• Structural Engineering and Mechanics
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• 제26권4호
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• pp.377-392
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• 2007

A discrete sliding mode control (SMC) method based on hybrid model of neural network and nominal model is proposed to reduce the vibration of flexible structures, which is a robust active controller developed by using a sliding manifold approach. Since the thick boundary layer will reduce the virtue of SMC, the multilayer feed-forward neural network is adopted to model the uncertainty part. The neural network is trained by Levenberg-Marquardt backpropagation. The design objective of the sliding mode surface is based on the quadratic optimal cost function. In course of running, the input signal of SMC come from the hybrid model of the nominal model and the neural network. The simulation shows that the proposed control scheme is very effective for large uncertainty systems.

• Journal of Electrical Engineering and Technology
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• 제4권4호
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• pp.429-437
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• 2009

This paper proposes a hybrid voltage controller based on a hierarchical control structure for implementation in the Jeju power system. The hybrid voltage controller utilizes the coordination of various reactive power devices such as generators, switched shunt devices and LTC to regulate the pilot voltage of an area or zone. The reactive power source can be classified into two groups based on action characteristics, namely continuous and discrete. The controller, which regulates the pilot bus voltage, reflects these characteristics in the coordination of the two types of reactive power source. However, the continuous type source like generators is a more important source than the discrete type for an emergency state such as a voltage collapse, thereby requiring a more reactive power reserve of the continuous type to be utilized in the coordination in order to regulate the pilot bus voltage. Results show that the hybrid controller, when compared to conventional methods, has a considerable improvement in performance when adopted to control the pilot bus voltage of the Jeju island system.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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• pp.568-579
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• 2018

Virtual power plants can be regarded as systems that have entered the network after restructure of power systems. In fact, these plants are a set of consumers capable of consuming and generating power. In response to widespread implementation of plug-in hybrid electric vehicles, further investigation of energy management in this type of power plants seems to be of great value. In effect, these vehicles are able to receive and inject power from/into the network. Hence, study of the effects of these vehicles on management of virtual power plants seems to be illuminative. In this paper, management of power consumption/generation in virtual power plants has been investigated in the presence of hybrid electric vehicles. The objective function of virtual power plants problem management is to minimize the overall costs including not only the costs of energy production in power generation units, fuels, and degradation of batteries of vehicles, but also the costs of purchasing electricity from the network. Furthermore, the constraints on the operational of plants, loads and hybrid vehicles, level of penalty for greenhouse gas emissions ($CO_2$ and $NO_x$) produced by power plants and vehicles, and demand response to the immediate price of market have all been attended to in the present study. GAMS/Cplex software system and sample power system have been employed to pursue computer implementation and simulation.

• 전력전자학회논문지
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• 제10권1호
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• pp.87-94
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• 2005

본 논문은 저가형 고정도 아날로그 디지털 혼합형 엔코더에서 센서 취부 오차에 의해 발생하는 정지 2축 좌표상의 두 아날로그신호의 크기, 위상오차 문제 보상에 관한 연구이다. 기존의 혼합형 엔코더에서 문제시되고 있는 두 아날로그 위치정보 신호의 크기문제는 상대 크기에 대하여 정규화 함으로써 해결하였으며, 센서 취부시 발생하는 위치오차문제는 정지 2축 좌표축을 센서의 위치 오차분을 보상할 수 있도록 회전함으로써 보상할 수 있었다. 또한 제안된 새로운 방식의 위치 검출기법을 DSP의 QEP기능과 A/D변환기를 사용한 실험을 통하여 그 타당성을 검증하였다.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2009년도 하계학술발표대회 논문집
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• pp.1061-1064
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• 2009

The objective of this study is to find the optimal operational planning of the hybrid cooling system, which is combined by ice storage system and the absorption chiller. The optimization technique used in this study is dynamic programming. The objective function is summed cost during a day including charge and discharge periods of ice storage system and operation time of absorption chiller. Assuming that initially ice storage tank is stored fully and the cooling load is perfectly predicted for the operational planning. This method provides the most efficient and economic combination of equipment operational planning for cooling with respect to energy consumption cost.

• Transactions on Control, Automation and Systems Engineering
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• 제2권1호
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• pp.69-75
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• 2000

The aim of the work described in this paper is to develop a complex underground acoustic system which detects and localizes the origin of an underground hammering sound using an array of hydrophones located about 100m underground. Three different methods for the sound localization will be presented, a time-delay method, a power-attenuation method and a hybrid method. In the time-delay method, the cross correlation of the signals received from the array of sensors is used to calculate the time delays between those signals. In the power-attenuation method, the powers of the received signals provide a measure of the distances of the source from the sensors. In the hybrid method, both informations of time-delays and power-ratios are coupled together to produce better performance of position estimation. A new acoustic imaging technique has been developed for improving the hybrid method. This new acoustic imaging method shows the multi-dimensional distribution of the normalized cost function, so as to indicate the trend of the minimizing direction toward the source location. For each method the sound localization is carried out in three dimensions underground. The distance between the true and estimated origins of the source is 28m for a search area of radius 250m.

• Geomechanics and Engineering
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• 제24권3호
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• pp.237-251
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• 2021

The main purpose of this study is to investigate the performance of the proposed hybrid teaching-learning based optimization algorithm on the optimum design of reinforced concrete (RC) cantilever retaining walls. For this purpose, three different design examples are optimized with 100 independent runs considering continuous and discrete variables. In order to determine the algorithm performance, the optimization results were compared with the outcomes of the nine powerful meta-heuristic algorithms applied to this problem, previously: the big bang-big crunch (BB-BC), the biogeography based optimization (BBO), the flower pollination (FPA), the grey wolf optimization (GWO), the harmony search (HS), the particle swarm optimization (PSO), the teaching-learning based optimization (TLBO), the jaya (JA), and Rao-3 algorithms. Moreover, Rao-1 and Rao-2 algorithms are applied to this design problem for the first time. The objective function is defined as minimizing the total material and labor costs including concrete, steel, and formwork per unit length of the cantilever retaining walls subjected to the requirements of the American Concrete Institute (ACI 318-05). Furthermore, the effects of peak ground acceleration value on minimum total cost is investigated using various stem height, surcharge loads, and backfill slope angle. Finally, the most robust results were obtained by HTLBO with 50 populations. Consequently the optimization results show that, depending on the increase in PGA value, the optimum cost of RC cantilever retaining walls increases smoothly with the stem height but increases rapidly with the surcharge loads and backfill slope angle.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.627-630
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• 2004

The PID type controller has been widely used in industrial application due to its simply control structure, ease of design, and inexpensive cost. However, control performance of the PID type controller suffers greatly from high uncertainty and nonlinearity of the system, large disturbances and so on. This paper presents a hybrid fuzzy logic proportional plus conventional integral derivative controller (fuzzy P+ID). In comparison with a conventional PID controller, only one additional parameter has to be adjusted to tune the fuzzy P+ID controller. In this case, the stability of a system remains unchanged after the PID controller is replaced by the fuzzy P+ID controller without modifying the original controller parameters. Finally, the proposed hybrid fuzzy P+ID controller is applied to BLDC motor drive. Simulation results demonstrated that the control performance of the proposed controller is better than that of the conventional controller.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.330-333
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• 2003

Initial cluster size for clustering of partitioning methods is very important to the clustering result. In K-means algorithm, the result of cluster analysis becomes different with optimal cluster size K. Usually, the initial cluster size is determined by prior and subjective information. Sometimes this may not be optimal. Now, more objective method is needed to solve this problem. In our research, we propose a hybrid genetic algorithm, a tree induction based evolution algorithm, for determination of optimal cluster size. Initial population of this algorithm is determined by the number of terminal nodes of tree induction. From the initial population based on decision tree, our optimal cluster size is generated. The fitness function of ours is defined an inverse of dissimilarity measure. And the bagging approach is used for saying computational time cost.

• 한국정보전자통신기술학회논문지
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• 제5권1호
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• pp.33-44
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• 2012

이 논문에서는 얼개가 새로운 회귀 신경망을 제안하고, 그 신경망이 어떤 이산 시간 동적 시스템도 동정화 할 수 있음을 보인다. 또한, 제안한 신경망을 써서 유한 상태 자동기계를 부호화, 동정화, 그리고 추출하는 데에 적용하여 그 성능을 살펴본다. 제안한 신경망에 고친 비용함수를 쓰고 혼합 그리디 모의 담금질 방법으로 학습시키면 유한 상태 자동기계를 동정화하는 성능이 일반적으로 다른 기법보다 더 낫다는 것을 모의실험으로 보인다.