• Journal of Biomedical Engineering Research
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• v.24 no.6 s.81
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• pp.487-494
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• 2003

The sleep stages provides the most basic evidence for diagnosing a variety of sleep diseases. for staging sleep by analysis of EEG(electroencephalogram), it is especially important to detect the characteristic waveforms from EEG. In this paper, sleep EEG signals were analyzed using Fourier transform and continuous wavelet transform as well as discrete wavelet transform. Proposeed system methods. Fourier and wavelet for detecting of important characteristic waves(hump, sleep spindles. K-complex, hill wave, ripple wave) in sleep EEG. Sleep EEG data were analysed using Daubechies wavelet transform method and FFT method. As a result of simulation, we suggest that our neural network system attain high performance in classification of characteristic waves.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.754-759
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• 2007

This study exploits the data discriminating capability of silhouette statistics, which combines wavelet-based vertical energy threshold technique for the purpose of extracting damage-sensitive features and clustering signals of the same class. This threshold technique allows to first obtain a suitable subset of the extracted or modified features of our data, i.e., good predictor sets should contain features that are strongly correlated to the characteristics of the data without considering the classification method used, although each of these features should be as uncorrelated with each other as possible. The silhouette statistics have been used to assess the quality of clustering by measuring how well an object is assigned to its corresponding cluster. We use this concept for the discriminant power function used in this paper. The simulation results of damage detection in a truss structure show that the approach proposed in this study can be successfully applied for locating both open- and breathing-type damage even in the presence of a considerable amount of process and measurement noise.

• Journal of Electrical Engineering and Technology
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• v.11 no.3
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• pp.683-689
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• 2016

Most outdoor solid insulators may suffer from surface degradations due to non-stationary currents that flow on the insulator surface. These currents may be classified as leakage, discharge and tracking currents due to their disturbing potencies respectively. The magnitude of these currents depends on the degree of the contamination of surface. The leakage signals are followed by discharge signals and tracking signals which are capable of forming carbonized tracking paths on the surface between high voltage and earth contacts (surface tracking). Surface tracking is one of the most breakdown mechanisms observed on the solid insulators, especially polymers which may cause severely reduced lifetime. In this study the degradations observed on polyester resin based insulators are investigated according to the IEC 587 Inclined Plane Test Standard. The signals are monitored and recorded during tests until surface tracking initiated. In order to prevent total breakdown of an insulator, early detection of tracking signals is vital. Continuous Wavelet Transform (CWT) is proposed for classification of signals and their energy levels observed on the surface. The application of CWT for processing and classification of the surface signals which are prone to display high frequency oscillations can facilitate real time monitoring of the system for diagnosis.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.707-719
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• 2018

Various time series representation methods have been proposed for efficient time series clustering and classification. Lin et al. (DMKD, 15, 107-144, 2007) proposed a symbolic aggregate approximation (SAX) method based on symbolic representations after approximating the original time series using piecewise local mean. The performance of SAX therefore depends heavily on how well the piecewise local averages approximate original time series features. SAX equally divides the entire series into an arbitrary number of segments; however, it is not sufficient to capture key features from complex, large-scale time series data. Therefore, this paper considers data-adaptive local constant approximation of the time series using the unbalanced Haar wavelet transformation. The proposed method is shown to outperforms SAX in many real-world data applications.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06e
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• pp.395-398
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• 1998

본 논문에서는 웨이브렛 변환에서 구해진 파라미터와 신경회로망을 이용하여 후두의 양성종양과 정상상태를 구분하는 실험을 행하였다. 식별 파라미터로는 웨이브렛변환으로부터 도출된 ECS 파라미터와 jitter, shimmer를 이용하였으며 신경회로망은 한 개의 은닉층을 갖는 다층구조 신경망을 이용하였다. 신경망의 입력으로는 세가지 파라미터의 조합을 두 개 또는 세 개를 입력하여 각각의 경우의 식별율을 조사하였다. 실험결과 75%에서 93%에 이르는 식별율을 얻었다.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.853-854
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• 2008

The terrain perception technology using passive sensors plays a key role to enhance autonomous mobility for UGV. We present an effective method to classify terrain covers based on the color information. Considering a real-time implementation, neural network is applied for the terrain classifier and wavelet features extracted from the images are used. Test results show that the proposed algorithm has a promising classification performance.

• Journal of Power System Engineering
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• v.14 no.2
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• pp.40-47
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• 2010

This paper proposes a fault detection system to detect the strip rupture in six-high stand Cold Rolling Mills based on transient current signal of an electrical motor. For this work, signal smoothing technique is used to highlight precise feature between normal and fault condition. Subtracting the smoothed signal from the original signal gives the residuals that contains the information related to the normal or faulty condition. Using residual signal, discrete wavelet transform is performed and acquire the signal presenting fault feature well. Also, feature extraction and classification are executed by using PCA, KPCA and SVM. The actual data is acquired from POSCO for validating the proposed method.

• Journal of the Korean Society for Railway
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• v.4 no.3
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• pp.79-86
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• 2001

The signal pattern recognition method by acoustic emission signal is applied to detect and classify the defects of a journal bearing in a power plant. AE signals of main defects such as overheating, wear and corrosion are obtained from a small scale model. To detect and classify the defects, AE signal pattern recognition program is developed. As the classification methods, the wavelet transformation analysis, the frequency domain analysis and time domain analysis are used. Among three analyses, the wavelet transformation analysis is most effective to detect and classify the defects of the journal bearing..

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.3-6
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• 2008

베어링은 각종 설비에서 활용하는 중요한 기계요소 중 하나이다. 설비고장의 상당수는 베어링의 결함이나 파손에 기인하고 있다. 따라서 베어링에 대한 온라인모니터링기술은 설비의 정지를 예방하고 손실을 줄이는 데 필수적이다. 본 논문은 진동신호를 이용하여 베어링의 상태를 예측하기 위한 온라인모니터링에 대해 연구한다. 프로파일로 주어지는 진동신호는 이산웨이블릿변환을 통해 분석되고, 분해수준별 웨이블릿계수로부터 얻은 통계적 특징 중 유의한 것을 선별하고자 분산분석 (ANOVA)을 이용한다. 선별된 특징벡터는 Support Vector Machine (SVM)의 입력이 되는 데, 본 논문에서는 다중클래스 분류문제를 다루기 위한 계층적 SVM 네트워크를 제안한다.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.439-442
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• 1997

In our research, we will extract diagnostic parameters by LPC method and wavelet transform. Then, we will design artificial neural network which is based on RBF that can express input features in terms of fuzzy. Because PVC(Premature Ventricular Contraction) has possibility to cause heart attack, the detection of PVC is a very significant problem. To deal with this problem, LPC method which gives different coefficients or different morphologies and wavelet transform which has superior localization nature of time-frequency, are used to extract effective parameters or classification of normal and PVC. Because RBF network can allocate an input feature to the membership degree of each category, total system will be more flexible.