Performance evaluation of an importance sampling technique in a Jackson network

Importance sampling is a technique that is commonly used to speed up Monte Carlo simulation of rare events. However, little is known regarding the design of efficient importance sampling algorithms in the context of queueing networks. The standard approach, which simulates the system using an a priori fixed change of measure suggested by large deviation analysis, has been shown to fail in even the simplest network settings. Estimating probabilities associated with rare events has been a topic of great importance in queueing theory, and in applied probability at large. In this article, we analyse the performance of an importance sampling estimator for a rare event probability in a Jackson network. This article carries out strict deadlines to a two-node Jackson network with feedback whose arrival and service rates are modulated by an exogenous finite state Markov process. We have estimated the probability of network blocking for various sets of parameters, and also the probability of missing the deadline of customers for different loads and deadlines. We have finally shown that the probability of total population overflow may be affected by various deadline values, service rates and arrival rates.     

Optimum design of large steel skeletal structures using chaotic firefly optimization algorithm based on the Gaussian map

Structural and Multidisciplinary Optimization - In this paper, a new chaotic firefly algorithm based on Gaussian map (CGFA) is proposed for structural optimization problems. Different chaotic maps...     

Three-dimensional solution of low-velocity impact on sandwich panels with hybrid nanocomposite face sheets

In this article, a three-dimensional solution based on Fourier's series and the generalized differential quadrature method is presented to model the low-velocity impact on sandwich panels with hybrid nanocomposite face sheets. Navier's equations are derived and displacements are substituted by their corresponding Fourier's series. The contact force is considered as a Fourier's series of impactor displacement and deflection of contact point. To verify the theoretical model, experiments are performed on a polyurethane foam-cored sandwich panel with epoxy/woven-fiberglass/nanosilica hybrid nanocomposite face sheets. Contact force and lateral displacement of contact point histories are compared with the theoretical model.     

Novel design and simulation of reversible ALU in quantum dot cellular automata

The Journal of Supercomputing - Quantum dot cellular automata (QCA) technology is considered as one of the most suitable replacements to reduce the CMOS-based digital circuit design problems at the...     

Automatic fire detection based on soft computing techniques: review from 2000 to 2010

Automatic fire detection system is a system that is capable of assessing environmental factors and their effects on the environment as well as predicting the occurrence of fire in the early stages and even before the outbreak. There are two perspectives in fire detection: fire detection in forests or jungles and fire detection in occupied or residential areas. Automatic fire detection has attracted increased attention due to its importance in decreasing fire damage. There are many studies that have considered appropriate techniques for early fire detection. In recent years researches have been studying technical developments in this field aimed at exploiting wireless communications networks, detection systems and fire prediction systems design. In this paper the automatic fire detection researches using intelligent techniques from 2000 to 2010 is reviewed. We could classify researches to four categories: fire detectors, reduce false alarms systems, fire data analysis and fire predictors. We also classify the intelligent techniques outlined in the researches for each category.     

Predicting the combined effect of TiO2 nano-particles and welding input parameters on the hardness of melted zone in submerged arc welding by fuzzy logic

Submerged arc welding (SAW) is a high-quality arc welding process used in heavy industries for welding thick plates. In this process, selecting appropriate values for the input parameters is required for high productivity and cost effectiveness. A very important weld quality characteristic affected by welding input parameters is the hardness of melted zone (HMZ). This paper reports the applicability of fuzzy logic (FL) to predict HMZ in the SAW process which is affected by the combined effect of TiO₂ nano-particles and welding input parameters. The arc voltage, welding current, welding speed, contact tip-to-plate distance, and TiO₂ nano-particles were used as input parameters and HMZ as the response to develop FL model. A five-level five-factor central composite rotatable design (CCRD) was used in the experiments to generate experimental data. Experiments were performed, and HMZs were measured. The predicted results from FL were compared with the experimental data. The correlation factor value obtained was 99.99% between the measured and predicted values of HMZ. The results showed that FL is an accurate and reliable technique for predicting HMZ because of its low error rate.     

Study of the alloying additives and alkaline zincate solution effects on the commercial aluminum as galvanic anode for use in alkaline batteries

The corrosion behavior of different grades of commercial aluminum such as AA1040, AA5083, AA6060 and AA7075 in ZnO-containing 4 M NaOH has been determined by using open circuit potential-time measurements (OCP), galvanostatic and potentiodynamic polarization and electrochemical impedance spectroscopy (EIS). The results of scanning electron microscopy (SEM) and energy dispersive analysis of X-ray (EDAX) reveal that ZnO produces the inhibition effect by the formation of a zinc-containing deposit layer on the surface of aluminum electrodes. Although the influence of zincating on the performance of aluminum alloys and considering the amount of alloying elements such as zinc, magnesium and manganese in AA7075 and AA5083 alloys is much more than AA6060 one, the AA6060 aluminum exhibits negligible corrosion rate. Alloying aluminum with other elements and modifying the composition of the electrolyte is a necessary condition for reducing the self-corrosion of the aluminum anodes, whereas the proportion of the amount of additive elements is sufficient and important condition. As AA6060 with a low amount of Zn and Mg, but the high value of the ratio of (Mg/Zn) content (>400) can serve as a good galvanic anode in the alkaline media.     

Joint detection, channel estimation, and interference cancellation in downlink MC-CDMA communication systems using complex-valued multilayer neural networks

Data detection in the presence of interference is one of the main challenges in multicarrier code division multiple access (MC-CDMA) communication systems. In this paper, a new detection technique for downlink MC-CDMA systems is proposed. This technique uses complex-valued multilayer neural networks at the receiver side. With the new definition for desired responses (±(1+J) instead of ±1, where $ J = \sqrt {{ - 1}} $ ), the convergence rate is increased (in the training process) regarding to saturation of imaginary output and the performance is increased because of increasing Euclidean distance of output neuron inputs in two states of desired outputs (with factor of $ \sqrt {2} $ ). The performance of the proposed method is improved further by considering two various saturation coefficients (in the activation function of output layer) in the training and test processes. Since the last performance improving lead to low convergence rate, this effect is compensated by correcting the coefficient of training rate in the output layer. Simulation results confirm the high convergence rate, low computational complexity, and also good performance of the proposed method in wide range of SNRs.     

Fog-based healthcare systems: A systematic review

Multimedia Tools and Applications - The healthcare system aims to provide a reliable and organized solution to enhance the health of human society. Studying the history of patients can help...     

Towards effective offloading mechanisms in fog computing

Fog computing is considered a formidable next-generation complement to cloud computing. Nowadays, in light of the dramatic rise in the number of IoT devices, several problems have been raised in cloud architectures. By introducing fog computing as a mediate layer between the user devices and the cloud, one can extend cloud computing's processing and storage capability. Offloading can be utilized as a mechanism that transfers computations, data, and energy consumption from the resource-limited user devices to resource-rich fog/cloud layers to achieve an optimal experience in the quality of applications and improve the system performance. This paper provides a systematic and comprehensive study to evaluate fog offloading mechanisms' current and recent works. Each selected paper's pros and cons are explored and analyzed to state and address the present potentialities and issues of offloading mechanisms in a fog environment efficiently. We classify offloading mechanisms in a fog system into four groups, including computation-based, energy-based, storage-based, and hybrid approaches. Furthermore, this paper explores offloading metrics, applied algorithms, and evaluation methods related to the chosen offloading mechanisms in fog systems. Additionally, the open challenges and future trends derived from the reviewed studies are discussed.