• Proceedings of the KIPE Conference
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• 2012.07a
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• pp.154-155
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• 2012

The dynamic voltage restorer (DVR) is an effective protection device for wind turbine generator based on doubly-fed induction generator (DFIG) operated under the unbalanced voltage dip conditions. The compensating voltages of DVR depend on the voltage dips and on the influence of the zero sequence components. If the $Y_0/{\Delta}$ step-up transformers are used, there are no zero sequence components on the DFIG side. However, if the $Y_0/Y_0$ step-up transformers are used, the zero sequence components will appear during faults. The zero sequence components result in the high insulation costs and the asymmetric of the terminal voltages. This paper proposes a method for controlling zero sequence components in DVR to protect DFIG under unbalanced voltage dips. Simulation results are presented to verify the effectiveness of the proposed control method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.8
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• pp.368-373
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• 2002

The inverse z-transform of a z-domain expression of a sequence can be Performed in many different methods among which the recursive computational method is based on the difference equation. In applying this method, a few initial values of the sequence should be obtained separately. Although the existing method generates the right initial values of the sequence, its derivation and justification are not theoretically in view of the definition of z-transform and its shift theorems. In this paper a general approach for formulating a difference equation and for obtaining required initial values of a sequence is proposed, which completely complies to the definition of the z-transform and an interpretation of the validity of the existing method which is theoretically incorrect.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.52-59
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• 2011

In this paper, we propose an indexing method to support efficient similarity search for sequence databases. We present a new distance measurement called minimum DTW distance to enhance the filtering effects. The minimum DTW distance is to measure the minimum distance between a sequence data and the group of similar sequences. It enables similarity search through hierarchical index structure by filtering sequence databases. Finally, we show the superiority of our method through some experiments.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.1
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• pp.213-221
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• 1996

An assembly sequence is considered to be optimal when is minimizes assembly cost while satisfying assembly constraints. To derive such an optimal sequence for robotic assembly, this paper proposes a method using a simulated annealing algorithm. In this method, an energy funciton is derived inconsideration of both the assembly constraints and the assembly cost. The energy function thus derived is iteratively minimized until no further change in energy occurs. During the minimization, the energy is occationally perturbed probabilistically in order to escape from local minima. The minimized energy yields an optimal assembly sequence. To show the effectiveness of the proposed method, case studies are presented for industrial products such as an electrical relay and an automobil alternator. The performance is analyzed by comparing the results with those of a neural network-based method, based upon the optimal solutions of an expert system.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.335-341
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• 2022

In this paper, we adopt a transformer that shows remarkable performance in natural language processing as an acoustic model of hybrid speech recognition. The transformer acoustic model uses attention structures to process sequential data and shows high performance with low computational cost. This paper proposes a method to improve the performance of transformer AM by applying each of the four algorithms of sequence discriminative training, a weighted finite-state transducer (wFST)-based learning used in the existing DNN-HMM model. In addition, compared to the Cross Entropy (CE) learning method, sequence discriminative method shows 5 % of the relative Word Error Rate (WER).

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.599-617
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• 2006

Recently many LBS(Location Based Service) systems are issued in mobile computing systems. Spatial-Temporal Moving Sequence Pattern Mining is a new mining method that mines user moving patterns from user moving path histories in a sensor network environment. The frequent pattern mining is related to the items which customers buy. But on the other hand, our mining method concerns users' moving sequence paths. In this paper, we consider the sequence of moving paths so we handle the repetition of moving paths. Also, we consider the duration that user spends on the location. We proposed new Apriori_msp based on the Apriori algorithm and evaluated its performance results.

• Journal of Communications and Networks
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• v.16 no.1
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• pp.92-97
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• 2014

A novel method for extracting frequency slippage signal from radar full pulse sequence is presented. For the radar full pulse sequence received by radar interception receiver, radio frequency (RF) and time of arrival (TOA) of all pulses constitute a two-dimensional information sequence. In a complex and intensive electromagnetic environment, the TOA of pulses is distributed unevenly, randomly, and in a nonstationary manner, preventing existing methods from directly analyzing such time series and effectively extracting certain signal features. This work applies Gaussian noise insertion and structure function to the TOA-RF information sequence respectively such that the equalization of time intervals and correlation processing are accomplished. The components with different frequencies in structure function series are separated using empirical mode decomposition. Additionally, a chaos detection model based on the Duffing equation is introduced to determine the useful component and extract the changing features of RF. Experimental results indicate that the proposed methodology can successfully extract the slippage signal effectively in the case that multiple radar pulse sequences overlap.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.7
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• pp.1881-1897
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• 1994

This paper presents a neural network-based computational scheme to generate the optimized robotic assembly sequence for an assembly product consisting of a number of parts. An assembly sequence is considered to be optimal when it meets a number of conditions : it must satisfy assembly constraints, keep the stability of in-process subassemblies, and minimize assembly cost. To derive such an optimal sequence, we propose a scheme using both the Hopfield neural network and the expert system. Based upon the inferred precedence constraints and the assembly costs from the expert system, we derive the evolution equation of the network. To illustrate the suitability of the proposed scheme, a case study is presented for industrial product of an electrical relay. The result is compared with that obtained from the expert system.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.1-10
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• 2005

Multiple sequence alignment(MSA) is a fundamental technique of DNA and Protein sequence analysis. Biological sequences are aligned vertically in order to show the similarities and differences among them. In this Paper, we Propose an effcient group alignment method, which is based on clustering divergency, to Perform the alignment between two groups of sequences. The Proposed algorithm is a clustering divergence(CDMS)-based multiple sequence alignment and a top-down approach. The algorithm builds the tree topology for merging. It is so based on the concept that two sequences having the longest distance should be spilt into two clusters. We expect that our sequence alignment algorithm improves its qualify and speeds up better than traditional algorithm Clustal-W.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.10 s.89
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• pp.996-1004
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• 2004

1-Dimensional Cluster-Based Sequence Equalizer(1-D CBSE) lessens computational load, compared with the classic maximum likelihood sequence estimation(MLSE) equalizers, and has the superiority in the nonlinear channels. In this paper, we proposed an algorithm of searching for optimal training sequence that estimates the cluster centers instead of time-varying multipath fading channel estimation. The proposed equalizer not only resolved the problems in 1-D CBSE but also improved the bandwidth efficiency using the shorten length of taming sequence to improve bandwidth efficiency. In experiments, the superiority of the new method is demonstrated by comparing conventional 1-D CBSE and related analysis.