• Bulletin of the Korean Space Science Society
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• 1996.05a
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• pp.20-20
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• 1996

• Advances in concrete construction
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• v.13 no.2
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• pp.123-132
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• 2022

This paper aims to adapt Multilinear regression (MLR) to predict the strength and toughness of SIFCON containing various pozzolanic materials. Slurry Infiltrated Fibrous Concrete (SIFCON) is one of the most common terms used in concrete manufacturing, known for its benefits such as high ductility, toughness and high ultimate strength. Assessment of compressive strength (CS.), flexural strength (F.S.), splitting tensile strength (STS), dynamic elasticity modulus (DME) and impact energy (I.E.) using the experimental approach is too costly. It is time-consuming, and a slight error can lead to a repeat of the test and, to solve this, alternative methods are used to predict the strength and toughness properties of SIFCON. In the present study, the experimentally investigated SIFCON data about various mix proportions are used to predict the strength and toughness properties using regression analysis-multilinear regression (MLR) models. The input parameters used in regression models are cement, fibre, fly ash, Metakaolin, fine aggregate, blast furnace slag, bottom ash, water-cement ratio, and the strength and toughness properties of SIFCON at 28 days is the output parameter. The models are developed and validated using data obtained from the experimental investigation. The investigations were done on 36 SIFCON mixes, and specimens were cast and tested after 28 days of curing. The MLR model yields correlation between predicted and actual values of the compressive strength (C.S.), flexural strength, splitting tensile strength, dynamic modulus of elasticity and impact energy. R-squared values for the relationship between observed and predicted compressive strength are 0.9548, flexural strength 0.9058, split tensile strength 0.9047, dynamic modulus of elasticity 0.8611 for impact energy 0.8366. This examination shows that the MLR model can predict the strength and toughness properties of SIFCON.

• Publications of The Korean Astronomical Society
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• v.11 no.1
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• pp.263-277
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• 1996

We have intensively carried out numerical calculations on flare predictions from the solar activity data for photospheric sunspots, chromospheric flare and plages, coronal X-ray intensities and 2800MHz radio fluxes, by using multilinear regression method. Intensities of solar flares for the next day have been predicted from the solar data between 1977-1982 and 1993-1996. Firstly, we have calculated flare predictions with the multilinear regression method, by using separate solar data in growth and decay phase of sunspot area and magnetic field strength from the whole data on solar activities. Secondly, the same operations as above have been made for the remaining data after removal of the data with large deviation from the mean calculated by the above prediction method. we have reached a conclusion that average hit ratio of correct predictions to total predictions of flares with class of M5 over has been as high as 70% for the first case and that of correct prediction number to total observation number has been shown as 61%.

• Journal of The Korean Astronomical Society
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• v.40 no.4
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• pp.99-106
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• 2007

Statistical analyses were performed to investigate the relative success and accuracy of daily maximum X-ray flux (MXF) predictions, using both multilinear regression and autoregressive time-series prediction methods. As input data for this work, we used 14 solar activity parameters recorded over the prior 2 year period (1989-1990) during the solar maximum of cycle 22. We applied the multilinear regression method to the following three groups: all 14 variables (G1), the 2 so-called 'cause' variables (sunspot complexity and sunspot group area) showing the highest correlations with MXF (G2), and the 2 'effect' variables (previous day MXF and the number of flares stronger than C4 class) showing the highest correlations with MXF (G3). For the advanced three days forecast, we applied the autoregressive timeseries method to the MXF data (GT). We compared the statistical results of these groups for 1991 data, using several statistical measures obtained from a $2{\times}2$ contingency table for forecasted versus observed events. As a result, we found that the statistical results of G1 and G3 are nearly the same each other and the 'effect' variables (G3) are more reliable predictors than the 'cause' variables. It is also found that while the statistical results of GT are a little worse than those of G1 for relatively weak flares, they are comparable to each other for strong flares. In general, all statistical measures show good predictions from all groups, provided that the flares are weaker than about M5 class; stronger flares rapidly become difficult to predict well, which is probably due to statistical inaccuracies arising from their rarity. Our statistical results of all flares except for the X-class flares were confirmed by Yates' $X^2$ statistical significance tests, at the 99% confidence level. Based on our model testing, we recommend a practical strategy for solar X-ray flare predictions.

• Earthquakes and Structures
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• v.16 no.2
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• pp.197-208
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• 2019

The objective of this study is to propose new seismic intensity parameters based on the Hilbert spectrum and to associate them with the seismic damage potential. In recent years the assessment of even more seismic features derived from the seismic acceleration time-histories was associated with the structural damage. For a better insight into the complex seismic acceleration time-history, Hilbert-Huang Transform (HHT) analysis is utilized for its processing, and the Hilbert spectrum is obtained. New proposed seismic intensity parameters based on the Hilbert spectrum are derived. The aim is to achieve a significant estimation of the seismic damage potential on structures from the proposed new intensity parameters confirmed by statistical methods. Park-Ang overall structural damage index is used to describe the postseismic damage status of structures. Thus, a set of recorded seismic accelerograms from all over the word is applied on a reinforced concrete frame structure, and the Park-Ang indices through nonlinear dynamic analysis are provided and considered subsequently as reference numerical values. Conventional seismic parameters, with well-known seismic structural damage interrelation, are evaluated for the same set of excitations. Statistical procedures, namely correlation study and multilinear regression analysis, are applied on the set of the conventional parameters and the set of proposed new parameters separately, to confirm their interrelation with the seismic structural damage. The regression models are used for the evaluation of the structural damage indices for every set of parameters, respectively. The predicted numerical values of the structural damage indices evaluated from the two sets of seismic intensity parameters are inter-compared with the reference values. The numerical results confirm the ability of the proposed Hilbert spectrum based new seismic intensity parameters to approximate the postseismic structural damage with a smaller Standard Error of Estimation than this accomplished of the conventional ones.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.2
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• pp.78-84
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• 2007

Quantitative analysis of dynamic brain PET data using a tracer kinetic modeling has played important roles in the investigation of functional and molecular basis of various brain diseases. Parametric imaging of the kinetic parameters (voxel-wise representation of the estimated parameters) has several advantages over the conventional approaches using region of interest (ROI). Therefore, several strategies have been suggested to generate the parametric images with a minimal bias and variability in the parameter estimation. In this paper, we will review the several approaches for parametric imaging with linearized methods which include graphical analysis and mulilinear regression analysis.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.6
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• pp.175-182
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• 2013

Lately, customers think highly of the emotional satisfaction and as a result, issues on odor are matters of concern. The cases are odor of interior material and air-conditioner of vehicles. In particualar, with respect to the odor of air-conditioner, customers strongly claimed defects with provocative comments : "It smells like something rotten," "It smells like a foot odor," "It stinks like a rag." Generally, it is known that mold of evaporator core in the air-conditioning system decays and this produce VOCs which causes the odor to occur. In this study, partial least squares regression model is applied to predict the strength of the odor and select of important VOCs which affect car air conditioning smell. The PLS method is basically a particular multilinear regression algorithm which can handle correlated inputs and limited data. The number of latent variable is determined by the point which is stabilized mean absolute deviations of VOCs data. Also multiple linear regression is carried out to confirm the validity of PLS method.

• Computers and Concrete
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• v.24 no.3
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• pp.271-282
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• 2019

Concrete undergoes a series of thermo-based physio-chemical changes once exposed to elevated temperatures. Such changes adversely alter the composition of concrete and oftentimes lead to fire-induced explosive spalling. Spalling is a multidimensional, complex and most of all sophisticated phenomenon with the potential to cause significant damage to fire-exposed concrete structures. Despite past and recent research efforts, we continue to be short of a systematic methodology that is able of accurately assessing the tendency of concrete to spall under fire conditions. In order to bridge this knowledge gap, this study explores integrating novel artificial intelligence (AI) techniques; namely, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA), together with traditional statistical analysis (multilinear regression (MLR)), to arrive at state-of-the-art procedures to predict occurrence of fire-induced spalling. Through a comprehensive datadriven examination of actual fire tests, this study demonstrates that AI techniques provide attractive tools capable of predicting fire-induced spalling phenomenon with high precision.

• Safety and Health at Work
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• v.11 no.3
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• pp.314-321
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• 2020

Background: Occupational accidents occur for a variety of reasons, such as unsafe behaviors of workers and insufficient safety equipment at the workplace, but there are also various economic and social factors that can impact working conditions and working environment. This study analyzed the relationship between changes in economic factors and the occurrence of occupational accidents in Korea. Methods: Multilinear regression analysis was used as the analysis model. The general to specific method was also used, which consecutively removes statistically insignificant variables from a general model that includes dependent variables and lagged variables of dependent variables. Results: The frequency of occupational accidents was found to have a statistically significant relationship to economic indicators. The monthly number of cases of occupational injury and disease and fatal occupational injuries were found to be closely related to manufacturing capacity utilization, differences in the production index in the services sector, and commencements of building construction. The increase in equipment investment indicators was found to reduce fatal occupational injuries. Conclusion: The results of this study may be used to develop occupational accident trends or leading indicators, which in turn can be used by organizations that manage and monitor occupational accidents toward taking administrative action designed to reduce occupational accidents. The results also imply that short-term and mid- to long-term economic and social changes that can impact workers, workplaces and working conditions, and workplace organizations must be taken into account if more effective government policies are to be established and implemented toward further prevention of occupational accidents.

• YAKHAK HOEJI
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• v.46 no.5
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• pp.324-330
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• 2002

This experiment was carried out to determine non-destructively the hydrogen peroxide concentration of 3％ antiseptic hydrogen peroxide solutions by portable near-infrared (NIR) system. Hydrogen peroxide standards were prepared ranging from 0 to 25.6 w/w％ and the NIR spectra of hydrogen peroxide standard solutions were collected by using a quartz cell in 1 mm pathlength. We found the variation of absorbance band due to OH vibration of hydrogen peroxide depending on the concentration around 1400 nm in the second derivatives spectra. Partial least square regression (PLSR) and multilinear regression (MLR) were explored to develop a calibration model over the spectral range 1100-1720 nm. The model using PLSR was better than that using MLR. The calibration showed good results with a standard error of prediction (SEP) of 0.16％. In order to validate the developed calibration model, routine analyses were performed using commercial antiseptic hydrogen peroxide solutions. The hydrogen peroxide values from the NIR calibration model were compared with the values from a redox titration method. The NIR routine analyses results showed good correlation with those of the redox titration method. This study showed that the rapid and non-destructive determination of hydrogen peroxide in the antiseptic solution was successfully performed by portable NIR system without very harmful solvents.