• Transactions of Materials Processing
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• v.12 no.5
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• pp.479-486
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• 2003

This paper presents an elastoplastic analysis for spent nuclear fuel disposal container and its 50 cm thick bentonite buffer to predict the collapse of the container while the horizontal asymmetric sudden rock movement of 10 cm is applied on the composite structure. This sudden rock movement is anticipated by the earthquake etc. at a deep underground. Elastoplastic material model is adopted. Drucker-Prager yield criterion is used for the material yield prediction of the bentonite buffer and von-Mises yield criterion is used for the material yield prediction of the container. Analysis results show that even though very large deformations occur beyond the yield point in the bentonite buffer, the container structure still endures elastic small strains and stresses below the yield strength. Hence, the asymmetric 50 cm thick bentonite buffer can protect the container safely against the 10 cm sudden rock movement by earthquake etc.. Analysis results also show that bending deformations occur in the container structure due to the shear deformation of the bentonite buffer. The finite element analysis code, NISA, is used for the analysis.

• Transactions of Materials Processing
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• v.25 no.3
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• pp.189-194
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• 2016

In the current study, spring-back of DP980 steel sheet was numerically evaluated for U-bending using a yield function with a hardening model. For spring-back prediction, two types of yield functions - Hill'48 and Yld2000-2d - were considered. Additionally, isotropic hardening and the Yoshida-Uemori model were used to investigate the spring-back behavior. The parameters for each model were obtained from uniaxial tension, uniaxial tension-compression, uniaxial tension-unloading and hydraulic bulging tests. The numerical simulations were performed using the commercial software, PAM-STAMP 2G. The results were compared with experimental data from a U-bending process.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.207-209
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• 2003

Quantifying crop production is one of the most important applications of remote sensing in which the temporal and up-to-date data can play very important role in avoiding any immediate insufficiency in agricultural production. A combination of climatic data and biophysical parameters derived from Landsat7 ETM+ was used to develop a mathematical model for wheat yield forecast in different geographically wide Wheat growing districts in Egypt. Leaf Area Index (LAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) with temperature were used in the modeling. The model includes three sub-models representing the correlation between the reported yield and each individual variable. Simulation results using district statistics showed high accuracy of the derived correlations to estimate wheat production with a percentage standard error (%S.E.) of 1.5% in El- Qualyobia district and average (%S.E.) of 7% for the whole wheat areas.

• International Journal of Concrete Structures and Materials
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• v.2 no.1
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• pp.57-62
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• 2008

When the yield line theory is used to estimate the ultimate strength of a concrete barrier, it is of primary importance that the correct assumption is made for the failure mode of the barrier. In this study, a static test was performed on two full-scale concrete barrier specimens of Korean standard shape that simulate the actual behavior of a longitudinally continuous barrier. This was conducted in order to verify the failure mode presented in the AASHTO LRFD specification. The resulting shape of the yield lines differed from that presented in AASHTO when subjected to an equivalent crash load. Furthermore, the ultimate strengths of the specimens were lower than the theoretical prediction. The main causes of these differences can be attributed to the characteristics of the barrier shape and to a number of limitations associated with the classical yield line theory. Therefore, a revised failure mode with corresponding prediction equations of the strength were proposed based on the yield lines observed in the test. As a result, a strength that was more comparable to that of the test could be obtained. The proposed procedure can be used to establish more realistic test levels for barriers that have a similar shape.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.2
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• pp.132-139
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• 2009

Yield is a very important measure that can expresses simply for productivity and performance of company. So, yield is used widely in many industries nowadays. With the development of the information technology and online based real-time process monitoring technology, many industries operate the production lines that are developed into automation system. In these production lines, the product structures are very complexity and variety. So, there are many multi-variate processes that need to be monitored with many quality characteristics and associated process variables at the same time. These situations have made it possible to obtain super-large manufacturing process data sets. However, there are many difficulties with finding the cause of process variation or useful information in the high capacity database. In order to solve this problem, neural networks technique is a favorite technique that predicts the yield of process for process control. This paper uses a neural networks technique for improvement and maintenance of yield in manufacturing process. The purpose of this paper is to model the prediction of a sub process that has much effect to improve yields in total manufacturing process and the prediction of adjustment values of this sub process. These informations feedback into the process and the process is adjusted. Also, we show that the proposed model is useful to the manufacturing process through the case study.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.19-26
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• 2002

In this paper, the nonlinear structural analysis for the composite structure of the spent nuclear fuel disposal container and the 50cm thick bentonite buffer is carried out to predict the collapse of the container while the sudden rock movement of 10cm is applied on the composite structure. This sudden rock movement is anticipated by the earthquake etc. at a deep underground. Horizontal symmetric rock movement is assumed in this structural analysis. Elastoplastic material model is adopted. Drucker-Prager yield criterion is used for the material yield prediction of the bentonite buffer and von-Mises yield criterion is used for the material yield prediction of the container(cast iron insert, copper outer shell and lid and bottom). Analysis results show that even though very large deformations occur beyond the yield point in the bentonite buffer, the container structure still endures elastic small strains and stresses below the yield strength. Hence, the 50cm thick bentonite buffer can protect the container safely against the 10cm sudden rock movement by earthquake etc.. Analysis results also show that bending deformations occur in the container structure due to the shear deformation of the bentonite buffer. The elastoplastic nonlinear structural analysis for the composite structure of the container and the bentonite buffer is performed using the finite element analysis code, NISA.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.10-12
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• 2003

This study was to monitor changes of leaf area index (LAI) and normalized difference vegetation index (NDVI), calculated from ground-based remotely sensed high resolution reflectance spectra, during rice (Oryza sativa L. cv. TNG 67) growth so as to determine their relationships and the optimum time period to use these parameters for yield prediction. Field experiments were conducted at the experimental farm of TARI to obtain various scales of grain yield and values of LAI and NDVI in the first and the second cropping seasons of 2001-2002. It was found that LAI and NDVI can be mutually estimated through an exponential relationship, and hence plant growth information and spectral remote sensing data become complementary counterparts through this linkage. Correlation between yield and LAI was best fitted to a nonlinear function since about 7 weeks after transplanting (WAT). The accumulated and the mean values of LAI from 15 days before heading (DBH) to 15 days after heading (DAH) were the optimum time period to predict rice yield for First Crops, while values calculated from 15 DBH to 10 DAH were the optimal timing for Second Crops.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.17-28
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• 2023

Today Agriculture segment is a significant supporter of Indian economy as it represents 18% of India's Gross Domestic Product (GDP) and it gives work to half of the nation's work power. Farming segment are required to satisfy the expanding need of food because of increasing populace. Therefore, to cater the ever-increasing needs of people of nation yield prediction is done at prior. The farmers are also benefited from yield prediction as it will assist the farmers to predict the yield of crop prior to cultivating. There are various parameters that affect the yield of crop like rainfall, temperature, fertilizers, ph level and other atmospheric conditions. Thus, considering these factors the yield of crop is thus hard to predict and becomes a challenging task. Thus, motivated this work as in this work dataset of different states producing different crops in different seasons is prepared; which was further pre-processed and there after machine learning techniques Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, Ridge Regression, Polynomial Regression, Linear Regression are applied and their results are compared using python programming.

• Research in Plant Disease
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• v.19 no.3
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• pp.196-200
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• 2013

This study was carried out to investigate yield loss due to soybean leaf spot disease caused by Cercospora sojina Hara and to determine the economic threshold level. The investigations revealed highly significant correlations between disease severity (diseased leaf area) and yield components (pod number per plant, total grain number per plant, total grain weight per plant, percent of ripened grain, weight of hundred seed, and yield). The correlation coefficients between leaf spot severity and each component were -0.90, -0.90, -0.92, -0.99, -0.90 and -0.94, respectively. The yield was inversely proportional to the diseased leaf area increased. The regression equation, yield prediction model, between disease severity (x) and yield (y) was obtained as y = -3.7213x + 354.99 ($R^2$ = 0.9047). Based on the yield prediction model, economic injury level and economic threshold level could be set as 3.3% and 2.6% of diseased leaf area of soybean.

• Journal of Environmental Science International
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• v.16 no.12
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• pp.1319-1324
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• 2007

A tank model in conjunction with Kalman filter is developed for prediction of sediment yield from an upland watershed in Northwestern Mississippi. The state vector of the system model represents the parameters of the tank model. The initial values of the state vector were estimated by trial and error. The sediment yield of each tank is computed by multiplying the total sediment yield by the sediment yield coefficient. The sediment concentration of the first tank is computed from its storage and the sediment concentration distribution(SCD); the sediment concentration of the next lower tank is obtained by its storage and the sediment infiltration of the upper tank; and so on. The sediment yield computed by the tank model using Kalman filter was in good agreement with the observed sediment yield and was more accurate than the sediment yield computed by the tank model.