• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.229-231
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• 2006

NLPCA(Nonlinear Principal Component Analysis) is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis. NLPCA operates by a feedforward neural network called AANN(Auto Associative Neural Network) which performs the identity mapping. In this work, a sensor fault detection system based on NLPCA is presented. To verify its applicability, simulation study on the data supplied from sensor network is executed.

• KIEE International Transactions on Electrophysics and Applications
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• v.3C no.4
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• pp.130-135
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• 2003

This study describes neural network based dissolved gas analysis using composition patterns of gas concentrations for transformer fault diagnosis. DGA samples were gathered from related literatures and classified into six types of faults and then a neural network was trained using the DGA samples. Diagnosis tests were performed by the trained neural network with DGA samples of serviced transformers, fault causes of which were identified by actual inspection. Diagnosis results by the neural network were in good agreement with actual faults.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.109-118
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• 2023

Energy awareness is an essential design flaw in wireless sensor network. Clustering is the most highly regarded energy-efficient technique that offers various benefits such as energy efficiency and network lifetime. Clusters create hierarchical WSNs that introduce the efficient use of limited sensor node resources and thus enhance the life of the network. The goal of this paper is to provide an analysis of the various energy efficient clustering algorithms. Analysis is based on the energy efficiency and network lifetime. This review paper provides an analysis of different energy-efficient clustering algorithms for WSNs.

• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.3
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• pp.13-19
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• 2016

Element-focused network analysis method for truss structure is proposed. The propagation process of loads from external loads to connected other elements is similar to that of connections between nodes in accordance with attachment rule in a network. Here nodes indicate elements in a truss structure and edges represent propagated loads. Therefore, the flows of loads in a truss structure can be calculated using the network analysis method, and consequently the structure can also be analyzed. As a first step to analyze a truss structure as a network, we propose a local load transfer rule in accordance with the topology of elements, and then analyze the loads of the truss elements. Application of this method reveal that the internal loads and reactions caused by external loads can be accurately estimated. Consequently, truss structures can be considered as networks and network analysis method can be applied to further complex truss structures.

• Genomics & Informatics
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• v.5 no.3
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• pp.133-136
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• 2007

The Gene Set network viewer (GSnet) visualizes the functional enrichment of a given gene set with a protein interaction network and is implemented as a plug-in for the Cytoscape platform. The functional enrichment of a given gene set is calculated using a hypergeometric test based on the Gene Ontology annotation. The protein interaction network is estimated using public data. Set operations allow a complex protein interaction network to be decomposed into a functionally-enriched module of interest. GSnet provides a new framework for gene set analysis by integrating a priori knowledge of a biological network with functional enrichment analysis.

• Korea Trade Review
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• v.46 no.1
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• pp.113-134
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• 2021

This study analyzed the centrality of the GVCs network and the value-added-based production structure of the electrical and electronic industries using ADB-MIRO and social network analysis methods. According to the analysis, the centrality and power of the GVSc intermediate goods network were differentiated into China, the United States, and the EU due to the advancement of industrial structure in Asia. In the 2000 network, the United States and Japan had a very strong influence in all aspects, including connectivity and strength. However, in 2017, China's power index rose to number one among 62 countries in the network. Furthermore, this study presented strategic implications of the Korean electrical and electronic industries to respond to the reorganization of GVSs based on the analysis results.

• Journal of the Korean Society for information Management
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• v.31 no.4
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• pp.49-68
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• 2014

The purpose of this study is to perform content analysis of research articles using the language network analysis method in Korea and catch the basic point of the language network analysis method. Six analytical categories are used for content analysis: types of language text, methods of keyword selection, methods of forming co-occurrence relation, methods of constructing network, network analytic tools and indexes. From the results of content analysis, this study found out various features as follows. The major types of language text are research articles and interview texts. The keywords were selected from words which are extracted from text content. To form co-occurrence relation between keywords, there use the co-occurrence count. The constructed networks are multiple-type networks rather than single-type ones. The network analytic tools such as NetMiner, UCINET/NetDraw, NodeXL, Pajek are used. The major analytic indexes are including density, centralities, sub-networks, etc. These features can be used to form the basis of the language network analysis method.

• International Journal of Knowledge Content Development & Technology
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• v.8 no.4
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• pp.75-98
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• 2018

In this study, the network analysis of researchers, institutions, and research principal agent was conducted to understand structure characteristics of international cooperation research project implemented from 1997 to 2018. The network of researchers and institutions were decentralized structure. On the other hands, the network of research principal agent was centralized structure. The Soul National University is the leading organization of international cooperation research project. In terms of research principal agent, corporation is the leading principal agent. In additions, the results of the network centroid analysis of the researchers and institutions were correlated with the research funds. As a result, it was confirmed that the network centroid of research organization was linearly related to research funds.

• International Journal of High-Rise Buildings
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• v.8 no.1
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• pp.49-56
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• 2019

Against the background of the rapid development of the Shanghai Metro network, this paper attempts to establish an analytical approach to evaluate the impact of multiple transport network accessibilities on commercial space use in metro station areas. Ten well-developed metro station areas in central Shanghai are selected as samples. Commercial space floor area and visitors in these areas are collected. Using ArcGIS and Spatial Design Network Analysis, the Shanghai Metro network and road network are modeled to compute diversified transport accessibilities. Evidence from land use and commercial space floor area within a 0-to-500-meter buffer zone of stations is consistent with location and land-use theory: commercial land use is concentrated closer to stations. Correlation analysis suggests that hourly visitors to the shopping mall are mainly influenced by metro network accessibility, while retail stores and restaurants are affected by both metro and pedestrian accessibility.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.141-148
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• 2016

In this paper, we propose a topic network analysis approach which integrates topic modeling and social network analysis. We collected 2,039 scientific papers from five top journals in the field of data mining published from 1996 to 2015, and analyzed them with the proposed approach. To identify topic trends, time-series analysis of topic network is performed based on 4 intervals. Our experimental results show centralization of the topic network has the highest score from 1996 to 2000, and decreases for next 5 years and increases again. For last 5 years, centralization of the degree centrality increases, while centralization of the betweenness centrality and closeness centrality decreases again. Also, clustering is identified as the most interrelated topic among other topics. Topics with the highest degree centrality evolves clustering, web applications, clustering and dimensionality reduction according to time. Our approach extracts the interrelationships of topics, which cannot be detected with conventional topic modeling approaches, and provides topical trends of data mining research fields.