• Food Science of Animal Resources
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• v.39 no.4
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• pp.565-575
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• 2019

This study evaluated Campylobacter jejuni risk in ground meat products. The C. jejuni prevalence in ground meat products was investigated. To develop the predictive model, survival data of C. jejuni were collected at $4^{\circ}C-30^{\circ}C$ during storage, and the data were fitted using the Weibull model. In addition, the storage temperature and time of ground meat products were investigated during distribution. The consumption amount and frequency of ground meat products were investigated by interviewing 1,500 adults. The prevalence, temperature, time, and consumption data were analyzed by @RISK to generate probabilistic distributions. In 224 samples of ground meat products, there were no C. jejuni-contaminated samples. A scenario with a series of probabilistic distributions, a predictive model and a dose-response model was prepared to calculate the probability of illness, and it showed that the probability of foodborne illness caused by C. jejuni per person per day from ground meat products was $5.68{\times}10^{-10}$, which can be considered low risk.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.50-56
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• 2020

With the rapid development of deep neural network theory and application research, the effect of face detection has been improved. However, due to the complexity of deep neural network calculation and the high complexity of the detection environment, how to detect face quickly and accurately becomes the main problem. This paper is based on the relatively simple model of the MTCNN model, using FDDB (Face Detection Dataset and Benchmark Homepage), LFW (Field Label Face) and FaceScrub public datasets as training samples. At the same time of sorting out and introducing MTCNN(Multi-Task Cascaded Convolutional Neural Network) model, it explores how to improve training speed and Increase performance at the same time. In this paper, the dynamic image pyramid technology is used to replace the traditional image pyramid technology to segment samples, and OHEM (the online hard example mine) function in MTCNN model is deleted in training, so as to improve the training speed.

• Journal of Korean Academy of Nursing
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• v.51 no.3
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• pp.280-293
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• 2021

Purpose: This study aimed to identify the risk factors for diabetic foot ulceration (DFU) to develop and evaluate the performance of a DFU prediction model and nomogram among people with diabetes mellitus (DM). Methods: This unmatched case-control study was conducted with 379 adult patients (118 patients with DM and 261 controls) from four general hospitals in South Korea. Data were collected through a structured questionnaire, foot examination, and review of patients' electronic health records. Multiple logistic regression analysis was performed to build the DFU prediction model and nomogram. Further, their performance was analyzed using the Lemeshow-Hosmer test, concordance statistic (C-statistic), and sensitivity/specificity analyses in training and test samples. Results: The prediction model was based on risk factors including previous foot ulcer or amputation, peripheral vascular disease, peripheral neuropathy, current smoking, and chronic kidney disease. The calibration of the DFU nomogram was appropriate (χ² = 5.85, p = .321). The C-statistic of the DFU nomogram was .95 (95% confidence interval .93~.97) for both the training and test samples. For clinical usefulness, the sensitivity and specificity obtained were 88.5% and 85.7%, respectively at 110 points in the training sample. The performance of the nomogram was better in male patients or those having DM for more than 10 years. Conclusion: The nomogram of the DFU prediction model shows good performance, and is thereby recommended for monitoring the risk of DFU and preventing the occurrence of DFU in people with DM.

• Journal of the Korean Data and Information Science Society
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• v.14 no.1
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• pp.83-92
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• 2003

In this paper, we derive noninformative priors for the ratio of parameters in the Burr model. We obtain Jeffreys' prior, reference prior and second order probability matching prior. Also we prove that the noninformative prior matches the alternative coverage probabilities and a HPD matching prior up to the second order, respectively. Finally, we provide simulated frequentist coverage probabilities under the derived noninformative priors for small and moderate size of samples.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.723-731
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• 1998

In this paper, we consider a hierarchical Bayes estimation of the parameter, the reliability and hazard rate function based on samples from a Burr type X failure model. Bayes calculations can be implemented by means of the Gibbs sampler and a numerical study us provided.

• Korean Journal of Child Studies
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• v.19 no.2
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• pp.97-113
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• 1998

The purpose of this paper was to evaluate hypothesized alternative models for the factor structure of the Korean Version of the Aberrant Behavior Checklist(K-ABC) using standardized samples. Confirmatory factor analyses of correlated factor models using the Jeroskog method were carried out. Analyses supported the two-factor processing model. When the achievement scale was added, a three factor model (two processing factors and an achievement factor) emerged. When factorially uncorrelated models were analyzed, fit indices proved to be improper.

• Journal of the Korean Geophysical Society
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• v.8 no.3
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• pp.153-157
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• 2005

A discussion of a method has been used with success in terrain modelling to estimate the height at any point on the land surface from irregularly distributed samples. The special requirements of terrain modelling are discussed as well as a detailed description of the algorithm and an example of its application.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.881-889
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• 2009

Support vector machines(SVMs) are known as flexible and efficient classifier of multivariate observations, producing a hyperplane or hyperdimensional curved surface in multidimensional feature space that best separates training samples by known groups. As various methodological extensions are made for SVM classifiers in recent years, it becomes more difficult to understand the constructed model intuitively. The aim of this paper is to visualize various SVM classifications tuned by several parameters in reduced dimensions, so that data analysts secure the tangible image of the products that the machine made.

• Journal of Korean Society for Quality Management
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• v.25 no.4
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• pp.71-78
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• 1997

In this paper, we consider a hierarchical Bayes estimation of the parameter, the reliability and failure rate functions based on type-II censored samples from a Burr type-？ failure time model. The Gibbs sampler a, pp.oach brings considerable conceptual and computational simplicity to the calculation of the posterior marginals and reliability. A numerical study is provided.

• Journal of Korean Society for Quality Management
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• v.13 no.1
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• pp.2-11
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• 1985

We study some Bayes esimators of the reliability of k out of m stress-strength model under quadratic loss and various prior distributions. We obtain Bayes estimators, Bayes risk, predictive bounds and asymtotic distribution of Bayes estimator. We investigate behaviours of Bayes estimator in moderate samples.