Predicting remaining useful life (RUL) becomes significant to implement prognostics and health management of industrial systems. The relevant studies have contributed to creating RUL prediction models and validating their acceptable performance; however, they are confined to drive reasonable preventive maintenance strategies derived from and connected with such predictive models. This paper proposes a data-driven preventive maintenance method that predicts RUL of industrial systems and determines the optimal replacement time intervals to lead to cost minimization in preventive maintenance. The proposed method comprises: (1) generating RUL prediction models through learning historical process data by using machine learning techniques including random forest and extreme gradient boosting, and (2) applying the system failure time derived from the RUL prediction models to the Weibull distribution-based minimum-repair block replacement model for finding the cost-optimal block replacement time. The paper includes a case study to demonstrate the feasibility of the proposed method using an open dataset, wherein sensor data are generated and recorded from turbofan engine systems.

When considering military operations that require rapid response time, forward supply operation of various type of ammunition is essential. Also, t is necessary to supply ammunition in a timely manner before an ammunition shortage situation occurs. In this study, we propose a mathematical model for allocation of ammunition to ammunition storehouse at the Ammunition Supply Post (ASP). The model has several objectives. First, it ensures that the frequent used ammunition is stored in a distributed manner at a high workability ammunition storehouses. Second, infrequent used ammunition is required to be stored intensively at a single storehouse as much as possible. Third, capacity of the storehouse and compatible storage restriction required to be obeyed. Lastly, criticality of ammunition should be considered to ensure safety distance. We propose an algorithm to find the pareto-based optimal solution using the mathematical model in a reasonable computation time. The computational results show that the suggested model and algorithm can solve the real operational scale of the allocation problem.

With the increased interest in the quality of life of modern people, the implementation of the five-day working week, the increase in traffic convenience, and the economic and social development, domestic and international travel is becoming commonplace. Furthermore, in the past, there were many cases of purchasing packaged goods of specialized travel agencies. However, as the development of the Internet improved the accessibility of information about the travel area, the tourist is changing the trend to plan the trip such as the choice of the destination. Web services have been introduced to recommend travel destinations and travel routes according to these needs of the customers. Therefore, after reviewing some of the most popular web services today, such as Stubby planner (http://www.stubbyplanner.com) and Earthtory (http://www.earthtory.com), they were supposed to be based on traditional Traveling Salesman Problems (TSPs), and the travel routes recommended by them included some practical limitations. That is, they were not considered important issues in the actual journey, such as the use of various transportation, travel expenses, the number of days, and lodging. Moreover, although to recommend travel destinations, there have been various studies such as using IoT (Internet of Things) technology and the analysis of cyberspatial Big Data on the web and SNS (Social Networking Service), there is little research to support travel routes considering the practical constraints. Therefore, this study proposes a new mathematical model for applying to travel route recommendation service, and it is verified by numerical experiments on travel to Jeju Island and trip to Europe including Germany, France and Czech Republic. It also expects to be able to provide more useful information to tourists in their travel plans through linkage with the services for recommending tourist attractions built in the Internet environment.

본 연구에서는 태풍 나크리에 의한 해운대 해수욕장의 쇄파대 수리특성을 SWASH 모형을 이용하여 분석하였다. 국립해양 조사원에서 제공하는 파랑관측자료를 바탕으로 태풍 나크리 내습 시의 대표파를 선정하였다. 수치모형에서 입사파는 JONSWAP Spectrum에 의한 불규칙파로 선정하였다. SWASH 모형에 의해 산정된 해빈류 패턴은 현지관측자료와 비교하였으며 수치모형에서 산정된 최대소상고는 비디오 모니터링 자료 및 경험식과 비교하였다. 최대소상고의 위치는 비디오 모니터링 자료에 나타난 파흔을 이용하여 유추하였으며 태풍 NAKRI(1412) 내습 시 S 계열의 파랑이 지배적으로 작용하였으며 동백섬측에서 미포측으로 연안류가, 해운대 해수욕장 중앙부근에서 이안류가 발생하였다. SWASH 모형을 이용하여 산정한 최대소상고(1.15 m)는 비디오 모니터링 자료(1.26 m)와 유사한 경향성을 나타냈으며 Stockdon et al.에 의해 제시된 경험식(1.33m)과 비교적 유사하게 나타났다.

본 연구에서는 연승 수하식 양식시설의 파랑 중 해조류의 꼬임 현상에 영향을 미치는 연승줄의 간격, 계류 블록의 이동에 따른 양식장 형상변화 등을 수리모형실험을 통해 검토하고자 하였다. 실험결과로부터 해조류의 꼬임현상은 연승간격이 감소함에 따라 낮은 파고에서도 발생하였으며, 연승줄 간격이 클수록 보다 큰 파랑조건에서도 해조류 꼬임현상이 발생하지 않는 안정한 상태를 유지하였다. 이는 해조류의 꼬임 현상은 연승간격에 민감하게 영향을 받음을 나타낸다. 또한 블록 중량 및 주기가 길어짐에 따라 전체적으로 계류블록이 이동하기 시작한 임계파고가 커지는 경향을 나타내었다. 입사파랑 조건과 계류 블록 중량을 달리한 실험결과로부터 전면 블록 중량이 3.0 ton에서 8.0 ton으로 증가함에 따라 안정성이 크게 확보되는 결과를, 반대로 후면에서는 블록의 거동이 거의 발생하지 않았다. 이때 전면 계류삭의 최대 장력은 약 3.0 ton/m을 나타내었다.

In this paper, a mathematical model is developed for finding a degree of influence of risks affecting the project. At risk identification step, all risks are structured in forms of block diagrams after risks of project activities are identified. At risk analysis step, reliability functions are derived based on block diagrams of risks. Reliability function represents the probability of the project success. Based on the derived reliability functions, risks importance is defined as a criterion for expressing a degree of influence of each risk affecting the success of the project, which is. Risks importance includes the structural importance of risk, the probability of risk, and the impact of risk. It could be applied to determine the priorities of risks which should be controlled, and the use of such priorities could guarantee successful project activities. Finally, examples of the proposed mathematical models will be given.

It is a significant issue for several country including Korea, where the natural and the weather conditions are severe, to keep the safety against disasters which occur frequently every year, especially in urban region crowded with population. In order to implement suitable and effective measures against various disasters in such area, development of method for evaluation of disaster prevention performance based on various disaster risks and effective disaster damage mitigation technologies is independable. In this paper, methods for hazard evaluation, vulnerability evaluation and loss evaluation, and damage technologies are proposed targetting man-made disaster and natural one like flood, earthquake and tsunami and so on. The method proposed in this paper is based on the research of USA and Japan for man-made disaster and natural disaster. The proposed method will be developed in detail in four years during research period funded by government.

본 논문에서는 시스템 연령(年齡)에 의해 보전 활동의 효과를 설명하는 일반 수리(修理) 개념을 이용한 최적 보전(保全) 정책에 대한 연구를 수행하였다. 본 논문에서는 주기적인 일반 수리와 고장 시 최소 수리가 적용되는 최적 보전 정책을 고려하였다. 따라서 일반 수리에 따른 보전 정책의 비용 함수를 도출하였고 최적 보전 정책을 도출하는 알고리즘을 제시하였고 예제를 통해 알고리즘의 성능을 분석하였다. 이 연구를 통해 시스템을 운영하는데 있어서 어느 수준의

This paper is intended to develop a Bayesian decision model for the repair of deteriorating system. A non-homogeneous Poisson process with a power law failure intensity function is used to describe the behavior of the deteriorating repairable system. The decision on whether to have minimal repair or imperfect repair should be made on the occurrence of a failure. However, it is difficult to make a reasonable decision due to many uncertainties intrinsic in repair actions. In this paper, prior distributions are used in order to analyze the uncertainties embedded in the decision alternatives. Especially, a prior distribution for imperfect repair with probabilistic reduction in the failure intensity is proposed. In addition, mathematical expressions to calculate the expected prior loss of each repair alternative are proposed.

This paper presents the application of integrated mathematical programming approach for the design of cellular manufacturing. The proposed approach is carried out in two phases: The first phase concerning exceptional elements(EEs) in cell formation and th

This paper is intended to develop a Bayesian decision model for the repair of deteriorating system. A non-homogeneous Poisson process with a power law failure intensity function is used to describe the behavior of the deteriorating repairable system. The decision on whether to have minimal repair or imperfect repair should be made on the occurrence of a failure. However, it is difficult to make a reasonable decision due to many uncertainties intrinsic in repair actions. In this paper, prior distributions are used in order to analyze the uncertainties embedded in the decision alternatives. Especially, a prior distribution for imperfect repair with probabilistic reduction in the failure intensity is proposed. In addition, mathematical expressions to calculate the expected prior loss of each repair alternative are proposed.

This paper presents the application of integrated mathematical programming approach for the design of cellular manufacturing. The cores of the proposed approach are two phases; concurrently a dealing with exceptional elements(EEs) and cell formation and facilities layout design. A policy dealing with EEs considers minimizing the total costs of three important costs ; (1) intercellular transfer (2) machine duplication and (3) subcontracting. And important issue is the calculation of the number of machines needed by considering the maximum utilization of machines and the available capacity of a machine that can be transferred for intercell moving is an key. Facilities layout design is considered to reflect the real field data such as the operation sequence of the parts to be manufactured. quadratic. The model is formulated as mixed integer programming that is presented to find the optimal solution.

This paper is to develop replacement models under minimal repair with exponential polynomial wavelet failure rate function. Wavelets have good time-frequency localization, fast algorithms and parsimonious representation. Also this study is presented along with numerical examples using sensitivity analysis for exponential polynomial trigonometric failure rate function.

본연구는 시설내 다양한 환경조건하에서 오이의 호흡속도에 관한 수리적 모형을 개발하고자 실시하였다. 개개 오이 식물에 대한 총광합성속도의 8.55%가 호흡에 사용되었다. 생장호흡계수는 0.0935로 추정되었고 유지호흡속도는 24℃ 온도에서 0.00158g CH2O.g-1.h-1로 추정되었다. 그리고 그것은 온도상승에 따라 지수적으로 증가하였다. 호흡속도는 저장 탄수화물량이 낮아짐에 따라 비례적으로 감소했고 뿌리의 이온흡수호흡속도는 0.6648g CH2O.(gN)-1로 추정되었다.

시설 오이재배에서 조절가능한 환경요인들, 즉 광도, CO2 농도, 온도 그리고 엽중 질소 농도의 변화에 따른 양액재배 오이 엽의 총광합성 속도를 측정하였다. 광보상점은 10~20μmol.m-2 .s-1 정도로 낮았고 광포화점은 1000μmol.m-2 .s-1 이상이었으며, 오이의 총광합성 속도는 온도가 상승할수록 증가속도는 감소하지만 지속적인 증가를 보였으나 24~32℃ 사이에서 광합성 속도는 큰 차이를 보이지 않아 이 범위가 오이 생육에 대한 적정온도인 것으로 나타났다. CO2 보상점은 20-40μmol.mol-1 사이에 위치하였고 CO2포화점은 1200μmol.mol-1이상으로 나타났으며 엽중은 질소함량의 증가에 따른 잎의 총광합성 속도의 변화는 sigmoid형의 증가추세를 보였다. 요인들간의 상호작용 효과에서는 모든 경우 상승적으로 나타나, 한 요인의 수준이 증가함에 따라 타 요인의 수준의 증가에 따른 총광합성 속도도 상승적을 증가하였다. 각환경요인의 변화와 요인들간의 상호작용에 따른 총광합성 속도의 변화에 대한 수리적 모형을 개발하였다. 이들 모형은 시설 내 환경변이에 따른 오이의 생육 내지는 수량에서의 차이를 밝히는데 이용될 수 있으며 오이의 식물생장 모형이나 더 나아가 경영합리화를 위한 오이 생산 전문가 시스템의 개발에 필요한 기초 자료로 이용될 수 있을 것이다.