• 한국통신학회논문지
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• 제21권1호
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• pp.251-268
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• 1996

In this paper, a higher-order feedforward neural network called ridge polynomial network (RPN) which shows good approximation capability for nonlnear continuous functions defined on compact subsets in multi-dimensional Euclidean spaces, is presented. This network provides more efficient and regular structure as compared to ordinary higher-order feedforward networks based on Gabor-Kolmogrov polynomial expansions, while maintating their fast learning property. the ridge polynomial network is a generalization of the pi-sigma network (PSN) and uses a specialform of ridge polynomials. It is shown that any multivariate polynomial can be exactly represented in this form, and thus realized by a RPN. The approximation capability of the RPNs for arbitrary continuous functions is shown by this representation theorem and the classical weierstrass polynomial approximation theorem. The RPN provides a natural mechanism for incremental function approximation based on learning algorithm of the PSN. Simulation results on several applications such as multivariate function approximation and pattern classification assert nonlinear approximation capability of the RPN.

• 지식경영연구
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• 제5권2호
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• pp.25-51
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• 2004

The purpose of this study is to investigate the knowledge creating pattern of technology catching-up and pioneering phase in the new product development process. This paper first reviews the knowledge conversion, absorptive capability, learning orientation and the trigger of learning. The paper then presents the integrative model of knowledge creating. Based on the integrative model, in-depth case analysis was conducted on the new product development process in Phicom. As a result, the paper discuss that the pattern of absorptive capability building, knowledge conversion and knowledge transfer is different from technology catching-up and pioneering phase. Finally, the implications and limitations of the study are discussed.

• 지식경영연구
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• 제2권1호
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• pp.109-132
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• 2001

The purpose of this article is to develop a dynamic model of organizational capabilities and knowledge creation, and at the same time identify the organizational capability factors for effective knowledge creation, by empirically analyzing the history of new Quinolone antibacterial drug compound (LB20304a) discovery process at LG, as a case in point. Major findings of this study are as follows. First, in a science-based area such as drug development, the core of successful knowledge creation lies in creative combination of different bodies of scientific explicit knowledge. Second, the greater the difficulty of learning external knowledge, the more tacit knowledge is needed for the recipient firm to effectively exploit that knowledge. Third, in science-based sector such as pharmaceutical industry, the key for successful knowledge creation lies in the capability of recruiting and retaining star scientists. Finally, for effective knowledge creation, a firm must keep its balance among three dimensions of organizational capabilities: local, process, architectural capabilities.

• 한국컴퓨터정보학회논문지
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• 제21권1호
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• pp.169-180
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• 2016

In this paper, we propose a CT-SPI Model based on a physical computing for improving Computational Thinking(CT) for elementary students. This study analyzes the results obtained from applying the CT-SPI model to fourth-grade class. The model is designed to embrace the learning activities and CT elements for three different stages: System thinking, Prototyping and Interaction. For providing curriculum with CT-SPI model, physical SW educational module is developed, so that learners could focus on internalizing CT. The study results indicate that a learning satisfaction and a degree of learner interest improve significantly. Comprehensive CT capability assessment results in three levels show that the capability in lowest level (score is below 4 out of 10) has decreased by 46.6% but capability in highest level (over 7 out of 10) has increased by 20%.

• The Journal of Asian Finance, Economics and Business
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• 제8권7호
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• pp.203-213
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• 2021

This paper aims to analyze the influence of innovation capabilities on the relationship between strategic orientation and the performance of women small and medium entrepreneurs (SMEs). The strategic orientation in this study used three constructs, namely market orientation, learning orientation, and technology orientation. The method of data collection was the survey method and was collected from 149 SMEs in the typical culinary industry in Indonesia using probability samples. The data analysis method uses path analysis. The results showed that the influence of strategic orientation on business performance has strengthened previous studies. However, in this study strategic orientation that directly affects business performance is market orientation and learning orientation, while technology orientation has no direct effect. The existence of innovation capability as a mediation variable strengthens the influence of strategic orientation on business performance. The results of this study also showed that the ability of innovation has a positive and significant effect on the performance of women entrepreneurs in Indonesia. The contribution of this research pays special attention to the strategic orientation of women entrepreneurs engaged in the culinary business in Indonesia.

• 전자공학회논문지B
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• 제31B권1호
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• pp.107-114
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• 1994

Hardware implementation of the on-chip learning artificial neural networks is important for real-time processing. A pRAM model is based on probabilistic firing of a biological neuron and can be implemented in the VLSI circuit with learning capability. We derive a backpropagation learning algorithm for the pRAM networks and present its circuit implementation with stochastic computation. The simulation results confirm the good convergence of the learning algorithm for the pRAM networks.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1950-1955
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• 1991

A fuzzy dynamic learning controller is proposed and applied to control of time delayed, non-linear and unstable chemical processes. The proposed fuzzy dynamic learning controller can self-adjust its fuzzy control rules using the external dynamic information from the process during on-line control and it can create th,, new fuzzy control rules autonomously using its learning capability from past control trends. The proposed controller shows better performance than the conventional fuzzy logic controller and the fuzzy self organizing controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.402-406
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• 1989

Since the control parameters of classical PID controller are fixed for all control period, it is not easy to produce a desired transition phenomena. We incorporate an iterative learning scheme to the linear controller so that it has more flexibility and adaptation capability especially in the transition period. In this paper a hybrid type learning controller is proposed in which fixed linear controller guides learning at the beginning stage. Once a perfect learning is achieved, then the control action is performed by only the learning controller. A computer simulation result demonstrates better performance during transition time than that with only linear PD controller.

• 정보교육학회논문지
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• 제10권1호
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• pp.143-152
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• 2006

기업에서 실제로 수행된 블렌디드 학습(Blended Learning)의 효과성을 규명해보고 이를 토대로 온라인 학습과 오프라인 학습을 효과적으로 병행하기 위한 시사점을 도출하고자 본 연구가 수행되었다. 연구 목적을 달성하기 위해 블렌디드 학습의 효과성에 대한 설명적 혼합설계 방식으로 체제적 분석을 시도하였다. 블렌디드 학습의 효과에 대한 양적 분석 결과, 종합진단과 타인진단에서는 블렌디드 학습이 리더십 역량을 향상시키는데 유의미한 영향을 미치는 것으로 나타났고, 사후 설문조사 및 핵심 면담대상자집단과의 면담 결과 블렌디드 학습의 유용성을 확인할 수 있었다. 이러한 연구 결과를 바탕으로, 블렌디드 학습 방식으로 운영한 프로그램의 분석을 통해 블렌디드 학습 효과에 영향을 주는 특성을 분석하고, 블렌디드 학습의 효과성 검증에 대한 방향을 제시하고자 하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.49.4-49
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• 2001

Digit recognition based on backpropagation neural networks, as an important application of pattern recognition, was attracted much attention. Although it has the advantages of parallel calculation, high error-tolerance, and learning capability, better recognition effects can only be achieved with some specific fixed format input of the digit image. Therefore, digit image preprocessing ability directly affects the accuracy of recognition. Here using Matlab software, the digit image was enhanced by resizing and neutral-rotating the extracted digit image, which improved the digit recognition capability of the backpropagation neural network under practical conditions. This method may also be helpful for recognition of other patterns with backpropagation neural networks.