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Distributed Event-Triggered Synchronization of Interconnected Linear Two-Time-Scale Systems With Switching Topology

IEEE TRANSACTIONS ON CYBERNETICS 2022年第12期52卷 13714-13726页

作者： Lei, Yan Wang, Yan-Wu Liu, Xiao-Kang Liu, Zhi-Wei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Peoples R China

This article investigates the synchronization problem of interconnected linear two-time-scale systems (TTSSs) with switching topology. By utilizing the Chang transformation, a distributed synchronization protocol is proposed with event-triggered communication. Static and dynamic event-triggered mechanisms are proposed successively, which both contain two separated event-triggering conditions corresponding to the slow and the fast subsystems. The existence of a strictly positive time period between any two successive transmissions is ensured regardless of the initial states. The main difficulty of this study lies in that the state jump and parametric uncertainty appear because of the system transformation. To overcome the difficulty, the system is first modeled as an uncertain hybrid system. Then, the control gain is properly designed by solving Riccati-like equations dependent on the rough bounds of the eigenvalues of communication graph Laplacians, and a piecewise quadratic Lyapunov function is proposed with which the jump caused by the switching topology is subtly evaluated. Sufficient conditions are thus established to achieve the event-triggered synchronization. Furthermore, the results are also extended to solve the synchronization problem of the interconnected impulsive linear TTSSs. Finally, three numerical examples are provided to demonstrate the effectiveness of the proposed theoretical results.

关键词： Switches Topology Synchronization Analytical models control systems Symmetric matrices Lyapunov methods Event-triggered communication switching topology two-time-scale system (TTSS)

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Neuroadaptive Impulsive control on Consensus of Uncertain Multiagent Systems Using Continuous and Sampled Information

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第8期34卷 5086-5098页

作者： Han, Yiyan Xiao, Qiang Zeng, Zhigang Huazhong Univ Sci & Technol Educ Minist China Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Peoples R China

This article considers the consensus problem of uncertain multiagent systems, which is addressed by neuroadaptive impulsive control schemes. The proposed control schemes indicate that the communication among agents only occurs impulsively, while the dynamics uncertainty is addressed by adaptive schemes using neural networks. Based on such approaches, two specific control schemes are designed. One is that with impulsive feedback, the control scheme uses continuous-time information, which implies that the adaptive process is continuous over time. Another is that by adopting sampled information, the update of all systems, including the feedbacks on agents, the update of neural networks, and the estimation for uncertainty, can be executed only at impulsive instants. The latter case can reduce the energy cost for communication and control, but extra assistant systems are required. The estimation and consensus prove to be achieved with errors if some conditions are fulfilled. Numerical simulations, including a practical system example, are presented.

关键词： Multi-agent systems Neural networks Estimation Costs Uncertainty Protocols Directed graphs Consensus neuroadaptive impulsive control sampled information uncertain system

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Sparse representation and modified density peak clustering-based state identification for multimode processes

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第2期34卷 025016-025016页

作者： Wang, Zhaojing Chen, Bin Zheng, Ying Wang, Yang Yan, Xiaoyun Wuhan Text Univ Hubei Prov Engn Res Ctr Intelligent Text & Fash Sch Comp Sci & Artificial Intelligence Wuhan 430200 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

Industrial processes with high-dimensional data are generally operated with mixed normal/faulty states in different modes, making it difficult to automatically and accurately identify the faults. In this paper, a state identification framework is proposed for multimode processes. First, a key variable selection approach is presented based on sparse representation to eliminate redundant variables. Then, modified density peak clustering is proposed to identify different states, in which a distance measurement with a time factor is constructed to select all the possible cluster centers. Then, the sum of squared errors-based approach is developed to determine the optimal cluster centers automatically. Further, considering that the mode attributes may be mixed with the fault attributes, a two-step 'coarse-to-fine identification' strategy is designed to precisely identify the modes and the faults in each mode. Finally, three cases including a numerical simulation, Tennessee Eastman benchmark process and an actual semiconductor manufacturing process are presented to show the feasibility of the proposed method.

关键词： multimode process state identification modified density peak clustering sparse representation

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Camera distance helps 3D hand pose estimated from a single RGB image

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GRAPHICAL MODELS 2023年 127卷

作者： Cui, Yuan Li, Moran Gao, Yuan Gao, Changxin Wu, Fan Wen, Hao Li, Jiwei Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China CloudWalk Technol Guangzhou Peoples R China Wuhan Univ Elect Informat Sch Wuhan Peoples R China

Most existing methods for RGB hand pose estimation use root-relative 3D coordinates for supervision. However, such supervision neglects the distance between the camera and the object (i.e., the hand). The camera distance is especially important under a perspective camera, which controls the depth-dependent scaling of the perspective projection. As a result, the same hand pose, with different camera distances can be projected into different 2D shapes by the same perspective camera. Neglecting such important information results in ambiguities in recovering 3D poses from 2D images. In this article, we propose a camera projection learning module (CPLM) that uses the scale factor contained in the camera distance to associate 3D hand pose with 2D UV coordinates, which facilities to further optimize the accuracy of the estimated hand joints. Specifically, following the previous work, we use a two-stage RGB-to-2D and 2D-to-3D method to estimate 3D hand pose and embed a graph convolutional network in the second stage to leverage the information contained in the complex non-Euclidean structure of 2D hand joints. Experimental results demonstrate that our proposed method surpasses state-of-the-art methods on the benchmark dataset RHD and obtains competitive results on the STB and D+O datasets.

关键词： Hourglass GCN Hand pose estimation Root-relative joints Coordinates attentions

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Degradation-oriented real-time power control of fuel cell hybrid vehicles under uncertain driving conditions

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第78期48卷 30556-30569页

作者： Fu, Jun Zeng, Linghong Sheng, Chuang Guo, Ziang Li, Beijia Deng, Zhonghua Li, Xi Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Shenzhen Res Inst Shenzhen 518055 Peoples R China

This work provides a real-time power allocation algorithm to address uncertain actual driving situations for fuel cell hybrid vehicles. To predict the vehicle speed under nondeterministic driving conditions, a fusion prediction model is developed based on the advantages of the Markov chain and neural network. The optimal power splitting decision in each receding horizon is then solved using the Pontryagin's minimum principle (PMP) method, considering fuel consumption, State of Charge (SOC), and performance degradation. A degradation model of electrochemical active surface area (ECSA) based on Pt catalyst dissolution was developed. Then the effect of the energy management algorithm on fuel cell degradation was evaluated using the degradation model. Compared with the two conventional real-time power splitting strategies, the approach suggested in this research can better reduce the fuel consumption and maintain the stability of battery SOC with a lower fluctuation while taking into account the degradation of the fuel cell. (c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Hybrid power system Uncertain operating conditions Fusion velocity prediction Predictive control Degradation assessment

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Geometric Renormalization Reveals the Self-Similarity of Weighted Networks

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第2期10卷 426-434页

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

The geometric renormalization (GR) group of complex networks based on hidden metric space provides a powerful framework for studying the self-similarity of networks. Recent studies have shown that this framework can significantly reduce the size and complexity of the initial system. In this sense, the smaller-scale replica can be used as an alternative or guidance to the original large-scale network. In this article, we extend the GR framework to the weighted network and prove that this framework can sustain the self-similarity of synthetic weighted networks and real-world weighted networks. Furthermore, we assign the corresponding weights to all edges of reconstructed human connectomes at five different resolutions, and the results show that topological features of these networks exhibit self-similar behaviors. Remarkably, our results also suggest that the GR transform group can generate a series of low-resolution replica networks that are similar to the initial highest-resolution human connectome networks, which greatly promotes the network science, neuroscience, and physics understanding of brain mechanisms, and is of great significance to the research of brain science. Finally, a typical spin-like model is used to further verify the rationality of this framework.

关键词： Brain modeling Topology Solid modeling Neurons Integrated circuit modeling Geometric modeling Fractals Geometric renormalization (GR) self-similarity spin-like model weighted networks

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Expanding Hyperspherical Space for Few-Shot Class-Incremental Learning

Expanding Hyperspherical Space for Few-Shot Class-Incrementa...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Yao Deng Xiang Xiang Key Lab of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

In today’s ever-changing world, the ability of machine learning models to continually learn new data without forgetting previous knowledge is of utmost importance. However, in the scenario of few-shot class-incremental learning (FSCIL), where models have limited access to new instances, this task becomes even more challenging. Current methods use prototypes as a replacement for classifiers, where the cosine similarity of instances to these prototypes is used for prediction. However, we have identified that the embedding space created by using the relu activation function is incomplete and crowded for future classes. To address this issue, we propose the Expanding Hyperspherical Space (EHS) method for FSCIL. In EHS, we utilize an odd-symmetric activation function to ensure the completeness and symmetry of embedding space. Additionally, we specify a region for base classes and reserve space for unseen future classes, which increases the distance between class distributions. Pseudo instances are also used to enable the model to anticipate possible upcoming samples. During inference, we provide rectification to the confidence to prevent bias towards base classes. We conducted experiments on benchmark datasets such as CIFAR100 and miniimageNet, which demonstrate that our proposed method achieves state-of-the-art performance.

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Periodic Event-Triggered control for Networked control Systems With External Disturbance and Input and Output Delays

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第10期53卷 6386-6394页

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

This article investigates the problem of periodic event-triggered output-feedback control for networked control systems in the presence of external disturbance and input and output delays. With the aid of the prediction technique, we first develop the predictor-based-extended state observer to reconstruct the system information, including the unknown state and disturbance. The periodic event-triggered output-feedback control law is then designed via the disturbance/uncertainty estimation and attenuation (DUEA) method, such that the communication times can be remarkably reduced and, at the same time, the disturbance rejection ability can be effectively enhanced. Under the predictor-based event-triggered control method, the influence of the time delays is effectively attenuated, and the effect of external disturbance is considerably attenuated due to the prediction technique and the DUEA method. By using the small-gain arguments, this article gives some sufficient stability conditions for the overall control system, and the explicit computations of sampling/updating period and time delays are presented as well. Finally, we employ a practical example and show some comparative simulation results to demonstrate the advantages of the predictor-based event-triggered control method proposed in this article.

关键词： Delays control systems Delay effects Networked control systems Observers Stability analysis Sun Disturbance uncertainty estimation and attenuation (DUEA) input and output delays networked control system periodic event-triggered control (PETC)

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Bimodal Feature Propagation and Fusion for Real-time Semantic Segmentation on RGB-D images 7

Bimodal Feature Propagation and Fusion for Real-time Semanti...

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7th International Conference on intelligent Computing and Signal processing, ICSP 2022

作者： He, Ying Xiao, Li Sun, Zhigang Wang, Zhuo Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9781665478571

Semantic Segmentation is the foundation of scene understanding and automatic driving tasks. One of the challenges of semantic segmentation is the reduction of feature resolution as the network goes deep. In this paper, the low-resolution features are integrated into high-resolution features progressively, enabling high-resolution prediction. Currently, incorporating the auxiliary depth information into a semantic segmentation framework has proven to be helpful to improve accuracy. However, additional process of depth data immensely increases computational complexity, resulting in limitation for practical application. Here, a real-time bimodal semantic segmentation network that effectively extracts and fuses complementary information is investigated. Our network extends a light-weight architecture which can seamlessly take advantage of various prevalent backbone networks. Our model extracts RGB and depth features from two branches in parallel and integrates multi-level features of two modalities through bimodal feature fusion blocks. To reduce computation complexity, we employ depth-wise separable convolution while keep performance intact. Meanwhile, an attention mechanism is exploited to recalibrate the fusion modality features adaptively. Refinement components are retained to refine multi-resolution feature maps. We conduct ablative study to demonstrate the availability of our bimodal fusion blocks. Comprehensive experiments validate that the proposed network outperforms the state-of-the-arts and achieves 18 FPS on the challenging RGB-D dataset, NYUDv2. © 2022 IEEE.

关键词： Semantic Segmentation

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TranPulse: Remote Photoplethysmography Estimation With Time-Varying Supervision to Disentangle Multiphysiologically Interference

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Shao, Hang Luo, Lei Qian, Jianjun Chen, Shuo Hu, Chuanfei Yang, Jian Nanjing Univ Sci & Technol PCA Lab Key Lab Intelligent Percept & Syst High Dimens Inf Minist Educ Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Jiangsu Key Lab Image & Video Understanding Social Nanjing 210094 Peoples R China RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan Southeast Univ Sch Automat Key Lab Measurement & Control Complex Syst Engn Minist Educ Nanjing 211189 Peoples R China

Physical signs of the body that are too subtle to be observed by human eyes can reflect significant health indicators. Although many vision-based approaches have been devoted to recovery, they topically focus on recognizing explicit features such as colors, textures, and patches, and pay less attention to the entanglement and disentanglement among implicit biological characteristics. Meanwhile, existing deep networks for remote physiological detection are generally weak or deliberately neglectful in eliminating long-term time-varying interference and noise. To address these issues, we propose TranPulse, a novel remote estimation paradigm dedicated to video transformer-based polyphysiological disentanglement for robust heart rate (HR) prediction. Specifically, we improve existing single-stage transformer-based cardiac estimation backbones into a targeted two-stage architecture, which consists of a pair of asymmetric encoder and decoder. The encoder, which is the important module for heart waveform prediction, guides global attention to spatio-temporal enhancement and perception of periodic signals according to facial frame differences. The decoder, which is the core strategy pipeline for unwrapping time-varying disturbances, uses waveform gradient variations as the constraint for high-dimensional representations to separate spikes from multiple uncorrelated obstructions. Simultaneously, we integrate the synchronous computation of the encoder's fusion of appearance and frame difference to provide more detailed guidance for in-the-wild spatio-temporal modeling, and design a more reliable regression loss function to coordinate long-term temporal and frame-wise spatial supervision. We train, validate, and practice our proposed model on multiple publicly available datasets, where it achieves competitive performance in pulse estimation by extensive experimental results.

关键词： Frame-wise supervision heart rate (HR) remote biosignal estimation spatio-temporal modeling vision transformer (ViT) Frame-wise supervision heart rate (HR) remote biosignal estimation spatio-temporal modeling vision transformer (ViT)

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