• 한국정보전자통신기술학회논문지
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• 제16권4호
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• pp.203-209
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• 2023

Harmony search(HS)는 새로운 하모니를 구성할 때 HM을 참조하는 경우 개별 하모니의 평가를 이용하지 않지만 PSO(particle swarm optimization)는 개별 입자의 평가와 모집단의 평가를 이용하여 해를 찾아간다. 그러나 본 연구에서는 HS와 PSO의 유사점을 찾아 PSO의 입자 개선 과정을 HS에 적용하여 알고리즘의 성능을 향상시키고자 하였다. PSO 알고리즘을 적용하기 위해서는 개별 입자의 local best와 떼(swam)의 global best가 필요하다. 본 연구에서는 HS가 harmony memory(HM)에서 가장 나쁜 하모니을 개선하는 과정을 PSO와 매우 유사한 과정으로 보았다. 이에 따라 HM의 가장 나쁜 하모니를 입자의 PSO의 local best로, 가장 좋은 하모니는 PSO의 global best 최고로 간주하였다. 이와 같이 PSO의 입자 개선과정을 HS 하모니 개선과정에 도입하여 HS의 성능을 향상시킬 수 있었다. 본 연구의 결과는 다양한 함수에 대한 최적화 예시를 통해 비교 확인하였다. 그 결과 정확성과 일관성에 있어 기존 HS보다 제안한 HS-PSO가 매우 우수함을 알 수 있었다.

• Asia Marketing Journal
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• 제21권2호
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• pp.51-71
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• 2019

L&P Cosmetic, the leading company selling mask packs on the global market, produces MEDIHEAL, the number-one best-selling mask pack brand in Korea and the best-selling imported mask pack brand in China (2017). The company pioneered the premium market for mask packs through its launch of premium mask packs in 2009, and has subsequently achieved outstanding success in Korea and China. Three key factors have contributed to the success of L&P Cosmetic: product leadership with R&D capability, strategic marketing programs tailored for each market segment, and operational excellence focusing on strategic outsourcing and partnership management. Nonetheless, globalization beyond the Chinese market remains a major challenge for the potential of L&P Cosmetic. The company has embarked upon a collaboration with BTS, the world's top K-pop stars, as an optimally effective way to achieve its goals and a highly efficient strategy to manage the risks of globalization. The global branding collaboration project with BTS has succeeded in generating primary demand for mask packs on the global market, spreading brand awareness of MEDIHEAL, and establishing global channel networks. L&P Cosmetic will continue to grow worldwide on the basis of this outstanding performance.

• Structural Engineering and Mechanics
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• 제42권6호
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• pp.783-797
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• 2012

A new hybrid meta-heuristic optimization algorithm is presented for design of structures. The algorithm is based on the concepts of the charged system search (CSS) and the particle swarm optimization (PSO) algorithms. The CSS is inspired by the Coulomb and Gauss's laws of electrostatics in physics, the governing laws of motion from the Newtonian mechanics, and the PSO is based on the swarm intelligence and utilizes the information of the best fitness historically achieved by the particles (local best) and by the best among all the particles (global best). In the new hybrid algorithm, each agent is affected by local and global best positions stored in the charged memory considering the governing laws of electrical physics. Three different types of structures are optimized as the numerical examples with the new algorithm. Comparison of the results of the hybrid algorithm with those of other meta-heuristic algorithms proves the robustness of the new algorithm.

• 한국기후변화학회지
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• 제9권2호
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• pp.133-142
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• 2018

Climate change will affect not only the crop productivity but also the pattern of rice disease epidemics in Korea. Impact assessments for the climate change are conducted using various climate change scenarios from many global climate models (GCM), such as a scenario from a best GCM or scenarios from multiple GCMs, or a combination of both. Here, we evaluated the feasibility of using a climate change scenario from the best GCM for the impact assessment on the potential epidemics of a rice leaf blast disease in Korea, in comparison to a multi?model ensemble (MME) scenario from multiple GCMs. For this, this study involves analyses of disease simulation using an epidemiological model, EPIRICE?LB, which was validated for Korean rice paddy fields. We then assessed likely changes in disease epidemics using the best GCM selected for individual agro?climatic zones and MME scenarios constructed by running 11 GCMs. As a result, the simulated incidence of leaf blast epidemics gradually decreased over the future periods both from the best GCM and MME. The results from this study emphasized that the best GCM selection approach resulted in comparable performance to the MME approach for the climate change impact assessment on rice leaf blast epidemic in Korea.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.817-822
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• 2004

This paper provides a global path planning method using self-organizing feature map which is a method among a number of neural network. The self-organizing feature map uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• 한국정밀공학회지
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• 제23권3호
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• pp.94-101
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• 2006

A global path planning method using self-organizing feature map which is a method among a number of neural network is presented. The self-organizing feature map uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are moved toward the input vector On the other hand, the modified method in this research uses a predetermined initial weight vectors of 1-dimensional string and 2-dimensional mesh, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are moved toward the input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• 제어로봇시스템학회논문지
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• 제12권5호
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• pp.473-479
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• 2006

A global path planning algorithm using modified self-organizing feature map(SOFM) which is a method among a number of neural network is presented. The SOFM uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors of the 2-dimensional mesh, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the opposite direction of input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• 수산경영론집
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• 제43권2호
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• pp.95-106
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• 2012

This study analyzed the changes in productivity growth of 291 regional fisheries cooperatives area from 2001 to 2010 selected as target. The productivity growth analysis of operating offices calculates Global-Malmquist productivity index. Input variables are number of the persons and the nettable area, output variables are deposit, loans and earnings. To improve the homogeneity of industry, the operating conditions were considered. Global Malmquist index of Operating offices was reduced between 2001~2010. The cause of increase and decrease of productivity are divided by efficiency change(EC) and best-practice change(BPC). Operating offices with increased productivity existed between 2001~2002 and between 2002~2003 and between 2006~2007. There were operating offices with increased productivity by EC. Global Malmquist index of Operating offices with locations was highest relatively in metropolitan. Operating offices with increased productivity existed between 2003~2004 and between 2007~2008 and between 2008~2009 in all locations. There were operating offices with decreased productivity by BPC.

• 산업경영시스템학회지
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• 제41권2호
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• pp.32-46
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• 2018

Research in the Servitization of Manufacturing has become focused more on cases than concepts over the past decade. This is because governments have emphasized some practical research with policies to support their industries. Manufacturers need differentiated strategies to gain competitiveness by servitization in the global market. They should examine how common servitization has become in the same industries and markets. They should also make sure how it works and for what purpose it is done since it is necessary to make the best decision to be able to distribute the limited resources most effectively to defeat the global competitors. South Korea has the sixth largest trade volume in the world, but Korean SMEs' marketing capabilities fall short compared to that of major global companies. This paper seeks to develop the proper model and its application for the servitization with global cases which are recommended for Korean SMEs. They need to check the urgency in servitization according to their products, industries and target markets. In addition, factors such as purposes, time and types of the servitization are examined to see how they are related each other. The most significant implication of this study is that the processes for early-stage companies in servitazation are modeled to help them make the best choices.

• 제어로봇시스템학회논문지
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• 제11권2호
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• pp.137-143
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• 2005

Autonomous mobile robot has an ability to navigate using both map in known environment and sensors for detecting obstacles in unknown environment. In general, autonomous mobile robot navigates by global path planning on the basis of already made map and local path planning on the basis of various kinds of sensors to avoid abrupt obstacles. This paper provides a global path planning method using self-organizing feature map which is a method among a number of neural network. The self-organizing feature map uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.