• Structural Engineering and Mechanics
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• v.71 no.1
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• pp.1-12
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• 2019

This study investigates optimum designs of steel space truss towers under seismic loading by using Jaya optimization algorithm. Turkish Earthquake Code (2007) specifications are applied on optimum designs of steel space truss towers under the seismic loading for different local site classes depending on different soil groups. The proposed novel algorithm does not have any algorithm-specific control parameters and depends only a simple revision equation. Therefore, it provides a practical solution for structural optimization problems. Optimum solutions of the different steel truss examples are carried out by selecting suitable W sections taken from American Institute of Steel Construction (AISC). In order to obtain optimum solutions, a computer program is coded in MATLAB in corporated with SAP2000-OAPI (Open Application Programming Interface). The stress and displacement constraints are applied on the design problems according to AISC-ASD (Allowable Stress Design) specifications. Firstly, a benchmark truss problem is examined to see the efficiency of Jaya optimization algorithm. Then, two different multi-element truss towers previously solved with other methods without seismic loading in literature are designed by the proposed algorithm. The first space tower is a 582-member space truss with the height of 80 m and the second space tower is a 942-member space truss of about 95 m height. The minimum optimum designs obtained with this novel algorithm for the case without seismic loading are lighter than the ones previously attained in the literature studies. The results obtained in the study show that Jaya algorithm is a practical and robust optimization method for structural optimization problems. Moreover, incorporation of the seismic loading causes significant increase in the minimum design weight.

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.167-183
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• 2018

Electroencephalogram (EEG) recording provides a new way to support human-machine communication. It gives us an opportunity to analyze the neuro-dynamics of human cognition. Machine learning is a powerful for the EEG classification. In addition, machine learning can compensate for high variability of EEG when analyzing data in real time. However, the optimal EEG electrode location must be prioritized in order to extract the most relevant features from brain wave data. In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. We present useful results by using optimal rule numbers and non - symmetric membership function using EEG data for four movements with the right arm through various experiments.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.402-410
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• 2013

In this paper, a novel and robust Power System Stabilizer (PSS) is proposed as an effective approach to improve stability in electric power systems. The dynamic performance of proposed PSS has been thoroughly compared with Conventional PSS (CPSS). Both the Real Coded Genetic Algorithm (RCGA) and Particle Swarm Optimization (PSO) techniques are applied to optimum tune the parameter of both the proposed PSS and CPSS in order to damp-out power system oscillations. Due to the high sufficiency of both the RCGA and PSO techniques to solve the very non-linear objective, they have been employed for solution of the optimization problem. In order to verify the dynamic performance of these devices, different conditions of disturbance are taken into account in Single Machine Infinite Bus (SMIB) power system. Moreover, to ensure the robustness of proposed PSS in damping the power system multi-mode oscillations, a Multi Machine (MM) power system under various disturbances are considered as a test system. The results of nonlinear simulation strongly suggest that the proposed PSS significantly enhances the power system dynamic stability in both of the SMIB and MM power system as compared to CPSS.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.1
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• pp.1-9
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• 2009

OFDM(Orthogonal Frequency Division Multiplexing) system is robust to frequency selective fading and narrowband interference in high-speed data communications. However, an OPDM signal consists of a number of independently modulated subcarriers and the superposition of these subcarriers causes a problem that can give a large PARR(Peak-to-Average Power Ratio). PTS(Partial Transmit Sequence) scheme can reduce the PAPR by dividing OFDM signal into subblocks and then multiplying the phase weighting factors to each subblocks, but computational complexity for selecting of phase weighting factors increases exponentially with the number of subblocks. Therefore, in this paper, MG-PSO(Modified Greedy algorithm-Particle Swarm Optimization) algorithm that combines modified greedy algorithm and PSO(Particle Swarm Optimization) algorithm is proposed to use for the phase control method in PTS scheme. This method can solve the computational complexity and guarantee to reduce PAPR. We analyzed the performance of the PAPR reduction when we applied the proposed method to telecommunication systems.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.15-29
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• 2018

This paper proposes an enhanced object tracking algorithm to compensate the lack of temporal information in existing particle swarm optimization based object trackers using the trajectory of the target object. The proposed scheme also enables the tracking and documentation of the location of an online updated set of distractions. Based on the trajectories information and the distraction set, a rule based approach with adaptive parameters is utilized for occlusion detection and determination of the target position. Compare to existing algorithms, the proposed approach provides more comprehensive use of available information and does not require manual adjustment of threshold values. Moreover, an effective weight adjustment function is proposed to alleviate the diversity loss and pre-mature convergence problem in particle swarm optimization. The proposed weight function ensures particles to search thoroughly in the frame before convergence to an optimum solution. In the existence of multiple objects with similar feature composition, this algorithm is tested to significantly reduce convergence to nearby distractions compared to the other existing swarm intelligence based object trackers.

• The Journal of Information Technology
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• v.3 no.2
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• pp.73-85
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• 2000

The efficient algorithms are suggested in this study for solving the multicommodity network flow problems applied to Communications Systems. These problems are typical NP-complete optimization problems that require integer solution and in which the computational complexity increases numerically in appropriate with the problem size. Although the suggested algorithms are not absolutely optimal, they are developed for computationally efficient and produce near-optimal and primal integral solutions. We supplement the traditional Lagrangian method with a price-directive decomposition. It proceeded as follows. First, A primal heuristic from which good initial feasible solutions can be obtained is developed. Second, the dual is initialized using marginal values from the primal heuristic. Generally, the Lagrangian optimization is conducted from a naive dual solution which is set as ${\lambda}=0$. The dual optimization converged very slowly because these values have sort of gaps from the optimum. Better dual solutions improve the primal solution, and better primal bounds improve the step size used by the dual optimization. Third, a limitation that the Lagrangian decomposition approach has Is dealt with. Because this method is dual based, the solution need not converge to the optimal solution in the multicommodity network problem. So as to adjust relaxed solution to a feasible one, we made efficient re-allocation heuristic. In addition, the computational performances of various versions of the developed algorithms are compared and evaluated. First, commercial LP software, LINGO 4.0 extended version for LINDO system is utilized for the purpose of implementation that is robust and efficient. Tested problem sets are generated randomly Numerical results on randomly generated examples demonstrate that our algorithm is near-optimal (< 2% from the optimum) and has a quite computational efficiency.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.752-757
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• 2003

Recently, 3D structure recovery through self-calibration of camera has been actively researched. Traditional calibration algorithm requires known 3D coordinates of the control points while self-calibration only requires the corresponding points of images, thus it has more flexibility in real application. In general, self-calibration algorithm results in the nonlinear optimization problem using constraints from the intrinsic parameters of the camera. Thus, it requires initial value for the nonlinear minimization. Traditional approaches get the initial values assuming they have the same intrinsic parameters while they are dealing with the situation where the intrinsic parameters of the camera may change. In this paper, we propose new initialization method using the minimum 2 images. Proposed method is based on the assumption that the least violation of the camera’s intrinsic parameter gives more stable initial value. Synthetic and real experiment shows this result.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.702-704
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• 2004

This paper focuses on the problem of asymptotic stabilization for uncertain time-delay systems with saturating actuator. We propose a state feedback controller which maximizes the delay bound for guaranteeing stability of the system. Then, based on the Lyapunov method, a delay-dependent stabilization criterion is devised by taking the relationship between the terms in the Leibniz-Newton formula into account. The criterion is represented in terms of LMIs, which can be solved by various efficient convex optimization algorithm. Numerical examples are given to illustrate our main method.

• MALSORI
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• no.64
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• pp.121-135
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• 2007

Speech signal is distorted by channel characteristics or additive noise and then the performances of speaker or speech recognition are severely degraded. To cope with the noise problem, we propose a modified HMM decoder algorithm using SNR-based observation confidence, which was successfully applied for GMM in speaker identification task. The modification is done by weighting observation probabilities with reliability values obtained from SNR. Also, we apply PSO (particle swarm optimization) method to the confidence function for maximizing the speaker identification performance. To evaluate our proposed method, we used the ETRI database for speaker recognition. The experimental results showed that the performance was definitely enhanced with the modified HMM decoder algorithm.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.220-222
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• 2006

This paper presents intelligent digital redesign method of global approach for hybrid state space fuzzy-model-based controllers. For effectiveness and stabilization of continuous-time uncertain nonlinear systems under discrete-time controller, Takagi-Sugeno(TS) fuzzy model is used to represent the complex system. And global approach design problems viewed as a convex optimization problem that we minimize the error of the norm bounds between nonlinearly interpolated lineal operators to be matched. Also, by using the bilinear and inverse bilinear approximation method, we analyzed nonlinear system's uncertain parts more precisely. When a sampling period is sufficiently small, the conversion of a continuous-time structured uncertain nonlinear system to an equivalent discrete-time system have proper reason. Sufficiently conditions for the global state-matching of the digitally controlled system are formulated in terms of linear matrix inequalities (LMIs). Finally, a T-S fuzzy model for the chaotic Lorentz system is used as an example to guarantee the stability and effectiveness of the proposed method.