• 한국광학회지
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• 제21권1호
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• pp.1-5
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• 2010

본 논문에서는 실리콘 태양전지의 흡수효율 증가를 위해 도파모드 공진 특성을 이용한 격자 구조를 제안하였다. 도파모드 공진을 이용함으로써 두께를 ~200 nm 수준으로 줄이면서도 높은 흡수율을 기대할 수 있는 태양전지 설계가 가능함을 확인하였다. 제안된 구조는 은으로 된 반사경 위에 격자구조를 갖는 Poly-Si 유전체 층이 존재하는 1-D 구조로서 각 구조변수들 즉 격자의 주기, 유전체 두께, 격자 간격 및 깊이 등이 흡수 효율에 어떤 영향을 미치는지 알아보고, 변수들의 조절을 통해 최적의 구조를 찾고자 시도하였다. PSO알고리즘을 사용하여 제안된 구조의 적절성을 확인 하였으며, 이로부터 65.8%의 유효 흡수율을 얻을 수 있었다.

• Journal of Electrical Engineering and Technology
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• 제13권1호
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• pp.352-361
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• 2018

This paper proposes a sensing system of the Halbach array permanent magnet spherical motor(PMSM). The rotor position can be obtained by solving three rotation angles, which revolves around 3 reference axes of the stator. With the development of 3-D hall sensor, the position identification problem of the Halbach array PMSM based on rotor magnetic field is studied in this paper. A nonlinear and serious coupling relationship between the rotation angles and the measured magnetic flux density is established on the basis of the rotation transformation theory and the magnetic field model. In order to get rid of the influence on position detection caused by the harmonics of rotor magnetic field and the stator coil magnetic field, a sensor location combination scheme is proposed. In order to solve the nonlinear equation fast and accurately, a new position solution algorithm which combines the merits of gradient projection and particle swarm optimization(PSO) is presented. Then the rotation angles are obtained and the rotor position is identified. The validity of the sensing system is verified through the simulation.

• Wind and Structures
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• 제38권1호
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• pp.75-91
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• 2024

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.

• 정보처리학회논문지B
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• 제15B권4호
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• pp.331-340
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• 2008

특징 선택은 기계 학습에서 분류의 성능을 높이기 위해 사용되는 방법이다. 여러 방법들이 개발되고 사용되어 오고 있으나, 전체 데이터에서 최적화된 특징 부분집합을 구성하는 문제는 여전히 어려운 문제로 남아있다. 생태계 모방 알고리즘은 생물체들의 행동 원리 등을 기반으로하여 만들어진 진화적 알고리즘으로, 최적화된 해를 찾는 문제에서 매우 유용하게 사용되는 방법이다. 특징 선택 문제에서도 생태계 모방 알고리즘을 이용한 해결방법들이 제시되어 오고 있으며, 이에 본 논문에서는 생태계 모방 알고리즘을 이용한 특징 선택 방법을 개선하는 방안을 제시한다. 이를 위해 잘 알려진 생태계 모방 알고리즘인 유전자 알고리즘(GA)과 파티클 집단 최적화 알고리즘(PSO)을 이용하여 데이터에서 가장분류 성능이 우수한 특징 부분집합을 만들어 내도록 하고, 최종적으로 개별 특징의 사전 중요도를 설정하여 생태계 모방 알고리즘을 개선하는 방법을 제안하였다. 이를 위해 개별 특징의 우수도를 구할 수 있는 mRMR이라는 방법을 이용하였다. 이렇게 설정한 사전 중요도를 이용하여 GA와 PSO의 진화 연산을 수정하였다. 데이터를 이용한 실험을 통하여 제안한 방법들의 성능을 검증하였다. GA와 PSO를 이용한 특징 선택 방법은 그 분류 정확도에 있어서 뛰어난 성능을 보여주었다. 그리고 최종적으로 제시한 사전 중요도를 이용해 개선된 방법은 그 진화 속도와 분류 정확도 면에서 기존의 GA와 PSO 방법보다 더 나아진 성능을 보여주는 것을 확인하였다.

• Smart Structures and Systems
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• 제22권5호
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• pp.561-574
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• 2018

In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three wellknown optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.

• Geomechanics and Engineering
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• 제20권3호
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• pp.191-205
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• 2020

Soil shear strength parameters play a remarkable role in designing geotechnical structures such as retaining wall and dam. This study puts an effort to propose two accurate and practical predictive models of soil shear strength parameters via hybrid artificial neural network (ANN)-based models namely genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. To reach the aim of this study, a series of consolidated undrained Triaxial tests were conducted to survey inherent strength increase due to addition of polypropylene fibers to sandy soil. Fiber material with different lengths and percentages were considered to be mixed with sandy soil to evaluate cohesion (as one of shear strength parameter) values. The obtained results from laboratory tests showed that fiber percentage, fiber length, deviator stress and pore water pressure have a significant impact on cohesion values and due to that, these parameters were selected as model inputs. Many GA-ANN and PSO-ANN models were constructed based on the most effective parameters of these models. Based on the simulation results and the computed indices' values, it is observed that the developed GA-ANN model with training and testing coefficient of determination values of 0.957 and 0.950, respectively, performs better than the proposed PSO-ANN model giving coefficient of determination values of 0.938 and 0.943 for training and testing sets, respectively. Therefore, GA-ANN can provide a new applicable model to effectively predict cohesion of fiber-reinforced sandy soil.

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2388-2398
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• 2017

In this study, we propose a robust face recognition system to pose variations based on automatic pose estimation. Radial basis function neural network is applied as one of the functional components of the overall face recognition system. The proposed system consists of preprocessing and recognition modules to provide a solution to pose variation and high-dimensional pattern recognition problems. In the preprocessing part, principal component analysis (PCA) and 2-dimensional 2-directional PCA ($(2D)^2$ PCA) are applied. These functional modules are useful in reducing dimensionality of the feature space. The proposed RBFNNs architecture consists of three functional modules such as condition, conclusion and inference phase realized in terms of fuzzy "if-then" rules. In the condition phase of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by the Fuzzy C-Means (FCM) algorithm. In conclusion phase of rules, the connections (weights) are realized through four types of polynomials such as constant, linear, quadratic and modified quadratic. The coefficients of the RBFNNs model are obtained by fuzzy inference method constituting the inference phase of fuzzy rules. The essential design parameters (such as the number of nodes, and fuzzification coefficient) of the networks are optimized with the aid of Particle Swarm Optimization (PSO). Experimental results completed on standard face database -Honda/UCSD, Cambridge Head pose, and IC&CI databases demonstrate the effectiveness and efficiency of face recognition system compared with other studies.

• Earthquakes and Structures
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• 제25권6호
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• pp.417-427
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• 2023

If the center of mass and center of stiffness or strength of a structure plan do not coincide, the structure is considered asymmetric. During an earthquake, in addition to lateral vibration, the structure experiences torsional vibration as well. Lateraltorsional coupling in asymmetric structures in the plan will increase lateral displacement at the ends of the structure plan and, as a result, uneven deformation demand in seismically resistant frames. The demand for displacement in resistant frames depends on the magnitude of transitional displacement to rotational displacement in the plan and the correlation between these two. With regard to the inability to eliminate the asymmetrical condition due to various reasons, such as architectural issues, this study has attempted to use supplemental viscous dampers to decrease the correlation between lateral and torsional acceleration or displacement in the plan. This results in an almost even demand for lateral deformation and acceleration of seismic resistant frames. On this basis, using the concept of Torsional Balance, adequate distribution of viscous dampers for the decrease of this correlation was determined by transferring the "Empirical Center of Balance" (ECB) to the geometrical center of the structure plan and thus obtaining an equal mean square value of displacement and acceleration of the plan edges. This study analyzed stiff and flexible torsional structures with one-way and two-way mass asymmetry in the Opensees software. By implementing the Particle Swarm Optimization (PSO) algorithm, the optimum formation of dampers for controlling lateral displacement and acceleration is determined. The results indicate that with the appropriate distribution of viscous dampers, not only does the lateral displacement and acceleration of structure edges decrease but the lateral displacement or acceleration of the structure edges also become equal. It is also observed that the optimized center of viscous dampers for control of displacement and acceleration of structure depends on the amount of mass eccentricity, the ratio of uncoupled torsional-to-lateral frequency, and the amount of supplemental damping ratio. Accordingly, distributions of viscous dampers in the structure plan are presented to control the structure's torsion based on the parameters mentioned.

• Smart Structures and Systems
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• 제26권1호
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• pp.49-61
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• 2020

This paper proposes a novel high performance vibration control device, multiple tuned mass dampers-inerters (MTMDI), to suppress the oscillatory motions of structures. The MTMDI, similar to the MTMD, involves multiple tuned mass damper-inerter (TMDI) units. In order to reveal the basic performance of the MTMDI, it is installed on a single degree-of-freedom (SDOF) structure excited by the ground acceleration, and the dynamic magnification factors (DMF) of the structure-MTMDI system are formulated. The optimization criterion is determined as the minimization of maximum values of the relative displacement's DMF for the controlled structure. Based on the particle swarm optimization (PSO) algorithm to tune the optimum parameters of the MTMDI, its performance has been investigated and evaluated in terms of control effectiveness, strokes, stiffness and damping coefficient, inerter element force, and robustness in frequency domain. Meanwhile, further comparison between the MTMDI with MTMD has been conducted. Numerical results clearly demonstrate the MTMDI outperforms the MTMD in control effectiveness and strokes of mass blocks. Additionally, in the aspects of frequency perturbations on both earthquake excitations and structures, the robustness of the MTMDI is also better than the MTMD.

• 전기학회논문지
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• 제63권4호
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• pp.526-533
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• 2014

In this study, we introduce design methodology to develop a guidance for issuing heavy rainfall warning by using both RBFNNs(Radial basis function neural networks) and SVR(Support vector regression) model, and then carry out the comparative studies between two pattern classifiers. Individual classifiers are designed as architecture realized with the aid of optimization and pre-processing algorithm. Because the predictive performance of the existing heavy rainfall forecast system is commonly affected from diverse processing techniques of meteorological data, under-sampling method as the pre-processing method of input data is used, and also data discretization and feature extraction method for SVR and FCM clustering and PSO method for RBFNNs are exploited respectively. The observed data, AWS(Automatic weather wtation), supplied from KMA(korea meteorological administration), is used for training and testing of the proposed classifiers. The proposed classifiers offer the related information to issue a heavy rain warning in advance before 1 to 3 hours by using the selected meteorological data and the cumulated precipitation amount accumulated for 1 to 12 hours from AWS data. For performance evaluation of each classifier, ETS(Equitable Threat Score) method is used as standard verification method for predictive ability. Through the comparative studies of two classifiers, neuro-fuzzy method is effectively used for improved performance and to show stable predictive result of guidance to issue heavy rainfall warning.