• Journal of the Optical Society of Korea
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• v.14 no.3
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• pp.228-234
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• 2010

This paper presents the joint characteristic function of the first- and second-order polarization-modedispersion (PMD) vectors in installed optical fibers that are almost linearly birefringent. The joint characteristic function is a Fourier transform of the joint probability density function of these PMD vectors. We regard the random fiber birefringence components as white Gaussian processes and use a Fokker-Planck method. In the limit of a large transmission distance, our joint characteristic function agrees with the previous joint characteristic function obtained for highly birefringent fibers. However, their differences can be noticeable for practical transmission distances.

• Journal of the Optical Society of Korea
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• v.12 no.4
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• pp.240-243
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• 2008

Using the well-known Fokker-Planck method, this paper presents a standard way to find the joint-characteristic function for the first- and second-order polarization-mode-dispersion vectors originally derived by Foschini and Shepp. Compared with the Foschini and Shepp's approach, the Fokker-Planck approach gives a more accurate and straightforward way to find the joint-characteristic function.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.1
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• pp.45-60
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• 1995

It is attempted to estimate excitation force of a linear vibratory system using measured vibration responses. The excitation force is estimated from the relationship between the vibration response and system characteristic matrices which are extracted from both the mathematical model of the system and actual response in contrast to the usual approach of inverting the frequency response matrices. This extraction scheme is based on the fact that the vibration response can be expressed in term of linear combination of frequency domain modal vectors defined as mutually orthonormal basis vectors in frequency domain. The extracted frequency domain basis vectors are very stable in computational manipulation. It is found that the estimated excitation force is in good agreement with actually measured force except at the natural frequencies the structure, which is the common feature still to be overcome by the research efforts in this area. From the results of this paper, this disagreement is considered to come from the discrepancy between the model and actual value of the mass, damping and stiffness of the structure.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.3
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• pp.72-78
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• 2000

In this study, we propose a model verification algorithm based on DCT and neural network for ATM security system. We construct database about facial images after capturing thirty persons facial images in the same lumination and distance. To simulate model verification, we capture four learning images and test images per a man. After detecting edge in facial images, we detect a characteristic area of square shape using edge distribution in facial images. Characteristic area contains eye bows, eyes, nose, mouth and cheek. We extract characteristic vectors to calculate diagonally coefficients sum after obtaining DCT coefficients about characteristic area. Characteristic vectors is normalized between +1 and -1, and then used for input vectors of neural networks. Not considering passwords, simulations results showed 100% verification rate when facial images were learned and 92% verification rate when facial images weren't learned. But considering passwords, the proposed algorithm showed 100% verification rate in case of two simulations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1370-1376
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• 2012

This paper proposes a gender recognition algorithm which classifies a male or female speaker. In this paper, characteristic vectors for the male and female speaker are analyzed, and recognition experiments for the proposed gender recognition by a neural network are performed using these characteristic vectors for the male and female. Input characteristic vectors of the proposed neural network are 10 LPC (Linear Predictive Coding) cepstrum coefficients, 12 LPC cepstrum coefficients, 12 FFT (Fast Fourier Transform) cepstrum coefficients and 1 RMS (Root Mean Square), and 12 LPC cepstrum coefficients and 8 FFT spectrum. The proposed neural network trained by 20-20-2 network are especially used in this experiment, using 12 LPC cepstrum coefficients and 8 FFT spectrum. From the experiment results, the average recognition rates obtained by the gender recognition algorithm is 99.8% for the male speaker and 96.5% for the female speaker.

• Journal of KIISE:Software and Applications
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• v.33 no.10
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• pp.836-850
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• 2006

Nowadays, due to the rapidly increasing number of package software products, quality test has been emphasized for package software products. When testing software products, one of the most important factors is to select metrics which form the bases for tests. In this paper, the types of package software are represented as characteristic vectors having probabilistic relationships with metrics. The characteristic vectors could be regarded as indicators of software type. To assign the metrics for each software type, the past test metrics are collected and analyzed. Using Bayesian belief network, the dependency relationship network of the characteristic vectors and metrics is constructed. The dependency relationship network is then used to find the proper metrics for the test of new package software products.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1991.10a
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• pp.77-80
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• 1991

This paper proposes a new feature extraction method which is effectively used in character recognition, and validate the effectiveness through various computational methods for similiarity degree. To get feature vectors used in this method, Hough transform is applied to character image, which is used for edge extraction in image processing. By that transformation technique, strokes could be extracted and feature vectors constructed suitably. The characteristic of this method is solving the difficulties in stroke extraction through transform space analysis, which is induced by noise and blurring, and representing high recognition rate 99.3% within 10 candidates in relative low dimension.

• Communications of the Korean Mathematical Society
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• v.22 no.4
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• pp.587-596
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• 2007

In this paper, we study two-phase flow models. The chunk mix model of the two-phase flow equations is analyzed by a characteristic analysis. The model discussed herein has real characteristic values for all physically acceptable states and except for a set of measure zero has a complete set of characteristic vectors in state space.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.522-526
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• 1994

There are great needs for checking machine operation status precisely in the iron and steel plants. Rotating machines such as pumps, compressors, and motors are the most important objects in the plant maintenance. In this paper back-propagation neural network is utilized in diagnosing rotating machines. Like the finger print or the voice print of human, the abnormal vibrations due to axis misalignment, shaft bending, rotor unbalance, bolt loosening, and faults in gear and bearing have their own spectra. Like the pattern recognition technique, characteristic. feature vectors are obtained from the power spectra of vibration signals. Then we apply the characteristic feature vectors to a back propagation neural net for the weight training and pattern recognition.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.3
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• pp.104-113
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• 2000

In this study, we propose facial image recognition based on wavelet transform and neural network. This algorithm is proposed by following processes. First, two gray level images is captured in constant illumination and, after removing input image noise using a gaussian filter, differential image is obtained between background and face input image, and this image has a process of erosion and dilation. Second, a mask is made from dilation image and background and facial image is divided by projecting the mask into face input image Then, characteristic area of square shape that consists of eyes, a nose, a mouth, eyebrows and cheeks is detected by searching the edge of divided face image. Finally, after characteristic vectors are extracted from performing discrete wavelet transform(DWT) of this characteristic area and is normalized, normalized vectors become neural network input vectors. And recognition processing is performed based on neural network learning. Simulation results show recognition rate of 100 % about learned image and 92% about unlearned image.