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False Noise Attack Detection for differentially-private distributed control of microgrids

Automatica 2025年 179卷

作者： Ye, Feng Cao, Xianghui Cai, Lin Chow, Mo-Yuen School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Department of Electrical and Computer Engineering University of Victoria Victoria BCV8W 3P6 Canada UM-SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Privacy preserving in distributed control is getting more attention, and differential privacy (DP) is the common tool to protect data privacy, in which additive noise is applied in the algorithm function. However, DP can be leveraged by false noise (FN) attacks because attack vectors can be disguised as artificial noise in DP. FN attacks are a concern as the stealth attacks are hard to detect. Moreover, DP in distributed control makes FN attack detection more difficult. Hence, detecting FN attacks in privacy-preserving distributed control is critical and challenging. In this paper, taking distributed energy management systems as the control object, we propose a novel peer-to-peer attack detection approach, named False Noise Attack Detection (FNAD). In FNAD, each device observes the power decisions of its neighbors based on the data from its two-hop neighbors, estimates the power decisions of its neighbors by a Kalman filter, and updates the detection index of each neighbor according to the residues of the Kalman filter at each iteration. The detection index is developed based on information entropy, without any prior knowledge of the FN attacks. If a device's detection index is out of well-defined thresholds, its neighbors can perform a majority vote to decide whether it is malicious. We theoretically prove the detection effect of FNAD against three representative attacks in the literature and analyze the advantages of FNAD compared with the traditional methods. The effectiveness of FNAD is demonstrated by extensive simulations. © 2025 Elsevier Ltd

关键词： State estimation

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Indoor Layout Estimation by Fusing Monocular RGB Image Features Extracted with HRNet

Indoor Layout Estimation by Fusing Monocular RGB Image Featu...

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第三十九届中国控制会议

作者： Rong-Ze Huang Yin-Bo Liu Meh Jabeen Qing-Hao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and ControlSchool of Electrical and Information EngineeringTianjin University

Indoor layout estimation(ILE) is a challenging research that segments the natural indoor scene image into floor,walls and ceiling,and it has a wide range of application prospects in the fields such as scene understanding,reconstruction,robot positioning,and virtual *** ILE method by merging multiple feature s from a monocular red-green-blue(RGB) image was ***,the High-resolution networks(HRNet) was used to extract the features of indoor corner keypoints(CKs) and informative edges(IEs) in the form of ***,the extracted features were then selected through a room type(RT)classification ***,a feature fusion algorithm was proposed,which obtained the initial layout hypothe sis through CK features and then continuously optimized the layout hypothe sis with IE *** proposed ILE method was validated on the large scene understanding challenge(LSUN) dataset.

关键词： Indoor layout estimation Corner keypoint Informative edge Fusion algorithm High-resolution net

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Multimodal Information-Based Broad and Deep Learning Model for Emotion Understanding

Multimodal Information-Based Broad and Deep Learning Model f...

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第40届中国控制会议

作者： Min Li Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota 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 Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

Multimodal information-based broad and deep learning model（MIBDL） for emotion understanding is proposed, in which facial expression and body gesture are used to achieve emotional states recognition for emotion understanding. It aims to understand coexistence multimodal information in human-robot interaction by using different processing methods of deep network and broad network, which obtains the features of depth and width dimensions. Moreover, random mapping in the initial broad learning network could cause information loss and its shallow layer network is difficult to cope with complex tasks. To address this problem, we use principal component analysis to generate the nodes of the broad learning, and the stacked broad learning network is adapted to make it easier for the existing broad learning networks to cope with complex tasks by creating deep variations of the existing network. To verify the effectiveness of the proposal, experiments completed on benchmark database of spontaneous emotion expressions are developed, and experimental results show that the proposal outperforms the state-of-theart methods. According to the simulation experiments on the FABO database, by using the proposed method, the multimodal recognition rate is 17,54%, 1.24%, and 0.23% higher than those of the temporal normalized motion and appearance features（TN）,the multi-channel CNN（MCCNN）, and the hierarchical classification fusion strategy（HCFS）, respectively.

关键词： Convolution neural network broad learning emotion understanding facial expression body gesture

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Monocular Visual SLAM based on VGG Feature Point Extraction

Monocular Visual SLAM based on VGG Feature Point Extraction

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第三十九届中国控制会议

作者： Xu-Yang Dai Qing-Hao Meng Wen-Jian Zheng Shao-Kai Zhu Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and ControlSchool of Electrical and Information Engineering Tianjin University

Significant progress has been made in the field of visual SLAM（Simultaneous Localization and Mapping）. However, the localization accuracy of visual SLAM can be significantly reduced in low-texture and illumination-changing environments. To solve these problems, an enhanced visual SLAM algorithm based on VGG（Visual Geometry Group） network was proposed in this paper. Firstly, the VGG network for feature point extraction was incorporated into the visual odometry（VO） to achieve robust camera pose estimation. Secondly, an automatic corner annotation method was adopted to set up the training database, which could reduce the workload of data annotation. Thirdly, to make the backend optimization more suitable for the VGG based VO, the BA（bundle adjustment） optimization process was improved. Experimental results showed that, the proposed visual SLAM method outperformed the mainstream ORB-SLAM（Oriented FAST and Rotated BRIEF SLAM） method, in terms of the number of effective feature points, the robustness of feature matching process to light changes, and the accuracy of robot pose estimation.

关键词： monocular visual SLAM feature point extraction convolutional neural network

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Odor Evaluation of Vehicle Interior Materials Based on Portable E-nose

Odor Evaluation of Vehicle Interior Materials Based on Porta...

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第三十九届中国控制会议

作者： Zhe-Hua Sun Ke-Xian Liu Xue-Han Xu Qing-Hao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University

The evaluation of the odor emitted by the interior materials is of great significance to the air quality control in the vehicle. This paper focuses on the multi-label classification problem of the odor type(i.e. the type of material from which the odor is released) and the grade evaluation in the vehicle. Two multi-label classifiers based on binary relevance(BR) and classification chain(CC) were designed to distinguish odor types and grades simultaneously and to distinguish odor types first and then grades, respectively. Based on the self-designed portable E-nose, six grades of odors released from six types of common interior materials were detected and classified. Experimental results showed that both the multi-label classifiers achieved better recognition accuracy, but the CC had an average recognition rate of 88.17% slightly better than the BR. It is preliminarily verified that the portable E-nose based multi-label classifiers could be used to evaluate the odor of automotive interior materials.

关键词： E-nose vehicle interior materials multi-label classifier Odor evaluation

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Attitude Adjustment of a Multi-joint AUV Using an Improved LADRC

Attitude Adjustment of a Multi-joint AUV Using an Improved L...

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第三十九届中国控制会议

作者： Lin Yu Ke-Xian Liu Qing-Hao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and ControlSchool of Electrical and Information Engineering Tianjin University

To achieve vertical profile exploration of deep-sea hybrid structures, a multi-joint autonomous underwater vehicle(MJ-AUV) with the orthogonal joints structure was designed, and its virtual prototype was built using Automatic Dynamic Analysis of Mechanical systems(ADAMS). This paper focuses on the attitude control of the designed MJ-AUV which is a nonlinear system with strong coupling, big hysteresis, large inertia, and time-varying disturbance. According to the special structure of the MJ-AUV, an error based variable gain linear active disturbance rejection controller(EVG-LADRC) was proposed and compared with the traditional proportional-integral-derivative(PID) controller and LADRC. Simulation results demonstrated that EVG-LADRC outperformed the PID and LADRC in terms of rapidity, anti-interference, and accuracy.

关键词： MJ-AUV virtual prototype attitude control EVG-LADRC

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Accelerometer optimization based on real-time measurement of Hole Deviation INS with While-Drilling

Accelerometer optimization based on real-time measurement of...

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Chinese Automation Congress (CAC)

作者： Yuan-Qiu Liu Fang Jin Jun-Lei Song Kai-Feng Dong Wen-Qin Mo Ya-Juan Hui School of Automation China University of Geosciences Wuhan China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9781665426480

Well deviation measurement of While-Drilling(MWD) refers to the real-time measurement of well deviation and azimuth While-Drilling is the key for navigation in automatic drilling, real-time detection of drilling direction and automatic drilling control. In this paper, we solve the problem that the accelerometer could not accurately measure gravity because of the high-speed rotation caused by the eccentric installation of INS (Inertial Navigation System) and the acceleration of vibration line in the real-time measurement of borehole deviation and azimuth angle while drilling. Firstly, the dynamic physical model of accelerometer and gyroscope under eccentric installation is built, the non-gravity error of accelerometer measurement under eccentric motion is analyzed, and the algorithm optimization is made. Secondly, a calibration scheme for the compensation coefficients of centrifugal and tangential forces is given, which enables the carrier to make eccentric pure rotational motion on the rotating platform to determine the compensation calibration coefficient matrix. Finally, the first-order complementary filter algorithm is used to fuse the gyroscope angular velocity with the corrected acceleration for attitude calculation, and the algorithm is experimented. In this study, MPU6050 is selected as the chip of INS, and the attitude algorithm is realized by STM32. The experimental results show that the high dynamic error of the accelerometer measurement caused by eccentric motion is effectively reduced by the algorithm compensation.

关键词： Accelerometers Drilling Semiconductor device measurement Heuristic algorithms measurement uncertainty Vibration measurement Real-time systems

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A Generalized Probabilistic Monitoring Model with Both Randomand Sequential Data

arXiv

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arXiv 2022年

作者： Yu, Wanke Wu, Min Huang, Biao Lu, Chengda The 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 The Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G 2G6 Canada

Many multivariate statistical analysis methods and their corresponding probabilistic counterparts have been adopted to develop process monitoring models in recent decades. However, the insightful connections between them have rarely been studied. In this study, a generalized probabilistic monitoring model (GPMM) is developed with both random and sequential data. Since GPMM can be reduced to various probabilistic linear models under specific restrictions, it is adopted to analyze the connections between different monitoring methods. Using expectation maximization (EM) algorithm, the parameters of GPMM are estimated for both random and sequential cases. Based on the obtained model parameters, statistics are designed for monitoring different aspects of the process system. Besides, the distributions of these statistics are rigorously derived and proved, so that the control limits can be calculated accordingly. After that, contribution analysis methods are presented for identifying faulty variables once the process anomalies are detected. Finally, the equivalence between monitoring models based on classical multivariate methods and their corresponding probabilistic graphic models is further investigated. The conclusions of this study are verified using a numerical example and the Tennessee Eastman (TE) process. Experimental results illustrate that the proposed monitoring statistics are subject to their corresponding distributions, and they are equivalent to statistics in classical deterministic models under specific restrictions. Copyright © 2022, The Authors. All rights reserved.

关键词： Process monitoring

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A Multi-modal Panoramic Speaker Localization Method

A Multi-modal Panoramic Speaker Localization Method

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Chinese Automation Congress (CAC)

作者： Y. Z. Long Y. W. Jian Z. T. Liu School of Automation China University of Geosciences WuHan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems WuHan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9781665426480

As a supporting technology in the field of human-computer interaction, speaker localization method has been a research hotspot in recent years. However, the existing single-mode speaker location methods cannot meet the requirements of stability and rapidity. For improve the accuracy and effective positioning range of speaker localization system, a multi-modal panoramic speaker localization method is proposed in this paper. Firstly, the coarse position of the speaker was preliminarily obtained by sound source localization. Then, according to the initial positioning of the speaker, whether the image used for face recognition need to be stitched is decided. What’s more, the position of speaker is calculated by face detection and calibration. Finally, the results of voice and image localization are fused by coordinate transformation. The proposal is tested on self-built experimental system, and the experimental results prove the effectiveness of the proposed method.

关键词： Location awareness Image resolution Image edge detection Face recognition Standardization Cameras Robustness

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A Novel E-Nose Chamber Design for VOCs Detection in Automobiles

A Novel E-Nose Chamber Design for VOCs Detection in Automobi...

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第三十九届中国控制会议

作者： Lu Cheng Yin-Bo Liu Qing-Hao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and ControlSchool of Electrical and Information EngineeringTianjin University

To detect parts per billion（ppb）-level volatile organic compounds（VOCs） in automobiles, a pre-concentrator is generally needed for electronic noses（e-noses）, which can enrich ppb-level concentration to parts per million（ppm） level. However, the current e-nose chamber structure could dilute the pre-concentrated gases and then the detection performance is reduced. To solve this problem, a novel chamber design was proposed in this paper. The core design idea was to reduce the space for the gas flow in the chamber, taking into account the shape and size of the gas sensors. Specially, for the pin-type gas sensors, a series of cylindrical structure design was adopted, and for the micro-electro-mechanical system（MEMS） patch-type sensors, the headspace was compressed. The results of computational fluid dynamics（CFD） based simulation showed that the concentration of pre-concentrated gases in the novel chamber was higher than that of the traditional chamber. The designed chambers were manufactured using polylactic acid（PLA） material by 3D printing. Experimental gases were prepared from polyurethane（PU） leather and polyvinyl chloride（PVC） leather to verify the performance of the chambers, and the results demonstrated that the proposed new e-nose chamber was superior to the traditional one in terms of the pre-concentration effect, repeatability and classification performance.

关键词： e-nose chamber pre-concentrator parts per billion（ppb） volatile organic compounds（VOCs） repeatability classification

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