• Journal of Power Electronics
• /
• 제10권5호
• /
• pp.505-517
• /
• 2010

In this paper, an intelligent sliding-mode speed controller for achieving favorable decoupling control and high precision speed tracking performance of permanent-magnet synchronous motor (PMSM) drives is proposed. The intelligent controller consists of a sliding-mode controller (SMC) in the speed feed-back loop in addition to an on-line trained wavelet-neural-network controller (WNNC) connected in parallel with the SMC to construct a robust wavelet-neural-network controller (RWNNC). The RWNNC combines the merits of a SMC with the robust characteristics and a WNNC, which combines artificial neural networks for their online learning ability and wavelet decomposition for its identification ability. Theoretical analyses of both SMC and WNNC speed controllers are developed. The WNN is utilized to predict the uncertain system dynamics to relax the requirement of uncertainty bound in the design of a SMC. A computer simulation is developed to demonstrate the effectiveness of the proposed intelligent sliding mode speed controller. An experimental system is established to verify the effectiveness of the proposed control system. All of the control algorithms are implemented on a TMS320C31 DSP-based control computer. The simulated and experimental results confirm that the proposed RWNNC grants robust performance and precise response regardless of load disturbances and PMSM parameter uncertainties.

• 방송공학회논문지
• /
• 제8권2호
• /
• pp.204-208
• /
• 2003

웨이브릿 변환 기반의 부대역(subband) 분해과정을 새로운 수학적 모델로 표현한다. 제안된 모델은 많은 계층의 분해과정에 거쳐 정규적인 다해상도 해석을 수행할 수 있다. 이러한 접근방식은 단일채널 선형 공간불변 필터링문제를 다채널로 확장할 수 있게 해주는 동시에 선형 공간불변 영상복원문제와 주파수상에서 적응적 제약적 최소제곱(Constrained Least Square:CLS) 필터에 적용될 수 있다. 제안된 필터에서 우리는 부대역의 특징에 따라 적응적으로 다른 변수를 사용할 수 있다. 본 논문에서 제안한 주파수상의 적응적 CLS 필터를 S/W로 구현하였으며, 이 실험을 통해 부대역의 특징을 정화하게 측정할 경우 제안된 주파수상 적응적 CLS 필터는 기존의 단채널 필터에서 벗어나 현저히 화질을 개선할 수 있음을 보여준다.

• 대한전자공학회논문지SP
• /
• 제37권6호
• /
• pp.48-59
• /
• 2000

안테나 센서 어레이를 이용하여 수신되는 전파의 도래각을 추정하는 방식으로서 MUSIC(multiple signal classification)과 같은 고유분해(eigendecomposition)를 기반으로 한 방식은 백색잡음 환경하에서는 고분해능의 우수한 성능을 보이지만 유색잡음이 존재하는 환경에서는 성능이 크게 저하된다. 본 논문에서는 주기성을 가진 신호에 잡음이 더해진 선호를 웨이브렛 영역으로 변환하여 신호와 잡음을 분리하는 방법을 사용하여 유색잡음이 있는 환경에서 도래각 추정 문제를 접근하였다. 배경잡음만 있는 경우 센서 어레이 출력을 이산 웨이브렛 분해를 하여 얻은 멀티스케일 성분들의 공분산 행렬은 밴드화된 행렬로 근사화 할 수 있는데 비하여 협대역 신호는 멀티스케일 성분간의 상관성은 급속히 감소하는 현상을 보이지 않고 공분산 행렬에서는 신호성분이 전체 행렬에 분포한다. 어레이 출력의 공분산 행렬을 웨이브렛 영역으로 변환하여 유색잡음에 해당하는 특정 밴드를 삭제하고 MUSIC과 같은 기존의 공간 스펙트럼 추정방식을 적용하여 도래각을 추정 한 다음 그 결과로 부터 신호성분을 합성하여 삭제한 밴드를 채우는 과정을 반복하여 정확한 도래각을 얻는 방안을 제안하였다. 제안된 알고리즘의 성능을 여러 가지 형태의 상관함수 특성을 가진 유색잡음 환경에서 모의실험을 통하여 기존 방식과 비교 분석하였다.

• Smart Structures and Systems
• /
• 제5권2호
• /
• pp.153-171
• /
• 2009

In this paper, the analytical fault detection and diagnosis (FDD) is presented using model-based and signal-based methodology with wavelet analysis on signals obtained from sensors and sensor networks. In the model-based FDD, we present the modeling of contact interface found in soft materials, including the biomedical contacts. Fingerprint analysis and signal-based FDD are also presented with an experimental framework consisting of a mechanical pneumatic system typically found in manufacturing automation. This diagnosis system focuses on the signal-based approach which employs multi-resolution wavelet decomposition of various sensor signals such as pressure, flow rate, etc., to determine leak configuration. Pattern recognition technique and analytical vectorized maps are developed to diagnose an unknown leakage based on the established FDD information using the affine mapping. Experimental studies and analysis are presented to illustrate the FDD methodology. Both model-based and wavelet-based FDD applied in contact interface and manufacturing automation have implication towards better quality of life by applying theory and practice to understand how effective diagnosis can be made using intelligent FDD. As an illustration, a model-based contact surface technology an benefit the diabetes with the detection of abnormal contact patterns that may result in ulceration if not detected and treated in time, thus, improving the quality of life of the patients. Ultimately, effective diagnosis using FDD with wavelet analysis, whether it is employed in biomedical applications or manufacturing automation, can have impacts on improving our quality of life.

• Nuclear Engineering and Technology
• /
• 제52권8호
• /
• pp.1771-1776
• /
• 2020

Adverse effects in the measured gamma spectrum caused by radioactive statistical fluctuations, gamma ray scattering, and electronic noise can be reduced by energy spectrum denoising. Wavelet threshold denoising can be used to perform multi-scale and multi-resolution analysis on noisy signals with small root mean square errors and high signal-to-noise ratios. However, in traditional wavelet threshold denoising methods, there are signal oscillations in hard threshold denoising and constant deviations in soft threshold denoising. An improved wavelet threshold calculation method and threshold processing function are proposed in this paper. The improved threshold calculation method takes into account the influence of the number of wavelet decomposition layers and reduces the deviation caused by the inaccuracy of the threshold. The improved threshold processing function can be continuously guided, which solves the discontinuity of the traditional hard threshold function, avoids the constant deviation caused by the traditional soft threshold method. The examples show that the proposed method can accurately denoise and preserves the characteristic signals well in the gamma energy spectrum.

• Tribology and Lubricants
• /
• 제24권6호
• /
• pp.320-325
• /
• 2008

The recent industrial application requires technical methods to get the cutting fluid droplet surfaces in particular from the viewpoint of topography and micro texture. To characterize the surface topography of droplet, the combination of the confocal laser scanning microscope (CLSM) and wavelet filtering is well suited for obtaining the droplet geometry encountered in tribological research. This technique indicates a better agreement in obtaining an appropriate droplet surface obtained by the CLSM over a detail range of surface accuracy (resolution: $2{\mu}m$). And the results allow an excellent accuracy in a measurement of a droplet surface. The combination of extended focal depth measurement configured and multi-scale wavelet filtering has proven that it can construct a droplet surface in a successive and accurate way. A multi-scale approach of wavelet filtering was developed based on the decomposition and reconstruction of droplet surface by 2D wavelet transform using db9 (a mother wavelet of daubechies). Also this technique can be extended to characterize the quantification of droplet properties and other field in a wide range of scales. Finally this method is verified to be a better droplet surface modeling in a micro scale arising in a mist machining.

• Smart Structures and Systems
• /
• 제11권6호
• /
• pp.589-604
• /
• 2013

Identification of damage has become an evolving area of research over the last few decades with increasing the need of online health monitoring of the large structures. The visual damage detection can be impractical, expensive and ineffective in case of large structures, e.g., offshore platforms, offshore pipelines, multi-storied buildings and bridges. Damage in a system causes a change in the dynamic properties of the system. The structural damage is typically a local phenomenon, which tends to be captured by higher frequency signals. Most of vibration-based damage detection methods require modal properties that are obtained from measured signals through the system identification techniques. However, the modal properties such as natural frequencies and mode shapes are not such good sensitive indication of structural damage. Identification of damaged jacket type offshore platform members, based on wavelet packet transform is presented in this paper. The jacket platform is excited by simple wave load. Response of actual jacket needs to be measured. Dynamic signals are measured by finite element analysis result. It is assumed that this is actual response of the platform measured in the field. The dynamic signals first decomposed into wavelet packet components. Then eliminating some of the component signals (eliminate approximation component of wavelet packet decomposition), component energies of remained signal (detail components) are calculated and used for damage assessment. This method is called Detail Signal Energy Rate Index (DSERI). The results show that reduced wavelet packet component energies are good candidate indices which are sensitive to structural damage. These component energies can be used for damage assessment including identifying damage occurrence and are applicable for finding damages' location.

• 전력전자학회논문지
• /
• 제11권6호
• /
• pp.570-576
• /
• 2006

본 논문에서는 이산 웨이브렛 변환을 이용하여 원통형 동기발전기 회전자 권선의 층간단락을 진단하는 방법에 대해 기술하였다. 제안하는 방법에서는 회전자 권선의 층간단락 진단을 위해 다중해상도 분석을 이용하여 회전자 전류를 여러 스케일 영역으로 분할한 후, 각 영역에서의 신호의 에너지 값을 비교하였다. 실험적인 결과는 특정 회전자 슬롯 내에 25%, 42%, 67%, 83%, 99%단락이 발생한 경우와 정상적인 경우를 비교한 것으로, 그 편차는 층간단락 비율에 반비례하여 나타났다. 이러한 실험적인 결과는 제안한 이산 웨이브렛 변환을 이용한 방법이 원통형 동기발전기 회전자 권선의 충간단락 진단에 적절함을 보여준다.

• 한국수자원학회논문집
• /
• 제45권11호
• /
• pp.1187-1199
• /
• 2012

본 연구에서는 Wavelet Transform과 Support Vector Machine (SVM)을 결합한 Hybrid 상수도 수요량 예측 모형을 개발하였다. Wavelet Transform 방법을 활용하여 다양한 스케일이 존재하는 상수도 수요량 시계열을 분해하여 단순한 형태의 시계열로 변환하는데 이용하였으며, 비선형 예측모형인 SVM은 이들 단순화된 시계열을 예측하는데 활용하여 예측성능을 극대화시키는 방안을 수립하였다. 본 연구에서는 상수도 수요량 자료에서 내재되어 있는 주기의 특성과 비선형 예측모형의 장점을 서로 연계한 해석이 가능하였으며 시각적인 검토 및 모든 통계지표에서 개선된 예측결과를 확인할 수 있었다. 특히, 기존 ARIMA 모형 계열에서 나타나는 자기예측문제를 상당부분 개선한 결과를 보여줌으로서 실질적인 수요량 예측모형으로서 활용이 가능할 것으로 판단된다.

• Journal of Electrical Engineering and Technology
• /
• 제13권6호
• /
• pp.2485-2492
• /
• 2018

The rapid development of large-scale integrated circuits has brought great challenges to the circuit testing and diagnosis, and due to the lack of exact fault models, inaccurate analog components tolerance, and some nonlinear factors, the analog circuit fault diagnosis is still regarded as an extremely difficult problem. To cope with the problem that it's difficult to extract fault features effectively from masses of original data of the nonlinear continuous analog circuit output signal, a novel approach of feature extraction and dimension reduction for analog circuit fault diagnosis based on wavelet packet decomposition, local linear embedding algorithm, and clone selection algorithm (WPD-LLE-CSA) is proposed. The proposed method can identify faulty components in complicated analog circuits with a high accuracy above 99%. Compared with the existing feature extraction methods, the proposed method can significantly reduce the quantity of features with less time spent under the premise of maintaining a high level of diagnosing rate, and also the ratio of dimensionality reduction was discussed. Several groups of experiments are conducted to demonstrate the efficiency of the proposed method.