• 품질경영학회지
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• 제35권4호
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• pp.159-165
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• 2007

Using statistical methods a change-point analysis of oil supply disruption is conducted. The statistical distribution of oil supply disruption is a weibull distribution. The detection of the change-point is applied to Bayesian method and weibull parameters are estimated through Markov chain monte carlo and parameter approach. The statistical approaches to the estimation for the change-point and weibull parameters is implemented with the sets of simulated and real data with small sizes of samples.

• Communications for Statistical Applications and Methods
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• 제24권5호
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• pp.507-518
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• 2017

Volatility plays a crucial role in theory and applications of asset pricing, optimal portfolio allocation, and risk management. This paper proposes a combined model of autoregressive moving average (ARFIMA), generalized autoregressive conditional heteroscedasticity (GRACH), and skewed-t error distribution to accommodate important features of volatility data; long memory, heteroscedasticity, and asymmetric error distribution. A fully Bayesian approach is proposed to estimate the parameters of the model simultaneously, which yields parameter estimates satisfying necessary constraints in the model. The approach can be easily implemented using a free and user-friendly software JAGS to generate Markov chain Monte Carlo samples from the joint posterior distribution of the parameters. The method is illustrated by using a daily volatility index from Chicago Board Options Exchange (CBOE). JAGS codes for model specification is provided in the Appendix.

• 한국공작기계학회논문집
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• 제13권1호
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• pp.28-34
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• 2004

In this study, new way of evaluating manufacturing cost Is organized and applied. In real manufacturing circumstances, tolerances of parts and assemblies are closely related to the cost. Several researches have been tried to identify the relations and set models. Moreover tolerances have influences on the maintenance of the manufacturing facilities. However Past researches have not considered the processing cost for the failed products. Therefore maintenance costs are represented as stochastic expressions, which include reliability of assembly and facilities. The stochastic nature of the maintenance cost is modeled and solved using Markov chain approach. Results show that this approach gives reliable estimations with remarkable computing time reduction.

• 산업경영시스템학회지
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• 제22권51호
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• pp.231-240
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• 1999

A major concern in Concurrent Engineering is the control and management of workload in a period of process. As a general rule, leveling the peak of workload in certain period is difficult because concurrent processing is comprised of various processes, including overlapping, paralleling looping and so on. Therefore, the workload management with resource constraints is so beneficial that effective methods to analyze design process are momentous. This study presents the Timed Petri Nets approach of precedence logic networks, and provides an alternative for users to analyze constraint processes to resolve conflicts of resources. Another approach to Continuous Time Markov Chain using Stochastic Petri Nets is also proposed. These approaches are expected to facilitate resolving resource constrained scheduling problems more systematically in Concurrent Engineering environment.

• Communications for Statistical Applications and Methods
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• 제13권3호
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• pp.491-501
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• 2006

We consider a multiple change point model with autoregressive conditional heteroscedasticity (ARCH). The model assumes that all or the part of the parameters in the ARCH equation change over time. The occurrence of the change points is modelled as the discrete time Markov process with unknown transition probabilities. The model is estimated by Markov chain Monte Carlo methods based on the approach of Chib (1998). Simulation is performed using a variant of perfect sampling algorithm to achieve the accuracy and efficiency. We apply the proposed model to the simulated data for verifying the usefulness of the model.

• 정보과학회 논문지
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• 제44권7호
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• pp.719-725
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• 2017

약 네트워크의 관리 비용을 줄이고 성능을 향상시키기 위해 ETSI(European Telecommunication Standards Institute)는 클라우드 데이터 센터에서 네트워크 기능(Network Function)을 소프트웨어 형태로 구현할 수 있는 네트워크 기능 가상화(Network Function Virtualization) 개념을 도입했다. 네트워크 기능 가상화 구조 내에서 네트워크 기능을 물리적 노드(예: 범용 서버)에 네트워크 기능을 호스팅하여 실제 리소스를 공유할 수 있다. 네트워크 기능 가상화를 지원하는 네트워크 서비스 제공 업체의 경우, 효율적인 자원 할당 방법을 통해 운영비용(OPEX) 및 자본 비용(CAPEX)를 줄일 수 있다. 이에 본 논문에서는 최적화 방법을 통해 Network Service Chain Embedding 문제를 분석하고 Markov Approximation 프레임워크 기반 최적의 솔루션을 제안한다. 제안사항에 대한 시뮬레이션 결과는 평균 CPU 사용률이 73%, 링크 사용률이 최대 53% 증가함을 보여준다.

• Smart Structures and Systems
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• 제21권5호
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• Structural Engineering and Mechanics
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• 제63권1호
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• pp.47-53
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• 2017

The fundamental goal of this study is to minimize the uncertainty of the median fragility curve and to assess the structural vulnerability under earthquake excitation. Bayesian Inference with Markov Chain Monte Carlo (MCMC) simulation has been presented for efficient collapse response assessment of the independent intake water tower. The intake tower is significantly used as a diversion type of the hydropower station for maintaining power plant, reservoir and spillway tunnel. Therefore, the seismic fragility assessment of the intake tower is a pivotal component for estimating total system risk of the reservoir. In this investigation, an asymmetrical independent slender reinforced concrete structure is considered. The Bayesian Inference method provides the flexibility to integrate the prior information of collapse response data with the numerical analysis results. The preliminary information of risk data can be obtained from various sources like experiments, existing studies, and simplified linear dynamic analysis or nonlinear static analysis. The conventional lognormal model is used for plotting the fragility curve using the data from time history simulation and nonlinear static pushover analysis respectively. The Bayesian Inference approach is applied for integrating the data from both analyses with the help of MCMC simulation. The method achieves meaningful improvement of uncertainty associated with the fragility curve, and provides significant statistical and computational efficiency.

• The Journal of Asian Finance, Economics and Business
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• 제7권8호
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• pp.281-287
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• 2020

In capital budgeting practices, investment project evaluations based on the net present value (NPV) and the internal rate of return (IRR) represent the traditional evaluation techniques. Compared with the traditional methods, the modified internal rate of return (MIRR) gives the opportunity to evaluate an investment in certain projet, while taking the changes in cash flows over time and issuing shares such as dividing shares, bonuses, and dividend for each end of the investment year into account. Therefore, this study aims to evaluate an investment in the Malaysian construction sector utilizing financial data for 39 public listed companies operating in the Malaysian construction sector over the period from Jan 1, 2007, to December 30, 2018, based on the MIRR method. Stochastic was studied in this study to estimate the estimated probability by applying the Markov chain model to the MIRR method where the transition matrix has two possible movements of either Good (G) or Bad (B). it is found that the long-run probability of getting a good investment is higher than the probability of getting a bad investment in the long-run, where were the probabilities of good and bad are 0.5119, 0.4881, respectively. Hence, investment in the Malaysian construction sector is recommended.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권9호
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• pp.3258-3273
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• 2021

Malware is a severe threat to the computing system and there's a long history of the battle between malware detection and anti-detection. Most traditional detection methods are based on static analysis with signature matching and dynamic analysis methods that are focused on sensitive behaviors. However, the usual detections have only limited effect when meeting the development of malware, so that the manual update for feature sets is essential. Besides, most of these methods match target samples with the usual feature database, which ignored the characteristics of the sample itself. In this paper, we propose a new malware detection method that could combine the features of a single sample and the general features of malware. Firstly, a structure of Directed Cyclic Graph (DCG) is adopted to extract features from samples. Then the sensitivity of each API call is computed with Markov Chain. Afterward, the graph is merged with the chain to get the final features. Finally, the detectors based on machine learning or deep learning are devised for identification. To evaluate the effect and robustness of our approach, several experiments were adopted. The results showed that the proposed method had a good performance in most tests, and the approach also had stability with the development and growth of malware.