• Computers and Concrete
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• v.17 no.4
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• pp.489-498
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• 2016

In this paper, the flexural strength ($f_{fs}$) and splitting tensile strength ($f_{sts}$) of concrete containing different proportions of fly ash have been modeled by using gene expression programming (GEP). Two GEP models called GEP-I and GEP-II are constituted to predict the $f_{fs}$ and $f_{sts}$ values, respectively. In these models, the age of specimen, cement, water, sand, aggregate, superplasticizer and fly ash are used as independent input parameters. GEP-I model is constructed by 292 experimental data and trisected into 170, 86 and 36 data for training, testing and validating sets, respectively. Similarly, GEP-II model is constructed by 278 experimental data and trisected into 142, 70 and 66 data for training, testing and validating sets, respectively. The experimental data used in the validating set of these models are independent from the training and testing sets. The results of the statistical parameters obtained from the models indicate that the proposed empirical models have good prediction and generalization capability.

• Journal of Environmental Science International
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• v.23 no.9
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• pp.1537-1549
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• 2014

The objectives of this study were to analyze reservoir trophic state, based on Trophic State Index (TSI), spatial variation patterns of three zones (riverine, transition, and lacustrine zone), and empirical models through 20-years long-term data analysis. Trophic variables of TP and CHL-a were highest during the summer monsoon, and decreased along the main axis from the riverine to lacustrine zone. In the mean time, TN did not show the trend. Ratios of N:P and Secchi disc transparency (SD) increased from the riverine to lacustrine zone. The analysis of trophic state index (TSI) showed that mean TSI (TP) and TSI (CHL-a) were 62 and 57, respectively, and these values were highest in the transition zone during the summer. This zone should be managed well due to highest lake water pollution. The analysis of Trophic State Index Deviation (TSID) showed that algal growth was primarily limited by light penetration, and this was most pronounced in the monsoon season. The analysis of empirical models showed that the value of $R^2$, based on CHL-SD model, was 0.30 (p < 0.0001) in the transition zone and the $R^2$, based on TP-SD model, was 0.41 (p < 0.0001) in the transition zone.

• Journal of Ship and Ocean Technology
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• v.11 no.1
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• pp.11-24
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• 2007

Better prediction of a ship's manouevrabilty in initial design stage is becoming more, important as IMO manoeuvring criteria has been activated in the year of 2004. In the present study, in order to obtain more exact and reliable results for ship manoeuvrability in the initial design stage, numerical simulation is carried out by use of RANS equation based calculation of hydrodynamic forces exerted upon the ship hull. Other forces such as rudder force and propeller force are estimated by one of the empirical models recommended by MMG Group. Calculated hydrodynamic force coefficients are compared with those obtained by empirical models. Standard manoeuvring simulations such as turning circle and zig-zag are also carried out for a medium size Product Carrier and the results are compared with those of pure empirical models and manoeuvring sea trial. Generally good qualitative agreement is obtained in hydrodynamic forces due to steady oblique motion and steady turning motion between the results of CFD calculation and those of MMG model, which is based on empirical formulas. The results of standard manoeuvring simulation also show good agreement with sea trial results.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.489-490
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• 2015

We present population synthesis models for the calcium II triplet (CaT), currently the most popular metallicity indicator, based on high-resolution empirical spectral energy distributions (SEDs). Our new CaT models, based on empirical SEDs, show a linear correlation below [Fe/H] ~ -0.5, but the linear relation breaks down in the metal-rich regime by converging to the same equivalent width. This relation shows good agreement with the observed CaT of globular clusters (GCs) in NGC 1407 and the Milky Way. However, a model based on theoretical SEDs does not show this feature of the CaT and fails to reproduce observed GCs in the metal-rich regime. This linear relation may cause inaccurate metallicity determination for metal-rich stellar populations. We have also confirmed that the effect of horizontal-branch stars on the CaT is almost negligible in models based on both empirical and theoretical SEDs. Our new empirical model may explain the difference between the color distributions and CaT distributions of GCs in various early-type galaxies. Based on our model, we claim that the CaT is not a good metallicity indicator for simple stellar populations in the metal-rich regime.

• Journal of Korea Technology Innovation Society
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• v.5 no.2
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• pp.129-140
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• 2002

This study is try to do the empirical tests on the relationship among innovation cycle, investment cycle, and business cycle suggested in recent economic growth models. We apply co-spectra analysis to estimate dynamic correlations in the extraction HP filtered variables and first difference filtered variables in our data set. Our empirical results are; (i) an existing asynchronization between innovation cycle and investment cycle, (ii) in the long frequency, an existing positive correlation between innovation cycle and business cycle, (iii) in the short frequency, however, a finding the high negative correlation between the two cycle. Our empirical findings support the recent growth through cycle models and suggest some economic policy implementations for economic stabilization during a severe business cycle.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.355-368
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• 2023

This article introduces the R package ELCIC (https://cran.r-project.org/web/packages/ELCIC/index.html), which provides an empirical likelihood-based information criterion (ELCIC) for model selection that includes, but is not limited to, variable selection. The empirical likelihood is a semi-parametric approach to draw statistical inference that does not require distribution assumptions for data generation. Therefore, ELCIC is more robust and versatile in the context of model selection compared to the currently existing information criteria. This paper illustrates several applications of ELCIC, including its use in generalized linear models, generalized estimating equations (GEE) for longitudinal data, and weighted GEE (WGEE) for missing longitudinal data under the mechanisms of missing at random and dropout.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.6
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• pp.433-439
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• 2022

Muscle fatigue models to predict maximum endurance time (MET) are broadly classified as either 'empirical' or 'theoretical'. Empirical models are based on fitting experimental data and theoretical models on mathematical representations of physiological process. This paper examines the effectiveness of dynamic muscle fatigue model as theoretical model to predict maximum endurance time during forearm isometric contraction. Forty volunteers (20 females, 20 males) are participated in this study. Empirical models (exponential model and power model) and theoretical model (dynamic muscle fatigue model) are used to compare. Mean absolute deviation (MAD), correlation coefficient (r) and intraclass correlation (ICC) are calculated between theoretical model and empirical models. MAD are below 3.5%p, r and ICC are above 0.93 and 0.87, respectively. This results demonstrate that dynamic muscle fatigue model as theoretical model is valid to predict MET.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.338-346
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• 2023

During isometric elbow flexion, forearm length should be an important factor to determine not only joint torque but also maximum endurance time (MET), when the forearm is perpendicular to the direction of the force. The purpose of this paper is to examine the effect of forearm length as an additional factor on empirical models of MET such as an exponential model and a power model during isometric elbow flexion. Thirty volunteers participated in our experiment to measure factor variables such as circumferences and lengths of their upper and lower arms. Their METs were measured according to the percent of maximum voluntary contraction intensity (%MVC). For the multiple linear regression model of ln(MET) using these measurements, significant variables could be observed in %MVC and forearm lengths (P<0.05). The empirical models were assessed by these models using forearm length as the additional factor. Mean absolute deviations (MAD) between the measured METs amd the two empirical models were about 19.4 [s], but MAD using models applied forearm lengths were reduced to about 16.2 [s]. The correlation coefficients and intraclass correlation coefficients were about 0.87, but those applied forearm lengths were increased to about 0.91. These results demonstrated that forearm length was a significant additional factor to the empirical model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1518-1539
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• 2024

Artificial intelligence-enabled business models aim to improve decision-making, operational efficiency, innovation, and productivity. The presented systematic literature review is conducted to highlight elucidating the utilization of artificial intelligence (AI) methods and techniques within AI-enabled businesses, the significance and functions of AI-enabled organizational models and frameworks, and the design parameters employed in academic research studies within the AI-enabled business domain. We reviewed 39 empirical studies that were published between 2010 and 2023. The studies that were chosen are classified based on the artificial intelligence business technique, empirical research design, and SLR search protocol criteria. According to the findings, machine learning and artificial intelligence were reported as popular methods used for business process modelling in 19% of the studies. Healthcare was the most experimented business domain used for empirical evaluation in 28% of the primary research. The most common reason for using artificial intelligence in businesses was to improve business intelligence. 51% of main studies claimed to have been carried out as experiments. 53% of the research followed experimental guidelines and were repeatable. For the design of business process modelling, eighteen AI mythology were discovered, as well as seven types of AI modelling goals and principles for organisations. For AI-enabled business models, safety, security, and privacy are key concerns in society. The growth of AI is influencing novel forms of business.

• Journal of Food Hygiene and Safety
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• v.19 no.1
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• pp.19-24
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• 2004

Dose-response models in microbial risk assessment can be divided into biologically plausible models and empirical models. Biologically plausible models are formed by the assumptions in dose distribution of microbes, host sensitivity to microbes, and minimal infectious dose of microbes : there are Exponential model and $\beta$-Poisson model, representatively. Empirical models are mainly used to express the toxicity of chemicals : there are Weibull-Gamma model etc. Deviance function (Y) is used to fit available data to dose-response models, and some dose-response models for food-borne pathogens are developed in humans and experimental animals.