• Advances in Computational Design
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• v.5 no.3
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• pp.323-347
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• 2020

The enhancements of bioenergy production effectiveness require the comprehensively experimental study of several parameters affecting these bioprocesses. The interpretation of the obtained experimental results and the estimation of optimum yield are extremely complicated such as misinterpreting the results of an experiment. The use of mathematical modeling and statistical experimental designs can consistently supply the predictions of the potential yield and the identification of defining parameters and also the understanding of key relationships between factors and responses. This paper summarizes several mathematical models used to achieve an adequate overall and maximal production yield and rate, to screen, to optimize, to identify, to describe and to provide useful information for the effect of several factors on bioenergy production processes. The usefulness, the validity and, the feasibility of each strategy for studying and optimizing the bioenergy-producing processes were discussed and confirmed by the good correlation between predicted and measured values.

• The Korean Journal of Food & Health Convergence
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• v.4 no.2
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• pp.17-23
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• 2018

Carica papaya is an important medicinal plant used in the management of different disease conditions. Phytochemical screening was carried out using different chemical test, Percentage yield and total phenolic content was evaluated using Folin Ciocalteu method in different solvent system; methanol, ethanol, ethyl acetate, n-butanol and n-hexane respectively. The phytochemical screening of the studies showed the presence of flavonoids, saponins, tannins, terpenoids, glycosides, steroids, carbonhydrate, anthraquinone and alkaloids. The percentage yield of crude extract and total polyphenol content was high in methanol, ethanol and ethyl acetate when compared with n-butanol and n-hexane. The studies show that Carica papaya leave extracts is a potent source of secondary metabolites. The use of the plant in the management of diseases is justified.

• Journal of Biosystems Engineering
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• v.41 no.4
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• pp.408-417
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• 2016

Purpose: Yield monitoring systems are an essential component of precision agriculture. They indicate the spatial variability of crop yield in fields, and have become an important factor in modern harvesters. The objective of this paper was to review research trends related to yield monitoring sensors for grain crops. Methods: The literature was reviewed for research on the major sensing components of grain yield monitoring systems. These major components included grain flow sensors, moisture content sensors, and cutting width sensors. Sensors were classified by sensing principle and type, and their performance was also reviewed. Results: The main targeted harvesting grain crops were rice, wheat, corn, barley, and grain sorghum. Grain flow sensors were classified into mass flow and volume flow methods. Mass flow sensors were mounted primarily at the clean grain elevator head or under the grain tank, and volume flow sensors were mounted at the head or in the middle of the elevator. Mass flow methods used weighing, force impact, and radiometric approaches, some of which resulted in measurement error levels lower than 5% ($R^2=0.99$). Volume flow methods included paddle wheel type and optical type, and in the best cases produced error levels lower than 3%. Grain moisture content sensing was in many cases achieved using capacitive modules. In some cases, errors were lower than 1%. Cutting width was measured by ultrasonic distance sensors mounted at both sides of the header dividers, and the errors were in some cases lower than 5%. Conclusions: The design and fabrication of an integrated yield monitoring system for a target crop would be affected by the selection of a sensing approach, as well as the layout and mounting of the sensors. For accurate estimation of yield, signal processing and correction measures should be also implemented.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2006.06b
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• pp.295-303
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• 2006

The pulp yield was improved by about 4.5-5% when polysulfide (PS) and anthraquinone (AQ) were added to the kraft cooking liquor (white liquor). The exchange of the black liquor with fresh white liquor further increased the yield. The highest pulp yield was obtained when the PS cooking liquor containing 70% of total active alkali (AA) and 100% of AQ was used from the beginning of the reaction and the black liquor was exchanged with fresh white liquor containing the residual 30% of AA just after temperature reached $135^{\circ}C$. There was a good correlation between kraft pulp yields of a hardwood species and the ratios of the amount of xylose to glucose (X/G ratio), liberated by an acid hydrolysis of the pulps. However, the correlation was dependent on raw material wood species. Therefore, it is required in advance to establish a correlation between the yields and X/G ratios for raw material wood species of a target pulp in order to estimate pulp yield using X/G ratio. The X/G ratios of relatively high yield pulps showed higher values than those expected from the correlation. In a mill trial, the superiority of the PS-AQ isothermal cooking (ITC) process over the kraft ITC process was confirmed by examining X/G ratio of pulps obtained. The pulp yield in the PS-AQ ITC process was estimated at about 57.0%. This yield is very high, which indicates that reaction conditions of the PS-AQ ITC process are optimal.

• 한국생물공학회:학술대회논문집
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• 2003.10a
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• pp.420-420
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• 2003

• 전기의세계
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• v.29 no.11
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• pp.742-750
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• 1980

The problem of nonlinear time-invariant system identification by estimation of Wiener kernels is studied for discrete time systems with inputs having symmetric probability distributions. G-functionals are constructed. It is further shown that under idealized conditions, these seemingly different techniques yield the same results. The results of identification of asimulated second degree system is presented.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.177-191
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• 2022

This paper addresses the use of machine learning methods for causal estimation of treatment effects from observational data. Even though conducting randomized experimental trials is a gold standard to reveal potential causal relationships, observational study is another rich source for investigation of exposure effects, for example, in the research of comparative effectiveness and safety of treatments, where the causal effect can be identified if covariates contain all confounding variables. In this context, statistical regression models for the expected outcome and the probability of treatment are often imposed, which can be combined in a clever way to yield more efficient and robust causal estimators. Recently, targeted maximum likelihood estimation and causal random forest is proposed and extensively studied for the use of data-adaptive regression in estimation of causal inference parameters. Machine learning methods are a natural choice in these settings to improve the quality of the final estimate of the treatment effect. We explore how we can adapt the design and training of several machine learning algorithms for causal inference and study their finite-sample performance through simulation experiments under various scenarios. Application to the percutaneous coronary intervention (PCI) data shows that these adaptations can improve simple linear regression-based methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.1
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• pp.42-51
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• 2011

Poisson model and Gamma-Poisson model are popularly used to analyze statistical behavior from defective data. The methods are based on binary criteria, that is, good or failure. However, manufacturing industries prefer polytomous criteria for classifying manufactured products due to flexibility of marketing. In this paper, I introduce two multivariate Gamma-Poisson(MGP) models and estimation methods of the parameters in the models, which are able to handle polytomous data. The models and estimators are verified on defective pixels of LCD manufacturing. Experimental results show that both the independent MGP model and the multinomial MGP model have excellent performance in terms of mean absolute deviation and the choice of method depends on the purpose of use.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.503-512
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• 2006

In this paper, the Inverse Weibull distribution parameters have been estimated using a new estimation technique based on the non-parametric kernel density function that introduced as an alternative and reliable technique for estimation in life testing models. This technique will require bootstrapping from a set of sample observations for constructing the density functions of pivotal quantities and thus the confidence intervals for the distribution parameters. The performances of this technique have been studied comparing to the conditional inference on the basis of the mean lengths and the covering percentage of the confidence intervals, via Monte Carlo simulations. The simulation results indicated the robustness of the proposed method that yield reasonably accurate inferences even with fewer bootstrap replications and it is easy to be used than the conditional approach. Finally, a numerical example is given to illustrate the densities and the inferential methods developed in this paper.

• Journal of Mechanical Science and Technology
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• v.15 no.8
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• pp.1079-1089
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• 2001

This paper proposes J and CTOD estimation schemes applied to fracture toughness testing, covering typical homogeneous and bi-material specimens. Recommendations are based on the plastic limit analysis (either slip line field or finite element limit analyses), assuming the rigid plastic material behavior. The main outcome of the present study is that the J and CTOD estimation schemes (both codified and non-codified), recommended for homogeneous specimens, can be equally used for bi-material specimens with interface cracks. The effect of yield strength mismatch in bi-material specimens on the J-integral CTOD is discussed.