• Korean Journal of Veterinary Research
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• v.36 no.4
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• pp.941-948
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• 1996

The accuracy of rectal palpation and ultrasonography for predicting the presence of a functional corpus luteum in subestrous dairy cows was investigated, using the result of a radioimmunoassay for progesterone in plasma. Luteal status (high or low progesterone concentrations) was diagnosed in 820 cows, using rectal palpation and B-mode transrectal ultrasonography, and the results of rectal palpation and ultrasonography were compared in $2{\times}2$ contingency table with plasma progesterone concentrations. A $2{\times}2$ contingency table analysis allowed the calculation of sensitivity, specificity and predictive values for rectal palpation and ultrasonography. The sensitivity, specificity, predictive value of a positive test and predictive value of a negative test were 81.9%, 67.5%, 79.0% and 71.4% for rectal palpation, and 96.3%, 88.8%, 94.5% and 92.4% for ultrasonography, respectively. The percentages of observed agreement and expected agreement between rectal palpation and ultrasonography were 71.8% and 57.1%, respectively. An evaluation of agreement between rectal palpation and ultrasonography, the value of Kappa was 0.34. It was concluded that a ultrasonography was more sensitive and specific than rectal palpation in predicting the presence of a functional corpus luteum. Therefore, ultrasonographic examination is a reliable method for assessing the functional status of ovarian structures in subestrous dairy cows.

• Journal of The Geomorphological Association of Korea
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• v.24 no.3
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• pp.105-118
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• 2017

The debris slope landforms which are existent in Korean mountains is generally on the steep slopes and mostly covered by vegetation, it is difficult to investigate the landform. Therefore a scientific method is required to come up with an effective field investigation plan. For this purpose, the use of Remote Sensing and GIS technologies for a spatial analysis is essential. This study has extracted the potential area of debrisslope landform formation using Fuzzy set and Bayesian Predictive Discriminate Model as mathematical data integration methods. The first step was to obtain information about debris locations and their related factors. This information was verified through field investigation and then used to build a database. In the second step, the map that zoning the study area based on the degree of debris formation possibility was generated using two modeling methods, and then cross validation technique was applied. In order to quantitatively analyze the accuracy of two modeling methods, the calculated potential rate of debrisformation within the study area was evaluated by plotting SRC(Success Rate Curve) and calculating AUC(Area Under the Curve). As a result, the prediction accuracy of Fuzzy set model wes 83.1% and Bayesian Predictive Discriminate Model wes 84.9%. It showed that two models are accurate and reliable and can contribute to efficient field investigation and debris landform management.

• Journal of The Korean Society of Integrative Medicine
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• v.10 no.3
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• pp.107-117
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• 2022

Purpose : The purpose of this study was to establish a model of the predictive factors for success or failure of examinees undertaking the Korean physical therapist licensing examination (KPTLE). Additionally, we assessed the pass/fail cut-off point. Methods : We analyzed the results of 10,881 examinees who undertook the KPTLE, using data provided by the Korea Health Personnel Licensing Examination Institute. The target variable was the test result (pass or fail), and the input variables were: sex, age, test subject, and total score. Frequency analysis, chi-square test, descriptive statistics, independent t-test, correlation analysis, binary logistic regression, and receiver operating characteristic (ROC) curve analyses were performed on the data. Results : Sex and age were not significant predictors of attaining a pass (p>.05). The test subjects with the highest probability of passing were, in order, medical regulation (MR) (Odds ratio (OR)=2.91, p<.001), foundations of physical therapy (FPT) (OR=2.86, p<.001), diagnosis and evaluation for physical therapy (DEPT) (OR=2.74, p<.001), physical therapy intervention (PTI) (OR=2.66, p<.001), and practical examination (PE) (OR=1.24, p<.001). The cut-off points for each subject were: FPT, 32.50; DEPT, 29.50; PTI, 44.50; MR, 14.50; and PE, 50.50. The total score (TS) was 164.50. The sensitivity, specificity, and the classification accuracy of the prediction model was 99 %, 98 %, and 99 %, respectively, indicating high accuracy. Area under the curve (AUC) values for each subject were: FPT, .958; DEPT, .968; PTI, .984; MR, .885; PE, .962; and TS, .998, indicating a high degree of fit. Conclusion : In our study, the predictive factors for passing KPTLE were identified, and the optimal cut-off point was calculated for each subject. Logistic regression was adequate to explain the predictive model. These results will provide universities and examinees with useful information for predicting their success or failure in the KPTLE.

• Journal of Navigation and Port Research
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• v.48 no.1
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• pp.27-33
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• 2024

Following the COVID-19 pandemic, congestion within container terminals has led to a significant increase in waiting time and turnaround time for external trucks, resulting in a severe inefficiency in gate-in and gate-out operations. In response, port authorities have implemented a Vehicle Booking System (VBS) for external trucks. It is currently in a pilot operation. However, due to issues such as information sharing among stakeholders and lukewarm participation from container transport entities, its improvement effects are not pronounced. Therefore, this study proposed a deep learning-based predictive model for external trucks turnaround time as a foundational dataset for addressing problems of waiting time for external trucks' turnaround time. We experimented with the presented predictive model using actual operational data from a container terminal, verifying its predictive accuracy by comparing it with real data. Results confirmed that the proposed predictive model exhibited a high level of accuracy in its predictions.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.2
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• pp.160-171
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• 2008

MU's mobility patterns can be found from a movement history data. The prediction accuracy and model complexity depend on the degree of application of history data. The more data we use, the more accurate the prediction is. As a result, the location management cost is reduced, but complexity of the model increases. In this paper, we classify MU's mobility patterns into four types. For each type, we find the respective optimal number of application of history data, and predictive location area by using the simulation. The optimal numbers of four types are shown to be different. When we use more than three application of history data, the simulation time and data storage are shown to increase very steeply.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.388-394
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• 2007

In wireless network, we propose a predictive location update scheme which considers mobile user's(MU's) mobility patterns. MU's mobility patterns can be found from a movement history data. The prediction accuracy and model complexity depend on the degree of application of history data. The more data we use, the more accurate the prediction is. As a result, the location management cost is reduced, but complexity of the model increases. In this paper, we classify MU's mobility patterns into four types. For each type, we find the respective optimal number of application of history data, and predictive location area by using the simulation. The optimal numbers of four types are shown to be different. When we use more than three application of history data, the simulation time and data storage are shown to increase very steeply.

• Journal of the Korean Society for Advanced Composite Structures
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• v.5 no.4
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• pp.11-17
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• 2014

A progressive failure analysis procedure for composite laminates is completed in here. An anisotropic plastic constitutive model for fiber-reinforced composite material is implemented into computer program for a predictive analysis procedure of composite laminates. Also, in order to describe material behavior beyond the initial yield, the anisotropic work-hardening model and subsequent yield surface are implemented into a computer code, which is Predictive Analysis for Composite Structures (PACS). The accuracy and efficiency of the anisotropic plastic constitutive model and the computer program PACS are verified by solving a number of various fiber-reinforced composite laminates with and without geometric discontinuity. The comparisons of the numerical results to the experimental and other numerical results available in the literature indicate the validity and efficiency of the developed model.

• Proceedings of the KIEE Conference
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• summer
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• pp.1220-1222
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• 2004

In this paper, we propose a new discrete-time predictive current controller for a PM-LSM(permanent magnet linear synchronous motor). The main objectives of the current controllers are to ensure that the measured stator currents tract the command values accurately and to shorten the transient interval as much as possible, in order to obtain high-performance of ac drive system. A new control strategy is seen the scheme that gets the fast adaptation of transient current change, the fast transient response tracking and is proposed simplified calculation. Moreover, the simulation results will be verified the improvements of predictive controller and accuracy of the current controller.

• Proceedings of the KIPE Conference
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• 2015.07a
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• pp.522-523
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• 2015

This paper proposes a deadbeat current controller using predictive current method for three-phase shunt active power filter (SAPF). In proposed controller, the compensating current reference which is necessary for the deadbeat control is estimated by using the predictive current method. This method can improve the accuracy of compensation and ensure the fast dynamic response of SAPF. Simulations have been carried out to demonstrate the perfomance of SAPF using proposed control method.

• Communications for Statistical Applications and Methods
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• v.27 no.2
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• pp.211-224
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• 2020

Sensitivity and specificity are popular measures described by the receiver operating characteristic (ROC) curve. There are also two other measures such as the positive predictive value (PPV) and negative predictive value (NPV); however, the PPV and NPV cannot be represented by the ROC curve. Based on the total operating characteristic (TOC) curve suggested by Pontius and Si (International Journal of Geographical Information Science, 97, 570-583, 2014), explanatory methods are proposed to geometrically describe the PPV and NPV by the TOC curve. It is found that the PPV can be regarded as the slope of the right-angled triangle connecting the origin to a certain point on the TOC curve, while 1 - NPV can be represented as the slope of the right-angled triangle connecting a certain point to the top right corner of the TOC curve. When the neutral zone exists, the PPV and 1-NPV can be described as the slopes of two other right-angled triangles of the TOC curve. Therefore, both the PPV and NPV can be estimated using the TOC curve, whether or not the neutral zone is present.