• Journal of IKEEE
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• v.22 no.4
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• pp.1099-1103
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• 2018

Platooning of vehicles is an efficient traffic management model that improves traffic flow and fuel consumption. Especially, it is necessary to reduce computational load and networking overhead in automated connected vehicle systems. Because it is important to maintain the size of the platoon group appropriately for efficient platoon operation, this study proposed a dynamic grouping scheme for platooning in an automated vehicle system. The proposed scheme is analyzed by a mathematical model based on Markov chain. From the performance evaluation, it was confirmed that the proposed scheme appropriately controls the size of the platoon group.

• Nuclear Engineering and Technology
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• v.51 no.5
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• pp.1289-1296
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• 2019

Non-functional requirements plays a critical role in designing variety of applications domain ranges from safety-critical systems to simple gaming applications. Performance is one of the crucial non-functional requirement, especially in control and safety systems, that validates the design. System risk can be quantified as a product of probability of system failure and severity of its impact. In this paper, we devise a technique to do the performance analysis of safety critical and control systems and to estimate performance based risk factor. The technique elaborates Petri nets to estimate performability to ensure system dependability requirements. We illustrate the technique on a case study of Nuclear Power Plant system. The technique has been validated on 17 safety critical and control systems of Nuclear Power Plant.

• Asian Journal of Innovation and Policy
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• v.7 no.3
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• pp.625-645
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• 2018

This paper's objective is to draw a decision guideline to continue research and development (R&D) investments in biotech start-ups facing the "Valley of Death" syndrome - a long negative profit period during a financial crisis. The data include financial indices as Net income, Revenues, Total stockholders' equity, Cash & equivalents, and R&D expenses of 18 major biotech companies (nine in negative profit and nine positive, in FY2008) and 15 major pharmaceutical corporations as benchmarks both in FY2008 and in FY2016 derived from the US SEC Database, EDGAR. A first methodology dealing with real options analysis assumes Total stockholders' equity as a growth option. And a second methodology, Bayesian Markov chain Monte Carlo (MCMC) analysis, is applied to test the probability relationship between the Total stockholders' equity and the R&D expenses in these three groups. This study confirms that Total stockholders' equity can play the role of a call option to support continuing R&D investments even in negative profits.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.41.1-41.1
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• 2019

We consider dynamical dark energy models based on a minimally coupled scalar field with three different potentials: the inverse power-law, SUGRA and double exponential potentials. For each model, we derived perturbation initial conditions in the early epoch and performed the Markov Chain Monte Carlo (MCMC) analysis to explore the parameter space that is favored by the current cosmological observations like Planck CMB anisotropy, type Ia supernovae, and baryon acoustic oscillation data. The analysis has been done by using the modified CAMB/COSMOMC code in which the dynamical evolution of the scalar field perturbations are fully considered. The MCMC constraints on the cosmological as well as potential parameters are derived. In the talk we will present a progress report.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2747-2757
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• 2018

We consider the MLE (maximum likelihood estimate) and Bayesian estimates of three-parameter bathtub-shaped lifetime distribution based on the progressive type II censoring with binomial removal. Jung, Chung (2018) proposed the three-parameter bathtub-shaped distribution which is the extension of the two-parameter bathtub-shaped distribution given by Zhang (2004). Jung, Chung (2018) investigated its properties and estimations. The maximum likelihood estimates are computed using Newton-Raphson algorithm. Also, Bayesian estimates are obtained under the balanced loss function using MCMC (Markov chain Monte Carlo) method. In particular, BSEL (balanced squared error loss) function is considered as a special form of balanced loss function given by Zellner (1994). For comparing theirs MLEs with the corresponding Bayes estimates, some simulations are performed. It shows that Bayes estimates is better than MLEs in terms of risks. Finally, concluding remarks are mentioned.

• Journal of the Korean Physical Society
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• v.73 no.10
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• pp.1452-1457
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• 2018

Inclusion of the ground state of $^{16}O$ is investigated for a study of elastic ${\alpha}-^{12}C$ scattering for the l = 0 channel at low energies in effective field theory. We employ a Markov chain Monte Carlo method for the parameter fitting and find that the uncertainties of the fitted parameters are significantly improved compared to those of our previous study. We then calculate the asymptotic normalization constants of the $0^+$ states of $^{16}O$ and compare them with the experimental data and the previous theoretical estimates. We discuss implications of the results of the present work.

• Journal of Multimedia Information System
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• v.8 no.1
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• pp.31-34
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• 2021

The classical HMM is defined by a parameter triple �� = (��, A, B), where each parameter represents a collection of probability distributions: initial state, state transition and output distributions in order. This paper proposes a new stationary parameter e = (e1, e2, …, eN) where N is the number of states and et = P(|xt = i, y) for describing how an input pattern y ends in state xt = i at time t followed by nothing. It is often said that all is well that ends well. We argue here that all should end well. The paper sets the framework for the theory and presents an efficient inference and training algorithms based on dynamic programming and expectation-maximization. The proposed model is applicable to analyzing any sequential data with two or more finite segmental patterns are concatenated, each forming a context to its neighbors. Experiments on online Hangul handwriting characters have proven the effect of the proposed augmentation in terms of highly intuitive segmentation as well as recognition performance and 13.2% error rate reduction.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.99-118
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• 2021

In this paper, we introduce an extended form of the inverse power Lomax model via Marshall-Olkin approach. We call it the Marshall-Olkin inverse power Lomax (MOIPL) distribution. The four- parameter MOIPL distribution is very flexible which contains some former and new models. Vital properties of the MOIPL distribution are affirmed. Maximum likelihood estimators and approximate confidence intervals are considered under Type I censored samples. Maximum likelihood estimates are evaluated according to simulation study. Bayesian estimators as well as Bayesian credible intervals under symmetric loss function are obtained via Markov chain Monte Carlo (MCMC) approach. Finally, the flexibility of the new model is analyzed by means of two real data sets. It is found that the MOIPL model provides closer fits than some other models based on the selected criteria.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.357-367
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• 2021

In general, common cause failures (CCFs) have been modeled with the assumption that components within the same group are symmetric. This assumption reduces the number of parameters required for the CCF probability estimation and allows us to use a parametric model, such as the alpha factor model. Although there are various asymmetric conditions in nuclear power plants (NPPs) to be addressed, the traditional CCF models are limited to symmetric conditions. Therefore, this paper proposes the copulabased CCF model to deal with asymmetric as well as symmetric CCFs. Once a joint distribution between the components is constructed using copulas, the proposed model is able to provide the probability of common cause basic events (CCBEs) by formulating a system of equations without symmetry assumptions. In addition, Bayesian inferences for the parameters of the marginal and copula distributions are introduced and Markov Chain Monte Carlo (MCMC) algorithms are employed to sample from the posterior distribution. Three example cases using simulated data, including asymmetry conditions in total failure probabilities and/or dependencies, are illustrated. Consequently, the copula-based CCF model provides appropriate estimates of CCFs for asymmetric conditions. This paper also discusses the limitations and notes on the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.131-134
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• 2023

The age of information (AoI) was proposed to quantify the freshness of information about the status of a remote source system, which is defined as the amount of time that has elapsed since a packet was created at its source. This paper analyzes the age of information of a discrete time Geo/D/1/1 status update system. For this purpose, the system is modeled as a discrete-time two-state Markov chain. The stationary probability distributions for peak AoI and AoI are obtained. The average peak AoI, the average AoI, and the freshness ratio of information are also derived. Some numerical results of the analysis are presented.