• Structural Engineering and Mechanics
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• 제46권3호
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• pp.403-416
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• 2013

This paper describes new optimization strategy that offers significant improvements in performance over existing methods for geometry design of frame structures. In this study, an imperialist competitive algorithm (ICA) and ant colony optimization (ACO) are combined to reach to an efficient algorithm, called Imperialist Competitive Ant Colony Optimization (ICACO). The ICACO applies the ICA for global optimization and the ACO for local search. The results of optimal geometry for three benchmark examples of frame structures, demonstrate the effectiveness and robustness of the new method presented in this work. The results indicate that the new technique has a powerful search strategies due to the modifications made in search module of ICACO. Higher rate of convergence is the superiority of the presented algorithm in comparison with the conventional mathematical methods and non hybrid heuristic methods such as ICA and particle swarm optimization (PSO).

• 지식경영연구
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• 제9권2호
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• pp.1-13
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• 2008

The purpose of this study is to investigate the use of elements related to the competitive intelligence(CI) process, methods, and infrastructure in accordance with the degree of CI effectiveness of domestic firms and to propose a guideline for designing and operating an effective CI program in Korean films. The results of this study reveal that, for the elevation of CI effectiveness of Korean firms, it is important to actively utilize the overall CI process, including undisclosed information through human networks, public information through various media, and quantitative and qualitative analyses, an independent CI unit, various CI support systems, such as information and reward systems, and organizational culture of CI openness within an organization. However, CI outsourcing, CI primary objectives, and CI scale do not seem to have a significant influence on CI effectiveness of Korean firms. Based on these results, this research presents some important implications for effective competitive intelligence for Korean firms.

• 한국도서관정보학회지
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• 제35권2호
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• pp.155-173
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• 2004

이 연구는 21세기 지식기반사회에서 우리나라 전문도서관ㆍ정보센터가 서비스를 향상하고 조직의 경쟁력을 강화하기 위한 방안을 모색하였다. 이를 위해 우선 전문도서관ㆍ정보센터의 특성을 살펴보고 현재 전문도서관ㆍ정보센터가 처한 경쟁환경을 진단하였다. 다음으로 지식경영과 경쟁정보의 관점에서 전문도서관ㆍ정보센터의 경영전략을 모색하였다 나아가 우리 사회의 전문도서관ㆍ정보센터 현실에서 지식경영과 경쟁정보를 활용하기 위한 전략을 제시하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.466-469
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• 1996

In this paper, we present the global minimization condition by an informal analysis of the Langevine competitive learning neural network. From the viewpoint of the stochastic process, it is important that competitive learning guarantees an optimal solution for pattern recognition. By analysis of the Fokker-Plank equation for the proposed neural network, we show that if an energy function has a special pseudo-convexity, Langevine competitive learning can find the global minima. Experimental results for pattern recognition of handwritten numeral data indicate the superiority of the proposed algorithm.

• 전자공학회논문지B
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• 제33B권2호
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• pp.159-167
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• 1996

In this paper, a new competitive learning algorithm called dynamic competitive learning (DCL) is presented. DCL is a supervised learning mehtod that dynamically generates output neuraons and nitializes weight vectors from training patterns. It introduces a new parameter called LOG (limit of garde) to decide whether or not an output neuron is created. In other words, if there exist some neurons in the province of LOG that classify the input vector correctly, then DCL adjusts the weight vector for the neuraon which has the minimum grade. Otherwise, it produces a new output neuron using the given input vector. It is largely learning is not limited only to the winner and the output neurons are dynamically generated int he trining process. In addition, the proposed algorithm has a small number of parameters. Which are easy to be determined and applied to the real problems. Experimental results for patterns recognition of remote sensing data and handwritten numeral data indicate the superiority of dCL in comparison to the conventional competitive learning methods.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.255-258
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• 2001

This introduces a improved method of the forward kinematics analysis, which finds the 6DOF motions and velocities from the given six cylinder lengths in the Steward platform. The numerical method(Newton Raphson Mehotd)of the forward kinematics analysises has the disadvantage of the long calculated time. To overcome this, we propose the competitive method that determine a proper initial value. Through the competitive method, we can select a proper initial value so that the calculate time is reduced. therefore we can give the property of the real time process to the forward kinematics analysis. We show the result comparing between general Newton-Raphson method and proposed one. From the result we verify the performance of the proposed method.

• 한국병원경영학회지
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• 제21권3호
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• pp.53-64
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• 2016

The study empirically examines the classic hypothesis on resource-based view(RBV) theory, which is the possible relationship between competitive advantage and performance. For the study, we have surveyed 198 hospital administrators in Korea. By testing the hypotheses at conceptual level, a more robust approach, we found that (1) if a hospital possesses and exploits resources and capabilities that are both valuable and rare, it will attain a competitive advantage, and (2) the attaintment of such advantage will enable the hospital to improve its performance. The results may be interest to both academics and practitioners. From an academic standpoint, the study more accurately captures the dynamics of the theory by pairing resources-capabilities as opposed to individual resources or capabilities. From a practitioner perspective, it is suggested that hospital managers need no necessarily seek out novel resources and capabilities, but rather develop novel ways in which to combine those resources and capabilities to which they do have access.

• 전자공학회논문지C
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• 제34C권7호
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• pp.82-91
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• 1997

In this paper, we analyze the dynamics of langevine competitive learning neural network based on its fokker-plank equation. From the viewpont of the stochastic differential equation (SDE), langevine competitive learning equation is one of langevine stochastic differential equation and has the diffusin equation on the topological space (.ohm., F, P) with probability measure. We derive the fokker-plank equation from the proposed algorithm and prove by introducing a infinitestimal operator for markov semigroups, that the weight vector in the particular simplex can converge to the globally optimal point under the condition of some convex or pseudo-convex performance measure function. Experimental resutls for pattern recognition of the remote sensing data indicate the superiority of langevine competitive learning neural network in comparison to the conventional competitive learning neural network.

• 한국정보시스템학회지:정보시스템연구
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• 제11권1호
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• pp.105-127
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• 2002

The objective of this study is to analyze factors affecting usage of b2b e-marketplace. For this purpose, we suggest a research framework where the factors are e-business infrastructure, expectation of competitive advantages and perceived barriers with current and future usages as result variables. For empirical test, 219 companies are surveyed and 177 valid ones among them are analyzed. In result, e-business infrastructure and competitive advantage affect to both current and future usage positively and perceived barriers affect to future usage negatively. The results shows the following two: First, many companies expect improvement of competitive advantages such as bargaining power and efficiency by usage of e-marketplace. Second, for more aggressive usage of e-marketplace, it is important not only to build e-business infrastructure but also to resolve perceived barriers.

• 대한산업공학회지
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• 제26권2호
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• pp.101-109
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• 2000

A competitive coevolutionary algorithm is a probabilistic search method that imitates the biological process that two or more species competitively coevolve through evolutionary arms race. The algorithm has been used to efficiently solve adversarial problems that can be formulated as the search for a solution that is correct over a large space of test cases. We develop an efficient competitive coevolutionary algorithm to solve adversarial problems with high complexity. The algorithm developed in this paper employs three methods: tournament competitions, exchanging of entry fee, and localized coevolution. Analyzed in this paper are the effects of the methods on the performance of the proposed algorithm. The extensive experiments show that our algorithm can progress an evolutionary arms race between competitive coevolving species and then outperforms existing approaches to solving the adversarial problems.