• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.3
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• pp.485-492
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• 2017

GMM has been one of the most popular methods for scream sound detection. In the conventional GMM, the whole training data is divided into scream sound and non-scream sound, and the GMM is trained for each of them in the training process. Motivated by the idea that the process of scream sound detection is very similar to that of speaker recognition, the UBM which has been used quite successfully in speaker recognition, is proposed for use in scream sound detection in this study. We could find that UBM shows better performance than the traditional GMM from the experimental results.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.10 no.4
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• pp.204-210
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• 1998

This paper presents the development of the probability density function applicable for wave heights (peak-to-trough excursions) in finite water depth including shallow water depth. The probability distribution applicable to wave heights of a non-Gaussian random process is derived based on the concept of the maximum entropy method. When wave heights are limited by breaking wave heights (or water depth) and only first and second moments of wave heights are given, the probability density function developed is closed form and expressed in terms of wave parameters such as $H_m$(mean wave height), $H_{rms}$(root-mean-square wave height), $H_b$(breaking wave height). When higher than third moment of wave heights are given, it is necessary to solve the system of nonlinear integral equations numerically using Newton-Raphson method to obtain the parameters of probability density function which is maximizing the entropy function. The probability density function thusly derived agrees very well with the histogram of wave heights in finite water depth obtained during storm. The probability density function of wave heights developed using maximum entropy method appears to be useful in estimating extreme values and statistical properties of wave heights for the design of coastal structures.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.554-561
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• 2013

In this paper, we introduce a modeling and a process simulator developed with the aid of pRBFNNs for activated sludge process in the sewage treatment system. Activated sludge process(ASP) of sewage treatment system facilities is a process that handles biological treatment reaction and is a very complex system with non-linear characteristics. In this paper, we carry out modeling by using essential ASP factors such as water effluent quality, the manipulated value of various pumps, and water inflow quality, and so on. Intelligent algorithms used for constructing process simulator are developed by considering multi-output polynomial radial basis function Neural Networks(pRBFNNs) as well as Fuzzy C-Means clustering and Particle Swarm Optimization. Here, the apexes of the antecedent gaussian functions of fuzzy rules are decided by C-means clustering algorithm and the apexes of the consequent part of fuzzy rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The coefficients of the consequent polynomial of fuzzy rules and performance index are considered by the Least Square Estimation and Mean Squared Error. The descriptions of developed process simulator architecture and ensuing operation method are handled.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2087-2089
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• 2003

The presence of narrowband interference (NBI) in Direct-sequence code division multiple access (DS/CDMA) systems is an inevitable problem when the interference is strong enough. The improvement in the system performance employs by adaptive narrowband interference suppression techniques. Basically there have been two types of method for narrowband interference suppression estimator/subtracter approaches and transform domain approaches. In this paper the focus is on the type of estimator/subtracter approaches. However, the binary direct sequence (DS) signal, that acts as noise in the prediction process is highly non-Gaussian. The case of a Gaussian interferer with known in an autoregressive (AR) signal or a digital signal and also in a sinusoidal signal (Tone) that included in is paper. The proposed NBI suppression is presence in an adaptive IIR notch filter for lattice structure and more powerful by using a variable step-size algorithm. The simulation results show that the proposed algorithm can significantly increase the convergence rate and improved system performance when compare with adaptive least mean square algorithm (LMS).

• The Journal of the Acoustical Society of Korea
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• v.20 no.6
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• pp.87-93
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• 2001

As a new type of a linear decorrelating receiver, the Pseudo-Decorrelator was presented for asynchronous code division multiple access systems by the author. In this paper, the concept of the Pseudo-Decorrelator is extended to derive a receiver for WCDMA uplink systems over an additive white Gaussian noise channel. Starting with the analysis of the multiple access components of the decision statistics, a non-square cross-correlation matrix for each bit is obtained. This cross-correlation matrix is then inverted, and the inverted matrix is applied to the decision statistics obtained from a conventional receiver. In this receiver, the detection process can be started after the first three consecutive bits are received. Simulation results are presented for K-user systems over an additive white Gaussian noise channel under the circumstances in which synchronization errors, including time delay errors and carrier phase errors exist. It is shown that the proposed receiver performs better than a conventional receiver and parallel interference canceller.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.259-264
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• 2017

Birth weight of a new born baby provides very important information in evaluating many clinical issues such as fetal growth restriction. This paper analyzes birth weight data of babies born in Korea from 2011 to 2013, and it shows that there is a biologically implausible distribution of birth weights in the data. This implies that some errors may be generated in the data collection process. In particular, this paper analyzes the relationship between gestational period and birth weight, and it is shown that the birth weight data mostly of gestational periods from 28 to 32 weeks have noticeable errors. Therefore, this paper employs the finite Gaussian mixture model to classify the collected data points into two classes: non-corrupted and corrupted. After the classification the paper removes data points that have been predicted to be corrupted. This adjustment scheme provides more natural and medically plausible percentile values of birth weights for all the gestational periods.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2009.11a
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• pp.65-65
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• 2009

High temperature silicon carbide Schottky diode was fabricated with Au deposited on poly 3C-SiC thin film grown on p-type Si(100) using atmospheric pressure chemical vapor deposition. The charge transport mechanism of the diode was studied in the temperature range of 300 K to 550 K. The forward and reverse bias currents of the diode increase strongly with temperature and diode shows a non-ideal behavior due to the series resistance and the interface states associated with 3C-SiC. The charge transport mechanism is a temperature activated process, in which, the electrons passes over of the low barriers and in turn, diode has a large ideality factor. The charge transport mechanism of the diode was analyzed by a Gaussian distribution of the Schottky barrier heights due to the Schottky barrier inhomogeneities at the metal-semiconductor interface and the mean barrier height and zero-bias standard deviation values for the diode was found to be 1.82 eV and $s_0$=0.233 V, respectively. The interface state density of the diode was determined using conductance-frequency and it was of order of $9.18{\times}10^{10}eV^{-1}cm^{-2}$.

• Journal of the Society of Naval Architects of Korea
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• v.37 no.2
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• pp.57-69
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• 2000

A higher order panel method based on representation for both the geometry and the velocity potential is developed for the analysis of steady flow around marine propellers. The self-influence functions due to the normal dipole and the source are desingularized through the quadratic transformation, and then the singular part is integrated analytically whereas the non-singular part is integrated using Gaussian quadrature. A null pressure jump Kutta condition at the trailing edge is found to be effective in stabilizing the solution process and in predicting the correct solution. Numerical experiments indicate that the present method is robust and predicts the pressure distribution around lifting bodies with much fewer panels than existing low order panel methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.7
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• pp.913-917
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• 2013

During the restoration process of primary restorative transmission system, some over voltages may happen due to nonlinear interaction between unloaded transformers and transmission systems. These over voltages caused by harmonic resonance can be suppressed by inserting damping loads before energizing transformers. But it is very difficult to predict the occurrence possibility of harmonic resonance and complex simulation must be repeated to estimate the sufficient damping loads. This paper presents a damping loads prediction system to prevent harmonic resonance. Detailed analysis of the relationship between harmonic resonance and the amount of damping loads is discussed. The prediction system is developed using a curve fitting and a neural network based on this relationship. A curve fitting used a Gaussian function based on non-linear least square method and multi-layer back-propagation neural network is applied. The system is applied to primary restorative transmission lines in korean power system and the result showed satisfactory performance.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.137-144
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• 2017

Face detection is still a challenging task under severe face pose variations in complex background. This paper proposes an effective algorithm which can detect single or multiple faces based on skin color detection and depth information. We introduce Gaussian mixture model(GMM) for skin color detection in a color image. The depth information is from three dimensional depth sensor of Kinect V2 device, and is useful in segmenting a human body from the background. Then, a labeling process successfully removes non-face region using several features. Experimental results show that the proposed face detection algorithm can provide robust detection performance even under variable conditions and complex background.