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2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020

作者： Xia, Ye Li, Wei Zhang, Xiaoli Kang, Chaoqun Zhou, Hongjun Zheng, Bingtao China Electric Power Research Institute Protection Relay Department State Grid Secondary Equipment Evaluation Center Beijing China Shanghai Energy Interaction Research Institute Intelligent Power Distribution Technology Center Shanghai China Beijing Ponovo Power Co. Ltd Software Development Department Beijing China Changsha China

ISBN: (纸本)9781728166117

The local configuration scheme of relay protection has become the trend of substation development in the future. Based on the modular design idea, an automatic test system suitable for on-site relay protection device is developed, so as to realize synchronous closed-loop test for several local protection devices of the same type from different manufacturers. The automatic test system can synchronously modify the setting values, control words and pressing plates of multiple devices. It can synchronously apply excitation to multiple devices to complete the function test of electrical quantity, automatically generate the test report, which greatly shorten the test time of local protection device. At the same time, through the analysis of the data model of the local protection device, the protection function of the tested device configuration is obtained, and the corresponding test cases are generated intelligently, which can highly improve the development efficiency of the test cases. Under the premise of ensuring the reliability of detection, the research results are applied to three sets of local protection devices for synchronous automatic test, which extremely improves the detection efficiency of local protection devices, and it has achieved good results in preliminary application. © 2020 IEEE.

关键词： Relay protection

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GUARD: Multigranularity-based Unsupervised Anomaly Detection Algorithm for Multivariate Time Series

GUARD: Multigranularity-based Unsupervised Anomaly Detection...

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Fan Meng Qunli Yang Zhengda He Shangdong Yang Weidong Tang Public Credit Information Center Jiangsu Strategy and Development Research Center Nanjing China State Key Lab for Novel Software Technology Nanjing University Nanjing China School of Science China Pharmaceutical University Nanjing China The School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Customer Operations Department China Telecom Corporation Limited Nanjing Branch Nanjing China

ISBN: (纸本)9781665456579

Unsupervised anomaly detection on multivariate time series (MTS) is a valuable problem in practice because it significantly reduces labeling costs. Even though most classical methods begin to consider inter-series dependencies, the hierarchical relationships between and within-series data are often ignored. To overcome these limitations, in this paper, we propose a multiGranUlArity based unsupeRvised anomaly Detection (GUARD) algorithm that captures the hierarchical relationships in three granularities and merges these dependencies in the following anomaly detection task. Specifically, the proposed algorithm first captures the internal dependencies within the subsequence and learns a hidden representation based on a trained autoencoder. Then, a hierarchical mechanism is designed to capture the local and global dependencies between the given subsequence and the whole set and learn their hidden representation. The experimental results show that the proposed algorithm can effectively and stably exploit the potential relationship of MTS and detect anomalies more accurately than the basic approaches.

关键词： Representation learning Cloud computing Costs Fuses Computational modeling Time series analysis Labeling

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0151 Personalized Caffeine Recommendations to Achieve Optimal Alertness Levels

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Sleep 2024年第SUPPLEMENT_1期47卷 A66–A66页

作者： Vital-Lopez, Francisco Doty, Tracy Anlap, Ian Killgore, William Reifman, Jaques Department of Defense Biotechnology High Performance Computing Software Applications Institute Telemedicine and Advanced Technology Research Center U.S. Army Medical Research and Development Command Behavioral Biology Branch Walter Reed Army Institute of Research University of Arizona College of Medicine

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Reasonableness discussion and analysis for Hyperledger Fabric configuration

Reasonableness discussion and analysis for Hyperledger Fabri...

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IEEE International Conference on Blockchain and Cryptocurrency (ICBC)

作者： Song Hua Shenbin Zhang Bingfeng Pi Jun Sun Kazuhiro Yamashita Yoshihide Nomura Information Technology Laboratory Fujitsu Research & Development Center Suzhou China Software Laboratory Fujitsu Laboratories Kawasaki Japan

ISBN: (数字)9781728166803

ISBN: (纸本)9781728166810

Blockchain, as a distributed ledger technology, becomes more and more popular in both industry and academia. Hyperledger Fabric is permissioned blockchain platform hosted by Linux foundation. Fabric has various components such as peer, ordering service, chaincode and state database. The structure of Fabric network is very complicated to provide reliable permissioned blockchain service. Generally, developers must deal with hundreds of parameters to configure a network. That will cause many reasonableness problems in configurations. In this paper, we focus on how to detect reasonableness problems in Fabric configurations. Firstly, we discuss and provide a reasonableness problem knowledge database based on the perspectives of functionality, security and performance. Secondly, we implemented a detect tool for reasonableness check to Fabric. Finally, we collect 108 sample networks as the testing dataset in the experiment. The result shows our tool can help developers to locate reasonableness problems and understand their network better.

关键词： Fabrics Tools Syntactics Information technology

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Big Data Mining Analysis of Key Indicators of Online New Energy Vehicles

Big Data Mining Analysis of Key Indicators of Online New Ene...

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作者： Caidong Gu Zhaobin Liu Ligang Fang Jiangsu Modern Enterprise Informatization Application Support Software Engineering Technology Research and Development Center Suzhou Vocational University

In this paper, more than 300 new energy vehicle key indicators of online sales are mined, and big data visualization analysis is carried out. In October 2018, big data mining analysis was conducted on the brand, model, pure electric endurance, slow charging time, fast charging time, battery capacity, maximum power, maximum torque, monthly sales volume, annual cumulative sales volume, monthly transaction price, user score, number of public praise, manufacturer guidance price(low) and manufacturer guidance price(high) of each new energy vehicle market in Sina ***. The results show that the average pure electric endurance of new energy vehicles is 210.3 km(with a maximum endurance of 613 km), and the average fast charging time is 1.7 hours(with a minimum duration of half an hour). The best-known brand of new energy vehicles is BYD, with the highest market share of BYD and Toyota.

关键词： Big Data Mining Analysis of Key Indicators of Online New Energy Vehicles Key

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Reasonableness discussion and analysis for Hyperledger Fabric configuration

arXiv

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arXiv 2020年

作者： Hua, Song Zhang, Shenbin Pi, Bingfeng Sun, Jun Yamashita, Kazuhiro Nomura, Yoshihide Information Technology Laboratory Fujitsu Research & Development Center Suzhou China Software Laboratory Fujitsu Laboratories Kawasaki Japan

Blockchain, as a distributed ledger technology, becomes more and more popular in both industry and academia. Each peer in blockchain system maintains a copy of ledger and makes sure of data consistency through consensus protocol. Blockchain system can provide many benefits such as immutability, transparency and security. Hyperledger Fabric is permissioned blockchain platform hosted by Linux foundation. Fabric has various components such as peer, ordering service, chaincode and state database. The structure of Fabric network is very complicated to provide reliable permissioned blockchain service. Generally, developers must deal with hundreds of parameters to configure a network. That will cause many reasonableness problems in configurations. In this paper, we focus on how to detect reasonableness problems in Fabric configurations. Firstly, we discuss and provide a reasonableness problem knowledge database based on the perspectives of functionality, security and performance. Secondly, we implemented a detect tool for reasonableness check to Fabric. Finally, we collect 108 sample networks as the testing dataset in the experiment. The result shows our tool can help developers to locate reasonableness problems and understand their network better. Copyright © 2020, The Authors. All rights reserved.

关键词： Blockchain

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Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imaging

arXiv

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arXiv 2024年

作者： Yang, Jing Cheng, Jian Li, Cheng Fan, Wenxin Zou, Juan Wu, Ruoyou Wang, Shanshan Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Peng Cheng Laboratory Guangdong Shenzhen China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Yuhang District Hangzhou China

Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the in vivo human brain. However, to effectively capture the intricate characteristics of water diffusion at various directions and scales, it is important to employ comprehensive q-space sampling. Unfortunately, this requirement leads to long scan times, limiting the clinical applicability of dMRI. To address this challenge, we propose SSOR, a Simultaneous q-Space sampling Optimization and Reconstruction framework. We jointly optimize a subset of q-space samples using a continuous representation of spherical harmonic functions and a reconstruction network. Additionally, we integrate the unique properties of diffusion magnetic resonance imaging (dMRI) in both the q-space and image domains by applying l1-norm and total-variation *** experiments conducted on HCP data demonstrate that SSOR has promising strengths both quantitatively and qualitatively and exhibits robustness to noise. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction

arXiv

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arXiv 2022年

作者： Ji, Jiahao Wang, Jingyuan Jiang, Zhe Jiang, Jiawei Zhang, Hu State Key Laboratory of Software Development Environment School of Computer Science & Engineering Beihang University Beijing China Peng Cheng Laboratory Shenzhen China Department of Computer & Information Science & Engineering The University of Florida United States MOE Engineering Research Center of Advanced Computer Application Technology School of Computer Science & Engineering Beihang University Beijing China

High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities. The lack of integration between physical principles and data-driven models is an important reason for limiting the development of this field. In the literature, physics-based methods can usually provide a clear interpretation of the dynamic process of traffic flow systems but are with limited accuracy, while data-driven methods, especially deep learning with black-box structures, can achieve improved performance but can not be fully trusted due to lack of a reasonable physical basis. To bridge the gap between purely data-driven and physics-driven approaches, we propose a physics-guided deep learning model named Spatio- Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework. Specifically, we assume the traffic flow on road networks is driven by a latent potential energy field (like water flows are driven by the gravity field), and model the spatio-temporal dynamic process of the potential energy field as a differential equation network. STDEN absorbs both the performance advantage of data-driven models and the interpretability of physics-based models, so is named a physics-guided prediction model. Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin. A case study further verifies that STDEN can capture the mechanism of urban traffic and generate accurate predictions with physical meaning. The proposed framework of differential equation network modeling may also cast light on other similar applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Differential equations

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Spectral-spatial anomaly detection of hyperspectral data based on improved isolation forest

TechRxiv

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TechRxiv 2021年

作者： Song, Xiangyu Aryal, Sunil Ting, Kai Ming Liu, Zhen He, Bin Changchun130033 China The University of Chinese Academy of Sciences Beijing100049 China The School of Information Technology Deakin University 75 Pigdons Rd Waurn Ponds VIC3216 Australia National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Big Data Research Center and Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

Anomaly detection in hyperspectral image is affected by redundant bands and the limited utilization capacity of spectral-spatial information. In this article, we propose a novel Improved Isolation Forest (IIF) algorithm based on the assumption that anomaly pixels are more susceptible to isolation than the background pixels. The proposed IIF is a modified version of the Isolation Forest (iForest) algorithm, which addresses the poor performance of iForest in detecting local anomalies and anomaly detection in high-dimensional data. Further, we propose a spectral-spatial anomaly detector based on IIF (SSIIFD) to make full use of global and local information, as well as spectral and spatial information. To be specific, first, we apply the Gabor filter to extract spatial features, which are then employed as input to the Relative Mass Isolation Forest (ReMass-iForest) detector to obtain the spatial anomaly score. Next, original images are divided into several homogeneous regions via the Entropy Rate Segmentation (ERS) algorithm, and the preprocessed images are then employed as input to the proposed IIF detector to obtain the spectral anomaly score. Finally, we fuse the spatial and spectral anomaly scores by combining them linearly to predict anomaly pixels. The experimental results on four real hyperspectral data sets demonstrate that the proposed detector outperforms other state-of-the-art methods. © 2021, CC BY.

关键词： Anomaly detection

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Feature Selection Using Local Interpretable Model-Agnostic Explanations on Metagenomic Data 7th

Feature Selection Using Local Interpretable Model-Agnostic E...

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7th International Conference on Future Data and Security Engineering, FDSE 2020

作者： Thanh-Hai, Nguyen Tran, Toan Bao Tran, An Cong Thai-Nghe, Nguyen College of Information and Communication Technology Can Tho University Can Tho900100 Viet Nam Center of Software Engineering Duy Tan University Da Nang550000 Viet Nam Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam

ISBN: (纸本)9789813343696

Metagenomics is one of the emerging concepts in personalized medicine approaches to take care of and improve human health. Numerous studies have revealed that metagenomic data can associate with a vast of human diseases. Recent advancements in machine learning techniques and computation resources enable us to speed up the data processing and also improve the performance in diagnosis accuracy. However, we face difficulties to process metagenomic data due to its complexities and high dimension. This work proposes an approach based on an explanation model to perform feature selection tasks. The proposed approach selects a small set of features from the original features with Interpretable Model-agnostic Explanations that can obtain better performances than feature selection based on importance scores generated from a robust learning machine learning such as Random Forests. We are expected that the approach can be an efficient feature selection method compared to classic feature selection techniques. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

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