• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.431-440
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• 2013

We consider the problem of estimating the parameters of heavy tailed distributions. In general, maximum likelihood estimation(MLE) is the most preferred method of parameter estimation because it has good properties such as asymptotic consistency, normality and efficiency. However, MLE is not always the best solution because MLE is unstable or does not exist in some cases. This paper proposes another parameter estimation method, non-linear least squares(NLS) and compares its performance to MLE. The NLS estimator is achieved by minimizing sum of squared difference between empirical cumulative distribution function(CDF) and a theoretical distribution function. In this article, we compare the NLS method to MLE using simulated data from heavy tailed distributions. The NLS method is shown to perform better than MLE in Burr distribution when the sample size is small; in addition, it performs well in a Frechet distribution.

• Journal of Wetlands Research
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• v.18 no.2
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• pp.132-147
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• 2016

In this study the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) sensor onboard the Soil Moisture Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission-Water (GCOM-W1) based soil moisture retrievals were revised to obtain better accuracy of soil moisture and higher data acquisition rate over East Asia. These satellite-based soil moisture products are revised against a reference land model data set, called Global Land Data Assimilation System (GLDAS), using Cumulative Distribution Function (CDF) matching and regression approach. Since MIRAS sensor is perturbed by radio frequency interferences (RFI), the worst part of soil moisture retrieval, East Asia, constantly have been undergoing loss of data acquisition rate. To overcome this limitation, the threshold of RFI, DQX, and composite days were suggested to increase data acquisition rate while maintaining appropriate data quality through comparison of land surface model data set. The revised MIRAS and AMSR2 products were compared with in-situ soil moisture and land model data set. The results showed that the revising process increased correlation coefficient values of SMOS and AMSR2 averagely 27% 11% and decreased the root mean square deviation (RMSD) decreased 61% and 57% as compared to in-situ data set. In addition, when the revised products' correlation coefficient values are calculated with model data set, about 80% and 90% of pixels' correlation coefficients of SMOS and AMSR2 increased and all pixels' RMSD decreased. Through our CDF-based revising processes, we propose the way of mutual supplementation of MIRAS and AMSR2 soil moisture retrievals.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.45-52
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• 2013

Coordinated Multi-Point (CoMP) is considered as a technology in the 3rd Generation Partnership Project(3GPP) Long Term Evolution-Advanced (LTE-A) system. In this paper, we design and analyze the performance of the Coordinated Beamforming (CB) technique, which is one major category of CoMP. We perform Monte Carlo simulations with a Heterogeneous Network (HetNet) in LTE-A, and confirm the performance through a graph of the Cumulative Distribution Function (CDF). From the simulation results, we show significant performance gain with the CoMP technique, and better performance when we apply various schemes of scheduling and precoding.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.9
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• pp.3-11
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• 2016

Vehicle to Everything (V2X) communication has been growing to enhance traffic safety by employing advanced wireless communication systems. V2X communication is one core solution for governing and advancing future traffic safety and mobility. In this paper, we design the system level simulator (SLS) of Long Term Evolution (LTE)-based V2X and propose the adaptive transmission scheme for vehicle communication. The proposed scheme allocates the resource randomly in time and frequency domain, and transmit the message according to probability of transmission. The performance analysis is based on freeway case in periodic message transmission. Simulation results show that proposed scheme can improve the cumulative distribution function (CDF) of packet reception ratio (PRR) and average PRR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3188-3207
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• 2017

In this paper, we propose an image enhancement algorithm to improve license plate extraction rate in various environments (Day Street, Night Street, Underground parking lot, etc.). The proposed algorithm is composed of image enhancement algorithm and license plate extraction algorithm. The image enhancement method can improve an image quality of the degraded image, which utilizes a histogram information and overall gray level distribution of an image. The proposed algorithm employs an interpolated probability distribution value (PDV) in order to control a sudden change in image brightness. Probability distribution value can be calculated using cumulative distribution function (CDF) and probability density function (PDF) of the captured image, whose values are achieved by brightness distribution of the captured image. Also, by adjusting the image enhancement factor of each part region based on image pixel information, it provides a function that can adjust the gradation of the image in more details. This processed gray image is converted into a binary image, which fuses narrow breaks and long thin gulfs, eliminates small holes, and fills gaps in the contour by using morphology operations. Then license plate region is detected based on aspect ratio and license plate size of the bound box drawn on connected license plate areas. The images have been captured by using a video camera or a personal image recorder installed in front of the cars. The captured images have included several license plates on multilane roads. Simulation has been executed using OpenCV and MATLAB. The results show that the extraction success rate is more improved than the conventional algorithms.

• Korean Journal of Metals and Materials
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• v.46 no.9
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• pp.554-562
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• 2008

The distribution and prediction interval for the misorientation angle of grain boundary at which $Cr_2N$ was precipitated during heating at $900^{\circ}C$ for $10^4$ sec were newly estimated, and followed by the estimation of mathematical and median rank methods. The probability density function of the misorientation angle can be estimated by a statistical analysis. And then the ($1-{\alpha}$)100% prediction interval of misorientation angle obtained by the estimated probability density function. If the estimated probability density function was symmetric then a prediction interval for the misorientation angle could be derived by the estimated probability density function. In the case of non-symmetric probability density function, the prediction interval could be obtained from the cumulative distribution function of the estimated probability density function. In this paper, 95, 99 and 99.73% prediction interval obtained by probability density function method and cumulative distribution function method and compared with the former results by median rank regression or mathematical method.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.9
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• pp.87-94
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• 2007

This paper presents new criteria for DGA(Dissolved Gases Analysis) using CDF(Cumulative Distribution Function) obtained from the data from the diagnosis of transformers operated in KEPCO over a period of 16 years. Because of differences in operating environments, construction type, oil volume, and other factors, the interpretative criteria of DGA at KEPCO differs from other standards such as IEC-60599, or Rogers and Doernenburg. To suggest the most appropriate criteria, the DGA data from transformers under normal conditions as well as from developing fault transformers were collected. Using these data, this study suggests the limitative gas level of transformers under normal operating conditions and verifies the suitability of the criteria. Because the application of this new criterion to transformers at KEPCO increases the detectable ratio of incipient faults and reduces unnecessary follow-up sampling and analysis, the new criteria yields a more reliable prediction of transformer condition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1866-1883
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• 2019

As the youngest branch of information hiding, network covert timing channels conceal the existence of secret messages by manipulating the timing information of the overt traffic. The popular model-based framework for constructing covert timing channels always utilizes cumulative distribution function (CDF) of the inter-packet delays (IPDs) to modulate secret messages, whereas discards high-order statistics of the IPDs completely. The consequence is the vulnerability to high-order statistical tests, e.g., entropy test. In this study, a rich security model of covert timing channels is established based on IPD chains, which can be used to measure the distortion of multi-order timing statistics of a covert timing channel. To achieve rich security, we propose two types of covert timing channels based on nested lattices. The CDF of the IPDs is used to construct dot-lattice and interval-lattice for quantization, which can ensure the cell density of the lattice consistent with the joint distribution of the IPDs. Furthermore, compensative quantization and guard band strategy are employed to eliminate the regularity and enhance the robustness, respectively. Experimental results on real traffic show that the proposed schemes are rich-secure, and robust to channel interference, whereas some state-of-the-art covert timing channels cannot evade detection under the rich security model.

• Journal of applied mathematics & informatics
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• v.42 no.3
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• pp.647-661
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• 2024

This study aims to determine the optimal solution to transportation problems. We proposed a novel approach for tackling the initial basic feasible solution. This is a critical step toward achieving an optimal or near-optimal solution. The transportation issue is an issue of distributing goods from several sources to several destinations. The literature demonstrates many ways to improve IBFS. In this work, to solve the IBFS, we suggested a new method based on the statistical formula called cumulative distribution function (CDF) in exponential distribution, and inverse contra-harmonic mean (ICHM). The spreadsheet converts transportation cost values into exponential cost cell values. The stepping-stone method is used to identify an optimum solution. The results are compared with other existing methodologies, the suggested method incorporates balanced, and unbalanced, maximizing the profits, random values, and case studies which produce more effective outcomes.

• MALSORI
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• v.55
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• pp.131-142
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• 2005

A mismatch between the training and the test conditions often causes a drastic decrease in the performance of the speech recognition systems. In this paper, non-linear transformation techniques based on histogram equalization in the acoustic feature space are studied for reducing the mismatched condition. The purpose of histogram equalization(HEQ) is to convert the probability distribution of test speech into the probability distribution of training speech. While conventional histogram equalization methods consider only the probability distribution of a test speech, for noise-corrupted test speech, its probability distribution is also distorted. The transformation function obtained by this distorted probability distribution maybe bring about miss-transformation of feature vectors, and this causes the performance of histogram equalization to decrease. Therefore, this paper proposes a new method of calculating noise-removed probability distribution by using assumption that the CDF of noisy speech feature vectors consists of component of speech feature vectors and component of noise feature vectors, and this compensated probability distribution is used in HEQ process. In the AURORA-2 framework, the proposed method reduced the error rate by over $44\%$ in clean training condition compared to the baseline system. For multi training condition, the proposed methods are also better than the baseline system.