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Configuration Parameters (configuration + parameter)
Selected AbstractsStress,optical behaviour of polyester networksPOLYMER INTERNATIONAL, Issue 3 2002Evaristo Riande Abstract Thermoelastic networks were prepared by end-linking hydroxyl terminated chains of poly(neopentylglycol hexafluoroglutarate) (PNGHFG) and poly(diethylene glycol terephthalate) (PDET) using, respectively, tri(p -isocyanate-phenyl)-thiophosphate and 2,4-bis(p -isocyanate benzyl)- p -phenylisocyanate as crosslinking agents. The plots of birefringence versus stress for PNGHFG and PDET networks exhibit negative deviations from linearity at elongation ratios greater than 1.9 and 5, respectively. The values of the optical configuration parameter ,a for the former and latter networks are 2.98,Å3 at 5,°C and 20.80,Å3 at 30,°C, respectively. Theoretical calculations carried out using the conformational energies obtained from the critical analysis of the mean-square dipole moments of diethyl hexafluoroglutarate and poly(neopentylglycol hexafluoroglutarate) give the value of 2.24,Å3 at 5,°C for this parameter, in fair agreement with the experimental result. Similar calculations carried out on PDET networks give ,a,,=,3.74,Å3 at 30,°C, a value significantly lower than the experimental result. The cause of the strong discrepancy between the theoretical and experimental results observed for the optical configuration parameter of PDET is discussed. © 2002 Society of Chemical Industry [source] Simulation of resource synchronization in a dynamic real-time distributed computing environmentCONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 14 2004Chen Zhang Abstract Today, more and more distributed computer applications are being modeled and constructed using real-time principles and concepts. In 1989, the Object Management Group (OMG) formed a Real-Time Special Interest Group (RT SIG) with the goal of extending the Common Object Request Broker Architecture (CORBA) standard to include real-time specifications. This group's most recent efforts have focused on the requirements of dynamic distributed real-time systems. One open problem in this area is resource access synchronization for tasks employing dynamic priority scheduling. This paper presents two resource synchronization protocols that the authors have developed which meet the requirements of dynamic distributed real-time systems as specified by Dynamic Scheduling Real-Time CORBA (DSRT CORBA). The proposed protocols can be applied to both Earliest Deadline First (EDF) and Least Laxity First (LLF) dynamic scheduling algorithms, allow distributed nested critical sections, and avoid unnecessary runtime overhead. In order to evaluate the performance of the proposed protocols, we analyzed each protocol's schedulability. Since the schedulability of the system is affected by numerous system configuration parameters, we have designed simulation experiments to isolate and illustrate the impact of each individual system parameter. Simulation experiments show the proposed protocols have better performance than one would realize by applying a schema that utilizes dynamic priority ceiling update. Copyright © 2004 John Wiley & Sons, Ltd. [source] NEURAL NETWORK MODELING OF END-OVER-END THERMAL PROCESSING OF PARTICULATES IN VISCOUS FLUIDSJOURNAL OF FOOD PROCESS ENGINEERING, Issue 2010YANG MENG ABSTRACT Modeling of the heat transfer process in thermal processing is important for the process design and control. Artificial neural networks (ANNs) have been used in recent years in heat transfer modeling as a potential alternative to conventional dimensionless correlation approach and shown to be even better performers. In this study, ANN models were developed for apparent heat transfer coefficients associated with canned particulates in high viscous Newtonian and non-Newtonian fluids during end-over-end thermal processing in a pilot-scale rotary retort. A portion of experimental data obtained for the associated heat transfer coefficients were used for training while the rest were used for testing. The principal configuration parameters were the combination of learning rules and transfer functions, number of hidden layers, number of neurons in each hidden layer and number of learning runs. For the Newtonian fluids, the optimal conditions were two hidden layers, five neurons in each hidden layer, the delta learning rule, a sine transfer function and 40,000 learning runs, while for the non-Newtonian fluids, the optimal conditions were one hidden layer, six neurons in each hidden layer, the delta learning rule, a hyperbolic tangent transfer function and 50,000 learning runs. The prediction accuracies for the ANN models were much better compared with those from the dimensionless correlations. The trained network was found to predict responses with a mean relative error of 2.9,3.9% for the Newtonian fluids and 4.7,5.9% for the non-Newtonian fluids, which were 27,62% lower than those associated with the dimensionless correlations. Algebraic solutions were included, which could be used to predict the heat transfer coefficients without requiring an ANN. PRACTICAL APPLICATIONS The artificial neural network (ANN) model is a network of computational elements that was originally developed to mimic the function of the human brain. ANN models do not require the prior knowledge of the relationship between the input and output variables because they can discover the relationship through successive training. Moreover, ANN models can predict several output variables at the same time, which is difficult in general regression methods. ANN concepts have been successfully used in food processing for prediction, quality control and pattern recognition. ANN models have been used in recent years for heat transfer modeling as a potential alternative to conventional dimensionless correlation approach and shown to be even better performers. In this study, ANN models were successfully developed for the heat transfer parameters associated with canned particulate high viscous Newtonian and non-Newtonian fluids during an end-over-end rotation thermal processing. Optimized configuration parameters were obtained by choosing appropriate combinations of learning rule, transfer function, learning runs, hidden layers and number of neurons. The trained network was found to predict parameter responses with mean relative errors considerably lower than from dimensionless correlations. [source] DYNAMIC MODELING OF RETORT PROCESSING USING NEURAL NETWORKSJOURNAL OF FOOD PROCESSING AND PRESERVATION, Issue 2 2002C. R. CHEN ABSTRACT Two neural network approaches , a moving-window and hybrid neural network , which combine neural network with polynomial regression models, were used for modeling F(t) and Qv(t) dynamic functions under constant retort temperature processing. The dynamic functions involved six variables: retort temperature (116,132C), thermal diffusivity (1.5,2.3 × 10,7m2/s), can radius (40,61 mm), can height (40,61 mm), and quality kinetic parameters z (15,39C) and D (150,250 min). A computer simulation designed for process calculations of food thermal processing systems was used to provide the fundamental data for training and generalization of ANN models. Training data and testing data were constructed by both second order central composite design and orthogonal array, respectively. The optimal configurations of ANN models were obtained by varying the number of hidden layers, number of neurons in hidden layer and learning runs, and a combination of learning rules and transfer function. Results demonstrated that both neural network models well described the F(t) and Qv(t) dynamic functions, but moving-window network had better modeling performance than the hybrid ANN models. By comparison of the configuration parameters, moving-window ANN models required more neurons in the hidden layer and more learning runs for training than the hybrid ANN models. [source] | ||||||||||