• Nutrition Research and Practice
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• v.11 no.1
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• pp.51-56
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• 2017

BACKGROUND/OBJECTIVES: The purpose of this study was to assess the accuracy of a dietary reference intake (DRI) predictive equation for estimated energy requirements (EER) in female college tennis athletes and non-athlete students using doubly labeled water (DLW) as a reference method. MATERIALS/METHODS: Fifteen female college students, including eight tennis athletes and seven non-athlete subjects (aged between 19 to 24 years), were involved in the study. Subjects' total energy expenditure (TEE) was measured by the DLW method, and EER were calculated using the DRI predictive equation. The accuracy of this equation was assessed by comparing the EER calculated using the DRI predictive equation ($EER_{DRI}$) and TEE measured by the DLW method ($TEE_{DLW}$) based on calculation of percentage difference mean and percentage of accurate prediction. The agreement between the two methods was assessed by the Bland-Altman method. RESULTS: The percentage difference mean between the methods was -1.1% in athletes and 1.8% in non-athlete subjects, whereas the percentage of accurate prediction was 37.5% and 85.7%, respectively. In the case of athletic subjects, the DRI predictive equation showed a clear bias negatively proportional to the subjects' TEE. CONCLUSIONS: The results from this study suggest that the DRI predictive equation could be used to obtain EER in non-athlete female college students at a group level. However, this equation would be difficult to use in the case of athletes at the group and individual levels. The development of a new and more appropriate equation for the prediction of energy expenditure in athletes is proposed.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.567-574
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• 2003

The estimation problem of predictive value of a positive test(PVP), which is assessing the accuracy of a screening test is considered. Score methods discussed by Gart and Nam(1988) are proposed for constructing confidence interval for PVP. The simulation studies are conducted in evaluating the proposed methods and existing approximate ones.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.383-395
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• 1999

This study is concerned with the evaluation of predictive ability of classification models with ordered multiple categories. If categories can be ordered or ranked the spread of misclassification should be considered to evaluate the performance of the classification models using loss rate since the apparent error rate can not measure the spread of misclassification. Since loss rate is known to underestimate the true loss rate the bootstrap method were used to estimate the true loss rate. thus this study suggests the method to evaluate the predictive power of the classification models using loss rate and the bootstrap estimate of the true loss rate.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.4
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• pp.206-210
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• 2002

The transmission delays in the networked control systems affect the performance and stability. For covering the delays, this paper proposes NCS including the predictive strategy. The proposed method shows that the two cases such as stable and unstable system are well behaved.

• Journal of Korean Society of Water and Wastewater
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• v.33 no.6
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• pp.481-489
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• 2019

As aeration is an energy-intensive process, its control has become more important to save energy and to meet strict effluent limits. In this study, predictive aeration control based on the respirometric method has been applied to the sequencing batch reactor (SBR) process. The variation of the respiration rate by nitrification was great and obvious, so it could be a very useful parameter for the predictive aeration control. The maximum respiration rate due to nitrification was about 60 mg O₂/L·h and the maximum specific nitrification rate was about 7.5 mg N/g MLVSS·h. The aeration time of the following cycle of the SBR was daily adjusted in proportion to that which was previously determined based on the sudden decrease of respiration rate at the end of nitrification in the respirometer. The aeration time required for nitrification could be effectively predicted and it was closely related to influent nitrogen loadings. By the predictive aeration control the aerobic period of the SBR has been optimized, and energy saving and enhanced nitrogen removal could be obtained.

• The Korean Journal of Cytopathology
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• v.18 no.2
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• pp.119-125
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• 2007

Urine cytology is an important screening tool for urinary tract neoplasms. Liquid-based preparation methods, such as $ThinPrep^{(R)}$, have been introduced for non-gynecological samples. We aimed to assess the diagnostic accuracy of liquid-based preparations in urine cytology by comparing the results of the conventional Cytospin preparation method for the same samples. A total of 236 cases subject to urine cytology were enrolled in this study from January 2005 to December 2005. All cases were subjected to cystoscopy and if a malignancy was suspected, a biopsy was performed. Urine cytology slides were made using the $ThinPrep^{(R)}$ preparation method and the conventional Cytospin and/or direct smear method from the individual samples. The results of urine cytology were compared with the final cystoscopic or histological diagnoses. We analyzed the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of both cytology preparation methods. A total of 236 slides made using the liquid based method were satisfactory for slide quality, whereas 5 slides (2.1%) prepared by conventional methods were unsatisfactory because of air-drying, a thick smear, or a bloody or inflammatory background. The $ThinPrep^{(R)}$ method showed 53.1% sensitivity, 92.6% specificity, a 92,6% positive predictive value, a 94.1% negative predictive value and 85,6% accuracy, while the conventional method showed 51% sensitivity, 98.4% specificity, a 92.6% positive predictive value, a 98.4% negative predictive value and 88,6% accuracy. Although the diagnostic values were equivalent between the use of the two methods, the quality of the cytology slides and the time consumed during the microscopic examination for a diagnosis were superior for the $ThinPrep^{(R)}$ method than for the conventional method. In conclusion, our limited studies have shown that the use of the liquid based preparation method is beneficial to improve the quality of slides and reduce the duration for a microscopic examination, but did not show better sensitivity, accuracy and predictive values.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.662-668
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• 2015

This paper presents an improved predictive control of an induction machine fed by a matrix converter using N-switching vectors as the control action during a complete sampling period of the controller. The conventional model predictive control scheme based matrix converter uses a single switching vector over the same period which introduces high torque ripple. The proposed switching scheme for a matrix converter based model predictive control of an induction machine drive selects the appropriate switching vectors for control of electromagnetic torque with small variations of the stator flux. The proposed method can reduce the ripple of the electrical variables by selecting the switching state as well as the method used in the space vector modulation techniques. Simulation results are presented to verify the effectiveness of the improved predictive control strategy for induction machine fed by a matrix converter.

• Journal of Power Electronics
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• v.18 no.5
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• pp.1608-1617
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• 2018

The rapid and correct isolation of faulty submodules (SMs) is of great importance for improving the reliability of modular multilevel converters (MMCs). Therefore, a fast diagnosis method containing fault detection and fault location determination was presented in this paper. An improved incremental predictive model of arm current was proposed to detect failures, and the multi-step prediction method was used to eliminate the negative impact of disturbances. Moreover, a control method was proposed to strengthen the fault characteristics to rapidly locate faulty arms and faulty SMs by detecting the variation rate of the SM capacitor voltage. The proposed method can rapidly and easily locate faulty SMs under different load conditions without the need for additional sensors. The experimental results have validated the effectiveness of the proposed method by using a single-phase MMC with four SMs per arm.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.24-30
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• 2003

In this paper, we propose a novel predictive control method, which uses a wavelet neural network as a predictor, for the control of chaotic systems. In our method, we use the gradient descent method for training the parameter of a wavelet neural network. The control signals are directly obtained by minimizing the difference between a reference signal and the output of a wavelet neural network. To verify the efficiency of our method, we apply it to the Doffing and the Henon system, which are a representative continuous and discrete time chaotic system respectively, and compare with the results of generalized predictive control using multi-layer perceptron.

• Communications for Statistical Applications and Methods
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• v.23 no.4
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• pp.355-362
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• 2016

Ensemble methods often help increase prediction ability in various predictive models by combining multiple weak learners and reducing the variability of the final predictive model. In this work, we demonstrate that ensemble methods also enhance the accuracy of prediction under kernel ridge regression and kernel logistic regression classification. Here we apply bagging and random forests to two kernel-based predictive models; and present the procedure of how bagging and random forests can be embedded in kernel-based predictive models. Our proposals are tested under numerous synthetic and real datasets; subsequently, they are compared with plain kernel-based predictive models and their subsampling approach. Numerical studies demonstrate that ensemble approach outperforms plain kernel-based predictive models.