• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.3
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• pp.206-211
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• 2004

In phase controlled rectifier, it's been known that a fast response is achieved by predictive current control without any overshoot. The frequent sampling period is essential to improve the firing accuracy in conventional predict current control. However, improving the firing accuracy if difficult to reduce the period of sampling efficiently because current sampling and predictive current control is carried out in every period and the ON-OFF current control is performed by comparing two different one. To improve the firing accuracy at the predictive current control, the calculated firing angle is loaded into the high-accuracy hardware timer. So the calculation of exact crossing point between the predictive and actual current is the most important. In this paper, the flow chart for proposed firing angle calculation algorithm is obtained for the fastest current control performance in transient state. The performance of proposed algorithm is verified through simulations and experiments.

• Journal of Chest Surgery
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• v.29 no.1
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• pp.52-58
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• 1996

Sixty six patients who were operated as lung cancer during the period from Mar. 1991 to Sep. 1993 at the department of Thoracic and cardiovascular surgery, were reviewed retrospectively and the accuracy of regional lymph node in preoperative CT were compared with histopathologlc report obtained from operation. The age ranged from 30 to 72 years old (mean age : 56.5), and 51 patients were male and 15 patients were female. The author analysed the true positive, true negative, false positive and false negative and sensitivity, specificity, positive predictive index, negative predictive index and accuracy of each nodes. The result is that there were differences between seven nodal groups in specificity, sensitivity, positive predictive Index, negative predictive index and accuracy. The range of each nodal group is from 81.7 to 98.3% The nodes of the most poor accuracy are aortopulmonary area and hilar area.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.03a
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• pp.101-105
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• 1998

A large system predictor, which can perform prediction of sales trend in a huge number of distribution centers, is presented using neural predictive model. There are 20,000 number of distribution centers, and each distribution center need to forecast future demand in order to establish a reasonable inventory policy. Therefore, the number of forecasting models corresponds to the number of distribution centers, which is not possible to estimate that kind of huge number of accurate models in ERP (Enterprise Resource Planning)module. Multilayer neural net as universal approximation is employed for fitting the prediction model. In order to improve prediction accuracy, a sequential simulation procedure is performed to get appropriate network structure and also to improve forecasting accuracy. The proposed simulation procedure includes neural structure identification and virtual predictive model generation. The predictive model generation consists of generating virtual signals and estimating predictive model. The virtual predictive model plays a key role in tuning the real model by absorbing the real model errors. The complement approach, based on real and virtual model, could forecast the future demands of various distribution centers.

• Journal of Intelligence and Information Systems
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• v.1 no.2
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• pp.1-16
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• 1995

The field of human judgment and decision making provides useful methodologies for examining the human decision making process and substantive results. One of the methodologies is a lens model analysis which can examine valid nonlinearity in the human decision making process. Using the method, valid nonlinearity in human decision behavior can be successfully detected. Two linear(statistical) models of human experts and two nonlinear models of human experts are compared in terms of predictive accuracy (predictive validity). The results indicate that nonlinear models can capture factors(valid nonlinearity) that contribute to the expert's predictive accuracy, but not factors (inconsistency) that detract from their predictive accuracy. Then, it is argued that nonlinear models cab be more accurate than linear models, or as accurate as human experts, especially when human experts employ valid nonlinear strategies in decision making.

• Communications for Statistical Applications and Methods
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• v.23 no.4
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• pp.343-353
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• 2016

In doubly-censored data, an originating event time and a terminating event time are interval-censored. In certain analyses of such data, a researcher might be interested in the elapsed time between the originating and terminating events as well as regression modeling with risk factors. Therefore, in this study, we introduce a model evaluation method to measure the predictive ability of a model based on negative predictive values. We use a semiparametric estimate of the predictive accuracy to provide a simple and flexible method for model evaluation of doubly-censored survival outcomes. Additionally, we used simulation studies and tested data from a prostate cancer trial to illustrate the practical advantages of our approach. We believe that this method could be widely used to build prediction models or nomograms.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.19 no.4
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• pp.444-452
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• 2012

Purpose: To explore the usefulness of previous fall history as a triage variable for inpatients. Methods: Medical records of 21,382 patients, admitted to medical units of one tertiary hospital, were analyzed retrospectively. Inpatient falls were identified from the hospital's self-report system. Non-falls in 1,125 patients were selected by a stratified matching sampling with 125 patients with falls (0.59%). A comparative and predictive accuracy analysis was conducted to describe differences between the two groups with and without a history of falls. Logistic regression was used to measure the effect size of the fall history. Results: The fall history group showed higher prevalence by 9 fold than the non-fall history group. The relationships between falls and relevant variables which were significant in the non-fall history group, were not significant for the fall history group. Falls in the fall history group were 25 times more likely than in the non-fall group. Predictive accuracy of the risk assessment tool showed almost zero specificity in the fall history group. Conclusion: The presence of fall history, the fall prevalence, variables relevant to falls, and the accuracy of the risk tool were different, which support the usefulness of the fall history as a triage variable.

• Korean Journal of Community Nutrition
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• v.14 no.4
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• pp.462-473
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• 2009

The purpose of this study is to analyze the accuracy of predictive equations for resting metabolic rate (RMR) in Korean college students. Subjects were 60 healthy Korean college students (30 males, 30 females) aged 18-25 years. RMR was measured by indirect calorimetry. Predicted RMRs were calculated using the Harris-Benedict, Schofield (W)/(WH), FAO/ WHO/UNU(W)/(WH), Owen, Mifflin, Cunningham, Liu, IMNA and Henry (W)/(WH) equations. The accuracy of the equations was evaluated on basis of accurate prediction (the percentage of subjects whose RMR was predicted within 90% to 110% of the RMR measured), mean difference, RMSPE, mean % difference, limits of agreement of Bland- Altman method between predicted and measured RMR. Measured RMR of male and female students were $1833.4{\pm}307.4kcal/day$ and $1454.3{\pm}208.0kcal/day$, respectively. All predictive equations underestimated measured RMR. Of the predictive equations tested, the Harris-Benedict equation (mean difference: -80.4 kcal/day, RMSPE: 236 kcal/day, mean % difference: -3.1%) was the most accurate and precise, but accurate prediction of the equation was only 42%. Thus, this study suggests that the ethnicity-specific predictive equation from Korean people should be developed to improve the accuracy of predicted RMR for Koreans. (Korean J Community Nutrition 14(4) : 462${\sim}$473, 2009)

• Korean Journal of Community Nutrition
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• v.19 no.6
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• pp.568-580
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• 2014

Objectives: The purpose of this study was to measure the resting metabolic rate (RMR) and to assess the accuracy of RMR predictive equations for Korean farmers. Methods: Subjects were 161 healthy Korean farmers (50 males, 111 females) in Gangwon-area. The RMR was measured by indirect calorimetry for 20 minutes following a 12-hour overnight fasting. Selected predictive equations were Harris-Benedict, Mifflin, Liu, KDRI, Cunningham (1980, 1991), Owen-W, F, FAO/WHO/UNU-W, WH, Schofield-W, WH, Henry-W, WH. The accuracy of the equations was evaluated on the basis of bias, RMSPE, accurate prediction and Bland-Altman plot. Further, new RMR predictive equations for the subjects were developed by multiple regression analysis using the variables highly related to RMR. Results: The mean of the measured RMR was 1703 kcal/day in males and 1343 kcal/day in females. The Cunningham (1980) equation was the closest to measured RMR than others in males and in females (males Bias -0.47%, RMSPE 110 kcal/day, accurate prediction 80%, females Bias 1.4%, RMSPE 63 kcal/day, accurate prediction 81%). Body weight, BMI, circumferences of waist and hip, fat mass and FFM were significantly correlated with measured RMR. Thus, derived prediction equation as follow : males RMR = 447.5 + 17.4 Wt, females RMR = 684.5 - 3.5 Ht + 11.8 Wt + 12.4 FFM. Conclusions: This study showed that Cunningham (1980) equation was the most accurate to predict RMR of the subjects. Thus, Cunningham (1980) equation could be used to predict RMR of Korean farmers studied in this study. Future studies including larger subjects should be carried out to develop RMR predictive equations for Korean farmers.

• Imaging Science in Dentistry
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• v.45 no.3
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• pp.141-145
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• 2015

Purpose: This study evaluated the diagnostic accuracy of the reverse contrast mode in intraoral digital radiography for the detection of proximal dentinal caries, in comparison with the original digital radiographs. Materials and Methods: Eighty extracted premolars with no clinically apparent caries were selected, and digital radiographs of them were taken separately in standard conditions. Four observers examined the original radiographs and the same radiographs in the reverse contrast mode with the goal of identifying proximal dentinal caries. Microscopic sections $5{\mu}m$ in thickness were prepared from the teeth in the mesiodistal direction. Four slides prepared from each sample used as the diagnostic gold standard. The data were analyzed using SPSS (${\alpha}=0.05$). Results: Our results showed that the original radiographs in order to identify proximal dentinal caries had the following values for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy, respectively: 72.5%, 90%, 87.2%, 76.5%, and 80.9%. For the reverse contrast mode, however, the corresponding values were 63.1%, 89.4%, 87.1%, 73.5%, and 78.8%, respectively. The sensitivity of original digital radiograph for detecting proximal dentinal caries was significantly higher than that of reverse contrast mode (p<0.05). However, no statistically significant differences were found regarding specificity, positive predictive value, negative predictive value, or accuracy (p>0.05). Conclusion: The sensitivity of the original digital radiograph for detecting proximal dentinal caries was significantly higher than that of the reversed contrast images. However, no statistically significant differences were found between these techniques regarding specificity, positive predictive value, negative predictive value, or accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1879-1882
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• 1991

Kalman Filter application to model predictive control is discussed. Most of refinery and petrochemical processes contain uncertainties in their output. Simplified state estimation algorithm is merged to model predictive control to improve overall control accuracy.