• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.3
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• pp.313-319
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• 2009

The blasting has a lot of economic efficiency and speediness but it can damage to a neighbor structure, a domestic animal and a cultured fish due to the blasting vibration, then the public grievance is increased. Therefore, we need to manage the blasting vibration efficiently. The prediction of the correct vibration velocity is not easy because there are lots of different kinds of the scale of blasting vibration and it has a number of a variable effect. So we figure the optimum line through the least-squares regression by using the vibration data measured in hard rock blasting and compared with the design vibration prediction equation. As a result, we confirm that the vibration estimated in this paper is bigger than the design vibration prediction equation in the same charge and distance. If there is a Gaussian normal distribution data on the left-right side of the least squares regression, then we can estimate the vibration prediction equation on reliability 50%(${\beta}=0$), 90%(${\beta}=1.28$), 95%(${\beta}=1.64$). 99.9%(${\beta}=3.09$). As a result, it appears to be suitable that the reliability is 99% at the transverse component, the reliability 95% is at the vertical component, the reliability 90% is at the longitudinal component and the reliability is 95% at the peak vector sum component.

• Journal of Environmental Impact Assessment
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• v.33 no.2
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• pp.84-98
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• 2024

This study proposed a prediction equation for the estimation of blasting vibaration and blasting noise, utilizing 320 datasets for the blasting vibration and blasting noise acquired during urban blasting works in the Incheon, Suwon, Wonju, and Yangsan regions. The proposed blasting vibration prediction equation, derived from regression analysis, indicated correlation coefficients of 0.879 and 0.890 for SRSD and CRSD, respectively, with an R² value exceeding 0.7. In the case of the blasting noise prediction equation, stepwise regression analysis yielded a correlation coefficient of 0.911 between the prediction values and real measurements for the blasting nosie, and further analysis to determine the constant value revealed a correlation coefficient of 0.881, with an R² value also exceeding 0.7. These results suggest the feasibility of applying the proposed prediction equations when environmental impact assessments or education environment evaluation according to urban development or apartment construction projects is performed.

• Journal of the Korean Society of Tobacco Science
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• v.21 no.1
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• pp.95-101
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• 1999

This study was carried out to analyze chemical components in burley tobacco using near infrared spectroscopy(NIRS). Samples were collected in '96 and '97 crop year. Calibration equations were developed by modified partial least square. The standard error performance(SEP) of '96 crop year samples between NIRS and standard laboratory analysis were 0.25% for nicotine, 0.18% for total nitrogen, 0.59% for crude ash, 0.32% for ether extracts, and 0.14% for chlorine, respectively. The analytical results of '97 crop year samples were similar to those of '96 crop year samples. The analytical result of '97 crop year samples analyzed by '96 calibration equation was more inaccurate than that of '96 crop year samples. The SEP of '96 or '97 crop year samples applying calibration equation derived from '96 plus '97 crop year samples was similar to that of '96 or '97 crop year samples analyzed by '96 or '97 calibration equation, respectively. The SEP of '97 crop year samples analyzed by calibration equation derived from '96 plus '97 crop year samples was more accurate than that of '97 crop year samples analyzed by '96 calibration equation. To improve the analytical inaccuracy caused by the difference of crop year between calibration and prediction samples, we need to include the prediction sample spectra which were different from calibration sample spectra in recalibration sample spectra, and then develop recalibration equation. The NIRS can apply to analyze burley leaf tobacco, leaf process or tobacco manufacturing process which were required the rapid analytical result.

• Fire Science and Engineering
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• v.21 no.2 s.66
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• pp.48-53
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• 2007

The knowledge of the flash point of the mixtures is very important for prevention and protection of fire in the industrial field. The flash points for the water+2-propanol system were measured by using Tag open-cup apparatus(ASTM D1310-86). The experimental data were compared with the values calculated by the Raoult's law, the Van Laar equation and the NRTL(Non Random Two Liquids) equation. The calculated values based on the Van Laar and NRTL equations were found to be better than those based on the Raoult's law. It was concluded that Van Laar and NRTL equations were more effective than the Raoult' law at describing the activity coefficients for non-ideal solution such as the water+2-propanol system. And the predictive curve of the flash point prediction model based on the Van Law equation described the experimentally-derived data more effectively than was the case when the prediction model was based upon the NRTL equation.

• Animal Bioscience
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• v.34 no.10
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• pp.1623-1631
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• 2021

Objective: This study aimed to validate and evaluate the dry matter (DM) intake prediction model of the Korean feeding standards for dairy cattle (KFSD). Methods: The KFSD DM intake (DMI) model was developed using a database containing the data from the Journal of Dairy Science from 2006 to 2011 (1,065 observations 287 studies). The development (458 observations from 103 studies) and evaluation databases (168 observations from 74 studies) were constructed from the database. The body weight (kg; BW), metabolic BW (BW^0.75, MBW), 4% fat-corrected milk (FCM), forage as a percentage of dietary DM, and the dietary content of nutrients (% DM) were chosen as possible explanatory variables. A random coefficient model with the study as a random variable and a linear model without the random effect was used to select model variables and estimate parameters, respectively, during the model development. The best-fit equation was compared to published equations, and sensitivity analysis of the prediction equation was conducted. The KFSD model was also evaluated using in vivo feeding trial data. Results: The KFSD DMI equation is 4.103 (±2.994)+0.112 (±0.022)×MBW+0.284 (±0.020)×FCM-0.119 (±0.028)×neutral detergent fiber (NDF), explaining 47% of the variation in the evaluation dataset with no mean nor slope bias (p>0.05). The root mean square prediction error was 2.70 kg/d, best among the tested equations. The sensitivity analysis showed that the model is the most sensitive to FCM, followed by MBW and NDF. With the in vivo data, the KFSD equation showed slightly higher precision (R² = 0.39) than the NRC equation (R² = 0.37), with a mean bias of 1.19 kg and no slope bias (p>0.05). Conclusion: The KFSD DMI model is suitable for predicting the DMI of lactating dairy cows in practical situations in Korea.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.4
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• pp.299-308
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• 2004

In this paper, we propose a probabilistic framework to predict the interaction probability of proteins. The notion of domain combination and domain combination pair is newly introduced and the prediction model in the framework takes domain combination pair as a basic unit of protein interactions to overcome the limitations of the conventional domain pair based prediction systems. The framework largely consists of prediction preparation and service stages. In the prediction preparation stage, two appearance probability matrices, which hold information on appearance frequencies of domain combination pairs in the interacting and non-interacting sets of protein pairs, are constructed. Based on the appearance probability matrix, a probability equation is devised. The equation maps a protein pair to a real number in the range of 0 to 1. Two distributions of interacting and non-interacting set of protein pairs are obtained using the equation. In the prediction service stage, the interaction probability of a Protein pair is predicted using the distributions and the equation. The validity of the prediction model is evaluated for the interacting set of protein pairs in Yeast organism and artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in DIP database are used as teaming set of interacting protein pairs, very high sensitivity(86%) and specificity(56%) are achieved within our framework.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.9
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• pp.1335-1344
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• 2015

A set of prediction equations to estimate the nitrogen-corrected apparent metabolizable energy (AMEn) of individual ingredients and diets used in the poultry feed industry was evaluated. The AMEn values of three energy ingredients (maize, sorghum and defatted maize germ meal), four protein ingredients (soybean meal, maize gluten meal 60% crude protein, integral micronized soy and roasted whole soybean) and four diets (three containing four feedstuffs, complex diets, and one containing only corn-soybean meal, basal diet) were determined using a metabolism assay with male broilers from 1 to 7, 8 to 21, 22 to 35, and 36 to 42 days old. These values were compared to the AMEn values presented in the tables of energy composition or estimated by equation predictions based on chemical composition data of feedstuffs. In general, the equation predictions more precisely estimated the AMEn of feedstuffs when compared to the tables of energy composition. The equation AMEn (dry matter [DM] basis) = 4,164.187+51.006 ether extract (% in DM basis)-197.663 ash-35.689 crude fiber (% in DM basis)-20.593 neutral detergent fiber (% in DM basis) ($R^2=0.75$) was the most applicable for the prediction of the energy values of feedstuffs and diets used in the poultry feed industry.

• Journal of the Korean Geotechnical Society
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• v.19 no.1
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• pp.221-228
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• 2003

In land reclamation construction using marine clay, a measure of material function, that is, the relation between void ratio-effective stress and permeability, is very important aspect for the prediction of self-weight consolidation behavior. But reclaimed ground has very high water content, so there are many difficulties in the laboratory test for measuring material function. For this reason, some researches are carried out using slurry cconsolidometr to measure material function. In this study, material function was measured using slurry consolidometer, and to overcome the shortcoming of researches using slurry cosolidometer, an equation for the prediction of material function was proposed on the basis of column test's parameter. Material function was determined through low stress consolidation test and permeability test, and it also was calculated with the equation using column test parameter. The continuity of material function could be confirmed through these tests. Material function is easily determined with the equation proposed in this study, and can be used for the prediction of self-weight consolidation behavior.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.4
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• pp.615-622
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• 2020

Objective: The objectives were to investigate correlations between energy digestibility (digestible energy [DE]:gross energy [GE]) and various fiber types including crude fiber (CF), total dietary fiber (TDF), soluble dietary fiber (SDF), insoluble dietary fiber (IDF), neutral detergent fiber (NDF), and acid detergent fiber (ADF), and to develop prediction equations for estimating DE in feed ingredients and diets for growing pigs. Methods: A total of 289 data with DE values and chemical composition of feeds from 39 studies were used to develop prediction equations for DE. The equations were validated using values provided by the National Research Council. Results: The DE values in feed ingredients ranged from 2,011 to 4,590 kcal/kg dry matter (DM) and those in diets ranged from 2,801 to 4,203 kcal/kg DM. In feed ingredients, DE:GE was negatively correlated (p<0.001) with NDF (r = -0.84), IDF (r = -0.83), TDF (r = -0.82), ADF (r = -0.78), and CF (r = -0.72). A best-fitting model for DE (kcal/kg) in feed ingredients was: 1,356 + (0.704 × GE, kcal/kg) - (60.3 × ash, %) - (27.7 × NDF, %) with R² = 0.80 and p<0.001. In diets, DE:GE was negatively correlated (p<0.01) with NDF (r = -0.72), IDF (r = -0.61), TDF (r = -0.52), CF (r = -0.45), and ADF (r = -0.34). A best-fitting model for DE (kcal/kg) in diets was: 1,551 + (0.606 × GE, kcal/kg) - (22.1 × ash, %) - (25.6 × NDF, %) with R² = 0.62 and p<0.001. All variables are expressed as DM basis. The equation developed for DE in feed ingredients had greater accuracy than a published equation for DE. Conclusion: All fiber types are reasonably good independent variables for predicting DE of swine feeds. The best-fitting model for predicting DE of feeds employed neutral detergent fiber as an independent variable.

• Korean Journal of Community Nutrition
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• v.19 no.6
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• pp.568-580
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• 2014

Objectives: The purpose of this study was to measure the resting metabolic rate (RMR) and to assess the accuracy of RMR predictive equations for Korean farmers. Methods: Subjects were 161 healthy Korean farmers (50 males, 111 females) in Gangwon-area. The RMR was measured by indirect calorimetry for 20 minutes following a 12-hour overnight fasting. Selected predictive equations were Harris-Benedict, Mifflin, Liu, KDRI, Cunningham (1980, 1991), Owen-W, F, FAO/WHO/UNU-W, WH, Schofield-W, WH, Henry-W, WH. The accuracy of the equations was evaluated on the basis of bias, RMSPE, accurate prediction and Bland-Altman plot. Further, new RMR predictive equations for the subjects were developed by multiple regression analysis using the variables highly related to RMR. Results: The mean of the measured RMR was 1703 kcal/day in males and 1343 kcal/day in females. The Cunningham (1980) equation was the closest to measured RMR than others in males and in females (males Bias -0.47%, RMSPE 110 kcal/day, accurate prediction 80%, females Bias 1.4%, RMSPE 63 kcal/day, accurate prediction 81%). Body weight, BMI, circumferences of waist and hip, fat mass and FFM were significantly correlated with measured RMR. Thus, derived prediction equation as follow : males RMR = 447.5 + 17.4 Wt, females RMR = 684.5 - 3.5 Ht + 11.8 Wt + 12.4 FFM. Conclusions: This study showed that Cunningham (1980) equation was the most accurate to predict RMR of the subjects. Thus, Cunningham (1980) equation could be used to predict RMR of Korean farmers studied in this study. Future studies including larger subjects should be carried out to develop RMR predictive equations for Korean farmers.