• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.665-666
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• 2022

미분 진화는 연속 최적화 문제에 대한 효율적인 알고리즘이다. 그러나 대규모 최적화 문제를 해결하기 위해 미분 진화를 적용하면 성능이 빠르게 저하되고 런타임이 기하급수적으로 증가한다. 이 문제를 극복하기 위해 Spark(SparkDECC라고 함)를 기반으로 하는 새로운 협력 공진화 미분 진화를 제안한다. 분할 정복 전략은 SparkDECC에서 사용된다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.559-560
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• 2021

차등 진화는 연속 최적화 문제를 해결하기 위한 효율적인 알고리즘이다. 그러나 대규모 최적화 문제를 해결하기 위해 차등 진화를 적용하면 성능이 급격히 저하되고 런타임이 기하급수적으로 증가한다. 따라서 Spark(SparkDECC로 알려짐)를 기반으로 하는 새로운 협력 공진화 차동 진화가 제안된다. 분할 정복 전략은 SparkDECC에서 사용된다.

• 한국도서관정보학회지
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• 제51권1호
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• pp.91-114
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• 2020

ICT 환경의 정착 및 넷 미디어의 광범위한 보급으로 인해 정보접근성에 기반한 양적 정보격차는 정보활용 중심의 질적 정보격차의 형성으로 진화해 왔으며, 인터넷 상에 엄청난 양의 정보가 존재하는 현재의 정보환경에서는 기존의 양적, 질적 정보격차와는 다른 보다 심화된 지적 정보격차가 발생하고 있다. 본 연구에서는 정보환경의 진화에 따른 정보격차의 변화 양상을 확인하고, 지적 정보격차의 형성을 실증적으로 확인하였다. 또한 지적 정보격차는 기존의 양적, 질적 정보격차와 명확하게 구분되는 것이 아니라 기존의 정보격차의 요인들을 기반으로 정보에 대한 지적 능력의 차이로 인해 발생함을 실증하였다. 특히 정보격차의 핵심 요인으로 인식되어 온 교육수준과 경제적 수준은 여전히 양적, 질적 정보격차를 형성하고 있으며, 지적 정보격차의 형성에 있어서도 주요 요인으로 작용하고 있음을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1585-1588
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• 1997

In this paper, divide total workspace of robot manipulator into several subspaces and construct PID controller ineach subspace. Using EvolutionSTrategy we optimize the gains of PID controller in each subspace. But the gains may have a large difference on the boundary of subspaces, which can cause bad oscillatory performance. So we use Aritificial Neural Network to have continuous gain curves htrough the entire subspaces. Simualtion results show that the proposed method is quite useful.

• Journal of information and communication convergence engineering
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• 제19권3호
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• pp.155-160
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• 2021

Differential evolution is an efficient algorithm for solving continuous optimization problems. However, its performance deteriorates rapidly, and the runtime increases exponentially when differential evolution is applied for solving large-scale optimization problems. Hence, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC. First, the large-scale problem is decomposed into several low-dimensional subproblems using the random grouping strategy. Subsequently, each subproblem can be addressed in a parallel manner by exploiting the parallel computation capability of the resilient distributed datasets model in Spark. Finally, the optimal solution of the entire problem is obtained using the cooperation mechanism. The experimental results on 13 high-benchmark functions show that the new algorithm performs well in terms of speedup and scalability. The effectiveness and applicability of the proposed algorithm are verified.

• 대한전기학회논문지:전력기술부문A
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• 제48권8호
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• pp.1014-1021
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• 1999

PID controllers with constant gains have been widely used in various control systems. But it is difficult to have uniformly good control performance in all operating conditions. In this paper, we propose a variable PID controller for robot manipulators. We divide total workspace of manipulators into several subspaces. PID controllers in each subspace are optimized using evolution strategy which is a kind of global search algorithm. In real operation, the desired trajectories may cross several subspaces and we select the corresponding gains in each subspace. The gains may have large difference on the boundary of subspaces, which may cause oscillatory motion. So we use artificial neural network to have continuous smooth gain curves to reduce the oscillatory motion. From the experimental results, although the proposed variable PID controller for robots should pay for some computational burden, we have found that the controller is more superior to the conventional constant gain PID controller.

• 천문학회보
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• 제40권2호
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• pp.48.3-49
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• 2015

Scaling relations of early-type galaxies (ETG) provide a deep insight into their formation and evolution. Interestingly enough, most relations extending into the dwarf regimes display non-linear or broken-linear features, unlike the linear relations for normal (i.e., intermediate-mass to giant) ETGs only. Here we investigate the mass-size scaling relation of ETGs using a massive database of galaxies from SDSS DR12. We divide ETGs into two groups by the indication of star formation such as colors, and examine their distinction along the mass-size relation. We find that the mass-size distribution of blue, young normal galaxies is in good agreement with that of dwarf ETGs. Our result suggests that blue, young normal ETGs may serve as links between (passive) normal ETGs and dwarfs. We discuss the possibility of blue, young ETGs being progenitors of dwarf ETGs.

• 한국주거학회논문집
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• 제3권1호
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• pp.69-84
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• 1992

This study is a trial to syustematize the types of Minga(house of the lower class) that is distributed in the whole Korean penninsula. It is thought that, it can be systematised by using the method of five step' assortment shich is able to divide the types of Minga. Five steps are as follows: 1. The system of appearance and evolution of floor 2. The system of forms 3. Areal distribution of forms 4. State of gathering of living spaces 5. Variety of forms

• 국제초고층학회논문집
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• 제12권1호
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• pp.19-33
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• 2023

Air Rights techniques, including floor-area ratio (FAR) transfers, FAR bonuses, and FAR storage, have been widely applied among skyscraper constructions in New York City for profit maximization goals. Since 1916, air rights regulations in New York zoning system have been revised and improved over the years to cater the urban development needs of different periods, and they also result in typical skyscraper block typologies. This research firstly performed spatial overlay analysis to reveal the spatial correlation between skyscraper blocks and air rights application blocks; secondly, Spacematrix parameters and cluster analysis are applied to divide the skyscraper urban block of New York City into four categories. Compared with air rights application data, the research attempts to illustrate how various air rights techniques have acted on the formation and evolution of skyscraper block typologies in the pre-1916, 1916-1961, 1961-2010, and 2010-present periods respectively, in order to reveal the relationship between public policies and urban morphology in a broader sense and also provide references for policy making in future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권12호
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• pp.5972-5989
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• 2019

With the onset of the big data age, data is growing exponentially, and the issue of how to optimize large-scale data processing is especially significant. Large-scale global optimization (LSGO) is a research topic with great interest in academia and industry. Spark is a popular cloud computing framework that can cluster large-scale data, and it can effectively support the functions of iterative calculation through resilient distributed datasets (RDD). In this paper, we propose a hybrid mechanism of particle swarm optimization (PSO) and differential evolution (DE) algorithms based on Spark (SparkPSODE). The SparkPSODE algorithm is a parallel algorithm, in which the RDD and island models are employed. The island model is used to divide the global population into several subpopulations, which are applied to reduce the computational time by corresponding to RDD's partitions. To preserve population diversity and avoid premature convergence, the evolutionary strategy of DE is integrated into SparkPSODE. Finally, SparkPSODE is conducted on a set of benchmark problems on LSGO and show that, in comparison with several algorithms, the proposed SparkPSODE algorithm obtains better optimization performance through experimental results.