• Wind and Structures
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• v.36 no.2
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• pp.105-120
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• 2023

The present study is performed to find the effect of corner recession on a square plan-shaped tall building. A series of numerical simulations have been carried out to find the two orthogonal wind force coefficients on various model configurations using Computational Fluid Dynamics (CFD). Numerical analyses are performed by using ANSYS-CFX (k-ℇ turbulence model) considering the length scale of 1:300. The study is performed for 0° to 360° wind angle of attack. The CFD data thus generated is utilised to fit parametric equations to predict alongwind and crosswind force coefficients, C_fx and C_fy. The precision of the parametric equations is validated by employing a wind tunnel study for the 40% corner recession model, and an excellent match is observed. Upon satisfactory validation, the parametric equations are further used to carry out multiobjective optimization considering two orthogonal force coefficients. Pareto optimal design results are presented to propose suitable percentages of corner recession for the study building. The optimization is based on reducing the alongwind and crosswind forces simultaneously to enhance the aerodynamic performance of the building.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.81-87
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• 2016

This paper analysis the AM-SCS-MMA (Adaptive Modulus-Soft Constraint Satisfaction-MMA) based on the adaptive modulus and minimus-disturbance technique in order to improve the stability and robustness in low signal to noise power of current MMA adaptive equalization algorithm. In AM-SCS-MMA, it updates the filter coefficient applying the adaptive modulus and minimum-disturbance technique of deterministic optimization problem instead of LMS or gradient descend algorithm for obtain the minimize the cost function of adaptive equalization. It is possible to improve the equalizer filter stability, robustness to the various noise characteristic and simultaneous reducing the intersymbol interference due to the amplitude and phase distortion occurred at channel. The computer simulation were performed for confirming the improved performance of SCS-MMA. For these, the output signal constellation of equalizer, residual isi, MSE, EMSE (Excess MSE) which means the channel traking capability and SER which means the robustness were applied. As a result of computer simulation, the AM-SCS-MMA have slow convergence time and less residual quantities after steady state, more good robustness in the poor signal to noise ratio, but poor in channel tracking capabilities was confirmed than MMA.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2011.11a
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• pp.101-103
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• 2011

Various desired performances of concrete cannot be always obtained by current conventional mix proportion methods for recycled aggregate concrete (RAC). This paper suggests a new design method of mix proportion for RAC to reduce the number of trial mixes using genetic algorithm (GA) which has been an optimization technique to solve the multi-object problem. In mix design method by GA, several fitness functions for the required properties of concrete, i.e., slump, strength, price, and carbonation speed coefficient were considered based on conventional data or fitness function. As a result, various optimum mix proportions for RAC that meet required performances were obtained and the risk evaluation was also conducted for selected mixtures.

• Journal of Power System Engineering
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• v.11 no.1
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• pp.20-27
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• 2007

In this paper, finite element modeling was performed for vibration analysis of a rotor system installed in sunroof motor, and analysis process was developed for natural frequency and unbalance response analysis. At the same time, to reduce analysis modeling error caused by the difference between analysis and measured values, finite element model updating was conducted using an optimization algorithm, i.e. hybrid genetic algorithm and simulated annealing (HGASA) method. For this end experimental modal test was carried out and by using the measured frequency response function (FRF), model updating was performed considering both cases where core coil was removed and included. And acceptable result was obtained. Also, dynamic property coefficient of bush bearing which influences vibration response of the rotor system was estimated.

• ETRI Journal
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• v.46 no.3
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• pp.461-472
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• 2024

We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.64-71
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• 2017

Considering the volatility, intermittent and random of photovoltaic (PV) generation systems, accurate forecasting of PV power output is important for the grid scheduling and energy management. In order to improve the accuracy of short-term power forecasting of PV systems, this paper proposes a prediction model based on environmental factors and support vector machine optimized by genetic algorithm (GA-SVM). In order to improve the prediction accuracy of this model, weather conditions are divided into three types, and the gray correlation coefficient algorithm is used to find out a similar day of the predicted day. To avoid parameters optimization into local optima, this paper uses genetic algorithm to optimize SVM parameters. Example verification shows that the prediction accuracy in three types of weather will remain at between 10% -15% and the short-term PV power forecasting model proposed is effective and promising.

• Membrane and Water Treatment
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• v.9 no.5
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• pp.363-371
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• 2018

In this work, it was investigated the usability of artificial bee colony (ABC) and genetic algorithm (GA) in modeling adsorption of Co(II) onto drinking water treatment sludge (DWTS). DWTS, obtained as inevitable byproduct at the end of drinking water treatment stages, was used as an adsorbent without any physical or chemical pre-treatment in the adsorption experiments. Firstly, DWTS was characterized employing various analytical procedures such as elemental, FT-IR, SEM-EDS, XRD, XRF and TGA/DTA analysis. Then, adsorption experiments were carried out in a batch system and DWTS's Co(II) removal potential was modelled via ABC and GA methods considering the effects of certain experimental parameters (initial pH, contact time, initial Co(II) concentration, DWTS dosage) called as the input parameters. The accuracy of ABC and GA method was determined and these methods were applied to four different functions: quadratic, exponential, linear and power. Some statistical indices (sum square error, root mean square error, mean absolute error, average relative error, and determination coefficient) were used to evaluate the performance of these models. The ABC and GA method with quadratic forms obtained better prediction. As a result, it was shown ABC and GA can be used optimization of the regression function coefficients in modeling adsorption experiments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.9
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• pp.1531-1540
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• 2016

In this paper, we propose vehicle license plate recognition system based on Radial Basis Function Neural Networks (RBFNNs) with the use of local binarization functions and canny edge algorithm. In order to detect the area of license plate and also recognize license plate numbers, binary images are generated by using local binarization methods, which consider local brightness, and canny edge detection. The generated binary images provide information related to the size and the position of license plate. Additionally, image warping is used to compensate the distortion of images obtained from the side. After extracting license plate numbers, the dimensionality of number images is reduced through Principal Component Analysis (PCA) and is used as input variables to RBFNNs. Particle Swarm Optimization (PSO) algorithm is used to optimize a number of essential parameters needed to improve the accuracy of RBFNNs. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. Image data sets are obtained by changing the distance between stationary vehicle and camera and then used to evaluate the performance of the proposed system.

• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.193-198
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• 2009

The H.264/ AVC video coding standard supports the intra prediction with various block sizes for luma component and a 8x8 block size for chroma components. This new feature of H.264/AVC offers a considerably higher improvement in coding efficiency compared to previous compression standards. In order to achieve this, H.264/AVC uses the Rate-distortion optimization (RDO) technique to select the best intra prediction mode for each block size, and it brings about the drastic increase of the computation complexity of H.264 encoder. In this paper, a fast block size decision algorithm is proposed to reduce the computation complexity of the intra prediction in H.264/AVC. The proposed algorithm computes the smoothness based on AC and DC coefficient energy for macroblocks and compares with the nonparametric criteria which is determined by considering information on neighbor blocks already reconstructed, so that deciding the best probable block size for the intra prediction. Also, the use of non-parametric criteria makes the performance of intra-coding not be dependent on types of video sequences. The experimental results show that the proposed algorithm is able to reduce up to 30% of the whole encoding time with a negligible loss in PSNR and bitrates and provides the stable performance regardless types of video sequences.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.636-645
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• 2012

This study introduces an information granular-based fuzzy radial basis function neural networks (FRBFNN) based on multiobjective optimization and weighted least square (WLS). An improved multiobjective space search algorithm (IMSSA) is proposed to optimize the FRBFNN. In the design of FRBFNN, the premise part of the rules is constructed with the aid of Fuzzy C-Means (FCM) clustering while the consequent part of the fuzzy rules is developed by using four types of polynomials, namely constant, linear, quadratic, and modified quadratic. Information granulation realized with C-Means clustering helps determine the initial values of the apex parameters of the membership function of the fuzzy neural network. To enhance the flexibility of neural network, we use the WLS learning to estimate the coefficients of the polynomials. In comparison with ordinary least square commonly used in the design of fuzzy radial basis function neural networks, WLS could come with a different type of the local model in each rule when dealing with the FRBFNN. Since the performance of the FRBFNN model is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials present in the consequent parts of the rules, we carry out both structural as well as parametric optimization of the network. The proposed IMSSA that aims at the simultaneous minimization of complexity and the maximization of accuracy is exploited here to optimize the parameters of the model. Experimental results illustrate that the proposed neural network leads to better performance in comparison with some existing neurofuzzy models encountered in the literature.