• Journal of information and communication convergence engineering
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• 제4권4호
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• pp.174-179
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• 2006

In this paper we present a text detection method inspired by wavelet packet analysis and improved fuzzy clustering algorithm(IAFC).This approach assumes that the text and non-text regions are considered as two different texture regions. The text detection is achieved by using wavelet packet analysis as a feature analysis. The wavelet packet analysis is a method of wavelet decomposition that offers a richer range of possibilities for document image. From these multi scale features, we adapt the improved fuzzy clustering algorithm based on the unsupervised learning rule. The results show that our text detection method is effective for document images scanned from newspapers and journals.

• 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.

• 한국정밀공학회지
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• 제22권10호
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• pp.80-89
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• 2005

In this paper, time-frequency analysis using wavelet packet transform and advanced-MDSA (Multiple Dimensional Spectral Analysis) which based on wavelet packet transform is applied fur fault source identification and diagnosis of early detection of fault non-stationary sound/vibration signals. This method is analyzing the signal in the plane of instantaneous time and instantaneous frequency. The results of ordinary coherence function, which obtained by wavelet packet analysis, showed the possibility of early fault detection by analysis at the instantaneous time. So, by checking the coherence function trend, it is possible to detect which signal contains the major fault signal and to know how much the system is damaged. Finally, It is impossible to monitor the system is damaged or undamaged by using conventional method, because crest factor is almost constant under the range of magnitude of fault signal as its approach to normal signal. However instantaneous coherence function showed that a little change of fault signal is possible to monitor the system condition. And it is possible to predict the maintenance time by condition based maintenance for any stationary or non-stationary signals.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2327-2330
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• 2003

This paper deals with face detection and recognition using ellipsodal information and wavelet packet analysis. We proposed two methods. First, Face detection method uses general ellipsodal information of human face contour and we find eye position on wavelet transformed face images A novel method for recognition of views of human faces under roughly constant illumination is presented. Second, The proposed Face recognition scheme is based on the analysis of a wavelet packet decomposition of the face images. Each face image is first located and then, described by a subset of band filtered images containing wavelet coefficients. From these wavelet coefficients, which characterize the face texture, the Euclidian distance can be used in order to classify the face feature vectors into person classes. Experimental results are presented using images from the FERET and the MIT FACES databases. The efficiency of the proposed approach is analyzed according to the FERET evaluation procedure and by comparing our results with those obtained using the well-known Eigenfaces method. The proposed system achieved an rate of 97％(MIT data), 95.8％(FERET databace)

• 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.

• International Journal of Naval Architecture and Ocean Engineering
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• 제3권2호
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• pp.126-135
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• 2011

In this study, numerical analysis was carried out by using the finite element method to construct the first mode shape of damaged stiffened plates, and the damage locations were detected with two-dimensional discrete wavelet analysis. In the experimental analysis, four different damaged stiffened structures were observed. Firstly, each damaged structure was hit with a shaker, and then accelerometers were used to measure the vibration responses. Secondly, the first mode shape of each structure was obtained by using the wavelet packet, and the location of cracks were also determined by two-dimensional discrete wavelet analysis. The results of the numerical analysis and experimental investigation reveal that the proposed method is applicable to detect single crack or multi-cracks of a stiffened structure. The experimental results also show that fewer measurement points are required with the proposed technique in comparison to those presented in previous studies.

• 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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.3028-3030
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• 2000

In general. a speech coding is dedicated to the compression performance or the speech quality. But. the speech coding in this paper is focused on the performance of flexible transmission to the, network speed. For this. the subbanding coding is needed. which is used the wavelet packet concept in the signal analysis. The extraction of each frequency-band is difficult to general signal analysis methods, after coding each band, the reconstruction of these is also a difficult problem. But. with the wavelet packet concept(perfect reconstruction) and its fast computation algorithm. the extraction of each band and the reconstruction are more natural. Also, this paper describes a direct solution of the voice transmission on network and implement this algorithm at the TCP/IP network environment of PC.

• 한국정보통신학회논문지
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• 제10권3호
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• pp.542-548
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• 2006

본 논문에서는 컬러 정지 영상을 대상으로 상반신 인물 영상이 입력되었을 때, 얼굴 영역을 추출하고 검증하는 방법을 제안한다. 본 논문의 얼굴 추출과정은 1단계로 영상 내 피부색 영역을 추출한 다음, 후보 영역들에 대한 공간적 제한조건을 이용하여 1차 얼굴 후보 영역을 결정한다. 2단계에서는 얼굴 구성 요소 중 가장 두드러진 특징으로서 눈 영역을 탐색하고, 눈 영역을 기준으로 한국인의 얼굴에 대한 구조적 통계값을 적용한다. 이로서 얼굴 포함 최소 사각형 후보 영역을 결정한다. 마지막 3단계에서는 영상 내 색상 정보와 공간 정보 그리고 구조적 통계치로부터 결정된 얼굴 후보 영역에 대하여 얼굴 영역의 텍스춰(texture)를 Wavelet Packet Analysis를 이 용해 조사함으로써 얼굴 영역을 확정하게 된다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2307-2310
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• 2003

In this paper, a method for face recognition based on the wavelet packet decomposition is proposed. In the proposed method, the input image is decomposed by the 2-level wavelet packet transformation and then the face areas are defined by the Integral Projection technique applied to each of the 1-level subband images, HL and LH. After the defined face areas are divided into three areas, called top, bottom, and border, the mean and the variance of the three areas of the approximation image are computed, and the variance of the single predetermined face area for the rest of 15 detail images, from which the feature vectors of statistical measure are extracted. In this paper we use the wavelet packet decomposition, a generalization of the classical wavelet decomposition, to obtain its richer signal analysis features such as discontinuity in higher derivatives, self-similarity, etc. And we have shown that even with very simple statistical features such as mean values and variance we can make an excellent basis for face classification, if an appropriate probability distance is used.