• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.8 s.239
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• pp.1085-1092
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• 2005

The OHT(Over Head Transportation) Vehicle transports heavy products quickly and repeatedly at the industrial workplace. The belt in the OHT vehicle is used to support the weight of the OHT Cage. The fatigue of the belt is caused by the dynamic load during the operation time. Since the fatigue fracture of the belt affects the safety at the workplace, the correct prediction of the dynamic load is necessary to calculate the fatigue life of the belt on the design step. In this paper a computer aided analysis method is proposed for the belt in the early design stage using dynamic analysis, stress analysis, belt tensile test, belt fatigue test and fatigue lift prediction method. From the dynamic load time histories and the stress of the belt FE model, a dynamic stress time history is produced. Using linear damage law and cycle counting method, fatigue life cycle is calculated. The method developed in this paper is used to reduce the time and cost for designing the OHT belt in different environment and condition.

• Safety and Health at Work
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• v.2 no.3
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• pp.236-242
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• 2011

Objectives: To determine the feasibility of predicting static and dynamic peak back-compressive forces based on (1) static back compressive force values at the lift origin and destination and (2) lifting speed. Methods: Ten male subjects performed symmetric mid-sagittal floor-to-shoulder, floor-to-waist, and waist-to-shoulder lifts at three different speeds (slow, medium, and fast), and with two different loads (light and heavy). Two-dimensional kinematics and kinetics were captured. Linear regression analyses were used to develop prediction equations, the amount of predictability, and significance for static and dynamic peak back-compressive forces based on a static origin and destination average (SODA) backcompressive force. Results: Static and dynamic peak back-compressive forces were highly predicted by the SODA, with R2 values ranging from 0.830 to 0.947. Slopes were significantly different between slow and fast lifting speeds (p < 0.05) for the dynamic peak prediction equations. The slope of the regression line for static prediction was significantly greater than one with a significant positive intercept value. Conclusion: SODA under-predict both static and dynamic peak back-compressive force values. Peak values are highly predictable and could be readily determined using back-compressive force assessments at the origin and destination of a lifting task. This could be valuable for enhancing job design and analysis in the workplace and for large-scale studies where a full analysis of each lifting task is not feasible.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.12
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• pp.439-446
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• 2016

In this paper, an efficient prediction accuracy enhancement strategy is proposed for improving the performance of the prediction-based parallel event-driven gate-level timing simulation. The proposed new strategy adopts the static double prediction and the dynamic prediction for input and output values of local simulations. The double prediction utilizes another static prediction data for the secondary prediction once the first prediction fails, and the dynamic prediction tries to use the on-going simulation result accumulated dynamically during the actual parallel simulation execution as prediction data. Therefore, the communication overhead and synchronization overhead, which are the main bottleneck of parallel simulation, are maximally reduced. Throughout the proposed two prediction enhancement techniques, we have observed about 5x simulation performance improvement over the commercial parallel multi-core simulation for six test designs.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.11a
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• pp.677-681
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• 2011

Characteristics of vertical and horizontal axis wind turbines are explained. The speed and direction of wind on the blade of the Darrieus type turbine changes very severely. Therefore dynamic stall happens periodically and the wake from the front blade deteriorates the performance of rear blades. Blade element momentum theory(BEMT) is widely utilized for aerodynamic design and performace prediction of horizontal axis wind turbine(HAWT). Computation analysis and wind tunnel test are also performed for the performance prediction.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.5
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• pp.94-102
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• 1998

In this paper, we proposed a dynamic selection scheme of advnaced prediction mode(DAPM), which reduces computational cost and improves coding efficiency. We can select the mode between default prediction mode (DPM) and advanced prediction mode (APM) according to motion componenets in a frame dynamically. For this purpose, we defined error distribution of motion estimation (EDME) as sum of absolute difference(SAD) for each searching points. This distribution region is divided to four subregions. We calculate minimum values in each subregions and then, we determine whether block motion estimation is performed or not depending on the results. As a result, we reduced computational complexity to 30% without degradation of image quality compared to fixed APM(FAPM) by selecting DPM for linear movement.

• Wind and Structures
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• v.29 no.2
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• pp.129-137
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• 2019

The maximum along-wind displacement of a considerable amount of building under simulated wind loads is computed with the aim to produce a simple prediction model using multiple regression analysis with variables transformation. The Shinozuka and Newmark methods are used to simulate the turbulent wind and to calculate the dynamic response, respectively. In order to evaluate the prediction performance of the regression model with longer degree of determination, two complex structural models were analyzed dynamically. In addition, the prediction model proposed is used to estimate and compare the maximum response of two test buildings studied with wind loads by other authors. Finally, it was proved that the prediction model is reliable to estimate the maximum displacements of structures subjected to the wind loads.

• Journal of information and communication convergence engineering
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• v.5 no.2
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• pp.171-176
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• 2007

To give a guarantee a consistently high level of quality and reliability of Telematics traffic service, traffic flow forecasting is very important issue. In this paper, we proposed an adaptable integrated prediction model to predict the traffic flow in the future. Our model combines two methods, short-term prediction model and long-term prediction model with different combining coefficients to reflect current traffic condition. Short-term model uses the Kalman filtering technique to predict the future traffic conditions. And long-term model processes accumulated speed patterns which means the analysis results for all past speeds of each road by classifying the same day and the same time interval. Combining two models makes it possible to predict future traffic flow with higher accuracy over a longer time range. Many experiments showed our algorithm gives a better precise prediction than only an accumulated speed pattern that is used commonly. The result can be applied to the car navigation to support a dynamic shortest path. In addition, it can give users the travel information to avoid the traffic congestion areas.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1712-1732
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• 2011

Cloud provides dynamically scalable virtualized computing resources as a service over the Internet. To achieve higher resource utilization over virtualization technology, an optimized strategy that deploys virtual machines on physical machines is needed. That is, the total number of active physical host nodes should be dynamically changed to correspond to their resource usage rate, thereby maintaining optimum utilization of physical machines. In this paper, we propose a pattern-based prediction model for resource provisioning which facilitates best possible resource preparation by analyzing the resource utilization and deriving resource usage patterns. The focus of our work is on predicting future resource requests by optimized dynamic resource management strategy that is applied to a virtualized data center in a Cloud computing environment. To this end, we build a prediction model that is based on user request patterns and make a prediction of system behavior for the near future. As a result, this model can save time for predicting the needed resource amount and reduce the possibility of resource overuse. In addition, we studied the performance of our proposed model comparing with conventional resource provisioning models under various Cloud execution conditions. The experimental results showed that our pattern-based prediction model gives significant benefits over conventional models.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.309-314
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• 1992

Many dynamic mechanical systems, such as parts-feeders, walking machines, and percussive power tools, are described by equations of motion which are discontinuous. The discontinuities result from kinematic constraint changes which are difficult to foresee, especially in presence of impact. A simulation algorithm for these types of systems must be able to algorithmically predict and detect the kinematic constraint changes without any prior knowledge of the system's motion. This paper presents a rule-based approach to the prediction and detection of kinematic constraint changes between bodies with arc and line boundaries. The developed algorithm's ability to accurately and automatically detect the unpredicted changes of kinematic constraints is demonstrated with a numerical example.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.795-805
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• 1997

In this paper, we consider a Bayesian forecasting method for the analysis of repeated surveys. It is assumed that the parameters of the superpopulation model at each time follow a stochastic model. We propose Bayesian prediction procedures for the finite population total under dynamic generalized linear models. Some numerical studies are provided to illustrate the behavior of the proposed predictors.