• Journal of the Korea Society of Computer and Information
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• v.6 no.3
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• pp.23-28
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• 2001

There has been several meaning full efforts to save costs on system development and expand the life-time of a system in changeful IT circumstance. It was a module-based architecture that empower productivity at structured programming era. But it couldn't grow nor evolve, but could raise only calling frequency of module. But OOP or OO-method overcome limit of structured programing by class inheritance and/or overloading and/or over-riding. A component centric architecture, what is mixture of distributed systems, like CORBA or DCOM with OOP, can support not only high reusability or expansion of life-time but also Plug-&-Play between them. To assemble these component to build a new system in easy way, the well-formed specification of a component is highly required. At this study, the enhanced referential component architecture and its UML specification will be suggested.

• Smart Media Journal
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• v.9 no.3
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• pp.31-40
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• 2020

Currently, black-box-based machine learning algorithms are used to analyze big data in manufacturing. This algorithm has the advantage of having high analytical consistency, but has the disadvantage that it is difficult to interpret the analysis results. However, in the manufacturing industry, it is important to verify the basis of the results and the validity of deriving the analysis algorithms through analysis based on the manufacturing process principle. To overcome the limitation of explanatory power as a result of this machine learning algorithm, we propose a manufacturing big data analysis method using genetic programming. This algorithm is one of well-known evolutionary algorithms, which repeats evolutionary operators such as selection, crossover, mutation that mimic biological evolution to find the optimal solution. Then, the solution is expressed as a relationship between variables using mathematical symbols, and the solution with the highest explanatory power is finally selected. Through this, input and output variable relations are derived to formulate the results, so it is possible to interpret the intuitive manufacturing mechanism, and it is also possible to derive manufacturing principles that cannot be interpreted based on the relationship between variables represented by formulas. The proposed technique showed equal or superior performance as a result of comparing and analyzing performance with a typical machine learning algorithm. In the future, the possibility of using various manufacturing fields was verified through the technique.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.253-258
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• 2008

Recently, game players want new game requiring more various tactics and strategies in the complex environment beyond simple and repetitive play. Various artificial intelligence techniques have been suggested to make the game characters learn within this environment, and the recent researches include the neural network and the genetic algorithm. The Genetic programming(GP) has been used in this study for learning strategy of the agent in the pursuit-evasion problem which is used widely in the game theories. The suggested GP algorithm is faster than the existing algorithm such as neural network, it can be understood instinctively, and it has high adaptability since the evolving chromosomes can be transformed to the reasoning rules.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.1
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• pp.43-48
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• 2014

Usually, image deconvolution is applied as a preprocessing step in surveillance systems to reduce the effect of motion or out-of-focus blur problem. In this paper, we propose a blind-image deconvolution filtering approach based on genetic programming (GP). A numerical expression is developed using GP process for image restoration which optimally combines and exploits dependencies among features of the blurred image. In order to develop such function, first, a set of feature vectors is formed by considering a small neighborhood around each pixel. At second stage, the estimator is trained and developed through GP process that automatically selects and combines the useful feature information under a fitness criterion. The developed function is then applied to estimate the image pixel intensity of the degraded image. The performance of developed function is estimated using various degraded image sequences. Our comparative analysis highlights the effectiveness of the proposed filter.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.9
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• pp.81-92
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• 1998

A new hybrid technique using several sub-populations having completely different evolutionary behaviors is proposed to increase the possibility to quickly find the global optimum of continuous optimization problem. It has three sub-populations. Two NPOSA algorithms showing good performance in the problem having a rugged fitness function are applied to two sub-populations and a self-adaptive evolutionary algorithm to the other sub-population. Sub-populations evolve in different manners and the interaction among these sub-populations lead to the global optimum quickly. The efficiency of this technique is verified through benchmark test functions. Finally, the algorithm with three sub-populations has been applied to seek for the optimal camera calibration parameters. After an error function has been defined using measured feature points of a calibration block, it has been shown that the algorithm searches for the camera parameters that minimize the error function.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1998.10a
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• pp.153-158
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• 1998

In this paper, we present a design of optimal 2-DOF PID controller for control of gantry crane which has to control swing motion and trolley position. For tuning the parameter of 2-DOF PID controller, we used evolution strategy(ES). During operate the crane system in yard, the goal is transporting the load to a goal position as quick as possible without rope oscillation. The crane is generally operated by an expert operator, but recently an automatic control system with high speed and rapid transportation is required. However, we developed an optimal controller which has to control the crane system with disturbance.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.27-30
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• 2005

3개 이상의 DNA 혹은 단백질의 염기서열을 정렬하는 다중 서열 정렬(multiple sequence alignment, MSA)은 서열들 사이의 진화관계, 단백질의 구조와 기능에 관한 연구에 필수적인 도구이다. 최적화된 다중서열 정렬을 얻기 위해 사용되는 가장 유용한 방법은 동적 프로그래밍이다. 그러나 동적프로그래밍은 정렬하고자 하는 서열의 수가 증가함에 따라 시간도 지수함수($O(n^k)$)로 증가하기 때문에 다중 서열 정렬에는 효율적이지 못하다. 따라서, 본 논문에서는 최적의 MSA 문제를 해결하기 위해 클러스터링 기반의 새로운 다중 서열 정렬 (Clustering-based Multiple Sequence Alignment, CMSA) 알고리즘을 제안한다. 결과적으로 제안한 CMSA 알고리즘의 기여도는 다중 서열 정렬의 질적 향상과 처리 시간 단축($O(n^3L^2)$)이 기대된다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.277-279
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• 2005

DNA 마이크로어레이 기술의 발전으로 유전자 발현에 대한 많은 양의 정보가 쏟아지게 되었고, 이러한 정보들을 이용하여 유전자 조절 네트워크를 수학적으로 모델링하는 것이 시스템 생물학의 중요 관심사로 떠오르고 있다. 본 논문에서는 실험에서 얻어낸 데이터를 유전 프로그래밍을 이용한 기호 회귀를 통해 데이터 지점을 조정하고 유전 프로그래밍의 결과 함수를 이용해 각 지점에서의 미분값을 얻어내었다. 그 뒤, 불리안 네트워크를 표현하는 이진 배열과 S-시스템을 표현하는 실수 배열을 결합한 해를 사용하는 유전 알고리즘으로 앞에서 얻은 데이터를 이용해 원하는 S-시스템의 구조와 매개변수를 구해내었다.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.661-663
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• 1999

복수 염기서열 정렬(multiple sequence alignment)은 염기서열들 사이의 진화관계, 단백질의 구조와 기능에 관한 연구에 필수적인 도구이다. 다이나믹 프로그래밍(dynamic programming) 방법은 대부분의 경우에 있어 최적의 염기서열 정렬 결과를 제공할 수 있다. 그러나 그것이 사용하는 갭 비용함수 때문에 특별한 경우에 최적의 염기서열 정렬을 만들어 내지 못한다. 본 논문에서는 다이나믹 프로그래밍에 의해 획득된 염기서열을 개선하기 위한 휴리스틱 방법을 제안한 후, 실제 단백질 데이터를 가지고 성능 분석을 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.867-868
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• 2009

아키텍쳐를 기반으로 디자인 된 소프트웨어는 컴포넌트간의 낮은 결합력 때문에 재사용이나 부분적인 수정이 쉽다. 일반적으로 아키텍쳐는 디자인 단계에서 구성되며 아키텍쳐 디자인을 바탕으로 컴퍼넌트들을 구현한다. 그러나 프로그래밍 언어의 컴퍼넌트간 인터페이스는 아키텍쳐의 커넥터와 다르기 때문에 구현된 코드는 아키텍쳐 디자인을 있는 그대로 반영하지 못 한다. 결과적으로 차후 프로그램 코드의 수정이나 재사용이 아키텍쳐 디자인의 변경보다 복잡해진다. 본 논문에서는 아키텍쳐의 커넥터를 클래스를 통해 명확히 구현함으로써 아키텍쳐 디자인을 그대로 유지하는 코드 작성법을 제안한다.