• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.149-162
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• 2021

This paper established a new smart factory based on manufacturing data for an introductory company focusing on the personalized cosmetics manufacturing industry. We build on an example of a system that collects, manages, and analyzes documents and data that were previously managed by CGMP-based analog for data-driven use. To this end, we have established a system that can collect all data in real time at the production site by introducing artificial intelligence smart factory platform LINK5 MOS and POP system, collecting PLC data, and introducing monitoring system and pin board. It also aims to create a new business cluster space based on this project.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1477-1483
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• 2020

The stability guarantee of wireless network based control systems is still challenging due to the lossy links and node failures. This paper proposes a hierarchical cluster-based network protocol called robust wireless sensor and actuator network (R-WSAN) by combining time, channel, and space resource diversity. R-WSAN includes a scheduling algorithm to support the network resource allocation and a control task sharing scheme to maintain the control stability of multiple plants. R-WSAN was implemented on a real test-bed using Zolertia RE-Mote embedded hardware platform running the Contiki-NG operating system. Our experimental results demonstrate that R-WSAN provides highly reliable and robust performance against lossy links and node failures. Furthermore, the proposed scheduling algorithm and the task sharing scheme meet the stability requirement of control systems, even if the controller fails to support the control task.

• Journal of the Economic Geographical Society of Korea
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• v.26 no.3
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• pp.256-273
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• 2023

This study analyzed the structural changes in the global foreign direct investment (FDI) network using stock data from the International Monetary Fund's Coordinated Direct Investment Survey (CDIS) for 2009~2021. The results showed that the COVID-19 pandemic had a negative impact on the FDI links between countries and the activities of reciprocal relationships. The United States, the Netherlands, and the United Kingdom consistently play central roles in the global FDI network. The degree centrality of China has changed significantly over time in confronting the volatile situation of the world economy. Cross-tabulation analysis revealed a significant association between FDI clusters and geographic regions. Within each cluster, the linkage structure of FDI partners of closely connected individual countries has exhibited differential characteristics as the global economic environment changes.

• Convergence Security Journal
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• v.23 no.4
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• pp.61-70
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• 2023

Clustering is an unsupervised learning method that involves grouping data based on features such as distance metrics, using data without known labels or ground truth values. This method has the advantage of being applicable to various types of data, including images, text, and audio, without the need for labeling. Traditional clustering techniques involve applying dimensionality reduction methods or extracting specific features to perform clustering. However, with the advancement of deep learning models, research on deep clustering techniques using techniques such as autoencoders and generative adversarial networks, which represent input data as latent vectors, has emerged. In this study, we propose a deep clustering technique based on deep learning. In this approach, we use an autoencoder to transform the input data into latent vectors, and then construct a vector space according to the cluster structure and perform k-means clustering. We conducted experiments using the MNIST and Fashion-MNIST datasets in the PyTorch machine learning library as the experimental environment. The model used is a convolutional neural network-based autoencoder model. The experimental results show an accuracy of 89.42% for MNIST and 56.64% for Fashion-MNIST when k is set to 10.

• 한국노년학
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• v.38 no.4
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• pp.1057-1081
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• 2018

This study analyzed the case of elderly care cluster in Gwangjin-gu to explore the possibilities of social economy as a provider of community-based social services. Community-based means the approach by which community organizations build a voluntary and collaborative network to enhance collective problem-solving abilities. Therefore, it is very likely that the social economy that emphasizes people, labor, community, and democratic principles can contribute to community-based social services. This study analyzed social economic network by using four characteristics of social economy suggested by OECD community economy and employment program as an analysis framework. The results of this study are as follows: First, it is found that social economy would hardly supply community-based social services through network cooperation because of a large variation in community identity, investment to new product, and labor protection. Second, community users are not the consumers of the social economy and the products of the social economy stay in market products only for the organizations in social economy. In order to create good services that meet the needs of residents, community development approaches are required at the same time. The importance of community space where local residents and social economy meet is derived. Third, public support such as purchasing support has weakened the ecosystem of social economy by making the distinction between public economy and social economy more obscure. On the other hand, public investment in community infrastructure is an indirect aid to social economy to communicate with residents and to promote good supply and consumption. In the end, community-based social services need a platform where the social economy and the people meet. This type of public investment can create the ecosystem of the social economy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1131-1141
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• 2015

In this paper, we proposed a new approach of machine learning based method for detecting game-bots from normal players in MMORPG by inspecting the player's action log data especially in-game money increasing/decreasing event log data. DBSCAN (Density Based Spatial Clustering of Applications with Noise), an one of density based clustering algorithms, is used to extract the attributes of spatial characteristics of each players such as a number of clusters, a ratio of core points, member points and noise points. Most of all, even game-bot developers know principles of this detection system, they cannot avoid the system because moving a wide area to hunt the monster is very inefficient and unproductive. As the result, game-bots show definite differences from normal players in spatial characteristics such as very low ratio, less than 5%, of noise points while normal player's ratio of noise points is high. In experiments on real action log data of MMORPG, our game-bot detection system shows a good performance with high game-bot detection accuracy.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.201-213
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• 2018

In recent years, the number of traffic accidents caused by car accidents has been decreasing steadily due to traffic accident prevention activities in Korea. However, the number of accidents in Seoul is higher than that of other regions. Various studies have been conducted to prevent traffic accidents, which are human disasters. In particular, previous studies have performed the spatial analysis of traffic accidents by counting the number of traffic accidents by administrative districts or by estimating the density through kernel density method in order to identify the traffic accident cluster areas. However, since traffic accidents take place along the road, it would be more meaningful to investigate them concentrated on the road network. In this study, traffic accidents were assigned to the nearest road network in two ways and analyzed by hotspot analysis using Getis-Ord Gi* statistics. One of them was investigated with a fixed road link of 10m unit, and the other by computing the average traffic accidents per unit length per road section. As a result by the first method, it was possible to identify the specific road sections where traffic accidents are concentrated. On the other hand, the results by the second method showed that the traffic accident concentrated areas are extensible depending on the characteristic of the road links. The methods proposed here provide different approaches for visualizing the traffic accidents and thus, make it possible to identify those sections clearly that need improvement as for the traffic environment.

• Journal of Korea Spatial Information System Society
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• v.7 no.2 s.14
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• pp.47-56
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• 2005

Recently, with the rapid development of mobile computing, wireless positioning technologies, and the generalization of wireless internet, LBS (Location Based Service) which utilizes location information of moving objects is serving in many fields. In order to serve LBS efficiently, the location data server that periodically stores location data of moving objects is required. Formerly, GIS servers have been used to store location data of moving objects. However, GIS servers are not suitable to store location data of moving objects because it was designed to store static data. Therefore, in this paper, we designed and implemented an extended SLDS(Short-term Location Data Subsystem) for real-time Location Based Services. The extended SLDS is extended from the SLDS which is a subsystem of the GALIS(Gracefully Aging Location Information System) architecture that was proposed as a cluster-based distributed computing system architecture for managing location data of moving objects. The extended SLDS guarantees real-time service capabilities using the TMO(Time-triggered Message-triggered Object) programming scheme and efficiently manages large volume of location data through distributing moving object data over multiple nodes. The extended SLDS also has a little search and update overhead because of managing location data in main memory. In addition, we proved that the extended SLDS stores location data and performs load distribution more efficiently than the original SLDS through the performance evaluation.

• Journal of Korea Spatial Information System Society
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• v.10 no.3
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• pp.79-91
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• 2008

Recently, with highly Interest In Location-Based Service(LBS) utilizing location data of moving objects, the GALIS(Gracefully Aging Location Information System) which is a cluster-based distributed computing architecture was proposed as a more efficient location management system of moving objects. In the SLDS(Short-term location Data Subsystem) which Is a subsystem of the GALIS, since the SDP(Short-term Data Processor) Master transmits current location data and queries to every SDP Worker, the SDP Master reassembles and sends query results produced by SDP Workers to the client. However, the services are suspended during the SDP Master under failure and the response time to the client is increased if the load is concentrated on the SDP Master. Therefore, in this paper, the extended SLDS was designed and implemented to solve these problems. Though one SDP Master is under failure, the other can provide the services continually, and so the extended SLDS can guarantee the high reliability of the SLDS. The extended SLDS also can reduce the response time to the client by enabling two SDP Masters to perform the distributed query processing. Finally, we proved high reliability and high availability of the extended SLDS by implementing the current location data storage, query processing, and failure takeover scenarios. We also verified that the extended SLDS is more efficient than the original SLDS through the query processing performance evaluation.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.58-72
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• 2021

In July 2021, UNCTAD classified Korea as a developed country. After the Korean War in the 1950s, economic development was promoted despite difficult conditions, resulting in epoch-making national growth. However, in order to respond to the rapidly changing global economy, it is necessary to continuously study the domestic industrial ecosystem and prepare strategies for continuous change and growth. This study analyzed the industrial ecosystem of the automobile industry where it is possible to obtain transaction data between companies by applying complexity spatial network analysis. For data, 295 corporate data(node data) and 607 transaction data (link data) were used. As a result of checking the spatial distribution by geocoding the address of the company, the automobile industry-related companies were concentrated in the Seoul metropolitan area and the Southeastern(Dongnam) region. The node importance was measured through degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality, and the network structure was confirmed by identifying density, distance, community detection, and assortativity and disassortivity. As a result, among the automakers, Hyundai Motor, Kia Motors, and GM Korea were included in the top 15 in 4 indicators of node centrality. In terms of company location, companies located in the Seoul metropolitan area were included in the top 15. In terms of company size, most of the large companies with more than 1,000 employees were included in the top 15 for degree centrality and betweenness centrality. Regarding closeness centrality and eigenvector centrality, most of the companies with 500 or less employees were included in the top 15, except for automakers. In the structure of the network, the density was 0.01390522 and the average distance was 3.422481. As a result of community detection using the fast greedy algorithm, 11 communities were finally derived.