• Survey Research
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• v.12 no.3
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• pp.25-48
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• 2011

In this paper, we overview the sample design, sampling error, non-response rate and prediction errors of the exit poll conducted for 2010 local elections and discusses how to detect a prediction bias in exit poll. To investigate the bias problem in exit poll in regional(Si-Do) level, we analyze exit poll data for 2007 presidential election and 2006 local elections as well as 2010 local elections in Korea. The measure of predictive accuracy A proposed by Martin et al.(2005) is used to assess the exit poll bias. The empirical studies based on three exit polls clearly show that there exits systematic bias in exit poll and the predictive bias of candidates affiliated to conservative party (such as Hannara-Dang) is serious in the specific regions. The result of this study on systematic bias will be very useful to improving the exit poll methodology in Korea.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.5
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• pp.3057-3062
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• 2013

Background: Selecting chemotherapy regimens guided by chemosensitivity tests can provide individualized therapies for cancer patients. The 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2Htetrazolium, inner salt (MTS) assay is one in vitro assay which has become widely used to evaluate the sensitivity to anticancer agents. The aim of this study was to evaluate the clinical applicability and accuracy of MTS assay for predicting chemotherapeutic response in unresectable NSCLC patients. Methods: Cancer cells were isolated from malignant pleural effusions of patients by density gradient centrifugation, and their sensitivity to eight chemotherapeutic agents was examined by MTS assay and compared with clinical response. Results: A total of 37 patients participated in this study, and MTS assay produced results successfully in 34 patients (91.9%). The sensitivity rates ranged from 8.8% to 88.2%. Twenty-four of 34 patients who received chemotherapy were evaluated for in vitro-in vivo response analysis. The correlation between in vitro chemosensitivity result and in vivo response was highly significant (P=0.003), and the total predictive accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for MTS assay were 87.5%, 94.1%, 71.4%, 88.9%, and 83.3%, respectively. The in vitro sensitivity for CDDP also showed a significant correlation with in vivo response (P=0.018, r=0.522). Conclusion: MTS assay is a preferable in vitro chemosensitivity assay that could be use to predict the response to chemotherapy and select the appropriate chemotherapy regimens for unresectable NSCLC patients, which could greatly improve therapeutic efficacy and reduce unnecessary adverse effects.

• Clinics in Shoulder and Elbow
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• v.27 no.2
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• pp.149-159
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• 2024

Background: In rotator cuff repair, the long head of the biceps tendon (LHB) is commonly used as graft material. However, factors influencing LHB tear severity are poorly understood, and predicting grade II LHB tears is challenging. This study aimed to identify these factors preoperatively. Methods: The demographics, medical parameters, and pain severity of 750 patients who underwent arthroscopic surgery from January 2010 to February 2021 were evaluated to determine the factors associated with LHB tear severity and grade II tears. Both overall and large-to-massive rotator cuff tear (RCT) cohorts underwent ordinal and binary logistic regression analyses. Predictive accuracy for grade II LHB tears was determined using the area under the receiver operating characteristic curve (AUC). Results: In the overall cohort, high-sensitivity C-reactive protein (hs-CRP) >1 mg/L (P<0.001), subscapularis tear (P<0.001), hypothyroidism (P=0.031), and the tangent sign (P=0.003) were significantly associated with LHB tear severity, and hs-CRP>1 mg/L, subscapularis tear, and Patte retraction degree were significantly associated with grade II LHB tears (P<0.001). In the large-to-massive RCT cohort, hs-CRP>1 mg/L, hypertension, and age ≥50 years (P<0.05) were significantly associated with LHB tear severity, and hs-CRP>1 mg/L (P<0.001) and hypertension (P=0.026) were significantly associated with grade II LHB tears. In both cohorts, hs-CRP >1 mg/L demonstrated good predictive accuracy for grade II LHB tears (AUCs: 0.72 and 0.70). Conclusions: Serum hs-CRP >1 mg/L is associated with LHB tear severity and serves as a reliable predictor of grade II LHB tears, facilitating preoperative assessment of the LHB as potential graft material in arthroscopic rotator cuff repair. serves as a reliable predictor of grade II LHB tears, facilitating preoperative assessment of the LHB as potential graft material in arthroscopic rotator cuff repair. Level of evidence: III.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.9
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• pp.4284-4293
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• 2012

At the early construction project stage, the most important task is to estimate planned construction costs analyzed with detailed information. Therefore, in this study, Apartment Housing Projects at the Early Stage of Civil Construction Cost of the reasonable and accurate predictions of the Regression analysis to 170 of actual Construction Cost, and dependent variable regression to Civil Construction Cost, location based national land area based on a combination of private land, union land, public land to the use of predictive models by various analyses of the ease and accuracy. As a result, Civil Construction Cost of Apartment Housing Projects by the regression formula for the error rate estimates in national land predictive model 15.59%, private land predictive model 17.53%, union land predictive model 21.86%, public land predictive model 13.08%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.351-361
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• 2021

The seriousness of fire is rising because fire causes enormous damage to property and human life. Therefore, this study aims to predict various risk factors affecting fire by fire type. The predictive analysis of fire factors was carried out targeting Gyeonggi-do, which has the highest number of fires in the country. For the analysis, using machine learning methods SVM (Support Vector Machine), RF (Random Forest), GBRT (Gradient Boosted Regression Tree) the accuracy of each model was presented with a high fit model through MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error), and based on this, predictive analysis of fire factors in Gyeonggi-do was conducted. In addition, using machine learning methods such as SVM (Support Vector Machine), RF (Random Forest), and GBRT (Gradient Boosted Regression Tree), the accuracy of each model was presented with a high-fit model through MAE and RMSE. Predictive analysis of occurrence factors was achieved. Based on this, as a result of comparative analysis of three machine learning methods, the RF method showed a MAE = 1.765 and RMSE = 1.876, as well as the MAE and RMSE verification and test data were very similar with a difference between MAE = 0.046 and RMSE = 0.04 showing the best predictive results. The results of this study are expected to be used as useful data for fire safety management allowing decision makers to identify the sequence of dangers related to the factors affecting the occurrence of fire.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.272-283
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• 2017

In this paper, an improved predictive functional control (PFC) scheme for permanent magnet synchronous motor (PMSM) control system is proposed, on account of the standard PFC method cannot provides a satisfying disturbance rejection performance in the case of strong disturbances. The PFC-based method is first introduced in the control design of speed loop, since the good tracking and robustness properties of the PFC heavily depend on the accuracy of the internal model of the plant. However, in orthodox design of prediction model based control method, disturbances are not considered in the prediction model as well as the control design. A minimum-order observer (MOO) is introduced to estimate the disturbances, which structure is simple and can be realized at a low computational load. This paper adopted the MOO to observe the load torque, and the observations are then fed back into PFC model to rebuild it when considering the influence of perturbation. Therefore, an improved PFC strategy with torque compensation, called the PFC+MOO method, is presented. The validity of the proposed method was tested via simulation and experiments. Excellent results were obtained with respect to the speed trajectory tracking, stability, and disturbance rejection.

• Journal of Power Electronics
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• v.15 no.6
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• pp.1547-1558
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• 2015

A predictive functional control (PFC) scheme for permanent magnet synchronous motor (PMSM) servo systems is proposed in this paper. The PFC-based method is first introduced in the control design of speed loop. Since the accuracy of the PFC model is influenced by external disturbances and speed detection quantization errors of the low distinguishability optical encoder in servo systems, it is noted that the standard PFC method does not achieve satisfactory results in the presence of strong disturbances. This paper adopted the Kalman filter to observe the load torque, the rotor position and the rotor angular velocity under the condition of a limited precision encoder. The observations are then fed back into PFC model to rebuild it when considering the influence of perturbation. Therefore, an improved PFC method, called the PFC+Kalman filter method, is presented, and a high performance PMSM servo system was achieved. The validity of the proposed controller was tested via experiments. Excellent results were obtained with respect to the speed trajectory tracking, stability, and disturbance rejection.

• BMB Reports
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• v.37 no.6
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• pp.648-656
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• 2004

This paper deals with the development of a predictive probabilistic model, a composite dependency-reflecting model (CDRM), which was designed to detect core promoter regions and transcription start sites (TSS) in vertebrate genomic DNA sequences, an issue of some importance for genome annotation. The model actually represents a combination of first-, second-, third- and much higher order or long-range dependencies obtained using the expanded maximal dependency decomposition (EMDD) procedure, which iteratively decomposes data sets into subsets on the basis of dependency degree and patterns inherent in the target promoter region to be modeled. In addition, decomposed subsets are modeled by using a first-order Markov model, allowing the predictive model to reflect dependency between adjacent positions explicitly. In this way, the CDRM allows for potentially complex dependencies between positions in the core promoter region. Such complex dependencies may be closely related to the biological and structural contexts since promoter elements are present in various combinations separated by various distances in the sequence. Thus, CDRM may be appropriate for recognizing core promoter regions and TSSs in vertebrate genomic contig. To demonstrate the effectiveness of our algorithm, we tested it using standardized data and real core promoters, and compared it with some current representative promoter-finding algorithms. The developed algorithm showed better accuracy in terms of specificity and sensitivity than the promoter-finding ones used in performance comparison.

• Journal of Power Electronics
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• v.11 no.4
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• pp.583-590
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• 2011

Three-level NPC inverters have been put into practical use for years especially in high voltage high power grids. This paper researches three-level active power filters (APFs). In this paper a mathematical model in the d-q coordinates is presented for 3-phase 3-wire NPC APFs. The deadbeat control scheme is obtained by using state equations. Canceling the delay of one sampling period and providing the predictive value of the harmonic current is a key problem of the deadbeat control. Based on this deadbeat control, the predictive output current value is obtained by the state observer. The delay of one sampling period is remedied in this digital control system by the state observer. The predictive harmonic command current value is obtained by the repetitive predictor synchronously. The repetitive predictor can achieve a better prediction of the harmonic current with the same sampling frequency, thus improving the overall performance of the system. The experiment results indicate that the steady-state accuracy and the dynamic response are both satisfying when the proposed control scheme is implemented.

• Clinical and Experimental Reproductive Medicine
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• v.48 no.2
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• pp.156-162
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• 2021

Objective: The aim of the present study was to evaluate the predictive capability of fasting-state measurements of glucose and insulin levels alone for abnormal glucose tolerance in women with polycystic ovary syndrome (PCOS). Methods: In total, 153 Korean women with PCOS were included in this study. The correlations between the 2-hour postload glucose (2-hr PG) level during the 75-g oral glucose tolerance test (OGTT) and other parameters were evaluated using Pearson correlation coefficients and linear regression analysis. The predictive accuracy of fasting glucose and insulin levels and other fasting-state indices for assessing insulin sensitivity derived from glucose and insulin levels for abnormal glucose tolerance was evaluated using receiver operating characteristic (ROC) curve analysis. Results: Significant correlations were observed between the 2-hr PG level and most fasting-state parameters in women with PCOS. However, the area under the ROC curve values for each fasting-state parameter for predicting abnormal glucose tolerance were all between 0.5 and 0.7 in the study participants, which falls into the "less accurate" category for prediction. Conclusion: Fasting-state measurements of glucose and insulin alone are not enough to predict abnormal glucose tolerance in women with PCOS. A standard OGTT is needed to screen for impaired glucose tolerance and type 2 diabetes mellitus in women with PCOS.