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Fixed-time stabilization of switched two-time-scale systems: Mode-dependent control and event-triggered control

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2024年第11期361卷

作者： Zeng, Ze-Hong Wang, Yan-Wu Lei, Yan Xiao, Jiang-Wen Chen, Wu-Hua Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Guangxi Univ Sch Math & Informat Sci Nanning 530004 Guangxi Peoples R China

This paper concentrates on the fixed -time stabilization of switched two-time-scale systems (switched TTSSs). Unlike the existing asymptotical or exponential stabilization results on switched TTSSs, the fixed -time stabilization problem is solved for switched TTSS for the first time via switching control. The upper bounds of the convergence time and the singular perturbation parameter are given explicitly, respectively. Further, the fixed -time stabilization of switched TTSS is achieved by event-triggered control. Unlike the existing event-triggered mechanisms designed for non-switched TTSSs or single-time-scale systems, a mode -dependent and boundary-layer-related event-triggered mechanism is designed to maintain a fixed time convergence, as well as the explicit upper bound of the singular perturbation parameter and the exclusive of Zeno behavior. Finally, three numerical examples, including a comparison study, are given to show the proposed control methods' effectiveness and advantages.

关键词： Switched two-time-scale system Fixed-time stability Average dwell time Event-triggered controller

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A Multi-Scale Validation and Analysis of Flash Flooding Impacts on Urban Road Transportation

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION SYSTEMS 2024年第11期25卷 16869-16880页

作者： Du, Chongyang Ouyang, Min Fan, Chao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Clemson Univ Sch Civil & Environm Engn & Earth Sci Clemson SC 29634 USA

The impacts of flash flooding on road transportation, such as travel time delays, are crucial concerns by different stakeholders including neighborhoods and governments at all levels, which calls for a multi-scale flooding impact assessment. Although existing methods have such a capacity, they are not effectively validated, limiting our understanding of the impacts for successful flooding management. This paper develops a multi-scale flooding impact assessment framework and a validation method applied to road transportation in Wuhan, China. Using real-world traffic data in a historical flooding event, the validation results show the capability of the framework in predicting the post-flooding travel time of trips across different scales. The framework is further used to predict travel time delays in flooding at neighborhood, district, and city scales during peak-traffic periods. Results show that while flooding primarily impacts commuting times in central city areas, the integration of traffic accidents in flooding expands the impact to districts and communities farther away from the city center. At the city scale, our analysis indicates that the travel time to workplaces is mostly increased, compared to the increase in travel time to critical facilities. The proposed framework is also adopted to prioritize the treatment of flood-prone sites. These results show the potential of the framework to help flooding response and mitigation.

关键词： Floods Roads Urban areas Rain Transportation Analytical models Accidents Flash flooding impact model validation multi-scale assessment urban road transportation

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Memristor-Based Circuit Design of Biological Behavior Chain

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年

作者： Yang, Le Cai, Ren Cheng, Ming Ding, Zhixia Li, Sai Zeng, Zhigang Wuhan Inst Technol Sch Elect & Informat Engn Wuhan 430205 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China

Currently, studies on memristor-based operant conditioning circuits concentrate on the learning process of the single behaviors, without attention to the chaining process composed of multiple behaviors. This paper proposes a memristor-based circuit design of biological behavior chain, which consists of behavior modules, learning modules, delay modules, and satiety modules. After learning, the mouse can complete a chain process consisting of three behaviors: pressing button A to eliminate electrical stimulation, pressing button B to obtain food, and pressing button C to open the cage. Behavior and delay modules are used to simulate these three behaviors, the learning module is used to simulate the learning process of chaining between two behaviors and chaining between three behaviors. In addition, the study also explored the effects of mouse satiety on the experiment, as well as processes such as natural forgetting and relearning. The simulation results in PSPICE indicate that the circuit is capable of effectively simulating the aforementioned functions. Furthermore, Monte Carlo analysis and temperature simulation analysis are conducted on the circuit. The simulation results confirm that the circuit exhibits good stability.

关键词： Circuits Memristors Associative memory Integrated circuit modeling Electrical stimulation Pressing Adaptation models Organisms Presses Decision making Biological behavior chain memristor operant conditioning classical conditioning satiety

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Non-negative scaled edge-consensus of saturated networked systems via adaptive output-feedback control

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NEUROCOMPUTING 2024年 586卷

作者： Sun, Yaping Yang, Xinsong Zhao, Yini Su, Housheng Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China Sichuan Univ Sch Foreign Languages Chengdu 610065 Sichuan Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

This paper investigates fully distributed scaled non -negative edge -consensus problems of networked systems with actuator saturation and unmeasurable internal states. By using the adaptive control method, outputfeedback -based low -gain technique, and graph theory, a new adaptive algorithm is designed to obtain scaled edge -consensus conditions, under which the difficulties caused by the constraints on edge states and controllers are overcome. There are three interesting characteristics that any global information of networks is not used in this paper's algorithm and result, including the number of vertexes and edges;the feasible solutions of the scaled edge -consensus conditions exist and can be easily obtained;the convergence rate of the controller is adjustable. Furthermore, the designed algorithm is expanded in the cases without actuator saturation. Finally, three examples are given to verify the theoretical results.

关键词： Edge-consensus Actuator saturation Networked systems Observer Adaptive control

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AI WALKUP: A Computer-Vision Approach to Quantifying MDS-UPDRS in Parkinson's Disease

arXiv

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arXiv 2024年

作者： Xiang, Xiang Zhang, Zihan Ma, Jing Deng, Yao Key Lab of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Parkinson's Disease (PD) is the second most common neurodegenerative disorder. The existing assessment method for PD is usually the Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS) to assess the severity of various types of motor symptoms and disease progression. However, manual assessment suffers from high subjectivity, lack of consistency, and high cost and low efficiency of manual communication. We want to use a computer vision based solution to capture human pose images based on a camera, reconstruct and perform motion analysis using algorithms, and extract the features of the amount of motion through feature engineering. The proposed approach can be deployed on different smartphones, and the video recording and artificial intelligence analysis can be done quickly and easily through our APP. Copyright © 2024, The Authors. All rights reserved.

关键词： Computer vision

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Vision Transformer Off-the-Shelf: A Surprising Baseline for Few-Shot Class-Agnostic Counting 38

Vision Transformer Off-the-Shelf: A Surprising Baseline for ...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence

作者： Wang, Zhicheng Xiao, Liwen Cao, Zhiguo Lu, Hao Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

ISBN: (纸本)1577358872

Class-agnostic counting (CAC) aims to count objects of interest from a query image given few exemplars. This task is typically addressed by extracting the features of query image and exemplars respectively and then matching their feature similarity, leading to an extract-then-match paradigm. In this work, we show that CAC can be simplified in an extract-and-match manner, particularly using a vision transformer (ViT) where feature extraction and similarity matching are executed simultaneously within the self-attention. We reveal the rationale of such simplification from a decoupled view of the self-attention. The resulting model, termed CACViT, simplifies the CAC pipeline into a single pretrained plain ViT. Further, to compensate the loss of the scale and the order-of-magnitude information due to resizing and normalization in plain ViT, we present two effective strategies for scale and magnitude embedding. Extensive experiments on the FSC147 and the CARPK datasets show that CACViT significantly outperforms state-of-the-art CAC approaches in both effectiveness (23:60% error reduction) and generalization, which suggests CACViT provides a concise and strong baseline for CAC. Code will be available.

关键词： Artificial intelligence

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Mixture of Experts for EEG-Based Seizure Subtype Classification

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 4781-4789页

作者： Du, Zhenbang Peng, Ruimin Liu, Wenzhong Li, Wei Wu, Dongrui Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Belt & Rd Joint Lab Measurement & Control Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Minist Educ Image Proc & Intelligent Contr Wuhan 430074 Peoples R China

Epilepsy is a pervasive neurological disorder affecting approximately 50 million individuals worldwide. Electroencephalogram (EEG) based seizure subtype classification plays a crucial role in epilepsy diagnosis and treatment. However, automatic seizure subtype classification faces at least two challenges: 1) class imbalance, i.e., certain seizure types are considerably less common than others;and 2) no a priori knowledge integration, so that a large number of labeled EEG samples are needed to train a machine learning model, particularly, deep learning. This paper proposes two novel Mixture of Experts (MoE) models, Seizure-MoE and Mix-MoE, for EEG-based seizure subtype classification. Particularly, Mix-MoE adequately addresses the above two challenges: 1) it introduces a novel imbalanced sampler to address significant class imbalance;and 2) it incorporates a priori knowledge of manual EEG features into the deep neural network to improve the classification performance. Experiments on two public datasets demonstrated that the proposed Seizure-MoE and Mix-MoE outperformed multiple existing approaches in cross-subject EEG-based seizure subtype classification. Our proposed MoE models may also be easily extended to other EEG classification problems with severe class imbalance, e.g., sleep stage classification.

关键词： EEG mixture of experts seizure subtype classification class imbalance

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GTINet: Global Topology-Aware Interactions for Unsupervised Point Cloud Registration

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第7期34卷 6363-6375页

作者： Jiang, Yinuo Zhou, Beitong Liu, Xiaoyu Li, Qingyi Cheng, Cheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control MOE Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Point cloud registration is a critical task in various 3D applications. Supervised approaches are restricted by the difficulty and cost of acquiring ground-truth annotations. Thus, unsupervised point cloud registration has emerged as a promising alternative. However, existing unsupervised methods often overlook the importance of feature interactions, leading to feature matching ambiguity. To address these challenges, we propose an unsupervised point cloud registration framework termed Global Topology-aware Interactions Network (GTINet), which contains a global structural relations (GSR) module and a contextual topological interactions (CTI) module. The GSR module transforms local features into global features through global graph convolutions. Based on the obtained global features, the CTI module learns geometric feature similarities and relative positional knowledge for both the source and target point clouds. The CTI module further learns contextual feature interactions through topology-aware attention layers. By improving the discriminativeness of features, our GTINet reduces the feature matching ambiguity caused by local structural similarity. Extensive experiments demonstrate that our method achieves state-of-the-art unsupervised registration performance on the ModelNet40, 7Scene, and KITTI datasets. Our work provides a novel perspective for conducting unsupervised point cloud registration. We will release our code for future research.

关键词： Point cloud compression Feature extraction Circuits and systems Context modeling Training Three-dimensional displays Pipelines Unsupervised point cloud registration feature extraction global structural features contextual interactions

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Safety-Critical control With control Barrier Function Based on Disturbance Observer

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IEEE TRANSACTIONS ON AUTOMATIC control 2024年第7期69卷 4750-4756页

作者： Sun, Jiankun Yang, Jun 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 Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU Leics England

In this article, we investigate the continuous and sampled-data safety-critical control problems with control barrier functions in the presence of time-varying disturbances. To this end, a nonlinear disturbance observer is first designed to estimate the disturbance, and the continuous safe control design of the nominal systems is formulated as a quadratic program. We then design a continuous composite controller by integrating the disturbance compensation term and the state feedback term computed via solving the quadratic program, such that the undesirable influence of time-varying disturbances on both control performance and safety property can be effectively attenuated. It shows that under the proposed continuous control method, the robust safety property of dynamical systems can be strictly guaranteed in the presence of time-varying disturbances. Moreover, the results on the continuous safe control are extended into the sampled-data case, where the control input keeps the same in the intersample time intervals. A practical example of adaptive cruise control is introduced, and the simulation results are presented to verify the superiorities of the proposed control method.

关键词： Safety Dynamical systems control design Time-varying systems control systems Disturbance observers Estimation error control barrier function disturbance/uncertain estimation and attenuation nonlinear disturbance observer safety-critical control

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A Novel Sequence-to-Sequence-Based Deep Learning Model for Multistep Load Forecasting

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第1期36卷 638-652页

作者： Lu, Renzhi Bai, Ruichang Li, Ruidong Zhu, Lijun Sun, Mingyang Xiao, Feng Wang, Dong Wu, Huaming Ding, Yuemin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China Shanghai Elect Grp Co Ltd Cent Acad Shanghai 200070 Peoples R China Kanazawa Univ Inst Sci & Engn Kakuma Kanazawa Ishikawa 9201192 Japan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China Zhejiang Univ Dept Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China Minist Educ Key Lab Intelligent Control & Optimizat Ind Equip Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Tianjin Univ Ctr Appl Math Tianjin 300072 Peoples R China Univ Navarra Tecnun Sch Engn San Sebastian 20018 Spain

Load forecasting is critical to the task of energy management in power systems, for example, balancing supply and demand and minimizing energy transaction costs. There are many approaches used for load forecasting such as the support vector regression (SVR), the autoregressive integrated moving average (ARIMA), and neural networks, but most of these methods focus on single-step load forecasting, whereas multistep load forecasting can provide better insights for optimizing the energy resource allocation and assisting the decision-making process. In this work, a novel sequence-to-sequence (Seq2Seq)-based deep learning model based on a time series decomposition strategy for multistep load forecasting is proposed. The model consists of a series of basic blocks, each of which includes one encoder and two decoders;and all basic blocks are connected by residuals. In the inner of each basic block, the encoder is realized by temporal convolution network (TCN) for its benefit of parallel computing, and the decoder is implemented by long short-term memory (LSTM) neural network to predict and estimate time series. During the forecasting process, each basic block is forecasted individually. The final forecasted result is the aggregation of the predicted results in all basic blocks. Several cases within multiple real-world datasets are conducted to evaluate the performance of the proposed model. The results demonstrate that the proposed model achieves the best accuracy compared with several benchmark models.

关键词： Load modeling Predictive models Load forecasting Forecasting Autoregressive processes Data models Time series analysis Decomposition strategy long short-term memory (LSTM) neural network multistep load forecasting sequence-to-sequence (Seq2Seq) model temporal convolution network (TCN)

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