• 한국소음진동공학회논문집
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• 제19권9호
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• pp.884-891
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• 2009

This study investigates some of swarm intelligence algorithms to tackle a traditional damage detection problem having stiffness degradation or damage in mechanical structures. Particle swarm(PSO) and ant colony optimization(ACO) methods have been exploited for localizing and estimating the location and extent damages in a structure. Both PSO and ACO are population-based, stochastic algorithms that have been developed from the underlying concept of swarm intelligence and search heuristic. A finite element (FE) model updating is implemented to minimize the difference in a set of natural frequencies between measured and baseline vibration data. Stiffness loss of certain elements is considered to simulate structural damages in the FE model. It is numerically shown that PSO and ACO algorithms successfully completed the optimization process of model updating in locating unknown damages in a truss structure.

• 인터넷정보학회논문지
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• 제18권1호
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• pp.1-9
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• 2017

클라우드 컴퓨팅에서 바이오 영감 컴퓨팅 기술과 같은 연구들을 통해, 오프로딩 기법에서 새로운 차원의 솔루션이 개발되고 있다. 모바일 장비 사용의 증가 추세에 따라, 바이오 영감 기술은 모바일 클라우드 컴퓨팅의 발전에 기여하고 있다. 모바일 클라우드 컴퓨팅에서의 에너지효율적인 기법은 총 에너지 소비를 줄이기 위해 필요하지만, 지금까지의 연구는 태스크 분산을 위한 의사결정과정에서 에너지 소비에 관해 고려하지 않고 있다. 본 논문에서는 클라우드렛에서 데이터센터로의 오프로딩 전략으로 Particle Swarm Optimization (PSO) 방법을 제안하며, 이 과정에서 각 태스크는 입자(particle)로 표현된다. 입자의 수를 줄이기 위해 PSO를 적용하기 전에 K-means 클러스터링을 사용하여 수집한 태스크를 클라우드렛 상에서 분류하며, PSO 처리과정 중에는 모든 태스크를 대상으로 하지 않고 분류된 태스크에 따라 최적의 데이터 센터를 찾는다. 시뮬레이션 결과, 제안한 PSO기법이 처리 시간 관점에서는 전통적인 방법에 비해 조금 늦지만, 에너지 관점의 데이터 센터 선택에서는 우수함을 나타내었다.

• 한국전자통신학회논문지
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• 제13권3호
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• pp.587-592
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• 2018

위상 배열 안테나를 이용한 적응형 빔 형성 시스템은 간섭신호가 있는 통신환경에 적응형으로 빔을 형성하여 통신 품질을 향상시킨다. 적응형 빔 형성을 위해서는 위상 배열 안테나의 각 방사소자에 급전되는 신호의 위상을 우수한 조합을 산출해야 한다. 본 논문에서는 우수한 위상 천이 조합 산출 확률을 증가시키기 위해, 개치 밀도에 따른 재확산 절차가 추가된 개선된 개체 군집 최적화 알고리즘을 제안하였다.

• Journal of information and communication convergence engineering
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• 제10권4호
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• pp.390-395
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• 2012

This paper describes an implementation of automatic mutual localization of swarm robots using a particle filter. Each robot determines the location of the other robots using wireless sensors. The measured data will be used for determination of the movement method of the robot itself. It also affects the other robots' self-arrangement into formations such as circles and lines. We discuss the problem of a circle formation enclosing a target that moves. This method is the solution for enclosing an invader in a circle formation based on mutual localization of the multi-robot without infrastructure. We use trilateration, which does require knowing the value of the coordinates of the reference points. Therefore, specifying the enclosure point based on the number of robots and their relative positions in the coordinate system. A particle filter is used to improve the accuracy of the robot's location. The particle filter is operates better for mutual location of robots than any other estimation algorithms. Through the experiments, we show that the proposed scheme is stable and works well in real environments.

• 유통과학연구
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• 제11권3호
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• pp.5-11
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• 2013

Purpose - Going concern is one of fundamental concepts in accounting and auditing and sometimes the assessment of a company's going concern status that is a tough process. Various going concern prediction models' based on statistical and data mining methods help auditors and stakeholders suggested in the previous literature. Research design - This paper employs a data mining approach to prediction of going concern status of Iranian firms listed in Tehran Stock Exchange using Particle Swarm Optimization. To reach this goal, at the first step, we used the stepwise discriminant analysis it is selected the final variables from among of 42 variables and in the second stage; we applied a grid-search technique using 10-fold cross-validation to find out the optimal model. Results - The empirical tests show that the particle swarm optimization (PSO) model reached 99.92% and 99.28% accuracy rates for training and holdout data. Conclusions - The authors conclude that PSO model is applicable for prediction going concern of Iranian listed companies.

• Structural Engineering and Mechanics
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• 제54권3호
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• pp.453-474
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• 2015

A hybrid approach of Particle Swarm Optimization (PSO) and Swallow Swarm Optimization algorithm (SSO) namely Hybrid Particle Swallow Swarm Optimization algorithm (HPSSO), is presented as a new variant of PSO algorithm for the highly nonlinear dynamic truss shape and size optimization with multiple natural frequency constraints. Experimentally validation of HPSSO on four benchmark trusses results in high performance in comparison to PSO variants and to those of different optimization techniques. The simulation results clearly show a good balance between global and local exploration abilities and consequently results in good optimum solution.

• 한국정보통신학회논문지
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• 제13권7호
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• pp.1312-1320
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• 2009

본 논문은 Particle Swarm Optimization(PSO)을 이용하여 2차원 IIR 디지털필터의 설계방법을 제안하였다. 먼저 2차원 디지털필터의 설계문제를 PSO에 적용하기 위하여 최소화 문제로써 형식화 과정이 논의된다. 제안된 PSO 알고리즘을 이용한 설계방법은 기존의 PSO 알고리즘에 IIR 필터설계에서 요구되는 안정성을 보증하는 과정이 검토되어 개선된다. 본 논문에서 제안된 방법의 타당성을 설계예시를 통해 고찰한 결과, 설계된 디지털필터는 동일한 설계사양으로 기존의 설계방법으로 설계된 디지털필터보다 근사오차 면에서 우수한 결과를 얻을 수 있었다. 또한 제안한 설계방법에 의한 2차원 IIR 디지털필터는 설계과정에서 필터의 안정성을 보증할 수 있었다.

• Nuclear Engineering and Technology
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• 제53권1호
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Industrial Engineering and Management Systems
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• 제6권1호
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• pp.64-71
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• 2007

J. Kennedy and R. Eberhart first introduced the concept called as Particle Swarm Optimization (PSO). They applied it to optimize continuous nonlinear functions and demonstrated the effectiveness of the algorithm. Since then a considerable number of researchers have attempted to apply this concept to a variety of optimization problems and obtained reasonable results. In PSO, individuals communicate and exchange simple information with each other. The information among individuals is communicated in the swarm and the information between individuals and their swarm is also shared. Finally, the swarm approaches the optimal behavior. It is reported that reasonable approximate solutions of various types of test functions are obtained by employing PSO. However, if more precise solutions are required, additional algorithms and/or hybrid algorithms would be necessary. For example, the heading vector of the swarm can be slightly adjusted under some conditions. In this paper, we propose a hybrid algorithm to obtain more precise solutions. In the algorithm, when a better solution in the swarm is found, the neighborhood of a certain distance from the solution is searched. Then, the algorithm returns to the original PSO search. By this hybrid method, we can obtain considerably better solutions in less iterations than by the standard PSO method.

• Journal of Electrical Engineering and Technology
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• 제11권5호
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• pp.1404-1411
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• 2016