• 한국정보통신학회논문지
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• 제14권5호
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• pp.1085-1092
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• 2010

본 논문에서는 최적화 알고리즘인 PSO를 이용하여 한국인의 생활자계 노출실태 조사 시 확보한 16세 이하의 미취학 아동, 초등학생, 중학생 실측 데이터베이스를 활용하여, 자계노출의 정도를 실측에 의하지 않고 추정할 수 있는 '24시간 소아고노출 생활자계 추정식'을 개발하였다. 24시간 개인자계 노출량 추정식의 입력 데이터는 성, 연령, 주거형태, 주거지 크기, 선로이격거리 및 송전전압을 사용하였다. 그리고 16세 이하에 대해서 24시간 고노출 개인자계 노출분포, 자계노출의 특성, 특정 조건별 자계노출특성 등을 분석하였다.

• 한국통신학회논문지
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• 제35권1C호
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• pp.87-95
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• 2010

본 논문에서는 UWB (Ultra Wide Band) 시스템에서 PSO (Particle Swarm Optimization)를 사용하는 향상된 TDoA (Time Difference of Arrival) 무선측위 기법을 제안한다. 제안된 기법은 TDoA 파라미터 재추정과 태그(Tag) 위치 재측정을 수행하는 두 단계로 구성된다. 이들 두 단계에서 PSO 알고리즘은 무선측위 성능 향상을 위해 고용된다. 첫 번째 단계에서 TDoA 추정 오차를 줄이기 위해, 제안된 기법은 전형적인 TDoA 무선측위 방식으로부터 얻어진 TDoA 파라미터를 재추정한다. 두 번째 단계에서 무선측위 오차를 최소화시키기 위해, 첫 번째 단계에서 추정된 TDoA 파라미터를 가지고 제안된 기법은 태그의 위치를 다시 측정한다. 모의실험 결과, 제안된 기법은 LoS (Line-of-Sight)와 NLoS (Non-Line-of-Sight) 채널 환경에서 모두 전형적인 TDoA 무선측위 방식에 비해 우수한 무선측위 성능을 달성하는 것을 확인할 수 있었다.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.312-320
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• 2021

Increase in computational cost and exhaustive search can lead to more complexity and computational energy. Thus, there is need for effective and efficient scheme to reduce the complexity to achieve optimal energy utilization. This will improve the energy efficiency and enhance the proficiency in terms of the resources needed to achieve convergence. This paper primarily focuses on the development of hybrid swarm intelligence scheme for reducing the computational complexity in binary optimization. In order to reduce the complexity, both artificial bee colony (ABC) and particle swarm optimization (PSO) have been employed to effectively minimize the exhaustive search and increase convergence. First, a new approach using ABC and PSO has been proposed and developed to solve the binary optimization problem. Second, the scout for good quality food sources is accomplished through the deployment of PSO in order to optimally search and explore the best source. Extensive experimental simulations conducted have demonstrate that the proposed scheme outperforms the ABC approaches for reducing complexity and energy consumption in terms of convergence, search and error minimization performance measures.

• Journal of information and communication convergence engineering
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• 제22권2호
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• pp.127-132
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• 2024

Haptic actuators for large display panels play an important role in bridging the gap between the digital and physical world by generating interactive feedback for users. However, the generation of meaningful haptic feedback is challenging for large display panels. There are dead zones with low haptic sensations when a small number of actuators are applied. In contrast, it is important to control the traveling wave generated by the actuators in the presence of multiple actuators. In this study, we propose a particle swarm optimization (PSO)-based algorithm for the haptic localization of plates with electrostatic vibration actuators. We modeled the transverse displacement of a plate under the effect of actuators by employing the Kirchhoff-Love plate theory. In addition, starting with twenty randomly generated particles containing the actuator parameters, we searched for the optimal actuator parameters using a stochastic process to yield localization. The capability of the proposed PSO algorithm is reported and the transverse displacement has a high magnitude only in the targeted region.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 심포지엄 논문집 정보 및 제어부문
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• pp.77-78
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• 2008

This paper proposes a new algorithm for path planning and obstacles avoidance using the ant colony optimization algorithm and the particle swarm optimization. The proposed algorithm is a new hybrid algorithm that composes of the ant colony algorithm method and the particle swarm optimization method. At first, we produce paths of a mobile robot in the static environment. And then, we find midpoints of each path using the Maklink graph. Finally, the hybrid algorithm is adopted to get a shortest path. We prove the performance of the proposed algorithm is better than that of the path planning algorithm using the ant colony optimization only through simulation.

• Journal of Electrical Engineering and Technology
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• 제12권4호
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• pp.1586-1592
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• 2017

Voltage sourced converter (VSC) based direct-current (DC) grid has the ability to control power flow flexibly and securely, thus it has become one of the most valid approaches in aspect of large-scale renewable power generation, oceanic island power supply and new urban grid construction. To solve the optimal power flow (OPF) problem in DC grid, an adaptive particle swarm optimization (PSO) algorithm based on fuzzy control theory is proposed in this paper, and the optimal operation considering both power loss and voltage quality is realized. Firstly, the fuzzy membership curve is used to transform two objectives into one, the fitness value of latest step is introduced as input of fuzzy controller to adjust the controlling parameters of PSO dynamically. The proposed strategy was applied in solving the power flow issue in six terminals DC grid model, and corresponding results are presented to verify the effectiveness and feasibility of proposed algorithm.

• 전기학회논문지
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• 제64권1호
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• pp.8-15
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• 2015

This paper includes a method for estimating the parameter of a synchronous generator and exciter using the modified particle swarm optimization. A solid round rotor synchronous generator and exciter have been modeled with the saturation function. They are regarded as state of being cooperative to a infinite bus. The behavior characteristic of all particles assigned to a parameter needs to be reflected in the PSO algorithm to fine out more close result to the optimal solution. The results of the simulation to estimate the parameters of the synchronous generator and exciter in the modified PSO algorithm are described.

• 전기학회논문지
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• 제65권4호
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• pp.547-554
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• 2016

Analyze the customer daily load patterns, be used to determine the optimal charging and discharging schedule which can minimize the electrical charges through the battery energy storage system(BESS) installed in consumers is an object of this paper. BESS, which analyzes the load characteristics of customer and reduce the peak load, is essential for optimal charging and discharging scheduling to save electricity charges. This thesis proposes optimal charging and discharging scheduling method, using particle swarm optimization (PSO) and penalty function method, of BESS for reducing energy charge. Since PSO is a global optimization algorithm, best charging and discharging scheduling can be found effectively. In addition, penalty function method was combined with PSO in order to handle many constraint conditions. After analysing the load patterns of target BESS, PSO based on penalty function method was applied to get optimal charging and discharging schedule.

• Genomics & Informatics
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• 제9권3호
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• pp.134-135
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• 2011

DNA chips are used for experiments on genes and provide useful information that could be further analyzed. Using the data extracted from the DNA chips to find useful patterns or information has become a very important issue. In this paper, we explain the application developed for classifying DNA chip data using a classification method based on the Particle Swarm Optimization (PSO) algorithm. Considering that DNA chip data is extremely large and has a fuzzy characteristic, an algorithm that imitates the ecosystem such as the PSO algorithm is suitable to be used for analyzing such data. The application enables researchers to customize the PSO algorithm parameters and see detail results of the classification rules.

• Steel and Composite Structures
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• 제47권6호
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• pp.759-779
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• 2023

The paper proposes two hybrid metaheuristic optimization and artificial neural network (ANN) methods for the close prediction of the ultimate axial compressive capacity of concentrically loaded concrete filled double skin steel tube (CFDST) columns. Two metaheuristic optimization, namely genetic algorithm (GA) and particle swarm optimization (PSO), approaches enable the dynamic training architecture underlying an ANN model by optimizing the number and sizes of hidden layers as well as the weights and biases of the neurons, simultaneously. The former is termed as GA-ANN, and the latter as PSO-ANN. These techniques utilize the gradient-based optimization with Bayesian regularization that enhances the optimization process. The proposed GA-ANN and PSO-ANN methods construct the predictive ANNs from 125 available experimental datasets and present the superior performance over standard ANNs. Both the hybrid GA-ANN and PSO-ANN methods are encoded within a user-friendly graphical interface that can reliably map out the accurate ultimate axial compressive capacity of CFDST columns with various geometry and material parameters.