• Knowledge Management Research
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• v.17 no.3
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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.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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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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• v.81 no.5
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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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• 1996.10b
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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.

• Journal of Technology Innovation
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• v.30 no.2
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• pp.29-58
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• 2022

Startups lack resources and manpower to build internal capabilities to strengthen market competitiveness; external cooperation such as joint research and networking plays is important. In this study, we analyzed the effect of startups' industry-university cooperation on organizational learning capability and innovation performance. Empirical results demonstrate the mechanism by which government R&D investment strengthens organizational learning capability and creates innovative results by promoting cooperation between startups and universities. First, industry-university cooperation strengthened organizational learning capability. An empirical analysis shows that startups increase internal capabilities through external cooperation. Second, startups' organizational learning capability had a significant effect on innovation performance. We analyze how organizations with high learning capabilities positively develop corporate innovation performance by having a culture of discovery and sharing new ideas. Finally, industry-university cooperation had different effects on organizational learning capability and innovation performance according to the previous experiences of startup founders. In particular, small- and medium-sized (startup) businesses and individual-based experience groups positively affected the creation of organizational learning capabilities and innovation performance through industry-university cooperation. Small- and medium-sized businesses and individual founders have a relatively small cooperative network with the outside world compared to founders of large companies, universities, and research institutes; therefore, they strengthen organizational learning capabilities through cooperation with universities. This study demonstrates that government should create policy inducements for cooperation with universities to maximize the R&D performance of startups. Criticism exists that lending support to startups and universities will hinder innovation performance; nevertheless, government investment plays a role in expanding intangible resources such as accumulating technologies, fostering high-quality human resources, and strengthening innovation networks. Therefore, the government should appropriately utilize the its authority to strengthen investment strategies for startup growth.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.4
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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.

• The Journal of the Korea Contents Association
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• v.9 no.2
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• pp.97-105
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• 2009

This paper presents a combination of the generalized Cascade Correlation and generalized Recurrent Cascade Correlation learning algorithms. The new network will be able to grow with vertical or horizontal direction and with recurrent or without recurrent units for the quick solution of the pattern classification problem. The proposed algorithm was tested learning capability with the sigmoidal activation function and hyperbolic tangent activation function on the contact lens and balance scale standard benchmark problems. And results are compared with those obtained with Cascade Correlation and Recurrent Cascade Correlation algorithms. By the learning the new network was composed with the minimal number of the created hidden units and shows quick learning speed. Consequently it will be able to improve a learning capability.

• Journal of Internet Computing and Services
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• v.15 no.1
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• pp.143-156
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• 2014

The smart education has the goal of enhancing the capability of learners in the 21st century and especially address the improvement of the problem solving capability. This smart education based on the growth of smart devices and the effect of dramatical spread requires the ability of problem solving using the smart technology in accordance with time change. As the problem solving learning is a model used mainly for improving the capability of problem solving, this study develops the problem solving learning model focusing on the teaching-learning activity using the smart devices and also applies this model to the school field. As a result, the favorable response that using the smart devices is effective to the problem solving can be obtained. This study can contribute to achieve the goal of the smart education, and later can be effective to the successful smart education in the school field.

• Journal of the Korean Home Economics Association
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• v.50 no.7
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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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• v.14 no.4
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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.