• 한국심리학회지:학교
• /
• 제17권3호
• /
• pp.333-356
• /
• 2020

본 연구는 대학생의 공적 자의식과 사회불안의 관계에서 부정적 및 긍정적 평가에 대한 두려움과 긍정적인 사회적 결과 가치 절하의 영향을 알아보고자 하였다. 이때 사회불안을 사회적 상호작용 불안과 수행불안으로 구분하여 차별적 영향력을 확인하였다. 이를 위해 대학생 316명을 대상으로 설문조사를 실시하였다. 연구결과는 다음과 같다. 첫째, 공적 자의식, 부정적 평가에 대한 두려움, 긍정적 평가에 대한 두려움, 긍정적인 사회적 결과의 가치 절하, 사회적 상호작용 불안, 수행 불안은 모두 유의한 정적 관련을 보였다. 둘째, 공적 자의식과 사회불안의 관계에서 부정적 및 긍정적 평가에 대한 두려움과 긍정적인 사회적 결과 가치 절하의 매개효과가 유의한 것으로 나타났다. 셋째, 공적 자의식과 사회불안의 관계를 부정적 및 긍정적 평가에 대한 두려움과 긍정적인 사회적 결과의 가치 절하가 매개할 때, 수행 불안에 미치는 영향이 사회적 상호작용 불안에 미치는 영향보다 유의하게 크고, 부정적 평가에 대한 두려움이 매개할 때는 사회적 상호작용 불안에 미치는 영향이 수행 불안에 미치는 영향 보다 유의하게 큰 것으로 나타났다. 이러한 연구결과를 바탕으로 연구의 시사점과 후속 연구를 위한 제언을 논의하였다.

• Journal of Electrical Engineering and Technology
• /
• 제7권1호
• /
• pp.9-16
• /
• 2012

The present study presents the Phasor Discrete Particle Swarm Optimization (PDPSO) algorithm, an effective optimization technique, the multi-dimensional vectors of which consist of magnitudes and phase angles. PDPSO is employed in the configuration of micro-grids. Micro-grids are concepts of distribution system that directly unifies customers and distributed generations (DGs). Micro-grids could supply electric power to customers and conduct power transaction via a power market by operating economic dispatch of diverse cost functions through several DGs. If a large number of micro-grids exist in one distribution system, the algorithm needs to adjust the configuration of numerous micro-grids in order to supply electric power with minimum generation cost for all customers under the distribution system.

• ETRI Journal
• /
• 제40권6호
• /
• pp.726-735
• /
• 2018

Femtocells are good examples of the ultimate networking technology, offering enhanced indoor coverage and higher data rate. However, the dense deployment of femto base stations (FBSs) and the exploitation of subcarrier reuse between macrocell base stations and FBSs result in significant co-tier and cross-tier interference, thus degrading system performance. Therefore, appropriate resource allocations are required to mitigate the interference. This paper proposes a discrete bacterial foraging optimization (DBFO) algorithm to find the optimal resource allocation in two-tier networks. The simulation results showed that DBFO outperforms the random-resource allocation and discrete particle swarm optimization (DPSO) considering the small number of steps taken by particles and bacteria.

• Journal of Electrical Engineering and Technology
• /
• 제4권2호
• /
• pp.185-193
• /
• 2009

This paper presents a methodology for photovoltaic (PV) system allocation in distribution systems using a discrete particle swarm optimization (DPSO). The PV allocation problem is in the category of mixed integer nonlinear programming and its formulation may include multi-valued dis-crete variables. Thus, the PSO requires a scheme to deal with multi-valued discrete variables. This paper introduces a novel multi-level quantization scheme using a sigmoid function for discrete particle swarm optimization. The technique is employed to a standard PSO architecture; the same velocity update equation as in continuous versions of PSO is used but the particle's positions are updated in an alternative manner. The set of multi-level quantization is defined as integer multiples of powers-of-two terms to efficiently approximate the sigmoid function in transforming a particle's position into discrete values. A comparison with a genetic algorithm (GA) is performed to verify the quality of the solutions obtained.

• Structural Engineering and Mechanics
• /
• 제84권4호
• /
• pp.489-502
• /
• 2022

This paper is aimed at developing an optimization-based Finite Element model updating approach for structural damage identification and quantification. A modal flexibility-based error function is introduced, which uses modal assurance criterion to formulate the updating problem as an optimization problem. Because of the inexplicit input/output relationship between the candidate solutions and the error function's output, a robust and efficient optimization algorithm should be employed to evaluate the solution domain and find the global extremum with high speed and accuracy. This paper proposes a new multi-stage Selective Particle Swarm Optimization (SPSO) algorithm to solve the optimization problem. The proposed multi-stage strategy not only fixes the premature convergence of the original Particle Swarm Optimization (PSO) algorithm, but also increases the speed of the search stage and reduces the corresponding computational costs, without changing or adding extra terms to the algorithm's formulation. Solving the introduced objective function with the proposed multi-stage SPSO leads to a smart feedback-wise and self-adjusting damage detection method, which can effectively assess the health of the structural systems. The performance and precision of the proposed method are verified and benchmarked against the original PSO and some of its most popular variants, including SPSO, DPSO, APSO, and MSPSO. For this purpose, two numerical examples of complex civil engineering structures under different damage patterns are studied. Comparative studies are also carried out to evaluate the performance of the proposed method in the presence of measurement errors. Moreover, the robustness and accuracy of the method are validated by assessing the health of a six-story shear-type building structure tested on a shake table. The obtained results introduced the proposed method as an effective and robust damage detection method even if the first few vibration modes are utilized to form the objective function.