• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.1
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• pp.67-95
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• 1998

This research addresses flow time prediction in the dynamic unbalanced job shop scheduling environment. The specific purpose of the research is to develop the job flow time prediction model in the dynamic unbalance djob shop. Such factors as job characteristics, job shop status, characteristics of the shop workload, shop dispatching rules, shop structure, etc, are considered in the prediction model. The regression prediction approach is analyzed within a dynamic, make-to-order job shop simulation model. Mean Absolute Lateness (MAL) and Mean Relative Error (MRE) are used to compare and evaluate alternative regression models devloped in this research.

• Smart Structures and Systems
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• v.14 no.6
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• pp.1269-1289
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• 2014

With the sub-stepping technique, the numerical analysis in real-time dynamic hybrid testing is split into the response analysis and signal generation tasks. Two target computers that operate in real-time may be assigned to implement these two tasks, respectively, for fully extending the simulation scale of the numerical substructure. In this case, the integration time-step of solving the dynamic response of the numerical substructure can be dozens of times bigger than the sampling time-step of the controller. The time delay between the real and desired feedback forces becomes more striking, which challenges the well-developed delay compensation methods in real-time dynamic hybrid testing. This paper focuses on displacement prediction and force correction for delay compensation in the real-time dynamic hybrid testing with a large integration time-step. A new displacement prediction scheme is proposed based on recently-developed explicit integration algorithms and compared with several commonly-used prediction procedures. The evaluation of its prediction accuracy is carried out theoretically, numerically and experimentally. Results indicate that the accuracy and effectiveness of the proposed prediction method are of significance.

• 한국연소학회:학술대회논문집
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• 2014.11a
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• pp.19-20
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• 2014

Boiler design should be desinged to maximize thermal efficiency of the system under imposed load requirement and a boiler should be validated for transient operation. If a proper prediction is possible on the transient behavior and transient characteristics of a boiler, one may asses the performance of boiler component, control logics and operation procedures. In this work, dynamic modeling method of boiler is presented and dynamic simulation of load runback scenario was carried out on suprecritical oil-firing boiler.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.7
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• pp.91-98
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• 1997

Because of a complicated behavior of fatigue in mechanical structures, the analysis of fatigue is in need of much researches on life prediction. A method is developed for the dynamic tensile strength analysis by simple tensile test, which is for the failure life prediction by lethargy coefficient of various materials. Then it is programed to analyze the failure life prediction of mechanical system by virtue of fracture. Thus the dynamic tensile strength analysis is performed to evaluate life parameters as a numerical example, using the developed method.

• KIISE Transactions on Computing Practices
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• v.23 no.11
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• pp.632-640
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• 2017

Although solar insolation is the weather factor with the greatest influence on power generation in photovoltaic systems, the Meterological Agency does not provide solar insolation data for future dates. Therefore, it is essential to research prediction methods for solar insolation to efficiently manage photovoltaic systems. In this study, we propose a Dynamic Piecewise Prediction Model that can be used to predict solar insolation values for future dates based on information from the weather forecast. To improve the predictive accuracy, we dynamically divide the entire data set based on the sun altitude and cloudiness at the time of prediction. The Dynamic Piecewise Prediction Model is developed by applying a polynomial linear regression algorithm on the divided data set. To verify the performance of our proposed model, we compared our model to previous approaches. The result of the comparison shows that the proposed model is superior to previous approaches in that it produces a lower prediction error.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1734-1737
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• 2019

This paper presents a prediction structure with a Hybrid Dynamic Branch Prediction (HDBP) scheme which decreases the number of stalls. In the application, a branch history register is dynamically adjusted to produce more unique index values of pattern history table (PHT). The number of stalls is also reduced by using the modified gshare predictor with a long history register folding scheme. The aliasing rate decreased to 44.1% and the miss prediction rate decreased to 19.06% on average compared with the gshare branch predictor, one of the most popular two-level branch predictors. Moreover, Compared with the gshare, an average improvement of 1.28% instructions per cycle (IPC) was achieved. Thus, with regard to the accuracy of branch prediction, the HDBP is remarkably useful in boosting the overall performance of the superscalar processor.

• Earthquakes and Structures
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• v.26 no.2
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• pp.97-115
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• 2024

In this paper, a database consisting of the dynamic shear modulus ratio and damping ratio test data of clay obtained from 406 groups of triaxial tests is constructed with the starting area of Xiong'an New Area as the research background. The aim is to study the nonlinear dynamic properties of clay in this area under cyclic loading. The study found that the effective confining pressure and plasticity index have certain influences on the dynamic shear modulus ratio and damping ratio of clay in this area. Through data analysis, it was found that there was a certain correlation between effective confining pressure and plasticity index and dynamic shear modulus ratio and damping ratio, with fitting degree values greater than 0.1263 for both. However, other physical indices such as the void ratio, natural density, water content and specific gravity have only a small effect on the dynamic shear modulus ratio and the damping ratio, with fitting degree values of less than 0.1 for all of them. This indicates that it is important to consider the influence of effective confining pressure and plasticity index when studying the nonlinear dynamic properties of clays in this area. Based on the above, prediction models for the dynamic shear modulus ratio and damping ratio in this area were constructed separately. The results showed that the model that considered the combined effect of effective confining pressure and plasticity index performed best. The predicted dynamic shear modulus ratio and damping ratio closely matched the actual curves, with approximately 88% of the data falling within ±1.3 times the measured dynamic shear modulus ratio and approximately 85.1% of the data falling within ±1.3 times the measured damping ratio. In contrast, the prediction models that considered only a single influence deviated from the actual values, particularly the model that considered only the plasticity index, which predicted the dynamic shear modulus ratio and the damping ratio within a small distribution range close to the average of the test values. When compared with existing prediction models, it was found that the predicted dynamic shear modulus ratio in this paper was slightly higher, which was due to the overall hardness of the clay in this area, leading to a slightly higher determination of the dynamic shear modulus ratio by the prediction model. Finally, for the dynamic shear modulus ratio and damping ratio of the engineering site in the starting area of Xiong'an New Area, we confirm that the prediction formulas established in this paper have high reliability and provide the applicable range of the prediction model.

• 전기의세계
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• v.31 no.2
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• pp.126-131
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• 1982

Induction motors are known to cause voltage dip, oscillating torque and inrush current on the dynamic period. To compensate for these undesirable effects, the prediction of dynamic performances is required. The dynamic performances are determinated by circuit time constants. From this point of view, in this paper, the dynamic equivalent circuit included only three time-constant parameters are presented. To predict more simply dynamic performances, the new characteristics time constant parameters are analyzed, and now these parameters are described as the function of circuit time constants. This paper reviews and analyzes the use of series capacitance compensations, and the use of this analysis can make simply a prediction about oscillating conditions.

• Agricultural and Biosystems Engineering
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• v.1 no.2
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• pp.67-72
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• 2000

A dynamic draft model is necessary to analyze mechanics of tillage and to design optimal tillage tools. In order to deal with draft dynamics, a neural network paradigm was applied to develop dynamic draft models. For the development of the models, three kinds of tillage tools were used to measure drafts in the soil bin and a time lagged recurrent neural network was developed. The neural network had a structure to predict dynamic draft, having a function of one-step-ahead prediction. A procedure for network prediction model identification was established. The results show promising modeling of the dynamic drafts with developed neural network.

• ETRI Journal
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• v.43 no.6
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• pp.1058-1080
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• 2021

Crime is not a completely random event but rather shows a pattern in space and time. Capturing the dynamic nature of crime patterns is a challenging task. Crime prediction models that rely only on neighborhood influence and demographic features might not be able to capture the dynamics of crime patterns, as demographic data collection does not occur frequently and is static. This work proposes a novel approach for crime count and hotspot prediction to capture the dynamic nature of crime patterns using taxi data along with historical crime and demographic data. The proposed approach predicts crime events in spatial units and classifies each of them into a hotspot category based on the number of crime events. Four models are proposed, which consider different covariates to select a set of independent variables. The experimental results show that the proposed combined subset model (CSM), in which static and dynamic aspects of crime are combined by employing the taxi dataset, is more accurate than the other models presented in this study.