• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2000.11a
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• pp.183-187
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• 2000

This paper deals with multiresolution analysis of wavelet transform for partial discharge(PD), both corona and surface discharge. Multiresolution analysis was used for performing discrete wavelet transform. PD signals was decomposed into "approximation" and "detail" components until 4 levels by using discrete wavelet analysis. In this paper, daubechies family is adopted for the research of the characteristics of PD signals. The results show that in corona discharge the segment 7, 8, 9, 10, 11 values of defined variable is increased with discharge process, so phase distribution is characterized by 210~330 ranges. In case surface discharge in expoxy insulator inserted, defined variable values is fairly symmetric discharge pattern because coupled both corona and dielectric bounded discharges. We can confirmly discriminate the type PD source. the type PD source.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.6
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• pp.980-985
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• 2002

In this paper, we analyzed satellite image with a high resolution compression performance. We need much time in a fast processing on vast satellite image pixel data. On compressing and decompressing, we should keep the information about road, building, forest, etc. In conclusion, we did analyze and compare the performance of compression and decompression for JPEG and WSQ(wavelet scalar quantization) method. As a result, we knew that WSQ was more efficient than JPEG.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.84-89
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• 2001

Ultrasonic pulse echo method comes to be difficult to apply to the multi-layered structure with very thin layer, because the echoes from the top and the bottom of the layer are overlapped. Conventionally method, deconvolution technique has been used for the decomposition of overlapped UT signals, however it has disabilities when the waveform of the transmitted signal is distorted according to the propagation. In this paper, the wavelet transform based deconvolution (WTBD) technique is proposed as a new signal processing method that can decompose the overlapped echo signals in A-Scan signal with superior performances compared to the conventional deconvolution technique. Performances of the proposed method are shown by through computer simulations using model signal with noise and are demonstrated by through experiments for the fabricated acryl rod with a thin steel plate bonded to it.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.194-202
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• 1999

A method for pattern recognition based on wavelet transform is proposed in this paper. The boundary of the object to be recognized includes shape information for object of machine parts. The contour is first represented using a one-dimensional signal and normalized about translation, rotation and scale, then is used to build the wavelet transform representation of the object. Wavelets allow us to decompose a function into multi-resolution hierarchy of localized frequency bands. The recognition of 2-dimensional object based on the wavelet is described to analyze the shape of analysis technique; the discrete wavelet transform(DWT). The feature vectors obtained using wavelet analysis is classified using a multi-layer neural network. The results show that, compared with the use of fourier descriptors, recognition using wavelet is more stable and efficient representation. And particularly the performance for objects corrupted with noise is better than that of other method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.7
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• pp.1515-1522
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• 1997

Several image coding algorithms have been developed for the telecommunication and multimedia systems with high image quality and high compression ratio. In order to achieve low entropy and distortion, the system should pay great cost of computation time and memory. In this paper, the uniform cubic lattice is chosen for Lattice Vector Quantization (LVQ) because of its generic simplicity. As a transform coding, the Discrete Wavelet Transform (DWT) is applied to the images because of its multiresolution property. The proposed algorithm is basically composed of the biorthogonal DWT and the uniform cubic LVQ. The multiresolution property of the DWT is actively used to optimize the entropy and the distortion on the basis of the distortion-rate function. The vector codebooks are also designed to be optimal at each subimage which is analyzed by the biorthogonal DWT. For compression efficiency, the vector codebook has different dimension depending on the variance of subimage. The simulation results show that the performance of the proposed coding mdthod is superior to the others in terms of the computation complexity and the PSNR in the range of entropy below 0.25 bpp.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.9
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• pp.158-165
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• 2000

Reverse engineering is reproducing products by directly extracting geometric information from physical objects such as clay model wooden mock-up etc. The fundamental work in the reverse engineering is to acquire the geometric data for modeling the objects. This research proposes a novel method for data acquisition aiming at unmanned fast and precise measurement. This is come true by the sensor fusion with CCD camera using structured light beam and touch trigger sensor. The vision system provides global information of the objects data. In this case the number of data and position allocation for touch sensor is critical in terms of the productivity since the number of vision data is very huge. So we applied wavelet transform to reduce the number of data and to allocate the position of the touch probe. The simulated and experimental results show this method is good enough for data reduction.

• Journal of Biomedical Engineering Research
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• v.20 no.4
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• pp.435-442
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• 1999

A fuzzy-ART(adaptive resonance theory) network for the PVC(premature ventricular contraction) classification using wavelet coefficient is designed. This network consists of the feature extraction and learning of the fuzzy-ART network. In the first step, we have detected the QRS from the ECG signal in order to set the threshold range for feature extraction and the detected QRS was divided into several frequency bands by wavelet transformation using Haar wavelet. Among the low-frequency bands, only the 6th coefficient(D6) are selected as the input feature. After that, the fuzzy-ART network for classification of the PVC is learned by using input feature which comprises of binary data converted by applying threshold to D6. The MIT/BIH database including the PVC is used for the evaluation. The designed fuzzy-ART network showed the PVC classification ratio of 96.52%.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.8
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• pp.48-54
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• 1999

A new method for wavelet embedded image coding is presented extending the bases of the Shapiro's algorithm by incorporating edge detection, zerotree scheme, and classified VQ(CVQ). Generally edges in the image are regarded an visually important components and the previous literatures have proved that significant coefficients in wavelet transform domain correspond to the edges in spatial domain. Hence, by identifying the edge elements, the significant coefficient can be easily detected in wavelet domain without investigating descendant coefficients across layer. Hierarchical trees for the significant components are organized, and then CVQ method is applied to these trees. Since the significant information has higher priority in transmission, the simulation shows that our coder provides a superior performance over the conventional method and can be successfully applied to the application areas that require of progressive transmission.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.102-112
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• 2012

In this paper, we propose a texture classification using wavelet-domain BDIP (block difference of inverse probabilities) and BVLC (block variance of local correlation coefficients) features with WPCA (whitened principal component analysis) classifier. In the proposed method, the wavelet transform is first applied to a query image. The BDIP and BVLC operators are next applied to the wavelet subbands. Global moments for each subband of BDIP and BVLC are then computed and fused into a feature vector. In classification, the WPCA classifier, which is usually adopted in the face identification, searches the training feature vector most similar to the query feature vector. Experimental results show that the proposed method yields excellent texture classification with low feature dimension for test texture image DBs.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.759-765
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• 2006

In this paper, it is proposed an effective face expression recognition LDA mixed mode using a triangularity membership fuzzy function and wavelet basis. The proposal algorithm gets performs the optimal image, fuzzy wavelet algorithm and Expression recognition is consisted of face characteristic detection step and face Expression recognition step. This paper could applied to the PCA and LDA in using some simple strategies and also compares and analyzes the performance of the LDA mixed model which is combined and the facial expression recognition based on PCA and LDA. The LDA mixed model is represented by the PCA and the LDA approaches. And then we calculate the distance of vectors dPCA, dLDA from all fates in the database. Last, the two vectors are combined according to a given combination rule and the final decision is made by NNPC. In a result, we could showed the superior the LDA mixed model can be than the conventional algorithm.