• Journal of Electrical Engineering and Technology
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• 제13권4호
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• pp.1740-1751
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• 2018

This paper proposes a novel fault tolerant tracking control (FTTC) scheme for a class of nonlinear systems with actuator failures based on the policy iteration (PI) algorithm and the adaptive fault observer. The estimated actuator failure from an adaptive fault observer is utilized to construct an improved performance index function that reflects the failure, regulation and control simultaneously. With the help of the proper performance index function, the FTTC problem can be transformed into an optimal control problem. The fault tolerant tracking controller is composed of the desired controller and the approximated optimal feedback one. The desired controller is developed to maintain the desired tracking performance at the steady-state, and the approximated optimal feedback controller is designed to stabilize the tracking error dynamics in an optimal manner. By establishing a critic neural network, the PI algorithm is utilized to solve the Hamilton-Jacobi-Bellman equation, and then the approximated optimal feedback controller can be derived. Based on Lyapunov technique, the uniform ultimate boundedness of the closed-loop system is proven. The proposed FTTC scheme is applied to reconfigurable manipulators with two degree of freedoms in order to test the effectiveness via numerical simulation.

• 대한전기학회논문지:전력기술부문A
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• 제50권3호
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• pp.120-130
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• 2001

We present an approach to parallelizing optimal power flow that is suitable for distributed implementation and is applicable to very large interconnected power systems. This approach can be used by utilities to optimize economy interchange without disclosing details of their operating costs to competitors. Recently, it is becoming necessary to incorporate contingency constraints into the formulation, and more rapid updates of telemetered data and faster solution time are becoming important to better track changes in the system. This concern led to a research to develop an efficient algorithm for a distributed optimal power flow based on the Auxiliary Problem Principle and to study the convergence rate improvement of the distributed algorithm. The objective of this paper is to find a set of control parameters with which the Auxiliary Problem Principle (Algorithm - APP) can be best implemented in solving optimal power flow problems. We employed several IEEE Reliability Test Systems, and Korea Power System to demonstrate the alternative parameter sets.

• 산업경영시스템학회지
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• 제19권39호
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• pp.9-18
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• 1996

Many algorithms have been developed for optimizing the asymmectric traveling salesman problem known as a representative NP-Complete problem. The most efficient ones of them are branch and bound algorithms based on the subtour elimination approach. To increase efficiency of the branch and bound algorithm. number of decision nodes should be decreased. For this the minimum bound that is more close at the optimal solution should be found or an effective bounding strategy should be used. If the optimal solution has been known, we may apply it usefully to branching. Because a good feasible solution should be found as soon as possible and have similar features of the optimal solution. By the way, the upper bound solution in branch and bound algorithm is most close at the optimal solution. Therefore, the upper bound solution can be used instead of the optimal solution and information of which can be applied to new branching criteria. As mentioned above, this paper will propose an effective branching rule using the information of the upper bound solution in the branch and bound algorithm. And superiority of the new branching rule will be shown by comparing with Bellmore-Malone's one and carpaneto-Toth's one that were already proposed.

• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.39-45
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• 2020

In the production process, the buffer acts as a buffer to alleviate some of the problems such as delays in delivery and process control failures in unexpected situations. Determining the optimal buffer size can contribute to system performance, such as increased output and resource utilization. However, there are difficulties in allocating the optimal buffer due to the complexity of the process or the increase in the number of variables. Therefore, the purpose of this research is proposing an optimal buffer allocation that maximizes throughput. First step is to design the production process to carry out the research. The second step is to maximize the throughput through the harmony search algorithm and to find the buffer capacity that minimizes the lead time. To verify the efficiency, comparing the ratio of the total increase in throughput to the total increase in buffer capacity.

• Structural Engineering and Mechanics
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• 제87권4호
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• pp.305-315
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• 2023

The idea of the combination of the fractional-order operators with the brain emotional learning-based intelligent controller (BELBIC) is developed for implementation in seismic-excited structures equipped with active mass damper (AMD). For this purpose, a new design framework of the mentioned combination namely fractional-order BEBIC (FOBELBIC) is proposed based on a modified-teaching-learning-based optimization (MTLBO) algorithm. The seismic performance of the proposed controller is then evaluated for a 15-story building equipped with AMD subjected to two far-field and two near-field earthquakes. An optimal BELBIC based on the MTLBO algorithm is also introduced for comparison purposes. In comparison with the structure equipped with a passive tuned mass damper (TMD), an average reduction of 44.7% and 42.8% are obtained in terms of the maximum absolute and RMS top floor displacement for FOBELBIC, while these reductions are obtained as 30.4% and 30.1% for the optimal BELBIC, respectively. Similarly, the optimal FOBELBIC results in an average reduction of 42.6% and 39.4% in terms of the maximum absolute and RMS top floor acceleration, while these reductions are given as 37.9% and 30.5%, for the optimal BELBIC, respectively. Consequently, the superiority of the FOBELBIC over the BELBIC is concluded in the reduction of maximum and RMS seismic responses.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2617-2622
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• 2003

The pH neutralization process has long been taken as a representative benchmark problem of nonlinear chemical process control due to its nonlinearity and time-varying nature. For general nonlinear processes, it is difficult to control with a linear model-based control method so nonlinear controls must be considered. Among the numerous approaches suggested, the most rigorous approach is the dynamic optimization. However, as the size of the problem grows, the dynamic programming approach is suffered from the curse of dimensionality. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach was proposed by Bertsekas and Tsitsiklis (1996). The NDP approach is to utilize all the data collected to generate an approximation of optimal cost-to-go function which was used to find the optimal input movement in real time control. The approximation could be any type of function such as polynomials, neural networks and etc. In this study, an algorithm using NDP approach was applied to a pH neutralization process to investigate the feasibility of the NDP algorithm and to deepen the understanding of the basic characteristics of this algorithm. As the global approximator, the neural network which requires training and k-nearest neighbor method which requires querying instead of training are investigated. The global approximator requires optimal control strategy. If the optimal control strategy is not available, suboptimal control strategy can be used even though the laborious Bellman iterations are necessary. For pH neutralization process it is rather easy to devise an optimal control strategy. Thus, we used an optimal control strategy and did not perform the Bellman iteration. Also, the effects of constraints on control moves are studied. From the simulations, the NDP method outperforms the conventional PID control.

• 한국산학기술학회논문지
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• 제12권6호
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• pp.2698-2706
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• 2011

본 논문에서는 기존의 고압배전선로에 설치되어 운용되고 있는 선로전압조정장치(Step Voltage Regulator, 이하 SVR)의 최적 전압조정 알고리즘을 제시하고 이를 이용하여 수용가전압을 평가할 수 있는 최적 평가시스템을 제시한다. 현재 SVR은 일정전압 송출방식을 사용하고 있지만, 대규모의 태양광, 풍력 등의 분산전원이 배전계통에 도입되는 경우, 전압품질[과전압/저전압] 문제점을 발생시킬 수 있다. 따라서 본 논문에서는 선로전압조정장치의 LDC운용에 필수적인 정정치를 계산하기 위하여, 비선형 최적화수법인 선형회귀법을 사용하여 선로전압조정장치의 최적 운용 알고리즘을 제시하였고, 제안한 알고리즘을 바탕으로 평가시스템을 제작하여, 분산전원의 타입과 연계용량에 따라 다양한 시뮬레이션을 수행하여 제안한 알고리즘의 유효성을 확인하였다.

• 한국지능시스템학회논문지
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• 제13권1호
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• pp.12-17
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• 2003

데이터의 군집화를 수행할 때 최적 군집수 결정은 군집 결과의 성능에 많은 영향을 미친다. 특히 K-means 방법에서는 초기 군집수 K에 따라 군집결과의 성능 차이가 많이 나타난다. 하지만 대다수의 군집분석에서 초기 군집수의 결정은 경험을 바탕으로 하여 주관적으로 결정된다. 이때 개체수와 속성수가 증가하면 이러한 결정은 더욱 어려워지며 이때 결정된 군집수가 최적이 된다는 보장도 없다. 본 논문에서는 군집의 수를 자동으로 결정하고 그 결과의 유효성을 보장하기 위해 유전자 알고리즘에 기반한 최적 군집수 결정 방안을 제안한다. 데이터의 속성에 근거한 초기 해 집단이 생성되고, 해 집단 내에서 최적화된 군집수를 찾기 위해 교차 연산이 이루어진다. 적합도 값은 전체 군집화의 비 유사성의 합의 역으로 결정되어 전체적인 군집화 성능이 향상되는 방향으로 수렴된다. 또한 지역 국소값을 해결하기 위해 돌연변이 연산이 사용된다. 그리고 유전자 알고리즘의 학습 시간의 비용을 줄이기 위해 붓스트랩 기법이 적용된다

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2007년도 추계종합학술대회
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• pp.565-569
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• 2007

최적경로 탐색은 텔레매틱스에서 효용가치가 높은 기술이다. 그렇지만 기존 시스템의 최단 경로가 항상 최적 경로라고 볼 수 없다. 즉 도로위에서는 이동시간이 최소인 경로를 최적경로라고 정의할 수 있다. 이런 최적경로를 탐색하기 위한 여러 가지의 기술 및 알고리즘들이 존재한다. 계층 경로 알고리즘은 로드 네트워크를 주, 부 레이어로 나누어 경로를 탐색한다. 2단계로 나누어 경로를 탐색하기 때문에 경로 탐색 연산시간의 성능은 뛰어나다. 탐색되는 경로 또한 최적 경로에 가까운 결과를 가진다. 2단계로 계층을 분할 할 때, 부 도로를 포함하는 주요도로 영역의 할당 방법은 성능에 큰 영향을 미친다. 본 논문에서는 계층 경로 알고리즘에서의 주요도로 선정을 위한 탐색 영역 결정 기법에 대하여 제안한다. 그리고 제안한 기법을 계층 경로 탐색에 적용하는 방법을 기술한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권12호
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• pp.5842-5861
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• 2019

A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this paper proposes "An Optimal Driving Support Strategy(ODSS) based on an Genetic Algorithm for Autonomous Vehicles" which determines the optimal strategy of an autonomous vehicle by analyzing not only the external factors, but also the internal factors of the vehicle(consumable conditions, RPM levels etc.). The proposed ODSS consists of 4 modules. The first module is a Data Communication Module (DCM) which converts CAN, FlexRay, and HSCAN messages of vehicles into WAVE messages and sends the converted messages to the Cloud and receives the analyzed result from the Cloud using V2X. The second module is a Data Management Module (DMM) that classifies the converted WAVE messages and stores the classified messages in a road state table, a sensor message table, and a vehicle state table. The third module is a Data Analysis Module (DAM) which learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. The fourth module is a Data Visualization Module (DVM) which displays the optimal driving strategy and the current driving conditions on a vehicle monitor. This paper compared the DCM with existing vehicle gateways and the DAM with the MLP and RF neural network models to validate the ODSS. In the experiment, the DCM improved a loss rate approximately by 5%, compared with existing vehicle gateways. In addition, because the DAM improved computation time by 40% and 20% separately, compared with the MLP and RF, it determined RPM, speed, steering angle and lane changes faster than them.