• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.225-230
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• 2009

We propose a template matching method for component inspection of SMD assembly system. To discriminate wrong assembled components, the input image of component is matched with its standard image by template matching algorithm. For a fast inspection system, the calculation time of matching algorithm should be reduced. Since the standard images of all components located in a PCB are stored in computer, it is desirable to reduce the memory size of standard image. We apply the discrete wavelet transformation to reduce the image size as well as the calculation time. Only 7% memory of the BMP image is used to discriminate goodness or badness of components assembly. Comparative results are presented to verify the usefulness of the proposed method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.7
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• pp.1122-1130
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• 2001

In this paper, by applying the spectral domain wavelet concept to Green function, a fast spectral domain calculation of scattered fields is proposed to get the solution for the radiation integral. The spectral domain wavelet transform to represent Green function is implemented equivalently in space via the constant-Q windowing technique. The radiation integral can be calculated efficiently in the spectral domain using the windowed Green function expanded by its eigen functions around the observation region. Finally, the same formulation as that of the conventional fast multipole method (FMM) is obtained through the windowed Green function and the spectral domain calculation of the radiation integral.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.8
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• pp.1492-1501
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• 2011

A wide-area protection intelligent technique has been used to improve a reliability in power systems and to prevent a blackout. Nowadays, voltage and current phasor estimation has been executed by GPS-based synchronized PMU, which has become an important way of wide-area blackout protection for the prevention of expending faults in power systems. As this technique has the difficulties in collecting and sharing of information, there have been used a FNET method for the wide-area intelligent protection. This technique is very useful for the prediction of the inception fault and for the prevention of fault propagation with accurate monitoring frequency and frequency deviation. It consists of FDRs and IMS. It is well known that FNET can detect the dynamic behavior of system and obtain the real-time frequency information. Therefore, FDRs must adopt a optimal frequency estimation method that is robust to noise and fault. In this paper, we present comparative studies for the frequency estimation method using IRDWT(improved recursive discrete wavelet transform), for the frequency estimation method using FRDWT(fast recursive discrete wavelet transform). we used the Republic of Korea 345kV power system modeling data by EMTP-RV. The user-defined arbitrary waveforms were used in order to evaluate the performance of the proposed two kinds of RDWT. Also, the frequency variation data in various range, both large range and small range, were used for simulation. The simulation results showed that the proposed frequency estimation technique using FRDWT can be the optimal frequency measurement method applied to FDRs.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.7
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• pp.518-525
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• 2003

In this paper, we present a fast hardware architecture to implement a parallel 2-dimensional discrete wavelet transform(DWT)based on the lifting scheme DWT framework. The conventional 2-D DWT had a long initial and total latencies to get the final 2D transformed coefficients because the DWT used an entire input data set for the transformation and transformed sequentially The proposed architecture increased the parallel performance at computing the row directional transform using new data splitting method. And, we used the hardware resource sharing architecture for improving the total throughput of 2D DWT. Finally, we proposed a scheduling of hardware resource which is optimized to the proposed hardware architecture and splitting method. Due to the use of the proposed architecture, the parallel computing efficiency is increased. This architecture shows the initial and total latencies are improved by 50% and 66%.

• Proceedings of the KIEE Conference
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• 1999.07e
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• pp.2311-2313
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• 1999

웨이블렛 기법은 비주기적 신호의 해석에 있어 새롭게 적용되고 있는 방법이다. 이 방법은 Fourier Transform(FT), the Fast Fourier Transform(FFT), Least Square Method 방법과는 달리 시간 -주파수 분석을 통해 비주기적 과도 신호의 감지와 특징 추출이 용이하다. 이에 본 연구에서는 이러한 통계적 기법과는 달리 부분 방전 발생 신호를 시간-주파수 영역에서 연속적으로 분석 가능한 웨이블렛 기법을 이용하여 시간에 따른 절연체의 열화를 해석 및 진단하기로 한다. 부분 방전 현상을 나타내는 데이터는 방대하므로 진단을 위해 해석 정보에서 특정한 특징을 추출하며 이를 바탕으로 체계화된 데이터 베이스를 구성하는 기반을 마련한다. 해석에 필요한 신호는 복합감지 시스템(부분방전 시스템, 음향방출 시스템)을 이용하여 수집하였다.

• Journal of Korea Multimedia Society
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• v.10 no.10
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• pp.1271-1283
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• 2007

To detect defect of FPD(flat panel displays) is very difficult due to uneven illumination on FPD panel image. This paper presents a method to detect various types of defects using the approximated image of the uneven illumination by B-spline. To construct a approximated surface, corresponding to uneven illumination background intensity, while reducing random noises and small defect signal, only the lowest smooth subband is used by wavelet decomposition, resulting in reducing the computation time of taking B-spline approximation and enhancing detection accuracy. The approximated image in lowest LL subband is expanded as the same size as original one by wavelet reconstruction, and the difference between original image and reconstructed one becomes a flat image of compensating the uneven illumination background. A simple binary thresholding is then used to separate the defective regions from the subtracted image. Finally, blob analysis as post-processing is carried out to get rid of false defects. For applying in-line system, the wavelet transform by lifting based fast algorithm is implemented to deal with a huge size data such as film and the processing time is highly reduced.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.524-527
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• 2005

A society has progressed rapidly toward the highly advanced digital information age. However, noise is generated by several causes, when signal is processed. Therefore, methods for eliminating those noises have researched. There were the existing FFT(fast fourier transform) and STFT(short time fourier transform) for removing noise but it's impossible to know information about time and time-frequency localization capabilities have conflictive relationship. Therefore, for overcoming these limits, wavelet-based denoising methods that are capable of multiresolution analysis are applied to the signal processing field. However, existing threshold- and correlation-based denoising methods consider only statistical characteristics for noise, accordingly a lot of noise is acceptable as an edge and are impossible to remove AWGN and impulse noise, at the same time. Hence, in this paper we proposed wavelet-based new denoising algorithm and compared existing methods with it.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1920-1923
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• 2003

This paper presents compression rate improvement for SPIHT algorithm though redundancy bit removing. Proposed SPIHT algorithm uses a method to select of optimized threshold from feature of wavelet transform coefficients and removes sign bit if coefficient of LL area. Experimental results show that the proposed algorithm achieves more improvement bit rate and more fast progressive transmission with low bit rate.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.379-382
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• 2004

적응신호처리 분야에서 LMS알고리듬은 수식이 간단하고, 적은 계산량으로 인해 널리 사용되고 있지만, 시간영역의 적응알고리즘은 입력신호의 고유치 분포폭이 넓게 분포한 때는 수렴속도가 느려지는 단점이 있다. 이런 문제점을 개선하기 위하여 본 논문에서는 시간영역의 적응 알고리즘을 변환영역에서 수행하고, 변환영역에서 수렴성능 향상과 계산량을 줄이기 위하여 웨이블렛기반의 고속 적응 알고리즘을 제안하였다. 제안한 알고리즘을 OFDM 적응등화기에 적용하여, 기존의 OFDM 등화기 알고리즘과 비교하여 제안한 적응알고리즘의 성능이 우수함을 보인다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.27-32
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• 2006

Verbal communication is the most commonly used mean of communication. A spoken word carries a lot of informations about speakers and their emotional states. In this paper we designed a model to recognize emotional states in a speech, a first phase of two phases in developing a toy machine that recognizes emotional states in a speech. We conducted an experiment to extract and analyse the emotional state of a speaker in relation with speech. To analyse the signal output we referred to three characteristics of sound as vector inputs and they are the followings: frequency, intensity, and period of tones. Also we made use of eight basic emotional parameters: surprise, anger, sadness, expectancy, acceptance, joy, hate, and fear which were portrayed by five selected students. In order to facilitate the differentiation of each spectrum features, we used the wavelet transform analysis. We applied ANFIS (Adaptive Neuro Fuzzy Inference System) in designing an emotion recognition model from a speech. In our findings, inference error was about 10%. The result of our experiment reveals that about 85% of the model applied is effective and reliable.