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Kernel function based regression approaches for estimating the oxygen transfer performance of plunging hollow jet aerator

Kumar M., Tiwari N. K., Ranjan S.

Journal of Achievements in Materials and Manufacturing Engineering

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2019

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Vol. 95, nr 2

74--84

EN

Purpose: To evaluate the capability of various kernels employed with support vector regression (SVR) and Gaussian process regression (GPR) techniques in estimating the volumetric oxygen transfer coefficient of plunging hollow jets. Design/methodology/approach: In this study, a data set of 81 observations is acquired from laboratory experiments of hollow jets plunging on the surface of water in the tank. The jet variables: jet velocity, jet thickness, jet length, and water depth are varied accordingly and the values of volumetric oxygen transfer coefficient is computed. An empirical relationship expressing the oxygenation performance of plunging hollow jet aerator in terms of jet variables is formulated using multiple nonlinear regression. The performance of this nonlinear relationship is compared with various kernel function based SVR and GPR models. Models developed with the training data set (51 observations) are checked on testing data set (24 observations) for performance comparison. Sensitivity analysis is carried out to examine the influence of jet variables in effecting the oxygen transfer capabilities of plunging hollow jet aerator. Findings: The overall comparison of kernels yielded good estimation performance of Radial Basis Function kernel (RBF) and Pearson VII Function kernel (PUK) using the SVR technique which is followed by nonlinear regression, and other kernel function based regression models. Research limitations/implications: The results of the study pertaining to the performance of kernels are based on the current experimental conditions and the estimation potential of the regression models may fluctuate beyond the selection of current data range due to datadependant learning of the soft computing models. Practical implications: Volumetric oxygen transfer coefficient of plunging hollow jets can be predicted precisely using SVR model by employing RBF as kernel function as compared to empirical correlation and other kernel function based regression models. Originality/value: The comparative analysis of kernel functions is conducted in this study. In previous studies, the predictive modelling approaches are implemented in simulating the aeration properties of cylindrical solid jets only, while this paper simulates the volumetric oxygen transfer coefficient of diverging hollow jets with the jet variables by utilizing polynomial, normalized polynomial, PUK, and RBF kernels in SVR and GPR.

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Use of machine learning algorithm for the better prediction of SR peculiarities of WEDM of Nimonic-90 superalloy

Singh Nain S., Sai R., Sihag P., Vambol S., Vambol V.

Archives of Materials Science and Engineering

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2019

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Vol. 95, nr 1

12--19

EN

Purpose: With the end goal to fulfil stringent structural shape of the component in aeronautics industry, machining of Nimonic-90 super alloy turns out to be exceptionally troublesome and costly by customary procedures, for example, milling, grinding, turning, etc. For that reason, the manufacture and design engineer worked on contactless machining process like EDM and WEDM. Based on previous studies, it has been observed that rare research work has been published pertaining to the use of machine learning in manufacturing. Therefore the current research work proposed the use of SVM, GP and ANN methods to evaluate the WEDM of Nimonic-90. Design/methodology/approach: The experiments have been performed on the WEDM considering five process variables. The Taguchi L 18 mixed type array is used to formulate the experimental plan. The surface roughness is checked by using surface contact profilometre. The evolutionary algorithms like SVM, GP and ANN approaches have been used to evaluate the SR of WEDM of Nimonic-90 super alloy. Findings: The entire models present the significant results for the better prediction of SR peculiarities of WEDM of Nimonic-90 superalloy. The GP PUK kernel model is dominating the entire model. Research limitations/implications: The investigation was carried for the Nimonic-90 super alloy is selected as a work material. Practical implications: The results of this study provide an opportunity to conduct contactless processing superalloy Nimonic-90. At the same time, this contactless process is much cheaper, faster and more accurate. Originality/value: An experimental work has been reported on the WEDM of Udimet-L605 and use of advance machine learning algorithm and optimization approaches like SVM, and GRA is recommended. A study on WEDM of Inconel 625 has been explored and optimized the process using Taguchi coupled with grey relational approach. The applicability of some evolutionary algorithm like random forest, M5P, and SVM also tested to evaluate the WEDM of Udimet-L605.The fuzzy- inference and BP-ANN approached is used to evaluate the WEDM process. The multi-objective optimization using ratio analysis approach has been utilized to evaluate the WEDM of high carbon & chromium steel. But this current research work proposed the use of SVM, GP and ANN methods to evaluate the WEDM of Nimonic-90.

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Fault tolerant control using Gaussian processes and model predictive control

Yang X., Maciejowski J. M.

International Journal of Applied Mathematics and Computer Science

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2015

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Vol. 25, no. 1

133--148

EN

Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the second. We therefore propose to use the two together to obtain fault-tolerant control functionality. Our proposal is illustrated by several reasonably realistic examples drawn from flight control.

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Zastosowanie metody Gaussowskiego procesu do identyfikacji charakterystyk zagęszczenia gruntów ziarnistych

Kłos M., Słoński M., Waszczyszyn Z.

Zeszyty Naukowe Politechniki Rzeszowskiej. Budownictwo i Inżynieria Środowiska

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2012

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z. 59, nr 3/IV

41-48

PL

W pracy zastosowano metodę bayesowską Gaussowskiego Procesu (GP). Metoda wyróżnia się brakiem wektora wag i użyciem funkcji kernelowskich oraz macierzy kowariancji w przestrzeni danych wejściowych. Dzięki temu w GP można było formułować stosunkowo proste algorytmy i procedury komputerowe. GP zastosowano do identyfikacji dwóch charakterystyk zagęszczenia gruntów ziarnistych, tj. wilgotności optymalnej oraz maksymalnej gęstości objętościowej. Celem sprawdzenia numerycznej efektywności GP zastosowano ją do analizy nowych danych pomiarowych opisanych w [2], analizowanych w [4] za pomocą semi-baysowskiej sieci neuronowej (SBNN). Wykazano, że dokładność identyfikacji metodą GP jest porównywalna z zaletami SBNN.

EN

In the paper the Gaussian Process (GP) model is discussed as a simple Bayesian for approach to identification analysis. In GP model the weight vector is not applied, which makes the algorithms and computational procedures simpler than those formulated in the Semi-Bayesian Neural Network (SBNN). In the paper it was numerically proved that the application of GP to the identification of compaction parameters for granular soils is numerically efficient, comparable for GP and SBNN applications.