• 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 Software Assessment and Valuation
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• v.17 no.2
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• pp.1-10
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• 2021

The ability to recognize human emotions by computer vision is a very important task, with many potential applications. Therefore the demand for emotion recognition using not only RGB images but also thermal images is increasing. Compared to RGB images, thermal images has the advantage of being less affected by lighting conditions but require a more sophisticated recognition method with low-resolution sources. In this paper, we propose a Divide and Conquer-based CNN training strategy to improve the performance of facial thermal image-based emotion recognition. The proposed method first trains to classify difficult-to-classify similar emotion classes into the same class group by confusion matrix analysis and then divides and solves the problem so that the emotion group classified into the same class group is recognized again as actual emotions. In experiments, the proposed method has improved accuracy in all the tests than when recognizing all the presented emotions with a single CNN model.

• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.139-145
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• 2012

Generally, Quicksort selects the pivot from leftmost, rightmost, middle, or random location in the array. This paper suggests Quicksort using middle range pivot $P_0$ and continually divides into 2. This method searches the minimum value $L$ and maximum value $H$ in the length n of list $A$. Then compute the initial pivot key $P_0=(H+L)/2$ and swaps $a[i]{\geq}P_0$,$a[j]<P_0$ until $i$=$j$ or $i$>$j$. After the swap, the length of list $A_0$ separates in two lists $a[1]{\leq}A_1{\leq}a[j]$ and $a[i]{\leq}A_2{\leq}a[n]$ and the pivot values are selected by $P_1=P_0/2$, $P_2=P_0+P_1$. This process repeated until the length of partial list is two. At the length of list is two and $a$[1]>$a$[2], swaps as $a[1]{\leftrightarrow}a[2]$. This method is simpler pivot key process than Quicksort and improved the worst-case computational complexity $O(n^2)$ to $O(n{\log}n)$.