• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.620-626
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• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.

• 한국수자원학회논문집
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• 제39권8호
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• pp.677-690
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• 2006

본 연구에서는 LH-OAT (Latin Hypercube Ore factor At a Time) 민감도분석 방법과 SCE-UA (Shuffled Complex Evolution at University of Arizona) 최적화 기법을 적용하여 보청천 유역에서 SWAT모형에 대한 자동보정 방법을 제시하였다. LH-OAT 방법은 전역 민감도분석과 부분 민감도 분석의 장점을 조합하여 가용매개변수 공간에 대하여 효율적으로 매개변수의 민감도 분석이 가능하게 하였다. LH-OAT민감도 분석으로부터 결정된 매개변수의 민감도 등급은 SWAT 모형의 자동보정 과정에서 요구되는 보정대상 매개변수의 선택에 유용하게 적용될 수 있다. SCE-UA 방법을 적용한 SWAT모형의 자동보정 해석결과는 보정자료, 보정매개변수, 통계적 오차의 선택에 따라서 모형의 성능이 좌우되었다. 보정기간과 보정매개변수가 증가함에 따라 검증기간에 대한 RMSE (Root Mean Square Error), NSEF (Nash-Sutcliffe Model Efficiency), RMAE (Relative Mean Absolute Error), NMSE (Normalized Mean Square Error) 등의 모형오차는 감소하였지만, NAE (Normalized Average Error) 및 SDR(Standard Deviation Ratio)은 개선되지 않았다. SWAT모형의 보정에 적용되는 보정자료, 보정매개변수 및 모형평가를 위한 통계적 오차 선택이 해석결과에 미치는 복잡한 영향을 이해하기 위하여 다양한 대표유역을 대상으로 추가적인 연구가 필요하다.

• 전자공학회논문지
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• 제53권6호
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• pp.101-109
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• 2016

카메라 센서는 사람의 눈에 비해 제한적인 다이나믹 레인지를 갖기 때문에 영상 획득 시 실제 보이는 것과 다른 모습의 영상을 획득하게 된다. 이러한 문제를 영상 처리를 통해 해결하고자 톤 맵핑 함수를 이용한 방법들과 사람의 눈을 모델링한 레티넥스 이론 기반의 방법들이 연구되었다. 하지만 이러한 방법들은 후광 효과가 발생하거나 영상 개선 시 전역 또는 국부 콘트라스트 향상이 제한적이라는 단점이 있다. 제안하는 방법에서는 영상의 광원 정보를 레티넥스 이론을 활용하여 추정한 후 이를 영상의 품질 향상을 위해 다이나믹 레인지를 최적화시키는데 이용한다. 이 과정에서 후광 효과가 발생하는 것을 방지하기 위해 유사 밝기 영역에서만 평탄화가 이루어지고 밝기 차가 나는 영역은 밝기 차를 유지하도록 한다. 또한 톤 맵핑 함수 적용 시 하나의 화소가 아닌 주변 영역 정보와 추정된 광원 정보를 모두 고려하여 전역 및 국부 콘트라스트가 동시에 향상되는 알고리즘을 제안한다. 실험 결과들을 통해 제안하는 방법이 기존 방법에 비해 국부 콘트라스트 수치가 약 0.4 향상 되었고, 시각적인 면에서도 콘트라스트 향상과 함께 암부와 명부를 동시에 효과적으로 표현한 것을 확인 할 수 있다.

• 대한전기학회논문지:전력기술부문A
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• 제54권8호
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• pp.375-387
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• 2005

This paper presents an application of parallel Genetic Algorithm-Tabu Search (GA-TS) algorithm to search an optimal solution of a service restoration in distribution systems. The main objective of service restoration of distribution systems is, when a fault or overload occurs, to restore as much load as possible by transferring the do-energized load in the out of service area via network reconfiguration to the appropriate adjacent feeders at minimum operational cost without violating operating constraints, which is a combinatorial optimization problem. This problem has many constraints with many local minima to solve the optimal switch position. This paper develops parallel GA-TS algorithm for service restoration of distribution systems. In parallel GA-TS, GA operators are executed for each processor. To prevent solutions of low fitness from appearing in the next generation, strings below the average fitness are saved in the tabu list. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper $10\%$ of the population to enhance the local searching capabilities. With migration operation, best string of each node is transferred to the neighboring node after predetermined iterations are executed. For parallel computing, we developed a PC cluster system consists of 8 PCs. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through ethernet switch based fast ethernet. To show the validity of the proposed method, proposed algorithm has been tested with a practical distribution system in Korea. From the simulation results, we can find that the proposed algorithm is efficient for the distribution system service restoration in terms of the solution quality, speedup, efficiency and computation time.

• 대한전기학회논문지:전력기술부문A
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• 제53권10호
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• pp.556-564
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• 2004

This paper presents an application of parallel Genetic Algorithm-Tabu Search(GA-TS) algorithm to search an optimal solution of a reconfiguration in distribution system. The aim of the reconfiguration of distribution systems is to determine switch position to be opened for loss minimization in the radial distribution systems, which is a discrete optimization problem. This problem has many constraints and very difficult to solve the optimal switch position because it has many local minima. This paper develops parallel GA-TS algorithm for reconfiguration of distribution systems. In parallel GA-TS, GA operators are executed for each processor. To prevent solution of low fitness from appearing in the next generation, strings below the average fitness are saved in the tabu list. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper 10% of the population to enhance the local searching capabilities. With migration operation, best string of each node is transferred to the neighboring node aster predetermined iterations are executed. For parallel computing, we developed a PC-cluster system consisting of 8 PCs. Each PC employs the 2 GHz Pentium Ⅳ CPU and is connected with others through ethernet switch based fast ethernet. To show the usefulness of the proposed method, developed algorithm has been tested and compared on a distribution systems in the reference paper. From the simulation results, we can find that the proposed algorithm is efficient and robust for the reconfiguration of distribution system in terms of the solution qualify. speedup. efficiency and computation time.

• 한국태양에너지학회:학술대회논문집
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• 한국태양에너지학회 2012년도 춘계학술발표대회 논문집
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• pp.400-403
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• 2012

$Cu_2ZnSnSe_4$(CZTSe) is emerged as a promising material for thin-film solar cells because of non-toxic, inexpensive and earth abundant more than $Cu(In,Ga)Se_2$ materials. For fabricating compound semiconductor thin-film solar cells, CdS is widely used for a buffer layer which fabricated by a chemical bath deposition method (CBD). Through the experiment, we controlled deposition temperature and mol ratio of solution conditions to find the proper grain 크기 and exact composition. The optimum CdS layers were characterized in terms of surface morphology by using a scanning electron microscope (SEM) and atomic force microscope (AFM). The optimized CdS layer process was applied on CZTSe thin-films. The thickness of buffer layer related with device performance of solar cells which controlled by deposition time. Local surface potential of CdS/CZTSe thin-films was investigated by Kelvin probe force microscopy (KPFM). From these results, we can deduce local electric properties with different thickness of buffer layer on CZTSe thin-films. Therefore, we investigated the effect of CdS buffer layer thickness on the CZTSe thin-films for decreasing device losses. From this study, we can suggest buffer layer thickness which contributes to efficiencies and device performance of CZTSe thin-film solar cells.

• KIEE International Transactions on Power Engineering
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• 제5A권2호
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• pp.116-124
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• 2005

This paper presents an application of the parallel Genetic Algorithm-Tabu Search (GA- TS) algorithm, and that is to search for an optimal solution of a reconfiguration in distribution systems. The aim of the reconfiguration of distribution systems is to determine the appropriate switch position to be opened for loss minimization in radial distribution systems, which is a discrete optimization problem. This problem has many constraints and it is very difficult to solve the optimal switch position because of its numerous local minima. This paper develops a parallel GA- TS algorithm for the reconfiguration of distribution systems. In parallel GA-TS, GA operators are executed for each processor. To prevent solution of low fitness from appearing in the next generation, strings below the average fitness are saved in the tabu list. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper 10$\%$ of the population to enhance the local searching capabilities. With migration operation, the best string of each node is transferred to the neighboring node after predetermined iterations are executed. For parallel computing, we developed a PC-cluster system consisting of 8 PCs. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through switch based rapid Ethernet. To demonstrate the usefulness of the proposed method, the developed algorithm was tested and is compared to a distribution system in the reference paper From the simulation results, we can find that the proposed algorithm is efficient and robust for the reconfiguration of distribution system in terms of the solution quality, speedup, efficiency, and computation time.

• KIEE International Transactions on Power Engineering
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• 제5A권3호
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• pp.269-279
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• 2005

This paper presents an application of the parallel Adaptive Evolutionary Algorithm (AEA) to search an optimal solution of a reconfiguration in distribution systems. The aim of the reconfiguration is to determine the appropriate switch position to be opened for loss minimization in radial distribution systems, which is a discrete optimization problem. This problem has many constraints and it is very difficult to find the optimal switch position because of its numerous local minima. In this investigation, a parallel AEA was developed for the reconfiguration of the distribution system. In parallel AEA, a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner are used in order to combine the merits of two different evolutionary algorithms: the global search capability of GA and the local search capability of ES. In the reproduction procedure, proportions of the population by GA and ES are adaptively modulated according to the fitness. After AEA operations, the best solutions of AEA processors are transferred to the neighboring processors. For parallel computing, a PC-cluster system consisting of 8 PCs·was developed. Each PC employs the 2 GHz Pentium IV CPU, and is connected with others through switch based fast Ethernet. The new developed algorithm has been tested and is compared to distribution systems in the reference paper to verify the usefulness of the proposed method. From the simulation results, it is found that the proposed algorithm is efficient and robust for distribution system reconfiguration in terms of the solution quality, speedup, efficiency, and computation time.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2014년도 추계학술대회
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• pp.342-345
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• 2014

기계학습을 이용한 네트워크 침입탐지시스템은 선택된 특징 조합에 따라 정확성 및 효율성 측면에서 크게 영향을 받는다. 하지만 일반적으로 사용되는 침입탐지용 특징들로부터 최적의 조합을 찾아내는 일은 많은 계산량을 요구한다. 예를 들어 n개로 구성된 특징들로부터 가능한 특징조합은 $2^n-1$ 개이다. 본 논문에서는 이러한 문제를 해결하기 위한 최적 특징 선택 알고리즘을 제시한다. 제안한 알고리즘은 최적화 문제 해결을 위한 대표적인 메타 휴리스틱 알고리즘인 지역탐색 알고리즘에 기반 한다. 또한 특징 조합을 평가를 위해 선택된 특징 요소와 k-means 군집화 알고리즘을 이용해 구해진 군집화의 정확성을 비용함수로 사용한다. 제안한 특징 선택 알고리즘의 평가를 위해 NSL-KDD 데이터와 인공 신경망을 사용해 특징 모두를 사용한 경우와 비교한다.

• 대한토목학회논문집
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• 제26권2A호
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• pp.285-292
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• 2006

이 연구는 탄소섬유쉬트-콘크리트 부착이음 실험 결과로부터 국부적인 부착모텔(부착응력-미끄럼 모델)을 결정하는 방법을 제안하고, 실제 실험 결과와 비교하여 이러한 부착 조건에 적용 기능한 부착모델을 제시한다. 부착모델의 형상은 임의의 곡선 형태를 고려할 수 있도록 디중선형곡선(multi-linear curve)으로 가정하였으며, 수치적인 방법으로 부착이음의 해를 계산하여 실험 결과와 오차를 최소화하는 방법으로 부착모델을 결정하였다. 이중선형곡선(bilinear curve)을 도입한 부착모델 역시 최적화를 수행하여 다중선형모텔과 비교하였다. 최적화의 대상은 동일 조건의 부착모텔에 대해 여러 실험체로부터 구한 극한하중-부착길이 곡선과 개별 실험체의 하중-변위 곡선이다. 최적화를 위한 정식화는 physical programming을 사용하였으며 최적화 방법은 유전알고리즘(genetic algorithm)을 이용하였다.