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Model-free adaptive iterative learning control of high-order pseudo partial derivatives for nonlinear systems

IEEE Access 2025年 13卷 99366-99376页

作者： Cao, Wei Wang, Ting Qiao, Jinjie Chai, Buning Qiqihar University College of Computer and Control Engineering Qiqihar161006 China Qiqihar University College of Economics and Management Qiqihar161006 China

In this work, a model-free adaptive iterative learning control of high-order pseudo partial derivatives is presented for a category of nonlinear discrete-time systems that have features defined by non-repetitive disturbances. Firstly, the nonlinear system is initially transformed into a dynamic linearized data model, taking into account external non-repetitive disturbances by the iterative dynamic linearized method. Simultaneously, a high-order estimation method is developed utilizing historical batch input and output data, and an iterative extended state observer is constructed for estimating non-repetitive disturbances in the linearized data model to compensate for actual disturbances;Secondly, a model-free adaptive iterative learning control approach was devised, this approach employs estimated values of high-order pseudo partial derivatives and non-repetitive disturbances. The idea of compressive mapping was used to illustrate the provided algorithm's convergence, and the conditions required for its convergence were given. The study's findings show that, within a limited period of time, the suggested method may lower the tracking error to the expected trajectory's neighborhood, independent of explicit mathematical model information, and the proposed technique enhances tracking precision compared with low-order control algorithms. Finally, It has been shown through simulation results that the suggested strategy is effective in improving tracking accuracy. © 2013 IEEE.

关键词： Discrete time control systems

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Using join learning of multi-level features for automated essay scoring

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Neural Computing and Applications 2025年第16期37卷 10197-10214页

作者： Li, Shi Gao, Yan College of Computer and Control Engineering Northeast Forestry University Wenzheng Street Heilongjiang Province Harbin150006 China

Recently, the approach of extracting features of essays at different levels for joint learning and scoring using hybrid models has achieved excellent results. However, there are still some issues that need to be improved: (1) Splitting the essays into sentences of the same length when extracting sentence-level features may result in the truncation of sentence-level semantics;(2) multi-level representations of essays obtained through pre-trained language models (PLMs) are not sufficiently comprehensive and in depth to provide limited improvement in model performance;and (3) in extracting features from multi-level essay representations, finer-grained local features in sentences and dependencies among features are ignored. To address these issues, we propose an improved method for joint learning of multi-level features based on PLMs. Specifically, we first split the essays according to different semantic scales through a text splitter to maximize the retention of semantic and structural features at sentence level;secondly, we integrate the essay representations derived from the PLM and mine more comprehensive and deeper multi-level features from it;and finally, we aggregate sentence-level features and construct dependencies among them by a feature extraction fusion method. The value of average quadratic weighted kappa (QWK) on the Kaggle ASAP competition dataset is 0.809, which validates the effectiveness of our proposed method in the automated essay scoring (AES) task. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantics

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Research on incentive mechanism and evaluation of cross-enterprise distributed research and development resource sharing under networked collaborative design mode

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Soft Computing 2024年第21期28卷 12543-12559页

作者： Hu, Yongqing Su, Weixing Chen, Hanning He, Maowei Liu, Fang College of Control Science and Engineering Tiangong University Tianjin China College of Computer Science and Technology Tiangong University Tianjin China

The promotion and application of model-based systems engineering (MBSE) suffer from the lack of effective sharing of research and design (R&D) resources among enterprises in the networked collaborative design environment. This paper establishes a cross-enterprise R&D resource sharing incentive evolutionary game model, which considers multiple factors about resource sharers, sharing process and shared resource value information. The model analyses how various factors influence the evolution trend of sharing behavior. In addition, in order to realize the dynamic multidimensional evaluation and feedback of shared R&D resources and promote the positive development of resource sharing, this paper establishes a new dynamic double-objective resource evaluation model. The digital information resource in R&D resources is taken as an example to carry out experimental simulation and verification. Compared with the traditional evaluation methods, the new dynamic double-objective evaluation model established in this paper owns better performance on improving the cross-enterprise R&D resources collaborative sharing and further promoting the deep integration between R&D resources and R&D process under the networked collaborative design and MBSE development mode than others. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Evolutionary game model Incentive mechanism Multi-objective evaluation Networked collaborative design Resource sharing

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Federated Learning With Meta-Layers Training for Privacy-Preserving in Vehicular Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 621-629页

作者： Shen, Xiaoyang Li, Haibin Li, Yaqian Zhang, Wenming Alenazi, Mohammed J.F. Agarwal, Kadambri Yanshan University College of Electrical Engineering Hebei Qinhuangdao066000 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Hebei Qinhuangdao066000 China King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia ABES Engineering College Department of Computer Science and Engineering Ghaziabad201009 India

Vehicular consumer electronics, such as autonomous vehicles (AVs), need collecting large amounts of private user information, which face the risk of privacy leakage. To protect the privacy of consumers, researchers have proposed to apply federated learning (FL) to privacy-preserving vehicular consumer electronics, that is, leveraging FL for collaborative training of a decision model without exchanging the sensitive information generated by AVs. However, FL generally needs to exchange the gradients of client models periodically with the central server, which can be attacked by adversaries to infer user information. Thereby, it may still face the risk of privacy leakage. To solve that challenge, we put forward a novel FL framework, called FL with Meta-Layers Training (FL-MLT). Instead of exchanging the gradients of the client models, it exchanges, meta-layers with the central servers. Since meta-layers are only a slice of the client models, exchanging them helps protect privacy. On the other hand, they contain meta-knowledge to help the FL training process. In the experiments, we conduct extensive visual classification simulation to evaluate FL-MLT, and the experimental results demonstrate the superior performance of FL-MLT. © 1975-2011 IEEE.

关键词： Federated learning

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Harnessing Trend Theory to Enhance Distributed Proximal Point Algorithm Approaches for Multi-Area Economic Dispatch Optimization

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computers, Materials & Continua 2025年第3期82卷 4503-4533页

作者： Yaming Ren Xing Deng College of Mechanical and Control Engineering Guilin University of TechnologyGuilin541006China School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China

The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power *** complexity necessitates the employment of distributed solution methodologies,which are not only essential but also highly *** the realm of computational modelling,the multi-area economic dispatch problem(MAED)can be formulated as a linearly constrained separable convex optimization *** proximal point algorithm(PPA)is particularly adept at addressing such mathematical constructs *** study introduces parallel(PPPA)and serial(SPPA)variants of the PPA as distributed algorithms,specifically designed for the computational modelling of the *** PPA introduces a quadratic term into the objective function,which,while potentially complicating the iterative updates of the algorithm,serves to dampen oscillations near the optimal solution,thereby enhancing the convergence ***,the convergence efficiency of the PPA is significantly influenced by the parameter *** address this parameter sensitivity,this research draws on trend theory from stock market analysis to propose trend theory-driven distributed PPPA and SPPA,thereby enhancing the robustness of the computational *** computational models proposed in this study are anticipated to exhibit superior performance in terms of convergence behaviour,stability,and robustness with respect to parameter selection,potentially outperforming existing methods such as the alternating direction method of multipliers(ADMM)and Auxiliary Problem Principle(APP)in the computational simulation of power system dispatch *** simulation results demonstrate that the trend theory-based PPPA,SPPA,ADMM and APP exhibit significant robustness to the initial value of parameter c,and show superior convergence characteristics compared to the residual balancing ADMM.

关键词： Multi-area economic dispatch problem proximal point algorithm trend theory

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An Influence Maximization Algorithm Based on Improved K-Shell in Temporal Social Networks

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computers, Materials & Continua 2023年第5期75卷 3111-3131页

作者： Wenlong Zhu Yu Miao Shuangshuang Yang Zuozheng Lian Lianhe Cui College of Computer and Control Engineering of Qiqihar University Qiqihar161006China College of Teacher Education of Qiqihar University Qiqihar161006China

Influence maximization of temporal social networks(IMT)is a problem that aims to find the most influential set of nodes in the temporal network so that their information can be the most widely *** solve the IMT problem,we propose an influence maximization algorithm based on an improved K-shell method,namely improved K-shell in temporal social networks(KT).The algorithm takes into account the global and local structures of temporal social ***,to obtain the kernel value Ks of each node,in the global scope,it layers the network according to the temporal characteristic of nodes by improving the K-shell ***,in the local scope,the calculation method of comprehensive degree is proposed to weigh the influence of ***,the node with the highest comprehensive degree in each core layer is selected as the ***,the seed selection strategy of KT can easily lose some influential ***,by optimizing the seed selection strategy,this paper proposes an efficient heuristic algorithm called improved K-shell in temporal social networks for influence maximization(KTIM).According to the hierarchical distribution of cores,the algorithm adds nodes near the central core to the candidate seed *** then searches for seeds in the candidate seed set according to the comprehensive *** showthatKTIMis close to the best performing improved method for influence maximization of temporal graph(IMIT)algorithm in terms of effectiveness,but runs at least an order of magnitude faster than ***,considering the effectiveness and efficiency simultaneously in temporal social networks,the KTIM algorithm works better than other baseline algorithms.

关键词： Temporal social network influence maximization improved K-shell comprehensive degree

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FIDC-YOLO: Improved YOLO for Detecting Pine Wilt Disease in UAV Remote Sensing Images via Feature Interaction and Dependency Capturing

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 14674-14687页

作者： Xu, Zekun Zhou, Yipeng Wen, Shiting Jing, Weipeng Northeast Forestry University College of Computer and Control Engineering Harbin150040 China Macquarie University School of Computing Sydney Australia NingboTech University School of Computer and Data Engineering Ningbo315100 China

Unmanned aerial vehicle (UAV) remote sensing technology has been widely applied in pine wilt disease (PWD) detection. UAV-collected PWD images are characterized by complex backgrounds, small target sizes, and uneven spatial distribution. State-of-the-art generic detection methods typically use Transformers or Mamba-based backbones to mitigate background interference by enhancing the global receptive field. However, directly applying these generic detectors to detect PWD results in suboptimal performance due to insufficient utilization of local features. Although current PWD detection methods use the attention mechanisms to improve the recognition of infected targets, the limitations of Convolutional Neural Networks (CNNs) in capturing long-range dependencies hinder their ability to separate targets from the background. To overcome these challenges, a novel PWD detection method called feature interaction and dependency capturing-YOLO (FIDCYOLO) is proposed in this article. Firstly, to effectively extract the discriminative features of PWD targets, the shuffle efficient layer aggregation network (SELAN) is proposed to promote information interaction between features, improving the model's learning capability. Then, to address the interference of complex backgrounds, the bidirectional extension of Mamba (Hydra) is improved by employing the partial dependency capture (PDC) module, which captures both global and local dependencies for suppressing background information. Additionally, the feature aggregation-spatial pyramid pooling fast (FA-SPPF) module is introduced in front of PDC, which aggregates cross-level features to enhance detection performance. Finally, we use ADown as the downsampling module to avoid the loss of target information. Extensive experiments on our self-constructed PWD dataset and the public DIOR dataset demonstrate the excellent performance of FIDC-YOLO. © 2008-2012 IEEE.

关键词： Drones

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Design and Verification of Parallel Hip Exoskeleton Considering Output Torque Anisotropy

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Journal of Bionic engineering 2024年第3期21卷 1305-1320页

作者： Jilong Xu Yunzhan Niu Fucai Liu Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan UniversityQinhuangdao066004China Key Laboratory of Industrial Computer Control Engineering of Heibei Province Yanshan UniversityQinhuangdao066004China College of Mechanical Engineering Yanshan UniversityQinhuangdao066004China

In rehabilitation training,it is crucial to consider the compatibility between exoskeletons and human legs in ***,most exoskeletons today adopt an anthropomorphic serial structure,which results in rotational centers that are not precisely aligned with the center of the hip *** address this issue,we introduce a novel exoskeleton called the Parallel Hip Exoskeleton(PH-Exo)in this ***-Exo is meticulously designed based on the anisotropic law of output *** the friction of the drive components,a dynamic model of the human-machine complex is *** analysis demonstrates that PH-Exo not only exhibits outstanding torque performance but also achieves high controllability in both flexion/extension and adduction/abduction ***,a robust controller is designed to address model uncertainty,friction,and external *** experiments indicate that under the control of the robust controller,each motor achieves excellent tracking performance.

关键词： Hip exoskeleton Parallel mechanism Misalignment Anisotropy Output torque

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Research of interleaved hysteresis control based on soft-switching power amplifier 7

Research of interleaved hysteresis control based on soft-swi...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Qiu, Guangjuan Zhang, Xinyue Wei, Jian Chen, Jingxin Peng, E. College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798350359558

This paper delves into the interleaved hysteresis control method based on soft-switching power amplifiers. Firstly, through modeling and analysis, the basic topology and mode transition of the soft-switching power amplifier are clearly defined, revealing the conditions for implementing soft-switching. Secondly, a strategy for interleaved soft-switching hysteresis control is proposed, detailing its working principle, and the frequency range of the interleaved hysteresis loop is obtained through analysis. In order to optimize the design of the filter, this paper also deeply analyzes the design methods and parameter selection of the filter inductance, providing a theoretical foundation for the implementation of the power amplifier. The research results show that the proposed interleaved soft-switching hysteresis control method can reduce switch losses and electromagnetic interference, thereby improving the efficiency and reliability of the power amplifier. This study provides beneficial theoretical support and practical guidance for the application of high-performance motor drive systems. © 2024 IEEE.

关键词： Power amplifiers

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Optimizing Multimodal Emotion Recognition: Evaluating the Impact of Speech, Text, and Visual Modalities

Optimizing Multimodal Emotion Recognition: Evaluating the Im...

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2024 International Conference on Electronics and Devices, Computational Science, ICEDCS 2024

作者： Wang, Hongsheng Northeastern Forestry University College of Computer and Control Engineering Harbin China

ISBN: (纸本)9798331527624

This research aims to explore and optimize multimodal emotion recognition to enhance its performance. Multimodal emotion recognition involves analyzing information from different modalities - speech, vision, and text - to identify and classify emotional states accurately. This study investigates the roles of speech and text modalities and the auxiliary effect of visual modalities in multimodal emotion recognition. Study employs a two-stage multimodal information fusion neural network based on graph and attention mechanisms. The core idea is to achieve an effective fusion of multimodal information through graph convolutional networks and cross-modal attention mechanisms to improve emotion recognition performance. This study uses two datasets, Interactive Emotional Dyadic Motion Capture (IEMOCAP) and Multimodal Emotion Lines Dataset (MELD), to analyze the impact of speech and text modalities on visual modalities and their potential side effects. Results show that while the visual modality alone is less effective, adding speech or text significantly improves performance. However, introducing the speech modality and the text modality is less beneficial. Experimental outcomes reveal that the speech modality's effect is not as significant as the text modality, and its inclusion does not always lead to positive results, sometimes even degrading overall recognition performance. © 2024 IEEE.

关键词： Convolutional neural networks

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