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arXiv 2020年

作者： Liang, Chang-Duo Ge, Ming-Feng Liu, Zhi-Wei Wang, Yan-Wu Karimi, Hamid Reza School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan430074 China Department of Mechanical Engineering Politecnico di Milano Milan20156 Italy

In this paper, we present a new terminal sliding mode control to achieve predefined-time stability of robot manipulators. The proposed control is developed based on a novel predefined-time terminal sliding mode (PTSM) surface, on which the states are forced to reach the origin in a predefined time, i.e., the settling time is independent to the initial condition and can be explicitly user-defined via adjusting some specific parameters called the predefined-time parameters. It is also demonstrated that the proposed control can provide satisfactory steady-state performance in the case of both external disturbances and parametric uncertainties. Besides, we present a formal systemic analysis method to derive the sufficient conditions for guaranteeing the predefined-time convergence of the closed-loop system. Finally, the effectiveness and performance of the presented control scheme are illustrated through both theoretical comparisons and numerical simulations. Copyright © 2020, The Authors. All rights reserved.

关键词： Sliding mode control

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Stability of homogeneous positive systems with time-varying delays

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AUTOMATICA 2023年第1期152卷

作者： Xiao, Qiang Huang, Zhenkun Zeng, Zhigang Huang, Tingwen Lewis, Frank L. Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Jimei Univ Sch Sci Xiamen Peoples R China Texas A&M Univ Qatar Doha Qatar Univ Texas Arlington UTA Res Inst Ft Worth TX USA

Stability analysis of positive linear systems has been well-developed, while attention to positive homogeneous nonlinear systems is notably less paid. This note deepens and extends some previous results on stability of homogeneous systems with bounded time-varying delays. Based on positivity property, exponential stability of a class of delayed homogeneous system with degree confined to an interval is studied, and its state boundary is further investigated in any fixed time, which is more precise than the former result in some cases. Additionally, finite-time stability is obtained for a delay-free homogeneous system and an explicit boundary is presented. (c) 2023 Published by Elsevier Ltd.

关键词： Positive system Homogeneous system Exponential stability Boundedness Time delay

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Finite-size scaling of geometric renormalization flows in complex networks

arXiv

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arXiv 2020年

作者： Chen, Dan Su, Housheng Wang, Xiaofan Pan, Gui-Jun Chen, Guanrong School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan430074 China Department of Automation Shanghai University Shanghai200072 China Faculty of Physics and Electronic Science Hubei University Wuhan430062 China Department of Electrical Engineering City University of Hong Kong Hong Kong Hong Kong

Recently, the concept of geometric renormalization group provides a good approach for studying the structural symmetry and functional invariance of complex networks. Along this line, we systematically investigate the finite-size scaling of structural and dynamical observables in geometric renormalization flows of synthetic and real evolutionary networks. Our results show that these observables can be well characterized by a certain scaling function. Specifically, we show that the critical exponent implied by the scaling function is independent of these observables but only depends on the small-world properties of the network, namely, all networks located in the small-world phase have a uniform scaling exponent, while those located in the non-small-world phase and in their critical regions have another uniform scaling. More importantly, we perform extensive experiments on real evolutionary networks with small-world characteristics, and our results show that these observables also have uniform scaling in their geometric renormalization flows. Therefore, in a sense this exponent can be used as an effective measure for classifying universal small-world and non-small-world network models. © 2020, CC BY-SA.

关键词： Geometry

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A Novel Nonconvex Low-Rank Tensor Completion Approach for Traffic Sensor Data Recovery From Incomplete Measurements

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Chen, Xiaobo Wang, Kaiyuan Li, Zuoyong Zhang, Yu Ye, Qiaolin Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Shandong Peoples R China Jiangsu Key Lab Image & Video Understanding Social Nanjing 210094 Peoples R China Minjiang Univ Coll Comp & Control Engn Fujian Prov Key Lab Informat Proc & Intelligent Co Fuzhou 350121 Peoples R China Lehigh Univ Dept Bioengn Bethlehem PA 18015 USA Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing 210037 Peoples R China

Complete traffic sensor data are considered to be one of the critical ingredients for intelligent transportation systems (ITSs). However, traffic measurements prevalently suffer from inevitable and ubiquitous missing values (MVs). Current missing data completion algorithms are difficult to leverage the global low-rank property and the fine-grained spatial-temporal structure simultaneously. To circumvent this problem, this work presents a novel collaborative nonconvex low-rank spatial-temporal data tensor completion model that can take full advantage of the inherent spatial-temporal characteristics of traffic measurement data. First, the tensor Schatten p-norm, as an effective nonconvex surrogate of rank function, is used to exploit the global multidimensional correlation of traffic data. Then, we present an elastic net self-representation method and utilize an autoregression model in order to simultaneously capture the self-similarity and the temporal continuity of traffic data acquired in the same sensor network. By integrating the above elements in a unified nonconvex learning model, our method can explore the inherent structure of traffic data from the viewpoints of both global multidimensional correlation and fine-grained spatial and temporal dependency. Then, in view of the general framework of the alternating directions method of multipliers (ADMMs), an efficient iterative algorithm is designed to solve our model. Besides, to optimize the parameter combination of the model, a Bayesian optimization (BO)-based parameter selection algorithm is developed, which avoids manual parameter adjustment. Extensive experiments and analyses on two real-world traffic datasets are carried out. The results demonstrate the advantages of our model under diverse missing patterns and missing ratios.

关键词： Alternating directions method of multipliers (ADMMs) low-rank tensor completion (LRTC) spatial-temporal correlation traffic sensor networks

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Reduced-order interval observer based consensus for MASs with time-varying interval uncertainties

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AUTOMATICA 2022年 135卷 109989-109989页

作者： Wang, Xiaoling Wang, Xiaofan Su, Housheng Lam, James Nanjing Univ Posts & Telecommun Coll Automat & AI Nanjing 210023 Peoples R China Jiangsu Engn Lab IOT Intelligent Robots IOTRobot Nanjing 210023 Peoples R China Shanghai Univ Dept Automat Shanghai 200072 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Luoyu Rd 1037 Wuhan 430074 Peoples R China Univ Hong Kong Dept Mech Engn Pokfulam Rd Hong Kong Peoples R China

In this paper, the consensus problem of a continuous-time uncertain multi-agent system (MAS) with time-varying interval uncertainties in the system matrix and unknown initial states is investigated via output feedback. Relating only to the interval bounds on the uncertainties, a reduced-order framer, which includes two Luenberger-like observers, is first derived. Then, a distributed control algorithm relying only on the information of the framers associated with each agent and its neighbors is proposed. It is shown that the control algorithm can render the reduced-order framer to be an exponential reduced-order interval observer, and simultaneously drive the uncertain MAS to reach robust consensus exponentially. Finally, numerical simulations for a large-scale MAS are used to verify the theoretical results. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Consensus Interval observer Multi-agent system Reduced-order observer Uncertain system

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Pattern phase transitions of self-propelled particles: gases, crystals, liquids, and mills

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NEW JOURNAL OF PHYSICS 2016年第10期18卷 103005-103005页

作者： Cheng, Zhao Chen, Zhiyong Vicsek, Tamas Chen, Duxin Zhang, Hai-Tao Huazhong Univ Sci & Technol Sch Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA Univ Newcastle Sch Elect Engn & Comp Sci Callaghan NSW 2308 Australia Eotvos Lorand Univ Dept Biol Phys Pazmany Petersetany 1A H-1117 Budapest Hungary

To understand the collective behaviors of biological swarms, flocks, and colonies, we investigated the non-equilibrium dynamic patterns of self-propelled particle systems using statistical mechanics methods and H-stability analysis of Hamiltonian systems. By varying the individual vision range, we observed phase transitions between four phases, i.e., gas, crystal, liquid, and mill-liquid coexistence patterns. In addition, by varying the inter-particle force, we detected three distinct milling sub-phases, i.e., ring, annulus, and disk. Based on the coherent analysis for collective motions, one may predict the stability and adjust the morphology of the phases of self-propelled particles, which has promising potential applications in natural self-propelled particles and artificial multi-agent systems.

关键词： Vicsek model collective motions complex networks

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image Fuzzy Edge Information Segmentation Based on Computer Vision and Machine Learning

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JOURNAL OF GRID COMPUTING 2023年第4期21卷 56-56页

作者： Luo, Tianye Li, Shijun Li, Ji Guo, Jie Feng, Ruilong Mu, Ye Hu, Tianli Sun, Yu Guo, Ying Gong, He Jilin Agr Univ Coll Informat Technol Changchun 130118 Jilin Peoples R China Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou 543002 Guangxi Peoples R China Wuzhou Univ Guangxi Coll & Univ Key Lab Image Proc & Intellige Wuzhou 543002 Guangxi Peoples R China Jilin Agr Univ Jilin Prov Agr Internet Things Technol Collaborat Changchun 130118 Jilin Peoples R China Jilin Agr Univ Jilin Prov Intelligent Environm Engn Res Ctr Changchun 130118 Jilin Peoples R China Jilin Agr Univ Jilin Prov Informat Technol & Intelligent Agr Engn Changchun 130118 Jilin Peoples R China

image segmentation is a key problem in the field of machine vision. Its core goal is to separate the target and background in the region of interest from the image and directly affect the accuracy of subsequent operations such as target recognition and image understanding. In the past decades, there have been many good image segmentation algorithms. In recent years, the deep learning method represented by deep learning has made great progress in the field of image segmentation. In this paper, some commonly used image segmentation algorithms based on machine learning were reviewed, and their theoretical and experimental studies were carried out. In this paper, the application prospect of machine learning in image segmentation was prospected. The existing image segmentation methods are mainly divided into the following categories: threshold-based segmentation methods, region-based segmentation methods, edge-based segmentation methods, and segmentation methods based on specific theories. In recent years, with the rapid progress of computer vision technology, the requirements for the accuracy of object image edge information segmentation have become increasingly high. The main reason for image segmentation is to better obtain object information. However, due to interference conditions such as lighting and noise, image blurry edge information segmentation has become the most difficult point in the development of computer vision technology. In the comparative experiment of algorithms, the results showed that in the training set, the response time of Deep Neural Network (DNN) algorithm, Cluster Analysis (CA) algorithm, and Support Vector Machine (SVM) algorithm was 13.72 s, 16.88 s and 17.29 s when the number of samples was 150. In the test set, when the sample number was 50, the recognition rate of DNN algorithm was 93.7%;the recognition rate of CA algorithm was 87.9%;the recognition rate of SVM algorithm was 84.3%. Therefore, the research of image fuzzy edge information

关键词： Blurred Edges image Segmentation Machine Learning Computer Vision Deep Neural Network Cluster Analysis

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Hierarchical Extreme Learning Machine for Unsupervised Representation Learning

Hierarchical Extreme Learning Machine for Unsupervised Repre...

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International Joint Conference on Neural Networks

作者： Wentao Zhu Jun Miao Laiyun Qing Guang-Bin Huang Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China School of Electrical & Electronic Engineering Nanyang Technological University Nanyang Avenue Singapore 639798

ISBN: (纸本)9781479919611

Learning representations from massive unlabeled data is a hot topic for high-level tasks in many applications. The recent great improvements on benchmark data sets, which are achieved by increasingly complex unsupervised learning methods and deep learning models with lots of parameters, usually require many tedious tricks and much expertise to tune. However, filters learned by these complex architectures are quite similar to standard hand-crafted features visually, and training the deep models costs quite long time to fine-tune their weights. In this paper, Extreme Learning Machine-Autoencoder (ELM-AE) is employed as the learning unit to learn local receptive fields at each layer, and the lower layer responses are transferred to the last layer (trans-layer) to form a more complete representation to retain more information. In addition, some beneficial methods in deep learning architectures such as local contrast normalization and whitening are added to the proposed hierarchical Extreme Learning Machine networks to further boost the performance. The obtained trans-layer representations are followed by block histograms with binary hashing to learn translation and rotation invariant representations, which are utilized to do high-level tasks such as recognition and detection. Compared to traditional deep learning methods, the proposed trans-layer representation method with ELM-AE based learning of local receptive filters has much faster learning speed and is validated in several typical experiments, such as digit recognition on MNIST and MNIST variations, object recognition on Caltech 101. State-of-the-art performances are achieved on both Caltech 101 15 samples per class task and 4 of 6 MNIST variations data sets, and highly impressive results are obtained on MNIST data set and other tasks.

关键词： Hierarchical Extreme Learning Machine Unsupervised Representation Learning deep learning models Learning machines Dataset representation method Recognition (Psychology) Receptive field Learning representations Religious Missions Hierarchical

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High Information Density and Low Coverage Data Storage in DNA with Efficient Channel Coding Schemes

arXiv

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arXiv 2024年

作者： Ding, Yi He, Xuan Nguyen, Tuan Thanh Song, Wentu Yakhini, Zohar Yaakobi, Eitan Pan, Linqiang Tang, Xiaohu Cai, Kui Information Coding and Transmission Key Lab of Sichuan Province Southwest Jiaotong University Sichuan Chengdu611756 China Cluster Singapore University of Technology and Design 487372 Singapore Faculty of Computer Science Technion - Israel Institute of Technology Haifa3200003 Israel School of Computer Science RUNI Herzliya4615200 Israel Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

DNA-based data storage has been attracting significant attention due to its extremely high data storage density, low power consumption, and long duration compared to conventional data storage media. Despite the recent advancements in DNA data storage technology, significant challenges remain. In particular, various types of errors can occur during the processes of DNA synthesis, storage, and sequencing, including substitution errors, insertion errors, and deletion errors. Furthermore, the entire oligo may be lost. In this work, we report a DNA-based data storage architecture that incorporates efficient channel coding schemes, including different types of error-correcting codes (ECCs) and constrained codes, for both the inner coding and outer coding for the DNA data storage channel. We also carried out large scale experiments to validate our proposed DNA-based data storage architecture. Specifically, 1.61 and 1.69 MB data were encoded into 30,000 oligos each, with information densities of 1.731 and 1.815, respectively. It has been found that the stored information can be fully recovered without any error at average coverages of 4.5 and 6.0, respectively. This experiment achieved the highest net information density and lowest coverage among existing DNA-based data storage experiments (with standard DNA), with data recovery rates and coverage approaching theoretical optima. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel coding

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A Born-Again Takagi-Sugeno-Kang Fuzzy Classifier with Decoupled Fuzzy Dark Knowledge Distillation

SSRN

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SSRN 2024年

作者： Zhang, Xiongtao Yin, Zezong Jiang, Yunliang Jiang, Yizhang Sun, Danfeng Liu, Yong School of Information Engineering Huzhou University Huzhou31300 China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China School of Computer Science Hangzhou Dianzi University Hangzhou310018 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

In order to enpower the hight performance as well as interpretability of low-order TSK fuzzy classifier, a born-again TSK fuzzy classier embedded with decoupled fuzzy dark knowledge distillation called HTSK-LLM-DKD is proposed in this study. HTSK-LLM DKD achieves the following distinctive characteristics: 1) It takes high-order TSK as teacher model and low-order TSK fuzzy classifier as student model, and leverages the proposed Least Learning Machine based Decoupling Knowledge Distillation to distill the fuzzy dark knowledge from high-order TSK fuzzy classifier to low-order TSK fuzzy classifier, which resulting in low-order TSK fuzzy classifier endowed with enhanced performance surpassing or at least comparable to high-order TSK fuzzy classifier;specifically 2) The Negative Euclidean distance between the output of teacher model and each class is employed to obtain the teacher logits, and then it computes teacher/student soft labels by the softmax function with distilling temperature parameter;3) By reformulating the Kullback Leibler divergence, it decouples fuzzy dark knowledge into target class knowledge and non-target class knowledge, and transfers them to student model, which taking HTSK-LLM-DKD with outstanding performance and relatively fast running speed. Extensive experiment results on UCI benchmarking datasets and real-world dataset Cleveland Heart Disease demonstrate the effectiveness of the proposed HTSK-LLM-DKD. © 2024, The Authors. All rights reserved.

关键词： Students

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