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IEEE International Conference on Cyber Technology in Automation, control, and intelligent Systems

作者： Song Hyok Ri Jian Huang Myong Ho Kim Ji Yong Kim Gwangmin Choe Chol Jun Rim School of Automation Huazhong University of Science and Technology Wuhan CHINA Department of Control Science University of Science Pyongyang D. P. R. of Korea (Democratic People's Republic of Korea) Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan CHINA Department of Control Science University of Science Pyongyang D.P.R. of Korea (Democratic People's Republic of Korea) School of Computer Science Kim II Sung university Pyongyang D. P. R. of Korea (Democratic People's Republic of Korea)

ISBN: (纸本)9781479987313

An approach of direct adaptive fuzzy sliding-mode control which combines the fuzzy control with the sliding-mode control, is proposed for the control of a class of unknown nonlinear dynamic systems. The control goal is to obtain a direct adaptive fuzzy sliding-mode control law and a constructive Lyapunov synthesis approach with respect to a class of nonlinear systems without the knowledge of uncertainties. For improving the approximate performance of the fuzzy system, the proposed approach in this study not only online updates the parameter values in the consequence fuzzy sets, but also updates the shape parameters of the membership functions of the prime fuzzy sets. The fuzzy control rules are updated through the online adaptive learning, which makes the output of fuzzy control to approximate to a sliding-mode equivalent control. The asymptotic stability of the overall system based on Lyapunov theory is proved. Some numerical simulation results show the efficiency of the proposed approach.

关键词： Adaptive systems Nonlinear dynamical systems Sliding mode control Fuzzy control Adaptation models

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Size-invariance matters: rethinking metrics and losses for imbalanced multi-object salient object detection 24

Size-invariance matters: rethinking metrics and losses for i...

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Proceedings of the 41st International Conference on Machine Learning

作者： Feiran Li Qianqian Xu Shilong Bao Zhiyong Yang Runmin Cong Xiaochun Cao Qingming Huang Institute of Information Engineering Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China and School of Control Science and Engineering Shandong University Jinan China and Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China and Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD.

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Combining Attractor Propagation with Differential Evolution for Three-echelon logistics distribution optimization

SSRN

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SSRN 2022年

作者： Zhang, Haifei Ge, Hongwei Yang, Jieming Su, Shuzhi Tong, Yubing School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi214122 China Key Laboratory of Advanced Process Control for Light Industry Jiangnan University Ministry of Education Wuxi214122 China School of Computer Science and Engineering Anhui University of Science & Technology Anhui Huainan232001 China Medical Image Processing Group Department of Radiology University of Pennsylvania PhiladelphiaPA19104 United States

In the process of urban logistics distribution, how to achieve efficiently and timely logistics distribution within the distribution time limit has become an urgent problem to be solved in logistics management. Three-echelon logistics distribution is considered to be an effective strategy to alleviate urban congestion, improve liquidity and reduce pollution. In this paper, a mathematical model of three-echelon logistics distribution problem considering time window constraint is established based on directed graph, and a double-tier intelligent algorithm coordinated by Distance Entropy-based Attractor Propagation (DEBAP) clustering algorithm - Crossover-and-Selection based Differential Evolution (CSBDE) is proposed. DEBAP is responsible for the optimization of region partitioning and facility allocation. The obtained region partitioning and facility allocation scheme are transmitted to CSBDE for path planning. CSBDE feeds back the optimized path planning scheme to DEBAP, and the two-tier intelligent algorithm cooperates to complete the optimization of three-echelon logistics distribution system. In addition, this paper proposes a search strategy based on reachable distribution region and a facility allocation optimization strategy based on path length as weight to improve the efficiency of the algorithm. The example calculation shows that the mathematical model and combined intelligent algorithm proposed in this paper can effectively solve 3-echelon logistics distribution problem(3E-LDP), provide theoretical and methodological guidance for multi-echelon logistics distribution, and can be well implemented in practice to help logistics operators reduce operation costs and improve customer service. © 2022, The Authors. All rights reserved.

关键词： Clustering algorithms

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Unsupervised Learning of Depth and Pose Based on Monocular Camera and Inertial Measurement Unit (IMU)

Unsupervised Learning of Depth and Pose Based on Monocular C...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yanbo Wang Hanwen Yang Jianwei Cai Guangming Wang Jingchuan Wang Yi Huang University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Weitong Vision Technology Co. Ltd.

The main content of the research in this paper is the estimation of depth and pose based on monocular vision and Inertial Measurement Unit (IMU). The usual depth estimation network and pose estimation network require depth ground truth or pose ground truth as a supervised signal for training, while the depth ground truth and pose ground truth are hard to obtain, and monocular vision based depth estimation cannot predict absolute depth. In this paper, with the help of IMU, which is inexpensive and widely used, we can obtain angular velocity and acceleration information. Two new supervision signals are proposed and the calculation expressions are given. Among them, the model trained with acceleration constraint shows a good ability to estimate the absolute depth during the test. It can be considered that the model can estimate the absolute depth. We also derive the method of estimating the scale factor during the test from the acceleration constraint, and also achieve good results as the acceleration constraint does. In addition, this paper also studies the method of using IMU information as pose network input and as selecting conditions. Moreover, it analyzes and discusses the experimental results. At the same time, we also evaluate the effect of the pose estimation of the relevant models. This article starts by reviewing the achievements and deficiencies of the work in this field, combines the use of IMU, puts forward three new methods such as a new loss function, and conducts a test analysis and discussion of relevant indicators on the KITTI data set.

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Graph Convolutional Networks Based Muti-Label Deep Cross-Modal Hashing

Graph Convolutional Networks Based Muti-Label Deep Cross-Mod...

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International Conference on Information Science and Technology (ICIST)

作者： Yi Peng Xin Jiang Nian Zhang Jiang Xiong School of Electronic and Information Engineering Chongqing Three Gorges University Chongqing China Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Chongqing China Department of Electrical and Computer Engineering University of the District of Columbia Washington D.C. USA College of Mathematics and Statistics Chongqing Three Gorges University Chongqing China

Recently, multi-label deep cross-modal hashing (MDCH), which incorporates deep neural networks, hashing and multi-label learning for cross-modal retrieval tasks, has achieved excellent cross-modal retrieval results and thus became a highly popular area of research. Nevertheless, many existing MDCH methods concentrate on extracting information from multi-modal data, while neglecting the abundant semantic information in multiple labels. Few MDCH methods incorporate multi-label information, but they often treat labels as independent entities, ignoring the relationships between categories, which hinders the establishment of semantic connections among multi-modal data. In order to tackle the aforementioned challenges, we propose a graph convolutional networks based multi-label deep cross-modal hashing method (GMCH) in this paper. GMCH leverages two deep neural networks to generate hash representations from the original image-text pairs, during this process, a graph convolutional network is introduced to capture the category correlations of multi-labels and supervise the training of hash mapping. Experimental results on two commonly employed datasets validate the efficacy of the proposed GMCH method. You can find the code for our proposed GCMH at https://***/licher12/***.

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Cross-Modality Person Re-Identification Based on Shared Feature Attention Aggregation Algorithm

Cross-Modality Person Re-Identification Based on Shared Feat...

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International Conference on Information Science and Technology (ICIST)

作者： Jiong Wang Linjuan Xie Nian Zhang Jiang Xiong XiaoLing Song Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Chongqing China Department of Electrical and Computer Engineering University of the District of Columbia Washington D.C. USA College of Mathematics and Statistics Chongqing Three Gorges University Chongqing China College of Computer Science and Engineerings Chongqing Three Gorges University Chongqing China

Person re-identification in the visible-infrared spectrum (VI-ReID) is a challenging task due to significant difference of modalities, making it difficult to extract salient features across modalities. To solve this problem, in this paper, we propose a novel Cross-Modality Shared Feature Attention Aggregation algorithm (cm-SFAA) for VI-ReID. In the cm-SFAA approach, we first design a Random Channel Alternation Erasure Network to better learn robust features against occlusions and noise, as well as diverse feature representations under different channel combinations. This enhances the generalization ability of the model. Furthermore, a Local Feature Aggregation module is introduced in cm-SFAA, which utilizes an adaptive weighting mechanism to aggregate features from different levels and domains, thereby improving the performance of the model. Extensive experiments on two mainstream datasets SYSU-MMOI and RegDB show that cm-SFAA outperforms several baseline methods and acquires state-of-the-art performance. The source code of our proposed cm-SFAA is available at https://***/wdqlndVcm-SFAA.

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Design and Implementation of Telemedicine System Using Light Fidelity and PIC16F877A Microcontroller

Design and Implementation of Telemedicine System Using Light...

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International Conference on Information Science and Technology (ICIST)

作者： M.R. Ezilarasan Man-Fai Leung Xiangguang Dai Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Instituteof Science and Technology Chennai India Faculty of Science and Engineering School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Wanzhou Chongqing China

ISBN: (数字)9798350353334

ISBN: (纸本)9798350353341

Medical Body Area Networks (MBANs) increasingly utilize wireless communication technologies to enhance the freedom and convenience for patients and caregivers. Currently, radio frequency (RF) is the prevalent medium in healthcare applications; however, the potential for electromagnetic interference with precision medical equipment remains a concern. This study introduces a novel design and implementation of a medical healthcare information system using the newly developed Wireless Visible Light Communication (VLC) technology, also known as Light Fidelity (Li-Fi). Visible Light Emitting Diodes (LEDs), expected to soon surpass traditional incandescent and fluorescent bulbs as the primary light source due to their energy efficiency, facilitate this technology. In this research, patient health is monitored using sensors, with results displayed on an LCD. An LED lightbulb transmits the data as light signals, which are captured by a photo-detector at the receiver's end. This setup not only displays the output on an LCD but also alerts caregivers through an auditory signal, beeping every 15 seconds for respiration and every 30 seconds for heart rate monitoring. Moreover, these lighting fixtures serve dual functions: illumination and wireless data transmission, using the rapid switching capability of LEDs. This prototype VLC-based system offers a promising alternative for data services and monitoring in hospital areas where RF use is restricted.

关键词： Wireless communication Radio frequency Wireless sensor networks Lighting Light emitting diodes Light fidelity Liquid crystal displays Communication system security Monitoring Visible light communication

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Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review

arXiv

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arXiv 2021年

作者： Wu, Dongrui Xu, Jiaxin Fang, Weili Zhang, Yi Yang, Liuqing Xu, Xiaodong Luo, Hanbin Yu, Xiang The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China The School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China The College of Public Administration Huazhong University of Science and Technology Wuhan430074 China The University of Michigan Ann ArborMI48109 United States The School of Management and Sino European Institute for Intellectual Property Huazhong University of Science and Technology Wuhan430074 China

Physiological computing uses human physiological data as system inputs in real time. It includes, or significantly overlaps with, brain-computer interfaces, affective computing, adaptive automation, health informatics, and physiological signal based biometrics. Physiological computing increases the communication bandwidth from the user to the computer, but is also subject to various types of adversarial attacks, in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output, leading to possible user confusion, frustration, injury, or even death. However, the vulnerability of physiological computing systems has not been paid enough attention to, and there does not exist a comprehensive review on adversarial attacks to them. This paper fills this gap, by providing a systematic review on the main research areas of physiological computing, different types of adversarial attacks and their applications to physiological computing, and the corresponding defense strategies. We hope this review will attract more research interests on the vulnerability of physiological computing systems, and more importantly, defense strategies to make them more secure. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Analysis of non-Markovian passive quantum linear systems’ response to single-photon input fields*

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IFAC-PapersOnLine 2023年第2期56卷 5179-5184页

作者： Zhengyi Sun Shibei Xue Zibo Miao Zhiyuan Dong Dewei Li Lulu Pan Min Jiang Department of Automation Shanghai Jiao Tong University Shanghai 200240 P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 P. R. China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen 518055 P. R. China School of Electronics and Information Engineering Soochow University Suzhou 215006 P. R. China

In this paper, we analyze the response of passive quantum linear systems in a non-Markovian environment to single photon input fields. Based on an augmented modelling method for non-Markovian quantum systems, analytic forms of output intensity and output covariance function in steady-state are derived. These results allow us to quantitatively analyze the influence of non-Markovian environment on system response under a framework of linear system theory.

关键词： Non-Markovian quantum systems single photon state passive quantum linear system response theory

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fMRI-based Brain Disease Diagnosis: A Graph Network Approach

TechRxiv

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TechRxiv 2023年

作者： Chen, Qi-Qi Gao, Yubing Tong, Wei Li, Ping Wu, Edmond Q. The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Nanjing University of Science and Technology Jiangsu210008 China The School of Biological Science and Medical Engineering Beihang University Beijing100191 China The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

As a non-invasive brain imaging technology, functional magnetic resonance imaging (fMRI) provides a basic tool for brain functional network modeling and brain disease diagnosis. Problems, such as large number of parameters, low training efficiency, and poor interpretability, are encountered in mainstream models because of the high complexity of fMRI and brain networks. To solve these problems, a novel structure feature combined graph neural network (SFC-GNN) with a low number of parameters is proposed. In particular, SFC-GNN is composed of 1) the graph convolution layer of the brain region perception and 2) the node pooling layer of the graph structure feature (GSF). It also receives the sparse brain graph modeled by each subject’s fMRI as input. Especially, the GSF layer can select brain regions that are important for classification, thereby localizing all active regions related to brain disease. Moreover, a group network is constructed according to the correlation among subjects, and SFC-GNN can be extended further to a node classification model to achieve better diagnosis performance. The proposed method has been validated on the ABIDE and ADNI datasets, thereby showing the effectiveness of our proposed method in various experiments. © 2023, CC BY.

关键词： Magnetic resonance imaging

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