• Journal of Korean Society for Quality Management
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• v.11 no.2
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• pp.25-31
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• 1983

Often arrangement for effective product assessment and audit have not been completely satisfactory. The underlying reasons are: (a) The lack of early evidence of new unit quality. (b) The collection and processing of data. (c) Ineffective data analysis techniques. (d) The variability of information on which decision making is based. Because of the nature of the product the essential outputs from an affective QA organization would be: (a) Confirmation of new unit quality. (b) Detection of failures which are either epidemic or slowly degradatory. (c) Identification of failure cases. (d) Provision of management information at the right time to effect the necessary corrective action. The heart of an effective QA scheme is the acquisition and processing of data. With the advent of data processing for quality monitoring becomes feasible in an automotive testing environment. This paper shows how the method enables us to use Automotive Testing data for the cost benefits of QA management.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.651-659
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• 2012

In this paper we developed a statistical model for circular data with noises. In this case, model fitting by single circular model has a lack-of-fit problem. To overcome this problem, we consider some mixture models that include circular uniform distribution and apply an EM algorithm to estimate the parameters. Both von Mises and Wrapped skew normal distributions are considered in this paper. Simulation studies are executed to assess the suggested EM algorithms. Finally, we applied the suggested method to fit 2008 EHFRS(Epidemic Hemorrhagic Fever with Renal Syndrome) data provided by the KCDC(Korea Centers for Disease Control and Prevention).

• Communications for Statistical Applications and Methods
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• v.19 no.6
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• pp.899-904
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• 2012

High-dimensional categorical data models with small sample sizes have not been used extensively in genomic sequences that involve count (or discrete) or purely qualitative responses. A basic task is to identify differentially expressed genes (or positions) among a number of genes. It requires an appropriate test statistics and a corresponding multiple testing procedure so that a multivariate analysis of variance should not be feasible. A family wise error rate(FWER) is not appropriate to test thousands of genes simultaneously in a multiple testing procedure. False discovery rate(FDR) is better than FWER in multiple testing problems. The data from the 2002-2003 SARS epidemic shows that a conventional FDR procedure and a proposed test statistic based on a pseudo-marginal approach with Hamming distance performs better.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.11a
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• pp.490-501
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• 2002

기업의 생산성향상과 이익률에 영향을 줄 수 있는 지식이 경제 전반에 확산되어 나가는 과정은 한 나라의 경제발전속도에 영향을 미치는 중요한 요인이다. 기업 측면에서는 도입하려는 기술이 도입 후에 그 기업의 이익을 높여 줄 수 있다면 도입하지 않을 이유가 없다. 하지만 미래 수요의 불확실성이나 기술발전 방향의 불확실성 등으로 해서 기업으로서는 도입 후의 이익을 정확히 사전적으로 측정하기는 어렵다. 본 논문에서는 학계에서 일반적으로 사용되고 있는 두 가지 지식확산 모델을 설명하고자 한다. 그 하나는 하나의 새로운 기술이나 상품이 시간이 흐름에 따라 어떻게 전체 사용 가능자(population)에게 확산되는 지를 보여주는 1) Epidemic Diffusion Model (흔히 S자형 - Sigmoid - 모델이라고도 한다.)과 어떤 도입자가 어느 시점에서 대상이 된 새로운 기술을 도입할 것인지 아닌지를 결정하는 모델로서 2) Probit Diffusion Model (프로빗 모델)을 중심으로 한다. 그리고 이러한 지식확산과정과 속도에 영향을 줄 수 있는 기업 내부적 요인으로서 도입하고자 하는 기업의 누적된 경험이 중요하다는 것과 기업 외부적 요인으로서 네트웍 효과와 같은 요인들을 설명하였다.

• ETRI Journal
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• v.28 no.5
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• pp.692-695
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• 2006

Recently, there has been a constant barrage of worms over the Internet. Besides threatening network security, these worms create an enormous economic burden in terms of loss of productivity not only for the victim hosts, but also for other hosts, as these worms create unnecessary network traffic. Further, measures taken to filter these worms at the router level incur additional network delays because of the extra burden placed on the routers. To develop appropriate tools for thwarting the quick spread of worms, researchers are trying to understand the behavior of worm propagation with the aid of epidemiological models. In this study, we present an optimization model that takes into account infection and treatment costs. Using this model we can determine the level of treatment to be applied for a given rate of infection spread.

• Journal of Yeungnam Medical Science
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• v.38 no.2
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• pp.83-94
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• 2021

The global obesity epidemic and the growing elderly population largely contribute to the increasing incidence of type 2 diabetes. Insulin resistance acts as a critical link between the present obesity pandemic and type 2 diabetes. Naturally occurring reactive oxygen species (ROS) regulate intracellular signaling and are kept in balance by the antioxidant system. However, the imbalance between ROS production and antioxidant capacity causes ROS accumulation and induces oxidative stress. Oxidative stress interrupts insulin-mediated intracellular signaling pathways, as supported by studies involving genetic modification of antioxidant enzymes in experimental rodents. In addition, a close association between oxidative stress and insulin resistance has been reported in numerous human studies. However, the controversial results with the use of antioxidants in type 2 diabetes raise the question of whether oxidative stress plays a critical role in insulin resistance. In this review article, we discuss the relevance of oxidative stress to insulin resistance based on genetically modified animal models and human trials.

• Nonlinear Functional Analysis and Applications
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• v.28 no.2
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• pp.473-495
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• 2023

The use of mathematical modelling in the study of epidemiological disorders continues to grow substantially. In order to better support global policy initiatives and explain the possible consequence of an outbreak, mathematical models were constructed to forecast how epidemic illnesses spread. In this paper, fractional derivatives and (${\varpi}$ - F_𝓒)-contractions are used to explore the existence and uniqueness solutions of the novel coronavirus-19 model.

• Journal of Veterinary Science
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• v.24 no.2
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• pp.21.1-21.5
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• 2023

Under the current African swine fever (ASF) epidemic situation, a science-based ASF-control strategy is required. An ASF transmission mechanistic model can be used to understand the disease transmission dynamics among susceptible epidemiological units and evaluate the effectiveness of an ASF-control strategy by simulating disease spread results with different control options. The force of infection, which is the probability that a susceptible epidemiological unit becomes infected, could be estimated by applying an ASF transmission mechanistic model. The government needs to plan an ASF-control strategy based on an ASF transmission mechanistic model.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.22
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• pp.9731-9737
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• 2014

Large scale secular registry or surveillance systems have been accumulating vast data that allow mathematical modeling of cancer incidence and mortality rates. Most contemporary models in this regard use time series and APC (age-period-cohort) methods and focus primarily on predicting or analyzing cancer epidemiology with little attention being paid to implications for designing cancer registry, surveillance or evaluation initiatives. This research models age-specific cancer incidence rates using logistic growth equations and explores their performance under different scenarios of data completeness in the hope of deriving clues for reshaping relevant data collection. The study used China Cancer Registry Report 2012 as the data source. It employed 3-parameter logistic growth equations and modeled the age-specific incidence rates of all and the top 10 cancers presented in the registry report. The study performed 3 types of modeling, namely full age-span by fitting, multiple 5-year-segment fitting and single-segment fitting. Measurement of model performance adopted adjusted goodness of fit that combines sum of squred residuals and relative errors. Both model simulation and performance evalation utilized self-developed algorithms programed using C# languade and MS Visual Studio 2008. For models built upon full age-span data, predicted age-specific cancer incidence rates fitted very well with observed values for most (except cervical and breast) cancers with estimated goodness of fit (Rs) being over 0.96. When a given cancer is concerned, the R valuae of the logistic growth model derived using observed data from urban residents was greater than or at least equal to that of the same model built on data from rural people. For models based on multiple-5-year-segment data, the Rs remained fairly high (over 0.89) until 3-fourths of the data segments were excluded. For models using a fixed length single-segment of observed data, the older the age covered by the corresponding data segment, the higher the resulting Rs. Logistic growth models describe age-specific incidence rates perfectly for most cancers and may be used to inform data collection for purposes of monitoring and analyzing cancer epidemic. Helped by appropriate logistic growth equations, the work vomume of contemporary data collection, e.g., cancer registry and surveilance systems, may be reduced substantially.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.2979-2995
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• 2017

In recent years, there has been an increasing number of studies focused on identifying a set of spreaders to maximize the influence of spreading in complex networks. Although the k-core decomposition can effectively identify the single most influential spreader, selecting a group of nodes that has the largest k-core value as the seeds cannot increase the performance of the influence maximization because the propagation sphere of this group of nodes is overlapped. To overcome this limitation, we propose a neighborhood coreness cover and discount heuristic algorithm named "NCCDH" to identify a set of influential and decentralized seeds. Using this method, a node in the high-order shell with the largest neighborhood coreness and an uncovered status will be selected as the seed in each turn. In addition, the neighbors within the same shell layer of this seed will be covered, and the neighborhood coreness of the neighbors outside the shell layer will be discounted in the subsequent round. The experimental results show that with increases in the spreading probability, the NCCDH outperforms other algorithms in terms of the affected scale and spreading speed under the Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models. Furthermore, this approach has a superior running time.