• The Transactions of the Korean Institute of Electrical Engineers B
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• v.52 no.5
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• pp.221-229
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• 2003

This paper suggests a control algorithm of 3-phase 3-wire series active power filter. This suggested algorithm can compensate source harmonics and reactive power in 3-phase 3-wire power distribution systems. These harmonics are generated by nonlinear loads such as diode rectifiers and thyristor converters. This control algorithm extracts a compensation voltage reference from performance function without phase transformation. Therefore, this control algorithm is simpler than any other conventional control algorithms. 3-phase 3-wire series active power filters which have a harmonic voltage source and a harmonic current source are manufactured and experiments are carried out to verify the effectiveness of suggested control algorithm.

• The Journal of the Acoustical Society of Korea
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• v.19 no.2
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• pp.13-18
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• 2000

This paper presents the result of a technique to enhance TDAC in the AC-3 algorithm. To reduce block boundary noise without decreasing the performance of transform coding, We propose new special windows which improve the defect of the AC-3 algorithm that could not properly cancel aliasing in the transient period. In addition, a fast MDCT calculation algorithm based on a fast Fourier transform, is adopted.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.3
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• pp.63-69
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• 2002

Detailed routing is a problem assigning each net to a track after global routing. The most popular algorithms for detailed routing include left-edge algorithm, dogleg algorithm, and greedy channel routing algorithm. In this paper we propose a genetic algorithm searching solution space for the detailed routing problem. We compare the performance of proposed genetic algorithm(GA) for detailed routing with that of greedy channel routing algorithm by analyzing the results of each implementation.

• Industrial Engineering and Management Systems
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• v.10 no.3
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• pp.203-208
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• 2011

Job shop scheduling is well-known as one of the hardest combinatorial optimization problems and has been demonstrated to be NP-hard problem. In the past decades, several researchers have devoted their effort to develop evolutionary algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for job shop scheduling problem. Differential Evolution (DE) algorithm is a more recent evolutionary algorithm which has been widely applied and shown its strength in many application areas. However, the applications of DE on scheduling problems are still limited. This paper proposes a one-stage differential evolution algorithm (1ST-DE) for job shop scheduling problem. The proposed algorithm employs random key representation and permutation of m-job repetition to generate active schedules. The performance of proposed method is evaluated on a set of benchmark problems and compared with results from an existing PSO algorithm. The numerical results demonstrated that the proposed algorithm is able to provide good solutions especially for the large size problems with relatively fast computing time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.654-661
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• 2011

This paper is about a personal identification algorithm using an ECG that has been studied by a few researchers recently. Previously published algorithm can be classified as two methods. One is the method that analyzes of ECG features and the other is the morphological analysis of ECG. The main characteristic of proposed algorithm can be classified the method of analysis ECG features. Proposed algorithm adopts DSTW(Down Slope Trace Wave) for extracting ECG features, and applies SVM(Support Vector Machine) to training and testing as a classifier algorithm. We choose 18 ECG files from MIT-BIH Normal Sinus Rhythm Database for estimating of algorithm performance. The algorithm extracts 100 heartbeats from each ECG file, and use 40 heartbeats for training and 60 heartbeats for testing. The proposed algorithm shows clearly superior performance in all ECG data, amounting to 93.89% heartbeat recognition rate and 100% ECG recognition rate.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.297-307
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• 2022

The simple procedural segment selection algorithm commonly used in Dynamic Adaptive Streaming over HTTP (DASH) reveals severe weakness to provide high-quality streaming services in the integrated mobile networks of various wired and wireless links. A major issue could be how to properly cope with dynamically changing underlying network conditions. The key to meet it should be to make the segment selection algorithm much more adaptive to fluctuation of network traffics. This paper presents a system architecture that replaces the existing procedural segment selection algorithm with a deep reinforcement learning algorithm based on the Asynchronous Advantage Actor-Critic (A3C). The distributed A3C-based deep learning server is designed and implemented to allow multiple clients in different network conditions to stream videos simultaneously, collect learning data quickly, and learn asynchronously, resulting in greatly improved learning speed as the number of video clients increases. The performance analysis shows that the proposed algorithm outperforms both the conventional DASH algorithm and the Deep Q-Network algorithm in terms of the user's quality of experience and the speed of deep learning.

• Journal of the Korea Society of Computer and Information
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• v.17 no.9
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• pp.139-147
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• 2012

This paper suggests simple search algorithm for optimal solution in assignment problem. Generally, the optimal solution of assignment problem can be obtained by Hungarian algorithm. The proposed algorithm reduces the 4 steps of Hungarian algorithm to 1 step, and only selects the minimum cost of row and column then gets the optimal solution simply. For the 27 balanced and 7 unbalanced assignment problems, this algorithm finds the optimal solution but the genetic algorithm fails to find this values. This algorithm improves the time complexity O($n^3$) of Hungarian algorithm to O(n). Therefore, the proposed algorithm can be general algorithm for assignment problem replace Hungarian algorithm.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.37 no.10
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• pp.9-17
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• 2000

In this paper, the improved initial cell searching algorithm is proposed for 3GPP(The third Generation Partnership Project) W-CDMA system. The key objective of the proposed algorithm is to reduce searching slot and to increase the reliability in the first stage of cell searching algorithm in order to accomplish the second stage. So the proposed algorithm makes the mobile station transfers to the second stage from the first stage, just after the slot synchronization is declared successively at the same time-offset. In order to compare the proposed algorithm with the conventional one, the simulations are accomplished for cell search algorithm for 3GPP W-CDMA systems in the multipath Rayleigh fading channel. The first stage of conventional algorithm is also analyzed in a Rayleigh fading channel in order to prove simulation to be reasonable. In this paper, the proposed algorithm presents the better performance than conventional one. We also propose some parameters for optimal performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.5
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• pp.970-976
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• 2004

In this paper, we analyze channel estimation algorithms in a RAKE receiver with MRC. There are 3 popular channel estimation algorithms, which are WMSA(Weighted Multi-Slot Averaging) algorithm, EGE(Equal Gain Estimation) algorithm, SSE(Symbol-to-Symbol Estimation) algorithm. We analyze asynchronous IMT-2000(3GPP) which employ 3 different channel estimation algorithms by HP-ADS(Advanced Design System) simulation tool. We used lakes fading channel model for the analysis. from simulation results, we could observe that the performance of WMSA algorithm is better than others in low Doppler effect(3Km/h). However, in the case of high Doppler effect(120km1h), the EGE algorithm is more efficient. In this case the simple estimator with EGE algorithm seems to be more useful.

• Korean Management Science Review
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• v.27 no.3
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• pp.15-31
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• 2010

We consider a reverse logistics network design problem for recycling. The problem consists of three stages of transportation. In the first stage products are transported from retrieval centers to disassembly centers. In the second stage disassembled modules are transported from disassembly centers to processing centers. Finally, in the third stage modules are transported from either processing centers or a supplier to a manufacturer, a recycling site, or a disposal site. The objective is to design a network which minimizes the total transportation cost. We design a cooperative coevolutionary algorithm to solve the problem. First, the problem is decomposed into three subproblems each of which corresponds to a stage of transportation. For subproblems 1 and 2, a population of chromosomes is constructed. Each chromosome in the population is coded as a permutation of integers and an algorithm which decodes a chromosome is suggested. For subproblem 3, an heuristic algorithm is utilized. Then, a performance evaluation procedure is suggested which combines the chromosomes from each of two populations and the heuristic algorithm for subproblem 3. An experiment was carried out using test problems. The experiments showed that the cooperative coevolutionary algorithm generally tends to show better performances than the previous genetic algorithm as the problem size gets larger.