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Model-Free Event-Triggered Consensus Algorithm for Multiagent Systems Using Reinforcement Learning Method

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第8期52卷 5212-5221页

作者： Long, Mingkang Su, Housheng Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

In this article, we study the consensus issues of multiagent systems (MASs) without any information of the system model by using the reinforcement learning (RL) method and event-based control strategy. First, we design an adaptive event-based consensus control protocol using the local sampled state information so that the consensus errors of all agents are uniformly ultimately bounded. The validity of the above event-triggered adaptive control protocol is confirmed by excluding the Zeno behavior within finite time. Then, based on the RL approach, we present a model-free algorithm to get the feedback gain matrix, and accomplish constructing the adaptive event-triggered control strategy without the knowledge of model information. Distinct with the existing related works, this RL-based event-triggered adaptive control algorithm only relies on the local sampled state information, irrelevant to any model information or global network information. Finally, we provide some examples to demonstrate the validity of the above adaptive event-based consensus algorithm.

关键词： Adaptation models Consensus algorithm Heuristic algorithms Protocols Computational modeling Multi-agent systems Adaptive control Consensus event triggered model-free algorithm multiagent systems (MASs) reinforcement learning (RL)

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Predictor-Based Extended State Observer for Disturbance Rejection control of Multirate Systems With Measurement Delay

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第3期70卷 3003-3012页

作者： Sun, Jiankun Liu, Xiangyang Yang, Jun Zeng, Zhigang Li, Shihua Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Key Lab Image Proc & Intelligent Control Educ Mini Wuhan 430074 Peoples R China Southeast Univ Sch Automat Key Lab Measurement & Control CSE Minist Educ Nanjing 210096 Peoples R China Loughborough Univ Dept Aeronaut & Automot Engn Leicestershire LE11 3TU England

This article investigates the multirate disturbance rejection control problem for linear control systems with mismatched disturbance and measurement delay using predictor-based extended state observer. A new extended state observer together with output predictor is first designed to obtain the estimation values of system state and mismatched disturbance, where output predictor is used to compensate the influences of measurement delay and sampling of output. To attenuate the undesirable influence of mismatched disturbance, we then design a new sampled-data robust controller with disturbance compensation, and the updating rate of the proposed controller is allowed to be different from that of the sensor. Thanks to prediction and disturbance/uncertainty estimation and attenuation techniques, the disturbance rejection property of the resultant closed-loop control systems is enhanced despite the multirate and measurement delay. Some sufficient conditions are presented to ensure the stability property of the resultant control systems. We finally consider the application of the visual servoing control system for an inertially stabilized platform, and the experiment results verify superiorities of the predictor-based disturbance rejection control method proposed in the article.

关键词： Disturbance/uncertainty estimation and attenuation (DUEA) measurement delay predictor-based ex-tended state observer sampled-data control

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Unsupervised image Translation with GAN Prior 5th

Unsupervised Image Translation with GAN Prior

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5th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Tu, Pengqi Gao, Changxin Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China

ISBN: (纸本)9783031189067;9783031189074

Unsupervised image translation aims to learn the translation between two domains without paired data. Although impressive progress has been made in recent years, existing methods are difficult to build mapping between domains with drastic visual discrepancies. In this paper, we propose a novel framework for unsupervised image translation with large discrepancies. The key insight is to leverage the generative prior from pretrained GANs for the target domain (e.g., StyleGAN), to learn rich prior information of the target domain. We propose a two-stage framework, the GAN of the target domain is pretrained to obtain the prior information in the first stage, the pretrained GAN for the target domain is embedded as the decoder of the translation network and the translation network is trained with the guidance of the prior information in the second stage. Experimental results show the superiority of our method compared to the existing state-of-the-art methods for the translation between challenging and distant domains.

关键词： image translation Generative prior Two-stage

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Siamese Tracking with Bilinear Features 1

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6th Asian Conference on Pattern Recognition (ACPR)

作者： Pi, Zhixiong Gao, Changxin Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China

ISBN: (数字)9783031024443

ISBN: (纸本)9783031024443;9783031024436

Bilinear features arise in fine-grained visual recognition. They are advantageous to encode detailed representations and attributes to differentiate visually similar objects. The apparent similarity is challenging in visual tracking where background distractors interfere siamese trackers to localize the target object. Especially when distractors and the target belong to the same object category. To increase the discrimination between similar appearance objects, we propose an efficient bilinear encoding method for siamese tracking. The proposed method consists of a self-bilinear encoder and an cross-bilinear encoder. The bilinear features generated via the self-bilinear encoder and the cross-bilinear encoder represent target variations itself and target distractor difference, respectively. To this end, the proposed bilinear encoders advance siamese trackers to capture target appearance variations while differentiating the target and background distractors. Experiments on the benchmark datasets show the effectiveness of bilinear features. Our tracker performs favorably against state-of-the-art approaches.

关键词： Siamese network Bilinear feature Visual tracking

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User Identity Protection in EEG-Based Brain–Computer Interfaces

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 3576-3586页

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

A brain-computer interface (BCI) establishes a direct communication pathway between the brain and an external device. Electroencephalogram (EEG) is the most popular input signal in BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals;however, EEG signals also contain rich private information, e.g., user identity, emotion, and so on, which should be protected. This paper first exposes a serious privacy problem in EEG-based BCIs, i.e., the user identity in EEG data can be easily learned so that different sessions of EEG data from the same user can be associated together to more reliably mine private information. To address this issue, we further propose two approaches to convert the original EEG data into identity-unlearnable EEG data, i.e., removing the user identity information while maintaining the good performance on the primary BCI task. Experiments on seven EEG datasets from five different BCI paradigms showed that on average the generated identity-unlearnable EEG data can reduce the user identification accuracy from 70.01% to at most 21.36%, greatly facilitating user privacy protection in EEG-based BCIs.

关键词： Brain-computer interfaces machine learning privacy protection

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Parametric Design Optimization of a Universal Supernumerary Robotic Limb

Parametric Design Optimization of a Universal Supernumerary ...

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2021 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2021

作者： Huo, Jun Yang, Bo Ru, Hongge Huang, Jian Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Wuhan430074 China

ISBN: (纸本)9781665407731

It is a positive trend for hemiplegia with wearable robots in rehabilitation training. Recently, wearable Supernumerary Robotic Limb (SRL) is rising to a hot spot. The difficulty in modeling SRL for hemiplegia is how to make the SRL mechanism match the human upper and lower limbs' size and function simultaneously. In this paper, a simplified model of a four Degree of Freedoms (DOFs) universal SRL (USRL) is introduced. Based on workspace similarity, which is evaluated by Jaccard Index (JI), we establish a multi-object optimization (MOO) model. By applying the Particle Swarm Optimization (PSO) algorithm, we have found the optimal SRL mechanism parameters that meet the requirement of the human upper limb and the cane simultaneously. Meanwhile, a few impact factors are discussed briefly in the present proposal. The simulation results show that the USRL has a sound match with the human and could assist the hemiplegia subject as a supplemental limb. © 2021 IEEE.

关键词： Robotics

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Dead Time Adaptive Expansion-Based Sensorless Restart Strategy for Free-Wheeling SPMSM Drives

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第3期19卷 2442-2452页

作者： Tang, Qipeng Shen, Yi Sheng, Minggang Shen, Anwen Luo, Pan Luo, Xin Xu, Jinbang 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 Sci & Technol Reactor Syst Design Technol Lab Chengdu 610000 Peoples R China

This article proposes a novel rotational restart strategy based on the dead time adaptive expansion for the sensorless surface-mounted permanent magnet synchronous machines drive system. With the proposed method, the interval time of zero-voltage pulses (ZVPs) is replaced by the dead time and each pulsewidth modulation (PWM) period consists of the dead time and action time of ZVPs. By regulating the dead time based on the stator-current amplitude feedback, the stator current can be controlled within the reasonable setting range, which not only can achieve the adaptive selection of the action time and interval time of ZVPs, but also avoid the potential overcurrent and overcome the current sampling noise. Meanwhile, aiming at two continuous and discontinuous current modes caused by the inserted dead time, the corresponding calculation expressions of the rotor position are deduced in detail. Finally, experimental results indicate the effectiveness and feasibility of the proposed strategy.

关键词： Rotors Stators Pulse width modulation Short-circuit currents Inverters Informatics Switches Dead time expansion rotational restart surface-mounted permanent magnet synchronous machine (SPMSM) zero-voltage-pulse (ZVP)

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Bioinformatics-guided analysis uncovers TIGIT as an epigenetically regulated immunomodulator affecting immunotherapeutic sensitivity of gastric cancer

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CANCER BIOMARKERS 2022年第3期33卷 349-358页

作者： Ma, Jianfei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Luoyu Rd 1037 Wuhan 430074 Hubei Peoples R China

BACKGROUND: Immunomodulatory genes play significant roles in the regulation of immunological properties of gastric cancer, but the effect of epigenetic regulation of these genes on the immune properties is unknown. METHOD: I analyzed the methylation-expression correlation among all immunomodulators and compared with the nonimmunomodulators. The association between epigenetically regulated immunomodulators (ERI) and tumor microenvironment is evaluated. A key immunomodulator TIGIT is further selected to investigate the potential value in the regulation of immunologic properties. Furthermore, the prognostic value and the immunotherapeutic potential of TIGIT are also explored. RESULT: Four genes are identified as ERIs based on the negative correlation between expression and methylation. Association analysis shows that three ERIs participate in the regulation of the immune microenvironment of gastric cancer. Among these ERIs, TIGIT is identified as a key immunomodulator. TIGIT is found to be significantly associated with immune properties. The high TIGIT expression group tends to display an active immune landscape. TIGIT expression is also found to be associated with survival and immunotherapeutic sensitivity. High TIGIT expression group has a favorable prognosis and is more likely to respond to immunotherapy than the low expression group. CONCLUSION: TIGIT is an epigenetically regulated immunomodulator of gastric cancer which can modify the immune activity and affect immunotherapeutic sensitivity. These findings can promote the research of epigenetic therapies and improve the survival of cancer patients by sensitizing tumors to immune therapies.

关键词： Immunomodulator methylation immunotherapy gastric cancer prognosis

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Protecting Multiple Types of Privacy Simultaneously in EEG-based Brain-Computer Interfaces

arXiv

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arXiv 2024年

作者： Meng, Lubin Jiang, Xue Jia, Tianwang Wu, Dongrui Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is the preferred input signal in non-invasive BCIs, due to its convenience and low cost. EEG-based BCIs have been successfully used in many applications, such as neurological rehabilitation, text input, games, and so on. However, EEG signals inherently carry rich personal information, necessitating privacy protection. This paper demonstrates that multiple types of private information (user identity, gender, and BCI-experience) can be easily inferred from EEG data, imposing a serious privacy threat to BCIs. To address this issue, we design perturbations to convert the original EEG data into privacy-protected EEG data, which conceal the private information while maintaining the primary BCI task performance. Experimental results demonstrated that the privacy-protected EEG data can significantly reduce the classification accuracy of user identity, gender and BCI-experience, but almost do not affect at all the classification accuracy of the primary BCI task, enabling user privacy protection in EEG-based BCIs. © 2024, CC BY.

关键词： Differential privacy

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Global Asymptotic Synchronization of Coupled Neural Networks with Reaction Diffusions and Unbounded Time-varying Delays

Global Asymptotic Synchronization of Coupled Neural Networks...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Yun Xing Yin Sheng The Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This article investigates global asymptotic synchronization of coupled neural networks with reaction diffusions and unbounded time-varying delays (RDUDCNNs). Through designing a centralized adaptive controller and corresponding updating laws of time-varying control gains, under two different upper bound of control gains, an asymptotic convergence lemma under unbounded time-varying delays and the Barbarat lemma are used to derive global asymptotic synchronization of RDUDCNNs, respectively. The effectiveness of the obtained conclusion is verified through a numerical simulation.

关键词： Upper bound Automation Neural networks Numerical simulation Delays Synchronization Convergence

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