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distributed monitoring of Power System Oscillations Using Multiblock Principal Component Analysis and Higher-order Singular Value Decomposition

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Journal of Modern Power Systems and Clean Energy 2022年第4期10卷 818-828页

作者： Arturo Román-Messina Alejandro Castillo-Tapia David A.Román-García Marcos A.Hernández-Ortega Carlos A.Morales-Rergis Claudia M.Castro-Arvizu Center for Research and Advanced Studies(CINVESTAV)of the National Polytechnic Institute of Mexico GuadalajaraJal.45017Mexico Instituto Tecnológico y de Estudios Superiores de Monterrey GuadalajaraJal.45070Mexico Autonomous University of Guadalajara GuadalajaraJal.45129Mexico Centro de Enseñanza Técnica Industrial and he is also with UAGGuadalajaraJal.44638Mexico

The primary goal in the analysis of hierarchical distributed monitoring and control architectures is to study the spatiotemporal patterns of the interactions between areas or *** this paper,a novel conceptual framework for distributed monitoring of power system oscillations using multiblock principal component analysis(MB-PCA)and higher-order singular value decomposition(HOSVD)is proposed to understand,characterize,and visualize the global behavior of the power *** proposed framework can be used to evaluate the influence of a given area or utility on the oscillatory behavior,uncover low-dimensional structures from high-dimensional data,and analyze the effects of heterogeneous data on the modal characteristics and interpretation of power *** metrics are then investigated to examine the relationships between the dynamic patterns and participation of individual data blocks in the global behavior of the *** application of these techniques is demonstrated by case studies of two systems:a 14-machine test system and a 5449-bus 635-generator equivalent model of a large power system.

关键词： distributed monitoring multiblock principal component analysis(MB-PCA) higher-order singular value decomposition(HOSVD) Tucker decomposition

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The distributed Network monitoring Model with Bounded Delay Constraints

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Wuhan University Journal of Natural Sciences 2004年第4期9卷 429-434页

作者： LIUXiang-hui YINJian-ping LUXi-cheng CAIZhi-ping ZHAOJian-min SchoolofComputerScience NationalUniversityofDefenseTechnologyChangsha410073HunanChina SchoolofComputerScience ZhejiangNormalUniversityJinhua321004ZhejiangChina

We address the problem of optimizing a distributed monitoring system and the goal of the optimization is to reduce the cost of deployment of the monitoring infrastructure by identifying a minimum aggregating set subject to delay constraint on the aggregating path. We show that this problem is NP-hard and propose approximation algorithm proving the approximation ratio with lnm+1, where is the number of monitoring nodes. At last we extend our modal with more constraint of bounded delay variation. Key words network - distributed monitoring - delay constraint - NP-hard CLC number TP 393 Foundation item: Supported by the National Natural Science Foundation of China (60373023)Biography: LIU Xiang-hui(1973-), male, Ph. D. candidate, research direction: algorithm complexity analysis, QoS in Internet.

关键词： network distributed monitoring delay constraint NP-hard

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ECA Rule-Based Configurable Frame of distributed System monitoring

ECA Rule-Based Configurable Frame of Distributed System Moni...

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The 2010 IEEE International Conference on Progress in Informatics and Computing

作者： Jingxin Peng Jian Cao Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

＜正＞Due to the developing of network technology,the increasing size of enterprises,the rising of cloud computing,the monitoring of large scale distributed computing is more and more *** studies of distributed monitoring on resources,programs,services,home and abroad,are lack of userconfigurable and run-time *** paper proposed an ECA rule-based configurable monitor frame for distributed system to solve this *** this frame,the monitoring probes are configured by users to gather necessary monitoring information,and probes can be deployed through software remote deployment *** monitoring information are put into complex event engine through form of events,we use complex event processing technology to extract the abstract management event,which are more significant and more *** the management event occurs,according to the ECA rules established by users,monitor frame can make warning or improvement measures,so can manage distributed systems run-time.

关键词： ECA rule distributed monitoring runtime management complex event processing

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Combining Gprof and Event-Driven monitoring for Analyzing distributed Programs:A Rough View of NCSA Mosaic

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Journal of Computer Science & Technology 1996年第4期11卷 427-432页

作者：彭澄廉 R.Klar DepartmentofComputerScienceFudanUniversity Shanghai200433 IMMDVII，UniversityofErlangen－Nuernbery Ger

There are several purposes of analyzing a program: functional or performance analysis, debugging or, more recently, mapping a program to a new parallel or distributed architecture. In this paper, we introduce an effective method leading to the Execution Graph (EG) from a program. First,the Unix profiling tool Gprof is used to get the Execution Model (EM) of a C-program. Then the event-driven monitoring tool AICOS-SIMPLE is used to get the EG which includes not only the call graph but also the execution time table of the program. This method is suitable for analyzing modern distributed programs. As the example of the analysis, the well known HTTP protocol under the NCSA Mosaic is chosen. An EG of NCSA Mosaic on the routing level is given.

关键词： distributed monitoring performance analysis Gprof profiling

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Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and distributed Manners:A Recommendation System

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Computers, Materials & Continua 2024年第4期79卷 351-372页

作者： Ameni Ellouze Nesrine Kadri Alaa Alaerjan Mohamed Ksantini Control and Energy Management Laboratory National Engineering SchoolPreparatory Institute for Engineering StudiesUniversity of SfaxSfax3029Tunisia Higher Institute of Computer Science and Communication Technologies University of SousseSousseBN19RHTunisia College of Computer and Information Sciences Jouf UniversityPO Box 2014SakakahSaudi Arabia

Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic *** and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or ***,smartphones and their associated sensing devices operate in distributed and unstable ***,collecting their data and extracting useful information is a significant *** this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical *** the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical *** system is based on the calculation of calories burned by each user during physical *** this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical *** identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional *** human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information *** Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal *** results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and *** overcome this constraint and improve performance(96%),we proposed to use the

关键词： Human physical activities smartphone sensors deep learning distributed monitoring recommendation system uncertainty healthy calories

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