The effect of na and Sr modification on surface tension and volumetric shrinkage of A356 alloy and their influence on porosity formation

Modification of the eutectic Si in Al-Si foundry alloys by adding strontium or sodium is, unfortunately, accompanied by an increase of porosity in the casting. In an attempt to understand the nature of this problem, this study used a sessile-drop method to investigate the effect of Sr and Na on surface tension and volumetric shrinkage, two probable causes of porosity occur-rence. The addition of 0.01 wt pct Sr and 0.005 wt pct Na to A356 alloy decreases the surface tension of the liquid by about 19 and 10 pct, respectively, and may increase the volume shrink-age by about 12 pct. These changes to surface tension and volumetric shrinkage promote the early formation of the pores during solidification and give the availability of a longer period of growth prior to complete solidification, resulting in a larger pore size. The effect of surface tension on the pores is more significant than volumetric shrinkage. Although the predicted pore diameter increases with lower surface tension or higher volumetric shrinkage, these two effects alone do not seem able to completely account for the observed increase in porosity that is associated with modification.     

Proactive task migration with a self-adjusting migration threshold for dynamic thermal management of multi-core processors

Request for more computation power steadily forces designers to provide more powerful processors using more number of cores on a single chip. The increasing complexity of processors leads to higher integration density, power density, and temperature. For avoiding thermal emergencies, various dynamic thermal management techniques have been presented. In this paper, we present a novel online self-adjusting temperature threshold schema for dynamic thermal management to minimize both average and peak temperature with very low performance overhead. Our proposed algorithm adjusts migration threshold according to workload and hardware platforms. The experimental results indicate that our technique can significantly decrease the average and peak temperature compared to Linux standard scheduler, and two well-known thermal management techniques: PDTM and TAS.     

Predictive model for shear strength estimation in reinforced concrete beams with recycled aggregates using Gaussian process regression

Neural Computing and Applications - In order to attain sustainable development, recycled concrete aggregates (RCAs) are increasingly utilized in civil engineering projects. Therefore, it is vital...     

A new set of sufficient conditions based on coupling parameters for synchronization of Hopfield like Chaotic Neural Networks

In this paper, we consider a Hopfield like Chaotic Neural Networks which have both self-coupling and non-invertible activation functions. We show that the interactions between neurons can be used as a means of chaos generation or suppression to neuron’s outputs when more adaptability or stability is required. Furthermore, a new set of sufficient conditions based on coupling weights is proposed so that the synchronization of all neuron’s outputs with each other is guaranteed, when all neuron’s have identical activation functions. Finally, the effectiveness of the proposed approach is evaluated by performing simulations on three illustrative examples.     

Numerical solution of fuzzy relational equations based on smooth fuzzy norms

In this paper, we study and formulate a BP learning algorithm for fuzzy relational neural networks based on smooth fuzzy norms for functions approximation. To elaborate the model behavior more, we have used different fuzzy norms led to a new pair of fuzzy norms. An important practical case in fuzzy relational equations (FREs) is the identification problem which is studied in this work. In this work we employ a neuro-based approach to numerically solve the set of FREs and focus on generalized neurons that use smooth s-norms and t-norms as fuzzy compositional operators.     

A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods

Artificial neural network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have an extensive range of applications in water resources management. Wavelet transformation as a preprocessing approach can improve the ability of a forecasting model by capturing useful information on various resolution levels. The objective of this research is to compare several data-driven models for forecasting groundwater level for different prediction periods. In this study, a number of model structures for Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet-ANN and Wavelet-ANFIS models have been compared to evaluate their performances to forecast groundwater level with 1, 2, 3 and 4 months ahead under two case studies in two sub-basins. It was demonstrated that wavelet transform can improve accuracy of groundwater level forecasting. It has been also shown that the forecasts made by Wavelet-ANFIS models are more accurate than those by ANN, ANFIS and Wavelet-ANN models. This study confirms that the optimum number of neurons in the hidden layer cannot be always determined by using a specific formula but trial-and-error method. The decomposition level in wavelet transform should be determined according to the periodicity and seasonality of data series. The prediction of these models is more accurate for 1 and 2 months ahead (for example RMSE?=?0.12, E?=?0.93 and R ²?=?0.99 for wavelet-ANFIS model for 1 month ahead) than for 3 and 4 months ahead (for example RMSE?=?2.07, E?=?0.63 and R ²?=?0.91 for wavelet-ANFIS model for 4 months ahead).     

Position sensorless control

In this article, a generalized sensorless method for SRM drives has been explained. By continuous monitoring of the back-EMF, the best version of the sensorless technique is selected and engaged. The robust performance obtained from this technique makes it as attractive candidate for industrial as well as domestic applications. Furthermore, a four-quadrant strategy under sensorless control has been designed and implemented. The proposed method demonstrates simplicity in computation while providing high-precision position information with no extra hardware or memory. The proposed algorithms have been implemented on an experimental SRM test setup. Our findings show that four-quadrant sensorless control of the SRM drive is a feasible technique and can be considered as a technology ready for application. This technique is especially helpful where the characteristics of the application calls for operating in two/four-quadrants or when a closed-loop speed/position control with sensorless strategy is demanded.     

Characterization of polar organic compounds in the organic film on indoor and outdoor glass windows

Organic films on an impervious surface (window glass) were sampled at paired indoor-outdoor sites in July 2000 and characterized for their paraffinic and polar organic compositions along an urban-rural transect. Four classes of polar compounds (C11-C31 aliphatic monocarboxylic, C6-C14 dicarboxylic, nine aromatic polycarboxylic, and five terpenoid acids) constituted between 81 and 95% (w/w) of the total organic fraction analyzed comprising n-alkanes (C10-C36), 46 PAH, 97 PCBs, and 18 OC pesticides. Concentrations of the polar compounds plus their precursors, n-alkanes, ranged from 8 to 124 microg m(-20 and were dominated by monocarboxylic acids (67-89%, w/w). On outdoor windows, n-alkanes, aromatic acids, and terpenoid acids decreased in concentration along the urban-rural transect. The carbon preference index values and the interpretations of individual compounds indicate that the main sources of n-alkanes were plant waxes followed by petrogenic sources; monocarboxylic and dicarboxylic acids were from plant waxes and animal fats. Results of principal component analysis showed closer correspondence between outdoor and indoor signatures than among locations. In outdoor films, these compounds are suggested to play an important role in mediating chemical fate in urban areas by air-film exchange and facilitating "wash-off" due to their surfactant-like properties. In indoor films, these compounds provide a medium for the accumulation of more toxic compounds.     

A non-reference image fusion metric based on mutual information of image features

The widespread usage of image fusion causes an increase in the importance of assessing the performance of different fusion algorithms. The problem of introducing a suitable quality measure for image fusion lies in the difficulty of defining an ideal fused image. In this paper, we propose a non-reference objective image fusion metric based on mutual information which calculates the amount of information conducted from the source images to the fused image. The considered information is represented by image features like gradients or edges, which are often in the form of two-dimensional signals. In this paper, a method of estimating the joint probability distribution from marginal distributions is also presented which is employed in calculation of mutual information. The proposed method is compared with the most popular existing algorithms. Various experiments, performed on several databases, certify the efficiency of our proposed method which is more consistent with the subjective criteria.