• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.51-59
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• 2019

Due to the exponential growth of access information on the web, the need for predicting web users' next access has increased. Various models such as markov models, deep neural networks, support vector machines, and fuzzy inference models were proposed to handle web access prediction. For deep learning based on neural network models, training time on large-scale web usage data is very huge. To address this problem, deep neural network models are trained on cluster of computers in parallel. In this paper, we investigated impact of several important spark parameters related to data partitions, shuffling, compression, and locality (basic spark parameters) for training Multi-Layer Perceptron model on Spark standalone cluster. Then based on the investigation, we tuned basic spark parameters for training Multi-Layer Perceptron model and used it for tuning Spark when training Multi-Layer Perceptron model for web access prediction. Through experiments, we showed the accuracy of web access prediction based on our proposed web access prediction model. In addition, we also showed performance improvement in training time based on our spark basic parameters tuning for training Multi-Layer Perceptron model over default spark parameters configuration.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1542-1550
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• 2019

Legacy studies for classifying arrhythmia have been studied to improve the accuracy of classification, Neural Network, Fuzzy, etc. Deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose optimal parameter extraction method based on a deep learning. For this purpose, R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 97.84% in PVC classification.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.3-9
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• 2015

The wireless communication system adopts an appropriate retransmission scheme on each system protocol layer to improve reliability of data transmission. In each system protocol layer, the retransmission scheme operates in independently other layers and operates based on the parameters without reference to end-to-end performance of wireless communication system. For this reason, it is difficult to design the optimal system parameters that satisfy the QoS requirements for each service class. Thus, the performance analysis of wireless communication system is needed to design the optimal system parameters according to the end-to-end QoS requirements for each service class. In this paper, we derive the mathematical model to formulate the end-to-end performance of wireless communication system. We also evaluate the performance at the MAC and transport layers in terms of average spectral efficiency and average transmission delay. Based on the results of performance evaluations, we design the optimal system parameters according to the QoS requirements of service classes. From the results, the HARQ combined with AMC is appropriate for the delay-sensitive service and the ARQ combined with AMC is appropriate for a service that is insensitive to transmission delay. Also, the TCP can be applied for the delay-insensitive service only.

• Aerospace Engineering and Technology
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• v.6 no.2
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• pp.73-78
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• 2007

In this study, we reviewed the variations of GSD, line rate of a electro-optical payload caused by the changes of operational altitude and attitude of a satellite by applying design parameters of the EC6 which is under development. we also reviewed adjustable increments/decrements of line_rate which are limited by CEU(Camera Electronic Unit) design and then the effect on the MIF(Modulation Transfer Function) performance due to the un-synchronization between line_rate of EOS and ground scan velocity of the satellite based on the design parameters of CEU to show that CEU design is appropriate in terms of line_rate control of EOS.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.5
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• pp.407-415
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• 2016

In this paper, an aviation image fusion method is proposed for creating an informative fused image through gray scale images within noise. The proposed method is based on an adaptive guided filter which adjusts regulation parameter of the filter based on peak signal noise ratio (PSNR) in order to behave as an edge-preserving filtering property. Simulation results demonstrate that the proposed method preserves the edge information of the input image and reduces the noise effect while maintaining designed PSNR.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.6C
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• pp.492-496
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• 2012

This paper presents a decoding technique for speech recognition using uncertainty information from feature compensation method to improve the speech recognition performance in the low SNR condition. Traditional feature compensation algorithms have difficulty in estimating clean feature parameters in adverse environment. Those algorithms focus on the point estimation of desired features. The point estimation of feature compensation method degrades speech recognition performance when incorrectly estimated features enter into the decoder of speech recognition. In this paper, we apply the uncertainty information from well-known feature compensation method, such as IMM, to the recognition engine. Applied technique shows better performance in the Aurora-2 DB.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.2
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• pp.23-28
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• 2009

This study describes an emotion classification method using two different feature parameters of four-channel EEG signals. One of the parameters is linear prediction coefficients based on AR modelling. Another one is cross-correlation coefficients on frequencies of ${\theta}$, ${\alpha}$, ${\beta}$ bands of FFT spectra. Using the linear predictor coefficients and the cross-correlation coefficients of frequencies, the emotion classification test for four emotions, such as anger, sad, joy, and relaxation is performed with an artificial neural network. The results of the two parameters showed that the linear prediction coefficients have produced the better results for emotion classification than the cross-correlation coefficients of FFT spectra.

• The Journal of the Acoustical Society of Korea
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• v.20 no.5
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• pp.48-53
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• 2001

In speaker verification based on LPC analysis the prediction residues are ignored and LPCC(LPC cepstrum) are only used to compose feature vectors. In this study, LPCC and RCEP (residual cepstrum) extracted from residues are used as feature parameters in the various environmental speaker verification. We propose the weighting function which can enlarge inter-speaker variation by weighting pitch, speaker inherent vector, included in residual cepstrum. Simulation results show that the average speaker verification rate is improved in the rate of 6％ with RCEP and LPCC at the same time and is improved in the rate of 2.45% with the proposed weighted RCEP and LPCC at the same time compared with no weighting.

• 한국가시화정보학회:학술대회논문집
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• 2005.04a
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• pp.115-154
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• 2005

SI 엔진의 연소특징은 비정상 난류 예혼합 화염이며 여기서 내부 유동은 직접 화염 전파에 영향을 미치며 난류와 거시적 유동의 패턴 모두 중요한 역할을 한다. 내연기관 연소에서 난류는 매우 중요한 역할을 하고 통상 엔진 속도($\approx$흡입유동 속도)에 비례하며 그 주요 역할은 고속 운전 시 해당 사이클 내에 연소가 완료되는 데 기여하지만 출력저하, 제어 및 측정 그리고 사이클 변동과 관련하여 실질적으로 난류 제어를 통한 엔진 성능 개선은 사실상 불가능하다. 실물 엔진의 성능 파라미터로 주로 유동의 거시적 거동이 사용되며 이 유동과 연료 분사계가 혼합기 분포 상태와 화염 전파 방향을 결정하여 최종적으로 엔진의 성능을 지배한다. 따라서 가시화를 통한 연소 진단도 이 현상에 주목할 필요가 있으며 거시적 파라미터를 성능에 연관하는 다양한 기법이 존재하고 이들은 매우 풍부한 데이터베이스를 통해 비교적 정확한 성능의 예측을 가능하게 하고 이 점에 주목한 엔진만 성공을 거두었다. 이 거시적 현상에 주목하여 가시화를 통해 성층화 현상을 실험적으로 해석한 예를 제시하였다. SI 엔진 가시화에서 기법보다 중요한 것은 현상의 이해이다. 이를 위해 성공적 가시화 진단을 위해서는 우선 현상에 대한 모델링이 필요하고 이 모델에서 가시화를 통해 규명 가능한 현상을 추출해 내는 것이다.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.510-513
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• 2018

본 논문은 다양한 워드 임베딩 모델(word embedding model)들과 하이퍼 파라미터(hyper parameter)들을 조합하였을 때 특정 영역에 어떠한 성능을 보여주는지에 대한 연구이다. 3 가지의 워드 임베딩 모델인 Word2Vec, FastText, Glove의 차원(dimension)과 윈도우 사이즈(window size), 최소 횟수(min count)를 각기 달리하여 총 36개의 임베딩 벡터(embedding vector)를 만들었다. 각 임베딩 벡터를 Fast and Accurate Dependency Parser 모델에 적용하여 각 모들의 성능을 측정하였다. 모든 모델에서 차원이 높을수록 성능이 개선되었으며, FastText가 대부분의 경우에서 높은 성능을 내는 것을 알 수 있었다.