• Journal of Information Processing Systems
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• v.8 no.4
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• pp.669-684
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• 2012

Discrete wavelet transforms are extensively preferred in biomedical signal processing for denoising, feature extraction, and compression. This paper presents a new denoising method based on the modeling of discrete wavelet coefficients of ECG in selected sub-bands with Kernel density estimation. The modeling provides a statistical distribution of information and noise. A Gaussian kernel with bounded support is used for modeling sub-band coefficients and thresholds and is estimated by placing a sliding window on a normalized cumulative density function. We evaluated this approach on offline noisy ECG records from the Cardiovascular Research Centre of the University of Glasgow and on records from the MIT-BIH Arrythmia database. Results show that our proposed technique has a more reliable physical basis and provides improvement in the Signal-to-Noise Ratio (SNR) and Percentage RMS Difference (PRD). The morphological information of ECG signals is found to be unaffected after employing denoising. This is quantified by calculating the mean square error between the feature vectors of original and denoised signal. MSE values are less than 0.05 for most of the cases.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.385-394
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• 2009

Face tracking and recognition are difficult problems because the face is a non-rigid object. The main reasons for the failure to track and recognize the faces are the changes of a face pose and environmental illumination. To solve these problems, we propose a nonlinear manifold framework for the face pose and the face illumination normalization processing. Specifically, to track and recognize a face on the video that has various pose variations, we approximate a face pose density to single Gaussian density by PCA(Principle Component Analysis) using images sampled from training video sequences and then construct the GMM(Gaussian Mixture Model) for each person. To solve the illumination problem for the face tracking and recognition, we decompose the face images into the reflectance and the illuminance using the SSR(Single Scale Retinex) model. To obtain the normalized reflectance, the reflectance is rescaled by histogram equalization on the defined range. We newly approximate the illuminance by the trained manifold since the illuminance has almost variations by illumination. By combining these two features into our manifold framework, we derived the efficient face tracking and recognition results on indoor and outdoor video. To improve the video based tracking results, we update the weights of each face pose density at each frame by the tracking result at the previous frame using EM algorithm. Our experimental results show that our method is more efficient than other methods.

• 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.

• Journal of the Earthquake Engineering Society of Korea
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• v.14 no.1
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• pp.25-34
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• 2010

A method is presented for evaluating the seismic failure probability of bridge structures which show a nonlinear hysteretic dynamic behavior. Bridge structures are modeled as a bilinear dynamic system with a single degree of freedom. We regarded that the failure of bridges will occur when the displacement response of a deck level firstly crosses the predefined limit state during a duration of strong motion. For the estimation of the first-crossing probability of a nonlinear structural system excited by earthquake motion, we computed the average frequency of crossings of the limit state. We presented the non-Gaussian closure method for the approximation of the joint probability density function of response and its derivative, which is required for the estimation of the average frequency of crossings. The failure probabilities are estimated according to the various artificial earthquake acceleration sets representing specific seismic characteristics. For the verification of the accuracy and efficiency of presented method, we compared the estimated failure probabilities with the results evaluated from previous methods and the exact values estimated with the crude Monte-Carlo simulation method.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.649-663
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• 2019

The 2018 United Nations Development Programme (UNDP) report announced that deforestation in North Korea is the most extreme situation and in terms of climate change, this deforestation is a global scale issue. To respond deforestation, various study and projects are conducted based on remote sensing, but access to public data in North Korea is limited, and objectivity is difficult to be guaranteed. In this study, the forest detection based on density estimation in statistic using Landsat imagery was conducted in Gangwon province which is the only administrative district divided into South and North. The forest spatial data of South Korea was used as data for the labeling of forest and Non-forest in the Normalized Difference Vegetation Index (NDVI), and a threshold (0.6658) for forest detection was set by Gaussian Probability Density Function (PDF) estimation by category. The results show that the forest area decreased until the 2000s in both Korea, but the area increased in 2010s. It is also confirmed that the reduction of forest area on the local scale is the same as the policy direction of urbanization and industrialization at that time. The Kappa value for validation was strong agreement (0.8) and moderate agreement (0.6), respectively. The detection based on the Gaussian PDF estimation is considered a method for complementing the statistical limitations of the existing detection method using satellite imagery. This study can be used as basic data for deforestation in North Korea and Based on the detection results, it is necessary to protect and restore forest resources.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.95.1-95
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• 2001

This paper is concerned with a novel S-stability (stochastic-stability) concept in linear time-invariant stochastic systems, where a stochastic mode in dynamics depends on both the external disturbance and the inner-parameter variations. This leads to an EAG (eigenstructure assignment gaussian) problem; that is, the problem of associating S-eigenvalues (stochastic-eigenvalues), S-eigenvectors (stochastic-eigenvectors), and their PDFs (probability density functions) with the stochastic information of the systems with the required stochastic specifications. These results explicitly characterize how S-eigenvalues, S-eigenvectors and their PDFs in the complex plane may impose S-stability on stochastic systems.

• The Journal of the Acoustical Society of Korea
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• v.19 no.2E
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• pp.27-31
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• 2000

Evaluating log-likelihood of Gaussian mixture density is major computational burden for the keyword spotting system using continuous HMM. In this paper, we employ the bucket box intersection (BBI) algorithm to reduce the computational complexity of keyword spotting. We make some modification in implementing BBI algorithm in order to increase the discrimination ability among the keyword models. According to our keyword spotting experiments, the modified BBI algorithm reduces 50% of log-likelihood computations without performance degradation, while the original BBI algorithm under the same condition reduces only 30% of log-likelihood computations.

• Proceedings of the Optical Society of Korea Conference
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• 2003.02a
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• pp.246-247
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• 2003

In recent years, soaring traffic volumes over optical communications networks lead to the rapid advances in information communications equipments. In backbone communications networks, there has been an advance in high-density transmission through DWDM, which can simultaneously transmit numerous signals with different wavelengths. When the channel spacing is narrower, the cross-talk between channels an important parameter that guarantees to the high performance of a whole system, becomes critical. (omitted)

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.4
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• pp.869-875
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• 1996

This paper investigates a method for dtermining a threshold value based on the probability distribution function for color image segmentation. Principal components of normalized color is nalyzed and found that there are effective color transforms for outdoor scents. We esplain the functional relationship of the treshold and the probability of a regiona detection, asuming bivarate Gaussian probability density function. Experimental results show that the probability of detection is proportional to the segmented area.

• Smart Structures and Systems
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• v.25 no.5
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• pp.593-604
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• 2020

Temperature may have more significant influences on structural responses than operational loads or structural damage. Therefore, a comprehensive understanding of temperature distributions has great significance for proper design and maintenance of bridges. In this study, the temperature distribution of the steel box girder is systematically investigated based on the structural health monitoring system (SHMS) of the Sutong Cable-stayed Bridge. Specifically, the characteristics of the temperature and temperature difference between different measurement points are studied based on field temperature measurements. Accordingly, the probability density distributions of the temperature and temperature difference are calculated statistically, which are further described by the general formulas. The results indicate that: (1) the temperature and temperature difference exhibit distinct seasonal characteristics and strong periodicity, and the temperature and temperature difference among different measurement points are strongly correlated, respectively; (2) the probability density of the temperature difference distribution presents strong non-Gaussian characteristics; (3) the probability density function of temperature can be described by the weighted sum of four Normal distributions. Meanwhile, the temperature difference can be described by the weighted sum of Weibull distribution and Normal distribution.