• Composites Research
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• 제35권1호
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• pp.31-37
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• 2022

본 연구에서는 복합재료 블레이드의 최적 구조설계 프레임워크를 개발하고 이를 헬리콥터 블레이드에 적용하여 구조설계를 수행하였다. 개발된 최적 설계 프레임워크는 유전자 알고리즘과 입자 군집 최적화 알고리즘을 결합한 PSGA를 활용해 구성하였다. 이는 블레이드 단면에 대한 유한요소 모델 생성, 2차원 단면 유한요소 해석, 그리고 1차원 회전 보 해석의 단계를 거쳐 최적화 결과를 도출해낸다. 설계 과정에서 각 단면들은 B-spline으로 구성되며, 유한요소 생성 프로그램인 Gmsh를 활용해 모델링 된다. 이를 활용하여 최적화 과정에서 각 변수마다 대응되는 2차원 유한요소모델을 생성해 블레이드의 구조해석을 수행했다. 본 연구에서 제안한 프레임워크를 HART II 블레이드에 적용하여 최적 구조 설계를 수행했다. 최적 설계 결과 회전익 로터에서 요구하는 구조적 특징을 유지하면서, 공진회피와 질량 등의 조건이 개선된 블레이드 형상을 도출하였다.

• Journal of Electrical Engineering and Technology
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• 제3권3호
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• pp.320-330
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• 2008

This paper presents an improved dynamic programming (IDP) approach to solve the economic power dispatch problem including transmission losses in power systems. A detailed mathematical derivation of recursive dynamic programming approach for the economic power dispatch problem with transmission losses is presented. The transmission losses are augmented with the objective function using price factor. The generalized expression for optimal scheduling of thermal generating units derived in this article can be implemented for the solution of the economic power dispatch problem of a large-scale system. Six-unit, fifteen-unit, and forty-unit sample systems with non-linear characteristics of the generator, such as ramp-rate limits and prohibited operating zones are considered to illustrate the effectiveness of the proposed method. The proposed method results have been compared with the results of genetic algorithm and particle swarm optimization methods reported in the literature. Test results show that the proposed IDP approach can obtain a higher quality solution with better performance.

• Nuclear Engineering and Technology
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• 제54권6호
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• pp.2107-2119
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• 2022

Accurate remaining useful life (RUL) prediction for critical components of nuclear power equipment is an important way to realize aging management of nuclear power equipment. The electric gate valve is one of the most safety-critical and widely distributed mechanical equipment in nuclear power installations. However, the electric gate valve's extended service in nuclear installations causes aging and degradation induced by crack propagation and leakages. Hence, it is necessary to develop a robust RUL prediction method to evaluate its operating state. Although the particle filter(PF) algorithm and its variants can deal with this nonlinear problem effectively, they suffer from severe particle degeneracy and depletion, which leads to its sub-optimal performance. In this study, we combined the whale algorithm with regularized particle filtering(RPF) to rationalize the particle distribution before resampling, so as to solve the problem of particle degradation, and for valve RUL prediction. The valve's crack propagation is studied using the RPF approach, which takes the Paris Law as a condition function. The crack growth is observed and updated using the root-mean-square (RMS) signal collected from the acoustic emission sensor. At the same time, the proposed method is compared with other optimization algorithms, such as particle swarm optimization algorithm, and verified by the realistic valve aging experimental data. The conclusion shows that the proposed method can effectively predict and analyze the typical valve degradation patterns.

• 제어로봇시스템학회논문지
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• 제19권6호
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• pp.527-533
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• 2013

BCI (Brain-Computer Interface) is a system that transforms a subject's brain signal related to their intention into a control signal by classifying EEG (electroencephalograph) signals obtained during the imagination of movement of a subject's limbs. The BCI system allows us to control machines such as robot arms or wheelchairs only by imaging limbs. With the exact same experiment environment, activated brain regions of each subjects are totally different. In that case, a simple approach is to use as many channels as possible when measuring brain signals. However the problem is that using many channels also causes other problems. When applying a CSP (Common Spatial Pattern), which is an EEG extraction method, many channels cause an overfitting problem, and in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest an optimal channel selection method using a BPSO (Binary Particle Swarm Optimization), BPSO with channel impact factor, and GA. This paper examined optimal selected channels among all channels using three optimization methods and compared the classification accuracy and the number of selected channels between BPSO, BPSO with channel impact factor, and GA by SVM (Support Vector Machine). The result showed that BPSO with channel impact factor selected 2 fewer channels and even improved accuracy by 10.17~11.34% compared with BPSO and GA.

• Smart Structures and Systems
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• 제29권3호
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• pp.433-444
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• 2022

Concrete is a most utilized material in the construction industry that have main components. The strength of concrete can be improved by adding some admixtures. Evaluating the impact of fly ash (FA) and silica fume (SF) on the long-term compressive strength (CS) of concrete provokes to find the significant parameters in predicting the CS, which could be useful in the practical works and would be extensible in the future analysis. In this study, to evaluate the effective parameters in predicting the CS of concrete containing admixtures in the long-term and present a fitted equation, the multivariate adaptive regression splines (MARS) method has been used, which could find a relationship between independent and dependent variables. Next, for optimizing the output equation, biogeography-based optimization (BBO), particle swarm optimization (PSO), and hybrid PSOBBO methods have been utilized to find the most optimal conclusions. It could be concluded that for CS predictions in the long-term, all proposed models have the coefficient of determination (R²) larger than 0.9243. Furthermore, MARS-PSOBBO could be offered as the best model to predict CS between three hybrid algorithms accurately.

• 한국유체기계학회 논문집
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• 제16권1호
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• pp.11-16
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• 2013

A numerical study was conducted to improve the performance of an impeller of centrifugal compressor. Nine design variables were chosen with constraints. Only meridional contours and blade profile were adjusted. ANN (Artificial Neural Net) was adopted as a main optimization algorithm with PSO (Particle Swarm Optimization) in order to reduce the optimization time. At first, ANN was learned and trained with the design variable sets which were obtained using DOE (Design of Experiment). This ANN was continuously improved its accuracy for each generation of which population was one hundred. New design variable set in each generation was selected using a non-gradient based method of PSO in order to obtain the global optimized result. After $7^{th}$ generation, the prediction difference of efficiency and pressure ratio between ANN and CFD was less than 0.6%. From more than 1,200 design variable sets, a pareto of efficiency versus pressure ratio was obtained and an optimized result was selected based on the multi-objective function. On this optimized impeller, the efficiency and pressure ratio were improved by 1% and 9.3%, respectively.

• International Journal of Naval Architecture and Ocean Engineering
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• 제12권1호
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• pp.596-604
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• 2020

Previous studies on pipe auto-routing algorithms generally used such algorithms as A*, Dijkstra, Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization, to satisfy the relevant constraints of its own field and improve the output quality. On the other hand, this study aimed to significantly improve path-finding speed by applying the Jump Point Search (JPS) algorithm, which requires lower search cost than the abovementioned algorithms, for pipe routing. The existing JPS, however, is limited to two-dimensional spaces and can only find the shortest path. Thus, it requires several improvements to be applied to pipe routing. Pipe routing is performed in a three-dimensional space, and the path of piping must be parallel to the axis to minimize its interference with other facilities. In addition, the number of elbows must be reduced to the maximum from an economic perspective, and preferred spaces in the path must also be included. The existing JPS was improved for the pipe routing problem such that it can consider the above-mentioned problem. The fast path-finding speed of the proposed algorithm was verified by comparing it with the conventional A* algorithm in terms of resolution.

• 융합신호처리학회논문지
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• 제19권2호
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• pp.77-88
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• 2018

본 논문은 입력 포화를 가지는 불확실한 전기 구동 로봇 시스템에 대해 입자 군집 최적화(PSO)를 이용한 방사형 기저 함수 신경망(RBFNN) 기반 분산 적응 추종 제어 기법을 제안한다. 실제적으로 로봇 시스템에서는 구동기의 포화로 인해 입력 전압과 전류 신호 크기가 제한된다. 제안된 제어기는 이러한 입력 포화를 극복하며, 어떠한 로봇 링크 및 구동기의 모델 파라미터들을 요구하지 않는다. 제시된 PSO 기법에서 쓰인 적합도 함수는 추종 오차만이 아니라 전압과 전류의 크기를 포함하는 다중 목적 함수로 표현된다. PSO 기법을 이용하여 제어 이득과 방사형 기저 함수의 개수가 자동으로 조정되어 제어 시스템의 성능이 개선된다. 리아푸노프 안정도 해석에 의해 전체 제어 시스템의 안정도가 보장된다. 제안된 제어 기법의 타당성과 강인성이 시뮬레이션 결과를 통해 검증된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.520-541
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• 2023

In CRNs, SS is of utmost significance. Every CR user generates a sensing report during the training phase beneath various circumstances, and depending on a collective process, either communicates or remains silent. In the training stage, the fusion centre combines the local judgments made by CR users by a majority vote, and then returns a final conclusion to every CR user. Enough data regarding the environment, including the activity of PU and every CR's response to that activity, is acquired and sensing classes are created during the training stage. Every CR user compares their most recent sensing report to the previous sensing classes during the classification stage, and distance vectors are generated. The posterior probability of every sensing class is derived on the basis of quantitative data, and the sensing report is then classified as either signifying the presence or absence of PU. The ISVM technique is utilized to compute the quantitative variables necessary to compute the posterior probability. Here, the iterations of SVM are tuned by novel GO-PSA by combining GOA and PSO. Novel GO-PSA is developed since it overcomes the problem of computational complexity, returns minimum error, and also saves time when compared with various state-of-the-art algorithms. The dependability of every CR user is taken into consideration as these local choices are then integrated at the fusion centre utilizing an innovative decision combination technique. Depending on the collective choice, the CR users will then communicate or remain silent.

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.830-839
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• 2017

Support vector machine (SVM) is introduced as an effective fault diagnosis technique based on dissolved gases analysis (DGA) for oil-immersed transformers with maximum generalization ability; however, the applicability of the SVM is highly affected due to the difficulty of selecting the SVM parameters appropriately. Therefore, a novel approach combing SVM with improved imperialist competitive algorithm (IICA) for fault diagnosis of oil-immersed transformers was proposed in the paper. The improved ICA, which is proved to be an effective optimization approach, is employed to optimize the parameters of SVM. Cross validation and normalizations were applied in the training processes of SVM and the trained SVM model with the optimized parameters was established for fault diagnosis of oil-immersed transformers. Three classification benchmark sets were studied based on particle swarm optimization SVM (PSOSVM) and IICASVM with four multiple classification schemes to select the best scheme for transformer fault diagnosis. The results show that the proposed model can obtain higher diagnosis accuracy than other methods. The comparisons confirm that the proposed model is an effective approach for classification problems.