• 대한예방한의학회지
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• 제23권1호
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• pp.1-13
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• 2019

Backgrounds : The market of Complementary Alternative Medicine(CAM) in the United State(U.S.) accounts for a large proportion of the global CAM market and has a high growth rate. The recent introduction of Obama Care has brought the change in the health insurance system for CAM, and we need to analyze it for its implication to Korean system. Objectives : The purpose of this study is to investigate the current status of acupuncture and chiropractic health insurance in the U.S., and to draw implications for expanding the health insurance coverage for Korean traditional medicine through the comparison between the U.S. and Korean health insurance systems. Methods : We examined the data through the literature search and from the websites of both U.S. government departments and related organizations for the health insurance policy. Based on the collected data, we analyzed its CAM health insurance system in Korea. Results : The acupuncture covered by public health insurance in the U.S. has a limit in the number of treatments and a range of applied diseases compared with Korea. In addition, the practice of acupuncture is not subdivided. However, the chiropractic in the U.S. which also has a limited number of coverage and only three categories of practices are similar to that of Korea. Conclusions : Although the use of CAM by public health insurance is not active in the U.S., but the organizations such as Veterans Health Administration in Vermont is already discussing the use of acupuncture to solve the problem of opioid overuse. Thus Korea also needs to discuss to promote the expansion of the insurance system for CAM.

• 응용통계연구
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• 제20권3호
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• pp.541-550
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• 2007

시계열자료를 분석하는데 있어 중요한 목적 중에 하나가 미래값에 대한 예측이다. 일반적으로 자기회귀이동평균모형에서는 백색잡음이 정규분포를 따른다는 가정 하에서 모수의 추론과 예측 및 예측구간의 추정이 이루어지고 있다. 그러나 자료가 이러한 가정을 만족하지 않는 경우, 자료를 가정에 맞게 변환시킨 후 분석하는 방법을 생각해 볼 수 있다. 이 논문에서는 변환된 자료를 분석하여 얻은 결과를 이용하여 본래의 척도에서의 미래값에 대한 예측구간을 추정하는 문제에 대해 알아본다. 제안하는 방법에서는 먼저 적절한 변환을 이용하여 자료를 정규가정을 만족하도록 변환시키고 변환된 자료를 이용하여 미래값에 대한 예측구간을 추정한 후, 역변환을 이용하여 예측구간을 추정한다. 이 논문에서는 시계열분석에서 모델링이 상대적으로 어려운 왜도의 문제를 해결하기 위해 Yeo-Johnson 변환을 중심으로 한 방법론을 소개한다. 모의실험 결과 제안된 방법에 의한 단측예측구간의 포함확률이 변환을 사용하지 않은 구간보다 명목수준에 가까운 것을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1260-1283
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• 2019

Low earth orbit (LEO) satellite broadband network is a crucial part of the space information network. LEO satellite constellation design is a top-level design, which plays a decisive role in the overall performance of the LEO satellite network. However, the existing works on constellation design mainly focus on the coverage criterion and rarely take network performance into the design process. In this article, we develop a unified framework for constellation optimization design in LEO satellite broadband networks. Several design criteria including network performance and coverage capability are combined into the design process. Firstly, the quality of service (QoS) metrics is presented to evaluate the performance of the LEO satellite broadband network. Also, we propose a network stability model for the rapid change of the satellite network topology. Besides, a mathematical model of constellation optimization design is formulated by considering the network cost-efficiency and stability. Then, an optimization algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) is provided for the problem of constellation design. Finally, the proposed method is further evaluated through numerical simulations. Simulation results validate the proposed method and show that it is an efficient and effective approach for solving the problem of constellation design in LEO satellite broadband networks.

• International Journal of Contents
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• 제10권3호
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• pp.41-46
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• 2014

A cause-effect graph considers only the desired external behavior of a system by identifying input-output parameter relationships in the specification. When testing a software system with cause-effect graphs, it is important to derive a moderate number of tests while avoiding loss in fault detection ability. Pairwise testing is known to be effective in determining errors while considering only a small portion of the input space. In this paper, we present a new testing technique that generates pairwise tests from a cause-effect graph. We use a Boolean Satisbiability (SAT) solver to generate pairwise tests from a cause-effect graph. The Alloy language is used for encoding the cause-effect graphs and its SAT solver is applied to generate the pairwise tests. Using a SAT solver allows us to effectively manage constraints over the input parameters and facilitates the generation of pairwise tests, even in the situations where other techniques fail to satisfy full pairwise coverage.

• Management Science and Financial Engineering
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• 제15권2호
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• pp.101-118
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• 2009

In quantitative contexts, data mining is widely applied to the prediction of stock prices from financial time-series. However, few studies have examined the potential of data mining for shedding light on the qualitative problem-solving knowledge of experts who make stock price predictions. This paper presents a GA-based data mining approach to characterizing the qualitative knowledge of such experts, based on their observed predictions. This study is the first of its kind in the GA literature. The results indicate that this approach generates rules with higher accuracy and greater coverage than inductive learning methods or neural networks. They also indicate considerable agreement between the GA method and expert problem-solving approaches. Therefore, the proposed method offers a suitable tool for eliciting and representing expert decision rules, and thus constitutes an effective means of predicting the stock price index.

• 한국국방경영분석학회지
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• 제24권2호
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• pp.146-161
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• 1998

This article develops model of the nonstationary state behavior of the multiechelon spare parts provisioning systems. This study is concerned with a problem of determining the near optimal requirements level of the spare parts, especially Concurrent Spare Parts(CSP). CSP is supplied with the procurement of new equipment system, and is used to sustain the equipment without resupply during the initial coverage period. We consider this situation as a multiechelon inventory model with several bases and one depot. And we assume an equipment system which consists of many types of parts would grounded if one of the parts fail. Also this multiechelon CSP problem is considering the nonstationary poisson failure process and nonstationary exponential repair process in a dynamic environment. We develop an efficient computational procedure to find the near optimal number of spare parts minimizing the total expected cost, while achieving the required system availability. Finally we present a simple example of suggested method.

• Communications for Statistical Applications and Methods
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• 제21권2호
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• pp.193-200
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• 2014

It was recognized by some researchers that the disturbance variance in a censored regression model is frequently underestimated by the maximum likelihood method. This underestimation has implications for the estimation of marginal effects and asymptotic standard errors. For instance, the actual coverage probability of the confidence interval based on a maximum likelihood estimate can be significantly smaller than the nominal confidence level; consequently, a Bayesian estimation is considered to overcome this difficulty. The behaviors of the maximum likelihood and Bayesian estimators of disturbance variance are examined in a fixed effects panel regression model with a limited dependent variable, which is known to have the incidental parameter problem. Behavior under random effect assumption is also investigated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권11호
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• pp.4453-4468
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• 2015

This paper investigates optimization-based base station (BS) placement. An optimization model is defined and the BS placement problem is transformed to a lexicographical stratified programming (LSP) model for a given trajectory, according to different accuracy requirements. The feasible region for BS deployment is obtained from the positioning system requirement, which is also solved with signal coverage problem in BS placement. The LSP mathematical model is formulated with the average geometric dilution of precision (GDOP) as the criterion. To achieve an optimization solution, a tolerant factor based complete stratified series approach and grid searching method are utilized to obtain the possible optimal BS placement. Because of the LSP model utilization, the proposed algorithm has wider application scenarios with different accuracy requirements over different trajectory segments. Simulation results demonstrate that the proposed algorithm has better BS placement result than existing approaches for a given trajectory.

• Communications for Statistical Applications and Methods
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• 제14권3호
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• pp.633-640
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• 2007

We develop noninformative priors for the ratio of the lognormal means in equal variances case. The Jeffreys' prior and reference priors are derived. We find a first order matching prior and a second order matching prior. It turns out that Jeffreys' prior and all of the reference priors are first order matching priors and in particular, one-at-a-time reference prior is a second order matching prior. One-at-a-time reference prior meets very well the target coverage probabilities. We consider the bioequivalence problem. We calculate the posterior probabilities of the hypotheses and Bayes factors under Jeffreys' prior, reference prior and matching prior using a real-life example.

• 산업경영시스템학회지
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• 제21권47호
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• pp.87-99
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• 1998

This study is concerned with a problem of determining the optimal requirements level of the spare parts, especially Concurrent Spare Parts(CSP). CSP is supplied with the procurement of new equipment system, and is used to sustain the equipment without resupply during the initial coverage period. We consider this situation as a multiechelon inventory model with several bases and one depot. And we assume a equipment system which consists of many types of parts would grounded if one of the parts fail. Also this multiechelon CSP problem is considering a time-varing (dynamic) environment. We develop a computational procedure to find the optimal number of spare parts minimizing the total expected cost, while achieving the required system availability. Finally we present a simple example of suggested method.