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Electronic Voltage Transformer Metering Error Prediction Model Based on VMD and Correction Model

International Energy Journal 2025年第1期25卷 1-12页

作者： Li, Zhenhua Cui, Jiuxi Li, Zhenxing Mao, Yedong Xiao, Yanfeng Xiao, Yijun College of Electrical Engineering & New Energy Three Gorges University Hubei443002 China Provincial Engineering Research Center of Intelligent Energy Technology Three Gorges University Hubei443002 China Hubei Technology Innovation Center for Smart Hydropower Hubei430000 China China Yangtze Power Co. Ltd. Hubei443002 China

The deployment of Electronic Voltage Transformers (EVTs) within ultra-high voltage smart grids exceeding 110kV is progressively expanding, rendering their operational stability paramount for precisely acquiring voltage signals across power grids. This paper proposes a dual-stage metering error prediction model for EVTs, which integrates Variational Mode Decomposition (VMD) with a Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention Mechanism (CNN-BiGRU-AM). Initially, an optimization algorithm is applied to determine the optimal decomposition parameters in the variational mode. Non-stationary and stochastic raw ratio error sequences are then decomposed into different modal components through VMD. Subsequently, each component is modeled with a predictive framework incorporating BiGRU and an Attention Mechanism to obtain error deviation data based on the first stage's predicted ratio error results. Next, highly correlated features with EVT metering errors are identified using a random forest algorithm to construct a feature set. These features, combined with the error deviation obtained from the first stage, are used as joint inputs to establish an error correction process (second stage), resulting in the final prediction value. Empirical analysis indicates that the proposed method improves prediction accuracy and stability. © 2025 Regional Energy Resources Information center (RERIC), Asian Institute of technology. All rights reserved.

关键词： Convolutional neural networks

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Stability and bifurcation analysis of delayed tri-trophic food chain model in poisoned environment with fear effect and additional food

Stability and bifurcation analysis of delayed tri-trophic fo...

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作者： Ramasamy, Sivasamy Banjerdpongchai, David Park, PooGyeon Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Pohang University of Science and Technology Pohang Korea Republic of

One of the most important goals of theoretical ecologists is to find a strategy for controlling the chaos in ecological models to maintain healthy ecosystems. We investigate the influence of fear and the supply of additional food to predators in a delayed tri-trophic food chain model, where interference among the species is determined by Holling type-II functional response. The proposed model incorporates the fact that the growth rates of prey and middle predators are decreased due to the fear of middle and special predators, respectively. The additional food is supplied to both predators by either nature or external agencies. Furthermore, the poisoned prey in the predator's environment is taken into account. In the proposed model, we use two time delays: one for the growth term of the middle predator and another for the gestation delay of the special predator. We determine the conditions for the existence of ecologically feasible equilibrium points and their local and global stability. In addition, we establish the conditions for the existence of Hopf bifurcation around interior equilibrium to seek periodic behavior in nondelayed and delayed models. Numerical investigations are performed to justify the proposed theoretical findings through phase portraits, time series of solutions, and bifurcation diagrams. We observe that the chaotic dynamics of a tri-trophic food chain model can be controlled by the proper choice of the fear effect and by supplying additional food parameters. As a result, our findings provide more ecological insights into the dynamics of the delayed tri-trophic food chain model with the fear effect and additional food supply. © 2023 John Wiley & Sons, Ltd.

关键词： Hopf bifurcation

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Logarithmic Sliding-Mode control for High-Precision Rigid-Body Attitude Tracking 49

Logarithmic Sliding-Mode Control for High-Precision Rigid-Bo...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Dong, Hanlin Yang, Xuebo Chen, Zhongbo Liu, Xiyao School of Automation Northwestern Polytechnical University Xi'an China Research Institute of Intelligent Control Systems Harbin Institute of Technology Harbin China College of Control Science and Engineering Bohai University Jinzhou China Northwestern Polytechnical University Research Center for Intelligent Robotics Xi'an China

ISBN: (纸本)9798350331820

The problem of high-precision attitude tracking is studied in this paper under model uncertainty and external disturbance risk. First of all, the kinematics and dynamics model of rigid body is derived based on the quaternion theory. Then, a standard logarithmic sliding-mode function is constructed using the attitude quaternion and its derivatives of the rigid spacecraft, and the corresponding logarithmic sliding-mode controller is also derived. Finally, the stability of the controlled closed-loop system is demonstrated. Consistenting with theoretical proof, the numerical simulation results showed that the controlled rigid body can quickly track its desired attitude command, even if there are model uncertainty and external disturbance risks. © 2023 IEEE.

关键词： Disturbance rejection

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Comprehensive Communication Performance of Digital Road Traffic Signs under Various Environmental Conditions 24

Comprehensive Communication Performance of Digital Road Traf...

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24th COTA International Conference of Transportation Professionals: Resilient, intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Yin, Sheng Li, Wanjun Gao, Jian Yang, Qi Li, Hengyu Zhu, Jierui Research Institute of Highway Ministry of Transport Beijing China Transportation Industry Key Laboratory of Intelligent Transportation Technology Beijing China National Intelligent Transport Systems Center of Engineering and Technology Beijing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Digital road traffic signs, as a novel technology in modern traffic systems, have opened up new avenues for enhancing road safety and efficiency. This study aims to delve into the signal propagation performance of digital road traffic signs, particularly under various environmental conditions. We conducted both static and dynamic tests to analyze the relationship between signal strength and propagation distance, as well as the impact of device installation height and obstructions on signal propagation. Utilizing state-of-the-art digital traffic sign equipment, including high-performance transmitters and receivers, as well as specially designed vehicle-mounted devices, this study provides a comprehensive assessment of key factors such as transmission power, vehicle speed, and signal reception performance. The test results offer an in-depth understanding of the signal propagation characteristics of digital traffic signs. Through thorough data analysis, this study reveals several key factors affecting signal propagation performance and provides valuable guidance for the design and practical application of digital road traffic signs. Overall, this study underscores the importance of digital traffic signs in future traffic systems and offers strategic recommendations to ensure their effectiveness and reliability. © ASCE.

关键词： Traffic signs

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CMAGAN:classifier-aided minority augmentation generative adversarial networks for industrial imbalanced data and its application to fault prediction

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Advances in Manufacturing 2024年第3期12卷 603-618页

作者： Wen-Jie Wang Zhao Liu Ping Zhu School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai200240People’s Republic of China National Engineering Research Center of Automotive Power and Intelligent Control Shanghai Jiao Tong UniversityShanghai200240People’s Republic of China School of Design Shanghai Jiao Tong UniversityShanghai200240People’s Republic of China

Class imbalance is a common characteristic of industrial data that adversely affects industrial data mining because it leads to the biased training of machine learning *** address this issue,the augmentation of samples in minority classes based on generative adversarial networks(GANs)has been demonstrated as an effective *** study proposes a novel GAN-based minority class augmentation approach named classifier-aided minority augmentation generative adversarial network(CMAGAN).In the CMAGAN framework,an outlier elimination strategy is first applied to each class to minimize the negative impacts of ***,a newly designed boundary-strengthening learning GAN(BSLGAN)is employed to generate additional samples for minority *** incorporating a supplementary classifier and innovative training mechanisms,the BSLGAN focuses on learning the distribution of samples near classification ***,it can fully capture the characteristics of the target class and generate highly realistic samples with clear ***,the new samples are filtered based on the Mahalanobis distance to ensure that they are within the desired *** evaluate the effectiveness of the proposed approach,CMAGAN was used to solve the class imbalance problem in eight real-world fault-prediction *** performance of CMAGAN was compared with that of seven other algorithms,including state-of-the-art GAN-based methods,and the results indicated that CMAGAN could provide higher-quality augmented results.

关键词： Class imbalance Minority class augmentation Generative adversarial network(GAN) Boundary strengthening learning(BSL) Fault prediction

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Coumarin derivative dye sensitized NaYGdF4:Yb,Er nanoparticles with enhanced NIR Ⅱ luminescence for bio-vascular imaging

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Journal of Rare Earths 2023年第12期41卷 1843-1849,I0001页

作者： Yaqun Ma Zhan Wang Yanxing Wang Zhenghao Liu Yukun Wang Ruichan Lv Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information Engineering Research Center of Molecular and Neuro ImagingMinistry of EducationSchool of Life Science and TechnologyXidian UniversityXi'an710071China

Lanthanide-doped biocompatible nanoparticles have promising applications in near-infrared second region imaging due to their high chemical stability,enhanced photostability and sharp emission ***,the weak light absorption capacity limits the application of rare-earth nanoparticles(RENPs) for *** prepared a coumarin-derived dye sensitized NaYGdF4:Yb,Er nanoparticle probe,in which the organic dye enhances photon absorption through the sensitization process,improving the luminescence efficiency of the rare earth particles near 1000 and 1500 *** addition,good water solubility and stability of the probe are imparted by coating the particles with amphiphilic polymers distearoyl phosphatidylethanolamine-polyethylene glycol(DSPE-PEG) and polyacrylic *** composite probe with good biocompatibility and NIR Ⅱ luminescence properties can be used for vascular imaging,providing a tool for the detection of hematologic-related diseases.

关键词： Rare earth doped nanoparticles Dye sensitization NIR imaging Angiography

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Spatial Resolution Enhancement in OFDR Using a Modified Cohen's Class Analysis

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Journal of Lightwave technology 2025年第12期43卷 5915-5922页

作者： Liang, Changshuo Wang, Yu Gao, Yan Zhang, Hongjuan Jin, Baoquan Bai, Qing Taiyuan University of Technology College of Electrical and Power Engineering Taiyuan030024 China Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control Systems Ministry of Education and Shanxi Province Taiyuan030024 China Shanxi Transportation Technology Research & Development Co. Ltd Shanxi Taiyuan030006 China

We proposed a demodulation algorithm for optical frequency domain reflectometry (OFDR) sensing system which utilizes a Cohen's class analysis with a modified kernel function to improve spatial resolution and hold a reliable sensing accuracy. With the enhanced time-frequency concentration of Cohen's class analysis compared to conventional short-time Fourier transform (STFT), the trade-off between spatial resolution and sensing accuracy is mitigated. The procedure of the proposed Cohen's class analysis algorithm and the demodulation method with this algorithm are expounded in steps with the advantages explained in theory. The experiment results demonstrate that the OFDR system with the proposed algorithm is capable of sensing strain ranging from 0 to 1000 μϵ at 14 m sensing distance. As a comparison to conventional OFDR demodulation using STFT with window length of 200 points, the spatial resolution has improved from 6.05 mm to 0.16 mm, while the demodulation root mean square error (RMSE) reaches 2.91 × 10-3 nm in the strained area. © 1983-2012 IEEE.

关键词： Mean square error

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Drosophila Larvae-Inspired Soft Crawling Robot with Multimodal Locomotion and Versatile Applications

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research 2025年第1期7卷 35-46页

作者： Qin Fang Jingyu Zhang Yinhui He Nenggan Zheng Yue Wang Rong Xiong Zhefeng Gong Haojian Lu State Key Laboratory of Industrial Control and Technology Zhejiang UniversityHangzhou 310027China Institute of Cyber-Systems and Control Zhejiang UniversityHangzhou 310027China Medical School of Zhejiang University Hangzhou 310027China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Stomatology Hospital School of StomatologyZhejiang University School of MedicineZhejiang Provincial Clinical Research Center for Oral DiseasesKey Laboratory of Oral Biomedical Research of Zhejiang ProvinceCancer Center of Zhejiang UniversityEngineering Research Center of Oral Biomaterials and Devices of Zhejiang ProvinceHangzhou 310000China

Soft crawling robots have been widely studied and applied because of their excellent environmental adaptability and flexible ***,most existing soft crawling robots typically exhibit a single-motion mode and lack diverse *** by Drosophila larvae,this paper proposes a compact soft crawling robot(weight,13 g;length,165 mm;diameter,35 mm)with multimodal locomotion(forward,turning,rolling,and twisting).Each robot module uses 4 sets of high-power-density shape memory alloy actuators,endowing it with 4 degrees of motion *** analyze the mechanical characteristics of the robot modules through experiments and simulation *** plug-and-play modules can be quickly assembled to meet different motion and task *** soft crawling robot can be remotely operated with an external controller,showcasing multimodal motion on various material *** a narrow maze,the robot demonstrates agile movement and effective maneuvering around *** addition,leveraging the inherent bistable characteristics of the robot modules,we used the robot modules as anchoring units and installed a microcamera on the robot's head for pipeline *** robot completed the inspection in horizontal,vertical,curved,and branched pipelines,adjusted the camera view,and twisted a valve in the pipeline for the first *** research highlights the robot's superior locomotion and application capabilities,providing an innovative strategy for the development of lightweight,compact,and multifunctional soft crawling robots.

关键词： multimodal locomotion versatile applications Drosophila larvae soft crawling robot weight soft crawling robots shape memory alloy actuators

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Multi-modal knowledge graph inference via media convergence and logic rule

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CAAI Transactions on Intelligence technology 2024年第1期9卷 211-221页

作者： Feng Lin Dongmei Li Wenbin Zhang Dongsheng Shi Yuanzhou Jiao Qianzhong Chen Yiying Lin Wentao Zhu School of Information Science and Technology Beijing Forestry UniversityBeijingChina Engineering Research Center for Forestry-oriented Intelligent Information Processing National Forestry and Grassland AdministrationBeijingChina Michigan Technological University HoughtonMichiganUSA

Media convergence works by processing information from different modalities and applying them to different *** is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less *** address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been *** authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link ***,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic ***,logic rules of different lengths are mined from knowledge graph to learn new entity ***,knowledge graph inference is performed based on representing entities that converge multi-media *** experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.

关键词： logic rule media convergence multi-modal knowledge graph inference representation learning

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Active learning attraction basins of dynamical system

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Chinese Physics B 2025年第5期34卷 115-122页

作者： Xiao-Wei Cao Xiao-Lei Ru Gang Yan MOE Key Laboratory of Advanced Micro-Structured Materials and School of Physical Science and EngineeringTongji UniversityShanghai 200092China National Key Laboratory of Autonomous Intelligent Unmanned Systems MOE Frontiers Science Center for Intelligent Autonomous Systemsand Shanghai Research Institute of Intelligence Science and TechnologyTongji UniversityShanghai 200092China

Dynamical systems often exhibit multiple attractors representing significantly different functioning conditions.A global map of attraction basins can offer valuable guidance for stabilizing or transitioning system *** a map can be constructed without prior system knowledge by identifying attractors across a sufficient number of points in the state ***,determining the attractor for each initial state can be a laborious ***,we tackle the challenge of reconstructing attraction basins using as few initial points as *** each iteration of our approach,informative points are selected through random seeding and are driven along the current classification boundary,promoting the eventual selection of points that are both diverse and *** results across various experimental dynamical systems demonstrate that our approach requires fewer points than baseline methods while achieving comparable mapping ***,the reconstructed map allows us to accurately estimate the minimum escape distance required to transition the system state to a target basin.

关键词： complex system attraction basin active learning

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