• Korean System Dynamics Review
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• v.12 no.2
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• pp.69-94
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• 2011

Daejeon, encompassing Daedeok Science Town and Daedeok Innopolis, possesses the advantage of portraying relatively higher regional innovation capacity and facilitating network formation among regional professional research organizations. Applying the Social Network Analysis(SNA) techniques, this paper focuses on divulging structural and spatial characteristics of the Daejeon Information and Communication(ICT) industry network, analyzing co-research projects implemented by the Daejeon-based universities. For the analytical tool, it depends on NETMINER 3.0. Furthermore, based on the Systems Thinking approaches, this study suggests a couple of policy implications. Judging from the Korea Standard Industrial Classification principles, the existing ICT industry is subdivided into 11 sub-industries. The highest degree centralization value comes from the Mobile Communication sub-industry(188.668%), indicating that Mobile Communication sub-industry exerts the most significant impact on the regional innovation networking in Daejeon. Among various stakeholders, Korea Advanced Institute of Science and Technology(KAIST) records the top ranking in most categories, conspicuously leading the institute-industry linkage. In terms of the ICT spatial distribution, the intra-regional cooperation examples present the strongest linkage values, followed by Daejeon-the Capital Region ones. Finally, as well shown in a series of causal loop analyses, this study recommends that Daejeon should put top policy priority in strengthening the internal ICT network within Daejeon proper. Here, Daejeon should keep in mind the fact that there exist reinforcing loops between Daejeon's attractiveness and the entering of new ICT firms.

• Journal of Korea Game Society
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• v.15 no.1
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• pp.171-184
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• 2015

Looking at the existing game-related research, innumerable researches such as game acceptance, user satisfaction, continuance intention, wold-of-mouse intention have been carried out. However, there was no cases that applying the concept of service recovery in game researches. The purpose of this study is to evaluate the impact on the characteristic of the mobile social network game that based on domestic mobile social network game platform which focusing on KakaoTalk games on the service recovery. In addition, the study analyzed the characteristic factors of mobile social network game on the basis of Organizational Equity Theory and Innovation Diffusion Theory to identified the causal relationship between those factors and service recovery. As a result, except the independent variable-compatibility, all of the characteristics factors of mobile social network game showed direct or indirect effects on service recovery. The results of this study are expected as a reference for presenting a new business model, reducing risk factors and enriching the related research of the gaming industry.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.799-810
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• 2009

전 세계적으로 노령화 문제가 사회적 이슈로 대두되고 있는 가운데, 노인이 느끼는 전반적인 삶의 질 (Quality of Life, QoL) 이 하락하는 문제가 사회 전반에 걸쳐 중요한 문제로 인식되고 있다. 이에 다양한 형태로 발생하고 있는 노인들의 문제를 해결하기 위하여 노인학, 사회 복지학 등의 분야에서 많은 연구가 진행되고 있다. 이 중 노인의 사회적 관계 (Social Network, SN) 를 강화, 확장하여 노인의 삶의 만족도 향상을 추구하고자 하는 연구를 찾아볼 수 있다. 이러한 관점에서 노인의 사회적 관계를 인터넷을 이용해 개선함으로써, 노인의 삶의 질을 향상시키고자 하는 것이 본 연구의 궁극적인 목표이다. 이에 본 연구의 세부적인 목적은 현재 젊은 층을 중심으로 많이 사용되고 있는 SNS 를 노인들이 사용하지 않는 이유에 대한 분석, 노인을 위한 SNS 의 핵심적인 기능적, 서비스적 요소를 파악하는데 있다. 이를 위해 현재 인터넷을 잘 활용하고 있는 60 세 이상의 노인 22 명을 대상으로 개별 인터뷰, 참가자가 사용하는 서비스 분석, FGI 의 3 가지 방식으로 데이터를 수집하였다. 이후 수집한 데이터를 이용하여 정성적 분석 방법으로 Casual Network 를 도출하였으며, 정량적 분석 방법인 Laddering 분석으로 Hierarchical Map 도출하여 비교하였다. 또한 도출된 결과가 노인들만의 특징인지를 파악하기 위하여 대학생, 직장인 각 10 명씩을 대상으로 노인을 대상으로 한 연구 방식과 동일한 방법으로 데이터를 수집, 분석 하였다. 최종적으로 본 연구는 사회감성적 선택 이론 (Socioemotional Selectivity Theory, SST)를 바탕으로 인터넷에서의 Social Network 활동도 오프라인과 유사한 특징을 가지고 있으며, 현재 친하게 지내고 있는 사람들을 중심으로 SN 을 강화시키려 하는 강화형 타입과 새로운 SN을 생성하기 위하여 노력하는 확장형 타입으로 구분할 수 있었다. 추후 이러한 특징들을 반영하여 노인들을 위한 SNS 가 지녀야 할 기능적, 시스템적 요소를 제안하고, 추후 연구 및 SNS 개발과 관련한 계획을 정리하였다.

• Journal of Digital Convergence
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• v.12 no.1
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• pp.37-50
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• 2014

The purpose of this research is to look upon the smartphone market from the perspective of business ecosystems and to extract the critical success factors of OS platforms. Furthermore, this research aims to verify the effect of those factors on increasing utility resulting from the rising number of users as well as on intention of use. In order to do this, OS compatibility and OS upgradability were presented as the major characteristics of OS platforms and a logical causal relationship between network effect and intention to use which shows the increase of utility according to the number of users was established which was then followed by an empirical analysis. The results of the research showed that OS compatibility and OS upgradability both had positive effects on network effect and intention to use. By presenting the characteristics of OS platforms, a subject which has lacked pervious empirical studies, and establishing a logical causal relationship for the role platform characteristics play in the formation of business ecosystem in the smartphone market, it is expected that the findings of this research will contribute greatly not only academically but also in practical applications.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2143-2147
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• 2005

Human-Robot interaction (HRI) has recently become one of the most important issues in the field of robotics. Understanding and predicting the intentions of human users is a major difficulty for robotic programs. In this paper we suggest an interaction method allows the robot to execute the human user's desires in an intelligent room-based domain, even when the user does not give a specific command for the action. To achieve this, we constructed a full system architecture of an intelligent room so that the following were present and sequentially interconnected: decision-making based on the Bayesian belief network, responding to human commands, and generating queries to remove ambiguities. The robot obtained all the necessary information from analyzing the user's condition and the environmental state of the room. This information is then used to evaluate the probabilities of the results coming from the output nodes of the Bayesian belief network, which is composed of the nodes that includes several states, and the causal relationships between them. Our study shows that the suggested system and proposed method would improve a robot's ability to understand human commands, intuit human desires, and predict human intentions resulting in a comfortable intelligent room for the human user.

• Journal of the Korean Society of Safety
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• v.38 no.4
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• pp.54-59
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• 2023

The relationship between near misses and major accidents can be confirmed using the ratios proposed by Heinrich and Bird. Systematic reviews of previous national and international studies did not reveal the assessment process used in near-miss management systems. In this study, a model was developed for assessing near misses and major factors were derived through case application. By reviewing national and international literature, 14 factors were selected for each dimension of the P2T (people, procedure, technology) model. To identify the causal relationship between accidents and these factors, a near-miss assessment model was developed using a Bayesian network. In addition, a sensitivity analysis was conducted to derive the major factors. To verify the validity of the model, near-miss data obtained from the ethylene production process were applied. As a result, "PE2 (education)," "PR1 (procedure)," and "TE1 (equipment and facility not installed)" were derived as the major factors causing near misses in this process. If actual workplace data are applied to the near-miss assessment model developed in this study, results that are unique to the workplace can be confirmed. In addition, scientific safety management is possible only when priority is given through sensitivity analysis.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.689-697
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• 2024

According to the South Korean Ministry of Land, Infrastructure, and Transport, instances of defect dispute resolutions, primarily between construction contractors and apartment occupants, have been occurring at an annual average of over 4,000 cases since 2014 to the present day. To address the persistent issue of disputes between contractors and occupants regarding construction defects, it is crucial to use customer sentiment analysis to improve customer rights and guide construction companies in their efforts. This study presents a methodology for effectively managing customer complaints and enhancing feedback analysis in the context of defect repair services. The study begins with collecting and preprocessing customer feedback data. Semantic network analysis is used to understand the causes of discomfort in customer feedback, revealing insights into the emotional sentiments expressed by customers and identifying causal relationships between emotions and themes. This research combines text mining, and semantic network analysis to analyze customer feedback for decision-making. By doing so, defect repair service providers can improve service quality, address customer concerns promptly, and understand the factors behind emotional responses in customer feedback. Through data-driven decision-making, these providers can enhance customer rights and identify areas for construction companies to improve service quality.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.11-20
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• 2024

While artificial neural networks are adept at identifying patterns, they can struggle to distinguish between actual correlations and false associations between extracted facial features and criminal behavior within the training data. These associations may not indicate causal connections. Socioeconomic factors, ethnicity, or even chance occurrences in the data can influence both facial features and criminal activity. Consequently, the artificial neural network might identify linked features without understanding the underlying cause. This raises concerns about incorrect linkages and potential misclassification of individuals based on features unrelated to criminal tendencies. To address this challenge, we propose a novel region-based training approach for artificial neural networks focused on criminal propensity detection. Instead of solely relying on overall facial recognition, the network would systematically analyze each facial feature in isolation. This fine-grained approach would enable the network to identify which specific features hold the strongest correlations with criminal activity within the training data. By focusing on these key features, the network can be optimized for more accurate and reliable criminal propensity prediction. This study examines the effectiveness of various algorithms for criminal propensity classification. We evaluate YOLO versions YOLOv5 and YOLOv8 alongside VGG-16. Our findings indicate that YOLO achieved the highest accuracy 0.93 in classifying criminal and non-criminal facial features. While these results are promising, we acknowledge the need for further research on bias and misclassification in criminal justice applications

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.512-517
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• 2008

It is show that increasing of aged and handicapped people requires development of Ubiquitous computing technique to offer the specialized service for handicapped-people. For this, we need a development of Context Awareness and Service Reasoning Technique that the technique is supplied interaction between user and U-environment instead of the old unilateral relation. The old research of context awareness needed probabilistic presentation model like a Bayesian Network based on expert Systems for recognize given circumstance by a domain of uncertain real world. In this article, we define a domain of disorder activity assistant service application and context model based on ontology in diversified environment and minimized intervention of user and developer. By use this context model, we apply the structure learning of Bayesian Network and decide the service and activity to development of application service for handicapped people. Finally, we define the proper Conditional Probability Table of the structured Bayesian Network and if random situation is given to user, then present state variable of Activity and Service by given Causal relation of Bayesian Network based on Conditional Probability Table and it can be result of context awareness.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.769-777
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• 2021

This study investigated the change of the achievement rate of the target water quality conditioned on the occurrence of severe drought, to assess the effects of meteorological drought on the water quality management in the Nakdong River basin. Using three drought indices with difference time scales such as 30-, 60-, 90-day, i.e., SPI30, SPI60, SPI90, and three water quality indicators such as biochemical oxygen demand (BOD), total organic carbon (TOC), and total phosphorus (T-P), we first analyzed the relationship between severe drought occurrence water quality change in mid-sized watersheds, and identified the watersheds in which water quality was highly affected by severe drought. The Bayesian network models were constructed for the watersheds to probabilistically assess the relationship between severe drought and water quality management. Among 22 mid-sized watersheds in the Nakdong River basin, four watersheds, such as #2005, #2018, #2021, and #2022, had high environmental vulnerability to severe drought. In addition, severe drought affected spring and fall water quality in the watershed #2021, summer water quality in the #2005, and winter water quality in the #2022. The causal relationship between drought and water quality management is usufaul in proactive drought management.