• Title/Summary/Keyword: Prediction equation model

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The Lower Flash Points of the n-Butanol+n-Decane System

  • Dong-Myeong Ha;Yong-Chan Choi;Sung-Jin Lee
    • Fire Science and Engineering
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    • v.17 no.2
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    • pp.50-55
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    • 2003
  • The lower flash points for the binary system, n-butanol+n-decane, were measured by Pensky-Martens closed cup tester. The experimental results showed the minimum in the flash point versus composition curve. The experimental data were compared with the values calculated by the reduced model under an ideal solution assumption and the flash point-prediction models based on the Van Laar and Wilson equations. The predictive curve based upon the reduced model deviated form the experimental data for this system. The experimental results were in good agreement with the predictive curves, which use the Van Laar and Wilson equations to estimate activity coefficients. However, the predictive curve of the flash point prediction model based on the Willson equation described the experimentally-derived data more effectively than that of the flash point prediction model based on the Van Laar equation.

A Study for Examination of Road Noise Prediction Results According to 3-d Noise Prediction Models and Input Parameters (3차원 소음예측모델 및 입력변수 변화에 따른 도로소음 예측결과 검토에 대한 연구)

  • Sun, Hyosung
    • Journal of Environmental Impact Assessment
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    • v.23 no.2
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    • pp.112-118
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    • 2014
  • The application of a 3-d noise prediction model is increasing as a tool for performing actual noise assessment in order to investigate the noise impact of the residential facility around a development region. However, because the appropriate plans of applying a 3-d noise prediction model is insufficient, it is important to secure the reliability of the noise prediction results generated by a 3-d noise prediction model. Therefore, this study is focused on examining a 3-d noise prediction model, and a prediction equation and input data in it. For this, the 3-d noise prediction models such as SoundPLAN, Cadna-A, IMMI is applied in road noise. After the contents of road noise equations, input data of road noise source, and input data of road noise barrier are understood, the road noise prediction results are compared and examined according to the variation of 3-d noise prediction model, road noise equation, and input data of road noise source and road noise barrier.

A Proposal for Predicting the Compressive Strength of Ultra-high Performance Concrete Using Equivalent Age (등가재령을 활용한 초고성능 콘크리트의 압축강도 예측식 제안)

  • Baek, Sung-Jin;Park. Jae-Woong;Han Jun-Hui;Kim, Jong;Han, Min-Cheol
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2023.11a
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    • pp.149-150
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    • 2023
  • This study proposes the most suitable strength prediction model equation for UHPC by calculating the apparent activation energy of UHPC according to the curing temperature and deriving the integrated temperature and compressive strength prediction equation. The results are summarized as follows. The apparent activation energy was calculated using the Arrhenius function, which was calculated as 21.09 KJ/mol. A model equation suitable for UHPC was calculated, and when the Flowman model equation was used, it was confirmed that it was suitable for the properties of UHPC using a condensation promoting super plasticizing agent.

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Prediction of 2-Dimensional Unsteady Thermal Discharge into a Reservoir (온수의 표면방출에 의한 2차원 비정상 난류 열확산 의 예측)

  • 박상우;정명균
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.7 no.4
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    • pp.451-460
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    • 1983
  • Computational four-equation turbulence model is developed and is applied to predict twodimensional unsteady thermal surface discharge into a reservoir. Turbulent stresses and heat fluxes in the momentum and energy equations are determined from transport equations for the turbulent kinetic energy (R), isotropic rate of kinetic energy dissipation (.epsilon.), mean square temperature variance (theta. over bar $^{2}$), and rate of destruction of the temperature variance (.epsilon. $_{\theta}$). Computational results by four-equation model are favorably compared with those obtained by an extended two-equation model. Added advantage of the four-equation model is that it yields quantitative information about the ratio between the velocity time scale and the thermal time scale and more detailed information about turbulent structure. Predicted time scale ratio is within experimental observations by others. Although the mean velocity and temperature fields are similarly predicted by both models, it is found that the four-equation model is preferably candidate for prediction of highly buoyant turbulent flows.

A Domain Combination Based Probabilistic Framework for Protein-Protein Interaction Prediction (도메인 조합 기반 단백질-단백질 상호작용 확률 예측기법)

  • Han, Dong-Soo;Seo, Jung-Min;Kim, Hong-Soog;Jang, Woo-Hyuk
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • 2003.10a
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    • pp.7-16
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    • 2003
  • 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 pro-bability 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 fur 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 foaming set of interacting protein pairs, very high sensitivity(86%) and specificity(56%) are achieved within our framework.

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A Study on the Prediction Model Development for Environmental Noise of Mugungwha Train (무궁화 열차 환경소음 예측모델 개발에 관한 연구)

  • 조준호;김재철;최성훈;이찬우;한환수
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2004.05a
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    • pp.366-371
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    • 2004
  • For the reduction and efficient management of railway noise, first of all prediction of railway noise is necessarily requisted. At home and abroad many studies for prediction of raiiway nearby noise have been accomplished. But it is impossible to predict easily and exactly for the Korean Railway, because the acoustic powers for each rolling stock operated in Korea have not been built yet. So in this study, prediction model equation for environmental noise for Korean rolling stock Mugungwha was suggested using SEL of engine and rolling noise component separately. In this prediction model, the number of car, distance from the rail can be considered. Finally for the validation of prediction nlodel equation, the predicted Leq was compared to the measured Leq.

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A Consolidation Settlement Prediction Considering Primary and Secondary Consolidation (1차와 2차 침하를 고려한 압밀침하량 예측)

  • Lee, Dal-Won;Jeong, Seong-Gyu
    • Journal of The Korean Society of Agricultural Engineers
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    • v.47 no.1
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    • pp.61-68
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    • 2005
  • In this study, it was proposed that an equation for predicting consolidation settlement on soft clay ground, which separate total settlement into primary and secondary consolidation settlement equation. The consolidation settlements by the proposed equation and by the measured settlements from laboratory model test were compared and verified for its application. It was appeared that the proposed equation from the laboratory model test approach to be more realistic comparing to !the result of Terzaghi's equation. From the above application, it was concluded that the final settlement prediction by. the Hyperbolic, Asaoka methods is needed to the initial settlement but the proposed equation could be much applicable in the lacking condition of measured data of the initial period.

Numerical Analysis Model for Fatigue Life Prediction of Welded Structures (용접구조물의 피로수명예측을 위한 수치해석모델)

  • Lee, Chi-Seung;Lee, Jae-Myung
    • Journal of Welding and Joining
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    • v.27 no.6
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    • pp.49-54
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    • 2009
  • In this study, the numerical analysis model for fatigue life prediction of welded structures are presented. In order to evaluate the structural degradation of welded structures due to fatigue loading, continuum damage mechanics approach is applied. Damage evolution equation of welded structures under arbitrary fatigue loading is constructed as a unified plasticity-damage theory. Moreover, by integration of damage evolution equation regarding to stress amplitude and number of cycles, the simplified fatigue life prediction model is derived. The proposed model is compared with fatigue test results of T-joint welded structures to obtain its validation and usefulness. It is confirmed that the predicted fatigue life of T-joint welded structures are coincided well with the fatigue test results.

Evaluation of the equation for predicting dry matter intake of lactating dairy cows in the Korean feeding standards for dairy cattle

  • Lee, Mingyung;Lee, Junsung;Jeon, Seoyoung;Park, Seong-Min;Ki, Kwang-Seok;Seo, Seongwon
    • 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 (BW0.75, 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.

Optimal Pipe Replacement Analysis with a New Pipe Break Prediction Model (새로운 파괴예측 모델을 이용한 상수도 관의 최적 교체)

  • Park, Suwan;Loganathan, G.V.
    • Journal of Korean Society of Water and Wastewater
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    • v.16 no.6
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    • pp.710-716
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    • 2002
  • A General Pipe Break Prediction Model that incorporates linear and exponential models in its form is developed. The model is capable of fitting pipe break trends that have linear, exponential or in between of linear and exponential trend by using a weighting factor. The weighting factor is adjusted to obtain a best model that minimizes the sum of squared errors of the model. The model essentially plots a best curve (or a line) passing through "cumulative number of pipe breaks" versus "break times since installation of a pipe" data points. Therefore, it prevents over-predicting future number of pipe breaks compared to the conventional exponential model. The optimal replacement time equation is derived by using the Threshold Break Rate equation by Loganathan et al. (2002).