• 대한토목학회논문집
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• 제29권4D호
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• pp.555-561
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• 2009

굴착된 터널 형상 재현에서 단면의 종단 데이터는 터널의 유지를 위해서는 아주 중요하다. 터널이 완성되기전에 설계된 모델을 고려한 완성된 터널의 변형이 고려되어져야 한다. 그리고 변형은 터널 단면 전체를 따라 연속적으로 나타날 수 있다. 본 연구에서는 먼저 수학적 분석으로 접근하였고, 그것을 관측된 터널단면 데이터에 실험 하였다. 그 다음 선추적 방법, 유전자 알고리즘, 패턴 추적 방법 등으로 3D 터널 형상 재현을 비교하였다. 수학적 방법론은 철도 터널과 같은 간단한 원통형은 쉽게 해결이 되었으나, 도로터널과 같은 더욱 복잡한 모델(복심 곡선형과 비원통형)은 구속된 상태하에서 소프트 컴퓨팅 툴을 가지고 해결할 수 있었다. 유전자 알고리즘과 직접탐색법은 많은 계산 시간이 걸렸으나 복잡한 상태하에서 더욱 유연함을 보였으며, 선추적 방법은 초기값들이 제한된 범위 하에서 가장 빠르게 계산되어졌다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집C
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• pp.537-542
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• 2001

In recent engineering, the designer has become more and more dependent on computer simulation. But defining exact model using computer simulation is too expensive and time consuming in the complicate systems. Thus, designers often use approximation models, which express the relation between design variables and response variables. These models are called metamodel. In this paper, we introduce one of the metamodel, named Kriging. This model employs an interpolation scheme and is developed in the fields of spatial statistics and geostatistics. This class of interpolating model has flexibility to model response data with multiple local extreme. By reason of this multi modality, we can't use any gradient-based optimization algorithm to find global extreme value of this model. Thus we have to introduce global optimization algorithm. To do this, we introduce DE(Differential Evolution). DE algorithm is developed by Ken Price and Rainer Storn, and it has recently proven to be an efficient method for optimizing real-valued multi-modal objective functions. This algorithm is similar to GA(Genetic Algorithm) in populating points, crossing over, and mutating. But it introduces vector concept in populating process. So it is very simple and easy to use. Finally, we show how we determine Kriging metamodel and find global extreme value through two mathematical examples.

• Industrial Engineering and Management Systems
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• 제15권1호
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• pp.19-31
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• 2016

A design method of the network for automated transporters mounted on rails is addressed for automated container terminals. In the network design, the flow directions of some path segments as well as routes of transporters for each flow requirement must be determined, while the total transportation and waiting times are minimized. This study considers, for the design of the network, the waiting times of the transporters during the travel on path segments, intersections, transfer points below the quay crane (QC), and transfer points at the storage yard. An algorithm, which is the combination of a modified Dijkstra's algorithm for finding the shortest time path and a queuing theory for calculating the waiting times during the travel, is proposed. The proposed algorithm can solve the problem in a short time, which can be used in practice. Numerical experiments showed that the proposed algorithm gives solutions better than several simple rules. It was also shown that the proposed algorithm provides satisfactory solutions in a reasonable time with only average 7.22% gap in its travel time from those by a genetic algorithm which needs too long computational time. The performance of the algorithm is tested and analyzed for various parameters.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 춘계정기학술대회 e-Business를 위한 지능형 정보기술 / 한국지능정보시스템학회
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• pp.343-346
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• 2000

In this paper, a novel approach to intruder detection is introduced. The approach, based on the genetic algorithms, improved detection rate of the host systems which has traditionally relied on known intruder patterns and host addresses. Rather than making judgments on whether the access is instrusion or not, the systems can continuously monitor systems with categorized security level. With the categorization, when the intruder attempts repeatedly to access the systems, the security level is incrementally escalated. In the simulation of a simple intrusion, it was shown that the current approach improves robustness of the security systems by enhancing detection and flexibility. The evolutionary approach to intruder detection enhances adaptability of the system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.3013-3015
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• 2000

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Futhermore, a series of conventional techniques such as the pole placement and the optimal control based on the local linearizations have narrow stabilizable regions, At the same time, the fine tunings of their gain parameters are also troublesome, Thus, in this paper, an Evolving Neural Network ControlleY(ENNC) which its structure and its connection weights are optimized simultaneously by Real Variable Elitist Genetic Algorithm (RVEGA) was presented for stabilization of an IP system with nonlinearity, This proposed ENNC was described by a simple genetic chromosome. Through the simulation and experimental results, we showed that the finally acquired optimal ENNC was very useful in the stabilization control of IP system.

• 전기학회논문지
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• 제67권3호
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• pp.419-426
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• 2018

An useful and effective design method for the gas identification system is presented in this paper. The proposed gas identification system adopts hierarchical structure with two level rule base combining fuzzy sets with rough sets. At first, a hybrid genetic algorithm is used in grouping the array sensors of which the measured patterns are similar in order to reduce the dimensionality of patterns to be analyzed and to make rule construction easy and simple. Next, for low level identification, fuzzy inference systems for each divided group are designed by using TSK fuzzy rule, which allow handling the drift and the uncertainty of sensor data effectively. Finally, rough set theory is applied to derive the identification rules at high level which reflect the identification characteristics of each divided group. Thus, the proposed method is able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, we demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• 정보기술응용연구
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• 제1권
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• 한국유체기계학회 논문집
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• 제7권2호
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• pp.7-13
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• 2004

In an attempt to solve multiobjective optimization problems, weighted sum method is most widely used for the advantage that a designer can consider the relative significance of each object functions by weight values but it can be highly sensitive to weight vector and occasionally yield a deviated optimum from the relative weighting values designer designated because the multiobjective function has the form of simple sum of the product of the weighting values and the object functions in traditional approach. To search the design solution agree well to the designer's weighting values, we proposed new multiobjective function which was the functional of each normalized objective functions and considered to find the design solution comparing the distance between the characteristic line and the ideal optimum. In this study, proposed multiobjective function was applied to design high efficiency and low noise axial flow fan and the result shows this approach is effective for the case that the quality of the design can be highly affected by the designer's subjectiveness represented as weighting values in multiobjective design optimization process.

• 한국전자파학회논문지
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• 제16권1호
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• pp.40-48
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• 2005

본 논문에서는 NEC 코드와 Pareto 유전자 알고리즘 최적화 기법을 이용하여 초소형 유도결합 안테나를 설계하였다. 최적화된 유도결합 안테나 중 몇 가지 표본을 제작하고 성능을 측정하였다. 일반적으로 안테나의 크기가 작아질수록 입력 저항, 대역폭 및 효율이 감소하는데 비하여 제안된 방법으로 설계된 유도결합 안테나는 다른 부가적인 정합회로 없이 우수한 성능을 보인다. 간단한 회로 모델을 도입하여 제안된 유도결합 안테나의 동작원리를 설명하였고, Duroid 기판 위에 평면 구조로 제작하여 RFID 태그 안테나로써 성능을 입증하였다.

• 한국지능시스템학회논문지
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• 제15권6호
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• pp.730-735
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• 2005

배낭 문제는 단순한 것 같지만 조합 최적화 문제로서, 다항 시간(polynomial time)에 풀리지 않는 NP-hard 문제이다. 이 문제를 해결하기 위해 기존에는 GA(Genetic Algorithms)를 이용하여 해결하였다. 하지만 기존의 방법은 DNA의 정확한 특성을 고려하지 않아, 실제 실험과의 결과 차이가 발생하고 있다. 본 논문에서는 배낭 문제의 문제점을 해결하기 위해 DNA 컴퓨팅 기법에 DNA 코딩 방법을 적용한 ACO(Algorithm for Code Optimization)를 제안한다. ACO는 배낭 문제 중 (0,1)-배낭 문제에 적용하였고, 그 결과 기존의 방법보다 실험적 오류를 최소화하였으며, 또한 적합한 해를 빠른 시간내에 찾을 수 있었다.