• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.7
• /
• pp.957-963
• /
• 2007

In this paper, Nonlinear Autoregressive (NAR) method based on Least Square-Support Vector Regression (LS-SVR) is introduced and tested for nonlinear sustained vowel modeling. In the database of total 43 sustained vowel of Benign Vocal Fold Lesions having aperiodic waveform, this nonlinear synthesizer near perfectly reproduced chaotic sustained vowels, and also conserved the naturalness of sound such as jitter, compared to Linear Predictive Coding does not keep these naturalness. However, the results of some phonation are quite different from the original sounds. These results are assumed that single-band model can not afford to control and decompose the high frequency components. Therefore multi-band model with wavelet filterbank is adopted for substituting single band model. As a results, multi-band model results in improved stability. Finally, nonlinear sustained vowel modeling using NAR based on LS-SVR can successfully reconstruct synthesized sounds nearly similar to original voiced sounds.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.27 no.4A
• /
• pp.310-315
• /
• 2002

The Bark Coherence Function (BCF) defies a coherence function within perceptual domain as a new cognition module, robust to linear distortions due to the analog interface of digital mobile system. Our previous experiments have shown the superiority of BCF over current measures. In this paper, a new BCF suitable for VoIP is developed. The unproved BCF is based on the wavelet series expansion that provides good frequency resolution while keeping good time locality. The proposed Wavelet based Bark Coherence function (WBCF) is robust to variable delay often observed in packet-based telephony such as Voice over Internet Protocol (VoIP). We also show that the refinement of time synchronization after signal decomposition can improve the performance of the WBCF. The regression analysis was performed with VoIP speech data. The correlation coefficients and the standard error of estimates computed using the WBCF showed noticeable improvement over the Perceptual Speech Quality Measure (PSQM) that is recommended by ITU-T.

• Journal of Broadcast Engineering
• /
• v.26 no.5
• /
• pp.566-574
• /
• 2021

The arc is one of factors causing electrical fires. Over past decades, various researches have been carried out to detect arc occurrences. Even though frequency analysis, wavelet and statistical features have been used, arc detection performance is degraded due to diverse arc waveforms. On the contray, Deep neural network (DNN) direcly utilizes raw data without feature extraction, based on end-to-end learning. However, a disadvantage of the DNN is processing complexity, posing the difficulty of being migrated into a termnial device. To solve this, this paper proposes an arc detection method using a logistic regression that is one of simple machine learning methods.

• Journal of Multimedia Information System
• /
• v.3 no.2
• /
• pp.35-42
• /
• 2016

Electrocardiogram (ECG) signal gives a clear indication whether the heart is at a healthy status or not as the early notification of a cardiac problem in the heart could save the patient's life. Several methods were launched to clarify how to diagnose the abnormality over the ECG signal waves. However, some of them face the problem of lack of accuracy at diagnosis phase of their work. In this research, we present an accurate and successive method for the diagnosis of abnormality through Discrete Wavelet Transform (DWT), QRS complex detection and Support Vector Machines (SVM) classification with overall accuracy rate 95.26%. DWT Refers to sampling any kind of discrete wavelet transform, while SVM is known as a model with related learning algorithm, which is based on supervised learning that perform regression analysis and classification over the data sample. We have tested the ECG signals for 10 patients from different file formats collected from PhysioNet database to observe accuracy level for each patient who needs ECG data to be processed. The results will be presented, in terms of accuracy that ranged from 92.1% to 97.6% and diagnosis status that is classified as either normal or abnormal factors.

• The Korean Journal of Applied Statistics
• /
• v.25 no.4
• /
• pp.669-680
• /
• 2012

Noise removal of document images is a necessary step during preprocessing to recognize characters effectively because it has influences greatly on processing speed and performance for character recognition. We have considered using the spatial filters such as traditional mean filters and Gaussian filters, and wavelet transformed based methods for noise deduction in natural images. However, these methods are not effective for the noise removal of document images. In this paper, we present noise removal of document images using support vector regression. The proposed approach consists of two steps which are SVR training step and SVR test step. We construct an optimal prediction model using grid search with cross-validation in SVR training step, and then apply it to noisy images to remove noises in test step. We evaluate our SVR based method both quantitatively and qualitatively for noise removal in Korean, English and Chinese character documents, and compare it to some existing methods. Experimental results indicate that the proposed method is more effective and can get satisfactory removal results.

• Journal of Information Processing Systems
• /
• v.18 no.6
• /
• pp.719-728
• /
• 2022

The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction systems. The research on short-term traffic flow prediction has showed the considerable social value. At present, the support vector regression (SVR) intelligent prediction model that is suitable for small samples has been applied in this domain. Aiming at parameter selection difficulty and prediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters, which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out by comparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVR algorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparing the ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimization SVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experiments and verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing the ABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracy of the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained.

• Smart Structures and Systems
• /
• v.32 no.1
• /
• pp.61-81
• /
• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• Proceedings of the KSME Conference
• /
• 2003.04a
• /
• pp.678-683
• /
• 2003

Ultrasonic method using SH(shear horizontal) wave has been developed to determine the surface damage in fatigued material. Fatigue damages based on propagation energy were analyzed by multiregression analysis and phase measurement in interrupted fatigue test specimen including CrMoV and 12Cr alloy steel. From the test results, as the fatigue damage increased the propagation time of the launched waves increased and amplitude of wavelet decreased. Also, analysis for the waveform modulation showed a reliable estimation, with confidence limit of 97% for 12Cr steel and 95% for CrMoV steel, respectively. Therefore, It is thought that SH ultrasonic wave technique can be applied to determine fatigue damage of in-service component nondestructively.

• Journal of the Korean Geographical Society
• /
• v.51 no.4
• /
• pp.509-523
• /
• 2016

The soil prediction can provide quantitative soil information for sustainable mountainous ecosystem management. Visible near infrared spectroscopy, one of soil prediction methods, has been applied to predict several soil properties with effective costs, rapid and nondesctructive analysis, and satisfactory accuracy. Spectral preprocessing is a essential procedure to correct noisy spectra for visible near infrared spectroscopy. However, there are no attempts to evaluate various spectral preprocessing methods. We tested 5 different pretreatments, namely continuum removal, Savitzky-Golay filter, discrete wavelet transform, 1st derivative, and 2nd derivative to predict soil carbon(C) and nitrogen(N). Partial least squares regression was used for the prediction method. The total of 153 soil samples was split into 122 samples for calibration and 31 samples for validation. In the all range, absorption was increased with increasing C contents. Specifically, the visible region (650nm and 700nm) showed high values of the correlation coefficient with soil C and N contents. For spectral preprocessing methods, continuum removal had the highest prediction accuracy(Root Mean Square Error) for C(9.53mg/g) and N(0.79mg/g). Therefore, continuum removal was selected as the best preprocessing method. Additionally, there were no distinct differences between Savitzky-Golay filter and discrete wavelet transform for visual assessment and the methods showed similar validation results. According to the results, we also recommended Savitzky-Golay filter that is a simple pre-treatment with continuum removal.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2008.04a
• /
• pp.742-746
• /
• 2008

With the trend of factory automation, nowadays, much industrial machinery tends to be put into 24-hours operation a day. However, these trends in industrial equipments also increase the possibility of various mechanical problems and bring about innumerable maintenance cost. There is a strong need of the condition monitoring and diagnosis for industrial equipment, especially rotating machinery, since they are connected not only to the reduction in the maintenance costs but also connected to the enhancement of production efficiency. Generally, to evaluate the operating conditions in the machinery in the industrial field, various physical properties are monitored. Among them, vibration and Noise signals are the mist important indicator and it is effectively used in many diagnosis systems for machinery. Much previous research is based in the FFT (Fast Fourier Transform) method. The spectral analysis is assumed that the signal is stationary. However, almost random signals are non-stationary. The wavelet transform has been recognized an efficient Method. Most interesting sounds have time-varying features. Signal processing techniques for the analysis of transient sound have been not clearly given yet.