• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.123-125
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• 2002

여러 사람에게서 생체신호를 측정하여 특징을 추출하는 경우 피실험자마다 다른 신체적 또는 생리학적 특징에 의해 같은 클래스로 분류하고 싶어도 다른 클래스로 잘못 분류되는 경우가 발생한다. 이와 같이 N 명의 사람에게서 얻은 생체신호로 M 개의 클래스를 분류하도록 훈련하여 새로운 사람의 생체신호를 M 개의 클래스로 분류하고자 할 때 발생하는 문제를 해결하기 위한 방법으로 피실험자 독립적인 클러스터링 방법을 제안하고자 한다. 이를 위한 수학적 기반으로 동치관계들의 교집합과 합집합에 근거한 새로운 연산자를 정의하고 이를 이용하여 최대 공통 클러스터(Largest Common Cluster, LCC)라는 새로운 개념을 정의한다 이는 여러 사람에게서 얻은 정보에서 최대한 공통의 성질을 갖는 것들을 찾아내는 수학적이고 체계적인 방법이라 할 수 있다. 따라서 일단 LCC를 찾아내면 이를 특징(feature)으로 삼아 패턴분류기를 설계하면 여러 사람에게 적용가능한 생체신호 인식기를 설계할 수 있게 된다.

• Journal of the Korean Society for Nondestructive Testing
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• v.16 no.1
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• pp.34-45
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• 1996

The signal pattern recognition program which can perform signal acquisition and processing, the extraction and selection of features, the classifier design and the evaluation, is developed and applied to the classification of artificial defects in the weld zone of Austenitic STS304. The neural network classifier is compared with the linear discriminant function classifier and the empirical Bayesian classifier. The signal through a broadband sensor is compared with that through a resonance type sensor. In recognition rate, the neural network classifier is best, and the signal through a broadband sensor is better.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.4
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• pp.33-38
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• 2009

This paper proposes an automatic equalizer control method in automobile audio system. The proposed method discriminates the music segment from the consecutive real-time audio stream of the radio and the equalizer is controlled automatically according to the classified genre of the music segment. For enhancing the accuracy of the music genre classification in real-time, timbre feature and rhythm feature extracted from the consecutive audio stream is applied to GMM(Gaussian mixture model) classifier. The proposed method evaluates the performance of the music genre classification, which classified various audio segments segmented from the audio signal of the radio broadcast in automobile audio system into one of five music genres.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1531-1539
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• 2014

Premature ventricular contraction(PVC) is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. In other words, the design of algorithm that exactly detects abnormal signal and classifies PVC by analyzing the persons's physical condition and/or environment and variable QRS pattern is needed. Thus, PVC classification by personalized abnormal signal detection and QRS pattern variability is presented in this paper. For this purpose, we detected R wave through the preprocessing method and subtractive operation method and selected abnormal signal sets. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of abnormal beat detection and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 98.33% in abnormal beat classification error and 94.46% in PVC classification.

• Journal of Korea Multimedia Society
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• v.9 no.3
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• pp.279-286
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• 2006

Classification method used in this paper is applied for the first time to Korean traditional music. Among the frequency distribution vectors, average peak value is suggested and proved effective comparing to previous classification success rate. Mean, variance, spectral centroid, average peak value and ZCR are used to classify Korean traditional musical instruments. To achieve Korean traditional instruments automatic classification, Spectral analysis is used. For the spectral domain, Various functions are introduced to extract features from the data files. k-NN classification algorithm is applied to experiments. Taegum, gayagum and violin are classified in accuracy of 94.44% which is higher than previous success rate 87%.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.160-161
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• 2020

본 논문에서는 비디오 코딩 잔차신호를 보다 효율적으로 변환하기 위하여 오프라인으로 잔차신호를 학습하여 RD(Rate Distortion) Cost를 기반으로 분류된 몇 가지 변환 기저들을 생성하고, 비디오 복호화 과정 중 잔차신호를 역변환을 수행할 때 주변의 복호화가 완료된 신호들을 이용하여 최적의 변환 기저를 선택하여 해당 변환 기저로 역변환을 수행하여 효율적으로 잔차신호를 압축하는 방법에 대해 제안한다. 변환 기저 생성에는 분류된 잔차신호들에 대하여 2 차원 혹은 1 차원 KLT를 계산함으로써 얻어내어진다. 제안하는 방법은 VTM(VVC Test Model) version 10에서 실험하였으며 약 0.5% 정도의 성능향상을 보인다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.81-92
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• 2023

Traffic state classification is crucial for time-of-day (TOD) traffic signal control. This paper proposed a traffic state classification technique applying Deep-Embedded Clustering (DEC) method that uses a high dimensional traffic data observed at all signalized intersections in a traffic signal control sub area (SA). So far, signal timing plan has been determined based on the traffic data observed at the critical intersection in SA. The current method has a limitation that it cannot consider a comprehensive traffic situation in SA. The proposed method alleviates the curse of dimensionality and turns out to overcome the shortcomings of the current signal timing plan.

• Journal of Platform Technology
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• v.10 no.4
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• pp.47-55
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• 2022

Recently, diverse devices using different wireless technologies are gradually increasing in the IoT environment. In particular, it is essential to design an efficient feature extraction approach and detect the exact types of radio signals in order to accurately identify various radio signal modulation techniques. However, it is difficult to gather labeled wireless signal in a real environment due to the complexity of the process. In addition, various learning techniques based on deep learning have been proposed for wireless signal classification. In the case of deep learning, if the training dataset is not enough, it frequently meets the overfitting problem, which causes performance degradation of wireless signal classification techniques using deep learning models. In this paper, we propose a generative adversarial network(GAN) based on data augmentation techniques to improve classification performance when various wireless signals exist. When there are various types of wireless signals to be classified, if the amount of data representing a specific radio signal is small or unbalanced, the proposed solution is used to increase the amount of data related to the required wireless signal. In order to verify the validity of the proposed data augmentation algorithm, we generated the additional data for the specific wireless signal and implemented a CNN and LSTM-based wireless signal classifier based on the result of balancing. The experimental results show that the classification accuracy of the proposed solution is higher than when the data is unbalanced.

• Journal of Broadcast Engineering
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• v.19 no.6
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• pp.967-970
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• 2014

An efficient peak detection algorithm in magnitude spectrum is proposed to distinguish the M-frequency shift keying(FSK) signals from other digitally modulated signal. In addition, recognition of the modulation order estimation of FSK signals is also studied based on the fact that the magnitude spectrum of FSK signals reveals the number of peaks equal to the modulation order. When no a priori information about the signals, we utilize the histogram of the magnitude spectrum to determine the threshold which is important factor in peak detection algorithm. The simulation results show high probability of classification under 500 symbols and signal-to-noise ratio(SNR) higher than 4dB.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.54-61
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• 2001

A software package to classify acoustic emission (AE) signals using the wavelet transform and the neural network was developed Both of the continuous and the discrete wavelet transforms are considered, and the error back-propagation neural network is adopted as m artificial neural network algorithm. The signals acquired during the 3-point bending test of specimens which have artificial defects on weld zone are used for the classification of the defects. Features are extracted from the time-frequency plane which is the result of the wavelet transform of signals, and the neural network classifier is tamed using the extracted features to classify the signals. It has been shown that the developed software package is useful to classify AE signals. The difference between the classification results by the continuous and the discrete wavelet transforms is also discussed.