• 지식경영연구
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• 제17권3호
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• pp.227-248
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• 2016

Customer satisfaction dominates research on customer-firm performance relationships; however, with a few exceptions, the authors of most prior studies did not examine the possibility that an organizations' customer relationship management can increase its knowledge management. Building on previous literature of information processing theory and transaction cost perspective, this paper investigates the effect of various characteristics of customer relationship an organization cultivates on its own innovativeness. Specifically, we identify closeness, communication, sympathy as three critical components of managing customer relationship. Data from a multi-informant survey conducted to 442 organizations in Korean bank industry show that an organization's relationship with its customers has significant effects on its innovativeness. This study highlights the importance of customer relationship in terms of enhancing innovations, and helps to explain interactive effects among customer relationship, organizational learning, and innovativeness.

• 한국컴퓨터정보학회논문지
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• 제23권9호
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• pp.7-13
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• 2018

Recent advancement in data gathering technique improves the capability of information collecting, thus allowing the learning process between gathered data patterns and application sub-tasks. A pattern can be associated with multiple labels, demanding multi-label learning capability, resulting in significant attention to multi-label feature selection since it can improve multi-label learning accuracy. However, existing evolutionary multi-label feature selection methods suffer from ineffective search process. In this study, we propose a evolutionary search process for the task of multi-label feature selection problem. The proposed method creates large set of offspring or new feature subsets and then retains the most promising feature subset. Experimental results demonstrate that the proposed method can identify feature subsets giving good multi-label classification accuracy much faster than conventional methods.

• Structural Engineering and Mechanics
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• 제81권5호
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• pp.647-664
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• 2022

The powerful data mapping capability of computational deep learning methods has been recently explored in academic works to develop strategies for structural health monitoring through appropriate characterization of dynamic responses. In many cases, these studies concern laboratory prototypes and finite element models to validate the proposed methodologies. Therefore, the present work aims to investigate the capability of a deep learning algorithm called Sparse Autoencoder (SAE) specifically focused on detecting structural alterations in real-case studies. The idea is to characterize the dynamic responses via SAE models and, subsequently, to detect the onset of abnormal behavior through the Shewhart T control chart, calculated with SAE extracted features. The anomaly detection approach is exemplified using data from the Z24 bridge, a classical benchmark, and data from the continuous monitoring of the San Vittore bell-tower, Italy. In both cases, the influence of temperature is also evaluated. The proposed approach achieved good performance, detecting structural changes even under temperature variations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.944-947
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• 1996

Realization of autonomous agents that organize their own internal structure in order to behave adequately with respect to their goals and the world is the ultimate goal of AI and Robotics. Reinforcement learning gas recently been receiving increased attention as a method for robot learning with little or no a priori knowledge and higher capability of reactive and adaptive behaviors. In this paper, we present a method of reinforcement learning by which a multi robots learn to move to goal. The results of computer simulations are given.

• 기술혁신연구
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• 제30권2호
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• pp.29-58
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• 2022

벤처기업은 경쟁력 강화를 위한 내부 역량 구축에는 자원과 인력이 부족하기 때문에 공동연구, 네트워킹 등 외부와의 협력이 중요한 역할을 하고 있다. 이에 본 논문에서는 벤처기업의 산학협력 경험이 조직학습역량과 혁신성과에 미치는 영향에 대하여 살펴보고자 하였다. 지속적으로 확대되고 있는 정부 R&D 투자가 벤처기업과 대학의 협력을 촉진함으로써 조직학습역량을 강화하고 혁신성과를 창출하는 메커니즘을 실증 분석하였으며 연구결과는 다음과 같다. 첫째, 벤처기업의 산학협력 경험은 조직학습역량을 강화시키는 것으로 나타났다. 벤처기업은 대학과의 협력 및 자원 활용을 통해 내부 역량 강화에 중요한 역할을 하고 있음을 실증분석 한 것이다. 둘째, 벤처기업의 조직학습역량은 혁신성과에 유의한 영향을 미쳤다. 조직학습역량이 높은 조직은 새로운 아이디어를 발굴하고 공유하는 문화를 가지게 됨으로써 기업의 혁신성과 창출에도 긍정적인 역할을 하는 것으로 나타났다. 마지막으로 벤처기업 창업자의 배태조직(incubator organization)에 따른 산학협력과 조직학습역량을 분석한 결과 중소(벤처)기업 및 개인 경험 기반의 창업 그룹이 대학과의 협력을 통해 조직학습역량과 혁신성과 창출에 긍정적인 영향을 미치는 것으로 나타났다. 중소(벤처)기업과 개인기반의 창업자는 대기업, 대학 및 연구소 창업자에 비해 상대적으로 더 높은 기술역량을 보유한 대학과 협력함으로써 기업의 조직학습역량 강화에 도움을 받은 것으로 볼 수 있다. 본 연구를 통해 정부는 벤처기업의 R&D 성과를 극대화하기 위해 대학과 협력 유도하는 정책이 필요할 것이다. 물론 벤처기업과 대학에 나눠주기식 지원이 혁신성과를 저해하고 있다는 비판도 존재하지만 정부 투자는 기술 축적, 고급인력 양성, 혁신 네트워크 강화 등 무형자원 확충에 중요한 역할을 한다. 그렇기에 정부는 벤처기업의 성장을 위한 투자 전략성 강화를 위해 정부의 권한을 적절하게 활용해야 할 것이다.

• 한국경영과학회지
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• 제40권4호
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• pp.1-18
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• 2015

This paper proposes relational capability as a main driver of constructing inter-organizational collaboration networks. Based on social network theory and relational view literature, three components of relational capability are constructed and implemented by an agent-based model. The components include organizational capability, structural capability, and trust between a partner and a focal firm. These three components are updated by two micro mechanisms: structural mechanism and relational mechanism. Structural mechanism is a feedback loop in which the relational capability increases structural capability and vice versa. Relational mechanism is a learning-by-doing process in which a focal firm experiences success or failure of collaboration and the experience increases or decreases cumulative trust in a partner firm. Result of agent-based simulation shows that a collaboration network emerges through interactions of firm's relational capabilities and the characteristics of emerged networks vary with the contribution of structural capability and trust to relational capability. Specifically, in case structural capability contributes more to relational capability, the average degree centrality and collaboration proportion increases as time passes and enters into an equilibrium state. In that case, almost every firms participated in the network collaborates each other so that the emerged network becomes highly cohesive. In case trust contributes more to relational capability, the results are reversed. In an equilibrium state, the balance of contribution between structural capability and trust makes an emerged network larger and maximizes average degree centrality of the network.

• 한국콘텐츠학회논문지
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• 제9권2호
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• pp.97-105
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• 2009

본 논문에서는 일반화된 캐스케이드 코릴레이션 학습 알고리즘과 일반화된 순환 캐스케이드 코릴레이션 학습 알고리즘의 결합을 통한 새로운 알고리즘을 소개한다. 이 새로운 알고리즘은 패턴분류문제(pattern classification problem)의 신속한 해결을 위하여 비순환 뉴런이 유리한지 순환 뉴런이 유리한지 또는 수직성장이 유리한지 수평성장이 유리한지 고민할 필요 없이 후보뉴런의 학습 중에 네트워크의 구성을 스스로 결정한다. 이 알고리즘의 성능평가를 위하여 학습 알고리즘에서 중요한 기준 문제(benchmark problem) 중의 하나인 콘택트렌즈 문제(Contact lens problem)와 밸런스 스케일 문제 (Balance scale problem)에 대하여 실험하였고 기존의 캐스케이드 코릴레이션 알고리즘 및 순환 캐스케이드 코릴레이션 알고리즘과 성능을 비교 하였다. 이 실험에서 활성화 함수는 일반적으로 많이 사용하는 시그모이드 함수(sigmoidal function) 와 하이퍼볼릭탄젠트 함수(hyperbolic tangent function)를 사용하였다. 이 새로운 알고리즘은 학습을 통하여 기존의 알고리즘보다 적은 수의 은닉뉴런을 생성하여 보다 빠른 학습 속도를 보여주었다.

• 인터넷정보학회논문지
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• 제15권1호
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• pp.143-156
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• 2014

스마트교육은 21세기 학습자 역량 신장을 목표로 삼고 있으며, 그중에서 특히 문제해결력 향상을 강조하고 있다. 이러한 스마트교육은 스마트기기의 발전과 폭발적인 보급의 영향이 그 기저를 차지하고 있으며, 시대의 변화에 따라 스마트 테크놀로지를 활용한 문제해결력이 요구된다. 문제해결학습은 학생들의 문제해결력 향상에 초점을 맞추어 사용된 모형으로 본 연구에서는 문제해결력 향상을 극대화하기 위해 스마트기기를 활용한 교수 학습 활동 중심의 문제해결학습 모형을 구안하고 이를 학교 현장에 적용하였다. 그 결과 스마트기기의 활용이 문제해결에 많은 도움이 되었다는 긍정적인 반응을 얻을 수 있다. 본 연구를 통해 스마트교육이 추구 하고자 하는 목표를 달성하는데 기여하고, 향후 학교 현장에서 성공적인 스마트교육이 되기 위한 기초 연구가 되기를 기대한다.

• 대한가정학회지
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• 제50권7호
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• pp.1-12
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• 2012

The purpose of this study was to identify the factor structure of self-efficacy, consumer behavior, and the learning effects of the unit 'Consumption Life' and to study the effects of self-efficacy on the consumer behavior, and the learning effects of the unit 'Consumption Life'. Questionnaires were administered to 370 female middle school students living in the Metropolitan City of Daegu. The data was analyzed by using the frequency, descriptive statistics, factor analysis, reliability analysis, multiple regression, and t-test. The findings are as follow. Self-efficacy was composed of five factors such as the capability in work performance, rational performance, fear, anxiety, and the ability to challenge oneself. Consumer behavior was composed of five factors such as emphasis on product display, emphasis on information, emphasis on fashion, emphasis on appearance, and the products/information exchange. The learning effects of the unit 'Consumption Life' was composed of two factors in the economical consumption, and rational consumption. The effects of consumer behavior and the learning effects of the unit 'Consumption Life' on each of the self-efficacy factors like the capability in work performance, rational performance, fear, anxiety, the ability to challenge oneself were explained by factors such as emphasis on product display, emphasis on information, emphasis on fashion, emphasis on appearance and products/information exchange, and economical consumption and rational consumption.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권4호
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• pp.276-287
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• 2014

Neural fuzzy system (NFS) is basically a fuzzy system that has been equipped with learning capability adapted from the learning idea used in neural networks. Due to their outstanding system modeling capability, NFS have been widely employed in various applications. In this article, we intend to discuss several ideas regarding the learning of NFS for modeling systems. The first issue discussed here is about structure learning techniques. Various ideas used in the literature are introduced and discussed. The second issue is about the use of recurrent networks in NFS to model dynamic systems. The discussion about the performance of such systems will be given. It can be found that such a delay feedback can only bring one order to the system not all possible order as claimed in the literature. Finally, the mechanisms and relative learning performance of with the use of the recursive least squares (RLS) algorithm are reported and discussed. The analyses will be on the effects of interactions among rules. Two kinds of systems are considered. They are the strict rules and generalized rules and have difference variances for membership functions. With those observations in our study, several suggestions regarding the use of the RLS algorithm in NFS are presented.