• Journal of the Korean Data and Information Science Society
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• 제19권3호
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• pp.811-818
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• 2008

This paper is the generalization of the modified two-level skip-lot sampling plan(MTSkSP2) to n-level. The general formulas of the operating characteristic(OC) function, average sample number(ASN) and average outgoing quality(AOQ) for the plan are derived using Markov chain properties.

• Communications for Statistical Applications and Methods
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• 제7권1호
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• pp.211-224
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• 2000

The Bayes estimation of the proportion in successive occasions sampling with randomized response model is discussed by means of Acceptance Rejection sampling. Bayesian estimation of transition probabilities in two successive occasions is suggested via Markov Chain Monte Carlo algorithm and its applicability is represented in a numerical example.

• 대한산업공학회지
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• 제38권3호
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• pp.208-219
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• 2012

As the number of end-of-life vehicles (ELVs) has kept increasing, the management of ELV has also become one of the academic research focuses and European Union recently adopted the directive on ELVs. For the stakeholders has become a principle agent of dealing with all about ELVs, it is relevant investment decision to set up and to decide high-cost ELVs entity locations and to forecast future ELVs' amount in advance. In this paper, transition probability matrixes between months are made by using Markov Chain and the number of ELVs is predicted with them. This study will perform a great role as a fundamental material in Korea where just started having interests about recycling resources and studies related to the topic. Moreover, the forecasting method developed for this research can be adopted for other enhancements in different but comparable situations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권9호
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• pp.4568-4584
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• 2016

Steganography based on text generation has become a hot research topic in recent years. However, current text-generation methods which generate texts of normal style have either semantic or syntactic flaws. Note that texts of special genre, such as poem, have much simpler language model, less grammar rules, and lower demand for naturalness. Motivated by this observation, in this paper, we propose a text steganography that utilizes Markov chain model to generate Ci-poetry, a classic Chinese poem style. Since all Ci poems have fixed tone patterns, the generation process is to select proper words based on a chosen tone pattern. Markov chain model can obtain a state transfer matrix which simulates the language model of Ci-poetry by learning from a given corpus. To begin with an initial word, we can hide secret message when we use the state transfer matrix to choose a next word, and iterating until the end of the whole Ci poem. Extensive experiments are conducted and both machine and human evaluation results show that our method can generate Ci-poetry with higher naturalness than former researches and achieve competitive embedding rate.

• 한국습지학회지
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• 제5권2호
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• pp.1-13
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• 2003

In Korea, most of the rainfalls have been concentrated in the flood season and the flood study has received more attention than low flow analysis. One of the reasons that the analysis of low flows has less attention is the lacks of the required data like daily rainfall and so we have used the stochastic processes such as pulse noise, exponential distribution, and state model of Markov chain for the rainfall simulation in short term such as daily. Especially this study will pay attention to the state model of Markov chain. The previous study had performed the simulation study by the state model without considerations of the flood and non-flood periods and without consideration of the frequency of rainfall for the period of a state. Therefore this study considers afore mentioned two cases and compares the results with the known state model. As the results, the RMSEs of the suggested and known models represent the similar results. However, the PRE(relative percentage error) shows the suggested model is better results.

• Structural Engineering and Mechanics
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• 제81권1호
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• pp.103-115
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• 2022

The prediction error variances for frequencies are usually considered as unknown in the Bayesian system identification process. However, the error variances for mode shapes are taken as known to reduce the dimension of an identification problem. The present study attempts to explore the effectiveness of Bayesian approach of model parameters updating using Markov Chain Monte Carlo (MCMC) technique considering the prediction error variances for both the frequencies and mode shapes. To remove the ergodicity of Markov Chain, the posterior distribution is obtained by Gaussian Random walk over the proposal distribution. The prior distributions of prediction error variances of modal evidences are implemented through inverse gamma distribution to assess the effectiveness of estimation of posterior values of model parameters. The issue of incomplete data that makes the problem ill-conditioned and the associated singularity problem is prudently dealt in by adopting a regularization technique. The proposed approach is demonstrated numerically by considering an eight-storey frame model with both complete and incomplete modal data sets. Further, to study the effectiveness of the proposed approach, a comparative study with regard to accuracy and computational efficacy of the proposed approach is made with the Sequential Monte Carlo approach of model parameter updating.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권9호
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• pp.4180-4196
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• 2017

In this paper, we analyze average capacity of an amplify-and-forward (AF) cooperative communication system model in multi-relay multiuser networks. In contrast to conventional cooperative networks, relays in the considered network have no embedded energy supply. They need to rely on the energy harvested from the signals broadcasted by the source for their cooperative information transmission. Based on this structure, a two-step selection scheme is proposed considering both channel state information (CSI) and battery status of relays. Assuming each relay has infinite or finite energy storage for accumulating the energy, we use the infinite or finite Markov chain to capture the evolution of relay batteries and certain simplified assumptions to reduce computational complexity of the Markov chain analysis. The approximate closed-form expressions for the average capacity of the proposed scheme are derived. All theoretical results are validated by numerical simulations. The impacts of the system parameters, such as relay or user number, energy harvesting threshold and battery size, on the capacity performance are extensively investigated. Results show that although the performance of our scheme is inferior to the optimal joint selection scheme, it is still a practical scheme because its complexity is much lower than that of the optimal scheme.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2019년도 추계학술대회
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• pp.266-266
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• 2019

해마다 증가하고 있는 해양사고는 기관고장, 충돌, 좌초, 화재 등 다양하게 발생하고 있다. 이러한 해양사고는 대형 인명사고의 위험이 있어 사전에 사고를 예방 하는 게 무엇보다 중요하다. 이를 위해서는 해양사고 발생을 사전에 예측하고 이에 대응할 수 있는 예측 체계가 요구된다. 본 연구에서는 과거에 발생한 데이터를 근거로 미래를 예측할 수 있는 마코프 체인 프로세스(Markov Chain Process)를 적용하여 해양사고 발생을 사전에 예측하기 위한 모델링을 제안한다. 제시된 모델링을 적용하여 미래 발생 가능한 해양사고 발생 확률을 산출하고 실제 발생한 빈도와 비교하였다. 또한 많이 사용되는 다른 예측 분석 방법과 비교하여 예측의 정확성을 측정하였다. 이를 통해 해양사고 발생에 관한 예측 체계를 마련하는데 하나의 확률 모형을 제안하였으며, 나아가 다양한 해양사고의 문제를 예측하는데 기여할 것으로 기대된다.

• 융합보안논문지
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• 제14권4호
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• pp.19-26
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• 2014

모바일 악성코드는 웜에 의한 전파가 대표적이며, 웜의 확산 특징을 분석하기 위한 모델링 기법들이 제시되었지만 거시적인 분석만 가능하였고 특정 바이러스, 악성코드에 대해 예측하기는 한계점이 있다. 따라서 본 논문에서는 과거의 악성코드 데이터를 활용하여 미래의 악성코드의 발생을 예측 할 수 있는 마코프 체인을 기반으로 한 예측 방법을 제시하였다. 마코프 체인 예측 모델링에 적용할 악성코드 평균값은 전체 평균값, 최근 1년 평균값, 최근 평균값(6개월)의 세 가지 범위로 분류하여 적용하였고, 적용하여 얻어진 예측 값을 비교하여 최근 평균 값(6개월)을 적용하는 것이 악성코드 예측 확률을 높일 수 있음을 확인하였다.

• 대한토목학회논문집
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• 제28권4B호
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• pp.383-392
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• 2008

수공구조물을 설계하거나 수자원계획을 수립할 때 제한된 수문자료로 인해 수문모형의 매개변수를 추정하는데 어려움이 따르며 추정된 결과에 신뢰성을 부여하기 위해서 필수적으로 불확실성 분석이 필요하다 하겠다. 이러한 관점에서 본 연구에서는 국내외에서 주로 이용되고 있는 NWS-PC 강우-유출 모형을 대상으로 보다 진보된 매개변수 추정과 불확실성 분석이 가능한 Bayesian Markov Chain Monte Carlo 기법과 결합하여 국내 소양강댐 유역 일유입량 모의에 적용하였다. 실측 일유입량 자료를 대상으로 모형의 검정과정을 수행하였으며 NWS-PC 모형의 총 13개의 매개변수에 대한 사후분포를 추정하여 유출수문곡선의 불확실성 구간을 추정하였다. 검정 및 검증 모두에서 Bayesian Markov Chain Monte Carlo 기법이 모형의 적합성 측면에서 기존 방법론과 비교해보면 다소 우수하거나 비슷한 결과를 나타내었다. 실제로 유역에 발생하는 유출은 다양한 요인에 따라 변화될 수 있으며 이러한 점에서 Bayesian 방법은 강우-유출 관계에서 발생하는 이러한 불확실성을 매개변수의 불확실성으로 인지함으로서 우리가 예상치 못한 유출 사상에 대한 형태를 고려할 수 있는 장점이 있다. 따라서 댐 설계와 같은 대규모 수공 구조물 설계 시에 이러한 불확실성이 접목된 강우-유출 분석이 이루어진다면 보다 합리적인 방법으로 홍수 위험도 분석이 가능하며 더욱이 댐 규모 결정에 있어서 신뢰성 있는 의사 결정 수단을 제공할 수 있을 것으로 사료된다.