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Knowledge Transfer Learning via Dual Density Sampling for Resource-Limited Domain Adaptation

IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2269-2291页

作者： Zefeng Zheng Luyao Teng Wei Zhang Naiqi Wu Shaohua Teng the School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China the School of Information Engineering Guangzhou Panyu PolytechicGuangzhou 511483China the Faculty of Information Technology Monash University20 Exhibition Walk ClaytonVIC 3800Australia IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macao University of Science and TechnologyMacao 999078China

Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by ***,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of *** address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target *** analyses show that DDS avoids repeated sampling during the *** the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly *** addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA *** experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.

关键词： Cross-domain risk dual density sampling intra-domain risk maximum mean discrepancy knowledge transfer learning resource-limited domain adaptation

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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multi-physical analysis and motor design optimization. This paper proposes a new method for thermal steady state modeling and analysis based on transfer learning, which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on transfer learning is developed: the model is pre-trained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R2 and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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Specificity-preserving RGB-D saliency detection

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Computational Visual Media 2023年第2期9卷 297-317页

作者： Tao Zhou Deng-Ping Fan Geng Chen Yi Zhou Huazhu Fu School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Key Laboratory of System Control and Information Processing Ministry of EducationShanghaiChina Computer Vision Lab ETH Z¨urichZ¨urichSwitzerland School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’anChina School of Computer Science and Engineering Southeast UniversityNanjingChina Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Salient object detection(SOD)in RGB and depth images has attracted increasing research *** RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities,while few methods explicitly consider how to preserve modality-specific *** this study,we propose a novel framework,the specificity-preserving network(SPNet),which improves SOD performance by exploring both the shared information and modality-specific ***,we use two modality-specific networks and a shared learning network to generate individual and shared saliency prediction *** effectively fuse cross-modal features in the shared learning network,we propose a cross-enhanced integration module(CIM)and propagate the fused feature to the next layer to integrate cross-level ***,to capture rich complementary multi-modal information to boost SOD performance,we use a multi-modal feature aggregation(MFA)module to integrate the modalityspecific features from each individual decoder into the shared *** using skip connections between encoder and decoder layers,hierarchical features can be fully *** experiments demonstrate that our SPNet outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection *** project is publicly available at https://***/taozh2017/SPNet.

关键词： salient object detection(SOD) RGB-D cross-enhanced integration module(CIM) multi-modal feature aggregation(MFA)

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Neural Dynamics for Cooperative Motion control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1605-1620页

作者： Yufeng Lian Xingtian Xiao Jiliang Zhang Long Jin Junzhi Yu Zhongbo Sun the Department of Control Engineering Changchun University of TechnologyChangchun 130012China IEEE the College of Information Science and Engineering Northeastern UniversityShenyang 110819 the National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China the School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China the State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

关键词： Cooperative motion control noise-tolerant zeroing neural network(NTZNN) omnidirectional mobile manipulator(OMM) repetitive motion planning

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Dynamic and Heterogeneous Identity-Based Cooperative Co-Evolution for Distributed Lot-Streaming Flowshop Scheduling Problem

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Complex System Modeling and Simulation 2025年第1期5卷 86-106页

作者： Juan Wang Guanghui Zhang Xiaoling Li Yanxiang Feng School of Information Science and Technology Hebei Agricultural UniversityBaoding 071001China State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710049China School of Electronics and Control Engineering Chang’an UniversityXi’an 710064China School of Automation Science and Engineering Xi’an Jiaotong UniversityXi’an 710049China

In this research,a novel dynamic and heterogeneous identity based cooperative co-evolutionary algorithm(DHICCA)is proposed for addressing the distributed lot-streaming flowshop scheduling problem(DLSFSP)with the objective to minimize the makespan.A two-layer-vector representation is devised to bridge the solution space of DLSFSP and the search space of *** the evolution of DHICCA,population individuals are endowed with heterogeneous identities according to their quality,including superior individuals,ordinary individuals,and inferior individuals,which serve local exploitation,global exploration,and diversified restart,*** individuals with different identities require different evolutionary mechanisms to fully unleash their respective potentials,identity-specific evolutionary operators are devised to evolve them in a cooperative co-evolutionary *** is important to use limited population resources to solve complex optimization ***,exploitation is carried out on superior individuals by devising three exploitative operators with different intensities based on techniques of variable neighborhood,destruction-construction,and gene *** is executed on ordinary individuals by a newly constructed discrete Jaya algorithm and a probability crossover *** addition,restart is performed on inferior individuals to introduce new evolutionary individuals to the *** the cooperative co-evolution,all individuals with different identities are merged as a population again,and their identities are dynamically adjusted by new *** influence of parameters on the algorithm is investigated based on design-of-experiment and comprehensive computational experiments are used to evaluate the performance of all *** results validate the effectiveness of special designs and show that DHICCA performs more efficient than the existing state-of-the-art algorithms in solving the DLSFSP.

关键词： distributed flowshop scheduling lot-streaming scheduling identity division and merge heterogeneous evolution cooperative co-evolution

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A survey on hyperspectral image restoration: from the view of low-rank tensor approximation

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science China(information sciences) 2023年第4期66卷 33-63页

作者： Na LIU Wei LI Yinjian WANG Ran TAO Qian DU Jocelyn CHANUSSOT School of Information and Electronics and Beijing Key Laboratory of Fractional Signals and Systems Beijing Institute of Technology Department of Electrical and Computer Engineering Mississippi State University GIPSA-Lab University of Grenoble Alpes Aerospace Information Research Institute Chinese Academy of Sciences

The ability to capture fine spectral discriminative information enables hyperspectral images(HSIs) to observe, detect and identify objects with subtle spectral discrepancy. However, the captured HSIs may not represent the true distribution of ground objects and the received reflectance at imaging instruments may be degraded, owing to environmental disturbances, atmospheric effects, and sensors' hardware limitations. These degradations include but are not limited to complex noise, heavy stripes, deadlines,cloud/shadow occlusion, blurring and spatial-resolution degradation, etc. These degradations dramatically reduce the quality and usefulness of HSIs. Low-rank tensor approximation(LRTA) is such an emerging technique, having gained much attention in the HSI restoration community, with an ever-growing theoretical foundation and pivotal technological innovation. Compared to low-rank matrix approximation(LRMA),LRTA characterizes more complex intrinsic structures of high-order data and owns more efficient learning abilities, being established to address convex and non-convex inverse optimization problems induced by HSI restoration. This survey mainly attempts to present a sophisticated, cutting-edge, and comprehensive technical survey of LRTA toward HSI restoration, specifically focusing on the following six topics: denoising, fusion,destriping, inpainting, deblurring, and super-resolution. For each topic, state-of-the-art restoration methods are introduced, with quantitative and visual performance assessments. Open issues and challenges are also presented, including model formulation, algorithm design, prior exploration, and application concerning the interpretation requirements.

关键词： hyperspectral image image restoration low-rank tensor approximation multisource fusion remote sensing

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Deep supervised fused similarity hashing for cross-modal retrieval

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Multimedia Tools and Applications 2024年第39期83卷 86537-86555页

作者： Ng, Wing W. Y. Xu, Yongzhi Tian, Xing Wang, Hui Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering South China University of Technology Guangzhou China School of Artificial Intelligence South China Normal University Guangzhou China School of Electronics Electrical Engineering and Computer Science Queens University Belfast Belfast United Kingdom

The need for cross-modal retrieval increases significantly with the rapid growth of multimedia information on the Internet. However, most of existing cross-modal retrieval methods neglect the correlation between label similarity and intra-modality similarity in common semantic subspace training, which makes the trained common semantic subspace unable to preserve semantic similarity of original data effectively. Therefore, a novel cross-modal hashing method is proposed in this paper, namely, Deep Supervised Fused Similarity Hashing (DSFSH). The DSFSH mainly consists of two parts. Firstly, a fused similarity method is proposed to exploit the intrinsic inter-modality correlation of data while preserving the intra-modality relationship of data at the same time. Secondly, a novel quantization max-margin loss is proposed. The gap between cosine similarity and Hamming similarity is closed by minimizing this loss. Extensive experimental results on three benchmark datasets show that the proposed method yields better retrieval performance comparing to state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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Heterogeneous Image Knowledge Driven Visual Perception

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 255-257页

作者： Lan Yan Wenbo Zheng Fei-Yue Wang IEEE the College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China the School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan 430070China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter is concerned with visual perception closely related to heterogeneous *** the huge challenge brought by different image modalities,we propose a visual perception framework based on heterogeneous image knowledge,i.e.,the domain knowledge associated with specific vision tasks,to better address the corresponding visual perception problems.

关键词： Visual visual knowledge

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On Differentiation Among Pilot Signals of Multiple Mobile Targets in Retro-Reflective Beamforming for Wireless Power Transmission

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engineering 2023年第11期30卷 55-62页

作者： Xin Wang Long Li Tie Jun Cui Mingyu Lu School of Electrical Engineering Chongqing UniversityChongqing 400044China School of Electronic Engineering Xidian UniversityXi’an 710071China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China Department of Electrical and Computer Engineering West Virginia University Institute of TechnologyBeckleyWV 25801USA

An experimental study is conducted on several retro-reflective beamforming schemes for wireless power transmission to multiple wireless power receivers(referred to herein as“targets”).The experimental results demonstrate that,when multiple targets broadcast continuous-wave pilot signals at respective frequencies,a retro-reflective wireless power transmitter is capable of generating multiple wireless power beams aiming at the respective targets as long as the multiple pilot signals are explicitly separated from one another by the wireless power ***,various practical complications are identified when the pilot signals of multiple targets are not appropriately differentiated from each other by the wireless power ***,when multiple pilot signals are considered to be carried by the same frequency,the wireless power transmission performance becomes heavily dependent on the interaction among the pilot signals,which is highly undesirable in *** conclusion,it is essential for a retro-reflective wireless power transmitter to explicitly discriminate multiple targets’pilot signals among each other.

关键词： Wireless power transmission Microwave antenna array Retro-reflective beamforming Pilot signal Multiple access

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Towards Privacy-Aware Causal Structure Learning in Federated Setting

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IEEE Transactions on Big Data 2023年第6期9卷 1525-1535页

作者： Huang, Jianli Guo, Xianjie Yu, Kui Cao, Fuyuan Liang, Jiye Intelligent Interconnected Systems Laboratory of Anhui Province School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Taiyuan030006 China

Causal structure learning has been extensively studied and widely used in machine learning and various applications. To achieve an ideal performance, existing causal structure learning algorithms often need to centralize a large amount of data from multiple data sources. However, in the privacy-preserving setting, it is impossible to centralize data from all sources and put them together as a single dataset. To preserve data privacy, federated learning as a new learning paradigm has attached much attention in machine learning in recent years. In this paper, we study a privacy-aware causal structure learning problem in the federated setting and propose a novel federated PC (FedPC) algorithm with two new strategies for preserving data privacy without centralizing data. Specifically, we first propose a novel layer-wise aggregation strategy for a seamless adaptation of the PC algorithm into the federated learning paradigm for federated skeleton learning, then we design an effective strategy for learning consistent separation sets for federated edge orientation. The extensive experiments validate that FedPC is effective for causal structure learning in federated learning setting. © 2023 IEEE.

关键词： Big data

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