• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.14 no.1
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• pp.1-7
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• 2018

Materials in nuclear power plants can be embrittled by neutron irradiation. According to existing studies, the effect of the material property by irradiation embrittlement can be approximately simulated by cold working (pre-strain). In this study, finite element damage analysis method using the stress-modified fracture strain model is proposed to predict J-Resistance curves of irradiated SUS316 stainless steel. Experimental data of pre-strained SUS316 stainless steel material are obtained from literature and the damage model is determined by simulating the tensile and fracture toughness tests. In order to consider damage caused by the pre-strain, a pre-strain constant is newly introduced. Experimental J-Resistance curves for various degrees of pre-strain are well predicted.

• Journal of Oral Medicine and Pain
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• v.23 no.3
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• pp.263-270
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• 1998

Considering various factors contributing oral malodor, the accurate prediction of prognosis is very important to both clinician and patients. The present study has been performed to invetigate the relationship between treatment effeicacy and pre-treatment volatile sulfur compounds (VSC) level. Ninety patients were divided into three groups, A(<150ppb), B(150< <200ppb), and C(>200ppb) groups, according to pre-treatment VSC level detected by Halimeter, and each group included 30 patients. Routine therapeutic measures for oral were provided to each patient which consisted of oral prophylaxis, tooth brushing and flossing instruction, tongue scraping by proper device, and gargling of 0.25% ZnCl2 Solution. The group with high pre-treatment VSC level (>150ppb) showed significant reduction of VSC level at 1 and 3 weeks after. However, the group with low pre-treatment VSC level (<150ppb) did not show any significant reduction during the experimental periods. Collectively, the results suggested that patients with high pre-treatment VSC level show better prognosis.

• Ocean and Polar Research
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• v.23 no.2
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• pp.189-194
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• 2001

A practical algorithm for prediction of the sea surface temperatures (SST)from the satellite remote sensing data is presented in this paper. The fluctuations of SST consist of deterministic normals and stochastic anomalies. Due to large thermal inertia of sea water, the SST anomalies can be modelled by autoregressive or Markov process, and its near future values can be predicted provided the recent values of SST are available. The actual SST is predicted by superposing the pre-known SST normals and the predicted SST anomalies. We applied this prediction algorithm to the NOAA AVHRR weekly SST data for 18 years (1981-1998) in the seas adjacent to Korea (115-$145^{\circ}E$, 20-$55^{\circ}N$). The algorithm is applicable not only for prediction of SST in near future but also for nowcast of SST in the cloud covered regions.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.809-819
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• 2020

Gender prediction accuracy increases as convolutional neural network (CNN) architecture evolves. This paper compares voting and ensemble schemes to utilize the already trained five CNN models to further improve gender prediction accuracy. The majority voting usually requires odd-numbered models while the proposed softmax-based voting can utilize any number of models to improve accuracy. The ensemble of CNN models combined with one more fully-connected layer requires further tuning or training of the models combined. With experiments, it is observed that the voting or ensemble of CNN models leads to further improvement of gender prediction accuracy and that especially softmax-based voters always show better gender prediction accuracy than majority voters. Also, compared with softmax-based voters, ensemble models show a slightly better or similar accuracy with added training of the combined CNN models. Softmax-based voting can be a fast and efficient way to get better accuracy without further training since the selection of the top accuracy models among available CNN pre-trained models usually leads to similar accuracy to that of the corresponding ensemble models.

• Genomics & Informatics
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• v.16 no.4
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• pp.32.1-32.7
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• 2018

Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient's prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.

• New & Renewable Energy
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• v.18 no.2
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• pp.18-25
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• 2022

In this study, we propose a wind power generation prediction system that applies machine learning and data mining to predict wind power generation. This system increases the utilization rate of new and renewable energy sources. For time-series data, the data set was established by measuring wind speed, wind generation, and environmental factors influencing the wind speed. The data set was pre-processed so that it could be applied appropriately to the model. The prediction system applied the CNN (Convolutional Neural Network) to the data mining process and then used the LSTM (Long Short-Term Memory) to learn and make predictions. The preciseness of the proposed system is verified by comparing the prediction data with the actual data, according to the presence or absence of data mining in the model of the prediction system.

• Journal of the Korean Geotechnical Society
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• v.19 no.5
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• pp.15-26
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• 2003

Urban excavation requires highly reliable prediction technique for the design and construction of earth retaining structure in order to protect adjacent structures around deep excavation. Application of the pre-loading of bracing for deep excavation has been reported, and the known beneficial effects are not fully understood and recognized by many practitioners. Model tests have been carried out to evaluate the efficiency of pre-loading system in reducing ground settlement as well as prediction of structural damage around excavation in sand. The test results revealed that the applied pre-loading of 50% and 70% showed about 20% of reduction in horizontal wall displacement and 30∼40% reduction in ground settlement. Also, bracing forces and earth pressure distribution behind the wall have been monitored during pre-loading at various excavation stages.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.487-492
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• 2022

Universities are looking for various methods to enhance educational competence level suitable for the rapidly changing social environment. This study suggests a method to promote academic and educational achievements by reducing drop-out rate from their majors through implementation of pre-survey of satisfaction that revised and complemented survey items. To supplement the CQI method implemented after a general satisfaction survey, a pre-survey of satisfaction was carried out. To consolidate students' competences, this study made prediction and analysis of data with more importance possible using the Random Forest of the machine learning technique that can be applied to AI Medici platform, whose design is underway. By pre-processing the pre-survey of satisfaction, the students information enrolled in classes were defined as an explanatory variable, and they were classified, and a model was created and learning was conducted. For the experimental environment, the algorithms and sklearn library related in Jupyter notebook 3.7.7, Python 3.7 were used together. This study carried out a comparative analysis of change in educational satisfaction survey, carried out after classes, and trends in the drop-out students by reflecting the results of the suggested method in the classes.

• Atmosphere
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• v.24 no.4
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• pp.491-502
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• 2014

As a part of the KIAPS (Korea Institute of Atmospheric Prediction Systems) Package for Observation Processing (KPOP), we have developed the modules for Advanced Microwave Sounding Unit-A (AMSU-A) pre-processing and its bias correction. The KPOP system calculates the airmass bias correction coefficients via the method of multiple linear regression in which the scan-corrected innovation and the thicknesses of 850~300, 200~50, 50~5, and 10~1 hPa are respectively used for dependent and independent variables. Among the four airmass predictors, the multicollinearity has been shown by the Variance Inflation Factor (VIF) that quantifies the severity of multicollinearity in a least square regression. To resolve the multicollinearity, we adopted simple linear regression and Principal Component Regression (PCR) to calculate the airmass bias correction coefficients and compared the results with those from the multiple linear regression. The analysis shows that the order of performances is multiple linear, principal component, and simple linear regressions. For bias correction for the AMSU-A channel 4 which is the most sensitive to the lower troposphere, the multiple linear regression with all four airmass predictors is superior to the simple linear regression with one airmass predictor of 850~300 hPa. The results of PCR with 95% accumulated variances accounted for eigenvalues showed the similar results of the multiple linear regression.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1283-1293
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• 2022

The purpose of this study is to predict residual chlorine in order to maintain stable residual chlorine concentration in sedimentation basin by using artificial intelligence algorithms in water treatment process employing pre-chlorination. Available water quantity and quality data are collected and analyzed statistically to apply into mathematical multiple regression and artificial intelligence models including multi-layer perceptron neural network, random forest, long short term memory (LSTM) algorithms. Water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage data are used as the input parameters to develop prediction models. As results, it is presented that the random forest algorithm shows the most moderate prediction result among four cases, which are long short term memory, multi-layer perceptron, multiple regression including random forest. Especially, it is result that the multiple regression model can not represent the residual chlorine with the input parameters which varies independently with seasonal change, numerical scale and dimension difference between quantity and quality. For this reason, random forest model is more appropriate for predict water qualities than other algorithms, which is classified into decision tree type algorithm. Also, it is expected that real time prediction by artificial intelligence models can play role of the stable operation of residual chlorine in water treatment plant including pre-chlorination process.