• Journal of Appropriate Technology
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• v.6 no.2
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• pp.238-255
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• 2020

As COVID-19, which occurred at the end of 2019, has become a global pandemic, it has emerged as an unprecedented event that quickly destroys a nation's medical and healthcare system in both developed and developing countries. In the 21st century, most of the civil society that aimed for hyperconnected society is facing a new crisis that has not been experienced so far. Indeed, lack of personal protective equipment, isolation of clustered communities, disruption of medical systems necessary for diagnosis and treatment, and disruption of educational and economic activities due to social isolation are emerging. Since the COVID-19 has occurred, many of the difficulties that have occurred in the past six months indicate the basic infrastructure a society should have particularly in a pandemic. These include personal protective equipment (PPE), decontamination and quarantine tools essential for effective response, rapid and precise large-scale diagnosis, medical devices required for patient care, and identification and fast and wide on-line networks that can be used in social isolation. In this first part, we would like to introduce some representative examples of 1) personal protective equipment, 2) prevention of personal and community health, 3) social response through big data and networks within the framework of appropriate technology.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.150-163
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• 2022

Recently, the gated community has been increasing due to various reasons such as demand for differentiated areas and security, but various problems have been raised, including regional conflicts, traffic restrictions and disconnection of surrounding areas. Therefore, this study empirically considered what kind of spatial effect the gated community has on the surrounding area by analyzing the vitality using floating population big data and analyzing pedestrian accessibility using network analysis and social network analysis. As a result, it was found that the overall vitality in the study area was greatly affected by the land use and the building use. However, focusing on apartment complexes, even in the same land use, when the form of the complex is open to the outside, there is a lot of floating population, so the vitality is high. In terms of accessibility, assuming that the gated community is open, it was found that as the physical connectivity improved, there were more roads for pedestrians to choose from, and the accessibility improved as traffic and exchanges occurred in the disconnected space. The value of improving property rights and residential environment is also precious, but it is necessary to review how to reflect the improvement of local permeability in enhancing the publicness of cities and the value and direction of communities that can coexist with the region.

• Korean Journal of Cognitive Science
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• v.33 no.1
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• pp.23-50
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• 2022

Source memory is a cognitive process that combines the representation of the origin of the episodic experience with an item. By studying this daily process, researchers have made fundamental discoveries that make up the foundation of brain and behavior research, such as executive function and binding. In this paper, we review and conduct a bibliometric analysis on source memory papers published from 1989 to 2020. This review is based on keyword co-occurrence networks and author citation networks, providing an in-depth overview of the development of source memory research and future directions. This bibliometric analysis discovers a change in the research trends: while research prior to 2010 focused on individuality of source memory as a cognitive function, more recent papers focus more on the implication of source memory as it pertains to connectivity between disparate brain regions and to social neuroscience. Keyword network analysis shows that aging and executive function are continued topics of interest, although frameworks in which they are viewed have shifted to include developmental psychology and meta memory. The use of theories and models provided by source memory research seem essential for the future development of cognitive enhancement tools within and outside of the field of Psychology.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.440-446
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• 2018

This study examines the contrasting effects of an aging workforce on organizational innovation and the moderating role of age diversity. By utilizing a knowledge-based view, this study asserts that an aging workforce can contribute to organizational innovation to some degree due to accumulated firm-specific knowledge and experiences as well as various social networks within and outside the firm. On the contrary, as the portion of an aging workforce increases, the negative effect of the aging workforce on organizational innovation can overwhelm the positive effects because of the limitations of new knowledge creation. In addition, this study explores the effect of age diversity to moderate the relationship between aging workforce and organizational innovation.Empirical findings showed that an aging workforce and organizational innovation had an inverted-U relationship, and age diversity moderated this curvilinear relationship as this study predicted. These theoretical arguments and empirical findings highlight the necessity to explore the complex effects of an aging workforce on organizational innovation as well as additional factors influencing the relationship between aging workforce and firm innovation.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.367-378
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• 2007

기업이나 단체는 고객과의 관계형성 강화와 충성도를 높이기 위해 온라인 커뮤니티를 조직 차원에서 활성화하고 있고, 개인들은 사회 연결망(social network) 강화를 위해 개인의 관심사${\cdot}$가치${\cdot}$흥미${\cdot}$문화 등에 따라 특정 온라인 커뮤니티 사이트를 통해서 다양한 정보 획득과 구성들 간의 관계를 형성해 가고 있다. 이러한 추세에 힘입어 온라인 커뮤니티 사용자들은 상호작용성을 높이기 위해 아바타를 커뮤니케이션의 전달매체로 활용하는 경향이 있다. 본 연구는 아바타가 웹사이트 간 신뢰전이에 어떻게 영향을 미치고, 아바타 유무와 아바타 유형에 따라 신뢰전이가 어떤 차이가 나는지를 실증분석을 통해 검증하였다. 첫째, SERI와 SERI 포럼 사이트간에 신뢰전이가 부분적으로 발생하고 있으며, SERI 포럼 내에서는 변수들간의 모든 가설이 모두 유의하게 검증되었다. 둘째, 아바타 유형에 따라 SERI와 SERI 포럼 사이트간에 신뢰전이가 발생하는데 차이가 나타났다. 셋째, 모(母) 사이트의 신뢰는 자(子) 사이트의 품질요인과 포럼활동 만족도에 직${\cdot}$간접적으로 일부분 영향을 미쳤다. 다만, 중개자 신뢰는 시스템신뢰에는 유의한 영향을 주지 못하며, 피드백 메커니즘은 정보 품질과 시스템품질에 유희한 영향을 주지 못한다.

• Journal of Technology Innovation
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• v.24 no.2
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• pp.1-20
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• 2016

This case is to analyze the structure of technology convergence with social network analysis. More specifically, the convergence structure among input technologies mediated with products is analyzed with 2-mode social network analysis. Products are identified in project's goal and coded as the Korea Standard Industrial Classification. The input technologies are coded as the National Science Technology Classification. The subjects were 401 R&D projects applied to '2012 Convergence Technology Development Project for Small and Medium Businesses' promoted by Korea Technology & Information Promotion Agency for Small and Medium Enterprises. IT sectors had the structure of a particular input technology connected to many products, BT sectors also had a few input technology connected to many products but most were connected to specific products. Therefore We have realized that each convergence area had different convergence structure. There were the difference of connectivity centrality between input technologies and input technologies mediated with products. For IT sectors, the embedded S/W were the highest in both cases. For BT sectors, functional cosmetic development and fermentation technology were the highest in input technologies but fermentation technology was not the highest in input technologies mediated with products. This case defines the convergence based on the real projects and the use for managing and planing projects. Therefore, this case was to make a tool to analyze and design technology convergence projects.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Journal of the Korean association of regional geographers
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• v.16 no.4
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• pp.369-386
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• 2010

Migration usually derived from the gap of spatial efficiency and the attractiveness difference between the origin and the destination puts forth significant impact on the regional dynamics of population. Both migration and regional planning or policy are structurally interconnected, exerting impact each other within the circular loops. In order to observe migration characters in the regional dimension, this research depends on social network analysis(SNA) methods which easily portray interrelationship and its weight between the origin and the destination. In specific, it focuses on the application of centrality indicators in order to understand the in- and out-migration patterns and visualize them with spring graphs, pinpointing the in- and out-migration administrative units. This research deals with three migration patterns in 2001 and 2008, respectively: i) in- and out-migration between the nation and Chungbuk Province; ii) in- and out-migration within Chungcheong Areas; and iii) in- and out-migration within Chungbuk Province. Judging from Chungbuk examples, the highest in-migration was recorded at Heungduck District in terms of in- and out-migration between the nation and Chungbuk Province. Seoul and Gyeonggi were two major origins towards Chungbuk Province. Within Chungcheong Areas, Daejeon produced the highest in-migration in 2001. However, Chungnam took that position in 2008.

• Journal of the military operations research society of Korea
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• v.34 no.1
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• pp.99-115
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• 2008

This paper introduces the concept of knowledge management(KM) and current KM trend(such as community-of-practice, knowledge ecology, and social network analysis) with some successful KM practices, and discusses a chance to apply KM to defense organizations. Although defense organizations have several unique characteristics which differ from other private organizations such as critical security issues, implementing KM initiatives for them seems essential. Implications and future research directions are also discussed.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.183-200
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• 2009

Collaborative Filtering is one of the most used recommender systems. However, basically it cannot be used to recommend new products to customers because it finds products only based on the purchasing history of each customer. In order to cope with this shortcoming, many researchers have proposed the hybrid recommender system, which is a combination of collaborative filtering and content-based filtering. Content-based filtering recommends the products whose attributes are similar to those of the products that the target customers prefer. However, the hybrid method is used only for the limited categories of products such as music and movie, which are the products whose attributes are easily extracted. Therefore it is essential to find a more effective approach to recommend to customers new products in any category. In this study, we propose a new recommendation method which applies centrality concept widely used to analyze the relational and structural characteristics in social network analysis. The new products are recommended to the customers who are highly likely to buy the products, based on the analysis of the relationships among products by using centrality. The recommendation process consists of following four steps; purchase similarity analysis, product network construction, centrality analysis, and new product recommendation. In order to evaluate the performance of this proposed method, sales data from H department store, one of the well.known department stores in Korea, is used.