• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.279-284
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• 2014

This paper proposes a optimum design method for the Fuzzy PID controller of magnetic levitation-based Rail-Guided Vehicle(RGV). Since an attraction type levitation system is intrinsically unstable, it is difficult to completely satisfy the desired performance through the methods designed by conventional controllers. In the paper, the Fuzzy PID controller with fixed parameters are applied and then the optimum parameters of fuzzy PID controller are selected by genetic algorithm. For the fitness function of genetic algorithm, the performance index of PID controller is used. To verify the performance of the proposed method, we used Matlab/simulink model of Maglev and compared the proposed method with the performance of PID controller. The simulation results show that the proposed method is more effective than conventional PID controller.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2001.10a
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• pp.165-196
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• 2001

DNA chip 또는 microarray는 다수의 유전자 또는 유전자 조각을 (보통 수천내지 수만 개)칩상에 고정시켜 놓고 DNA hybridization 반응을 이용하여 유전자들의 발현 양상을 분석할 수 있는 기술이다. 이러한 high-throughput기술은 예전에는 생각하지 못했던 여러가지 분자생물학의 문제에 대한 해답을 제시해 줄 수 있을 뿐 만 아니라, 분자수준에서의 질병 진단, 신약 개발, 환경 오염 문제의 해결 등 그 응용 가능성이 무한하다. 이 기술의 실용적인 적용을 위해서는 DNA chip을 제작하기 위한 하드웨어/웻웨어 기술 외에도 이러한 데이터로부터 최대한 유용하고 새로운 지식을 창출하기 위한 bioinformatics 기술이 핵심이라고 할 수 있다. 유전자 발현 패턴을 데이터마이닝하는 문제는 크게 clustering, classification, dependency analysis로 구분할 수 있으며 이러한 기술은 통계학과인공지능 기계학습에 기반을 두고 있다. 주로 사용된 기법으로는 principal component analysis, hierarchical clustering, k-means, self-organizing maps, decision trees, multilayer perceptron neural networks, association rules 등이다. 본 세미나에서는 이러한 기본적인 기계학습 기술 외에 최근에 연구되고 있는 새로운 학습 기술로서 probabilistic graphical model (PGM)을 소개하고 이를 DNA chip 데이터 분석에 응용하는 연구를 살펴본다. PGM은 인공신경망, 그래프 이론, 확률 이론이 결합되어 형성된 기계학습 모델로서 인간 두뇌의 기억과 학습 기작에 기반을 두고 있으며 다른 기계학습 모델과의 큰 차이점 중의 하나는 generative model이라는 것이다. 즉 일단 모델이 만들어지면 이것으로부터 새로운 데이터를 생성할 수 있는 능력이 있어서, 만들어진 모델을 검증하고 이로부터 새로운 사실을 추론해 낼 수 있어 biological data mining 문제에서와 같이 새로운 지식을 발견하는 exploratory analysis에 적합하다. 또한probabilistic graphical model은 기존의 신경망 모델과는 달리 deterministic한의사결정이 아니라 확률에 기반한 soft inference를 하고 학습된 모델로부터 관련된 요인들간의 인과관계(causal relationship) 또는 상호의존관계(dependency)를 분석하기에 적합한 장점이 있다. 군체적인 PGM 모델의 예로서, Bayesian network, nonnegative matrix factorization (NMF), generative topographic mapping (GTM)의 구조와 학습 및 추론알고리즘을소개하고 이를 DNA칩 데이터 분석 평가 대회인 CAMDA-2000과 CAMDA-2001에서 사용된cancer diagnosis 문제와 gene-drug dependency analysis 문제에 적용한 결과를 살펴본다.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.846-851
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• 2005

In this paper, we propose Genetic Algorithms(GAs)-based Optimized Polynomial Neural Networks(PNN). The proposed algorithm is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional neural networks and can be generated in a dynamic manner. As each node of PNN structure, we use several types of high-order polynomial such as linear, quadratic and modified quadratic, and it is connected as various kinds of multi-variable inputs. The conventional PNN depends on the experience of a designer that select the number of input variables, input variable and polynomial type. Therefore it is very difficult to organize optimized network. The proposed algorithm leads to identify and select the number of input variables, input variable and polynomial type by using Genetic Algorithms(GAs). The aggregate performance index with weighting factor is proposed as well. The study is illustrated with tile NOx omission process data of gas turbine power plant for application to nonlinear process. In the sequel the proposed model shows not only superb predictability but also high accuracy in comparison to the existing intelligent models.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.11 s.114
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• pp.1065-1072
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• 2006

An OFDM(Orthogonal Frequency Division Multiplexing) system has the problem of the PAPR(Peak-to-Average Power Ratio) due to the overlapping phenomena of many sub-carriers. The previously proposed GA-SPW(Genetic Sub-block Phase Weighting) method not only improved the reduction of PAPR as the number of sub-blocks increases in an OFDM symbol but also decreased the number of calculations involved in the iterative phase searching yields to depend on the number of population and generation by using genetic algorithm not on the number of sub-blocks and phase elements. In this paper, we propose the modified GA-SPW method in order to improve the performance and to decrease the complexity. It is shown that the proposed modified GA-SPW method achieves the significant performance and the reduction of search complexity comparing to the ordinary technique, iterative flipping and previously proposed GA-SPW by the experimental results and analysis.

• Journal of Satellite, Information and Communications
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• v.7 no.1
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• pp.39-47
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• 2012

Currently, most of the frequency spectrum resources are allocated and due to the lack of frequency, low frequency band, optimal for wireless communication environment is not used. Therefore, Cognitive Radio (CR) is a critical issue to solve the spectrum scarcity and to improve frequency spectrum utilization in wireless communication. In this paper, we implement data transmission and receive in a real CR system using the USRP(Universal Software Radio Peripheral) board and GNU Radio package of an open source development kit. Concretely, we detect the Primary User by spectrum sensing, and then we send Primary User information to the database. After receiving the information, because the database already sent optimal transmit power, bandwidth and channel information to CR equipment, CR can communicate without any interference to Primary User.

• Journal of Korean Society of Transportation
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• v.19 no.1
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• pp.115-129
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• 2001

The purpose of this study is to develop a cycle-free signal timing model for minimizing delays based on Third-generation control concept using Genetic Algorithm. A special feature of this model is its ability to manage delays of turning movements on the cycle basis. The model produces a cycle-free based signal timing(cycles and green times) for each intersection to minimize delays of turning movements on the cycle basis. The performance of cycle-free signal timings was evaluated on normal (v/c = 0.7) and oversaturated (v/c=1.0) conditions. The performance measures are throughput and the number of queued vehicles at the end of green time. The result shows that the cycle free signal timing is superior to the fixed signal timing to manage traffic flows of intersections; (1) the proposed model accomplishes the basic objective of the research, producing cycle free signal timings on the cycle basis, (2) on normal conditions, cycle free signal timings produce less queued vehicles at the end of green time, and (3) on oversaturated conditions, the cycle free signal timing is superior to the fixed signal timing to manage saturated traffic flows of intersections.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.1
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• pp.1-6
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• 2002

Cogging torque is often a principal source of vibration and acoustic noise in high precision spindle motor applications. In this paper, cogging torque is analytically calculated using energy method to show that Fourier spectra of airgap permeance function and airgap MMF function are the most important design parameters to control cogging torque. To control these functions, stator pole shoe pairing and magnet arc design are proposed to minimize cogging torque. As for optimization technique, genetic algorithm is applied to handle trade-off effects of design parameters. Results show that the proposed method can reduce the cogging torque effectively.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.241-243
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• 2002

DNA 컴퓨팅은 생체 분자들이 갖는 막대한 병렬성을 정보 처리 기술에 적용한 기술이다. Adleman의 DNA 컴퓨팅은 랜덤한 고정길이의 형태로 문제를 표현하기 때문에 해를 찾지 못하거나 시간이 많이 걸리는 단점을 갖고 있다. 본 논문은 DNA 컴퓨팅에 DNA 코딩 방법을 적용하여 DNA 서열을 효율적으로 표현하고 반응횟수 만큼 합성과 분리 과정을 거쳐 최적의 코드를 생성하는 ACO(Algorithm for Code Optimization)를 제안한다. DNA 코딩 방법은 변형된 유전자 알고리즘으로 DNA 기능을 유지하며, 서열의 길이를 줄일 수 있으므로 최적의 서열을 생성할 수 있는 특징을 갖는다. ACO를 NP-complete 문제 중 Hamiltonian path problem에 적용하여 실험한 결과, Adleman의 DNA 컴퓨팅 보다 초기 문제 표현에서 높은 적합도 값을 갖는 서열을 생성했으며, 경로의 변화에도 능동적으로 대처하여 최적의 결과를 빠르게 탐색할 수 있었다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.702-705
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• 2011

본 논문에서는 초고층 전단벽-아웃리거 시스템에 대해, 기존의 근사해석법과 유전알고리즘을 이용하여, 물량최적설계 기반의 구성요소 단면 및 아웃리거 최적위치 결정에 관해 연구를 진행하였다. 아웃리거 시스템의 최적성은 아웃리거의 위치와 아웃리거 시스템을 구성하는 전단벽-아웃리거, 외곽기둥의 단면 성능의 복잡한 관계에 의해 역학적으로 결정된다. 하지만 기존의 아웃리거 시스템의 최적화 연구는 대부분 전단벽과 아웃리거, 외곽기둥의 단면은 고정된 상태에서, 아웃리거의 위치만 설계변수로 하여 아웃리거의 최적위치를 찾는 연구에 국한되어 있다. 이에 본 연구에서는 G.A.를 이용하여, 아웃리거 설치위치뿐만 아니라 전단벽과 아웃리거, 외곽기둥의 단면까지 설계변수로 하여 물량최적설계 조건을 만족시키는 아웃리거시스템의 최적설계 연구를 진행하였다. 또한 반복 계산의 시간을 줄이기 위해 기존의 근사해석법을 사용하였다. 본 연구의 결과는 초고층 구조물의 초기 설계 시에 구성요소의 단면 및 아웃리거 설치 층의 선정에 적극 활용될 수 있을 것이다.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.421-426
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• 2010

Due to the ease of deployment and the extended coverage, wireless mesh networks (WMNs) are gaining popularity and research focus. For example, the routing protocols that enhance the throughput on the WMNs and the link quality measurement schemes are among the popular research topics. However, most of these works assume that the locations of the mesh routers are predetermined. Since the operators in an Indoor mesh network can determine the locations of the mesh routers by themselves, it is essential to the WMN performance for the mesh routers to be initially placed by considering the performance issues. In this paper, we propose a mesh router placement scheme based on genetic algorithms by considering the characteristics of WMNs such as interference and topology. There have been many related works that solve similar problems such as base station placement in cellular networks and gateway node selection in WMNs. However, none of them actually considers the interference to the mesh clients from non-associated mesh routers in determining the locations of the mesh routers. By simulations, we show that the proposed scheme improves the performance by 30-40% compared to the random selection scheme.