• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.665-666
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• 2022

Differential evolution is an efficient algorithm for continuous optimization problems. However, applying differential evolution to solve large-scale optimization problems quickly degrades performance and exponentially increases runtime. To overcome this problem, a new cooperative coevolution differential evolution based on Spark (referred to as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.559-560
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• 2021

Differential evolution is an efficient algorithm for solving continuous optimization problems. However, applying differential evolution to solve large-scale optimization problems dramatically degrades performance and exponentially increases runtime. Therefore, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC.

• Journal of Korean Library and Information Science Society
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• v.51 no.1
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• pp.91-114
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• 2020

Due to the settlement of the ICT environment and the widespread dissemination of Net media, the quantitative digital divide based on access to information has evolved into the formation of a qualitative digital divide. In the current information environment where enormous amounts of information exist on the Internet, there is a deeper intellectual digital divide that is different from the existing quantitative and qualitative digital divide. This study identified the changes in digital divide resulted from the evolution of information environment and empirically verified the formation of the intellectual digital divide. It also demonstrated that the intellectual digital divide is not distinct from the existing quantitative and qualitative digital divide, but from differences in the intellectual capacity of the information based on the factors of the existing digital divide. In particular, it was confirmed that the educational and economic levels, which have been the key factors of the traditional digital divide, still form the quantitative and qualitative digital divide, and also act as the key factors in the formation of the intellectual digital divide.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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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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• v.19 no.3
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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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.8
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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.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.2
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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.

• Journal of the Korean housing association
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• v.3 no.1
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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

• International Journal of High-Rise Buildings
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• v.12 no.1
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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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• v.13 no.12
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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.