• 한국정밀공학회지
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• 제26권11호
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• pp.99-107
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• 2009

Since a long work piece influences the natural frequency of the entire system with a miniature high speed spindle, a bar-feeder is used for a long work piece to improve the vibration characteristics of a spindle system. Therefore, it is very important to design optimally support positions between a bar-feeder and a long work piece for a miniature high speed spindle system. The goal of the current paper is to present an optimization method for the design of support positions between a bar-feeder and a long work piece. This optimization method is effectively composed of the method of design of experiment (DOE), the artificial neural network (ANN) and the genetic algorithm (GA). First, finite element models which include a high speed spindle, a long work piece and the support conditions of a bar-feeder were generated from the orthogonal array of the DOE method, and then the results of natural vibration analysis using FEM were provided for the learning inputs of the neural network. Finally, the design of bar-feeder support positions was optimized by the genetic algorithm method using the neural network approximations.

• 한국컴퓨터정보학회논문지
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• 제26권9호
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• pp.27-35
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• 2021

인공생명체 연구는 자연 생명과 관련된 시스템이나 그 과정들, 진화 등을 평가해 다양한 응용과학 분야에 활용된다. 이러한 인공생명체의 원활한 활동을 위해 물리적 신체 설계와 행동 제어전략을 진화시키는 연구가 활발히 진행되었다. 그러나 형태와 신경망을 공진화시키는 것은 어렵기에 최적화된 움직임을 가진 인공생명체는 한 가지 형태에 한 가지 움직임만을 가지며 주변 환경 상황은 고려하지 않는 것이 대부분이다. 본 논문에서는 포식자-피식자 모델을 이용하여 형태와 신경망을 공진화하는 인공생명체가 환경적응형 움직임을 갖게 한다. 그런 다음 포식자-피식자 계층 구조를 최상위 포식자-중간 포식자-최하위 피식자 3단계로 확장하여 초기 개체군 밀도에 따라 시뮬레이션의 안정성을 판별하며 형태 진화와 개체군 역학 간의 상관관계를 분석한다.

• 한국지반공학회논문집
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• 제22권6호
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• pp.15-26
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• 2006

말뚝의 지지력과 거동을 예측하기 위하여 다양한 연구들이 수행되었음에도 불구하고, 메커니즘에 대한 전반적인 이해가 아직까지 미흡한 실정이다. 이는 많은 인자들이 서로 복잡한 연관성을 맺으며 말뚝의 거동에 영향을 미치기 때문이다. 따라서 지반조건과 말뚝조건 및 항타조건 등 과 관련된 많은 인자들 가운데 지지력에 중요한 영향을 미치는 인자들을 도출하기 어려우며, 또한 인자들 간의 복잡한 연관성을 지지력 공식에 적합하게 고려하기란 매우 어렵다. 본 연구에서는 항타말뚝들에 대한 동재하시험으로부터 선단 및 주면 지지력을 포함한 지지력을 예측하기 위하여 인공신경망이 적용되었다. 첫째로, 신경망 모델링에 근거한 민감도 분석를 통하여 지지력에 대한 각 영향인자들의 영향이 검토되었다. 둘째로, 지지력 예측을 위한 최적의 인공신경망 모델을 도출하기 위하여 인공신경망과 유전자 알고리즘으로 구성된 설계기법이 적용되었다. 이를 통해 토사지반에 관입된 항타말뚝의 지지력을 산정할 수 있는 최적의 인공신경망 모델을 제안하고자 하였다. 사용된 설계기법을 통하여 적합한 입력층 조합, 은닉층 노드수과 각 층 사이의 연결구조를 도출하였다. 도출된 인공신경망 모델을 적용함으로써 항타말뚝의 지지력을 간단하며 신뢰성 있게 예측할 수 있음을 알 수 있다.

• JSTS:Journal of Semiconductor Technology and Science
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• 제11권3호
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• pp.135-145
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• 2011

Accurate process characterization and optimization are the first step for a successful advanced process control (APC), and they should be followed by continuous monitoring and control in order to run manufacturing processes most efficiently. In this paper, process characterization and recipe optimization methods with multiple outputs are presented in high density plasma-chemical vapor deposition (HDP-CVD) silicon dioxide deposition process. Five controllable process variables of Top $SiH_4$, Bottom $SiH_4$, $O_2$, Top RF Power, and Bottom RF Power, and two responses of interest, such as deposition rate and uniformity, are simultaneously considered employing both statistical response surface methodology (RSM) and neural networks (NNs) based genetic algorithm (GA). Statistically, two phases of experimental design was performed, and the established statistical models were optimized using performance index (PI). Artificial intelligently, NN process model with two outputs were established, and recipe synthesis was performed employing GA. Statistical RSM offers minimum numbers of experiment to build regression models and response surface models, but the analysis of the data need to satisfy underlying assumption and statistical data analysis capability. NN based-GA does not require any underlying assumption for data modeling; however, the selection of the input data for the model establishment is important for accurate model construction. Both statistical and artificial intelligent methods suggest competitive characterization and optimization results in HDP-CVD $SiO_2$ deposition process, and the NN based-GA method showed 26% uniformity improvement with 36% less $SiH_4$ gas usage yielding 20.8 ${\AA}/sec$ deposition rate.

• 한국정보시스템학회지:정보시스템연구
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• 제26권3호
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• pp.63-90
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• 2017

Purpose This study proposes a novel system trading model using case-based reasoning (CBR) based on absolute similarity threshold. The proposed model is designed to optimize the absolute similarity threshold, feature selection, and instance selection of CBR by using genetic algorithm (GA). With these mechanisms, it enables us to yield higher returns from stock market trading. Design/Methodology/Approach The proposed CBR model uses the absolute similarity threshold varying from 0 to 1, which serves as a criterion for selecting appropriate neighbors in the nearest neighbor (NN) algorithm. Since it determines the nearest neighbors on an absolute basis, it fails to select the appropriate neighbors from time to time. In system trading, it is interpreted as the signal of 'hold'. That is, the system trading model proposed in this study makes trading decisions such as 'buy' or 'sell' only if the model produces a clear signal for stock market prediction. Also, in order to improve the prediction accuracy and the rate of return, the proposed model adopts optimal feature selection and instance selection, which are known to be very effective in enhancing the performance of CBR. To validate the usefulness of the proposed model, we applied it to the index trading of KOSPI200 from 2009 to 2016. Findings Experimental results showed that the proposed model with optimal feature or instance selection could yield higher returns compared to the benchmark as well as the various comparison models (including logistic regression, multiple discriminant analysis, artificial neural network, support vector machine, and traditional CBR). In particular, the proposed model with optimal instance selection showed the best rate of return among all the models. This implies that the application of CBR with the absolute similarity threshold as well as the optimal instance selection may be effective in system trading from the perspective of returns.