• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.1063-1066
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• 1996

On the prediction of driving performance, an acceleration performance is normally simulated in stall starting condition which is the engine status of full-throttle and high-speed. The lack of transient engine torque data makes the difficulty of predicting an acceleration performance on engine-idle start condition. A experimental equation of transient engine torque is derived from vehicle performance test data. It is applied to simulation the accleration performance prediction on idle starting condition.

• Proceedings of the KSME Conference
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• 2001.06e
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• pp.649-654
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• 2001

This paper describes aerodynamic preliminary design performance prediction and flow analysis for centrifugal compressor of the marine middle engine turbocharger. The performance characteristics of turbocharger compressor are investigated at various operating conditions using mass flow rate and revolution speed, and computational flow analysis for impeller and diffuser at design point are performed. Preliminary design results correspond to actual compressor geometric values comparatively by applying modified slip factor. Performance prediction and flow analysis results show good agreement with experiments. Therefore, this will provide the performance prediction in preliminary design, and help to increase the design capability for optimized impeller.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.872-875
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• 2009

In a bankruptcy prediction model, the accuracy is one of crucial performance measures due to its significant economic impacts. Ensemble is one of widely used methods for improving the performance of classification and prediction models. Two popular ensemble methods, Bagging and Boosting, have been applied with great success to various machine learning problems using mostly decision trees as base classifiers. In this paper, we analyze the performance of boosted neural networks for improving the performance of traditional neural networks on bankruptcy prediction tasks. Experimental results on Korean firms indicated that the boosted neural networks showed the improved performance over traditional neural networks.

• Journal of Biosystems Engineering
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• v.37 no.3
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• pp.131-139
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• 2012

In this study, we developed a simulation program for the prediction of tractive performance of a tractor, by applying a widely used empirical model for tractive performance prediction of single tire, Brixius. The tractive performance prediction program can readily predict and estimate tractive performance according to various soil conditions and different specifications of tractors. The program was developed with the considerations of tractor's specification-related parameters (e.g., weight, tire size, and wheelbase of the tractor), a soil parameter (i.e., cone index which represents the soil strength), and operating conditions of the tractor (e.g., theoretical speed and driving types such as 2WD and 4WD). Also, the program was designed to provide tractive performance prediction results of tractors such as gross traction, motion resistance, net traction, and tractive efficiency, in the form of not only numerical values but also graphical visualization. To evaluate the feasibility of the program, we input three different soil conditions (which have different cone indexes each other) and tractor operating conditions to the program and analyzed the tractive performance from each input condition. From the analysis, it can be concluded that the developed program can be effectively utilized to predict the tractive performance under various soil conditions and driving types of tractors with different specifications.

• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.2
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• pp.245-256
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• 2016

Prediction of TBM performance is very important for machine selection, and for reliable estimation of construction cost and period. The purpose of this research is to analyze the evaluation process of various prediction models for TBM performance and applied methodology. Based on the solid literature review since 2000, a classification system of TBM performance prediction model is proposed in this study. Classification system suggested in this study can be divided into two stages: selection of input parameter and application of prediction techniques. We also analyzed input and output parameters for prediction model and frequency of use. Lastly, the future research and development trend of TBM performance prediction is suggested.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.145-145
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• 2000

In this paper, we propose a hybrid d-step predictor which is composed of an adaptive predictor and a Kalman predictor. We prove the performance limit of the proposed predictor. Simulation is conducted to examine the performance of the proposed predictor. Simulation results show that the proposed combined predictor is superior to the adaptive predictor and the Kalman predictor. Proposed predictor is used for prediction of gun tip vibration of k1 tank. The result is compared with that of conventional adaptive predictor.

• Journal of information and communication convergence engineering
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• v.8 no.4
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• pp.370-376
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• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. However, its performance can be degraded due to multicollinearity problem where multiple classifiers of an ensemble are highly correlated with. This paper proposes genetic algorithm-based optimization techniques of SVM ensemble to solve multicollinearity problem. Empirical results with bankruptcy prediction on Korea firms indicate that the proposed optimization techniques can improve the performance of SVM ensemble.

• 유체기계공업학회:학술대회논문집
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• 2005.12a
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• pp.324-330
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• 2005

For axial-type turbines which operate at partial admission, a performance prediction model is developed. In this study, losses generated within the turbine are classified to windage loss, expansion loss and mixing loss. The developed loss model is compared with experimental results. Particularly, if a turbine operates at a very low partial admission rate, a circular-type nozzle is more efficient than a rectangular-type nozzle. For this case, a performance prediction model is developed and an experiment is conducted with the circular-type nozzle. The predicted result is compared with the measured performance, and the developed model quite well agrees with the experimental results. So the developed model could be applied to predict the performance of axial-type turbines which operate at various partial admission rates or with different nozzle shape.

• Journal of Digital Convergence
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• v.7 no.3
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• pp.25-33
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• 2009

Historically, performance prediction and sizing of server systems have been the key purchasing argument for customer. To accurate server's sizing and performance prediction, it is necessary to correctness guideline for sizing and performance prediction. But existing guidelines have many errors. So, we examine the benchmarks of performance organization such as SPEC and TPC. And then we consider to TPC-C and TPC-E benchmarks for OLTP server's sizing and performance prediction that is a basic concept of guidelines. Eventually, we propose improvement of errors in guidelines.

• The KSFM Journal of Fluid Machinery
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• v.9 no.4 s.37
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• pp.13-19
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• 2006

For axial-type turbines which operate at partial admission, a performance prediction model is developed. In this study, losses generated within the turbine are classified to windage loss, expansion loss and mixing loss. The developed loss model is compared with experimental results. Particularly, if a turbine operates at a very low partial admission rate, a circular-type nozzle is more efficient than a rectangular-type nozzle. For this case, a performance prediction model is developed and an experiment is conducted with the circular-type nozzle. The predicted result is compared with the measured performance, and the developed model quite well agrees with the experimental results. So the developed model could be applied to predict the performance of axial-type turbines which operate at various partial admission rates or with different nozzle shape.