• Advances in aircraft and spacecraft science
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• 제7권5호
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• pp.405-423
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• 2020

Non-linear energy sink (NES) is an emerging passive absorber able to mitigate the dynamic response of structures without any external energy supply, resonating with all the modes of the primary structure to control. However, its inherent non-linearities hinder its large-scale use and leads to complicated design procedures. For this purpose, an approximate design approach is herein proposed in a stochastic framework. Since loads are random in nature, the stochastic analysis of non-linear systems may be performed by means of computational intensive techniques such as Monte Carlo simulations (MCS). Alternatively, the Stochastic Linearisation (SL) technique has proven to be an effective tool to investigate the performance of different passive control systems under random loads. Since controlled systems are generally non-classically damped and most of SL algorithms operate recursively, the computational burden required is still large for those problems that make intensive use of SL technique, as optimal design procedures. Herein, a procedure to speed up the Stochastic Linearisation technique is proposed by avoiding or strongly reducing numerical evaluations of response statistics. The ability of the proposed procedure to effectively reduce the computational effort and to reliably design the NES is showed through an application on a well-known case study related to the vibrations mitigation of an aircraft wing.

• 한국안전학회지
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• 제28권5호
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• pp.1-4
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• 2013

The main objective was to design and develop a prototype robot system for assessing slip resistance. The developed robot system will be able to be used for stochastic nature of friction in the whole workplace. The second objective was to evaluate its operating condition in the laboratory, using a dreg sled type slipmeter(BOT-3000) as reference device. It was found that COF(Coefficient of Friction) measured with robot system was similar to that of BOT-3000 when sliding velocity was reached at 0.2m/s. The robot system might be the more promising one than any traditional measurement devices. A further evolution of prototype devices, as well as the development of test methods for that's various applications, is to be started in forthcoming studies.

• 동력기계공학회지
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• 제14권6호
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• pp.61-66
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• 2010

Engineering asset management (EAM) requires the accurate assessment of current and the prediction of future asset health condition. Suitable mathematical models that are capable of predicting time-to-failure and the probability of failure in future time are essential. In general reliability models, lifetime of component and system is estimated using failure time data. This paper deals with the reliability assessment of elevators using life of main components. Especially this work is concerned with the stochastic nature of life of elevator components. First, we investigate the Weibull statistical analysis of lifetime data for the components. The final goal is to establish the mathematical model for reliability assessment. This work provides more perspectives to future research in the fields of reliability and maintainability.

• Communications for Statistical Applications and Methods
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• 제9권2호
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• pp.367-379
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• 2002

We introduce a new concept of $\theta$ conditionally independent and positive and negative dependence of bivariate stochastic processes and their corresponding hitting times. We have further extended this theory to stronger conditions of dependence similar to those in the literature of positive and negative dependence and developed theorems which relate these conditions. Finally we are given some examples to illustrate these concepts.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.437-437
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• 2022

The prediction of dam inflow rate is crucial for the management of the largest multi-purpose dam in South Korea, the Soyang Dam. The main issue associated with the management of water resources is the stochastic nature of the reservoir inflow leading to an increase in uncertainty associated with the inflow prediction. The Autoregressive (AR) model is commonly used to provide the simulation and forecast of hydrometeorological data. However, because its estimation is based solely on the time-series data, it has the disadvantage of being unable to account for external variables such as climate information. This study proposes the use of the Autoregressive Exogenous Stochastic Volatility (ARXSV) model within a Bayesian modeling framework for increased predictability of the monthly dam inflow by addressing the exogenous and stochastic factors. This study analyzes 45 years of hydrological input data of the Soyang Dam from the year 1974 to 2019. The result of this study will be beneficial to strengthen the potential use of data-driven models for accurate inflow predictions and better reservoir management.

• 대한산업공학회지
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• 제15권2호
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• pp.33-44
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• 1989

This paper presents stochastic-statistical dominance rules which eliminate dominated alternatives thereby reduce the number of satisficing alternatives to a manageable size so that the decision maker can choose the best alternative among them when neither the utility function nor the probability distribution of outcomes is exactly known. Specifically, it is assumed that only the characteristics of the utility function and the value function are known. Also, it is assumed that prior probabilities of the mutually exclusive states of nature are not known, but their relative bounds are known. First, the notion of relative risk aversion is used to describe the decision maker's attitude toward risk, which is defined with the acknowledgement that the utility function of the decision maker is a composite function of a cardinal value function and a utility function with-respect to the value function. Then, stochastic-statistical dominance rules are developed to screen out dominated alternatives according to the decision maker's attitude toward risk represented in the form of the measure of relative risk aversion.

• 한국정밀공학회지
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• 제2권3호
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• pp.70-76
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• 1985

A four-bar mechanism in consideration of the tolerances on link lengths and the clearances in joints is optimally designed by the method of stochastic analysis. The random nature of clearances and tolerances establishes a stochastic optimization design equation in which the parameters in the equation are described by random variables. In order to solve the design equation, the stochastic problem is converted into an equivalent deterministic one. The synthesis of four-bar mechanism for minimum mechanical and structural errors is carried out by the optimization techni- ques using Chebyshev spacing of precision points. By the results from the synthesized mechanism, the generated and desired motions are examined.

• 대한기계학회논문집A
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• 제20권7호
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• pp.2141-2147
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• 1996

The strength of material is scattered owing to the inhomogenity of microstructure, in spite of the same material. Therefore, in order to design the mechanical structure with the reliability engineering, it is important to grasp the statistical nature of material strength. In this paper, effects of grain sezes for the statistical nature of the fatigue crack growth was discussed. And the statistical nature of mechanical properties was compared with the statistical nature of the fatigue crack growth rate.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2006년도 춘계학술대회
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• pp.425-428
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• 2006

Natural gas hydrate has been a major problem for its plugging nature in the pipeline. With the demand of deep-water production, the importance of flow assurance technology, preventing hydrate, asphaltene and wax in the pipeline becomes bigger Kinetic models combined with the flow simulator are being developed to explain the nature of hydrate plug formation in the pipeline. To simulate the hydrate plug formation, each stage including the nucleation, growth and agglomeration should be considered. The hydrate nucleation is known to be stochastic and is believed hard to be predicted. Recent publications showed hydrate growth and agglomeration can be observed rigorously using a particle size analyzer. However properties of the hydrate should be investigated to model the growth and agglomeration. The attractive force between hydrate particles, supposed to be the capillary force, was revealed to be stochastic. Alternative way to model the hydrate agglomeration is to simulate by the discrete element method. Those parameters, particle size distribution, attractive force, and growth rate are embedded into the kinetic model which is combined Into the flow simulator. When compared with the flowloop experimental data, hydrate kinetic model combined into a flow simulator showed good results. With the early results, the hydrate kinetic model is promising but needs more efforts to improve it.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1994년도 추계학술발표회 및 정기총회
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• pp.1-2
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• 1994

With the prevalence of computers in modern organizations, simulation is receiving more atention as an effectvie decision -making tool. Simualtion is a computer-based numerical technique which uses mathmatical and logical models to approximate the behaviror of a real-world system. However, iptimization of synamic stochastic systems often defy analytical and algorithmic soluions. Although a simulation approach is often free fo the liminting assumption s of mathematical modeling, cost and time consiceration s make simulation the henayst's last resort. Therefore, whenever possible, analytical and algorithmica solutions are favored over simulation. This paper discussed the issues and procedrues for using simulation as a tool for optimization of stochastic complex systems that are dmodeled by computer simulation . Its emphasis is mostly on issues that are speicific to simulation optimization instead of consentrating on the general optimizationand mathematical programming techniques . A simulation optimization problem is an optimization problem where the objective function. constraints, or both are response that can only be evauated by computer simulation. As such, these functions are only implicit functions of decision parameters of the system, and often stochastic in nature as well. Most of optimization techniqes can be classified as single or multiple-resoneses techniques . The optimization of single response functins has been researched extensively and consists of many techniques. In the single response category, these strategies are gradient based search techniques, stochastic approximate techniques, response surface techniques, and heuristic search techniques. In the multiple response categroy, there are basically five distinct strategies for treating the responses and finding the optimum solution. These strategies are graphica techniqes, direct search techniques, constrained optimization techniques, unconstrained optimization techniques, and goal programming techniques. The choice of theprocedreu to employ in simulation optimization depends on the analyst and the problem to be solved. For many practival and industrial optimization problems where some or all of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computersimulation is one of the most effective means of studying such complex systems. In this paper, after discussion of simulation optmization techniques, the applications of above techniques will be presented in the modeling process of many flexible manufacturing systems.