• 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.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.1178-1187
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• 2008

According to the operation data between $2007{\sim}2008$(January$\sim$June) for essential safety equipment including ATC, there were nothing special but stable and steady. And also emergency stop number of count are decreasing. But the frequency are still above the resonable count and it means driving condition should be improved through interface between ground and locomotive same as come to an understanding with driver. The number of count of emergency stop for VACMA(vigilance system) are also decreasing but also the frequency are above resonable count. This phenomenon caused by careless driving for the driver or the system malfunction. So the continuous monitoring for the data is necessary.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.295-304
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• 2019

This article deals with threshold-asymmetric volatility models for over-dispersed and zero-inflated time series of count data. We introduce various threshold integer-valued autoregressive conditional heteroscedasticity (ARCH) models as incorporating over-dispersion and zero-inflation via conditional Poisson and negative binomial distributions. EM-algorithm is used to estimate parameters. The cholera data from Kolkata in India from 2006 to 2011 is analyzed as a real application. In order to construct the threshold-variable, both local constant mean which is time-varying and grand mean are adopted. It is noted via a data application that threshold model as an asymmetric version is useful in modelling count time series volatility.

• Food Science of Animal Resources
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• v.44 no.2
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• pp.372-389
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• 2024

This study investigated the efficacy of ultraviolet-C (UV-C) irradiation in enhancing the quality of raw bovine milk by targeting microbial populations and lipid peroxidation, both of which are key factors in milk spoilage. We categorized the raw milk samples into three groups based on initial bacterial load: low (<3 Log 10 CFU/mL), medium (3-4 Log 10 CFU/mL), and high (>4 Log 10 CFU/mL). Using a 144 W thin-film UV-C reactor, we treated the milk with a flow rate of 3 L/min. We measured the bacterial count including standard plate count, coliform count, coagulase-negative staphylococci count, and lactic acid bacteria count and lipid peroxidation (via thiobarbituric acid reactive substances assay) pre- and post-treatment. Our results show that UV-C treatment significantly reduced bacterial counts, with the most notable reductions observed in high and medium initial load samples (>4 and 3-4 Log 10 CFU/mL, respectively). The treatment was particularly effective against coliforms, showing higher reduction efficiency compared to coagulase-negative staphylococci and lactic acid bacteria. Notably, lipid peroxidation in UV-C treated milk was significantly lower than in pasteurized or untreated milk, even after 72 hours. These findings demonstrate the potential of UV-C irradiation as a pre-treatment method for raw milk, offering substantial reduction in microbial content and prevention of lipid peroxidation, thereby enhancing milk quality.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.325-336
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• 2019

In this article we extend the discrete Weibull regression model in the presence of missing data. Discrete Weibull regression models can be adapted to various type of dispersion data however, it is not widely used. Recently Yoo (Journal of the Korean Data and Information Science Society, 30, 11-22, 2019) adapted the discrete Weibull regression model using single imputation. We extend their studies by using multiple imputation also with several various settings and compare the results. The purpose of this study is to address the merit of using multiple imputation in the presence of missing data in discrete count data. We analyzed the seventh Korean National Health and Nutrition Examination Survey (KNHANES VII), from 2016 to assess the factors influencing the variable, 1 month hospital stay, and we compared the results using discrete Weibull regression model with those of Poisson, negative Binomial and zero-inflated Poisson regression models, which are widely used in count data analyses. The results showed that the discrete Weibull regression model using multiple imputation provided the best fit. We also performed simulation studies to show the accuracy of the discrete Weibull regression using multiple imputation given both under- and over-dispersed distribution, as well as varying missing rates and sample size. Sensitivity analysis showed the influence of mis-specification and the robustness of the discrete Weibull model. Using imputation with discrete Weibull regression to analyze discrete data will increase explanatory power and is widely applicable to various types of dispersion data with a unified model.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.923-932
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• 2014

The Hurdle model can to analyze zero-inflated count data. This model is a mixed model of the logit model for a binary component and a truncated Poisson model of a truncated count component. We propose a new hurdle model with a general heterogeneous random effects covariance matrix to analyze longitudinal zero-inflated count data using modified Cholesky decomposition. This decomposition factors the random effects covariance matrix into generalized autoregressive parameters and innovation variance. The parameters are modeled using (generalized) linear models and estimated with a Bayesian method. We use these methods to carefully analyze a real dataset.

• International Journal of Highway Engineering
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• v.17 no.4
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• pp.135-141
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• 2015

PURPOSES: This study has been conducted to determine a homogeneous segment and integration to improve the efficiency of short-term traffic count. We have also attempted to reduce the traffic monitoring budget. METHODS: Based on the statistical approach, a homogeneous segment in the same road section is determined. Statistical analysis using t-test, mean difference, and correlation coefficient are carried out for 10-year-long (2004-2013) short-term count traffic data and the MAPE of fresh data (2014) are evaluated. The correlation coefficient represents a trend in traffic count, while the mean difference and t-score represent an average traffic count. RESULTS : The statistical analysis suggests that the number of target segments varies with the criteria. The correlation coefficient of more than 30% of the adjacent segment is higher than 0.8. A mean difference of 36.2% and t-score of 19.5% for adjacent segments are below 20% and 2.8, respectively. According to the effectiveness analysis, the integration criteria of the mean difference have a higher effect as compared to the t-score criteria. Thus, the mean difference represents a traffic volume similarity. CONCLUSIONS : The integration of 47 road segments from 882 adjacent road segments indicate 8.87% of MAPE, which is within an acceptable range. It can reduce the traffic monitoring budget and increase the count to improve an accuracy of traffic volume estimation.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.11 no.1
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• pp.41-48
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• 2004

Purpose: The purpose of this study was to analyze the colony count of airborne microbes contamination every hour in the Neurosurgical Intensive Care Unit (NSICU) in order to identify the relationship of colony count to person-visits. Method: Data were collected during from 11:00 a.m. September 5 to 11:00 a.m. September 6, 2002. This study used blood agar & nutrient agar and handtally counter (USA) for collection of airborne microbes and number of person-visits. Data was analyzed using the SPSSWIN 10.0 with means, Pearson correlation coefficient, and simple regression. Result: The result of this study are as follows. Total colony count of airborne microbes for 24 hours in the NSICU was 4,609. Total number of person-visits to the NSICU was 15,347. The highest scores fur the total colony count in different areas of the NSICU was the rear door, followed by the preparation room, and the front entrance, while the lowest count was in the isolation rooms. There was a statistically significant relationship between colony count and number of person-visits to the NSICU. The most frequently airborne microbes in the NSICU were Micrococcus, CNS, Staphylococcus Micrococcus, Aureus. Conclusion: These findings indicate that the number of person-visits in hospitals influences total colony count of airborne microbes. This study contributes to assessment of biological indoor air quality in hospital and in the development of an NSICU care plan.

• Food Science and Biotechnology
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• v.17 no.6
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• pp.1352-1356
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• 2008

Bootstrap method, a computer-intensive statistical technique to estimate the distribution of a statistic was applied to deal with uncertainty and variability of the experimental data in stochastic prediction modeling of microbial growth on a chill-stored food. Three different bootstrapping methods for the curve-fitting to the microbial count data were compared in determining the parameters of Baranyi and Roberts growth model: nonlinear regression to static version function with resampling residuals onto all the experimental microbial count data; static version regression onto mean counts at sampling times; dynamic version fitting of differential equations onto the bootstrapped mean counts. All the methods outputted almost same mean values of the parameters with difference in their distribution. Parameter search according to the dynamic form of differential equations resulted in the largest distribution of the model parameters but produced the confidence interval of the predicted microbial count close to those of nonlinear regression of static equation.

• Journal of Yeungnam Medical Science
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• v.14 no.2
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• pp.430-442
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• 1997

Due to the lowering of biological functions resulted from old age, the elderly is known to have many different clinical laboratory data compared with the young adults. But, in korea, such study is lacking. This research is to find the differences between the elderly and the young adults, and also to know the sexual differences, by comparing the outcomes of the clinical. laboratory data. Along with that, it is to help clinical usage of the data in the future. The age of the elderly was between 60 and 83(average age 63.8), and that of the young. controls was between 20 and 35. In both sexes, MCV, MCH, ESR, CRP, AST, ALT, ${\gamma}$GTP, ALP, BUN, total cholesterol were significantly higher in the elderly than in the controls. And lymphocyte count(%), total bilirubin, direct bilirubin, total protein, albumin, $T_3$ were significantly lower in the elderly than in the controls(P<0.05). Hemoglobin, Hct, platelet count, $T_4$ were significantly lower only in the male elderly, and eosinophil count(%), creatinine were significantly higher only in the female elderly(P<0.05). HDL cholesterol was significantly higher only in the male elderly(P<0.01). There were no significant difference between two groups regarding WBC count, segment neutrophil count(%), monocyte count(%), TSH. Many clinical laboratory data are different between the elderly and the young adults, and some clinical laboratory data also have sexual differences.