• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.626-628
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• 2018

In this paper, we compare the performances of SVM (Support Vector Machine) and ANN (Artificial Neural Network) machine learning models for predicting solar energy by using meteorological data. Two machine learning models were built by using fifteen kinds of weather data such as long and short wave radiation average, precipitation and temperature. Then the RBF (Radial Basis Function) parameters in the SVM model and the number of hidden layers/nodes and the regularization parameter in the ANN model were found by experimental studies. MAPE (Mean Absolute Percentage Error) and MAE (Mean Absolute Error) were considered as metrics for evaluating the performances of the SVM and ANN models. Sjoem Simulation results showed that the SVM model achieved the performances of MAPE=21.11 and MAE=2281417.65, and the ANN model did the performances of MAPE=19.54 and MAE=2155345.10776.

• The Journal of the Korean dental association
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• v.44 no.2 s.441
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• pp.115-122
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• 2006

Objective : The purpose of this study was to evaluate the dimensional errors between real tooth, 3D CT image and CARP model. Materials and Methods : Two maxilla and two mandible block bones with intact teeth were taken from two cadavers. Computed tomography was taken either in dry state and in wet state. After then, all teeth were extracted and the dimensions of the real teeth were measured using a digital caliper at mesio-distal and bucco-lingual width both in crown and cervical portion. 3D CT image was generated using the V-works $4.0^{TM}$ (Cybemed Inc., Seoul, Korea) software. Twelve teeth were randomly selected for CARP model fabrication. All the measurements of 3D Ct images and CARP models were made in the same manner of the real tooth group. Dimensional errors between real tooth, 3D CT image model and CARP model was calculated. Results : 1) Average of absolute error was 0.199 mm between real teeth and 3D CT image model, 0.169 mm between 3D CT image model and CARP model and 0.291 mm between real teeth and CARP model, respectively. 2) Average size of 3D CT image was smaller than real teeth by 0.149 mm and that of CARP model was smalier than 3D CT image model by 0.067mm. Conclusion : Within the scope of this study, CARP model with the 0.291 mm average of absolute eror can aid to enhance the success rate cf autogenous tooth transplantation due to the increased accuracy of recipient bone and donor tooth.

• International Journal of Highway Engineering
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• v.20 no.2
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• pp.127-135
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• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1439-1448
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• 2022

Background: We investigated the feasibility of in vitro radiosensitivity prediction with gene expression using deep learning. Methods: A microarray gene expression of the National Cancer Institute-60 (NCI-60) panel was acquired from the Gene Expression Omnibus. The clonogenic surviving fractions at an absorbed dose of 2 Gy (SF2) from previous publications were used to measure in vitro radiosensitivity. The radiosensitivity prediction model was based on the convolutional neural network. The 6-fold cross-validation (CV) was applied to train and validate the model. Then, the leave-one-out cross-validation (LOOCV) was applied by using the large-errored samples as a validation set, to determine whether the error was from the high bias of the folded CV. The criteria for correct prediction were defined as an absolute error<0.01 or a relative error<10%. Results: Of the 174 triplicated samples of NCI-60, 171 samples were correctly predicted with the folded CV. Through an additional LOOCV, one more sample was correctly predicted, representing a prediction accuracy of 98.85% (172 out of 174 samples). The average relative error and absolute errors of 172 correctly predicted samples were 1.351±1.875% and 0.00596±0.00638, respectively. Conclusion: We demonstrated the feasibility of a deep learning-based in vitro radiosensitivity prediction using gene expression.

• The Research Journal of the Costume Culture
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• v.2 no.2
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• pp.385-394
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• 1994

Data of the body surface is a necessary unit for the measuring of metabolism energy and activity energy. And also, these data are referring to check the degree of retaining warmth of clothes, to find the effect of heat insulation according to body surface, to calculate an average temperature of skin, and to study the several fields of clothing. In measuring of body surface, it si actually impossible to measure a subject's body surface in each experiment. As the experimental method, both gypsum method, by which the shape of body an be copied as it is, and the weighting method from which planed body surface area can be measured with consistent thickness of polyprophylene film as used. In fact, every female subject feels uncomfortable to measure her body surface as a naked body. There, it is providing a simple, accurate regressive equation with weight & height as variable factors in this study. This equation is as=117.02W+77.31H-3344.94 with average error : 0.1%, absolute average error : 2.07%.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.4
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• pp.316-324
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• 2022

This paper uses seasonal auto-regressive integrated moving average (S-ARIMA), which is efficient in seasonality between time-series models, to predict the degradation tendency for lithium-ion batteries and study a method for improving the predictive performance. The proposed method analyzes the degradation tendency and extracted factors through an electrical characteristic experiment of lithium-ion batteries, and verifies whether time-series data are suitable for the S-ARIMA model through several statistical analysis techniques. Finally, prediction of battery aging is performed through S-ARIMA, and performance of the model is verified through error comparison of predictions through mean absolute error.

• Journal of the Korean Society of Safety
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• v.19 no.2
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• pp.54-60
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• 2004

Flammable substances are frequently used chemical industry processes. An accurate knowledge of the ALTs(Autoignition Temperatures) is important in developing appropriate prevention and control measures in industrial fire protection. The AITs describe the minimum temperature to which a substance must be heated, without the application of a flame or spark, which will cause that substance to ignite. The AITs are dependent upon many factors, namely initial temperature, pressure, volume, fuel/air stoichiometry, catalyst material, concentration of vapor, ignition delay. This study measured relationship between the AITs and the ignition delay times by using ASTM E659-78 apparatus for methanol and ethanol. The A.A.P.E.(Average Absolute Percent Error) and the A.A.D.(Average Absolute Deviation) of the experimental and the calculated delay times by the AITs for methanol were 14.59 and 1.76 respectively. Also the A.A.P.E. and the A.A.D. of the experimental and the calculated delay times by the ATIs for ethanol were 8.33 and 0.88.

• Journal of the Korean Society of Safety
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• v.17 no.4
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• pp.126-132
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• 2002

By using the reference data, the empirical equations which describe the interrelationships of explosion properties and physical properties of n-chlorinated hydrocarbons have been derived. The properties which have been correlated are the lower and upper explosive limits, the stoichiometric coefficients, the heats of combustion, the carbon numbers. Also, the new equations using the mathematical and statistical methods for predicting the temperature dependence of lower explosive limits(LEL) of chlorinated hydrocarbons on the basis of the literature data are proposed. The fire and explosion properties calculated by the proposed equations in this research were a good agrement with literature data within a few A.A.P.E.(Average Absolute Percent Error) and A.A.D.(Average Absolute Deviation.) From a given explosive properties, by using the proposed equations, it is possible to predict to the fire and explosion characteristics for the other chlorinated hydrocarbons.

• Journal of IKEEE
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• v.19 no.3
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• pp.335-341
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• 2015

Recently a demand for high precision absolute position transducer is increasing in order to control thickness in steel industry. LVDT (linear variable differential transformer) is widely used to measure the absolute position in the linearly moving cylinder under poor factory environment. In this paper we implement the three phase LVDT with a high resolution of one micron and L/D (LVDT to digital) converter. First we designed U, V, and W three phase signaling using FPGA. Second a pulse output algorithm is designed for position information with A and B phase waveforms. Finally the performance is compared with previous sensors. Experiments show that the linearity deviation error is 0.009788 [mm] and the average sinusoidal THD is 0.0751%, which means 2.2% and 33% more improved result than the previous sensors respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.10B
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• pp.1832-1840
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• 2000

The error diffusion algorithm is good for reproducing continuous images to binary images. However the reproduction of edge characteristics is weak in power spectrum an analysis of display error. In this paper an edge enhanced error diffusion method is proposed to improve the edge characteristic enhancement. Spatial gradient information in original image is adapted for edge enhance in threshold modulation of error diffusion. First the horizontal and vertical second order differential values are obtained from the gradient of peripheral pixels(3x3) in original image. second weighting function is composed by function including absolute value and sign of second order differential values. The proposed method presents a good visual results which edge characteristics is enhanced. The performance of the proposed method is compared with that of the conventional edge enhanced error diffusion by measuring the edge correlation and the local average accordance over a range of viewing distances and the RAPSD of display error.