• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1017-1024
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• 2010

Under the Kyoto Protocol many countries have been requested to participate in emissions trading with the assigned $CO_2$ emissions. In this environment, it is inevitable to change the system and market operation in deregulated power systems, and then ensuring safety margin is becoming more important for balancing system security, economy and $CO_2$ emissions. Nowadays, available transfer capability (ATC) is a key index of the remaining capability of a transmission system for future transactions. This paper presents a novel approach to the ATC evaluation with $CO_2$ emissions using multi-objective particle swarm optimization (MOPSO) technique. This technique evolves a multi-objective version of PSO by proposing redefinition of global best and local best individuals in multi-objective optimization domain. The optimal power flow (OPF) method using MOPSO is suggested to solve multi-objective functions including fuel cost and $CO_2$ emissions simultaneously. To show its efficiency and effectiveness, the results of the proposed method is comprehensively realized by a comparison with the ATC which is not including $CO_2$ emissions for the IEEE 30-bus system, and is found to be quite promising.

• Structural Engineering and Mechanics
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• v.61 no.2
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• pp.283-293
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• 2017

The main contribution of the present paper is to propose an intelligent fuzzy inference system approach for modeling the debonding strength of masonry elements retrofitted with Fiber Reinforced Polymer (FRP). To achieve this, the hybrid of meta-heuristic optimization methods and adaptive-network-based fuzzy inference system (ANFIS) is implemented. In this study, particle swarm optimization with passive congregation (PSOPC) and real coded genetic algorithm (RCGA) are used to determine the best parameters of ANFIS from which better bond strength models in terms of modeling accuracy can be generated. To evaluate the accuracy of the proposed PSOPC-ANFIS and RCGA-ANFIS approaches, the numerical results are compared based on a database from laboratory testing results of 109 sub-assemblages. The statistical evaluation results demonstrate that PSOPC-ANFIS in comparison with ANFIS-RCGA considerably enhances the accuracy of the ANFIS approach. Furthermore, the comparison between the proposed approaches and other soft computing methods indicate that the approaches can effectively predict the debonding strength and that their modeling results outperform those based on the other methods.

• Journal of Electrical Engineering and Technology
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• v.2 no.4
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• pp.413-419
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• 2007

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 3D-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.12
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• pp.870-872
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• 2014

In this paper, we devise a cost function by considering the energy consumption rate and the remaining energy of a peer. Then, we propose a parent peer selection method that chooses the least cost peer in the system in a distributed manner. On the contrary to the conventional method that makes each peer select the least cost neighbor as a parent peer, the proposed method chooses a parent peer using the swarm intelligence formed among a set of peers. Therefore, the proposed method could extent distributedly the number of peers searched for parent peer selection. Thus, compared to the conventional method, the proposed method increases the probability of being a parent peer as the cost of a peer becomes smaller with less operational load.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1347-1355
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• 2015

In this study, we compare the recognition performance using PCA and RBFNNs for introducing robust face recognition system to pose variations based on pose estimation. proposed face recognition system uses Honda/UCSD database for comparing recognition performance. Honda/UCSD database consists of 20 people, with 5 poses per person for a total of 500 face images. Extracted image consists of 5 poses using Multiple-Space PCA and each pose is performed by using (2D)²PCA for performing pose classification. Linear polynomial function is used as connection weight of RBFNNs Pattern Classifier and parameter coefficient is set by using Particle Swarm Optimization for model optimization. Proposed (2D)²PCA-based face pose classification performs recognition performance with PCA, (2D)²PCA and RBFNNs.

• Journal of Electrical Engineering and Technology
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• v.11 no.3
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• pp.715-723
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• 2016

This paper proposes a design of optimal PI(D) controller for brushless DC (BLDC) motor speed control by the particle swarm optimization (PSO), one of the powerful metaheuristic optimization search techniques. The proposed control system is implemented on the TMS320F28335 DSP board interfacing to MATLAB/SIMULINK. With Back EMF detection, the proposed system is considered as a class of sensorless control. This scheme leads to the speed adjustment of the BLDC motor by PWM. In this work, the BLDC motor of 100 watt is conducted to investigate the control performance. As results, it was found that the speed response of BLDC motor can be regulated at the operating speed of 800 and 1200 rpm in both no load and full load conditions. Very satisfactory responses of the BLDC system can be successfully achieved by the proposed control structure and PSO-based design approach.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.325-328
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• 2008

본 논문에서는 비선형 모델의 설계를 위해 Type-2 퍼지 논리 집합을 이용하여 불확실성 문제를 다룬다. 퍼지 논리 시스템의 멤버쉽 함수와 규칙의 구조는 불확실성이 존재하는 언어적인 정보 또는 수치적 데이터를 바탕으로 설계된다. 기존의 Type-1 퍼지 논리 시스템은 외부의 노이즈와 같은 불확실성을 효율적으로 취급할 수 없다. 그러나 Type-2 퍼지 논리 시스템은 불확실한 정보까지 멤버쉽 함수로 표현함으로서 불확실성을 효과적으로 다룰 수 있다. 따라서 본 논문에서는 규칙의 전 ${\cdot}$ 후반부가 Type-2 퍼지 집합으로 구성된 Type-2 퍼지 논리 시스템을 설계하고 불확실성의 변화에 대한 비선형 모델의 성능을 비교한다. 여기서 규칙 전반부 멤버쉽 함수의 정점 선택은 C-means 클러스터링 알고리즘을 이용하고, 규칙 후반부 퍼지 집합의 정점 결정에는 입자 군집 최적화(PSO : Particle Swarm Optimization) 알고리즘을 사용한다. 마지막으로, 비선형 모델 평가에 대표적으로 이용되는 가스로 시계열 데이터를 제안된 모델에 적용하고, 입력 데이터에 인위적인 노이즈가 포함되었을 경우 Type-2 퍼지 논리 시스템이 기존의 Type-1 퍼지 논리 시스템보다 우수함을 보인다.

• Journal of KIISE
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• v.43 no.12
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• pp.1315-1324
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• 2016

Recently, the increasing interest in drones has resulted in development of new related technologies. Attention has been focused toward research on swarm flight which controls drones simultaneously without collision. Thus, complicated missions can be completed rapidly through collaboration between drones. Due to low position accuracy, GPS is not appropriate for the outdoor mission involving accurate flight. In addition, the inaccurate position estimation of GPS gives rise to the serious problem of collision, since many drones are controlled in a narrow space. In this study, we increased the accuracy of position estimation through various sensors with Real-Time Kinematic-GPS (RTK-GPS). The mode switching algorithm was proposed to minimize the problem of sensor error. In addition, we introduced the outdoor swarm flight system based on the proposed position estimation.

• Structural Engineering and Mechanics
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• v.61 no.3
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• pp.347-357
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• 2017

The applications of active control is being more popular nowadays. Several control algorithms have been developed to determine optimum control force. In this paper, a Chaotic Particle Swarm Optimization (CPSO) technique, based on Logistic map, is used to compute the optimum control force of active tendon system. A chaotic exploration is used to search the solution space for optimum control force. The response control of Multi-Degree of Freedom (MDOF) shear buildings, equipped with active tendons, is introduced as an optimization problem, based on Instantaneous Optimal Active Control algorithm. Three MDOFs are simulated in this paper. Two examples out of three, which have been previously controlled using Lattice type Probabilistic Neural Network (LPNN) and Block Pulse Functions (BPFs), are taken from prior works in order to compare the efficiency of the current method. In the present study, a maximum allowable value of control force is added to the original problem. Later, a twenty-story shear building, as the third and more realistic example, is considered and controlled. Besides, the required Central Processing Unit (CPU) time of CPSO control algorithm is investigated. Although the CPU time of LPNN and BPFs methods of prior works is not available, the results show that a full state measurement is necessary, especially when there are more than three control devices. The results show that CPSO algorithm has a good performance, especially in the presence of the cut-off limit of tendon force; therefore, can widely be used in the field of optimum active control of actual buildings.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.355-362
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• 2009

In this paper, we present a framework for managing group behaviors in multi-agent swarm systems. The framework explores the benefits by dynamic associations with the proposed artificial potential functions to realize complex swarming behaviors. A key development is the introduction of a set of flocking by dynamic association (DA) algorithms that effectively deal with a host of swarming issues such as cooperation for fast migration to a target, flexible and agile formation, and inter-agent collision avoidance. In particular, the DA algorithms employ a so-called systematic singular association (SSA) rule for fast migration to a target and compact formation through inter-agent interaction. The resulting algorithms enjoy two important interrelated benefits. First, the SSA rule greatly reduces time-consuming for migration and satisfies low possibility that agents may be lost. Secondly, the SSA is advantageous for practical implementations, since it considers for agents even the case that a target is blocked by obstacles. Extensive simulation presents to illustrate the viability and effectiveness of the proposed framework.