• 한국음향학회지
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• 제33권2호
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• pp.139-145
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• 2014

본 논문에서는 WPD (Wavelet Packet Decomposition) 계수에 Teager 에너지를 적용한 특징 계수를 임계값 알고리듬에 적용하여 잡음에 강인한 VAD 알고리듬을 제안하였다. 임계값은 비음성 구간의 평균과 표준편차를 추산하여 설정하였다. TIMIT 음성과 NOISEX 잡음 데이터베이스를 사용한 실험 결과, 제안된 알고리듬이 기존의 대표적인 비교 대상 알고리듬보다 우수함을 보였다. 정확도는 SNR 10 dB부터 -10 dB까지 ROC (Receiver Operating Characteristics) 곡선을 사용하여 비교하였다.

• Structural Engineering and Mechanics
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• 제46권4호
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• pp.459-486
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• 2013

A novel damage classification method based on wavelet packet transform and statistical analysis is developed in this study for structural health monitoring. The response signal of a structure under an impact load is normalized and then decomposed into wavelet packet components. Energies of these wavelet packet components are then calculated to obtain the energy distribution. Statistical similarity comparison based on an F-test is used to classify the structure from changes in the wavelet packet energy distribution. A statistical indicator is developed to describe the damage extent of the structure. This approach is applied to the test results from simply supported reinforced concrete beams in the laboratory. Cases with single and two damages are created from static loading, and accelerations of the structure from under impact loads are analyzed. Results show that the method can be used with no reference baseline measurement and model for the damage monitoring and assessment of the structure with alarms at a specified significance level.

• Journal of international Conference on Electrical Machines and Systems
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• 제2권3호
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• pp.336-341
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• 2013

Power converter plays a very important role in switched reluctance motor (SRM) systems, and it is also the easiest one to experience failures. Power converter faults will cause the motor to run in non equilibrium states, and a long time fault operation will lead to motor and other modules damaged, and make the system completely lose working stability. This paper uses an asymmetric bridge converter as the research object with three-phase SRM, employs the wavelet packet decomposition for the phase currents. It analyzes and studies the short circuit fault condition of IGBT, uses an energy discrete degree of the wavelet packet nodes as the fault characteristic, and conducts the corresponding experimental and simulation analysis to verify the effectiveness and practicality of the proposed method.

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

• 한국음향학회:학술대회논문집
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• 한국음향학회 1999년도 학술발표대회 논문집 제18권 2호
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• pp.85-88
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• 1999

Adapted Local Trigonometric Transforms은 매우 높은 energy compaction을 가지므로 음성 및 영상신호에 이용하려는 시도가 이루어지고 있다. [1] 그러나 이 경우 복원 된 신호에는 시간 영역에서 불연속점이 발생하여 일종의 tick noise가 발생한다. 또한 phase성분을 잃게 되어 금속성 잡음도 추가하여 나타난다. 본 논문에서는 이러한 문제점을 해결하기 위한 Polynomial fitting 방식과 Wavelet Packet Transforms 방식을 제안한다. Polynomial fitting 방식으로는 시간축상에서 발생하는 문제를 해결하고 Wavelet Packet Transforms으로 Phase 문제를 해결한다. [2,3] 실험결과, 압축이전의 신호와 비교할 때 SNR에 있어서 개선을 보이며 tick noise와 금속성 잡음이 제거된 개선된 신호음을 확인 할 수 있었다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 추계학술대회논문집
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• pp.619-624
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• 2004

In this research, the structural health monitoring method using wavelet packet analysis and artificial neural network (ANN) is developed. Wavelet packet Transform (WPT) is applied to the response acceleration of a 3 element-cantilever beam which is subjected to impulse load and Gaussian random load to decompose the response signal, then the energy of each component is calculated. The first ten largest components in magnitude among the decomposed components are selected as input to an ANN to identify the damage location and severity. This method successfully predicted the amount of damage in the structure when the structure is subjected to impulse load. However, when the beam is subjected to Gaussian random load which can be considered as ambient vibration it did not yield satisfactory results. This method is applicable to structures such as machinery gears that are subjected to repetitive loads.

• 한국구조물진단유지관리공학회 논문집
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• 제11권3호
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• pp.159-166
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• 2007

본 논문에서는 구조물의 건전성 평가를 위하여 지진하중을 받은 프레임 구조물의 응답 가속도를 웨이블렛펙킷 변환(Wavelet Packet Transform; WPT)을 이용하여 분해한 후 인공신경망을 이용하여 각 부재의 손상도를 평가하였다. 인공신경망에는 응답가속도의 분해된 성분 중 에너지가 가장 큰 5개의 성분이 입력 값으로 사용 되었는데 인공신경망의 출력층에 있는 2개의 노드는 각각 손상된 부재와 손상도를 나타낸다. 이 논문에서 제시된 방법을 이용하여 구조물의 손상된 부재와 손상도를 평가하였고 만족스러운 결과를 얻었다.

• Journal of Information Technology Applications and Management
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• 제10권4호
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• pp.159-168
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• 2003

This paper is concerned with the compression of EEG signals using wavelet-packet based techniques. EEG data compression is desirable for a number of reasons. Primarily it decreases for transmission time, archival storage space, and in portable systems, it decreases memory requirements or increases channels and bandwidth. Upon wavelet decomposition, inherent redundancies in the signal can be removed through thresholding to achieve data compression. We proposed the energy cumulative function for deciding of the threshold value and it works very innovative of EEG data.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.531-532
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• 2007

According to the characteristics of power signal, the compression algorithm based on wavelet packet transform and denoising is presented. Its performance is assessed in terms of percentage of zero coefficients and energy retanied in reconstructed signals.

• Smart Structures and Systems
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• 제12권3_4호
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• pp.345-361
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• 2013

Locating and assessing the severity of damage in large or complex structures is one of the most challenging problems in the field of civil engineering. Considering that the wavelet packet transform (WPT) has the ability to clearly reflect the damage characteristics of structural response signals and the artificial neural network (ANN) is capable of learning in an unsupervised manner and of forming new classes when the structural exhibits change, this paper investigates a multi-stage structural damage diagnosis method by using the WPT and ANN based on "energy-damage" theory, in which, the wavelet packet component energies are first extracted to be damage sensitive feature and then adopted as input into an improved back propagation (BP) neural network model for damage diagnosis in a step by step mode. To validate the efficacy of the presented approach of the damage diagnosis, the benchmark structure of the American Society of Civil Engineers (ASCE) is employed in the case study. The results of damage diagnosis indicate that the method herein is computationally efficient and is able to detect the existence of different damage patterns in the simulated experiment where minor, moderate and severe damages corresponds to involving in the loss of stiffness on braces or the removal bracing in various combinations.