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A Multi-Model Fusion Approach for Transformer Fault Detection

A Multi-Model Fusion Approach for Transformer Fault Detectio...

Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Ke Zhou Wei Zhang Ligang Li Shaoming Pan Chengwei Huang Guangxi Power Grid Equipment Monitoring and Diagnosis Technology Innovation Center Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Electric Power Research Institute of Guangxi Power Grid CO. LTD. Nanning State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences China Power Supply Bureau of Guangxi Hezhou Power Grid Co. Ltd.

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

Addressing the weak generalization capability and suboptimal prediction performance of single mechanistic and data-driven models, this paper proposes a transformer fault detection method based on the fusion of mechanism and data models. Firstly, vibration and current data are collected on-site to train the SVM vibration model and the Adaboost current model. Secondly, current simulation data is obtained using laboratory simulation software. This simulation data, combined with the current data collected under the same conditions on-site, is used as input to train the GBDT error compensation model. Finally, these three models are used as inputs to the GRNN, completing the overall model design. This fusion of mechanistic and data-driven models not only addresses the parameter mismatch issue of the mechanistic model under complex working conditions but also deeply mines time-series data. Experimental results show that The mean squared error of the fusion model is 0.002 lower than that of the data model, and the coefficient of determination is improved by 5.07% compared to the mechanistic model, enhancing the overall predictive accuracy of the model.

关键词： Vibrations Support vector machines Training Accuracy Biological system modeling Fault detection Predictive models Transformers Prediction algorithms Data models

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Towards Autonomous Navigation of a Mobile Robot in a Steep Slope Vineyard

Towards Autonomous Navigation of a Mobile Robot in a Steep S...

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Proceedings of the International Convention MIPRO

作者： I. Hrabar J. Goričanec Z. Kovačić University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Robotics and Intelligent Control Systems (LARICS) Zagreb Croatia

This paper explores potential applications of an autonomous all-terrain mobile manipulator (AMM) in the agricultural vineyard environment. A robotic arm mounted on a mobile base performs a spraying task while moving past vines and a bud rubbing task while stopping next to vines. We discuss the challenges of AMM navigation when performing tasks in steep vineyard terrain. The presented navigation approach is based on the use of a heterogeneous robotic system consisting of an unmanned aerial vehicle (UAV) and AMM. The navigation information provided by the UAV gives the waypoints for the AMM. We also present preliminary results from navigation simulation, based on digital elevation model (DEM). The model uses real-world data, acquired by UAV photogrammetric measurement campaign. We conclude by highlighting some open problems and related future research directions.

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Path-tracking of Mobile Robot Using PD-type Iterative Learning control with Forgetting Factor

Path-tracking of Mobile Robot Using PD-type Iterative Learni...

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第35届中国控制与决策会议

作者： Tao Zhang Jingzhe Fang Yun Teng Xintong Wang Yiqiang Zhang Xiyou Chen School of Electrical Engineering Dalian University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences School of Electrical Engineering Shenyang University of Technology

In this paper,a discrete iterative learning control strategy is proposed for the path-tracking problem of mobile robot trajectory tracking with repeated *** algorithm adds the forgetting factor to the PD-type discrete iterative learning control and considers the correction of the initial state of the *** convergence of the algorithm is proved rigorously by using the paradigm theory,and the sufficient conditions of the paradigm form to make the algorithm converge are ***,the effectiveness of the proposed algorithm is verified by simulation.

关键词： Mobile robot discrete iterative learning control forgetting factor path-tracking

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Fast Realization of Robot 3D Simulation Based on WebGL

Fast Realization of Robot 3D Simulation Based on WebGL

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第35届中国控制与决策会议

作者： Tao Zhang Xintong Wang Yun Teng Jingzhe Fang Yiqiang Zhang Xiyou Chen School of Electrical Engineering Dalian University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences School of Electrical Engineering Shenyang University of Technology

As science and technology have advanced,an increasing number of people are focusing their study on 3 D robot *** 3 D quick simulation of the mechanical arm can be achieved using 3 D modeling software and *** the third-party WebGL library *** to construct the simulation scenario,then interact with and render the 3 D scene and 3 D robot model directly on the Web,which is easier and faster than downloading *** essay briefly explains the benefits and applicability of *** in the creation of 3 D virtual simulations,then goes on to discuss the quick implementation process of 3 D robot simulations before summarizing the building technique.

关键词： webgl three.js 3D simulation robot

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Research on intrusion detection of industrial control system based on improved QPSO-SVM

Research on intrusion detection of industrial control system...

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2022 International Conference on Electronic Information Technology, EIT 2022

作者： Yu, Yajie Liu, Xianda Wei, Weixuan Chen, Hengfei Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China College of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China

ISBN: (纸本)9781510655096

Intrusion detection technology can effectively evaluate the security state of the system. However, the accuracy of traditional intrusion detection methods still needs to be improved. Therefore, this paper proposed a SVM model based on improved QPSO. QPSO is used to optimize the hyperparameters of SVM to improve its accuracy. In addition, the shrinkage expansion coefficient in QPSO is improved to make QPSO adaptive. Experiment shows that the proposed algorithm has good performance. © 2022 SPIE. All rights reserved.

关键词： Intrusion detection

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells

Application of Deep Learning Method to Estimate Bottomhole P...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry.

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Minimizing Data Collection Latency for Coexisting Time-Critical Wireless Networks with Tree Topologies

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IEEE Transactions on Network and Service Management 2025年

作者： Zhang, Jialin Liang, Wei Yang, Bo Shi, Huaguang Liang, Ying-Chang Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang Institute of Automation Shenyang110016 China Northwest A and F University College of Information Engineering Yangling712100 China Henan University School of Artificial Intelligence Zhengzhou450046 China University of Electronic Science and Technology of China Center for Intelligent Networking and Communications Chengdu611731 China

Time-Critical Wireless Network (TCWN) is a promising communication technology that can satisfy the low latency, high reliability, and deterministic requirements of mission-critical applications. Multiple TCWNs required by various applications inevitably coexist with each other. Most existing works aim to achieve acceptable latency or consider the simplest topology (i.e., line topology). As latency requirements become more stringent, exploring the minimum data collection latency becomes an interesting problem. In this paper, the coexisting system consists of multiple tree-topology-based TCWNs. We first establish a conversion framework to convert an arbitrary tree topology into multiple analogous line topologies to reduce the analysis complexity. We then propose a Time-Critical wireless network Scheduling (TCS) algorithm to minimize the data collection latency of coexisting TCWNs. The TCS algorithm consists of two phases. In the internetwork scheduling phase, we strictly derive a general expression to characterize the practical network requirements. In the intranetwork scheduling phase, we design two levels of priority assignment algorithms to accurately characterize the critical states and resource requirements of different nodes. We conduct extensive simulations to verify the effectiveness of the TCS algorithm. The evaluation results show that the TCS algorithm can achieve minimum data collection latency in more than 99.956% cases, and the maximum difference compared to the optimal value is one time slot. © 2004-2012 IEEE.

关键词： Scheduling algorithms

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An optimization Strategy for Deep Neural Networks Training

An optimization Strategy for Deep Neural Networks Training

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Tingting Wu Peng Zeng Chunhe Song State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665464697

Learning rate is one of the essential hyperparameters influencing the training process and the accuracy of deep neural networks. However, until now, it is challenging to determine an optimal learning rate. A large learning rate can accelerate the training process but may bring instability in the training process and miss the global optimum. In contrast, a small learning rate would be in a stable training process, but the training speed would be slow, and the training process is easy to fall into local optimum. In this paper, first, the impact of the learning rate is analyzed. It is found that a learning rate schedule should consist of two stages to take into account the speed and accuracy of the training process simultaneously. Based on this consideration, this paper proposes an improvement strategy of learning rate schedules: a two-stage integration strategy of a large fixed learning rate and a rapid decay learning rate. Second, the proposed strategy is applied to optimize a series of widely used learning rate settings. Extensive experiments on CIFAR-10 and CIFAR-100 datasets with VGG19, ResNets, ResNext, DenseNets, SeNet, and some other models demonstrate that the proposed strategy optimizes the learning rate settings, the performance of trained models is enhanced.

关键词： Training Deep learning Measurement Schedules Computational modeling Neural networks Predictive models

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Learning Disentangled Representations and Temporal-Correlation Dynamics for Robotic Anomaly Diagnosis

Learning Disentangled Representations and Temporal-Correlati...

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IEEE International Conference on robotics and Biomimetics

作者： Dong Liu Hongmin Wu Kezheng Sun Yisheng Guan Biomimetic and Intelligent Robotics Lab (BIRL) School of Electromechanical Engineering Guangdong University of Technology Guangzhou China Guangdong Key Laboratory of Modern Control Technology Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangzhou China Pazhou Lab Guangzhou China

ISBN: (纸本)9781665481106

Anomalous diagnosis is valuable for reducing potential damages in long-term autonomy robot manipulation tasks, especially in Human-robot collaboration scenarios. Deep learning-based methods have been widely investigated for robot anomaly diagnosis, which can effectively encode complex dynamics from multi-modal sensory data. However, the lacking of enough anomalous samples and the fusion of high-dimensional and modality correlation as well as time-dependent is still a challenging problem. In this paper, a novel framework is introduced to generate synthetic anomaly samples for data augmentation by learning the disentangled representation with sequential disentangled variational autoencoder (sDVAE), and a temporal-correlation VAE (tcVAE) model for robot anomaly diagnosis by learning the temporal correlation features of multimodal anomalies. To evaluate the proposed methods, 115 original anomalous samples from 7 representative anomalies that are first recorded on a self-developed human-robot kitting task. Results indicate that the proposed methods show the best performance of the highest precision (97%), f1-score (95%), and accuracy (93%) with synthetic samples across all baseline methods.

关键词： Learning systems Correlation Biomimetics Biological system modeling Collaboration Robot sensing systems Data models

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