• Korean Journal of Artificial Intelligence
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• v.11 no.3
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• pp.29-34
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• 2023

Recently, the rapid development of artificial intelligence technology, many studies are being conducted to predict the risk of heart disease in order to lower the mortality rate of cardiovascular diseases worldwide. This study presents exercise or dietary improvement contents in the form of a software app or web to patients with cardiovascular disease, and cardiovascular disease through digital devices such as mobile phones and PCs. LR, LDA, SVM, XGBoost for the purpose of developing "Life style Improvement Contents (Digital Therapy)" for cardiovascular disease care to help with management or treatment We compared and analyzed cardiovascular disease prediction models using machine learning algorithms. Research Results XGBoost. The algorithm model showed the best predictive model performance with overall accuracy of 80% before and after. Overall, accuracy was 80.0%, F1 Score was 0.77~0.79, and ROC-AUC was 80%~84%, resulting in predictive model performance. Therefore, it was found that the algorithm used in this study can be used as a reference model necessary to verify the validity and accuracy of cardiovascular disease prediction. A cardiovascular disease prediction analysis algorithm that can enter accurate biometric data collected in future clinical trials, add lifestyle management (exercise, eating habits, etc.) elements, and verify the effect and efficacy on cardiovascular-related bio-signals and disease risk. development, ultimately suggesting that it is possible to develop lifestyle improvement contents (Digital Therapy).

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.301-306
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• 2023

Attention is a fundamental aspect in the cognitive process of human. Cognitive system of human body requires to focus on selected information among a vast amount of information from sensory organs. It has widely studied that various environmental factors affected the level of attention; however, few researches have aimed to the effect of direct cryotherapy. In this research, level of attention was studied comparing sub-indexes of FAIR test between groups with different duration of direct cryotheapy to the back of neck. FAIR test is a evaluation tool for visual attention consisting of three sub-indexes. Selective attention, accuracy of attention, and persistence of attention can be independently analyzed by FAIR test. In the analysis of selective attention, cryotherapy for 5 to 20 minutes showed higher result than cryotherapy for 40 minutes. In the analysis of persistence of attention, cryotherapy for 5 to 15 minutes showed higher result than cryotherapy for 40 minutes. Overall, selective attention and persistence of attention turns out to be maximized between 5 to 20 minutes of cryotherapy and tends to decrease afterwards. However, accuracy of attention does not seem to be affected by the duration of cryotherapy. Correlation between selective attention and the skin temperature by cryotherapy tends to be negative supporting the findings by ANOVA and post-hoc test. Correlation between persistence of attention and the skin temperature showed similar results.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.215-225
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• 2024

With the rapid development of Internet of Things (IoT) and big data technology, a large amount of data will be generated during the operation of related industries. How to classify the generated data accurately has become the core of research on data mining and processing in IoT industry chain. This study constructs a classification model of IoT industry chain based on improved random forest algorithm and text analysis, aiming to achieve efficient and accurate classification of IoT industry chain big data by improving traditional algorithms. The accuracy, precision, recall, and AUC value size of the traditional Random Forest algorithm and the algorithm used in the paper are compared on different datasets. The experimental results show that the algorithm model used in this paper has better performance on different datasets, and the accuracy and recall performance on four datasets are better than the traditional algorithm, and the accuracy performance on two datasets, P-I Diabetes and Loan Default, is better than the random forest model, and its final data classification results are better. Through the construction of this model, we can accurately classify the massive data generated in the IoT industry chain, thus providing more research value for the data mining and processing technology of the IoT industry chain.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4102-4111
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• 2023

In order to better perform thermal hydraulic calculation and analysis of supercritical water reactor, based on the experimental data of supercritical water, the model training and predictive analysis of the heat transfer coefficient of supercritical water were carried out by using the support vector machine (SVM) algorithm. The changes in the prediction accuracy of the supercritical water heat transfer coefficient are analyzed by the changes of the regularization penalty parameter C, the slack variable epsilon and the Gaussian kernel function parameter gamma. The predicted value of the SVM model obtained after parameter optimization and the actual experimental test data are analyzed for data verification. The research results show that: the normalization of the data has a great influence on the prediction results. The slack variable has a relatively small influence on the accuracy change range of the predicted heat transfer coefficient. The change of gamma has the greatest impact on the accuracy of the heat transfer coefficient. Compared with the calculation results of traditional empirical formula methods, the trained algorithm model using SVM has smaller average error and standard deviations. Using the SVM trained algorithm model, the heat transfer coefficient of supercritical water can be effectively predicted and analyzed.

• International Journal of Contents
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• v.10 no.4
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• pp.75-82
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• 2014

The purpose of this paper was to identify suitable variables for financial distress prediction models and to compare the accuracy of MDA and LA for early warning signals for wind energy companies in Korea. The research methods, discriminant analysis and logit analysis have been widely used. The data set consisted of 15 wind energy SMEs in KOSDAQ with financial statements in 2012 from KIS-Value. We found that five financial ratio variables were statistically significant and the accuracy of MDA was 86%, while that of LA is 100%. The importance of this study is that it demonstrates empirically that financial distress prediction models are applicable to the wind energy industry in Korea as an early warning signs of impending bankruptcy.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.481-488
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• 2005

This study presents a elastic parabolic cable element for initial shaping analysis of cable structures. First, the compatibility condition and the tangent stiffness matrices of the elastic catenary cable element are shortly summarized. Next the force-deformation relations and the tangent stiffness matrices of the elastic parabolic cable elements are derived from the assumption that sag configuration under self-weights is small. To confirm the accuracy of this element, initial shaping analysis of cable-stayed bridges under dead loads is executed. Finally, the accuracy and the validity of the analysis-results are compared and analyzed through numerical examples.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.109-128
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• 2016

This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.

• Proceedings of the Korea Concrete Institute Conference
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• 1999.10a
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• pp.483-486
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• 1999

A series of dynamic and static tests were conducted to observe the actual responses of a 1:5 scale 3-story reinforced concrete (RC) frame which was designed only for gravity loads. One of the major objectives of these experiments are to provide the calibration to the available static and dynamic inelastic techniques. In this study, the experimental results were simulated by using a nonlinear analysis program for reinforced concrete frame, IDARC-2D. The evaluation of the degree of the simulation leads to the conclusion that while the global behaviors such as story drifts and shears can be in general simulated with the limited accuracy in the dynamic nonlinear analysis, it is rather easy and simple to get the fairly high level of accuracy in the prediction of global and local behaviors in the static nonlinear analysis by using IDARC-2D.

• Journal of computational fluids engineering
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• v.19 no.4
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• pp.37-44
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• 2014

A detailed satellite panel thermal model composed of more than thousands nodes can not be directly integrated into a spacecraft thermal model due to its node size and the limitation of commercial satellite thermal analysis programs. For the integration of the panel into the satellite thermal model, a reduced thermal model having proper accuracy is required. A thermal model reduction method was developed and validated by using a geostationary satellite panel. The temperature differences of main components between the detailed and the reduced thermal model were less than $1^{\circ}C$ in steady state analysis. Also, the dynamic responses of the detailed and the reduced thermal model show very similar trends. Thus, the developed reduction method can be applicable to actual satellite thermal design and analysis with resonable accuracy and convenience.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.905-907
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• 1991

One of the general methods to diagnose abnormalities of heart is stethoscopy. This method needs special skill and experiment of doctors and it lacks for objectivity. Electrocardiography(ECG) is another biomedical method which is commonly used to diagnoss abnormalities of heart. The development of PCG is required in recent years to improve objectivity of stethoscopy method. In this paper, PCG is implemented on personal computer and ECG is also included to help the analysis of PCG waveform. Time analysis is used so far, but in this paper the frequency analysis is also considered to improve the accuracy of disgonosis. As future research, recognition of PCG and ECG signal and the Expert System is required to improve the accuracy of diagnosis.