• The Journal of the Acoustical Society of Korea
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• v.22 no.8
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• pp.711-716
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• 2003

This paper proposes the method to select the number of optimal mixtures in each state in Continuous Density HMM (Hidden Markov Models), Previously, researchers used the same number of mixture components in each state of HMM regardless spectral characteristic of speaker, To model each speaker as accurately as possible, we propose to use a different number of mixture components for each state, Selection of mixture components considered the probability value of mixture by each state that affects much parameter estimation of continuous density HMM, Also, we use PCA (principal component analysis) to reduce the correlation and obtain the system' stability when it is reduced the number of mixture components, We experiment it when the proposed method used average 10% small mixture components than the conventional HMM, When experiment result is only applied selection of mixture components, the proposed method could get the similar performance, When we used principal component analysis, the feature vector of the 16 order could get the performance decrease of average 0,35% and the 25 order performance improvement of average 0.65%.

• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.354-362
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• 2010

This paper proposes a new method for a hierarchical analysis of human gait by dividing the motion into gait direction and gait posture using the tool of dynamic Bayesian network. Based on Factorial HMM (FHMM), which is a type of DBN, we design the Gait Motion Decoder (GMD) in a circular architecture of state space, which fits nicely to human walking behavior. Most previous studies focused on human identification and were limited in certain viewing angles and forwent modeling of the walking action. But this work makes an explicit and separate modeling of pedestrian pose and posture to recognize gait direction and detect orientation change. Experimental results showed 96.5% in pose identification. The work is among the first efforts to analyze gait motions into gait pose and gait posture, and it could be applied to a broad class of human activities in a number of situations.

• Journal of the Korean Society for Railway
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• v.12 no.6
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• pp.1043-1048
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• 2009

PES (Programmable Electronic System) is used by software development for the train control system. PES has been widely used in real world and consists of hardware, firmware and application software. The PES are easily apply to many applications because its implementation has high flexibility. Many safety critical functions are realized through software in safety critical system. Normally, it is difficult to detect failures for PES system because the PES is too sophisticated to identify sources of the failure. So, the reliability analysis is needed by using software fault tolerance techniques. Currently, there are the recovery block, distributed recovery block, N-version programming, N self-checking programming in fault tolerance techniques. In this paper, the models of recovery block and N-version programming in software fault tolerance techniques are suggested by using the Markov model. Also, the reliability in the train control system is analyzed through changing time. The fault occupancy rates of the program, adjustment test and voter are stationary. So, the relation between time and reliability is presented by using Matlab program. In the result of reliability, the reliability of recovery block is more high than N-version programming in case of the same number of substitution block.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1121-1124
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• 2008

저수분석(low flow analysis)은 수자원공학에서 중요한 분야 중 하나이며, 특히 저수량 빈도분석(low flow frequency analysis)의 결과는 저수(貯水)용량의 설계, 물 수급계획, 오염원의 배치 및 관개와 생태계의 보존을 위한 수량과 수질의 관리에 중요하게 사용된다. 그러므로 본 연구에서는 저수량 빈도분석을 위한 점빈도분석을 수행하였으며, 특히 빈도분석에 있어서의 불확실성을 탐색하기 위하여 Bayesian 방법을 적용하고 그 결과를 기존에 사용되던 불확실성 탐색방법과 비교하였다. 본 논문의 I편에서는 Bayesian 방법 중 사전분포(prior distribution)와 우도함수(likelihood function)의 복잡성에 상관없이 계산이 가능한 Bayesian MCMC(Bayesian Markov Chain Monte Carlo) 방법과 Metropolis-Hastings 알고리즘을 사용하기 위한 여러과정의 이론적 배경과 Bayesian 방법에서 가장 중요한 요소인 사전분포를 구축하고 이를 비교 및 평가하였다. 고려된 사전분포는 자료에 기반하지 않은 사전분포와 자료에 기반한 사전분포로써 두 사전분포를 이용하여 Metropolis-Hastings 알고리즘을 수행하고 그 결과를 비교하여 저수량 빈도분석에 합리적인 사전분포를 선정하였다. 또한 알고리즘의 수행과정에서 필요한 제안분포(proposal distribution)를 적용하여 그에 따른 알고리즘의 효율성을 채택률(acceptance rate)을 산정하여 검증해 보았다. 사전분포의 분석 결과, 자료에 기반한 사전분포가 자료에 기반하지 않은 사전분포보다 정확성 및 불확실성의 표현에 있어서 우수한 결과를 제시하는 것을 확인할 수 있었고, 채택률을 이용한 알고리즘의 효용성 역시 기존 연구자들이 제시하였던 만족스러운 범위를 가지는 것을 알 수 있었다. 최종적으로 선정된 사전분포는 본 연구의 II편에서 Bayesian MCMC 방법의 사전분포로 이용되었으며, 그 결과를 기존 불확실성의 추정방법의 하나인 2차 근사식을 이용한 최우추정(maximum likelihood estimation)방법의 결과와 비교하였다.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.677-693
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• 2011

In this paper, we consider Bayesian approaches to zero inflated Poisson model, one of the popular models to analyze zero inflated count data. To generate posterior samples, we deal with a Markov Chain Monte Carlo method using a Gibbs sampler and an exact sampling method using an Inverse Bayes Formula(IBF). Posterior sampling algorithms using two methods are compared, and a convergence checking for a Gibbs sampler is discussed, in particular using posterior samples from IBF sampling. Based on these sampling methods, a real data analysis is performed for Trajan data (Marin et al., 1993) and our results are compared with existing Trajan data analysis. We also discuss model selection issues for Trajan data between the Poisson model and zero inflated Poisson model using various criteria. In addition, we complement the previous work by Rodrigues (2003) via further data analysis using a hierarchical Bayesian model.

• Korean Journal of Clinical Pharmacy
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• v.26 no.1
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• pp.59-69
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• 2016

Background: The subcutaneous formulation of biologic disease-modifying antirheumatic drugs (DMARDs) was preferred due to favored self-administration and would be an economical treatment option for patients with rheumatoid arthritis. This study was to compare the economic impact of biologic DMARDs administered by subcutaneous injection in patients with rheumatoid arthritis who had inadequate response to conventional DMARDs. Methods: The cost-minimization analysis was conducted to estimate the lifetime health care costs of treatment sequences with subcutaneous biologic DMARDs as first-line therapy from a health care system perspective. The Markov model was developed to represent the transitions through treatment sequences based on American College of Rheumatology response rate and discontinuation rate. The health care costs comprised the cost of medications, administration, dispensing, outpatient visits, test/diagnostic examination, palliative therapy and treatment of serious infection. All costs were expressed in 2016 Korean Won (KRW) and discounted at 5%. Results: The mean lifetime health care cost per patient was lowest in the etanercept sequence, which was estimated at KRW 63,441,679. The incremental costs of the treatment sequence started with adalimumab, golimumab, abatacept, and tocilizumab were KRW 7,985,730, KRW 4,064,669, KRW 2,869,947, and KRW 4,282,833, respectively, relative to etanercept sequence. These differences in costs mainly were attributable to medication costs. One-way and probabilistic sensitivity analyses confirmed that etanercept represented the option with the lowest cost compared with comparators. Conclusion: This study found that etanercept is likely a cost-saving treatment option among subcutaneous biologic DMARDs in patients with rheumatoid arthritis.

• Smart Media Journal
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• v.1 no.2
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• pp.12-22
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• 2012

Today we are witnessing an increasingly widespread use of cameras in our lives for video surveillance, robot vision, and mobile phones. This has led to a renewed interest in computer vision in general and an on-going boom in human activity recognition in particular. Although not particularly fancy per se, human gait is inarguably the most common and frequent action. Early on this decade there has been a passing interest in human gait recognition, but it soon declined before we came up with a systematic analysis and understanding of walking motion. This paper presents a set of DBN-based models for the analysis of human gait in sequence of increasing complexity and modeling power. The discussion centers around HMM-based statistical methods capable of modeling the variability and incompleteness of input video signals. Finally a novel idea of extending the discrete state Markov chain with a continuous density function is proposed in order to better characterize the gait direction. The proposed modeling framework allows us to recognize pedestrian up to 91.67% and to elegantly decode out two independent gait components of direction and posture through a sequence of experiments.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.561-581
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• 2017

Bayesian statistics can play a key role in the design and analysis of clinical trials and this has been demonstrated for medical device trials. By 1995 Bayesian statistics had been well developed and the revolution in computing powers and Markov chain Monte Carlo development made calculation of posterior distributions within computational reach. The Food and Drug Administration (FDA) initiative of Bayesian statistics in medical device clinical trials, which began almost 20 years ago, is reviewed in detail along with some of the key decisions that were made along the way. Both Bayesian hierarchical modeling using data from previous studies and Bayesian adaptive designs, usually with a non-informative prior, are discussed. The leveraging of prior study data has been accomplished through Bayesian hierarchical modeling. An enormous advantage of Bayesian adaptive designs is achieved when it is accompanied by modeling of the primary endpoint to produce the predictive posterior distribution. Simulations are crucial to providing the operating characteristics of the Bayesian design, especially for a complex adaptive design. The 2010 FDA Bayesian guidance for medical device trials addressed both approaches as well as exchangeability, Type I error, and sample size. Treatment response adaptive randomization using the famous extracorporeal membrane oxygenation example is discussed. An interesting real example of a Bayesian analysis using a failed trial with an interesting subgroup as prior information is presented. The implications of the likelihood principle are considered. A recent exciting area using Bayesian hierarchical modeling has been the pediatric extrapolation using adult data in clinical trials. Historical control information from previous trials is an underused area that lends itself easily to Bayesian methods. The future including recent trends, decision theoretic trials, Bayesian benefit-risk, virtual patients, and the appalling lack of penetration of Bayesian clinical trials in the medical literature are discussed.

• Korean Journal of Clinical Pharmacy
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• v.28 no.2
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• pp.131-137
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• 2018

Background: In South Korea, 22.3% of women ${\geq}50years$ of age and 37% of women ${\geq}70years$ of age visit the doctor to obtain treatment for osteoporosis. According to the analysis of the National Health Insurance Services claim data between 2008 and 2012, the number and incidence of hip and vertebral fractures increased during the same period. Denosumab, a newly marketed medicine in Korea, is the first RANK inhibitor. Methods: A cost-utility analysis was conducted from a societal perspective to prove the superiority of denosumab to alendronate. A Markov cohort model was used to investigate the cost-effectiveness of denosumab. A 6-month cycle length was used in the model, and all patients were individually followed up through the model, from their age at treatment initiation to their time of death or until 100 years of age. The model consisted of eight health states: well; hip fracture; vertebral fracture; wrist fracture; other osteoporotic fracture; post-hip fracture; post-vertebral fracture; and dead. All patients began in the well-health state. In this model, 5% discounted rate, two-year maximum offset time, and persistence were adopted. Results: The total lifetime costs for alendronate and denosumab were USD 5,587 and USD 6,534, respectively. The incremental cost-effectiveness ratio (ICER) for denosumab versus alendronate was USD 20,600/QALY. Given the ICER threshold in Korea, the results indicated that denosumab was remarkably superior to alendronate. Conclusion: Denosumab is a cost-effective alternative to the oral anti-osteoporotic treatment, alendronate, in South Korea.

• Journal of Applied Reliability
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• v.17 no.3
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• pp.213-223
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• 2017

Purpose: The purpose of this study is to arrange the life data analysis literatures based on the Bayesian method quantitatively and provide it as tables. Methods: The Bayesian method produces a more accurate estimates of other traditional methods in a small sample size, and it requires specific algorithm and prior information. Based on these three characteristics of the Bayesian method, the criteria for classifying the literature were taken into account. Results: In many studies, there are comparisons of estimation methods for the Bayesian method and maximum likelihood estimation (MLE), and sample size was greater than 10 and not more than 25. In probability distributions, a variety of distributions were found in addition to the distributions of Weibull commonly used in life data analysis, and MCMC and Lindley's Approximation were used evenly. Finally, Gamma, Uniform, Jeffrey and extension of Jeffrey distributions were evenly used as prior information. Conclusion: To verify the characteristics of the Bayesian method which are more superior to other methods in a smaller sample size, studies in less than 10 samples should be carried out. Also, comparative study is required by various distributions, thereby providing guidelines necessary.