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6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Zhuang, Jiawei Wang, Yonghua Wan, Pin Zhang, Shunchao Zhang, Yongwei Li, Yi School of Automation Guangdong University of Technology Guangzhou510006 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Hubei Key Laboratory of Intelligent Wireless Communications South-Central University for Nationalities Wuhan430074 China

ISBN: (纸本)9783030578831

Spectrum sensing is a fundamental function for cognitive radio systems, which can improve spectrum utilization. In this article, a blind spectrum sensing method based on the sample covariance matrix and K-median clustering algorithm is proposed to further improve the sensing performance. Specifically, to obtain a two-dimensional signal feature vector, the received signal matrix is rebuilt into two sub-matrices by the decomposition and reorganization (DAR) method. Moreover, two statistical covariance matrices are constructed by the sub-matrices, respectively. The ratios between the sum of some elements from the sample covariance matrices and the sum of diagonal elements from those matrices are used as a signal feature vector. It is demonstrated that the new signal feature vector and the feature vector based on the improved covariance absolute value method are equivalent. Furthermore, K-median clustering algorithm is trained by signal feature vectors to obtain a classifier. Indeed, this classifier can directly be used to detect whether the PU signal is absent or not. Simulation results report that the proposed algorithm has better sensing performance than some popular sensing algorithms based on random matrix theory or information geometry. © 2020, Springer Nature Switzerland AG.

关键词： Covariance matrix

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A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks

arXiv

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arXiv 2022年

作者： Pan, Yu Su, Zeyong Liu, Ao Wang, Jingquan Li, Nannan Xu, Zenglin Harbin Institute of Technology Shenzhen Shenzhen China University of Electronic Science and Technology of China Chengdu China Tokyo Institute of Technology Tokyo Japan State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Pengcheng Laboratory Shenzhen China

Tensorial Convolutional Neural Networks (TCNNs) have attracted much research attention for their power in reducing model parameters or enhancing the generalization ability. However, exploration of TCNNs is hindered even from weight initialization methods. To be specific, general initialization methods, such as Xavier or Kaiming initialization, usually fail to generate appropriate weights for TCNNs. Meanwhile, although there are ad-hoc approaches for specific architectures (e.g., Tensor Ring Nets), they are not applicable to TCNNs with other tensor decomposition methods (e.g., CP or Tucker decomposition). To address this problem, we propose a universal weight initialization paradigm, which generalizes Xavier and Kaiming methods and can be widely applicable to arbitrary TCNNs. Specifically, we first present the Reproducing Transformation to convert the backward process in TCNNs to an equivalent convolution process. Then, based on the convolution operators in the forward and backward processes, we build a unified paradigm to control the variance of features and gradients in TCNNs. Thus, we can derive fan-in and fan-out initialization for various TCNNs. We demonstrate that our paradigm can stabilize the training of TCNNs, leading to faster convergence and better results. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Recurrent Attentional Reinforcement Learning for Fault Diagnosis of Hydraulic System

Recurrent Attentional Reinforcement Learning for Fault Diagn...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Zhenhui Tang Ru Liu Kang Lou Chaojian Gao Jingcheng Wang SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya China Department of Automation Shanghai Jiao Tong University Shanghai China Shanghai Marine Equipment Research Institute Shanghai China Department of Automation the Key Laboratory of System Control and Information Processing Ministry of Education of China and the Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University Xi'an China

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Recognizing fault types of machinery system is a fundamental but challenging task in industrial application. Although data-driven fault diagnosis method attains remarkable progress by learning fault features automatically, the existing data-driven fault diagnosis pipelines learn non-discriminative feature. To deal with these problems, a new data-driven fault diagnosis method with recurrent attentional module is proposed to find a sequence of informative features by localizing distinguishing time fragments about each individual faults iteratively. The contextual sequence dependency between the localized time fragments is enhanced. Then, the fault types are further predicted on localized time fragments. The proposed framework also explicitly models long-term dependencies among these attentional time fragments to capture historical fault information. Experiments on hydraulic system show that the proposed framework achieves superior diagnosis performance and computation efficiency.

关键词： Fault diagnosis Location awareness Accuracy Computational modeling Pipelines Reinforcement learning Hydraulic systems Computational efficiency Information technology

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An Intelligent Maritime Scene Frame Prediction Based on Digital Twins Technology

An Intelligent Maritime Scene Frame Prediction Based on Digi...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Zhong-Zheng Guo Kai Wang Jiang-Yi Qin Xian-Bin Li De-Chao Ran Yu Shen Unmanned Systems Research Center National Innovation Institute of Defense Technology Academy of Military Science Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This paper proposes a scene frame prediction system based on digital twin technology. The system is mainly composed of an unmanned perception module, a communication link module, a command center module that provides intelligent computing services, and an interaction and visualization module for human operators. These modules together constitute a system for predicting and recovery the time-series matrix composed of scene frames and object frames. At the same time, in view of the current problems of lack of real-time information and long response time in the maritime search and rescue (SAR) field, this paper instantiates the above system to improve the existing SAR system. Satellites and unmanned vehicles perceive part of the scene data of the real cyber-physical system in real-time and divide them into multiple frames according to the timestamps. In addition, this paper also proposes an environmental graph-attention network (Env-GAT) to achieve efficient and reliable frame predicting. Finally, this paper verifies that the performance of the spatiotemporal attention graph network model has reached the optimal level through computational experiments.

关键词： Satellites Digital twin Conferences Unmanned vehicles Search problems Real-time systems Trajectory

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Detection of stealthy false data injection attacks against networked control systems via active data modification

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Information Sciences 2021年 546卷 192-205页

作者： Zhong-Hua Pang Lan-Zhi Fan Jian Sun Kun Liu Guo-Ping Liu Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of Technology Beijing 100144 China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing 100081 China Department of Artificial Intelligence and Automation Wuhan University Wuhan 430072 China

This paper investigates the design and detection problems of stealthy false data injection (FDI) attacks against networked control systems from the different perspectives of an attacker and a defender, respectively. First, a Kalman filter-based output tracking control system is presented, where stealthy FDI attacks are designed for its feedback and forward channels so as to destroy the system performance while bypassing a traditional residual-based detector. Second, to successfully detect such two-channel stealthy attacks, an active data modification scheme is proposed, by which the measurement and control data are amended before transmitting them through communication networks. Theoretical analysis is then carried out for both ideal and practical cases to evaluate the effectiveness of the detection scheme. An interesting finding is that the attacks designed based on a false model obtained from those modified data can remain stealthy. Finally, simulation results are provided to validate the proposed attack design and detection schemes.

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Stochastic DoS Attack Allocation Against Collaborative Estimation in Sensor Networks

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IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1225-1234页

作者： Ya Zhang Lishuang Du Frank L.Lewis School of Automation Southeast Universityand also with the Key Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of EducationNanjing 210096China University of Texas at Arlington Research Institute Fort WorthTX 76118 USA IEEE

In this paper,denial of service(DoS)attack management for destroying the collaborative estimation in sensor networks and minimizing attack energy from the attacker perspective is *** the communication channels between sensors and a remote estimator,the attacker chooses some channels to randomly jam DoS attacks to make their packets randomly dropped.A stochastic power allocation approach composed of three steps is ***,the minimum number of channels and the channel set to be attacked are ***,a necessary condition and a sufficient condition on the packet loss probabilities of the channels in the attack set are provided for general and special systems,***,by converting the original coupling nonlinear programming problem to a linear programming problem,a method of searching attack probabilities and power to minimize the attack energy is *** effectiveness of the proposed scheme is verified by simulation examples.

关键词： Attack allocation denial of service(DoS)attack packet loss remote estimation sensor networks

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High-Order Disturbance Observer-Based Attitude control with Prescribed Performance for Hypersonic Vehicle

High-Order Disturbance Observer-Based Attitude Control with ...

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第40届中国控制会议

作者： GuangHui Yang Xinming Wang Beijing Aerospace Automatic Control Institute School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Eduaction

This paper presents a high-order disturbance observer-based attitude tracking controller design with prescribed performance for hypersonic vehicle. The lumped disturbances caused by parameters perturbations, external disturbances and unmodelled dynamics are estimated accurately by utilizing high-order disturbance observers. Based on the disturbances estimation,a composite attitude tracking controller with guaranteeing prescribed transient and steady performance is proposed and the stability analysis of the closed-loop system is rigorously analyzed using the Lyapunov technique. The proposed controller not only ensures the tracking errors are within the desired performance envelope but also considering the disturbance rejection. Finally,the effectiveness of the proposed method is verified by simulations.

关键词： Hypersonic Vehicle Disturbance Observer Prescribed Performance control Dynamics Surface control

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The influence of color temperature mismatch in star simulator on positioning accuracy and magnitude measurement by star sensor

The influence of color temperature mismatch in star simulato...

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2021 International Conference on Computer Technology, Information Engineering and Electron Materials, CTIEEM 2021

作者： Wang, Zhiyong Liu, Shishen Sun, Hao Zhang, Jingjing Li, Ruyou Liu, Chen School of Automation China University of Geosciences Hubei 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 China Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai200083 China

During the calibrating of star sensor, the calibration accuracy is greatly affected by the mismatch between the color temperature of the light and the to-be-measured star, which further affects the attitude measurement accuracy. This paper studied the near-infrared spectra of stars with different color temperatures, and analyzed the errors on star positioning and magnitude measurement of star sensor due to the color temperature mismatch. The results showed that in the central field of view, the spot centroid deviation caused by spectral mismatch is smaller than that in the edge field of *** the defocus of the imaging surface also affects the spot centroid deviation. Besides, when calibrating with 6000K color temperature light, the maximum measurement error can reach -1.9126 magnitude. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

关键词： Stars

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A Seamless control Based on the state Follower for the Energy Balance of the Bipolar-Type Modular Solid-state Transformer 14

A Seamless Control Based on the State Follower for the Energ...

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14th IEEE International Symposium on Power Electronics for Distributed Generation systems, PEDG 2023

作者： Li, Hongming Zhou, Jianqiao Zhang, Jianwen Shi, Gang Cai, Xu Wang, Yong Dong, Xiaofeng Bao, Wei Shanghai Jiao Tong University Key Laboratory of Control of Power Transmission and Conversion Ministry of Education Shanghai China Beijing Sifang Automation CO. Ltd Hvdc Transmission and Power Electronics Business Unit Beijing China Facilities Management Suzhou Power Supply Company Suzhou China State Grid Shanghai Municipal Electric Power Company Shanghai Electrical Apparatus Research Institute Group CO.Ltd Shanghai China

ISBN: (纸本)9798350328233

A novel solid-state transformer, the bipolar-type modular solid-state transformer (BM-SST), is proposed to extend a bipolar type LVDC port from the modular multilevel converter-based solid-state transformer (MMC-SST). Compared with MMC-SST, BM-SST has the advantages of flexible power supply, high reliability and high safety. In particular, both arm voltage regulation (AVR) control and active circulating current injection (ACCI) control are used to cope with the unbalanced power between the positive and negative ports of the LVDC. The two control schemes are selected according to the different load conditions. However, the transition process between the two control schemes have not been investigated, which will affect the dynamic performance of BMSST. This paper proposes a seamless control based on state follower to achieve the seamless switching between AVR and ACCI. The proposed scheme has the advantages of good dynamic performance and simple control design. Finally, the feasibility and effectiveness of the proposed scheme is verified by a 1MW BM-SST simulation model based on Simulink MATLAB. © 2023 IEEE.

关键词： Voltage regulators

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Fusionplanner: A Multi-Task Motion Planner for Mining Trucks Using Multisensor Fusion Method

SSRN

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SSRN 2023年

作者： Teng, Siyu Chen, Long Li, Luxi Li, Yuchen Hu, Xuein Li, Lingxi Ai, Yunfeng Department of Computer Science Hong Kong Baptist University Hong Kong The School of Artificial Intelligence Hubei University Wuhan China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Waytous Inc. Haidian Distinct Beijing China Indianapolis United States School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

In recent years, significant achievements have been made in motion planning for intelligent vehicles. However, as a typical unstructured environment, open-pit mining attracts limited attention due to its complex operational conditions and adverse environmental factors. A comprehensive paradigm for unmanned transportation in open-pit mines is proposed in this research, including a simulation platform, a testing benchmark, and a trustworthy and robust motion planner. Firstly, we introduce the Parallel Mining Simulator (PMS), a high-fidelity simulator specifically designed for open-pit mining scenarios. PMS enables to management and control of open-pit mine transportation from both the single-truck control and multi-truck scheduling perspectives. Then, we develop a novel benchmark called MiningNav, which offers three validation approaches suitable for transportation roads. The purpose of MiningNav is to evaluate the trustworthiness and robustness of algorithms in open-pit mines. Finally, we propose the first multi-task motion planning algorithm, called FusionPlanner, for the mining truck by the multisensor fusion method to adapt both lateral and longitudinal control tasks for unmanned transportation. We validate the performance of our planner using MiningNav in PMS, and empirical results demonstrate a significant reduction in the number of collisions and takeovers of FusionPlanner, thereby improving overall trustworthiness and robustness. © 2023, The Authors. All rights reserved.

关键词： Motion planning

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