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Transmission Line Fault Ranging Method Based on ICEEMDAN-HFWEO

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Transmission Line Fault Ranging Method Based on ICEEMDAN-HFW...

Cyber-Energy Systems and Intelligent Energy (ICCSIE), International Conference on

作者： Zhijie Song Linlin Li Gang Liu Zhiyong Yang Jiyao Zhang Minzhen Wang School of electrical and information engineering Changchun institute of technology Changchun China Da'an power supply center State grid jilin electric power company limited baicheng power supply company Baicheng China Substation Operation and Inspection Center State Grid Songyuan Power Supply Company and Engineering Songyuan China Research office Changchun university of architecture and Engineering Changchun China School of electronic information College of science and technology ning bo university Ningbo China

ISBN: (数字)9798350373318

ISBN: (纸本)9798350373325

In the traveling wave fault localization method, the fault traveling wave head extraction accuracy determines the fault localization accuracy, this paper proposes a transmission line fault ranging method based on ICEEMDAN-HFWEO as a way to improve the extraction accuracy of traveling wave head. Firstly, for the defect that the single line mode component of the traditional phase mode transform matrix cannot characterize all types of faults, the Clark phase mode transform matrix is improved so that the single line mode component can characterize all types of faults; on this basis, ICEEMDAN (Improved Complete Ensemble Empirical Mode Based on this, ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) and HFWEO (Higher-order Frequency Weighted Energy Operator) are proposed as a combination of travelling waveform header extraction methods, which improves the extraction accuracy of the faulty travelling waveheads and realizes the accurate fault localization. Simulation experiments have proved that the proposed method can realize accurate fault localization of transmission lines under different transition resistances, different fault types and different fault distances, and the localization errors are less than 150m.

关键词： Location awareness Accuracy Power transmission lines Transmission line matrix methods Empirical mode decomposition Noise Distance measurement Matrix decomposition

Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification 25

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Towards Multi-resolution Spatiotemporal Graph Learning for M...

34th ACM Web Conference, WWW 2025

作者： Fan, Wei Fei, Jingru Guo, Dingyu Yi, Kun Song, Xiaozhuang Xiang, Haolong Ye, Hangting Li, Min University of Oxford Oxford United Kingdom Beijing Institute of Technology Beijing China University of Chicago Chicago United States State Information Center of China North China Institute of Computing Technology Beijing China The Chinese University of Hong Kong Shenzhen Shenzhen China Nanjing University of Information Science and Technology Nanjing China Jilin University Changchun China School of Computer Science and Engineering Central South University Changsha China Xiangjiang Laboratory Central South University Changsha China Furong Laboratory Central South University Changsha China

ISBN: (纸本)9798400712746

Medical time series has been playing a vital role in real-world healthcare systems as valuable information in monitoring health conditions of patients. Traditional methods towards medical time series classification rely on handcrafted feature extraction and statistical methods;with the recent advancement of artificial intelligence, the machine learning and deep learning methods have become more popular. However, existing methods often fail to fully model the complex spatial dynamics under different scales, which ignore the dynamic multi-resolution spatial and temporal joint inter-dependencies. Moreover, they are less likely to consider the special baseline wander problem as well as the multi-view characteristics of medical time series, which largely hinders their prediction performance. To address these limitations, we propose a Multi-resolution Spatiotemporal Graph Learning framework, MedGNN, for medical time series classification. Specifically, we first propose to construct multi-resolution adaptive graph structures to learn dynamic multi-scale embeddings. Then, to address the baseline wander problem, we propose Difference Attention Networks to operate self-attention mechanisms on the finite difference for temporal modeling. Moreover, to learn the multi-view characteristics, we utilize the Frequency Convolution Networks to capture complementary information of medical time series from the frequency domain. In addition, we introduce the Multi-resolution Graph Transformer architecture to model the dynamic dependencies and fuse the information from different resolutions. Finally, we have conducted extensive experiments on multiple medical real-world datasets that demonstrate the superior performance of our method. © 2025 Copyright held by the owner/author(s).

关键词： Graph neural networks

StripeRust-Pocket:A Mobile-Based Deep Learning Application for Efficient Disease Severity Assessment of Wheat Stripe Rust

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Plant Phenomics 2024年第3期6卷 771-786页

作者： Weizhen Liu Yuxi Chen Zhaoxin Lu Xiaoyu Lu Ze Wu Ziyao Zheng Yongqiang Suo Caixia Lan Xiaohui Yuan School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhanHubei 430070China Wuhan University of Technology Chongqing Research Institute Chongqing 401120China Sanya Science and Education Innovation Park of Wuhan University of Technology SanyaHainan 572025China Hubei Hongshan Laboratory College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan 430070HubeiChina Engineering Research Centre of Chinese Ministry of Education for Edible and Medicinal Fungi Jilin Agricultural UniversityChangchunJilin 130118China

Wheat stripe rust poses a marked threat to global wheat *** and effective disease severity assessments are crucial for disease resistance breeding and timely management of field *** this study,we propose a practical solution using mobile-based deep learning and model-assisted ***-Pocket,a user-friendly mobile application developed based on deep learning models,accurately quantifies disease severity in wheat stripe rust leaf images,even under complex ***,StripeRust-Pocket facilitates image acquisition,result storage,organization,and *** underlying model employed by StripeRust-Pocket,called StripeRustNet,is a balanced lightweight 2-stage *** first stage utilizes MobileNetV2-DeepLabV3+for leaf segmentation,followed by ResNet50-DeepLabV3+in the second stage for lesion *** severity is estimated by calculating the ratio of the lesion pixel area to the leaf pixel *** achieves 98.65%mean intersection over union(MIoU)for leaf segmentation and 86.08%MIoU for lesion *** using an additional 100 field images demonstrated a mean correlation of over 0.964 with 3 expert visual *** address the challenges in manual labeling,we introduce a 2-stage labeling pipeline that combines model-assisted labeling,manual correction,and spatial *** apply this pipeline to our self-collected dataset,reducing the annotation time from 20 min to 3 min per *** method provides an efficient and practical solution for wheat stripe rust severity assessments,empowering wheat breeders and pathologists to implement timely disease *** also demonstrates how to address the"last mile"challenge of applying computer vision technology to plant phenomics.

关键词： application learning pocket deep assessment disease efficient mobile-based severity striperust

Aqua-Sim Fourth Generation: Towards General and Intelligent Simulation for Underwater Acoustic Networks

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arXiv 2024年

作者： Guo, Jiani Song, Shanshan Chen, Hao Huangfu, Bingwen Liu, Jun Cui, Jun-Hong The College of Computer Science and Technology Jilin University Changchun130012 China The School of Electronic and Information Engineering Beihang University Beijing100191 China The Shenzhen Ocean Information Technology Industry Research Institute Shenzhen518055 China

Simulators are essential to troubleshoot and optimize Underwater Acoustic Network (UAN) schemes (network protocols and communication technologies) before real field experiments. However, due to programming differences between the above two contents, most existing simulators concentrate on one while weakening the other, leading to non-generic simulations and biased performance results. Moreover, novel UAN schemes increasingly integrate Artificial Intelligence (AI) techniques, yet existing simulators lack support for necessary AI frameworks, failing to train and evaluate these intelligent methods. On the other hand, these novel schemes consider more UAN characteristics involving more complex parameter configurations, which also challenges simulators in flexibility and fineness. To keep abreast of advances in UANs, we propose the Fourth Generation (FG) ns-3-based simulator Aqua-Sim FG, enhancing the general and intelligent simulation ability. On the basis of retaining previous generations’ functions, we design a new general architecture, which is compatible with various programming languages, including MATLAB, C++, and Python. In this way, Aqua-Sim FG provides a general environment to simulate communication technologies, network protocols, and AI models simultaneously. In addition, we expand six new features from node and communication levels by considering the latest UAN methods’ requirements, which enhances the simulation flexibility and fineness of Aqua-Sim FG. Experimental results show that Aqua-Sim FG can simulate UANs’ performance realistically, reflect intelligent methods’ problems in real-ocean scenarios, and provide more effective troubleshooting and optimization for actual UANs. The basic simulator is available at https://***/JLU-smartocean/aqua-sim-fg. Copyright © 2024, The Authors. All rights reserved.

关键词： MATLAB

Chlorine-substituted chiral 2D perovskite microwires with enhanced lattice rigidity for sensitive circularly polarized light detection

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science China Chemistry 2023年第12期66卷 3602-3610页

作者： Meng Yuan Yuchen Qiu Yingjie Zhao Yuyan Zhao Hui Li Xiao Wei Gaosong Chen Jiangang Feng Hanfei Gao Jinjin Zhao Jiahui Zhao Lei Jiang Yuchen Wu Key Laboratory of Bio-inspired Materials and Interfacial Science Technical Institute of Physics and ChemistryChinese Academy of SciencesBeijing 100190China College of Chemistry Zhengzhou UniversityZhengzhou 450001China College of Chemistry Jilin UniversityChangchun 130012China Key Laboratory for Special Functional Materials of Ministry of Education National and Local Joint Engineering Research Center for High-Efficiency Display and Lighting TechnologyCollaborative Innovation Center of Nano Functional Materials and ApplicationsHenan UniversityKaifeng 475004China University of Chinese Academy of Sciences(UCAS) Beijing 100049China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117585Singapore Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215123China Ji Hua Laboratory Foshan 528000China Department of Physics Shanxi Datong UniversityDatong 037009China

Chiral two-dimensional(2D) perovskites offer numerous attractive features for optoelectronics owing to their soft, deformable lattices and a high degree of chemical tunability. While tremendous advances have been made in perovskite-based direct circularly polarized light(CPL) detection, the low circular polarization anisotropy factor and sensitivity of those photodetectors arising from large lattice distortion still limit practical applications. Herein, chiral 2D perovskite-based single-crystalline microwire arrays with enhanced circular dichroism(CD) absorption are fabricated with the synergy of the capillary-bridgeconfined assembly method and chlorine-substituted phenethylamine(Cl-MBA). Compared with phenethylamine(MBA)-inserted perovskites, the smaller lattice distortion and increased halogen–halogen interaction within Cl–MBA-inserted perovskites strengthen lattice rigidity and weaken electron–phonon coupling to improve carrier transport and thermal stability, resulting in high-performance CPL photodetectors with an anisotropy factor of 0.25, responsivity exceeding 95.7±9.3 A W^(-1)and detectivity exceeding(3.05±0.30)×10^(13)Jones. This work opens a new perspective to modulate circular polarization sensitivity and will be helpful to realize promising implementations in quantum computation and communication.

关键词： chiral perovskites microwire arrays lattice rigidity circular polarization photodetectors

A Multi-Task-Based Deep Multi-Scale Information Fusion Method for Intelligent Diagnosis of Bearing Faults

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SSRN

SSRN 2022年

作者： Xin, Ruihao Wang, Tiantian Miao, Fengbo Yu, Cuinan Feng, Xin College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin Jilin132000 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China School of Science Jilin Institute of Chemical Technology Jilin Jilin132000 China

The use of deep learning for fault diagnosis is already a commonly used method. However, complex operating conditions under different working conditions significantly impact bearing vibration signals. Integrating the discriminative information of fault type and size into the deep learning model to achieve rich multi-task fault feature diagnosis still deserves attention. This study proposes a deep multi-task-based multi-scale feature fusion network MEAT(multi-task-based deep multi-scale feature fusion network) model to address these issues. Solve the limitations and poor adaptability of traditional convolutional neural network models under complex work. This model considers two sub-tasks, fault scale discrimination, and fault type discrimination, in the fault diagnosis multi-classification task through multi-task mapping decomposition. First, multi-dimensional features are extracted by convolution at different scales to obtain multi-dimensional fault information at different levels. Then these features are weighted and fused using a multi-layer attention mechanism to get accurate diagnosis results of bearing faults. © 2022, The Authors. All rights reserved.

关键词： Failure analysis

Multimodal Hypothetical Summary for Retrieval-based Multi-image Question Answering

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arXiv 2024年

作者： Li, Peize Si, Qingyi Fu, Peng Lin, Zheng Wang, Yan School of Artificial Intelligence Jilin University Changchun China Institute of Information Engineering Chinese Academy of Sciences Beijing China Huawei Technologies Co. Ltd. Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University Changchun China

Retrieval-based multi-image question answering (QA) task involves retrieving multiple question-related images and synthesizing these images to generate an answer. Conventional "retrieve-then-answer" pipelines often suffer from cascading errors because the training objective of QA fails to optimize the retrieval stage. To address this issue, we propose a novel method to effectively introduce and reference retrieved information into the QA. Given the image set to be retrieved, we employ a multimodal large language model (visual perspective) and a large language model (textual perspective) to obtain multimodal hypothetical summary in question-form and description-form. By combining visual and textual perspectives, MHyS captures image content more specifically and replaces real images in retrieval, which eliminates the modality gap by transforming into text-to-text retrieval and helps improve retrieval. To more advantageously introduce retrieval with QA, we employ contrastive learning to align queries (questions) with MHyS. Moreover, we propose a coarse-to-fine strategy for calculating both sentence-level and word-level similarity scores, to further enhance retrieval and filter out irrelevant details. Our approach achieves a 3.7% absolute improvement over state-of-the-art methods on RETVQA and a 14.5% improvement over CLIP. Comprehensive experiments and detailed ablation studies demonstrate the superiority of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

Lightweight Deep Learning for Resource-Constrained Environments: A Survey

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arXiv 2024年

作者： Liu, Hou-I Galindo, Marco Xie, Hongxia Wong, Lai-Kuan Shuai, Hong-Han Li, Yung-Yui Cheng, Wen-Huang Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130000 China Faculty of Computing and Informatics Multimedia University Cyberjaya63100 Malaysia Hon Hai Research Institute Taipei114 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei106 Taiwan

Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and biomedical signal processing. While there have been remarkable improvements in model accuracy, deploying these models on lightweight devices, such as mobile phones and microcontrollers, is constrained by limited resources. In this survey, we provide comprehensive design guidance tailored for these devices, detailing the meticulous design of lightweight models, compression methods, and hardware acceleration strategies. The principal goal of this work is to explore methods and concepts for getting around hardware constraints without compromising the model's accuracy. Additionally, we explore two notable paths for lightweight deep learning in the future: deployment techniques for TinyML and Large Language Models. Although these paths undoubtedly have potential, they also present significant challenges, encouraging research into unexplored areas. Copyright © 2024, The Authors. All rights reserved.

关键词： computer hardware

Object Attribute Matters in Visual Question Answering

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arXiv 2023年

作者： Li, Peize Si, Qingyi Fu, Peng Lin, Zheng Wang, Yan School of Artificial Intelligence Jilin University Changchun China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China

Visual question answering is a multimodal task that requires the joint comprehension of visual and textual information. However, integrating visual and textual semantics solely through attention layers is insufficient to comprehensively understand and align information from both modalities. Intuitively, object attributes can naturally serve as a bridge to unify them, which has been overlooked in previous research. In this paper, we propose a novel VQA approach from the perspective of utilizing object attribute, aiming to achieve better object-level visual-language alignment and multimodal scene understanding. Specifically, we design an attribute fusion module and a contrastive knowledge distillation module. The attribute fusion module constructs a multimodal graph neural network to fuse attributes and visual features through message passing. The enhanced object-level visual features contribute to solving fine-grained problem like counting-question. The better object-level visual-language alignment aids in understanding multimodal scenes, thereby improving the model’s robustness. Furthermore, to augment scene understanding and the out-of-distribution performance, the contrastive knowledge distillation module introduces a series of implicit knowledge. We distill knowledge into attributes through contrastive loss, which further strengthens the representation learning of attribute features and facilitates visual-linguistic alignment. Intensive experiments on six datasets, COCO-QA, VQAv2, VQA-CPv2, VQA-CPv1, VQAvs and TDIUC, show the superiority of the proposed method. © 2023, CC BY.

关键词： Distillation