• 디지털산업정보학회논문지
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• 제10권2호
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• pp.157-171
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• 2014

A organization of social enterprises is to achieve various goals such as private interests, the public nature, and social policy. For fulfilling these goals, we have to understand the various success factors. These success factors were shared among peoples. This study explored a position of structure of social network formed by success factors of Social Enterprises with importance. A position within social network defined a number of link connected other nodes. A position is closely associated with to individual's behaviors, opinions and thinking. We used social network analysis with two mode method for explaining feathers of structure of social network formed by success factors shared among peoples. We choose degree centrality for determining a position within social network. Centrality is a key measure in social network analysis. Results is that shared success factors are operation capital(15.15%) totally, and by Buying experience of products of Social Enterprises, Business Compliance(14.39%) and planning(12.88%), and by usage time of smart devices, Business Support(17.05%) and planning(16.10%). and the dominant success factor was not explored.

• 지능정보연구
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• 제20권2호
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• pp.93-107
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• 2014

대부분의 인터넷 쇼핑몰은 자사 고객의 관심 분야를 파악하고 이를 상품 추천에 효과적으로 활용하기 위해 많은 노력을 기울이고 있다. 하지만 고객이 회원 가입 시 직접 입력한 개인 정보는 신뢰하기가 어렵고, 고객의 구매 패턴을 통해 파악한 관심 분야 정보는 자사 사이트 내에 진입한 이후에만 보인 한정된 패턴이라는 측면에서 해당 고객의 다양한 관심분야를 제대로 나타낸다고 보기 어렵다. 이러한 한계를 극복하기 위해 본 연구에서는 고객의 평소 인터넷 사용 기록을 통해 최근 방문 사이트들의 주제를 분석함으로써, 고객의 실제 관심 분야를 파악할 수 있는 방안을 제시하였다. 또한 토픽 분석을 통해 각 사이트의 주제를 도출하고 도출된 주제를 다시 동시 방문자 관점에서 군집화 함으로써, 고객 관점에서 의미가 있는 상위 수준의 새로운 테마를 발굴하기 위한 방법론을 제안하였다. 연구의 특징은 유사주제 중심의 군집화라는 기존 연구와는 달리 사용자 관점의 관심주제 중심 군집화라 할 수 있다. 향후 사용자 중심의 카테고리 설계를 비롯한 새로운 관점의 고객군 정의 등 보다 높은 차원의 마케팅 전략 수립에 활용이 가능할 것으로 기대된다. 사용자 관점의 이슈 군집화 과정은 크롤링, 토픽 분석, 액세스 패턴 분석, 네트워크 병합, 네트워크 변환 및 군집화와 같은 여섯 가지 주요단계로 구성되어있다. 이를 위해 텍스트 마이닝과 소셜 네트워크 분석 기법을 활용한 비정형 텍스트를 기반으로한 빅데이터의 활용 방법을 모색하였다. 제안 방법론의 실무 적용 가능성을 평가하기 위해, 국내 최대 포털 뉴스 사이트의 방문자 2,177명의 1년간 방문 기록과 뉴스기사 대한 분석을 수행하고 그 결과를 요약하여 제시하였다.

• 대한산업공학회지
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• 제42권2호
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• pp.138-150
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• 2016

This study aims to analyze the structure and characteristics of the domestic online game industry using network analysis. In particular, two-mode network analysis is employed to measure the network structure, centrality, and cluster for two types of online game platforms, online games and mobile games, from 1996 to 2014. We also conduct a dynamic analysis to capture the structural changes in the ecosystem by internal and external environmental changes before and after turning point for each online game platform. It is revealed that the online game econsystem has the higher number of clusters and higher concentration ratio than those of mobile game ecosystem. In dynamic analysis, both platforms exhibit similar trends over time with the increasing number of clusters, enlargement of largest cluster's size, and decreasing concentration ratio. This study is expected to provide fruitful implications for strategic decision making of online game companies and policy making for the online game industry.

• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1812-1824
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• 2016

The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.

• International Journal of Control, Automation, and Systems
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• 제4권5호
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• pp.545-558
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• 2006

In this paper, PD-like visual servoing is modified in two ways: a sliding-mode observer is applied to estimate the joint velocities, and a RBF neural network is used to compensate the unknown gravity and friction. Based on Lyapunov method and input--to-state stability theory, we prove that PD-like visual servoing with the sliding mode observer and the neuro compensator is robust stable when the gain of the PD controller is bigger than the upper bounds of the uncertainties. Several simulations are presented to support the theory results.

• International Journal of Aeronautical and Space Sciences
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• 제16권4호
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• pp.510-519
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• 2015

Concerning the issue of high-dimensions, hybrid uncertainties of randomness and intervals including implicit and highly nonlinear limit state function, reliability analysis based on the hybrid uncertainty reliability mode combining with back propagation neural network (HU-BP neural network) is proposed in this paper. Random variables and interval variables are as input layer of the neural network, after the training and approximation of the neural network, the response variables are obtained through the output layer. Reliability index is calculated by solving the optimization model of the most probable point (MPP) searching in the limit state band. Two numerical cases are used to demonstrate the method proposed in this paper, and finally the method is employed to solving an engineering problem of the aerospace friction plate. For this high nonlinear, small failure probability problem with interval variables, this method could achieve a good analysis result.

• 지식경영연구
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• 제24권4호
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• pp.177-193
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• 2023

이 연구는 대기업의 경쟁력 조직문화를 형성하는 데 있어 조직문화, 직무 특성, 일의 가치와 지향성이 어떤 역할을 하는지 살펴보고자 하였다. MR-QAP 분석을 통해 이러한 변인들이 조직문화 프로파일 중 경쟁력 속성에 미치는 영향을 살펴보는 것을 연구목적으로 하였다. 이를 위해 국내 본사를 둔 완성차 기업의 15개 다양한 팀에 속한 161명으로부터 수집된 데이터를 활용하였다. 연구 결과, 경쟁력 있는 조직문화와 연결된 네트워크 특성이 경쟁력에 미치는 영향을 밝혔다. 직무 특성 중 과업 다양성과 피드백은 경쟁력 있는 조직문화에 유의한 정적 영향을 미치는 것으로 나타났고, 지각된 현재 조직 문화의 조직구성원 간 불일치가 경쟁력에 부정적인 영향을 미칠 가능성이 있는 것으로 나타났다. 조직문화 프로파일 중에서는 사회적 책임, 지지성, 혁신, 성과지향성이 경쟁력 있는 조직문화에 유의한 정적 영향을 보이는 반면, 보상중시성과 안정성은 유의한 영향을 미치지 않는 것으로 나타났다. 이러한 연구결과를 토대로 조직문화의 복잡한 역학관계를 이해하고, 조직 경쟁력을 증진시키기 위한 전략적 접근을 도모하며 실무적 시사점을 제공하였다.

• International Journal of Control, Automation, and Systems
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• 제4권3호
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• pp.382-393
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• 2006

Mechanical system dynamics plays an important role in the area of computational structural biology. Elastic network models (ENMs) for macromolecules (e.g., polymers, proteins, and nucleic acids such as DNA and RNA) have been developed to understand the relationship between their structure and biological function. For example. a protein, which is basically a folded polypeptide chain, can be simply modeled as a mass-spring system from the mechanical viewpoint. Since the conformational flexibility of a protein is dominantly subject to its chemical bond interactions (e.g., covalent bonds, salt bridges, and hydrogen bonds), these constraints can be modeled as linear spring connections between spatially proximal representatives in a variety of coarse-grained ENMs. Coarse-graining approaches enable one to simulate harmonic and anharmonic motions of large macromolecules in a PC, while all-atom based molecular dynamics (MD) simulation has been conventionally performed with an aid of supercomputer. A harmonic analysis of a macroscopic mechanical system, called normal mode analysis, has been adopted to analyze thermal fluctuations of a microscopic biological system around its equilibrium state. Furthermore, a structure-based system optimization, called elastic network interpolation, has been developed to predict nonlinear transition (or folding) pathways between two different functional states of a same macromolecule. The good agreement of simulation and experiment allows the employment of coarse-grained ENMs as a versatile tool for the study of macromolecular dynamics.

• Journal of Mechanical Science and Technology
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• 제20권2호
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• pp.251-261
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• 2006

The objective of this work is to apply artificial neural networks for solving inverse problems in the structural optimization of a fiber optic pressure sensor. For the sensor under investigation to achieve a desired accuracy, the change in the distance between the tips of the two fibers due to the applied pressure should not interfere with the phase change due to the change in the density of the air between the two fibers. Therefore, accurate dynamic analysis and structural optimization of the sensor is essential to ensure the accuracy of the measurements provided by the sensor. To this end, a normal mode analysis and a transient response analysis of the sensor were performed by combining commercial finite element analysis package, MSC/NASTRAN, and MATLAB. Furthermore, a parametric study on the design of the sensor was performed to minimize the size of the sensor while fulfilling a number of constraints. In performing the parametric study, the need for a relationship between the design parameters and the response of the sensor was fulfilled by using a neural network. The whole process of the dynamic analysis using commercial finite element analysis package and the parameter optimization of the sensor were automated within the MATLAB environment.

• 한국IT서비스학회지
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• 제16권3호
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.