• Korean Journal of Social Welfare Studies
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• v.42 no.1
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• pp.305-331
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• 2011

This study analyzes the consumption patterns of the poor households. The first objective of this analysis is to show that the group living in poverty get not one consumption pattern but several types of consumption patterns. The second objective is to understand what factors effect the consumption patterns. This study use the data of Korea Welfare Panel Study in 2008 & 2009. In oder to achieve first goal, We conduct factor analysis and cluster analysis. And to achieve second goal, We conduct multinomial logistic Analysis. Major findings are as follows. First we find six patterns of consuming types of the poor households. They are education oriented consuming type, diet oriented type, social network oriented type, transportation-communication oriented type, health & medical oriented type, and housing expenditure oriented type. Second we find these consumption patterns are effected by not economic factors but socio-populational factors, especially by life cycle of members of household.

• Korea Trade Review
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• v.46 no.5
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• pp.65-81
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• 2021

The technological development in the era of the 4th industrial revolution is changing the paradigm of various industries. Various technologies such as big data, cloud, artificial intelligence, virtual reality, and the Internet of Things are used, creating synergy effects with existing industries, creating radical development and value creation. Among them, the logistics sector has been greatly influenced by quantitative data from the past and has been continuously accumulating and managing data, so it is highly likely to be linked with big data analysis and has a high utilization effect. The modern advanced technology has developed together with the data mining technology to discover hidden patterns and new correlations in such big data, and through this, meaningful results are being derived. Therefore, data mining occupies an important part in big data analysis, and this study tried to analyze data mining techniques that can contribute to the logistics field and common logistics using these data mining technologies. Therefore, by using the AHP technique, it was attempted to derive priorities for each type of efficient data mining for logisticalization, and R program and R Studio were used as tools to analyze this. Criteria of AHP method set association analysis, cluster analysis, decision tree method, artificial neural network method, web mining, and opinion mining. For the alternatives, common transport and delivery, common logistics center, common logistics information system, and common logistics partnership were set as factors.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.179-184
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• 2024

Franchise are now a red ocean in Food industry and they need to find other options to appeal for their product, the uprising content, food tech. The franchises are working on R&D to help franchisees with the operations. Through this paper, we analyze the franchise interest on food tech and to help find the necessity of development for franchisees who are in needs with hand, not of human, but of technology. Using Textom, a big data analysis tool, "franchise" and "food tech" were selected as keywords, and search frequency information of Naver and Daum was collected for a year from 01 January, 2023 to 31 December, 2023, and data preprocessing was conducted based on this. For the suitability of the study and more accurate data, data not related to "food tech" was removed through the refining process, and similar keywords were grouped into the same keyword to perform analysis. As a result of the word refining process, a total of 10,049 words were derived, and among them, the top 50 keywords with the highest relevance and search frequency were selected and applied to this study. The top 50 keywords derived through word purification were subjected to TF-IDF analysis, visualization analysis using Ucinet6 and NetDraw programs, network analysis between keywords, and cluster analysis between each keyword through Concor analysis. By using big data analysis, it was found out that franchise do have interest on food tech. "technology", "franchise", "robots" showed many interests and keyword "R&D" showed that franchise are keen on developing food tech to seize competitiveness in Franchise Industry.

• 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.

• Wind and Structures
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• v.39 no.1
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• pp.1-14
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• 2024

Widespread damages from extreme winds have attracted lots of attentions of the resilience assessment of power distribution systems. With many related environmental parameters as well as numerous power infrastructure components, such as poles and wires, the increased challenge of power asset management before, during and after extreme events have to be addressed to prevent possible cascading failures in the power distribution system. Many extreme winds from weather events, such as hurricanes, generate widespread damages in multiple areas such as the economy, social security, and infrastructure management. The livelihoods of residents in the impaired areas are devastated largely due to the paucity of vital utilities, such as electricity. To address the challenge of power grid asset management, power system clustering is needed to partition a complex power system into several stable clusters to prevent the cascading failure from happening. Traditionally, system clustering uses the Binary Decision Diagram (BDD) to derive the clustering result, which is time-consuming and inefficient. Meanwhile, the previous studies considering the weather hazards did not include any detailed weather-related meteorologic parameters which is not appropriate as the heterogeneity of the parameters could largely affect the system performance. Therefore, a fragility-based network hierarchical spectral clustering method is proposed. In the present paper, the fragility curve and surfaces for a power distribution subsystem are obtained first. The fragility of the subsystem under typical failure mechanisms is calculated as a function of wind speed and pole characteristic dimension (diameter or span length). Secondly, the proposed fragility-based hierarchical spectral clustering method (F-HSC) integrates the physics-based fragility analysis into Hierarchical Spectral Clustering (HSC) technique from graph theory to achieve the clustering result for the power distribution system under extreme weather events. From the results of vulnerability analysis, it could be seen that the system performance after clustering is better than before clustering. With the F-HSC method, the impact of the extreme weather events could be considered with topology to cluster different power distribution systems to prevent the system from experiencing power blackouts.

• Journal of Forest and Environmental Science
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• v.40 no.3
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• pp.210-226
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• 2024

Old-growth mixed-conifer forests in Bhutan are characterized by remarkable tree species compositional heterogeneity. However, our knowledge of tree species assemblages and their structural attributes in these forests has been limited. Therefore, forest classification has been reliant on a single dominant species. This study aimed to distinguish tree species assemblages in an old-growth mixed conifer forest in Western Bhutan and to describe their natural compositional and stand structural characteristics. Furthermore, the regeneration status of species was investigated and the quantity and quality of accumulated coarse woody debris were assessed. Ninety simple random sampling plots were surveyed in the study site between 3,000 and 3,600 meters above sea level. Tree, standing deadwood, regeneration, and coarse woody debris data were collected. Seven tree species assemblages were distinguished by Hierarchical Cluster Analysis and Indicator Species Analysis, representing five previously undescribed tree species associations with unique set of consistent species. Principal Component Analysis revealed two transitional pathways of species dominance along an altitudinal gradient, highly determined by relative topographic position. The level of stand stratification varied within a very wide range, corresponding to physiognomic composition. Rotated-sigmoid and negative exponential diameter distributions were formed by overstorey species with modal, and understorey species with negative exponential distribution. Overstorey dominant species showed extreme nurse log dependence during regeneration, which supports the formation of their modal distribution by an early natural selection process. This allows the coexistence of overstorey and understorey dominant species, increasing the sensitivity of these primary ecosystems to forest management.

• The KIPS Transactions:PartC
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• v.10C no.3
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• pp.377-386
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• 2003

This study suggested a technique to maintain an efficient core-based multicast tree using weighted clustering factors in mobile Ad-hoc networks. The biggest problem with the core-based multicast tree routing is to decide the position of core node. The distance of data transmission varies depending on the position of core node. The overhead's effect on the entire network is great according to the recomposition of the multicast tree due to the movement of core node, clustering is used. A core node from cluster head nodes on the multicast tree within core area whose weighted factor is the least is chosen as the head core node. Way that compose multicast tree by weighted clustering factors thus and propose keeping could know that transmission distance and control overhead according to position andmobility of core node improve than existent multicast way, and when select core node, mobility is less, and is near in center of network multicast tree could verification by simulation stabilizing that transmission distance is short.

• Journal of Korea Technology Innovation Society
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• v.21 no.1
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• pp.64-91
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• 2018

This study explores the interactive pattern among social issue, academic research, and governmental policy on science and technology during the last 20 years. In particular, we try understand wether the science and technology policy research and governmental policy meets social needs appropriately. In order to do this, we have collected text data from news articles, papers, and governmental documents. Based on these data, social network analysis and cluster analysis has been carried out. According to the results, we have found that science and technology policy researches tend to focus on fragmented technological innovation meeting urgent practical needs at the initial stage. However, recently, the main characteristics of science and technology policy research shows co-evolutionary patterns responding to society. Furthermore, time lag also has been observed in the process of interaction among the three bodies. Based on these results, we put forward some suggestions for upcoming researches in science and technology policy. Firstly, analysis levels are needed to be shifted from micro level to mezo or macro level. Secondly, more research efforts are required to be focused on policy process in science technology and its public management. Finally, we have to enhance the sensitiveness to social issues through studies on agenda setting in science and technology policy.

• Journal of the Korea Furniture Society
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• v.21 no.6
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• pp.515-524
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• 2010

In line with the government's policies for localization, furniture industry in the northern area in Gyeoggi-province at presence has been operated by several furniture industrial complexes in the region in small scale, but now it should be considered from overall view of furniture industry in order to develop more competitive industry. As a matter of this fact, Gyeonggi-province should be engaged in planning to make various industrial clusters of the furniture-related industry based on the northern area of province at structural as well as politic aspects, with the help of the analyzed status of the current furniture industry in region, in supporting them by the systemized policies and developing the overall program to foster furniture as an international-competitive industry. Therefore this study suggested 'furniture industry center' which will exclusively handle and maintain the network of each furniture company in the northern area of Gyeonggi-province and 'high-tech furniture industry complex' to keep pace with the globalization and to be competitive internationally and also 'common brand business' for the cooperation at technical phase. In order to realize and vitalize such suggestions, it is urgently necessary that the network consists of the furniture companies and the expert of the related department in local universities as the main body for furniture industry, of course Gyeonggi-province as well.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.63-83
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• 2016

As the usage of information resources produced in various media and forms has been increased, the importance of metadata as a tool of information organization to describe the information resources becomes increasingly crucial. The purposes of this study are to analyze and to demonstrate the intellectual structure in the field of metadata through co-word analysis. The data set was collected from the journals which were registered in the Core collection of Web of Science citation database during the period from January 1, 1998 to July 8, 2016. Among them, the bibliographic data from 727 journals was collected using Topic category search with the query word 'metadata'. From 727 journal articles, 410 journals with author keywords were selected and after data preprocessing, 1,137 author keywords were extracted. Finally, a total of 37 final keywords which had more than 6 frequency were selected for analysis. In order to demonstrate the intellectual structure of metadata field, network analysis was conducted. As a result, 2 domains and 9 clusters were derived, and intellectual relations among keywords from metadata field were visualized, and proposed keywords with high global centrality and local centrality. Six clusters from cluster analysis were shown in the map of multidimensional scaling, and the knowledge structure was proposed based on the correlations among each keywords. The results of this study are expected to help to understand the intellectual structure of metadata field through visualization and to guide directions in new approaches of metadata related studies.