• PNF and Movement
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• 제12권2호
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• pp.97-106
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• 2014

Purpose: The purpose of this study was to investigate changes in EEG through attention. Concentration training and performing tasks are important factors in the improvement of motor learning ability. Methods: In the experiment, 22 healthy people were divided into two groups: the trail making test (TMT) group and the computerized neurocognitive function test (CNT) group. A one-way Neuro Harmony M test to see whether there was a significant difference among the groups. Results: The TMT group showed a significant increase in ${\alpha}$ wave, ${\alpha}$ wave sequence, and ${\beta}$ wave sequence; however, there were no significant differences in SMR wave, SMR wave sequence, and ${\beta}$ wave. The CNT group showed increases in ${\alpha}$ wave, ${\alpha}$ wave sequence, SMR wave, SMR wave sequence, and ${\beta}$ wave sequence; however, there was no significant difference in ${\beta}$ wave. In EEGs before and after two performance tasks were changed, there were significant differences in ${\beta}$ wave, SMR wave, SMR wave sequence; however, there were no significant differences in ${\alpha}$ wave sequence, ${\beta}$ wave, and ${\beta}$ wave sequence. Conclusion: Attention training and concentration training offer feedback and repetition for constant stimulus and response. Moreover, attention training and concentration training can contribute to new studies and motivation by developing fast sensory and motor skills through acceptable visual and auditory stimulation.

• 한국통신학회논문지
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• 제34권1C호
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• pp.113-119
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• 2009

The training sequence with the appropriate length for equalization and initial synchronization is necessary before sending the pure data in the burst transmission type DS-UWB system. The length of the training sequence is one of the factors which make throughput decreased. The noble structure with the variable length of the training sequence whose length can be adaptively tailored according to the channel conditions (CM1,CM2,CM3,CM4) in the DS-USB systems is proposed. This structure can increase the throughput without sacrificing the performance than the method with fixed length of training sequence considering the worst case channel conditions. Simulation results under IEEE 802.15.3a channel model show that the proposed scheme can achieve higher throughput than a conventional one with the slight loss of BER performance. And this structure can reduce the computation complexity and power consumption with selecting the short length of the training sequence.

• 전기학회논문지
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• 제56권11호
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• pp.2033-2035
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• 2007

This paper proposes a novel iterative LMS-based decision feedback equalizer for short burst transmission with relatively short training sequence. In the proposed equalizer, the longer concatenated training sequence can provide the more sufficient channel information and the reused original training sequence can provide the correct decision feedback information. In addition, the overall adaptive processing is performed using the low complexity LMS algorithm. The study shows the performance of the proposed method is enhanced with the number of iterations and, furthermore, better than that of the conventional LMS-based DFEs with the training sequence of longer or equal length. Computational complexity is increased linearly with the number of iterations.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1901-1904
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• 2002

A good performance on communication systems is obtained by decreasing the length of training sequence In the initial stage of adaptive equalization. This paper presents a new approach to accomplish this, with the use of a training adaptive equalizer. The approach is based on combining the training and tracking modes, in which the training equalizer is updated by the LMS algorithm with the training sequence and then updated by a blind algorithm. By computer simulations, it is shown that a class of the proposed equalizers provides better performance than the conventional training equalizer.

• 한국컴퓨터산업학회논문지
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• 제2권4호
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• pp.537-546
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• 2001

DSSS(Direct-Sequence Spread-Spectrum) CDMA 시스템에서 MAI(Multiple Access Interference)와 원근 문제를 해결할 수 있는 단일-사용자 검파에 적합한 알고리즘으로 Griffiths’알고리즘과 LCCMA(Linearly Constrained Constant Modulus Algorithm)에 제안되었으며 MMSE 검파기에 적합한 다중-사용자 알고리즘인 MOE 알고리즘 또한 제안되었다. 본 논문은 training sequence의 요구 없이 시스템의 성능을 향상시킬 수 있는 이 세 가지 Blind 적합 알고리즘을 가지고 간섭 사용자의 수나 원하는 사용자의 데이터 업데이트율에 따라 각각의 알고리즘별 성능을 비교 분석하였다. 시뮬레이션 결과 간섭 사용자수와 원하는 사용자의 업데이트율의 변화에 따라 모두 LCCMA 알고리즘이 뛰어난 성능을 보았다. Blind 적용은 하나의 training sequence의 필요성을 없앰으로써 더욱 융통성 있는 네트웍디자인을 가능케 했다.

• 한국전자파학회논문지
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• 제15권10호
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• pp.996-1004
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• 2004

1차원 클러스터 기반의 시퀀스 등화기(1-D CBSE)는 시퀀스 등화기(MLSE)가 갖는 계산상의 복잡성을 효율적으로 해결하고 비선형 채널에서의 뛰어난 성능 개선을 가져온다. 본 논문에서는 다중 경로 페이딩 채널 추정에 대응하는 1-D CBSE의 클러스터 중심을 추정하기 위한 향상된 훈련 시퀀스 구성 기법을 제안하였다. 새로이 제안된 등화기는 기존의 방식에서 갖는 문제점을 해결하고, 보다 짧은 길이의 훈련 시퀀스를 이용함으로써 대역폭 효율을 증대시키는 향상된 결과를 가져왔다. 제안된 알고리즘의 우수성은, 기존의 방법과 제안된 최적의 훈련시퀀스를 적용한 1-D클러스터 기반의 새로운 중심 추정을 통한 방법을 비교를 통하여 보였다. 특히, 컴퓨터 시뮬레이션에 의한 심볼 에러율(SER)에 기반을 둔 비교 분석을 통하여 살펴보았다.

• Journal of Communications and Networks
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• 제11권1호
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• pp.42-46
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• 2009

Superimposed training (SIT) design for estimating of time-varying multipath channels is investigated for mobile orthogonal frequency division multiplexing (OFDM) systems. The design of optimum SIT consists of two parts: The optimal SIT sequence is derived by minimizing the channel estimates' mean square error (MSE); the optimal power allocation between training and information data is developed by maximizing the averaged signal to interference plus noise ratio (SINR) under the condition of equal powered paths. The theoretical analysis is verified by simulations. For the metric of the averaged SINR against signal to noise ratio (SNR), the theoretical result matches the simulation result perfectly. In contrast to an interpolated frequency-multiplexing training (FMT) scheme or an SIT scheme with random pilot sequence, the SIT scheme with proposed optimal sequence achieves higher SINR. The analytical solution of the optimal power allocation is demonstrated by the simulation as well.

• 한국음향학회지
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• 제41권3호
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• pp.335-341
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• 2022

본 논문에서는 기존 자연어 처리 분야에서 뛰어난 성능을 보이는 트랜스포머를 하이브리드 음성인식에서의 음향모델로 사용하였다. 트랜스포머 음향모델은 attention 구조를 사용하여 시계열 데이터를 처리하며 연산량이 낮으면서 높은 성능을 보인다. 본 논문은 이러한 트랜스포머 AM에 기존 DNN-HMM 모델에서 사용하는 가중 유한 상태 전이기(weighted Finite-State Transducer, wFST) 기반 학습인 시퀀스 분류 학습의 네 가지 알고리즘을 각각 적용하여 성능을 높이는 방법을 제안한다. 또한 기존 Cross Entropy(CE)를 사용한 학습방식과 비교하여 5 %의 상대적 word error rate(WER) 감소율을 보였다.

• International journal of advanced smart convergence
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• 제5권3호
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• pp.8-15
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• 2016

In recent years, one of deep learning models called Deep Belief Network (DBN) which formed by stacking restricted Boltzman machine in a greedy fashion has beed widely used for classification and recognition. With an ability to extracting features of high-level abstraction and deal with higher dimensional data structure, this model has ouperformed outstanding result on image and speech recognition. In this research, we assess the applicability of deep learning in dna classification level. Since the training phase of DBN is costly expensive, specially if deals with DNA sequence with thousand of variables, we introduce a new encoding method, using decimal-binary vector to represent the sequence as input to the model, thereafter compare with one-hot-vector encoding in two datasets. We evaluated our proposed model with different contrastive algorithms which achieved significant improvement for the training speed with comparable classification result. This result has shown a potential of using decimal-binary vector on DBN for DNA sequence to solve other sequence problem in bioinformatics.

• 한국통신학회논문지
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• 제33권5A호
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• pp.479-486
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• 2008

본 논문은 MIMO-OFDM 시스템을 기반으로 한 Gbps급 차세대 무선전송 시스템에서 m-sequence를 이용하여 효율적으로 훈련심볼을 설계하는 방법을 제안한다. 계산량을 고려하지 않는 일반적인 훈련심볼 설계방법에는 랜덤한 시퀀스에 대하여 시스템 요구사항을 하나하나 비교해가며 모든 사항이 만족되었을 때 훈련심볼로 채택하는 기법이 존재한다. 본 논문에서는 상관관계 특성이 우수하여 대역확산 통신 방식 등에서 사용하는 m-sequence를 기반으로 제한된 탐색공간 내에서 효율적인 MIMO-OFDM 시스템을 위한 훈련심볼의 단계별 설계 및 검증방법을 논의한다. 제안된 방법은 패킷 기반 MIMO-OFDM 무선통신 시스템에서 자동이득제어, 타이밍 동기, 주파수 및 시간옵셋 추정, MIMO 채널추정 등을 포함한 시스템에서 요구하는 조건을 만족시키는 훈련심볼 설계를 목표로 한다.