• Proceedings of the ESK Conference
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• 1997.10a
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• pp.482-486
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• 1997

인간과 기계의 인터페이스로서 EEG를 이용한 방법이 새로이 부각되고 있다. 두뇌 피질로부터 추출되 는 EEG 신호를 처리해서 컴퓨터로 하여금 사람의 생각을 예측사고 원하는 바를 처리해주도록 하자는 것 이 궁극적인 목표이다. 본 연구에서는 두뇌피질 부위 중 손과 팔의 움직임에 민감하게 반응하는 부분 으로부터 EEG 신호(signal)를 추출해서 오른손 움직임인지 왼손 움직임인지를 구분해 주는 운동 신호 분류 방법을 제안하고 실험했다. 제안된 방법에서 성공률은 최대 89%를 보였으며, 이 방법을 응용하면 간단한 작업을 EEG로 처리하는 인터페이스의 설계,구현이 가능할 것이다.

• The KIPS Transactions:PartC
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• v.12C no.1 s.97
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• pp.63-68
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• 2005

In this paper, performance of a maximum-likelihood modulation classification for quadrature amplitude modulation (QAM) is studied. Unlike previous works, the relative classification performance with respect to the available modulations and performance limit with single-sample observation are presented. For those purposes, all constellations are set to have the same minimum Euclidean distance between symbols so that a smaller constellation is a subset of the larger ones. And only one sample of received waveform is used for multiple hypothesis test. As a result, classification performance is improved with increase in signal-to-noise ratio in all the experiments. Especially, when the true modulation format used in the transmitter is 4 QAM, almost perfect classification can be achieved without any additional information or observation samples. Though the possibility of false classification due to the symbols shared by subset constellations always exists, correct classification ratio of $80{\%}$ can be obtained with the single-sample observation when the true modulation formats are 16 and 64 QAM.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.32-43
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• 2002

In this paper, we propose a pattern classification system for digital signal which is based on neural networks. The proposed system consists of two models of neural network. The first part is a wavelet neural network whose role is a feature extraction. For this part, we compare existing models of wavelet networks and propose a new model for feature extraction. The other part is a wavelet network for pattern classification. We modify the structure of previous wavelet network for pattern classification and propose a learning method. The inputs of the pattern classification wavelet network is connection weights, dilation and translation parameters in hidden nodes of feature extraction network. And the output is a class of the signal which is input of feature extraction network. The proposed system is, applied to classification of EEG signal based on frequency.

• The Journal of the Acoustical Society of Korea
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• v.28 no.1
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• pp.32-43
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• 2009

In this paper, we improve the performance of cardiac disorder classification by continuous heart sound signals using automatic segmentation and extreme learning machine (ELM). The accuracy of the conventional cardiac disorder classification systems degrades because murmurs and click sounds contained in the abnormal heart sound signals cause incorrect or missing starting points of the first (S1) and the second heart pulses (S2) in the automatic segmentation stage, In order to reduce the performance degradation due to segmentation errors, we find the positions of the S1 and S2 pulses, modify them using the time difference of S1 or S2, and extract a single period of heart sound signals. We then obtain a feature vector consisting of the mel-scaled filter bank energy coefficients and the envelope of uniform-sized sub-segments from the single-period heart sound signals. To classify the heart disorders, we use ELM with a single hidden layer. In cardiac disorder classification experiments with 9 cardiac disorder categories, the proposed method shows the classification accuracy of 81.6% and achieves the highest classification accuracy among ELM, multi-layer perceptron (MLP), support vector machine (SVM), and hidden Markov model (HMM).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1728-1736
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• 2015

Several algorithms have been developed to classify arrhythmia which either rely on specific ECG(Electrocardiogram) database. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. But it is difficult to detect the P and T wave signal because of person's individual difference. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose arrhythmia classification method using QRS Pattern of ECG signal according to personalized type. For this purpose, we detected R wave through the preprocessing method and define QRS pattern of ECG signal by QRS feature Also, we detect and modify by pattern classification, classified arrhythmia duplicated QRS pattern in realtime. Normal, PVC, PAC, LBBB, RBBB, Paced beat classification is evaluated by using 43 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.98%, 97.22%, 95.14%, 91.47%, 94.85%, 97.48% in PVC, PAC, Normal, BBB, Paced beat classification.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.193-204
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• 2019

The explosion of data due to the improvement of sensor technology and computing performance has become the basis for analyzing the situation in the industrial fields, and various attempts to detect events based on such data are increasing recently. In particular, sound signals collected from sensors are used as important information to classify events in various application fields as an advantage of efficiently collecting field information at a relatively low cost. However, the performance of sound-event classification in the field cannot be guaranteed if noise can not be removed. That is, in order to implement a system that can be practically applied, robust performance should be guaranteed even in various noise conditions. In this study, we propose a system that can classify the sound event after generating the enhanced sound signal based on the deep learning algorithm. Especially, to remove noise from the sound signal itself, the enhanced sound data against the noise is generated using SEGAN applied to the GAN with a VAE technique. Then, an end-to-end based sound-event classification system is designed to classify the sound events using the enhanced sound signal as input data of CNN structure without a data conversion process. The performance of the proposed method was verified experimentally using sound data obtained from the industrial field, and the f1 score of 99.29% (railway industry) and 97.80% (livestock industry) was confirmed.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.469-472
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• 2011

신체 부위를 움직이는 상상을 할 때, 일반적으로 뇌의 감각 및 운동 피질 영역에서 특정 주파수 대역의 EEG(Electroencephalography) 신호의 세기가 감소하거나 증가하는 ERD(Event-Related Desynchronization)/ERS(Event-Related Synchronization) 현상이 발생한다. 하지만 ERD/ERS는 현상은 피험자에 의존적이고 매시도마다 큰 차이를 보인다. 이러한 문제를 해결하기 위해, 본 논문에서 각 시간-주파수 공간에 대하여 서로 다른 공간 필터를 구성하는 비 동질(non-homogeneous) 공간 필터 최적화 방법을 제안한다. EEG 신호는 시간에 대하여 비정상적(non-stationary) 특징을 가지기 때문에 제안하는 방법과 같이 시간에 따라 변화하는 ERD/ERS 특징을 반영하여 공간적 특징을 추출하는 방법은 시간에 대한 변화를 고려하지 않은 기존의 방법보다 우수한 성능을 보인다. 본 논문에서는 International BCI Competition IV에서 제공하는 4가지 동작 상상(왼손, 오른손, 발, 혀)에 대한 EEG 신호 데이터를 사용하여 동작 상상 분류 실험을 하고 이 결과를 기존의 타 방법들과 비교 분석하였다. 실험 결과, 피험자에 따라 서로 다른 시간-주파수 특징이 추출됨을 확인하였고, 최적화된 공간 필터들이 시간에 따라 변화하는 것을 확인하였다. 또한 이러한 특징을 이용하여 분류를 수행하였을 때, 더욱 우수한 분류 결과를 보임을 확인하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.599-607
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• 2006

The ship radiated noise appear the various characteristic signals due to the mechanic system in the ship, the propeller and the interaction between ship body and sea water. Generally, it is classified two main components: the speed dependent signal and the speed independent signal. It is required that very complex procedure to classify the signal origin from the ship-radiated noise. This paper presents techniques to automatically detect and classify the tonal signals ken the ship-radiated noise, using the Q factor and the neural network.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.75-76
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• 2012

감시정찰 센서네트워크 시스템에서 사용하는 센서 신호 중 진동 신호는 무인정찰 시스템상에서 침입자의 발자국 신호와 차량의 움직임을 탐지하는데 유용하게 사용 된다. 특히 발자국 진동 신호는 특유의 impulsive 한 성질로 인해 다른 진동 신호와 구별이 용이하며 많은 훈련이 필요한 통계적 모델 링 대신 단순한 Pitch-Based 중점 탐지 기법을 도입하여 탐지 및 분류가 가능하고 이를 통해 보다 실시간 환경에 적합한 시스템을 구현할 수 있다.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.169-172
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• 2001

본 논문에서는 회전된 문서에서의 회전각 검출과 문서에 포함된 그림, 글자, 표, 직선과 같은 구성요소를 자동으로 분석하고 분류하는 방법을 제안한다. 본 연구는 입력영상을 획득하는 과정에서 발생되는 회전각에 의해 발생되는 오류를 최소화하기 위한 회전각 검출단계, 각 구성요소 검출에 불필요한 배경제거 단계, 각 구성요소의 특성을 통한 구성요소 분류단계로 이루어진다. 제안한 문서 인식 시스템의 성능 평가를 위해서 다양 한 문서에 제안한 방법을 적용하고, 성공적인 결과를 보인다.