• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 가을 학술논문 발표대회
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• pp.87-88
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• 2022

The use of converter slag as an aggregate for concrete is limited due to the risk of expansion. This study analyzed the substances causing the expansion of converter slag and evaluated the possibility of its use as an aggregate for concrete through separation and selection. As a result of the experiment, it was confirmed that CaO and MgO were concentrated in the slag particles inducing expansion, and it was confirmed that it was possible to separate them from non-expanded particles through magnetic.

• Computers and Concrete
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• 제17권5호
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• pp.613-628
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• 2016

In this study, the influence of coarse aggregate size and type on chloride penetration of concrete was investigated, and the grey correlation analysis was applied to find the key influencing factor. Furthermore, the proposed 6-10-1 artificial neural network (ANN) model was constructed, and performed under the MATLAB program. Training, testing and validation of the model stages were performed using 81 experiment data sets. The results show that the aggregate type has less effect on the concrete permeability, compared with the size effect. For concrete with a lower w/b, the coarse aggregate with a larger particle size should be chose, however, for concrete with a higher w/c, the aggregate with a grading of 5-20 mm is preferred, too large or too small aggregates are adverse to concrete chloride diffusivity. A new idea for the optimum selection of aggregate to prepare concrete with a low penetration is provided. Moreover, the ANN model predicted values are compared with actual test results, and the average relative error of prediction is found to be 5.62%. ANN procedure provides guidelines to select appropriate coarse aggregate for required chloride penetration of concrete and will reduce number of trial and error, save cost and time.

• Asian-Australasian Journal of Animal Sciences
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• 제17권2호
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• pp.159-163
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• 2004

Data pertaining to first lactation records of 316 Murrah buffaloes, progeny of 47 sires, maintained at NDRI Farm for a period of 18 years were analysed to construct selection indices and to examine their robustness by changing the relative economic values of different economic traits. A total of 120 selection indices were constructed for three sets of relative economic values ( 40 for each set) considering different combinations of seven first lactation traits viz. age at first calving (AFC), first lactation 305 day or less milk yield (FLMY), first lactation length (FLL), first calving interval (FCI), milk yield per day of first lactation length (MY/FLL), milk yield per day of first calving interval (MY/FCI) and milk yield per day age at second calving (MY/ASC). The three sets of relative economic values were based on economic values of different traits, 1% standard deviation of different traits and regression of different traits on FLMY. The 'optimum' indices for the first two sets had five traits each namely AFC, FLMY, FLL, FCI and MY/ASC giving improvement in aggregate genotype of Rupees 269.11 and Rs. 174.88, respectively. The accuracy of selection from both indices was 70.79 and 69.39%, respectively. The 'best' selection index from the third set of data again had five traits (AFC, FLMY, FLL, FCI and MY/FLL) giving genetic gain of Rs. 124.16 and accuracy of selection of 71.81%. The critcal levels or break-even points for FLMY for varying levels of AFC and FCI estimated from the "optimum index" suggested the need of enhancement of present production level of the herd or reduction of AFC or FCI. It was concluded that economic values of various first lactation traits were the most appropriate to construct selection indices as compared to other criteria of assigning relative economic weights in Murrah buffaloes.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2005년도 봄학술 발표회 논문집(II)
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• pp.89-92
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• 2005

An experimental study was conducted to study the mechanical properties of recycled aggregate concrete in accordance with the different replacement ratios of recycled fine and coarse aggregate, ranging from 0$\%$ to 30$\%$ and 0$\%$ to 50$\%$, respectively. According to increase of these replacement ratios, compressive strengths and elastic modulus are reduced down to $10\∼20\%$ and $15\∼30\%$, respectively. The reducing ratios of elastic modulus are more distinct than that of compressive strength. For the selection of replacement ratios of recycled aggregate for structural concrete properly, it is necessary to evaluate the elastic modulus carefully.

• Geomechanics and Engineering
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• 제23권2호
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• pp.115-125
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• 2020

This study aims to explore practical and useful equations for rapid evaluation of uniaxial compressive strength of concrete (UCS-C) during the preliminary design stage of aggregate selection. For this purpose, aggregates which were produced from eight different intact rocks were used in the production of concretes. Laboratory experiments involved the tests for uniaxial compressive strength (UCS-R), point load index (PLI-R), P wave velocity (UPV-R), apparent porosity (n-R), unit weight (UW-R) and aggregate impact value (AIV-R) of the rock samples. UCS-C, point load index (PLI-C) and P wave velocity (UPV-C) of concrete samples were also determined. Relationships between UCS-R-rock parameters and UCS-C-concrete parameters were developed by regression analyses. In the simple regression analyses, PLI-C, UPV-C, UCS-R, PLI-R, and UPV-R were found to be statistically significant independent variables to estimate the UCS-C. However, higher coefficients of determination (R²=0.97-1.0) were obtained by multiple regression analyses. The results of simple regression analysis were also compared to the limited number of previous studies. The strength conversion factor (k) values were found to be 14.3 and 14.7 for concrete and rock samples, respectively. It is concluded that the UCS-C can roughly be estimated from derived equations only for the specified rock types.

• 한국도로학회:학술대회논문집
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• 한국도로학회 2009년도 추계학술대회 논문집
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• pp.549-552
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• 2009

• 산업경영시스템학회지
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• 제38권3호
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• pp.159-168
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• 2015

Aggregate Production Planning determines levels of production, human resources, inventory to maximize company's profits and fulfill customer's demands based on demand forecasts. Since performance of aggregate production planning heavily depends on accuracy of given forecasting demands, choosing an accurate forecasting method should be antecedent for achieving a good aggregate production planning. Generally, typical forecasting error metrics such as MSE (Mean Squared Error), MAD (Mean Absolute Deviation), MAPE (Mean Absolute Percentage Error), and CFE (Cumulated Forecast Error) are utilized to choose a proper forecasting method for an aggregate production planning. However, these metrics are designed only to measure a difference between real and forecast demands and they are not able to consider any results such as increasing cost or decreasing profit caused by forecasting error. Consequently, the traditional metrics fail to give enough explanation to select a good forecasting method in aggregate production planning. To overcome this limitation of typical metrics for forecasting method this study suggests a new metric, WACFE (Weighted Absolute and Cumulative Forecast Error), to evaluate forecasting methods. Basically, the WACFE is designed to consider not only forecasting errors but also costs which the errors might cause in for Aggregate Production Planning. The WACFE is a product sum of cumulative forecasting error and weight factors for backorder and inventory costs. We demonstrate the effectiveness of the proposed metric by conducting intensive experiments with demand data sets from M3-competition. Finally, we showed that the WACFE provides a higher correlation with the total cost than other metrics and, consequently, is a better performance in selection of forecasting methods for aggregate production planning.

• Computers and Concrete
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• 제14권5호
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• pp.547-561
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• 2014

Artificial neural networks (ANN) has been proven to be able to predict the compressive strength and elastic modulus of recycled aggregate concrete (RAC) made with recycled aggregates (RAs) from different sources. However, ANN is itself like a black box and the output from the model cannot generate an exact mathematical model that can be used for detailed analysis. So in this study, sensitivity analysis is conducted to further examine the influence of each selected factor on the output value of the models. This is not only conducive to the determination and selection of the more important factors affecting the results, but also can provide guidance for researchers in adjusting mix proportions appropriately when designing RAC based on the variation of these factors.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2014년도 춘계 학술논문 발표대회
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• pp.270-271
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• 2014

In this study, a replacement ratio of blast furnace slag coarse aggregate and a water binder ratio by an optimum combination of PHC file was investigated. As a results, the target strength 78.5MPa was altogether satisfied in a mix proportion 28-G100-SG0 and W/B ratio 26 %. The surface rupture was generated in 28-G0-SG100 combination after curing with the autoclave. According to the result of measuring the ingredient, the majority were the MgOH2 hydrate.

• 한국전자통신학회논문지
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• 제9권2호
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• pp.187-194
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• 2014

본 논문에서는 다른 모바일 단말에서의 통합적 간섭을 고려하여 셀룰러 네트워크에서 매크로 다이버시티 결합을 사용하였을 경우의 Outage 확률을 분석한다. Outage 확률을 분석한 타 논문과 달리 본 논문에서는 상대적으로 간섭이 적은 기지국을 선택하였을 때의 다이버시티 이득을 분석한다. 분석을 위해 모바일 단말이 포아송 포인트 프로세스에 따라 확률적으로 분포한다고 가정하였다. 다수의 기지국에 가해지는 통합적 간섭의 다변수 분포를 다변수 로그노멀 분포로 근사시켜 분석을 수행하였다.