• Journal of Biosystems Engineering
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• v.15 no.3
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• pp.219-229
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• 1990

The compression creep behavior of grains when loaded depends not only on load but also on duration of load application. The most common methods of studying the load-time characteristics of agricultural products is by employing rheological models such as Burger's model. However it is sometimes not sufficient to describe the viscoelastic behavior of grains to be Burger's model. For this reason, this study was conducted to develop the rheological model which represented the creep compliance response of the rough rice kernel and was a function of initial stress applied and time. The effects of the initial stress applied and the moisture content on the compression creep behavior of the rough rice kernel were analyzed. The results were obtained from the study as follows: 1. Since the viscoelastic behavior of the rough rice kernel was nonlinear, the transient and steady state creep compliance was satisfactorily modelled as follows: $$J({\sigma},t)=A{\sigma}^B[C+Dt-exp(-Ft)]$$ But, for the every stress applied, the compression creep behavior of the samples tested can be well described by Burger's model respectively. 2. The creep compliance, the instantaneous elastic strain, the retarded elastic strain and the viscous strain of the sample tested generally increased in magnitude with increasing the applied initial stress and the moisture content used in the tests. At low moisture content, the creep compliance for the Japonica-type rough rice kernel Was a little higher than those for Indica-type and at high moisture content, vice versa at high moisture content. 3. The retardation times of the samples had not an uniform tendency by the initial stress and the moisture content. The retardation times ranged from 0.66 to 6.76 seconds, and the creep progressed from transient to steady state at a relatively high rate. 4. The less viscous strain than the instantaneous elastic strain for the samples tested indicated that rough rice kernel behaved as a viscoelastic body characterized by elasticity than viscosity.

• Archives of design research
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• v.16 no.2
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• pp.57-66
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• 2003

In order to develop color image, color simulation based on data processing techniques has been developed and applied to data interpretation tools or product design supporting systems. It has been a commonmethod to use image key words to search for data and provide color coordination samples that determine computer combination in computerized support systems until recently. However, this method does not reflect system designers and users taste or preference on making final choices of color coordination samples because the database was designed based on an assumption of standardized group that was established database from large scaled image evaluation research. In this study, we suggest a color coordination simulator that supports designer's final decision-making procedure on sample groups through the simulation of various color combination. The simulator allows communications with the system to explore a designer's color combination taste and preference, and provides a user for an efficient environment to judge the optimum result. The color coordination simulator was designed based upon Dual mapping model derived from a designer's thought process, and four steps of operations longrightarrowdefining color concept longrightarrowmaking color sample groupslongrightarrow simulation-determining ranking among final combination samples - will be assisting color design process.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.463-477
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• 2009

Parameter estimation methods such as maximum likelihood estimation method, probability weighted moments method, regression method have been popularly applied to various extreme value models in numerous literature. Among three methods above, the performance of regression method has not been rigorously investigated yet. In this paper the regression method is compared with the other methods via Monte Carlo simulation studies for estimation of parameters of the Generalized Extreme Value(GEV) distribution and the Generalized Pareto(GP) distribution. Our simulation results indicate that the regression method tends to outperform other methods under small samples by providing smaller biases and root mean square errors for estimation of location parameter of the GEV model. For the scale parameter estimation of the GP model under small samples, the regression method tends to report smaller biases than the other methods. The regression method tends to be superior to other methods for the shape parameter estimation of the GEV model and GP model when the shape parameter is -0.4 under small and moderately large samples.

• Composites Research
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• v.29 no.6
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• pp.401-407
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• 2016

IPMC is composed of thin ion conductive polymer film sandwiched between metallic electrodes plated on both surfaces. Ionic Polymer-Metal Composite (IPMC) generates voltages when bent by mechanical stimuli. IPMC has a potential for the variety of energy harvesting applications due to its soft and hydrophilic characteristics. However, the large-scale implementation is necessary to increase the output power. In this paper, the scale-up of surface area effect on voltage generation characteristics of IPMC was investigated using IPMC samples with different surface areas. Also, a circuit model simulating both the output voltage and its offset variations was designed for estimating the voltages from IPMC samples. The proposed model simulated the output voltages with offsets well corresponding to various frequencies of input bending motion. However, some samples showed that the increase of error between real and simulated voltages with time due to the nonlinear characteristic of offset variations.

• Smart Structures and Systems
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• v.26 no.4
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• pp.403-418
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• 2020

In recent years, the use of Fiber Reinforced Polymers (FRPs) as one of the most common ways to increase the strength of concrete samples, has been introduced. Evaluation of the final strength of these specimens is performed with different experimental methods. In this research, due to the variety of models, the low accuracy and impact of different parameters, the use of new intelligence methods is considered. Therefore, using artificial intelligent-based models, a new solution for evaluating the bond strength of FRP is presented in this paper. 150 experimental samples were collected from previous studies, and then two new hybrid models of Imperialist Competitive Algorithm (ICA)-Artificial Neural Network (ANN) and Artificial Bee Colony (ABC)-ANN were developed. These models were evaluated using different performance indices and then, a comparison was made between the developed models. The results showed that the ICA-ANN model's ability to predict the bond strength of FRP is higher than the ABC-ANN model. Finally, to demonstrate the capabilities of this new model, a comparison was made between the five experimental models and the results were presented for all data. This comparison showed that the new model could offer better performance. It is concluded that the proposed hybrid models can be utilized in the field of this study as a suitable substitute for empirical models.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1025-1038
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• 2014

Linear mixed model has often been utilized for genetic association analysis with family-based samples. The correlation matrix for family-based samples is constructed with kinship coefficient and assumes that parental phenotypes are independent and the amount of correlations between parent and offspring is same as that of correlations between siblings. However, for instance, there are positive correlations between parental heights, which indicates that the assumption for correlation matrix is often violated. The statistical validity and power are affected by the appropriateness of assumed variance covariance matrix, and in this thesis, we provide the linear mixed model with flexible variance covariance matrix. Our results show that the proposed method is usually more efficient than existing approaches, and its application to genome-wide association study of body mass index illustrates the practical value in real data analysis.

• Mass Spectrometry Letters
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• v.10 no.1
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• pp.11-17
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• 2019

Drosophila melanogaster (fruits fly) is a representative model system widely used in biological studies because its brain function and basic cellular processes are similar to human beings. The whole head of the fly is often used to obtain the key function in brain-related diseases like degenerative brain diseases; however the biomolecular distribution of the head may be slightly different from that of a brain. Herein, lipid profiles of the head and dissected brain samples of Drosophila were studied using electrospray ionization-mass spectrometry (ESI-MS). According to the sample types, the detection of phospholipid ions was suppressed by triacylglycerol (TAG), or the specific phospholipid signals that are absent in the mass spectrum were measured. The lipid distribution was found to be different in the wild-type and the microRNA-14 deficiency model ($miR-14{\Delta}^1$) with abnormal lipid metabolism. A few phospholipids were also profiled by comparison of the head and the brain in two fly model systems. The mass spectra showed that the phospholipid distributions in the $miR-14{\Delta}^1$ model and the wild-type were different, and principal component analysis revealed a correlation between some phospholipids (phosphatidylethanolamine (PE), phosphatidylinositol (PI), and phosphatidylserine (PS)) in $miR-14{\Delta}^1$. The overall results suggested that brain-related lipids should be profiled using fly samples after dissection for more accurate analysis.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.4
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• pp.27-39
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• 2024

Soil texture is determined by the proportions of sand, silt, and clay within the soil, which influence characteristics such as porosity, water retention capacity, electrical conductivity (EC), and pH. Traditional classification of soil texture requires significant sample preparation including oven drying to remove organic matter and moisture, a process that is both time-consuming and costly. This study aims to explore an alternative method by developing an AI model capable of predicting soil texture from images of pre-sorted soil samples using computer vision and deep learning technologies. Soil samples collected from agricultural fields were pre-processed using sieve analysis and the images of each sample were acquired in a controlled studio environment using a smartphone camera. Color distribution ratios based on RGB values of the images were analyzed using the OpenCV library in Python. A convolutional neural network (CNN) model, built on PyTorch, was enhanced using Digital Image Processing (DIP) techniques and then trained across nine distinct conditions to evaluate its robustness and accuracy. The model has achieved an accuracy of over 80% in classifying the images of pre-sorted soil samples, as validated by the components of the confusion matrix and measurements of the F1 score, demonstrating its potential to replace traditional experimental methods for soil texture classification. By utilizing an easily accessible tool, significant time and cost savings can be expected compared to traditional methods.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.60 no.1
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• pp.57-70
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• 2024

In order to present a predictive drift model, Jeju National University's training ship was tested for about 11 hours and 40 minutes, and 81 samples that selected one of the entire samples at ten-minute intervals were subjected to regression analysis after verifying outliers and influence points. In the outlier and influence point analysis, although there is a part where the wind direction exceeds 1 in the DFBETAS (difference in Betas) value, the CV (cumulative variable) value is 6%, close to 1. Therefore, it was judged that there would be no problem in conducting multiple regression analyses on samples. The standard regression coefficient showed how much current and wind affect the dependent variable. It showed that current speed and direction were the most important variables for drift speed and direction, with values of 47.1% and 58.1%, respectively. The analysis showed that the statistical values indicated the fit of the model at the significance level of 0.05 for multiple regression analysis. The multiple correlation coefficients indicating the degree of influence on the dependent variable were 83.2% and 89.0%, respectively. The determination of coefficients were 69.3% and 79.3%, and the adjusted determination of coefficients were 67.6% and 78.3%, respectively. In this study, a more quantitative prediction model will be presented because it is performed after identifying outliers and influence points of sample data before multiple regression analysis. Therefore, many studies will be active in the future by combining them.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.227-235
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• 1998

In this paper, we consider a hierarchical Bayes estimation of the parameter, the reliability and hazard rate function based on type-II censored samples from a Rayleigh failure model. Bayes calculations can be implemented easily by means of the Gibbs sampler. A numerical study is provided.