computer Vision Deep Learning Pose Estimation

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