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arXiv 2022年

作者： Qing, Zhiwu Zhang, Shiwei Huang, Ziyuan Xu, Yi Wang, Xiang Tang, Mingqian Gao, Changxin Jin, Rong Sang, Nong Key Laboratory of Image Processing Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China ARC National University of Singapore Singapore Dalian Unversity of Technology China

Natural videos provide rich visual contents for self-supervised learning. Yet most existing approaches for learning spatio-temporal representations rely on manually trimmed videos, leading to limited diversity in visual patterns and limited performance gain. In this work, we aim to learn representations by leveraging more abundant information in untrimmed videos. To this end, we propose to learn a hierarchy of consistencies in videos, i.e., visual consistency and topical consistency, corresponding respectively to clip pairs that tend to be visually similar when separated by a short time span and share similar topics when separated by a long time span. Specifically, a hierarchical consistency learning framework HiCo is presented, where the visually consistent pairs are encouraged to have the same representation through contrastive learning, while the topically consistent pairs are coupled through a topical classifier that distinguishes whether they are topic-related. Further, we impose a gradual sampling algorithm for proposed hierarchical consistency learning, and demonstrate its theoretical superiority. Empirically, we show that not only HiCo can generate stronger representations on untrimmed videos, it also improves the representation quality when applied to trimmed videos. This is in contrast to standard contrastive learning that fails to learn appropriate representations from untrimmed videos. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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The variant d-path Laplacian based consensus protocols for networked harmonic oscillators

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NEUROCOMPUTING 2021年 422卷 277-286页

作者： Fang, Rong Wang, Xiaoling Su, Housheng Xiao, Min Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Jiangsu Engn Lab IOT Intelligent Robots IOTRobot Nanjing 210023 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab EducMinist China Luoyu Rd 1037 Wuhan 430074 Peoples R China Qingdao Univ Sci & Technol Sch Math & Phys Qingdao 266061 Peoples R China

In this paper, we address the consensus protocol design problem of the networked harmonic oscillators interacting through a directed graph with peer pressure from the perspective of the output information, in which the peer pressure is in the expression of the variant d-path Laplacian. For networked harmonic oscillators without time delay, sufficient conditions in terms of coupling strength are given, while for net-worked harmonic oscillators with time delay, sufficient conditions in the term of coupling strength and the upper bound on the time delay are proposed. Finally, simulation examples are provided to verify the theoretical results. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Networked harmonic oscillator Consensus Variant d-path Laplacian Observer

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Resilient Consensus of Multi-Agent Systems With Switching Topologies: A Trusted-Region-Based Sliding-Window Weighted Approach

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2021年第7期68卷 2448-2452页

作者： Zhai, Yang Liu, Zhi-Wei Guan, Zhi-Hong Wen, Guanghui 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 Southeast Univ Sch Math Dept Syst Sci Nanjing 211189 Peoples R China

The resilient consensus of multi-agent systems with trusted agents under switching topologies is addressed in this brief. A novel assumption on switching topologies is proposed, which is different from the existing works dependent on the classical graph robustness condition. Correspondingly, the trusted-region-based sliding-window weighted (TSW) algorithm is proposed to filter the received sampled data. It is successfully shown that the resilient consensus can be guaranteed under the assumption on switching topologies and TSW algorithm. Finally, numerical examples are conducted to validate the results.

关键词： Topology Switches Robustness Convergence Switching circuits Multi-agent systems Heuristic algorithms Multi-agent system malicious attack resilient consensus switching topologies trusted agents

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Extended State Observer Based Fault-Tolerant Predictive control for a Four-Mecanum Wheels Mobile Robot

Extended State Observer Based Fault-Tolerant Predictive Cont...

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

作者： Binghao Yang Dongliang Wang Ziling Wen Wu Wei Wenji Li Zhun Fan Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou University Guangdong China School of Department of Electronic and Information Engineering Shantou University Guangdong China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangzhou Guangdong China School of Automation Science and Engineering South China University of Technology Guangzhou Guangdong China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

The application of unmanned vehicles in industrial cargo transportation is becoming increasingly widespread, particularly playing a significant role in transporting heavy-duty goods. However, when performing such tasks, whether in single-vehicle transportation or multi-vehicle collaborative transportation, the occurrence of wheel failures can potentially lead to serious safety accidents. Therefore, this paper proposes a fault-tolerant control scheme, establishing a kinematic model considering disturbances and failures, and adopts model predictive control for trajectory tracking. Additionally, an extended state observer is introduced to observe system states and counteract disturbances. To address potential failure issues, a controller reconstruction strategy is proposed. Finally, the effectiveness and superiority of the controller is validated through numerical simulation and comparative experiment.

关键词： Fault tolerance Trajectory tracking Fault tolerant systems Wheels Transportation Kinematics Predictive models Safety State estimation Predictive control

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kuo Guo Yifan Li Hao Chen Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh PA USA

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function.

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TransView: Inside, Outside, and Across the Cropping View Boundaries 18

TransView: Inside, Outside, and Across the Cropping View Bou...

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18th IEEE/CVF International Conference on Computer Vision (ICCV)

作者： Pan, Zhiyu Cao, Zhiguo Wang, Kewei Lu, Hao Zhong, Weicai Huazhong Univ Sci & Technol Minist Educ Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Huawei CBG Consumer Cloud Serv Search Prod & Big Xian Peoples R China

ISBN: (纸本)9781665428125

We show that relation modeling between visual elements matters in cropping view recommendation. Cropping view recommendation addresses the problem of image recomposition conditioned on the composition quality and the ranking of views (cropped sub-regions). This task is challenging because the visual difference is subtle when a visual element is reserved or removed. Existing methods represent visual elements by extracting region-based convolutional features inside and outside the cropping view boundaries, without probing a fundamental question: why some visual elements are of interest or of discard? In this work, we observe that the relation between different visual elements significantly affects their relative positions to the desired cropping view, and such relation can be characterized by the attraction inside/outside the cropping view boundaries and the repulsion across the boundaries. By instantiating a transformer-based solution that represents visual elements as visual words and that models the dependencies between visual words, we report not only state-of-the-art performance on public benchmarks, but also interesting visualizations that depict the attraction and repulsion between visual elements, which may shed light on what makes for effective cropping view recommendation.

关键词： Computer vision

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Sample Weighted Gradient Regularization for Source-Free Domain Adaptation

SSRN

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SSRN 2024年

作者： Long, Yunchong Xu, Bingrong Zhang, Qingyong Yin, Jianhua Zeng, Zhigang School of Automation Wuhan University of Technology Wuhan430070 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430063 China The Engineering Research Center of Transportation Information and Safety Ministry of Education Wuhan430063 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Image Processing and Intelligent Control Education Ministry of China Wuhan430074 China

Conventional Domain Adaptation(DA) algorithms typically rely on access to the source data,which may not always be practical due to privacy concerns or limited storage *** address these challenges,Source-Free Domain Adaptation(SFDA) has emerged as an alternative that relies solely on a pre-trained source model rather than the actual source *** SFDA,pseudo-label supervised learning is commonly employed and initially plays a significant role in enhancing model performance during the early stages of target domain ***,as adaptation progresses,the performance can stagnate or even deteriorate due to the noise inherent in pseudo-labels,a phenomenon known as Confirmation *** noise present in target domain pseudo-labels introduces additional uncertainty and overfitting risks to the *** this study,a novel framework named Sample Weighted Gradient Regularization (SWGR) was introduced to tackle this *** first propose the label Uncertainty Estimation Metric (LUEM),a reweighting strategy that assesses the reliability of *** metric evaluates label uncertainty from both the source domain pre-trained model and spatial distribution of target domain samples,allowing the model to achieve improved cross-domain transfer performance with high-confidence samples in the target domain. Additionally,we impose a constraint on the gradient descent process of the total loss function in the form of an inequality to prevent entropy from *** Gradient Regularization eliminates overfitting while also guiding the model in lowering prediction uncertainty *** approach reduces the uncertainty in the target domain and enhances the model's generalization *** results demonstrate that our method surpasses competing SFDA approaches and even outperforms many traditional DA methods which require extensive amounts of source domain data. © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Visual Navigation and Landing control of an Unmanned Aerial Vehicle on a Moving Autonomous Surface Vehicle via Adaptive Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021年第12期32卷 5345-5355页

作者： Zhang, Hai-Tao Hu, Bin-Bin Xu, Zhecheng Cai, Zhi Liu, Bin Wang, Xudong Geng, Tao Zhong, Sheng Zhao, Jin Iluazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol 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 Huazhong Univ Sci & Technol Key Lab Imaging Proc & Intelligence Control Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Guangdong HUST Ind Technol Res Inst Dongguan 523808 Peoples R China Huazhong Univ Sci & Technol Guangdong Prov Key Lab Digital Mfg Equipment Dongguan 523808 Peoples R China

This article presents a visual navigation and landing control paradigm for an unmanned aerial vehicle (UAV) to land on a moving autonomous surface vehicle (ASV). Therein, an adaptive learning navigation rule with a multilayer nested guidance is designed to pinpoint the position of the ASV and to guide and control the UAV to fulfill horizontal tracking and vertical descending in a narrow landing region of the ASV by means of merely relative position feedback. To ensure the feasibility of the proposed control law, asymptotical stability conditions are derived based on Lyapunov stability theory. Landing experimental results are reported for a UAV-ASV system consisting of an M-100 UAV and a self-developed three-meters-long HUSTER-30 ASV on a lake to substantiate the efficacy of the proposed landing control method.

关键词： Navigation Unmanned aerial vehicles Visualization Global Positioning System Trajectory Regulation Autonomous robots Adaptive learning autonomous surface vehicle (ASV) landing navigation unmanned aerial vehicle (UAV)

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Evolution of cooperation in Snowdrift game with Multi-memory mechanism

Evolution of cooperation in Snowdrift game with Multi-memory...

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第42届中国控制会议

作者： Yaojun Liu Xingwen Liu Nyima Tashi Lab.of Complex Systems & Intelligent Control Key Lab.of Electronic Information of State Ethnic Affairs CommissionCollege of Electrical EngineeringSouthwest Minzu University Engineering Research Center for Tibetan Information Processing School of Information Science and TechnologyTibet University

Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural *** memory can greatly influence the emergence of cooperative behavior,memory-based research on evolutionary games has proliferated in recent *** the present paper,inspired by the well-known "Stanford marshmallow experiment"(SME),we proposed a multi-memory mechanism in Snowdrift game(SDG) on spatial lattice networks whose core lies in what follows:Players are divided into two groups according to individual self-control;different memory lengths are set by group and by time in a single *** simulation results show that our proposed mechanism promotes cooperation level of the considered evolutionary *** specifically,as memory length increases to an intermediate length,the cooperation level increases as *** mechanism with short memory length,the effect of population structure is obvious and small values of adjusting factor can intensively facilitate high-frequency cooperative *** findings may be helpful to understand the connection between population heterogeneity and the emergence of high cooperation.

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