• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.735-741
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• 1997

In the present paper, we propose a new randomization device free mail survey method. The estimator based on proposed model is unbiased and more efficient than the estimator based on SIngh, Mangat and Singh model (SMS-model)(1993) when $\pi$<1/2, and more protective than SMS-model in view of the protection of privacy regardless of the values of $\pi$ and $\pi_Y$ only if we count the number of say 'Yes' from the respondents. However, If we consider the respondents that say 'No', the SMS-model is more protective than our model.

• Journal of The Korean Astronomical Society
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• v.29 no.spc1
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• pp.1-5
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• 1996

Recent discussions of observational constraints on the standard hot big bang model are reviewed and it is argued that now there is room for considering alternative cosmologies. The quasi-steady state cosmology is briefly described. This model seems to explain most of the observed features of the universe, including the m-z relation, radio source count, the light nuclear abundances and the microwave background.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.721-725
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• 2004

Multiple-count problem is occurred when rectangle objects span across several buckets. The Cumulative Density (CD) histogram is a technique which solves multiple-count problem by keeping four sub-histograms corresponding to the four points of rectangle. Although it provides exact results with constant response time, there is still a considerable issue. Since it is based on a query window which aligns with a given grid, a number of errors may be occurred when it is applied to real applications. In this paper, we proposed selectivity estimation techniques using the generalized cumulative density histogram based on two probabilistic models: (1) probabilistic model which considers the query window area ratio, (2) probabilistic model which considers intersection area between a given grid and objects. In order to evaluate the proposed methods, we experimented with real dataset and experimental results showed that the proposed technique was superior to the existing selectivity estimation techniques. The proposed techniques can be used to accurately quantify the selectivity of the spatial range query on rectangle objects.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.1-5
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• 2021

Cyberbullying is a problem that is faced in many cultures. Due to their popularity and interactive nature, social media platforms have also been affected by cyberbullying. Social media users from Arab countries have also reported being a target of cyberbullying. Machine learning techniques have been a prominent approach used by scientists to detect and battle this phenomenon. In this paper, we compare different machine learning algorithms for their performance in cyberbullying detection based on a labeled dataset of Arabic YouTube comments. Three machine learning models are considered, namely: Multinomial Naïve Bayes (MNB), Complement Naïve Bayes (CNB), and Linear Regression (LR). In addition, we experiment with two feature extraction methods, namely: Count Vectorizer and Tfidf Vectorizer. Our results show that, using count vectroizer feature extraction, the Logistic Regression model can outperform both Multinomial and Complement Naïve Bayes models. However, when using Tfidf vectorizer feature extraction, Complement Naive Bayes model can outperform the other two models.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.7
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• pp.910-914
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• 2001

The teat number of a sow plays an important role for weaning pigs and has been utilized in selection of swine breeding stock. Various linear models have been employed for genetic analyses of teat number although the teat number can be considered as a count trait. Theoretically, Poisson error mixed models are more appropriate for count traits than Normal error mixed models. In this study, the two models were compared by analyzing data simulated with Poisson error. Considering the mean square errors and correlation coefficients between observed and fitted values, the Poisson generalized linear mixed model (PGLMM) fit the data better than the Normal error mixed model. Also these two models were applied to analyzing teat numbers in four breeds of swine (Landrace, Yorkshire, crossbred of Landrace and Yorkshire, crossbred of Landrace, Yorkshire, and Chinese indigenous Min pig) collected in China. However, when analyzed with the field data, the Normal error mixed model, on the contrary, fit better for all the breeds than the PGLMM. The results from both simulated and field data indicate that teat numbers of swine might not have variance equal to mean and thus not have a Poisson distribution.

• Proceedings of the KOR-KST Conference
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• 2007.11a
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• pp.357-366
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• 2007

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.237-243
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• 2017

In this paper, the detection performance of the electro-optical telescopes which observes and surveils space objects including artificial satellites, is analyzed. To perform the Modeling & Simulation(M&S) based analysis, satellite orbit model, telescope model, and the atmospheric model are constructed and a detection scenario observing the satellite is organized. Based on the organized scenario, pointing accuracy is analyzed according to the Field of View(FOV), which is one of the key factors of the telescope, considering pointing angle command error. In accordance with the preceding result, detection possibility according to the pixel-count of the detector and the FOV of the telescope is analyzed by discerning detection by Signal-to-Noise Ratio(SNR). The result shows that pointing accuracy increases with larger FOV, whereas the detection probability increases with smaller FOV and higher pixel-count. Therefore, major specification of the telescope such as FOV and pixel-count should be determined considering the result of M&S based analysis performed in this paper and the operational circumstances.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8B
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• pp.667-680
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• 2008

In this work, we present the results of our empirical study on 802.11 wireless LAN network traffic. We collect the packet trace from existing campus wireless LAN infra-structure. We analyzed four different data sets: aggregate traffic, upstream traffic, downstream traffic, tcp only packet trace from aggregate traffic. We analyze the time series aspect of underlying traffic (byte count process and packet count process), marginal distribution of time series, and packet size distribution. We found that in all four data sets there exist long-range dependent property in byte count and packet count process. Inter-arrival distribution is well fitted with Pareto distribution. Upstream traffic, i.e. from the user to Internet, exhibits significant difference in its packet size distribution from the rests. Average packet size of upstream traffic is 151.7 byte while average packet size of the rest of the data sets are all greater than 260 bytes. Packets with full data payloads constitutes 3% and 10% in upstream traffic and the downstream traffic, respectively. Despite the significant difference in packet size distribution, all four data sets have similar Hurst values. The Hurst alone does not properly explain the stochastic characteristics of the underlying traffic. We model the underlying traffic using fractional-ARIMA (FARIMA) and fractional Gaussian Noise (FGN). While the fractional Gaussian Noise based method is computationally more efficient, FARIMA exhibits superior performance in accurately modeling the underlying traffic.

• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.19-29
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• 2011

A Blog provides commentary, news, or content on a particular subject. The important part of many blogs is interactive format. Sometimes, there is a heated debate on a topic and any article becomes a political or sociological issue. In this paper, we proposed a method to predict the popularity of an article in advance. First, we used hit count as a factor to predict the popularity of an article. We defined the saturation point and derived a model to predict the hit count of the saturation point by a correlation coefficient of the early hit count and hit count of the saturation point. Finally, we predicted the virtual temperature of an article using 4 types(explosive, hot, warm, cold). We can predict the virtual temperature of Internet discussion articles using the hit count of the saturation point with more than 70% accuracy, exploiting only the first 30 minutes' hit count. In the hot, warm, and cold categories, we can predict more than 86% accuracy from 30 minutes' hit count and more than 90% accuracy from 70 minutes' hit count.

• Journal of Korean Neurosurgical Society
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• v.67 no.2
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• pp.177-185
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• 2024

Objective : Delayed cerebral ischemia (DCI) is a major cause of disability in patients who survive aneurysmal subarachnoid hemorrhage (aSAH). Systemic inflammatory markers, such as peripheral leukocyte count and systemic immune-inflammatory index (SII) score, have been considered predictors of DCI in previous studies. This study aims to investigate which systemic biomarkers are significant predictors of DCI. Methods : We conducted a retrospective, observational, single-center study of 170 patients with SAH admitted between May 2018 and March 2022. We analyzed the patients' clinical and laboratory parameters within 1 hour and 3-4 and 5-7 days after admission. The DCI and non-DCI groups were compared. Variables showing statistical significance in the univariate logistic analysis (p<0.05) were entered into a multivariate regression model. Results : Hunt-Hess grade "4-5" at admission, modified Fisher scale grade "3-4" at admission, hydrocephalus, intraventricular hemorrhage, and infection showed statistical significance (p<0.05) on a univariate logistic regression. Lymphocyte and monocyte count at admission, SII scores and C-reactive protein levels on days 3-4, and leukocyte and neutrophil counts on days 5-7 exhibited statistical significance on the univariate logistic regression. Multivariate logistic regression analysis revealed that monocyte count at admission (odds ratio [OR], 1.64; 95% confidence interval [CI], 1.04-2.65; p=0.036) and SII score at days 3-4 (OR, 1.55; 95% CI, 1.02-2.47; p=0.049) were independent predictors of DCI. Conclusion : Monocyte count at admission and SII score 3-4 days after rupture are independent predictors of clinical deterioration caused by DCI after aSAH. Peripheral monocytosis may be the primer for the innate immune reaction, and the SII score at days 3-4 can promptly represent the propagated systemic immune reaction toward DCI.