• International Journal of Automotive Technology
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• v.7 no.6
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• pp.749-756
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• 2006

This paper presents a complex stochastic wheelbase preview control method of a motorcycle suspension based on hierarchical modeling method. As usual, a vehicle suspension system is controlled as a whole body. In this method, a motorcycle suspension with five Degrees of Freedom(DOF) is dealt with two local independent 2-DOF suspensions according to the hierarchical modeling method. The central dynamic equations that harmonize local relations are deduced. The vertical and pitch accelerations of the suspension center are treated as center control objects, and two local semi-active control forces can be obtained. In example, a real time Linear Quadratic Gaussian(LQG) algorithm is adopted for the front suspension and the combination of the wheelbase preview and LQG control method is designed for the rear suspension. The results of simulation show that the control strategy has less calculating time and is convenient to adopt different control strategies for front and rear suspensions. The method proposed in this paper provides a new way for the vibration control of multi-wheel vehicles.

• Steel and Composite Structures
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• v.19 no.6
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• pp.1369-1379
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• 2015

In this study, a progressive damage modeling is developed to predict functional failure pressure of GRP pipes subjected to internal hydrostatic pressure. The modeling procedure predicts both first-ply failure pressure and functional failure pressure associated with the weepage phenomenon. The modeling procedure is validated using experimental observations. The random parameters attributed to the filament winding production process are identified. Consequently, stochastic simulation is conducted to investigate the influence of induced inconsistencies on the functional failure pressures of GRP pipes. The obtained results are compared to realize the degree to which random parameters affect the performance of the pipe in operation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.852-856
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• 2015

Demand side management (DSM) program has been frequently used for reducing the system peak load because it gives utilities and independent system operator (ISO) a convenient way to control and change amount of electric usage of end-use customer. Planning and operating methods are needed to efficiently manage a DSM program. This paper presents a planning method for DSM program. A planning method for DSM program should include an electric load forecasting, because this is the most important factor in determining how much to reduce electric load. In this paper, load forecasting with the temperature stochastic modeling and the sensitivity to temperature of the electric load is used for improving load forecasting accuracy. The proposed planning method can also estimate the required day, hour and total capacity of DSM program using Monte-Carlo simulation. The results of case studies are presented to show the effectiveness of the proposed planning method.

• International Journal of Contents
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• v.6 no.4
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• pp.30-34
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• 2010

Effective job scheduling scheme is a crucial part of complex heterogeneous distributed systems. Gang scheduling is a scheduling algorithm for grid systems that schedules related grid jobs to run simultaneously on servers in different local sites. In this paper, we address grid jobs (gangs) schedule modeling using Stochastic reward nets (SRNs), which is concerned for static and dynamic scheduling policies. SRN is an extension of Stochastic Petri Net (SPN) and provides compact modeling facilities for system analysis. Threshold queue is adopted to smooth the variations of performance measures. System throughput and response time are compared and analyzed by giving reward measures in SRNs.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.395-398
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• 2011

This paper introduces stochastic cash-flow modeling integrated with simulation based scheduling. The system makes use of CPM schedule data exported from commercial scheduling software, computes the best fit probability distribution functions (PDFs) of historical activity durations, assigns the PDFs identified to respective activities, simulates the schedule network, computes the deterministic and stochastic project cash-flows, plots the corresponding cash flow diagrams, and estimates the best fit PDFs of overdraft and net profit of a project. It analyzes the effect of different distributions of activity durations on the distribution of overdrafts and net profits, and improves reliability compared to deterministic cash flow analysis.

• Management Science and Financial Engineering
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• v.19 no.1
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• pp.37-41
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• 2013

This study presents an application of stochastic model for limit order book (LOB) dynamics to Korean Stock Index Futures (KOSPI 200 Futures). Since KOSPI 200 futures market is widely known as one of the most liquid markets in the world, direct application of an existing model is hardly possible. Therefore, we modified an existing model to successfully model and predict the dynamics of extremely liquid KOSPI 200 futures market.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.36-41
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• 2005

페트리 네트(Petri Nets)는 이산 사건 시스템을 모델링할 수 있는 그래픽하고, 수학적인 도구이다. 본 연구는 유연제조 시스템을 확률적인 페트리 네트(Stochastic Petri Nets)중의 하나인 임베디드 마코프 체인(Embeded Markov Chain)에 도입하고, 임베디드 마코프 체인의 방법 중에 하나인 일반화된 확률적 페트리 네트(Generalized Stochastic Perti Nets)에 적용시켰다. 그리고 결과치의 정확성을 알아내기 위하여, 페트리 네트 시뮬레이션과 아레나를 사용하여 실행하였다.

• Journal of the Korean Society of Hazard Mitigation
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• v.1 no.2 s.2
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• pp.115-121
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• 2001

The purpose of the synthetic generation of monthly river flows based on the short term observed data by means of stochastic models is to provide abundant input data to the water resources systems of which the system performance and operation policy are to be determined beforehand. In this study, a multivariate autoregressive model has been applied to generate monthly flows of the multi sites considering the correlations between each site. The model performance was examined using statistical comparisons between the historical and generated monthly series such as mean, variance, skewness and correlation coefficients. The results of this study showed that the modeled generated flows were statistically similar to the historical flows.

• ETRI Journal
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• v.35 no.5
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• pp.849-858
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• 2013

In this paper, we propose a new adaptive single model to track a maneuvering target with abrupt accelerations. We utilize the stochastic differential equation to model acceleration of a maneuvering target with stochastic volatility (SV). We assume the generalized autoregressive conditional heteroscedasticity (GARCH) process as the model for the tracking procedure of the SV. In the proposed scheme, to track a high maneuvering target, we modify the Kalman filtering by introducing a new GARCH model for estimating SV. The proposed tracking algorithm operates in both the non-maneuvering and maneuvering modes, and, unlike the traditional decision-based model, the maneuver detection procedure is eliminated. Furthermore, we stress that the improved performance using the GARCH acceleration model is due to properties inherent in GARCH modeling itself that comply with maneuvering target trajectory. Moreover, the computational complexity of this model is more efficient than that of traditional methods. Finally, the effectiveness and capabilities of our proposed strategy are demonstrated and validated through Monte Carlo simulation studies.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1309-1317
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• 2012

Stochastic weather generators are commonly used to simulate time series of daily weather, especially precipitation amount. Recently, a generalized linear model (GLM) has been proposed as a convenient approach to fitting these weather generators. In this paper, a stochastic weather generator is considered to model the time series of daily precipitation at Seoul in South Korea. As a covariate, global temperature is introduced to relate long-term temporal scale predictor to short-term temporal predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate time series of seasonal total precipitation in the GLM weather generator as covariates. It is veri ed that the addition of these covariates does not distort the performance of the weather generator in other respects.