• Journal of KIISE:Software and Applications
• /
• v.36 no.8
• /
• pp.607-615
• /
• 2009

There has recently been a trend that IT industry is united with traditional industries such as military, aviation, automobile, and medical industry. Therefore, embedded software which controls hardware of the system should guarantee the high reliability, availability, and maintainability. To guarantee these properties, there are many attempts to develop the embedded software based on COTS (Commercial Off The Shelf) hardware components. However, it can cause additional faults due to software/hardware interactions beside general software faults in this methodology. We called the faults, Linkage Fault. These faults have high severity that makes overall system shutdown although their occurrence frequency is extremely low. In this paper, we propose a new software reliability model which considers those linkage faults in embedded software development with COTS hardware components. We use the Bayesian Analysis and Markov Chain Monte-Cairo method to validate the model. In addition, we analyze real linkage fault data to support the results of the theoretical model.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.1
• /
• pp.81-98
• /
• 2013

Since Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) were introduced, many revised or extended topic models have appeared. Due to the intractable likelihood of these models, training any topic model requires to use some approximation algorithm such as variational approximation, Laplace approximation, or Markov chain Monte Carlo (MCMC). Although these approximation algorithms perform well, training a topic model is still computationally expensive given the large amount of data it requires. In this paper, we propose a new method, called non-simultaneous sampling deactivation, for efficient approximation of parameters in a topic model. While each random variable is normally sampled or obtained by a single predefined burn-in period in the traditional approximation algorithms, our new method is based on the observation that the random variable nodes in one topic model have all different periods of convergence. During the iterative approximation process, the proposed method allows each random variable node to be terminated or deactivated when it is converged. Therefore, compared to the traditional approximation ways in which usually every node is deactivated concurrently, the proposed method achieves the inference efficiency in terms of time and memory. We do not propose a new approximation algorithm, but a new process applicable to the existing approximation algorithms. Through experiments, we show the time and memory efficiency of the method, and discuss about the tradeoff between the efficiency of the approximation process and the parameter consistency.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.22 no.6
• /
• pp.77-87
• /
• 2019

The first grade zones in Ecology and Nature Map are important regions for the conservation of the ecosystem, but it would be degraded by various anthropogenic factors. This study analyzes the relationship between potential land cover change and degradation of the first grade zones using land cover transition probability. As a result, it was shown that most of the first grade zones with degraded were converted from forest to urban(5.1%), cropland(27.2%), barren(11.0%) and grass(27.5%) in Gangwon and forest to urban(18.0%), cropland(15.3%), grass(28.4%), barren(12.3%) in Gyeonggi. The result of the logistic regression analysis showed that the probability of degradation of first grade zone was higher in area where was expected the higher probability of urban, cropland, barren, grass transition. The barren transition probability was the most influential and grass was the next highest. There were regional differences in the probability of urban transition and cropland transition, and the urban transition probability was more influential in Gyeonggi-do. This is because development pressure such as housing site development is high in Gyeonggi-do. Due to the limitations of the Act on Mountain Districts Management, even in the first grade zones, the grade may be degraded. Therefore, if Ecology and Nature Map are used to prevent deforestation or conversion of mountainous districts, it may contribute to the preservation of the ecosystem.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.4
• /
• pp.781-790
• /
• 2015

The Parrondo paradox is the counter-intuitive phenomenon: when we combine two losing games we can win the game or when we combine two winning games we can lose the game. In this paper, we assume that an investor adopts the rule of the history-dependent Parrondo game for investment in the stock market. Using the KRX (Korea Exchange) data from 2012 to 2014, we found the Parrondo paradox in the stock trading: the redistribution of profits among accounts can turn the decrease of the expected cumulative profit into the increase of the expected cumulative profit. We also found that the opposite case, namely the reverse Parrondo effect, can happen in the stock trading.

• Genomics & Informatics
• /
• v.10 no.2
• /
• pp.81-87
• /
• 2012

Large-scale copy number variants (CNVs) in the human provide the raw material for delineating population differences, as natural selection may have affected at least some of the CNVs thus far discovered. Although the examination of relatively large numbers of specific ethnic groups has recently started in regard to inter-ethnic group differences in CNVs, identifying and understanding particular instances of natural selection have not been performed. The traditional $F_{ST}$ measure, obtained from differences in allele frequencies between populations, has been used to identify CNVs loci subject to geographically varying selection. Here, we review advances and the application of multinomial-Dirichlet likelihood methods of inference for identifying genome regions that have been subject to natural selection with the $F_{ST}$ estimates. The contents of presentation are not new; however, this review clarifies how the application of the methods to CNV data, which remains largely unexplored, is possible. A hierarchical Bayesian method, which is implemented via Markov Chain Monte Carlo, estimates locus-specific $F_{ST}$ and can identify outlying CNVs loci with large values of FST. By applying this Bayesian method to the publicly available CNV data, we identified the CNV loci that show signals of natural selection, which may elucidate the genetic basis of human disease and diversity.

• The Korean Journal of Applied Statistics
• /
• v.33 no.2
• /
• pp.123-136
• /
• 2020

It is common to encounter correlated multiple outcomes measured on the same subject in various research fields. In developmental toxicity studies, presence of malformed pups and fetal weight are measured on the pregnant dams exposed to different levels of a toxic substance. Joint analysis of such two outcomes can result in more efficient inferences than separate models for each outcome. Most methods for joint modeling assume a normal distribution as random effects. However, in developmental toxicity studies, the response distributions may change irregularly in location and shape as the level of toxic substance changes, which may not be captured by a normal random effects model. Motivated by applications in developmental toxicity studies, we propose a Bayesian joint model for binary and continuous outcomes. In our model, we incorporate a skewed logit model for the binary outcome to allow the response distributions to have flexibly in both symmetric and asymmetric shapes on the toxic levels. We apply our proposed method to data from a developmental toxicity study of diethylhexyl phthalate.

• Industrial Engineering and Management Systems
• /
• v.7 no.2
• /
• pp.101-112
• /
• 2008

As charts to monitor the process fraction defectives, P, in the high-yield processes, Mishima et al. (2002) discussed a synthetic chart, the Synthetic CS chart, which integrates the CS (Confirmation Sample)$_{CCC(\text{Cumulative Count of Conforming})-r}$ chart and the CCC-r chart. The Synthetic CS chart is designed to monitor quality characteristics in real-time. Recently, Kotani et al. (2005) presented the EWMA (Exponentially Weighted Moving-Average)$_{CCC-r}$ chart, which considers combining the quality characteristics monitored in the past with one monitored in real-time. In this paper, we present an alternative chart that is more superior to the $EWMA_{CCC-r}$ chart. It is an integration of the $EWMA_{CCC-r}$ chart and the CCC-r chart. In using the proposed chart, the quality characteristic is initially judged as either the in-control state or the out-of-control state, using the lower and upper control limits of the $EWMA_{CCC-r}$ chart. If the process is not judged as the in-control state by the $EWMA_{CCC-r}$ chart, the process is successively judged, using the $EWMA_{CCC-r}$ chart. We compare the ANOS (Average Number of Observations to Signal) of the proposed chart with those of the $EWMA_{CCC-r}$ chart and the Synthetic CS chart. From the numerical experiments, with the small size of inspection items, the proposed chart is the most sensitive to detect especially the small shifts in P among other charts.

• Journal of KIISE:Information Networking
• /
• v.34 no.1
• /
• pp.41-47
• /
• 2007

The Hierarchical Mobile IPv6 (HMIPv6) has been proposed to accommodate frequent mobility of the Mobile Node and to reduce the signaling load. A Mobility Anchor Point is a router located in a network visited by the Mobile Node. The Mobile Node uses the Mobile Anchor Point as a local Home Agent. The absence of any protections between Mobile Node and Mobile Anchor Point may lead to malicious Mobile Nodes impersonating other legitimate ones or impersonating a Mobile Anchor Point. In this paper, we propose a mechanism of the secure Mobile Anther Point discovery in HMIPv6. The performance analysis and the numerical results presented in this paper show that our proposal has superior performance to other methods.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.6
• /
• pp.2893-2900
• /
• 2014

Background: Statistical methods are very important to precisely measure breast cancer patient survival times for healthcare management. Previous studies considered basic statistics to measure survival times without incorporating statistical modeling strategies. The objective of this study was to develop a data-based statistical probability model from the female breast cancer patients' survival times by using the Bayesian approach to predict future inferences of survival times. Materials and Methods: A random sample of 500 female patients was selected from the Surveillance Epidemiology and End Results cancer registry database. For goodness of fit, the standard model building criteria were used. The Bayesian approach is used to obtain the predictive survival times from the data-based Exponentiated Exponential Model. Markov Chain Monte Carlo method was used to obtain the summary results for predictive inference. Results: The highest number of female breast cancer patients was found in California and the lowest in New Mexico. The majority of them were married. The mean (SD) age at diagnosis (in years) was 60.92 (14.92). The mean (SD) survival time (in months) for female patients was 90.33 (83.10). The Exponentiated Exponential Model found better fits for the female survival times compared to the Exponentiated Weibull Model. The Bayesian method is used to obtain predictive inference for future survival times. Conclusions: The findings with the proposed modeling strategy will assist healthcare researchers and providers to precisely predict future survival estimates as the recent growing challenges of analyzing healthcare data have created new demand for model-based survival estimates. The application of Bayesian will produce precise estimates of future survival times.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.19 no.6
• /
• pp.1085-1094
• /
• 1994

We propose a traffic flow control scheme of B-NT with temporary cell buffering and selective cell discarding to prevent congestion state of the network nodes in B-ISDN systems to reduce or suppress output cell strams towards T interface. We define the states of the network nodes as normal, pre-congestion, and congestion. In a pre-congestion state, the loss-sensitive traffic is temporarily buffered to slow down the rate of the output traffic streams. In a congestion state, the delay-sensitive traffic is selectively discarded to suppress the output traffic streams as possible in addition to the cell buffering. We model the input cell streams and the states of the network nodes with Interrupted Bernoulli Process and 3-state Markov chain to analyze the performance of the proposed scheme in the B-NT system. The appropriate size of the cell buffer is explored by means of simulation and the influence on the performance of the proposed scheme by the network node state is discussed. As results, more than 2,00 cells of buffer size is needed for the control of medium of lower than the medium, degree of congestion occurrence in the network node while the control of high degree of congestion occurrence is nearly impossible.