• Journal of Animal Science and Technology
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• v.63 no.4
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• pp.827-840
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• 2021

Several studies have focused on Ca and P requirements for pigs. These requirements are estimated from their retention and bone formation. However, modern pig breeds have different responses to dietary Ca and P than traditional breeds, and their requirements are expected to change on an annual basis. Besides individual Ca and P needs, the Ca to P ratio (Ca/P) is an important factor in determining requirements. This study aimed to implement a linear and quadratic regression analysis to estimate Ca and P requirements based on average daily gain (ADG), apparent total tract digestibility (ATTD) of Ca (ATTD-Ca), ATTD of P (ATTD-P), and crude protein (CP) digestibility. Results show that Ca/P had linear and quadratic effects on ADG in the phytase-supplemented (PS) group in both the 6-11 kg and 11-25 kg categories. In the latter category, the CP digestibility was linearly increased in response to increasing Ca/P in the without-phytase (WP) group. In the 25-50 kg category, there was a linear response of ADG and linear and quadratic responses of CP digestibility to Ca/P in the PS group, while a linear and quadratic increase in CP digestibility and a quadratic effect on ATTD-Ca were observed in the WP group. In the 50-75 kg category, Ca/P had significant quadratic effects on ADG in the PS and WP groups, along with significant linear and quadratic effects on ATTD-Ca. In addition, Ca/P had significant quadratic effects on ATTD-P and led to a significant linear and quadratic increase in the CP digestibility in the WP group. In the 75-100 kg category, analysis showed a significant decrease in ATTD-Ca and ATTD-P in the PS and WP groups; in the latter, ATTD-P and ATTD-Ca were linearly decreased by increasing Ca/P. In conclusion, our equations predicted a higher Ca/P in the 6-25 kg bodyweight categories and a lower Ca/P in the 50-100 kg category than that recommended in the literature.

• Journal of Environmental Health Sciences
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• v.38 no.2
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• pp.136-141
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• 2012

Objectives: The objective of this study was to investigate whether the ammonia nitrogen concentration of aqueous samples such as drinking water can be determined by measuring the saturation of the samples colored by indophenol method. Methods: A color saturation measurement system was constructed by connecting a notebook computer to an image acquisition device composed of a PC camera and a light source, and was then used to measure the saturation of samples colored by blue indophenol complex. Results: Between two available light sources, a fluorescent lamp was selected due to its demonstrating better linearity between color saturation and ammonia nitrogen concentration. Prediction by quadratic regression was more accurate than by linear regression, and prediction by quadratic regression in the concentration range of 0.1-1.0 $mg/l$ was more accurate than in the concentration range of 0.0-1.0 $mg/l$. Regression-based predictions over 0.25 $mg/l$, 0.55 $mg/l$ and 0.75 $mg/l$ concentrations were implemented both by spectrophotometric method and by measuring color saturation. In the case of 0.25 $mg/l$, the predicted concentration by spectrophotometric method was $0.256{\pm}0.0076\;mg/l$ and the predicted concentration by measuring color saturation was $0.246{\pm}0.0086\;mg/l$ (p=0.051). In the case of 0.55 $mg/l$, they were $0.561{\pm}0.0068\;mg/l$ and $0.564{\pm}0.0166\;mg/l$ (p=0.660). In the case of 0.75 $mg/l$, they were $0.755{\pm}0.0139\;mg/l$ and $0.762{\pm}0.0088\;mg/l$ (p=0.215). Conclusions: There were no statistically significant differences (p>0.05) between the data from the two methods in all three of the concentrations. Therefore, the color saturation measurement method proposed in this paper may be considered applicable for determining the ammonia nitrogen concentration of aqueous samples such as drinking water.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.9
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• pp.1387-1399
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• 2020

Objective: The objective of this study was to estimate the genetic parameters and trends for milk, fat, and protein yields in the first three lactations of Thai dairy cattle using a 3-trait,-3-lactation random regression test-day model. Methods: Data included 168,996, 63,388, and 27,145 test-day records from the first, second, and third lactations, respectively. Records were from 19,068 cows calving from 1993 to 2013 in 124 herds. (Co) variance components were estimated by Bayesian methods. Gibbs sampling was used to obtain posterior distributions. The model included herd-year-month of testing, breed group-season of calving-month in tested milk group, linear and quadratic age at calving as fixed effects, and random regression coefficients for additive genetic and permanent environmental effects, which were defined as modified constant, linear, quadratic, cubic and quartic Legendre coefficients. Results: Average daily heritabilities ranged from 0.36 to 0.48 for milk, 0.33 to 0.44 for fat and 0.37 to 0.48 for protein yields; they were higher in the third lactation for all traits. Heritabilities of test-day milk and protein yields for selected days in milk were higher in the middle than at the beginning or end of lactation, whereas those for test-day fat yields were high at the beginning and end of lactation. Genetics correlations (305-d yield) among production yields within lactations (0.44 to 0.69) were higher than those across lactations (0.36 to 0.68). The largest genetic correlation was observed between the first and second lactation. The genetic trends of 305-d milk, fat and protein yields were 230 to 250, 25 to 29, and 30 to 35 kg per year, respectively. Conclusion: A random regression model seems to be a flexible and reliable procedure for the genetic evaluation of production yields. It can be used to perform breeding value estimation for national genetic evaluation in the Thai dairy cattle population.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.3
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• pp.135-144
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• 2004

In this paper, we propose competitive fuzzy polynomial neurons-based advanced Self-Organizing Neural Networks(SONN) architecture for optimal model identification and discuss a comprehensive design methodology supporting its development. The proposed SONN dwells on the ideas of fuzzy rule-based computing and neural networks. And it consists of layers with activation nodes based on fuzzy inference rules and regression polynomial. Each activation node is presented as Fuzzy Polynomial Neuron(FPN) which includes either the simplified or regression polynomial fuzzy inference rules. As the form of the conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership (unction are studied and the number of the premise input variables used in the rules depends on that of the inputs of its node in each layer. We introduce two kinds of SONN architectures, that is, the basic and modified one with both the generic and the advanced type. Here the basic and modified architecture depend on the number of input variables and the order of polynomial in each layer. The number of the layers and the nodes in each layer of the SONN are not predetermined, unlike in the case of the popular multi-layer perceptron structure, but these are generated in a dynamic way. The superiority and effectiveness of the Proposed SONN architecture is demonstrated through two representative numerical examples.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.12 no.4
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• pp.354-362
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• 2000

Optimal supervisory control strategy for the set points of controlled variables in the central cooling system has been studied by computer simulation. A quadratic linear regression equation for predicting the total cooling system power in terms of the controlled and uncontrolled variables was developed using simulated data collected under different values of controlled and uncontrolled variables. The optimal set temperatures such as supply air temperature, chilled water temperature, and condenser water temperature, are determined such that energy consumption is minimized as uncontrolled variables, load, ambient wet bulb temperature, and sensible heat ratio, are changed. The chilled water loop pump and cooling tower fan speeds are controlled by the PID controller such that the supply air and condenser water set temperatures reach the set points designated by the optimal supervisory controller. The influences of the controlled variables on the total system and component power consumption was determined. It is possible to minimize total energy consumption by selecting the optimal set temperatures through the trade-off among the component powers. The total system power is minimized at lower supply, higher chilled water, and lower condenser water set temperature conditions.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.675-677
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• 1998

In this paper, we propose the fuzzy inference algorithm with multi-layer structure. MFIS(Multi-layer Fuzzy Inference System) uses PNN(Polynomial Neural networks) structure and the fuzzy inference method. The PNN is the extended structure of the GMDH(Group Method of Data Hendling), and uses several types of polynomials such as linear, quadratic and cubic, as well as the biquadratic polynomial used in the GMDH. In the fuzzy inference method, the simplified and regression polynomial inference methods are used. Here, the regression polynomial inference is based on consequence of fuzzy rules with the polynomial equations such as linear, quadratic and cubic equation. Each node of the MFIS is defined as fuzzy rules and its structure is a kind of neuro-fuzzy structure. We use the training and testing data set to obtain a balance between the approximation and the generalization of process model. Several numerical examples are used to evaluate the performance of the our proposed model.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.254-259
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• 1998

A new design methology is proposed to identify the structure and parameters of fuzzy model using PNN and a fuzzy inference method. The PNN is the extended structure of the GMDH(Group Method of Data Handling), and uses several types of polynomials such as linear, quadratic and cubic besides the biquadratic polynomial used in the GMDH. The FPNN(Fuzzy Polynomial Neural Networks) algorithm uses PNN(Polynomial Neural networks) structure and a fuzzy inference method. In the fuzzy inference method, the simplified and regression polynomial inference methods are used. Here a regression polynomial inference is based on consequence of fuzzy rules with a polynomial equations such as linear, quadratic and cubic equation. Each node of the FPNN is defined as fuzzy rules and its structure is a kind of neuro-fuzzy architecture. In this paper, we will consider a model that combines the advantage of both FPNN and PNN. Also we use the training and testing data set to obtain a balance between the approximation and generalization of process model. Several numerical examples are used to evaluate the performance of the our proposed model.

• Journal of the Korean Society of Tobacco Science
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• v.21 no.2
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• pp.109-118
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• 1999

A model predicted describing the effect of cigarette making materials on the level of filter ventilation was developed and evaluated. The developed model was expressed in terms of a linear and quadratic relationship which was validated with experimental measurements for different porosity of plug wrap and tipping paper, unencapsulated pressure drop of filter plug and cigarette column and vent position. Forty-six experimental frequencies were determined as a result of using three levels with five factors Box-Behnken design and analyzed by the multiple regression analysis with backward stepwise in STATISTICA/PC under restricted conditions. The four factors, except filter pressure drop variable, were statistically significant at the level of 0.05 but most of all linear by linear interactions were comparatively lower significant. By the analysis of linear and quadratic regression coefficient, filter ventilation of the cigarette was affected by porosity of plugwrap (5.87, -4.25), porosity of tip paper (5.68, -1.00), vent position (-3.87, 3.08), tobacco column pressure drop (2.56, 0.66), and filter pressure drop (1.50, 0.58) in the decreasing order. It should be emphasized that the major conclusion of this study was not that any particular parameter was linear or quadratic on any limit scale, but that there were highly significant relationships among factors involving linear, quadratic and their interaction and perhaps even linearity between and within factors. While, there is also quite strong evidence that vent position from mouth end and cigarette making materials are reverse relationship on this experimental model. On the basis of the result, it can be concluded that the porosity of the plug wrap and tipping paper has a marked effect on degree of filter ventilation rate. The F-value of plug wrap and tipping paper porosity among five factors were 39.2 and 36.8 respectively with P-value of 0.000 indicating higher significant for both factors. According to the analysis of variance, the model fitted for filter ventilation was significant at 5% confidence level and the coefficient of determination ($R^2$=0.84) was the proportion to variability in the data well fitted for by the model.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.2
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• pp.70-78
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• 2005

In this paper, a novel rotor flux estimation method of an induction motor using support vector regression(SVR) is presented. Two well-known different flux models with respect to voltage and current are necessary to estimate the rotor flux of an induction motor. Training of SVR which the theory of the SVR algorithm leads to a quadratic programming(QP) problem. The proposed SVR rotor flux estimator guarantees the improvement of performance in the transient and steady state in spite of parameter variation circumstance. The validity and the usefulness of proposed algorithm are throughly verified through numerical simulation.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.201-209
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• 2013

For censored regression, it is often the case that some input variables are not important, while some input variables are more important than others. We propose a novel algorithm for selecting such important input variables for censored kernel regression, which is based on the penalized regression with the weighted quadratic loss function for the censored data, where the weight is computed from the empirical survival function of the censoring variable. We employ the weighted version of ANOVA decomposition kernels to choose optimal subset of important input variables. Experimental results are then presented which indicate the performance of the proposed variable selection method.