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Sectorial coverage control with load balancing in non-convex hollow environments?

AUTOMATICA 2023年第1期157卷

作者： Zhai, Chao Fan, Pengyang Zhang, Hai -Tao China Univ Geosci Sch Automat Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Peoples R China China Univ Geosci Engn Res Ctr Intelligent Technol Geoexplorat Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Engn Res Ctr Autonomous Intelligent Unmanned Syst Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipments & Technol Wuhan 430074 Peoples R China

It has long been a challenging task for multi-agent systems (MASs) to inexpensively service probable events in non-convex environments. Coverage control provides an efficient framework to address MAS deployment problem for optimizing the cost of tackling unknown events. By means of the divideand-conquer methodology, this paper proposes a sectorial coverage formulation to configure MASs in non-convex hollow environments while ensuring load balancing among subregions. Thereby, a distributed controller is designed to drive each agent towards a desirable configuration that minimizes the coverage cost by simultaneously adopting sectorial partition mechanism. Theoretical analysis is conducted to ensure the asymptotic stability of closed-loop MASs with exponential convergence of equitable partition. In addition, a circular search algorithm is proposed to identify desirable solutions to such a sectorial coverage problem, which guarantees approximating the optimal deployment of MASs with arbitrarily small tolerance. Finally, both numerical simulations and multi-robot experiments are conducted to substantiate the efficiency of the present sectorial coverage approach.& COPY;2023 Elsevier Ltd. All rights reserved.

关键词： Coverage Cooperative control Multi -agent systems Distributed control

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A machine learning-based detection framework against intermittent electricity theft attack

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年第1期150卷

作者： Fang, Hongliang Xiao, Jiang-Wen Wang, Yan-Wu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

The widespread installation of advanced metering infrastructure (AMI) brings convenience to applications including but not limited to load management and demand response. However, AMI is also at risk of electricity theft and non-technical loss. Using the smart meter data provided by AMI to dig out user electricity consumption behavior is an effective way to construct electricity theft detectors. In this paper, a new intermittent electricity theft attack behavior is presented which switches between committing electricity theft and honestly consuming electricity alternately to skillfully evade the existing detectors. Based on the assumption that the labels of intermittent adversaries are unavailable, a new machine learning-based detection framework is proposed to detect this attack. Initially electricity features are constructed based on time intervals divided by a numerical iteration method. Then light gradient boosting method (LightGBM) is used to classify the normal users and adversaries. Further, the disperse degree of users is designed for capturing the differences between the intermittent adversaries and others. A new 2D label set is then constructed by combining the predicted labels of LightGBM and the disperse degree. Finally, a variational Bayesian Gaussian mixture model is employed based on the 2D label set to sort the users into the normal users and adversaries visually. Results of the case studies show that the presented attack can evade state-of-the-art detectors but still gain high profits. In addition, the proposed machine learning-based detector outperforms state-of-the-art detectors on both persistent attacks and intermittent attacks.

关键词： Advanced metering infrastructure Intermittent electricity theft Light gradient boosting method Disperse degree Variational Bayesian Gaussian mixture model

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Adversarial filtering based evasion and backdoor attacks to EEG-based brain-computer interfaces

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INFORMATION FUSION 2024年 107卷

作者： Meng, Lubin Jiang, Xue Chen, Xiaoqing Liu, Wenzhong Luo, Hanbin Wu, Dongrui Huazhong Univ Sci & Technol Belt & Rd Joint Lab Measurement & Control Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China

A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is a common input signal for BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals, while ignoring their security. Recent studies have shown that machine learning models in BCIs are vulnerable to adversarial attacks. This paper proposes adversarial filtering based evasion and backdoor attacks to EEG-based BCIs, which are very easy to implement. Experiments on three datasets from different BCI paradigms demonstrated the effectiveness of our proposed attack approaches. To our knowledge, this is the first study on adversarial filtering for EEG-based BCIs, raising a new security concern and calling for more attention on the security of BCIs.

关键词： Brain-computer interfaces Machine learning Adversarial attack Adversarial filtering

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Recovering unknown topology in a two-layer multiplex network:One layer infers the other layer

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Science China(Technological Sciences) 2022年第7期65卷 1493-1505页

作者： LIU Hui SHANG ZhiCheng REN ZiYi LI Yan ZENG ZhiGang LU JunAn School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074China School of Mathematics and Statistics Wuhan UniversityWuhan 430072China

There have been increasing interests in studying multiplex dynamical networks *** paper focuses on topology identiflcation of two-layer multiplex networks with peer-to-peer interlayer *** a two-layer network model in which different layers have different coupling patterns,we propose novel methods to recover unknown topological structure of one layer,using the information of the other layer known as a *** proposed methods make full use of the measured evolutional states of the multiplex network itself,and treat the layer with a known structure as an auxiliary layer which is designed to identify the unknown topological *** with the traditional synchronization-based identiflcation method,the proposed methods are in no need of constructing an additional auxiliary network to identify the unknown topological layer,and thus greatly reduce the cost of topology ***,numerical simulations validate the effectiveness of the proposed methods.

关键词： multiplex network synchronization topology identiflcation

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EfficientMatting: Bilateral Matting Network for Real-Time Human Matting 7th

EfficientMatting: Bilateral Matting Network for Real-Time Hu...

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7th Chinese Conference on Pattern Recognition and Computer Vision

作者： Luo, Rongsheng Wei, Rukai Zhang, Huaxin Tian, Ming Gao, Changxin Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Wuhan Peoples R China

ISBN: (纸本)9789819788576;9789819788583

Recent human matting methods typically suffer from two drawbacks: 1) high computation overhead caused by multiple stages, and 2) limited practical application due to the need for auxiliary guidance (e.g., trimap, mask, or background). To address these issues, we propose EfficientMatting, a real-time human matting method using only a single image as input. Specifically, EfficientMatting incorporates a bilateral network composed of two complementary branches: a transformer-based context information branch and a CNN-based spatial information branch. Furthermore, we introduce three novel techniques to enhance model performance while maintaining high inference efficiency. Firstly, we design a Semantic Guided Fusion Module (SGFM), which empowers the model to dynamically acquire valuable features with the assistance of context information. Secondly, we design a lightweight Detail Preservation Module (DPM) to achieve detail preservation and mitigate image artifacts during the upsampling process. Thirdly, we introduce the Supervised-Enhanced Training Strategy (SETS) to explicitly provide supervision on hidden features. Extensive experiments on P3M-10k, Human-2K, and PPM-100 datasets show that EfficientMatting outperforms state-of-the-art real-time human matting methods in terms of both model performance and inference speed.

关键词： EfficientMatting Semantic guided fusion module Detail preservation module Supervised-enhanced training strategy

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A Novel Hybrid-Action-Based Deep Reinforcement Learning for Industrial Energy Management

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第10期20卷 12461-12475页

作者： Lu, Renzhi Jiang, Zhenyu Yang, Tao Chen, Ying Wang, Dong Peng, Xin Huazhong Univ Sci & Technol Engn Res Ctr Autonomous Intelligent Unmanned Syst Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Syst Control & Informat Proc Minist Educ Shanghai 200240 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Tsinghua Univ Elect Engn Beijing 100084 Peoples R China Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China

As environmental pollution becomes increasingly serious and industrial energy consumption continuously rises, an intelligent and efficient industrial energy management policy is urgently needed to reduce costs and maximize the benefits of industrial energy systems. However, modern industrial energy systems are characterized by hybrid industrial equipment actions, diverse objectives, and highly intermittent and stochastically distributed renewable energy sources. Therefore, efficient operation and control are difficult. This article presents a novel, model-free energy management policy using a hybrid action deep reinforcement learning algorithm for energy scheduling of industrial equipments operating in various modes. Specifically, the interaction process between the industrial energy management center and each equipment is modeled as a Markov decision process that minimizes the daily operating cost of the energy system and maximizes the revenue of the production equipment. Then, a double parameterized deep Q-networks that does not require an explicit environmental model is developed to learn the hybrid action signals using actor and critic networks, in which the double Q value mechanism avoids value overestimation and improves the algorithm efficiency. In addition, the policy gradient of the proposed algorithm is derived and its convergence proof is discussed. Finally, numerical studies are conducted using real-world data to evaluate algorithm performance and verify its effectiveness.

关键词： Energy management Costs Power generation Renewable energy sources Optimization Load modeling Uncertainty Deep reinforcement learning (DRL) energy management hybrid actions industrial energy system

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CFN-ESA: A Cross-Modal Fusion Network With Emotion-Shift Awareness for Dialogue Emotion Recognition

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2024年第4期15卷 1919-1933页

作者： Li, Jiang Wang, Xiaoping Liu, Yingjian Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Brain Inspired Intelligent Syst Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

Multimodal emotion recognition in conversation (ERC) has garnered growing attention fromresearch communities in various fields. In this paper, we propose a Cross-modal Fusion Network with Emotion-Shift Awareness (CFN-ESA) for ERC. Extant approaches employ each modality equally without distinguishing the amount of emotional information in these modalities, rendering it hard to adequately extract complementary information from multimodal data. To cope with this problem, in CFN-ESA, we treat textual modality as the primary source of emotional information, while visual and acoustic modalities are taken as the secondary sources. Besides, most multimodal ERC models ignore emotion-shift information and overfocus on contextual information, leading to the failure of emotion recognition under emotion-shift scenario. We elaborate an emotion-shift module to address this challenge. CFN-ESA mainly consists of unimodal encoder (RUME), cross-modal encoder (ACME), and emotion-shift module (LESM). RUME is applied to extract conversation-level contextual emotional cues while pulling together data distributions between modalities;ACME is utilized to perform multimodal interaction centered on textual modality;LESM is used to model emotion shift and capture emotion-shift information, thereby guiding the learning of the main task. Experimental results demonstrate that CFN-ESA can effectively promote performance for ERC and remarkably outperform state-of-the-art models.

关键词： Emotion recognition in conversation multimodal fusion cross-modal association emotion shift

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A Novel Flux-Torque Decoupling Based Direct Torque control Strategy for Surface Permanent Magnet Synchronous Motor Drives

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2024年第4期39卷 2202-2215页

作者： Tang, Qipeng Luo, Pan Xu, Sang Sheng, Minggang Shen, Anwen Luo, Xin Xu, Jinbang Huazhong Univ Sci andTechnol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Huazhong Univ Sci andTechnol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Sci & Technol Reactor Syst Design Technol Lab Chengdu 610200 Peoples R China

Direct torque control (DTC) is increasingly attracted for high performance control of surface permanent magnet synchronous motors (SPMSMs) due to its easy implementation and fast dynamic response. However, the conventional switching-table-based method inevitably produces large torque and stator flux ripples because the selection of inverter's active-voltage vector is depending on the sign of torque and flux errors through hysteresis comparators regardless of their amplitude errors. To resolve these problems, a novel DTC strategy based on flux-torque decoupling is proposed in this paper. With the proposed method, two desired voltage vectors are firstly constructed to achieve the decoupling control of stator flux and torque by analyzing the relationships between applied voltage and torque and stator flux derivative. Afterwards, by applying vector synthesis to analyze the generation of two desired voltage vectors, the corresponding inverter's active-voltage vectors and their duty ratios are determined, so as to achieve the precise regulation of stator flux and torque with little ripples. Furthermore, in order to minimize the influence of machine parameters variations, a simple and real-time identification method for the ratio between permanent magnet flux linkage and stator inductance is presented. Finally, the experimental results indicate the effectiveness and feasibility of the proposed strategy.

关键词： DTC SPMSMs flux-torque decoupling two desired voltage vectors

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Harmonic Spectra Analysis of Digital SPWM in VSI With DC Bus Ripple and Dead-Time Effects

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2023年第8期38卷 9494-9513页

作者： Wang, Haozhe Xu, Jinbang Ye, Jie Li, Baojin Huang, Songtao Huang, Yukai Liu, Dong Shen, Anwen Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

This article presents a closed-form analytical solution to the harmonic spectrum of output voltage of a single-phase H-bridge inverter, and gives the precise harmonic spectrum considering the effects of dead time and dc bus voltage ripple in a digitally controlled system. Since the H-bridge is the basic block in converters, this solution and spectrum can be extended and applied to a variety of pulsewidth-modulation-based topologies. The interaction between modulation waveform and triangular during the modulation process is fully analyzed from a mathematical perspective so that the combined effects of digital sampling, dead time, and dc bus voltage ripple on the output voltage are summarized in time and frequency domains. Furthermore, the presented harmonic spectra do not need to judge the current polarity in calculation, which means that the exact amplitude and phase of each output harmonic can be obtained accurately, providing the important theoretical basis for system parameter design and specific harmonic compensation or elimination. The presented analysis results are verified by simulations and experiments.

关键词： DC bus ripple dead time digital sinusoidal pulsewidth modulation (DSPWM) double fourier series harmonic analysis voltage source inverter (VSI)

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An Accurate Dead Time Compensation Method for SPWM Voltage Source Inverters

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2023年第4期38卷 4894-4908页

作者： Ye, Jie Huang, Yukai Huang, Songtao Wang, Haozhe Li, Baojin Xu, Jinbang Shen, Anwen Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Dead time is generally used to avoid the short circuit of the dc source, which causes harmonics in the output voltage and current of the voltage source inverters (VSIs). Based on an accurate harmonics calculation model of digital sinusoidal pulsewidth modulation, harmonics caused by dead time can be analyzed and calculated accurately. The proposed method obtains the compensation waveforms by the derived calculation module and detailed operation steps and then injects these into the sinusoidal reference voltage to eliminate the corresponding frequency harmonics caused by the dead time effect. The phase delay of the fundamental voltage caused by dead time is also considered. Moreover, this method avoids detecting the current polarity, affecting the compensation effect. Simulation and experimental results of the proposed method are presented to validate the effectiveness.

关键词： Dead time compensation digital sinusoidal pulsewidth modulation harmonics voltage source inverter (VSI)

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