• Proceedings of the Korea Concrete Institute Conference
• /
• 2003.05a
• /
• pp.237-242
• /
• 2003

The object of this study is to investigate the strength development properties and the strength prediction of three-component concrete using the fly ash and the blast-furnace slag by a maturity method. The results were as follows. The values of the activation energy on this experiment are calculated as 38.69, 36.47, 32.46, 30.99 KJ/mol in the W/B 60, 55, 50, 45%. And it is considered that the equivalent age can be used to predict strength of the three-component concrete in the optional age. Also the strength of the three-component concrete can be predicted from the result of high correlation between predicted strength and measured strength.

• Proceedings of the KIEE Conference
• /
• 2004.11b
• /
• pp.69-71
• /
• 2004

This paper presents a method to predict failure probability related to aging. To calculate failure probability, the Weibull distribution is used due to age-related reliability. The Weibull distribution has shape and scale parameters. Each estimated parameter is obtained from Data Analytic Method (Type II Censoring) which is relatively simpler and faster than the traditional calculation ways for estimating parameters. Also, this paper shows the calculation procedures of a probabilistic failure prediction through a stochastic data analysis. Consequently, the proposed methods would be likely to permit that the new deregulated environment forces utilities to reduce overall costs while maintaining an age-related reliability index.

• Journal of Animal Science and Technology
• /
• v.52 no.5
• /
• pp.367-374
• /
• 2010

Telomeres at the end of chromosomes consist of tandem repeats of (TTAGGG)n DNA sequence and associated proteins. Telomeres have the essential functions in chromosome stability and genome integrity and are hence related to cell senescence and cancer. This study was carried out to quantify the amount of telomeric DNA and establish age prediction equations by using the quantity of telomeric DNA for cattle. Analysis of the telomere quantity of the lymphocytes was performed at different age, across breeds and between different sexes of cattle. We quantified the amount of telomeric DNA by the Q-FISH technique using the telomeric DNA probe in 460 cattle at age of 1~166 months in Korean Cattle and Holstein breeds. In results, we found that the amount of telomeric DNA decreased gradually with age. The amount of telomeric DNA of Korean Cattle was significantly higher than that of Holstein breed (P<0.01). In addition, the amount of telomeric DNA in male was significantly higher than that in female (P<0.01). Using the relationship between age and the amount of telomeric DNA in cattle, age predicting equations were established as a result of regression analysis. Because sex and breeds influenced telomeric DNA quantity, the age prediction equations were estimated separately in Korean Cattle females and Holstein females. The regression equations were $\hat{Y}$=$38.102X^2$-220.103X + 318.309 (P<0.0001, $R^2$=0.8019) in Korean Cattle females and $\hat{Y}$ = $42.799X^2$ - 199.682X + 242.106 (P<0.0001, $R^2$ = 0.8379) in Holstein females, where the X was quantity of telomeric DNA and Y was predicted age in months. These equations predicted the age of cattle with high significance and accuracy and have high R square values. Thus, it could be possible to scientifically predict the age using the above equations for Korean Cattle and Holstein females.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2012.11a
• /
• pp.107-108
• /
• 2012

The study on the strength prediction using Maturity is mainly focused on, but the study on the concrete mixing blast furnace slag powder is insufficient. The purpose of this study is to investigate the relationships between compressive strength and equivalent age by Maturity function and is to compare and examine the strength prediction of concrete mixing Blast Furnace Slag Power using ACI and Logistic Curve prediction equation. So it is intended that fundamental data are presented for quality management and process management of concrete mixing Blast Furnace Slag Power in the construction field.

• Journal of Forest and Environmental Science
• /
• v.28 no.2
• /
• pp.68-74
• /
• 2012

A total of 45 Pinus sylvestnis var. mongolica trees from 9 plots in northeast China were destructively sampled to develop aboveground prediction equations for inventory application. Sampling plots covered a range of stand ages (12-47-years-old) and densities (450-3,840/ha). The distribution of aboveground biomass of whole-trees and tree component (stems, branches and leaves) of individual trees were studied and 4 equations were developed as functions of diameter at breast height (DBH), total height (HT). All the equations have good estimation effect with high prediction precision over 90%. Forest biomass was estimated based on the individual biomass prediction equations. It was found forest biomass of all organs increased with the increasing of stand age and density. And the period of 45-50 years was the suitable harvest time for Pinus sylvesris plantation.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2012.05a
• /
• pp.251-252
• /
• 2012

The exiting studies on the strength prediction by maturity method is mainly focused on concrete using OPC, meanwhile the study on the concrete mixing blast furnace slag powder (BFSP) is insufficient. The purpose of this study is to investigate the relationships between compressive strength and equivalent age by existing Maturity functions, i.e., Nurse-saul function Arrhenius function. This study also compared and examined the strength prediction of concrete mixing BGSP using ACI model and Logistic Curve prediction equation. Therefore, it is intended that fundamental data are presented for quality management and process management of concrete mixing BFSP.

• Journal of Integrative Natural Science
• /
• v.8 no.4
• /
• pp.305-312
• /
• 2015

The present study showed WHtR to be significantly better than BMI and WC for prediction of metabolic-related diseases in the middle-aged and older people in Korea, based on Bayesian ordered probit model analysis. The variations of WC, BMI and WHtR were compared according to the number of metabolic-related diseases such as hypertension, dyslipidemia, stroke, myocardial infarction, angina pectoris and diabetes. It was found that the three measures showed the similar variation except a very few extreme cases for age less than 40. For subjects over the age of 40, WC was not significant and WHtR gave more influence in greater variability than BMI on the number of metabolic diseases. Also, the rate of change for WHtR was higher than for BMI as the number of metabolic-related diseases increased. Specifically, the difference of the marginal effect of WHtR between no disease and only one disease was 1.81 times higher than that of BMI. Moreover, it was pointed out that the threshold value of WHtR for obesity should be considered differently by age.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.31 no.5
• /
• pp.457-466
• /
• 2007

A numerical and experimental study on the evaluation and the prediction of cleanliness level in the mini-environment system was carried out. Using the concept of local mean air-age (LMA) and effective flow rate, the new direct method for estimating the mini-environment was developed and compared with the previous performance index of airflow pattern characteristics. It was found out that the airflow pattern analysis is a restricted method to estimate the real performance of the mini-environment. The reason is that the airflow pattern cannot predict the effect of the increment of the ventilation rate on the cleanliness level in the mini-environment. While LMA is capable of showing the effects of the contaminant accumulation caused by turbulent intensity, eddy, and the increment of the effective flow rate. This result showed that LMA is more exact and effective performance index than the previous one like the airflow pattern characteristics.

• Journal of Convergence for Information Technology
• /
• v.11 no.7
• /
• pp.1-6
• /
• 2021

The purpose of this study is to improve the performance of the artificial neural network system for facial image analysis through the image landmark selection technique. For landmark selection, a CNN-based multi-layer ResNet model for classification of facial image age is required. From the configured ResNet model, a heat map that detects the change of the output node according to the change of the input node is extracted. By combining a plurality of extracted heat maps, facial landmarks related to age classification prediction are created. The importance of each pixel location can be analyzed through facial landmarks. In addition, by removing the pixels with low weights, a significant amount of input data can be reduced.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
• /
• v.49 no.3
• /
• pp.135-141
• /
• 2023

Objectives: This study aimed to develop and validate machine learning (ML) models using H₂O-AutoML, an automated ML program, for predicting medication-related osteonecrosis of the jaw (MRONJ) in patients with osteoporosis undergoing tooth extraction or implantation. Patients and Methods: We conducted a retrospective chart review of 340 patients who visited Dankook University Dental Hospital between January 2019 and June 2022 who met the following inclusion criteria: female, age ≥55 years, osteoporosis treated with antiresorptive therapy, and recent dental extraction or implantation. We considered medication administration and duration, demographics, and systemic factors (age and medical history). Local factors, such as surgical method, number of operated teeth, and operation area, were also included. Six algorithms were used to generate the MRONJ prediction model. Results: Gradient boosting demonstrated the best diagnostic accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.8283. Validation with the test dataset yielded a stable AUC of 0.7526. Variable importance analysis identified duration of medication as the most important variable, followed by age, number of teeth operated, and operation site. Conclusion: ML models can help predict MRONJ occurrence in patients with osteoporosis undergoing tooth extraction or implantation based on questionnaire data acquired at the first visit.