• Journal of the Korean Society of Physical Medicine
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• v.19 no.3
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• pp.47-54
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• 2024

PURPOSE: This study aimed to identify the potential clinically predictive indicators of the age-related loss of skeletal muscle mass (ALSMM) in middle-aged women. METHODS: The data from a cross-sectional study involving 2,066 community-dwelling female participants aged 40 to 49 years were analyzed. Complex sampling analyses were used to ensure a nationally representative analysis, incorporating the individual weights provided by KNHANES. This approach accounted for the stratified, clustered, and multistage probability sampling design of the survey. The participants were screened for ALSMM, and various potential predictive indicators were assessed, including age, height, weight, body mass index, waist circumference, skeletal muscle mass index, smoking and drinking status, systolic and diastolic blood pressure, fasting glucose levels, triglyceride levels, and cholesterol levels. RESULTS: Significant potential predictive indicators for ALSMM included height, weight, body mass index, waist circumference, skeletal muscle mass index, and fasting glucose (p < .05). The systolic blood pressure, diastolic blood pressure, triglyceride levels triglyceride, and drinking and smoking status were found to be non-significant variables (p > .05). CONCLUSION: The study identified the potential predictive indicators for ALSMM among community-dwelling middle-aged women. These findings enhance the current understanding of ALSMM and highlight the potential predictive indicators associated with its development in middle-aged women.

• Journal of Power Electronics
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• v.18 no.2
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• pp.445-455
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• 2018

A novel algorithm is proposed to regulate the neutral point potential in neutral point clamped three-level inverters. Oscillations of the neutral point potential and an unbalanced dc-link voltage cause distortions of the output voltage. Large capacitors, which make the application costly and bulky, are needed to eliminate oscillations. Thus, the algorithm proposed in this paper utilizes the finite-control-set model predictive control and the multistage medium vector to solve these issues. The proposed strategy consists of a two-step prediction and a cost function to evaluate the selected multistage medium vector. Unlike the virtual vector method, the multistage medium vector is a mixture of the virtual vector and the original vector. In addition, its amplitude is variable. The neutral point current generated by it can be used to adjust the neutral point potential. When compared with the virtual vector method, the multistage medium vector contributes to decreasing the regulation time when the modulation index is high. The vectors are rearranged to cope with the variable switching frequency of the model predictive control. Simulation and experimental results verify the validity of the proposed strategy.

• Journal of the Korean Statistical Society
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• v.22 no.2
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• pp.235-247
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• 1993

The predictive density function of a potential future observation and its first four moments are obtained in this paper to study the effects of a non-normal prior of the unknown mean of a normal population. The derived predictive density function is modified to study changes in utility curves, used to choose the optimum treatment from a given set of treatments, at a given level of stimulus due to slight deviations from normality of the prior distribution. Numerical illustrations are provided to exhibit some effectsl.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.11
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• pp.48-56
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• 2005

Predictive filter theory proposed recently can be characterized by inherent advantages of estimating modelling error and overcoming the disadvantage of the Kalman filter theory. A one-step ahead error is minimized to produce optimized filter performance in the form of the predictive filter. The main advantage of this filter lies in the ability to estimate both state vector and system model error. In this paper, attitude estimation results based upon the predictive filter theory is addressed. Mathematical formulation for estimating bias signal is peformed by using the predictive filter theory, and attitude estimation based upon vector observation is presented. From the results of this study, the potential applicability of the predictive filter is highlighted.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1068-1071
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• 1993

An algorithm of fuzzy predictive sliding control is proposed to design a jet engine control system. Sliding control using predictive scheme is adopted to compensate the time delay of fuel injector. Fuzzy rule-base is also introduced to adjust the command input for suppressing the surge. The potential of the proposed algorithm is shown through simulations utilizing a typical engine-only model.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.149-156
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• 2020

The genesis of ore bodies is a very diverse and complex process, and the target depth of mineral exploration increases. These create a need for predictive mineral exploration, which may be facilitated by the advancement of machine learning and geological database. In this study, we confirm that the faults and igneous rocks distributions and magnetic data can be used as input data for potential mapping using deep neural networks. When the input data are constructed with faults, igneous rocks, and magnetic data, we can build a potential mapping model of the metal deposit that has a predictive accuracy greater than 0.9. If detailed geological and geophysical data are obtained, this approach can be applied to the potential mapping on a mine scale. In addition, we confirm that the magnetic data, which provide the distribution of the underground igneous rock, can supplement the limited information from the surface igneous rock distribution. Therefore, rather than simply integrating various data sets, it will be more important to integrate information considering the geological correlation to genesis of minerals.

• Journal of Power Electronics
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• v.13 no.6
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• pp.1016-1023
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• 2013

Predictive current control offers the potential for achieving more precise current control with a minimum of distortion and harmonic noise. However, the predictive method is difficult to implement and has a greater computational burden. This paper introduces a theoretical analysis and experimental verification for an improved predictive current control technique applied to single phase grid connected voltage source inverters (VSI). The proposed technique has simple calculations. An ATmega1280 microcontroller board is used to implement the proposed technique for a simpler and cheaper control system. To enhance the current performance and to obtain a minimum of current THD, an improved tri-level PWM switching strategy is proposed. The proposed switching strategy uses six operation modes instead of four as in the traditional strategy. Simulation results are presented to demonstrate the system performance with the improved switching strategy and its effect on current performance. The presented experimental results verify that the proposed technique can be implemented using fixed point 8-bit microcontroller to obtain excellent results.

• Journal of Preventive Medicine and Public Health
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• v.39 no.5
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• pp.433-437
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• 2006

Objectives: The aim of this study is to develop a methodology for estimating a nationwide statistic for hernia operations with using the claim database of the Korea Health Insurance Cooperation (KHIC). Methods: According to the insurance claim procedures, the claim database was divided into the electronic data interchange database (EDI_DB) and the sheet database (Paper_DB). Although the EDI_DB has operation and management codes showing the facts and kinds of operations, the Paper_DB doesn't. Using the hernia matched management code in the EDI_DB, the cases of hernia surgery were extracted. For drawing the potential cases from the Paper_DB, which doesn't have the code, the predictive model was developed using the data mining technique called SEMMA. The claim sheets of the cases that showed a predictive probability of an operation over the threshold, as was decided by the ROC curve, were identified in order to get the positive predictive value as an index of usefulness for the predictive model. Results: Of the claim databases in 2004, 14,386 cases had hernia related management codes with using the EDI system. For fitting the models with applying the data mining technique, logistic regression was chosen rather than the neural network method or the decision tree method. From the Paper_DB, 1,019 cases were extracted as potential cases. Direct review of the sheets of the extracted cases showed that the positive predictive value was 95.3%. Conclusions: The results suggested that applying the data mining technique to the claim database in the KHIC for estimating the nationwide surgical statistics would be useful from the aspect of execution and cost-effectiveness.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.227-242
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• 2015

This paper proposes a weighting factor optimization method in predictive control algorithm for torque ripple reduction in an induction motor fed by an indirect matrix converter (IMC). In this paper, the torque ripple behavior is analyzed to validate the proposed weighting factor optimization method in the predictive control platform and shows the effectiveness of the system. Therefore, an optimization method is adopted here to calculate the optimum weighting factor corresponds to minimum torque ripple and is compared with the results of conventional weighting factor based predictive control algorithm. The predictive control algorithm selects the optimum switching state that minimizes a cost function based on optimized weighting factor to actuate the indirect matrix converter. The conventional and introduced weighting factor optimization method in predictive control algorithm are validated through simulations and experimental validation in DS1104 R&D controller platform and show the potential control, tracking of variables with their respective references and consequently reduces the torque ripple.

• 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.