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Investigation of a Simulated Robot Model when it is Trained to Set Trajectories in the Task of controlling Assembly Parameters at Remote Visualization

Investigation of a Simulated Robot Model when it is Trained ...

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2022 International Russian automation Conference, RusAutoCon 2022

作者： Malkarov, A.Yu. Arkhipov, M.V. Kocherevskaya, L.B. Moscow Polytechnic University Faculty of Mechanical Engineering Department of Automation and Control Moscow Russia Moscow Aviation Institute National Research University Foreign Language Department for Aerospase Specialities Moscow Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

The science of robotics is becoming more and more relevant for industrial production. Visualization of the production system involves the exact modeling of its components as accurately as possible. For a robotic technological complex, such a main component is an industrial robot. The robot model should most accurately reflect what is happening in real production, since the robot is the basis of robotic production. Modeling allows you to perform a comprehensive analysis of the production system without direct design and start-up and, if possible, easily redesign it. An application was developed for building a virtual model of the robot and the workspace. An analysis of the behavior of the manipulator makes it possible to consistently improve the accuracy of modeling and the efficiency of designing a robotic technological complex. © 2022 IEEE.

关键词： Visualization

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A UAV-based compressive algorithm for minimizing the energy consumption of large-scale WSNs 2

A UAV-based compressive algorithm for minimizing the energy ...

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2023 2nd International Symposium on Intelligent Unmanned Systems and Artificial Intelligence, SIUSAI 2023

作者： Lv, Cuicui Yang, Linchuang Zhang, Xinxin Wang, Peijin Du, Zhenbin Li, Xiangming Wang, Peng School of Computer and Control Engineering YanTai University YanTai264005 China Central Research Institute Yantai Jereh Oilfield Services Group Co. Ltd. YanTai264003 China School of Environment and Materials Engineering YanTai University YanTai264005 China

In large-scale environmental monitoring, massive sensor nodes regularly collect data and transmit them over long distances, which burdens the network and leads to the increased energy consumption. To deal with this problem, we develop an Unmanned Aerial Vehicle (UAV)-based compressive algorithm. In this algorithm, the network clustering technologies and Compressive Sensing are exploited to decrease the amount of data throughout the network. Monte Carlo method is used to group sensor nodes into clusters. The UAV is dispatched as a mobile agent to collect data from the cluster heads. Compared with the benchmark algorithms, the simulations demonstrate that the algorithm proposed in this paper can decrease the network's energy consumption and obtain better environmental monitoring results. © Published under licence by IOP Publishing Ltd.

关键词： Energy utilization

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Multi-branch low-light enhancement algorithm based on spatial transformation

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Multimedia Tools and Applications 2025年第18期84卷 19647-19667页

作者： Wang, Wenlu Sun, Ying Zou, Chunlong Tang, Dalai Fang, Zifan Tao, Bo Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China School of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot010051 China College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442000 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Hydroelectric Design & Maintenance China Three Gorges University Yichang443002 China

Many state-of-the-art low-light image enhancement techniques now suffer from issues like color distortion, detail blurring, and the halo effect, hindering their ability to produce visual effects. This paper presents a multi-branch low-light enhancement algorithm based on spatial transformation to significantly improve the visual impact of images under low-light settings, overcoming the limitations of existing approaches. The process involves dividing the initial low-light picture into three distinct layers. Bootstrap filtering and Contrast Limited Adaptive Histogram Equalization are used to process the main feature layer. Primary feature extraction is used to extract features for the compensation layer, which is then integrated with Contrast Limited Adaptive Histogram Equalization and MSRCR. The auxiliary layer utilizes spatial transformation to apply gamma correction to the V-channel and performs color space transformation and additional processing. Ultimately, these three layers are combined and refined to create the final improved image. Based on the experimental results, the proposed method outperforms the standard MSRCR algorithm by 52.47%, 71.58%, and 12.10%, respectively, on the assessment metrics AG, SD, and IE. LOE declines by 22.3%. The processed image presents a good visual quality, effectively enhancing the image brightness while retaining a large amount of information and improving the image details. This paper proposes an effective method to deal with the problem of low illumination image enhancement, which provides a strong reference for future research and application in related fields. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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Temporal Feature Impact Explanation via Dynamic Sliding Window Sampling

Temporal Feature Impact Explanation via Dynamic Sliding Wind...

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Consumer Electronics and computer engineering (ICCECE), 2021 IEEE International Conference on

作者： Yan Zhou Xiaodong Li Kedong Zhu Feng Li Huibiao Yang Yong Ren College of Computer Science and Software Engineering Hohai University Nanjing China Power Automation Department China Electric Power Research Institute Nanjing China Ningxia Electric Power Research Institute Yinchuan China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Model interpretation methods are essential for understanding the predictions of machine learning models. How-ever, when applied to dynamic multivariate time series data, existing approaches encounter significant challenges, primarily due to the temporal correlations within the same feature at different time points. Ignoring temporal dependencies of time series data during interpretation usually leads to inadequate explanation results. To address these challenges, we propose a novel algorithm called Dynamic Sliding Window Sampling (DSWS) for explaining time series models. This algorithm dynam-ically generates importance scores for the same feature across various time steps by iteratively removing feature sets from specific time periods. Leveraging a sliding time window with an adaptable length, DSWS effectively captures the temporal dependencies of dynamic features and delineates their influence boundaries. Experiments on both synthetic and real world data sets validate DSWS’ applicability for time series data with dynamic temporal dependencies. Furthermore, it outperforms state-of-the-art methods in computational efficiency.

关键词： Adaptation models Correlation Heuristic algorithms Computational modeling Time series analysis Machine learning Predictive models Computational efficiency Consumer electronics

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Reinforcement Learning-Based Fault-Tolerant control of Uncertain Strict-Feedback Nonlinear Systems With Intermittent Actuator Faults

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IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 10464-10478页

作者： Qinmin Yang Huaying Li Zhengwei Ruan Bo Fan Shuzhi Sam Ge State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Hangzhou Huawei Communication Technology Company Ltd. Hangzhou China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China Department of Electrical and Computer Engineering Institute for Functional Intelligent Materials National University of Singapore Queenstown Singapore

In this work, a novel reinforcement learning-based adaptive fault-tolerant control (FTC) scheme with actuator redundancy is presented for a nonlinear strict-feedback system with nonlinear dynamics and uncertainties. A learning-based switching function technique is established to steer different groups of actuators automatically and successively to mitigate the impact of faulty actuators by observing a switching performance index. The optimal tracking control problem (OTCP) of strict-feedback nonlinear systems is transformed into an equivalent optimal regulation problem of each affine subsystem via adaptive feedforward controllers. Subsequently, the designed objective functions associated with Hamilton–Jacobi–Bellman (HJB) estimate errors caused by neural network (NN) approximations can be minimized by the reinforcement learning algorithm without value or policy iterations. It is proved that the tracking objective can be achieved and all signals in the closed-loop system can be guaranteed to be bounded, as long as the minimum time interval between two successive failures is bounded. Theoretical results are verified by simulations.

关键词： Actuators Switches Fault tolerant systems control systems Optimal control Redundancy Nonlinear dynamical systems Adaptive control Regulation Industrial control

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Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2222-2232页

作者： Juanjuan Li Junqing Li Xiao Wang Rui Qin Yong Yuan Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the School of Artificial Intelligence Anhui UniversityHefei 266114 the Qingdao Academy of Intelligent Industries Qingdao 230031China the School of Mathematics Renmin University of ChinaBeijing 100872China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive *** security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing *** this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary ***,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and ***,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic *** incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading *** temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation ***,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

关键词： Auction data trading markets multi-blockchain pricing mechanisms

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TSCCL: A Contrastive Learning Framework for Time Series Clustering with Hurst Exponent

TSCCL: A Contrastive Learning Framework for Time Series Clus...

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Consumer Electronics and computer engineering (ICCECE), 2021 IEEE International Conference on

作者： Yan Zhou Xiaodong Li Feng Li Kedong Zhu Huibiao Yang Yong Ren College of Computer Science and Software Engineering Hohai University Nanjing China Power Automation Department China Electric Power Research Institute Nanjing China Ningxia Electric Power Research Institute Yinchuan China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Time series clustering plays a vital role in various fields, including power grids and finance. In recent years, contrastive learning has been widely adopted to improve the performance of clustering algorithms. However, these methods were initially designed for image data. When applied to time series, which exhibit unique characteristics such as mean reversion, the labels of generated samples often do not match the labels of the original samples. This misalignment occurs because the nature of time series data is not sufficiently preserved during augmentation. Therefore, ensuring that the inherent properties of time series data are maintained during augmentation has become a critical issue. In this paper, we firstly introduce a novel data augmentation method called HTGAN (Hurst-Time series Generative Adversarial Network), which is integrated into a contrastive learning framework. Moreover, we propose a new clustering framework, TSCCL (Contrastive Learning Framework for Time Series Clustering), specifically designed for financial time series data clustering. Experimental results demonstrate that TSCCL outperforms all baseline methods, highlighting the effectiveness of HTGAN in financial time series data augmentation.

关键词： Learning systems Time series analysis Finance Clustering algorithms Contrastive learning Data augmentation Generative adversarial networks Power grids Consumer electronics

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Momentum-Imbued Langevin Dynamics (MILD) for Faster Sampling

Momentum-Imbued Langevin Dynamics (MILD) for Faster Sampling

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Nishanth Shetty Manikanta Bandla Nishit Neema Siddarth Asokan Chandra Sekhar Seelamantula Department of Electrical Engineering Indian Institute of Science Bangalore Department of Computer Science and Automation Indian Institute of Science Bangalore Microsoft Research Lab India (MSRI) Bangalore

Score-based generative models have emerged as the state-of-the-art in generative modeling. In this paper, we introduce a novel sampling scheme that can be combined with pre-trained score-based diffusion models to speed up sampling by a factor of two to five in terms of the number of function evaluations (NFEs) with a superior Fréchet Inception distance (FID), compared to Annealed Langevin dynamics in noise-conditional score network (NCSN) and improved noise-conditional score network (NCSN++). The proposed sampling algorithm is inspired by momentum-based accelerated gradient descent used in convex optimization techniques. We validate the sampling efficiency of the proposed algorithm in terms of FID on CIFAR-10 and CelebA datasets.

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滚动轴承静态和动态不对中对转子系统非线性振动特性的影响

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Journal of Central South University 2023年第3期30卷 871-903页

作者：王鹏飞杨阳徐宏阳马辉韩清凯罗忠闻邦椿 School of Mechanical Engineering and Automation Northeastern UniversityShenyang 110819China China North Vehicle Research Institute Beijing 100072China Key Laboratory of Vibration and Control of Aero-Propulsion System of Ministry of Education(Northeastern University) Shenyang 110819China

在滚动轴承不对中问题的研究中,以往的研究中很少考虑轴承不对中对转子系统振动特性的影响。基于现有研究的局限性,首先,给出了一种考虑轴承不对中的5自由度非线性力模型,该模型综合考虑了轴承的平行和角不对中、静态与动态不对中、内... 详细信息

在滚动轴承不对中问题的研究中,以往的研究中很少考虑轴承不对中对转子系统振动特性的影响。基于现有研究的局限性,首先,给出了一种考虑轴承不对中的5自由度非线性力模型,该模型综合考虑了轴承的平行和角不对中、静态与动态不对中、内圈与外圈不对中等多种情况。其次,将该模型应用在转子有限元模型中,分析了不对中对轴承动态接触特性和转子系统振动特性的影响。然后,给出了衡量轴承不对中程度的评价指标。最后,对比分析了平行和角不对中、静态与动态不对中的异同点。结果表明,轴承不对中提高了转子的共振转速,在共振区出现了幅值跳跃现象,表现出硬式非线性特征。从频率特征上来看,滚动轴承动态平行不对中和动态角不对中分别导致转子旋转频率成分fr和2fr的幅值增大。动态不对中导致轴承的承载区随着轴承内圈的旋转而时刻变化。该研究可为转子-轴承系统的故障识别提供理论依据和有价值的参考。

关键词：滚动轴承-转子系统不对中非线性动力学分岔故障诊断

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Combined Effect of Concept Drift and Class Imbalance on Model Performance During Stream Classification

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computers, Materials & Continua 2023年第4期75卷 1827-1845页

作者： Abdul Sattar Palli Jafreezal Jaafar Manzoor Ahmed Hashmani Heitor Murilo Gomes Aeshah Alsughayyir Abdul Rehman Gilal Department of Computer and Information Sciences Universiti Teknologi PETRONAS(UTP)Seri Iskandar32610Malaysia Centre for Research in Data Science UTPPerak32610Malaysia High Performance Cloud Computing Centre(HPC3) UTPPerak32610Malaysia School of Engineering and Computer Science Victoria University of WellingtonWellington6012New Zealand AI Institute University of Waikato WellingtonHamilton3240New Zealand Anti-Narcotics Force Ministry of Narcotics ControlIslamabad46000Pakistan College of Computer Science and Engineering Taibah UniversityMadinah42353Saudi Arabia

Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more *** existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s *** analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream ***, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority *** study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.

关键词： Classification data streams class imbalance concept drift class imbalance ratio

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