• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.21-28
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• 2007

Using multi-resolution, the mutiresolution proportional-integral-derivative(MRPID) controller functions as a filter to eliminate noise and disturbance which are included in error signals. If the sampling frequency is high, the response time will be delayed because of the remaining high frequency component although the overshoot is removed. However, if the sampling frequency is low, the response time will be enhanced by getting rid of signal components while the overshoot is increased. In this paper, the sampling frequency tuning method is used the response of the proportional integral derivative(PID) controller and the MRPID controller, and the parameter tuning method is considered the characteristic of the MRPID controller. The proposal method is verified by computer simulations.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.123-130
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• 2022

During the past decades, detection of gear defects remains as a major problem, especially when the gears are subject to non-stationary phenomena. The idea of this paper is to mixture a multilevel wavelet transform with a fast EMD decomposition in order to early detect gear defects. The sensitivity of a kurtosis is used as an indicator of gears defect burn. When the gear is damaged, the appearance of a crack on the gear tooth disrupts the signal. This is due to the presence of periodic pulses. Nevertheless, the existence of background noise induced by the random excitation can have an impact on the values of these temporal indicators. The denoising of these signals by multilevel wavelet transform improves the sensitivity of these indicators and increases the reliability of the investigation. Finally, a defect diagnosis result can be obtained after the fast transformation of the EMD. The proposed approach consists in applying a multi-resolution wavelet analysis with variable decomposition levels related to the severity of gear faults, then a fast EMD is used to early detect faults. The proposed mixed methods are evaluated on vibratory signals from the test bench, CETIM. The obtained results have shown the occurrence of a teeth defect on gear on the 5th and 8th day. This result agrees with the report of the appraisal made on this gear system.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.157-162
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• 2010

In this paper we propose a fractal analysis based on the discrete wavelet transform. It is well known that Fourier Transform is widely used for frequency analysis of random signal. However, the frequency domain is not used for expressing the sudden signal change and non-stationary signal at the time-axis by this method. Maximum value in the wavelet modules can be expressed by the Lipschitz exponent, which is useful to represent the characteristics of signal or the edge of an image. It is possible to reconstruct the original image only by using the few maximum points. The v possible image It iusing oil was acquired to interpret the maximum value. ufter that, it was applied to the v possible image of a ship model. In addition, the fractal dimens by by the conlapse process of the sediment particle was examined. In this paper, the fractal dimens by has been obtained by the maximum value and the experiment obtained from the visualized image also acquired the same result as existing methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.422-423
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• 2018

In this paper, we implement Frenelet transform for decomposition of the fringe pattern and analyze its characteristics. The implemented wavelet-like basis functions are well suited for reconstruction and processing of optically generated Fresnel holograms. After analyzing the characteristics of the B-spline function, we will discuss the wavelet-like multi-resolution analysis method. Through this process, we implemented a transform tool that can decompose fringe patterns effectively. We have implemented a B-spline function with various decomposition properties and showed the results of decomposing the fringe pattern.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.231-236
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• 2002

The classification of the speech-sound block comes from the multi-resolution analysis property of the discrete wavelet transform, which is used to reduce the computational time for the pre-processing of speech recognition. The merging algorithm is proposed to extract vapid speech-sounds in terms of position and frequency range. It performs unvoiced/voiced classification and denoising. Since the merging algorithm can decide the processing parameters relating to voices only and is independent of system noises, it is useful for extracting valid speech-sounds. The merging algorithm has an adaptive feature for arbitrary system noises and an excellent denoising signal-to-noise ratio and a useful system tuning for the system implementation.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.129-132
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• 2002

본 논문은 웨이브렛 변환 다중해상도 분해(multi-resolution Analysis : MRA)와 적응성 뉴로-퍼지 인터페이스 시스템(Adaptive Neuro-Fuzzy Inference System : ANFIS)을 기반으로 한 웨이브렛 신경망을 가지고 임의의 비선형 함수 학습 근사화를 개선하는 것이다. ANFIS 구조는 벨형 퍼지 함수로 구성이 되었고, 웨이브렛 신경망은 전파 알고리즘과 역전파 신경망 알고리즘으로 구성되었다. 여기 웨이브렛 구성은 단일 크기이고, ANFIS 기반 웨이브렛 신경망의 학습을 위해 역전파 알고리즘을 사용하였다. 1차원과 2차원 함수에서 웨이브렛 전달 파라미터 학습과 ANFIS의 벨형 소속 함수를 이용한 ANFIS 모델 기반 웨이브렛 신경망의 웨이브렛 기저 수 감소와 수렴 속도 성능이 기존의 알고리즘 보다 개선되었음을 확인하였다.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.33-36
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• 2001

본 논문에서는 기존의 음성인식에서 사용하는 특징벡터인 MFCC(Mel-Frequency Cepstral Cefficients)를 대신하여 웨이블렛 변환을 이용한 새로운 특징벡터를 추출하는 방법을 제안한다. 새 특징벡터로는 MRA(Multi-Resolution Analysis)를 이용하여 구성하였다. 웨이블렛 변환을 이용한 새로운 특징벡터의 추출 목적은 시간축과 주파수축에서의 더 좋은 해상도를 가지는 성질을 이용하는 것이다. 실험결과에서 웨이블렛 변환을 이용한 새로운 특징벡터를 이용한 인식이 기존의 방식보다 더 좋은 인식률을 보이고 있음을 확인하였다.

• Science of Emotion and Sensibility
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• v.16 no.4
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• pp.527-534
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• 2013

In this study, we analyzed spatial-frequency relationship related brain function for change of the time during attentional visual stimulus through the analysis of Coherence. With experimentation about ERP(Event Related Potential)data, it revealed that change of the phase synchronization between different scalp locations at ${\theta}$, ${\alpha}$ band. ERP between left and right frontal lobes, between the frontal and central lobes showed the phase synchronization at the P100, N200, ERP between the frontal and occipital lobes showed the phase synchronization at the P300 related information of visual stimulus. Compared to STFT using the window of a fixed length, CWT is able to multi-resolution analysis with the adjustment of parameters of mother wavelet. Thus, coherence results with CWT was found to be effective for analysis of time-varying spatial-frequency relationship in ERP. The phase synchronization for inattentional visual stimulus was not observed.

• Computers and Concrete
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• v.4 no.3
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• pp.167-186
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• 2007

Ductility based design of reinforced concrete structures implicitly assumes certain damage under the action of a design basis earthquake. The damage undergone by a structure needs to be quantified, so as to assess the post-seismic reparability and functionality of the structure. The paper presents an analytical method of quantification and location of seismic damage, through system identification methods. It may be noted that soft ground storied buildings are the major casualties in any earthquake and hence the example structure is a soft or weak first storied one, whose seismic response and temporal variation of damage are computed using a non-linear dynamic analysis program (IDARC) and compared with a normal structure. Time period based damage identification model is used and suitably calibrated with classic damage models. Regenerated stiffness of the three degrees of freedom model (for the three storied frame) is used to locate the damage, both on-line as well as after the seismic event. Multi resolution analysis using wavelets is also used for localized damage identification for soft storey columns.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.431-437
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• 2004

This paper propose a facial recognition method based on an ubiquitous computing that is one of next generation intelligence technology fields. The facial images are acquired by a mobile device so-called cellular phone camera. We consider a mobile security using facial feature extraction and recognition process. Facial recognition is performed by the PCA and fuzzy LDA algorithm. Applying the discrete wavelet based on multi-resolution analysis, we compress the image data for mobile system environment. Euclidean metric is applied to measure the similarity among acquired features and then obtain the recognition rate. Finally we use the mobile equipment to show the efficiency of method. From various experiments, we find that our proposed method shows better results, even though the resolution of mobile camera is lower than conventional camera.