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arXiv 2023年

作者： Li, Qiyue Luo, Huan Cheng, Hong Deng, Yuxing Sun, Wei Li, Weitao Liu, Zhi School of Electrical Engineering and Automation Hefei Unviersity of Techonology Hefei China Department of Computer and Network Engineering The University of Electro-Communications Japan

Incipient fault detection in power distribution systems is crucial to improve the reliability of the grid. However, the non-stationary nature and the inadequacy of the training dataset due to the self-recovery of the incipient fault signal, make the incipient fault detection in power distribution systems a great challenge. In this paper, we focus on incipient fault detection in power distribution systems and address the above challenges. In particular, we propose an ADaptive Time-Frequency Memory (AD-TFM) cell by embedding wavelet transform into the Long Short-Term Memory (LSTM), to extract features in time and frequency domain from the non-stationary incipient fault signals. We make scale parameters and translation parameters of wavelet transform learnable to adapt to the dynamic input signals. Based on the stacked AD-TFM cells, we design a recurrent neural network with ATtention mechanism, named AD-TFM-AT model, to detect incipient fault with multi-resolution and multi-dimension analysis. In addition, we propose two data augmentation methods, namely phase switching and temporal sliding, to effectively enlarge the training datasets. Experimental results on two open datasets show that our proposed AD-TFM-AT model and data augmentation methods achieve state-of-the-art (SOTA) performance of incipient fault detection in power distribution system. We also disclose one used dataset logged at State Grid Corporation of China to facilitate future research. © 2023, CC BY.

关键词： Long short-term memory

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Computing Technology Acoustic Inversion engineering Seismic Data and Its Application for Construction Purposes

Computing Technology Acoustic Inversion Engineering Seismic ...

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International Conference on Actual Problems of Electronic Instrument engineering (APEIE)

作者： Elena Yu. Goshko Sergey M. Zerkal Sergey S. Ivanov Automation and Computer Engineering Novosibirsk State Technical University Novosibirsk Russia Department of Computer Science Siberian University of Consumer Cooperation Novosibirsk Russia

The Shallow-depth engineering seismic studies are carried out in the upper part of the geological section, which is characterized by sharp vertical and horizontal variability of the physical properties of rocks. Additional difficulties in observing and recording caused by seismic vibrations of the soil are associated with the influence of unavoidable interference: ground (wind, sound noise), geological interference (noise of rock cracking during unloading of tectonic stress), man-made interference (the influence of power lines on signal reception, trembling of the soil during the work of construction or quarry mechanisms, cars, trains). The allocation of a useful signal against the background of interference requires the organization of seismic work with repeated repetition of excitations - the accumulation of signals, as well as the development of special computational algorithms and the use of new technologies for processing engineering seismic data. The article considers a computational algorithm of acoustic inversion, which transforms a wave seismic section into a discrete section of pulsed reactions of the medium - effective reflection coefficients. It has a compact discrete appearance and is easier for visual observation and further physico-geological interpretation. The computer technology of application of the acoustic inversion algorithm is implemented. The application of this technology to real engineering seismic data obtained on the territory of the construction of a high-rise building made it possible to determine the parameters of the geological foundation lying under the ground surface of the construction site, namely: the depth of occurrence, relief, areas of increased disintegration. The established properties of the geological foundation are necessary for the analysis of the strength and stability of soils during the construction and operation of a high-rise structure for its safety.

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Notice of Removal: Removal of ocular artifacts using ICA and adaptive filter for motor imagery-based BCI

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IEEE/CAA Journal of Automatica Sinica 2023年 1-8页

作者： Kim, Chung Song Sun, Jinwei Liu, Dan Wang, Qisong Paek, Sung Gyun School of Electrical Engineering and Automation Harbin Institute of Technology 92 West Dazhi Street Harbin 150001 China and also with the Department of Automation Kimchaek University of Technology Pyongyang 950003 Democratic People U+02BC s Republic of Korea School of Electrical Engineering and Automation Harbin Institute of Technology 92 West Dazhi Street Harbin 150001 China School of Computer Science and Technology Harbin Institute of Technology 92 West Dazhi Street Harbin 150001 China and also with the Institute of Information Technology Kimchaek University of Technology Pyongyang 950003 Democratic People U+02BC s Republic of Korea

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A connected components based layout analysis approach for educational documents 16

A connected components based layout analysis approach for ed...

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16th IEEE International Conference on computer Science and Education, ICCSE 2021

作者： Liu, Ruiying Yu, Shenbao Yang, Fan Pan, Yinghui Zeng, Yifeng Xiamen University Department of Automation China Shenzhen University College of Computer Science and Software Engineering China Northumbria University Department of Computer and Information Sciences

ISBN: (纸本)9781665414685

Layout analysis, which aims to detect and categorize areas of interest on document images, is an increasingly important part in document image processing. Existing researches have conducted layout analysis on various documents, but none has been proposed for documents yielded from teaching, i.e. exam papers and workbooks, which are worth studying. In this paper, we propose a novel layout analysis system to achieve two tasks for workbook pages and exam papers respectively. On one hand, we segment text and non-text areas of workbook pages. On the other hand, we extract regions of interest on exam papers. Our system is based on connected component (CC) analysis, specifically, it extracts geometric features and spatial information of CCs to recognize page elements. We carried out experiments on images collected from real-world scenarios, and promising results confirmed the applicability and effectiveness of our system. © 2021 IEEE.

关键词： Image analysis

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Digital Twin Definition for Detecting Abnormal Perspiration Patterns in High-Stress Professions 23

Digital Twin Definition for Detecting Abnormal Perspiration ...

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Vizitiu, Cristian Turcin, Ioan Dominey, Kevin Alexander Softic, Selver Dinculescu, Adrian-Catalin Bocanet, Vlad I. Dugan, Cosmin Moraru, Sorin-Aurel The Space Applications and Technologies Laboratory Institute of Space Science-Subsidiary of INFLPR Magurele Romania Transilvania University of Brasov Faculty of Electrical Engineering and Computer Science Department of Automatics and Information Technology Brasov Romania CAMPUS 02 University of Applied Sciences Department Automation Technology Graz Austria CAMPUS 02 University of Applied Sciences Department IT and Business Informatics Graz Austria Robotics and Production Management Technical University of Cluj-Napoca Cluj-Napoca Faculty of Industrial Engineering Department of Manufacturing Engineering Romania Bucharest University Emergency Hospital Bucharest Romania

ISBN: (纸本)9798331540388

Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological responses and optimizing human performance especially in professions such as emergency medical services and military operations, as well as for individuals exposed to Isolated Confined Extreme (ICE) environments such as astronauts and space analog mission participants. This paper defines a Digital Twin (DT) model, that will be integrated with an existing physical sweat simulator module (SSM) prototype. Its goal is to simulate human perspiration under high-stress conditions by integrating environmental, physiological, and psychological data. The system architecture is developed via Model-Based Systems engineering (MBSE). The scope of the designed virtual model is to accurately reflect physical perspiration, detect abnormal perspiration patterns, and generate recommendations regarding stress countermeasures. The goal is to influence the behavioral and/or physiological state of the physical twin, thereby creating a dynamic, responsive system. Machine learning (ML) models will be used to identify patterns in the environmental, physiological, and psychological inputs and predict sweat volume, chemical composition, and regional sweat distributions across the body. ML will also be used to detect abnormal perspiration patterns based on input data. The application of generative AI for creating test data from simulation templates is anticipated to facilitate the comparative analysis of two to three distinct profiles, such as astronauts, soldiers, and paramedics or firefighters, using simulated values. The SSM, with its integrated sensors, will serve the dual purpose of validating the model and providing realistic physical simulation. © 2024 IEEE.

关键词： Physiological models

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A comparative study of different information entropy index in personalized exercise recommendation 16

A comparative study of different information entropy index i...

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16th IEEE International Conference on computer Science and Education, ICCSE 2021

作者： Zheng, Qiulei Gao, Huifan Yang, Fan Pan, Yinghui Zeng, Yifeng Xiamen University Department of Automation China Shenzhen University College of Computer Science and Software Engineering China Northumbria University Department of Computer and Information Sciences

ISBN: (纸本)9781665414685

It is meaningful to recommend exercises to students in an online education system with a large amount of learning resource. Many recommendation methods usually rely on strategy in the recommendation system in order to predict an exercise score. In this paper, we compare different information entropy index in the exercise recommendation. These index consider how well exercise matches the student's knowledge ability. In order to compare different methods, we introduce an interactive platform for dynamic exercise recommendation. We conduct a set of exercise recommendation experiments, compare the effect of different index and find the optimal index in the experiment. © 2021 IEEE.

关键词： Students

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Characterisation of Nanocrystalline and Iron Powder Mix Cores at 77K

Characterisation of Nanocrystalline and Iron Powder Mix Core...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： McKeown, Sean Wadsworth, Aaron Pearce, Matthew G.S. Thrimawithana, Duleepa J. Zhao, Lei Li, Zongzheng Abb Power Conditioning Electrification Napier New Zealand The University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Chongqing Univeristy College of Automation Chongqing China Jiangsu Jitri Advanced Energy Materials Research Jiangsu China

ISBN: (纸本)9798350316445

Superconducting motors are an enabling technology for future large scale electric transport, such as freight, aviation, and shipping. As cryogenics are essential for superconductivity, the power electronics used to drive the motor could also be cryogenically cooled to further boost efficiency and power density. This paper investigates potential cores which could be used in a cryogenic environment for a filter inductor. Results show that nanocrystalline cores are the most promising candidate for cryogenic electronics, although at cryogenic temperatures the B-H curve becomes significantly less linear. Iron powder mix cores are shown to have the least increase in core loss at cryogenic temperatures, but a higher overall loss than nanocrystalline. Error analysis is conducted on the partial cancellation method showing that it greatly reduces error in loss measurement compared to the conventional two winding method. © 2023 IEEE.

关键词： Core Loss Measurement Cryogenic Electronics Magnetics Nanocrystalline Power Electronics

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Exponential Weighted Moving Average-Based Variational Mode Decomposition Method for Fault Diagnosis of Vehicle Batteries 13

Exponential Weighted Moving Average-Based Variational Mode D...

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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Wu, Penghao Tao, Hongfeng Tian, Engang Yin, Tangwen Chen, Hongtian Chen, Yiyang School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Jiangnan University Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Wuxi214122 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9798350361674

In recent years, the rapid advancement of electric vehicles has heightened concerns regarding the safety of high-energy batteries. Consequently, there has been a significant focus on the development of fault diagnosis technologies for batteries. Intelligent fault diagnosis (IFD), particularly data-driven approaches, represents a crucial methodology in addressing this concern. This paper proposes a data-driven approach, for IFD in high-energy batteries utilized in electric vehicles. The proposed Exponentially Weighted Average Variational Mode Decomposition (EWVMD) method overcomes these limitations by employing the exponential weighted moving average (EWMA) technique to smooth collected time-series voltage data. This process reduces noise, accentuates abnormal change trends, and yields secondary data with enhanced features. Subsequently, variational mode decomposition (VMD) is applied to the secondary data. The resulting decomposed sub-mode curves (intrinsic mode functions - IMFs) and the change trends of the residual curve, in conjunction with a fault threshold tailored to actual working conditions, are utilized to ascertain the occurrence of faults. Design verification experiments, the C42MSA electric vehicle power supply battery undergoes charging and discharging tests with injected faults at fixed intervals. The collected fault signals are then subjected to fault diagnosis using EWVMD. Results demonstrate the algorithm's superior detection effectiveness and increased sensitivity to fault signals compared to traditional methods. © 2024 IEEE.

关键词： Variational mode decomposition

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Prompt Based Tri-Channel Graph Convolution Neural Network for Aspect Sentiment Triplet Extraction

Prompt Based Tri-Channel Graph Convolution Neural Network fo...

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2024 SIAM International Conference on Data Mining, SDM 2024

作者： Peng, Kun Jiang, Lei Peng, Hao Liu, Rui Yu, Zhengtao Ren, Jiaqian Hao, Zhifeng Philip, S.Yu. Institute of Information Engineering Chinese Academy of Sciences China School of Cyberspace Security University of Chinese Academy of Sciences China School of Cyber Science and Technology Beihang University China Faculty of Information Engineering and Automation Kunming University of Science and Technology China College of Science University of Shantou China University of Illinois at Chicago Department of Computer Science United States

ISBN: (纸本)9781611978032

Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a given sentence's triplets, which consist of aspects, opinions, and sentiments. Recent studies tend to address this task with a table-filling paradigm, wherein word relations are encoded in a two-dimensional table, and the process involves clarifying all the individual cells to extract triples. However, these studies ignore the deep interaction between neighbor cells, which we find quite helpful for accurate extraction. To this end, we propose a novel model for the ASTE task, called Prompt-based Tri-Channel Graph Convolution Neural Network (PT-GCN), which converts the relation table into a graph to explore more comprehensive relational information. Specifically, we treat the original table cells as nodes and utilize a prompt attention score computation module to determine the edges' weights. This enables us to construct a target-aware grid-like graph to enhance the overall extraction process. After that, a triple-channel convolution module is conducted to extract precise sentiment knowledge. Extensive experiments on the benchmark datasets show that our model achieves state-of-the-art performance. The code is available at https://***/KunPunCN/PT-GCN. Copyright © 2024 by SIAM.

关键词： Extraction

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MusicalBSI - musical genres responses to fMRI signals analysis with prototypical model agnostic meta-learning for brain state identification in data scarce environment

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computers in Biology and Medicine 2025年 188卷 109795-109795页

作者： Dutta, Subhayu Goswami, Saptiva Debnath, Sonali Adhikary, Subhrangshu Majumder, Anandaprova Department of Computer Science & Engineering Dr. B.C. Roy Engineering College West Bengal Durgapur713206 India Department of Research & Development Spiraldevs Automation Industries Pvt. Ltd. West Bengal Raiganj733123 India

Functional magnetic resonance imaging is a popular non-invasive brain-computer interfacing technique to monitor brain activities corresponding to several physical or neurological responses by measuring blood flow changes at different brain parts. Recent studies have shown that blood flow within the brain can have signature activity patterns in response to various musical genres. However, limited studies exist in the state of the art for automatized recognition of the musical genres from functional magnetic resonance imaging. This is because the feasibility of obtaining these kinds of data is limited, and currently available open-sourced data is insufficient to build an accurate deep-learning model. To solve this, we propose a prototypical model agnostic meta-learning framework for accurately classifying musical genres by studying blood flow dynamics using functional magnetic resonance imaging. A test with open-sourced data collected from 20 human subjects with consent for 6 different mental states resulted in up to 97.25 ± 1.38% accuracy by training with only 30 samples surpassing state-of-the-art methods. Further, a detailed evaluation of the performances confirms the model's reliability. © 2025 Elsevier Ltd

关键词： Contrastive Learning

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