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Research Square 2021年

作者： Meng, Lubin Huang, Jian Zeng, Zhigang Jiang, Xue Yu, Shan Jung, Tzyy-Ping Lin, Chin-Teng Chavarriaga, Ricardo Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China La Jolla CA United States Center for Advanced Neurological Engineering Institute of Engineering in Medicine UCSD La Jolla CA United States Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Australia ZHAW DataLab Zürich University of Applied Sciences Winterthur8401 Switzerland

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to the wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks, e.g., the attacker can add tiny adversarial perturbations to a test sample to fool the model, or poison the training data to insert a secret backdoor. Previous research has shown that adversarial attacks are also possible for EEG-based BCIs. However, only adversarial perturbations have been considered, and the approaches are theoretically sound but very difficult to implement in practice. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is more feasible in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs. © 2021, CC BY.

关键词： Electroencephalography

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Domain-invariant similarity activation map contrastive learning for retrieval-based long-term visual localization

arXiv

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arXiv 2020年

作者： Hu, Hanjiang Wang, Hesheng Liu, Zhe Chen, Weidong Autonomous Robot Lab Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The State Key Laboratory of Robotics and System HIT The Department of Computer Science and Technology University of Cambridge United Kingdom

Visual localization is a crucial component in the application of mobile robot and autonomous driving. Image retrieval is an efficient and effective technique in image-based localization methods. Due to the drastic variability of environmental conditions, e.g. illumination, seasonal and weather changes, retrieval-based visual localization is severely affected and becomes a challenging problem. In this work, a general architecture is first formulated probabilistically to extract domain-invariant feature through multi-domain image translation. And then a novel gradient-weighted similarity activation mapping loss (Grad-SAM) is incorporated for finer localization with high accuracy. We also propose a new adaptive triplet loss to boost the contrastive learning of the embedding in a self-supervised manner. The final coarse-to-fine image retrieval pipeline is implemented as the sequential combination of models without and with Grad-SAM loss. Extensive experiments have been conducted to validate the effectiveness of the proposed approach on the CMU-Seasons dataset. The strong generalization ability of our approach is verified on RobotCar dataset using models pre-trained on urban part of CMU-Seasons dataset. Our performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision, especially under the challenging environments with illumination variance, vegetation and night-time images. The code and pretrained models are available on https://***/HanjiangHu/DISAM. Copyright © 2020, The Authors. All rights reserved.

关键词： Image retrieval

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Self-learning Attention Global Pooling Based Image Representation for Visual Place Recognition

Self-learning Attention Global Pooling Based Image Represent...

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Chinese automation Congress (CAC)

作者： Xiaoquan Huang Song Zheng College of Electrical Engineering and Automation Fuzhou University Fuzhou China Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou University Fuzhou China

The visual place recognition is based on the image to determine whether the current scene match the scene in the index data set and localization. However, due to the scene appearance and the shooting position changing with time, such as distance, viewpoint, illumination, weather, season, traffic flow, etc., it is a challenge to extract more distinguishing and representative image representation by overcoming multiple variables. Recently based on convolutional neural networks, many global pooling methods have been proposed and reached the cutting edge of the state of the art, but they ignore the correlation of local regions in different channels. Therefore we propose a self-learning attention global pooling model for feature maps and performs end-to-end fine-tuning training for contrastive loss through Siamese network. The experimental results show that our method outperforms the state-of-the-art global pooling method on two public test datasets: ROxford and RParis.

关键词： Feature extraction Image representation Visualization Image recognition Training Tensile stress Convolution

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Topology inference for multi-agent cooperation under unmeasurable latent input

arXiv

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arXiv 2020年

作者： Jiao, Qing Li, Yushan He, Jianping Shi, Ling Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

Topology inference is a crucial problem for cooperative control in multi-agent systems. Different from most prior works, this paper is dedicated to inferring the directed network topology from the observations that consist of a single, noisy and finite time-series system trajectory, where the cooperation dynamics is stimulated with the initial network state and the unmeasurable latent input. The unmeasurable latent input refers to intrinsic system signal and extrinsic environment interference. Considering the time-invariant/varying nature of the input, we propose two-layer optimization-based and iterative estimation-based topology inference algorithms (TO-TIA and IE-TIA), respectively. TO-TIA allows us to capture the separability of global agent state and eliminates the unknown influence of time-invariant input on system dynamics. IE-TIA further exploits the identifiability and estimability of more general time-varying input and provides an asymptotic solution with desired convergence properties, with higher computation cost compared with TO-TIA. Our novel algorithms relax the dependence of observation scale and leverage the empirical risk reformulation to improve the inference accuracy in terms of the topology structure and edge weight. Comprehensive theoretical analysis and simulations for various topologies are provided to illustrate the inference feasibility and the performance of the proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

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Study on the Preparation and Application of Vector Reference Map

Study on the Preparation and Application of Vector Reference...

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IEEE information technology and Mechatronics Engineering Conference (ITOEC)

作者： Yaqiang Shi Jingyuan Bao Li Xu Zhenpo Tian Delie Ming National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China The Second Navy Representative Office Wuhan China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9781728143231

ISBN: (纸本)9781728143248

In scene matching, we use vector data instead of raster data to prepare a reference map. The BSP -algorithm is used to manage the vector data and then it is loaded on the aircraft. During the guidance process, reference maps are real-time prepared based on the track information and camera parameters. As the vector reference map is very different from the real-time map, conditional generation adversarial networks are used to convert the real-time map to a vector-style image and then match it to the vector reference map, which can achieve 86% matching accuracy.

关键词： Real-time systems Aircraft Cameras Buildings Generators Aircraft navigation Roads

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Enhancing Adaptive Event-Triggered Protocols for Multi-Agent Consensus with External Disturbances

Enhancing Adaptive Event-Triggered Protocols for Multi-Agent...

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International Conference on control, automation, Robotics and Vision (ICARCV)

作者： Xianwei Li Yang Tang Bing Zhu Shaoyuan Li Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai China Seventh Research Division School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

This paper studies the design of adaptive event-triggered protocols for consensus of linear multi-agent systems (MASs) with external disturbances. Different from most of the existing results that deal with undirected graphs, this paper addresses directed graphs, specifically graphs that are assumed to be strongly connected. Inspired by a recent development [1], this paper devises novel adaptive event-triggered protocols for linear MASs with all agents subject to external disturbances. Two specific designs of composite triggering conditions are analysed and discussed. Compared with the disturbance-free case, additional constraints need to be introduced to deal with external disturbances and moreover the time-dependent terms are allowed to take any finite functions, rather than a class of L1 functions.

关键词： Protocols Eigenvalues and eigenfunctions Laplace equations Directed graphs Symmetric matrices Closed loop systems automation

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$\mathcal H_{2}$ Performance Analysis and Applications of 2-D Hidden Bernoulli Jump System

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2019年第10期49卷 2097-2107页

作者： Zheng-Guang Wu Ying Shen Hongye Su Renquan Lu Tingwen Huang State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Guangdong Key Laboratory of IoT Information Processing School of Automation Guangdong University of Technology Guangzhou China Texas A&M University at Qatar Doha Qatar

The asymptotic mean square stability and H 2 performance of the 2-D hidden Bernoulli jump system is analyzed in this paper. This system, essentially, has a bi-jumping property, in other words, its jumps depend on two jumping parameters, one is the Bernoulli process, the other is dependent on the Bernoulli process via a conditional probability matrix, they together determine which subsystem is active and are coined as hidden Bernoulli model. By means of Lyapunov function method, a sufficient condition is derived, which reveals that the system is asymptotically mean square stable and has a certain H 2 performance if a set of matrix inequalities are satisfied. Furthermore, the issues of asynchronous control and filtering are addressed for 2-D Bernoulli jump system based on the hidden Bernoulli model and the derived result. Finally, numerical simulations indicate that these proposed theories and methods are reliable and efficient.

关键词： Markov processes Switches Performance analysis Boundary conditions Indexes Switched systems

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Guest Editorial:Advances in Bio-inspired Heuristics for Computing

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CAAI Transactions on Intelligence technology 2019年第3期4卷 127-128页

作者： XINCHAO ZHAO MAOGUO GONG XINGQUAN ZUO LINQIANG PAN School of Science Beijing University of Posts and TelecommunicationsBeijing 100876PR China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian UniversityXi’an 710071PR China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876PR China Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of AutomationHuazhong University of Science and TechnologyWuhan 430074PR China

Bio-inspired computing (BIC), short for biologically inspired computing, is a field of study that loosely knits together subfields related to the topics of connectionism, social behaviour and emergence. The field of bio-inspired computing brings together researchers from many disciplines, including biology, computer science, mathematics, physics and genetics.

关键词： Guest Editorial Advances Bio-inspired Heuristics Computing

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AI-driven target screening and microfluidic sonication-assembled oral delivery of fucoxanthin-loaded probiotic vesicles for targeted alleviation of diet-induced obesity

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Chemical Engineering Journal 2025年 518卷

作者： Kuiyou Wang Xueqian Li Xiaoyu Xu Jingru Chang Mingyang Sun Kexin Huang Shasha Huang Hongyu Jiang Huihui Wang Mingqian Tan Wentao Su State Key Laboratory of Marine Food Processing and Safety Control National Engineering Research Center of Seafood Dalian Polytechnic University Dalian 116034 Liaoning China Dalian Key Laboratory for Precision Nutrition Academy of Food Interdisciplinary Science School of Food Science and Technology Dalian Polytechnic University Dalian 116034 Liaoning China Collaborative Innovation Center of Seafood Deep Processing Dalian Polytechnic University Dalian 116034 Liaoning China School of Mechanical Engineering and Automation Dalian Polytechnic University Dalian 116034 Liaoning China School of Information Science and Engineering Dalian Polytechnic University Dalian 116034 Liaoning China

Obesity is associated with numerous diseases, and its increasing prevalence poses serious public health challenges. Current pharmacological treatments, including appetite suppressants and fat absorption inhibitors, exhibit limited efficacy and adverse side effects. This study developed a Lactobacillus -derived vesicle (LCEV) system using microfluidic-assisted ultrasound technology to encapsulate fucoxanthin (Fx). In parallel, AI-based target screening was employed to evaluate its role in regulating lipid metabolism both in vitro and in vivo . Through simulation modeling and experimental validation, the microfluidic ultrasound technology generated a stable, uniformly distributed acoustic pressure field. It also optimized energy input and minimized localized high-pressure damage to vesicle structures, thereby enhancing encapsulation efficiency. In vitro , Fx-loaded LCEV (LCEV@Fx), assembled with microfluidic ultrasound, inhibited free fatty acid-induced lipid accumulation in liver cells, reduced oxidative stress, and suppressed adipocyte differentiation and lipid synthesis in 3T3-L1 preadipocytes. In vivo , vesicle encapsulation prolonged retention in the gastrointestinal tract, enhanced absorption across the serosal layer, and increased liver accumulation. AI-driven target screening and molecular docking analysis identified AMPKα1 as a key target for Fx in regulating lipid metabolism, providing a theoretical basis for its mechanism of action. Further experiments confirmed that LCEV@Fx reduced white adipose tissue accumulation and improved hepatic steatosis in HFD-induced mice. It promoted fatty acid oxidation through the AMPK/SIRT1 pathway while downregulating the expression of SREBP1 and FAS to inhibit lipid synthesis. Overall, this study, through AI-driven target screening and verification, clarified the mechanism of action of LCEV@Fx, providing new insights for its application in metabolic diseases, particularly demonstrating significant therapeutic potential in r

关键词： AI-driven target screening Fucoxanthin Microfluidic ultrasound Obesity Vesicles

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Energy Efficient Predictive control for Vapor Compression Refrigeration Cycle Systems

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IEEE/CAA Journal of Automatica Sinica 2018年第5期5卷 953-960页

作者： Xiaohong Yin Shaoyuan Li the School of Automation and Electronic Engineering Qingdao University of Science and TechnologyQingdao 266042China the Department of Automation Shanghai Jiao Tong Universityand the Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai 200240China

Abstract--Vapor compression refrigeration cycle （VCC） system is a high dimensional coupling thermodynamic system for which the controller design is a great challenge. In this paper, a model predictive control based energy efficient control strategy which aims at maximizing the system efficiency is proposed. Firstly, according to the mass and energy conservation law, an analysis on the nonlinear relationship between superheat and cooling load is carried out, which can produce the maximal effect on the system performance. Then a model predictive control （MPC） based controller is developed for tracking the calculated setting curve of superheat degree and pressure difference based on model identified from data which can be obtained from an experimental rig. The proposed control strategy maximizes the coefficient of performance （COP） which depends on operating conditions, in the meantime, it meets the changing demands of cooling capacity. The effectiveness of the control performance is validated on the experimental rig. Index Terms--Cooling load, model predictive control （MPC）, superheat, vapor compression refrigeration cycle （VCC）.

关键词： Cooling load model predictive control(MPC) superheat vapor compression refrigeration cycle(VCC)

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