• Journal of Korea Foundry Society
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• v.32 no.5
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• pp.225-230
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• 2012

In this study, a more practical and simulation approach which can predict the mechanical properties of aluminum alloys is proposed. First, cooling rate, micro-structure, and mechanical properties of casting product were measured through casting experiment. The relation between cooling rate and SDAS decrease exponentially and the linearly decreasing relation exist between SDAS and mechanical properties. Then, the cooling rate was calculated by casting process simulation and the mechanical properties were predicted by using the relations that were derived through experiment. Experimentally measured mechanical properties and predicted values by simulation were in the range of relatively small difference. The mechanical properties of various Al alloys are expected to be predicted by the casting process simulation before actual casting.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.11a
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• pp.54-57
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• 2009

This study was aimed to investigate the effects of visual texture on color emotion and to establish prediction models for color emotion by both physical color properties and visual texture characteristics. A variety of fabrics were printed by digital printer according to hue and tone combinations. Subjective sensation was evaluated in terms of visual texture for fabrics printed in gray whereas color emotion for those in chromatically printed. As results, fabric clusters by visual texture showed significant differences in color emotion factors and the differences were clearer for grayish tone fabrics. Prediction models for color emotion factors by both physical color properties and visual texture clusters were proposed as for all fabrics and grayish ones, respectively.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.6
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• pp.1465-1473
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• 1994

This paper describes the development strategy and contents of a fatigue life evaluation system, FLEVA. The system is composed of 4 parts; material properties, load histories, cycle counting and life prediction. The cycle counting is based on the rain-flow counting method and peak counting method, and the life prediction is performed based on the linear damage rule. Material properties(static, fatigue) are also provided as a database obtained by a computer aided test system. Case study is performed to verify the developed program.

• Journal of international Conference on Electrical Machines and Systems
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• v.2 no.2
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• pp.165-170
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• 2013

This paper presents the iron loss prediction of rotating electric machines taking account of the vector hysteretic properties of electrical steel sheet. The E&S vector hysteresis model is adopted to describe the vector hysteretic properties of a non-oriented electrical steel sheet, and incorporated into finite element analysis (FEA) for magnetic field analysis and iron loss prediction. A permanent magnet synchronous generator is taken as a numerical model, and the analyzed magnetic field distribution and predicted iron loss by using the proposed method is compared with those from a conventional method which employs an empirical iron loss formula with FEA based on a non-linear B-H curve. Through the comparison the effectiveness of the presented method for the iron loss prediction of the rotating machine is verified.

• Bulletin of the Korean Chemical Society
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• v.34 no.4
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• pp.1043-1046
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• 2013

Prediction of physicochemical properties of organic molecules is an important process in chemistry and chemical engineering. The MSEP approach developed in our lab calculates the molecular surface electrostatic potential (ESP) on van der Waals (vdW) surfaces of molecules. This approach includes geometry optimization and frequency calculation using hybrid density functional theory, B3LYP, at the 6-31G(d) basis set to find minima on the potential energy surface, and is known to give satisfactory QSPR results for various properties of organic molecules. However, this MSEP method is not applicable to screen large database because geometry optimization and frequency calculation require considerable computing time. To develop a fast but yet reliable approach, we have re-examined our previous work on organic molecules using two semi-empirical methods, AM1 and PM3. This new approach can be an efficient protocol in designing new molecules with improved properties.

• Journal of Biosystems Engineering
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• v.30 no.6 s.113
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• pp.360-365
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• 2005

Precision agriculture attempts to improve cropping efficiency by variable application of crop treatments such as fertilizers and pesticides, within field on a point-by-point basis. Therefore, a more complete understanding of the relationships between yield and soil properties is of critical importance in precision agriculture. In this study, the functional relationships between measured soil properties and rice yield were investigated. A supervised back-propagation neural network model was employed to relate soil chemical properties and rice yields on a point-by point basis, within individual site-years. As a results, a positive correlation was found between practical yields and predicted yields in 1999, 2000, 2001, and 2002 are 0.916, 0.879, 0.800 and 0.789, respectively. The results showed that significant overfitting for yields with only the soil chemical properties occurred so that more of environmental factors, such as climatological data, variety, cultivation method etc., would be required to predict the yield more accurately.

• Journal of Information Technology Applications and Management
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• v.18 no.4
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• pp.41-53
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• 2011

Trust prediction between users in social network based on the trust propagation assumes properties of transitivity and composability of trust propagation. But it has been hard to find studies which test on how those properties have been operated in real social network. This study aims to validate if the longer the distance of trust paths and the less the numbers of trust paths, the higher prediction error occurs using two real social network data set. As a result, the longer the distance of trust paths, we can find higher prediction error when predicting level of trust between source and target users. But we can not find decreasing trend of prediction error though the possible number of trust paths between source and target users increases.

• Fibers and Polymers
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• v.6 no.3
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• pp.269-276
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• 2005

In this study, cross-cultural comparison of sound sensation for Korean traditional silk fabrics between Korea and America was performed and prediction models for sound sensation by objective measurements including sound parameters such as level pressure of total sound (LPT), Zwicker's psychoacoustic characteristics, and mechanical properties by Kawabata Evaluation System were established for each nation to explore the objective parameters explaining sound sensation of the Korean traditional silk. As results, Koreans felt the silk fabric sounds soft and smooth while Americans were revealed as perceiving them hard and rough. Both Koreans and Americans were pleasant with sounds of Gongdan and Newttong and especially Newttong was preferred more by Americans in terms of sound sensation. In prediction models, some of subjective sensation were found as being related mainly with mechanical properties of traditional silk fabrics such as surface and compressional characteristics.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.2
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• pp.264-274
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• 2001

An integrated fatigue strength assessment system was computerized. The system developed consists of 9 modules: user interface, cycle counting, load history construction, data searching, fatigue properties estimation, fatigue data analysis, true stress and strain analysis, expert system for crack initiation life prediction, fatigue crack initiation and propagation life prediction. Fatigue strength database also was included in this system. The fatigue expert system helps a beginner to predict a fatigue crack initiation life in fatigue strength assessment. The expert system module in this system is developed on the personal computer by using C language and UNiK, an expert system developing tool. To evaluate the system, the results of test under variable loading of SAE and failure data from a field were analyzed. The evaluation show that the system provided fatigue life prediction within 3-scatter band and gave reasonable predictions. To get more accurate predictions of fatigue life without fatigue properties, we recommend utilizing the system along with the fatigue strength database.

• Computers and Concrete
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• v.16 no.3
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• pp.343-356
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• 2015

1078 sets of mixtures in total that include fly ash, slag, and/or silica fume have been collected for prediction on concrete properties. A new database platform (Compos) has been developed, by which the stepwise multiple linear regression (SMLR) and BP artificial neural networks (BP ANNs) programs have been applied respectively to identify correlations between the concrete properties (strength, workability, and durability) and the dosage and/or quality of raw materials'. The results showed obvious nonlinear relations so that forecasting by using nonlinear method has clearly higher accuracy than using linear method. The forecasting accuracy rises along with the increasing of age and the prediction on cubic compressive strength have the best results, because the minimum average relative error (MARE) for 60-day cubic compressive strength was less than 8%. The precision for forecasting of concrete workability takes the second place in which the MARE is less than 15%. Forecasting on concrete durability has the lowest accuracy as its MARE has even reached 30%. These conclusions have been certified in a ready-mixed concrete plant that the synthesized MARE of 7-day/28-day strength and initial slump is less than 8%. The parameters of BP ANNs and its conformation have been discussed as well in this study.