• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.1045-1049
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• 2001

Before building houses or apartments, we need to predict noise propagation to eliminate possible noise problems to residents. However, we do not try to predict noise propagation during estimation of noise effect for the developing area since we did not have a good mathematical model to predict noise level due to a traffic noise. In this article, an adequate mathematical model has been developed and proved to predict noise effect to living area due to a traffic noise.

• Computers and Concrete
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• v.21 no.1
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• pp.103-110
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• 2018

This study introduces the supervised committee fuzzy model as a hybrid fuzzy model to predict compressive strength (CS) of geopolymers prepared from alumina-silica products. For this purpose, more than 50 experimental data that evaluated the effect of $Al_2O_3/SiO_2$, $Na_2O/Al_2O_3$, $Na_2O/H_2O$ and Na/[Na+K] on (CS) of geopolymers were collected from the literature. Then, three different Fuzzy Logic (FL) models (Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL)) were adopted to overcome the inherent uncertainty of geochemical parameters and to predict CS. After validating the model, it was found that the SFL model is superior to MFL and LFL models, but each of the FL models has advantages to predict CS. Therefore, to achieve the optimal performance, the supervised committee fuzzy logic (SCFL) model was developed as a hybrid method to combine the benefits of individual FL models. The SCFL employs an artificial neural network (ANN) model to re-predict the CS of three FL model predictions. The results also show significant fitting improvement in comparison with individual FL models.

• Geomechanics and Engineering
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• v.30 no.6
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• pp.551-564
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• 2022

Fragmenting the rock mass is considered as the most important work in open-pit mines. Ground vibration is the most hazardous issue of blasting which can cause critical damage to the surrounding structures. This paper focuses on developing an explicit model to predict the ground vibration through an multi objective evolutionary polynomial regression (MOGA-EPR). To this end, a database including 79 sets of data related to a quarry site in Malaysia were used. In addition, a gene expression programming (GEP) model and several empirical equations were employed to predict ground vibration, and their performances were then compared with the MOGA-EPR model using the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²) and a20-index. Comparing the results, it was found that the MOGA-EPR model predicted the ground vibration more precisely than the GEP model and the empirical equations, where the MOGA-EPR scored lower MAE and RMSE, 𝜇 and 𝜎 closer to the optimum value, and higher R² and a20-index. Accordingly, the proposed MOGA-EPR model can be introduced as a useful method to predict ground vibration and has the capacity to be generalized to predict other blasting effects.

• Genomics & Informatics
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• v.6 no.4
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• pp.223-226
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• 2008

We introduce a computational approach for analysis of glycosylation in Post-PKS tailoring steps. It is a computational method to predict the deoxysugar biosynthesis unit pathway and the substrate specificity of glycosyltransferases involved in the glycosylation of polyketides. In this work, a directed and weighted graph is introduced to represent and predict the deoxysugar biosynthesis unit pathway. In addition, a homology based gene clustering method is used to predict the substrate specificity of glycosyltransferases. It is useful for the rational design of polyketide natural products, which leads to in silico drug discovery.

• Clinical and Experimental Pediatrics
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• v.48 no.10
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• pp.1033-1037
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• 2005

Pediatric nephrotic syndrome is a well-known illness for its high relapsing rate. If we can predict the relapsing rate and the responses to the steroid therapy of individual patients with nephrotic syndrome, the predictability will be helpful in building a therapeutic plan. Here is my review of research articles on the risk factors for the prediction of relapsing nephrotic syndrome.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.42.1-42
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• 2002

A model to predict population by sex, age and district over a long-range period is proposed based on fuzzy theories. First, a fuzzy model is described. Second, a method to estimate the social increase by sex and age in each district is proposed based on a fuzzy clustering method for dealing with long-range socioeconomic changes in population migration. By the proposed methods, it became possible to predict the population by sex, age and district over a long-range period. Third, the structure and characteristics of the three models of employment model, time distance model, and land use model constructed to predict various socioeconomic indicators, which are require...

• International Journal of Aerospace System Engineering
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• v.3 no.1
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• pp.5-9
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• 2016

This paper briefly introduces efforts into space debris modeling towards a better understanding of space debris environment. Space debris modeling mainly consists of debris generation and orbit propagation. Debris generation can characterize and predict physical properties of fragments originating from explosions or collisions. Orbit propagation can characterize, track, and predict the behavior of individual or groups of space objects. Therefore, space debris modeling can build evolutionary models as essential tools to predict the stability of the future space debris populations. Space debris modeling is also useful and effective to improve the efficiency of measurements to be aware of the present environment.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.149-155
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• 2020

Air pollution is a problem of environmental health risk in big cities. Recently, researchers have proposed using various artificial intelligence technologies to predict air pollution. The proposed model is Cooperative of Artificial Neural Network (ANN) and Fuzzy Inference System (FIS), to predict air pollution of Korean cities using Python. Data air pollutant variables were collected and the Air Korean Web site air quality index was downloaded. This paper's aim was to predict on the health risks and the very unhealthy values of air pollution. We have predicted the air pollution of the environment based on the air quality index. According to the results of the experiment, our model was able to predict a very unhealthy value.

• Nuclear Engineering and Technology
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• v.29 no.2
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• pp.158-166
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• 1997

Integrated performance test of Chemical and Volume Control System was successfully performed in 1994. However, an extensive effort to correct hardware and software problems in the letdown line was required mainly due to the lack of adequate simulation code to predict the test accurately. Although the LTC computer code was used during the YGN 3'||'&'||'4 NSSS design process, the code can not satisfactorily predict the test due to it insufficient letdown line modeling. This study developed a numerical model to simulate the letdown test by modifying the current LTC code, and then verified the model by comparing with the test data. The comparison shows that the modified LTC computer code can predict the transient behavior of letdown system lese very well. Especially, the model was verified to be able to predict the "Stiction (composition of stick and friction)" phenomena which caused instantaneous fluctuations in the letdown backpressure and flowrate. Therefore, it is concluded that the modified LTC computer code with the ability of calculating the "Stiction" phenomena will be very useful for future plant design and test predictions.predictions.

• Journal of the Korean Graphic Arts Communication Society
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• v.16 no.3
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• pp.97-108
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• 1998

Spectral reflectance of the object should be measured to predict the color of object under various illuminants. The spectral reflectance can be represented in a multi-dimension space. Generally the information of inputed image by digital camera and color scanner is represented with 3-dimension color signals such as RGB. In other to predict the color of inputed image under any illuminant, we should be estimated spectral reflectance of the object. In this paper, we described the method to predict spectral reflectance by einenvector using the skin color of printed image, confirmed availability and propriety through experiment. we estimated spectral reflectance of skin color taken by RGB color signals and than reproduced skin color according to various illuminants on CRT.