Navigation in the social internet-of-things (SIoT) for discovering the influential service-providers using distributed learning automata

The Journal of Supercomputing - The integration of the social network concepts and IoT has led to the creation of a new concept called Social IoT, wherein objects or things can interact and provide...     

A new range‐free PCA‐based localization algorithm in wireless sensor networks

Range‐free localization algorithms in wireless sensor networks have been an interesting field for researchers over the past few years. The combining of different requirements such as storage space, computational capacities, communication capabilities, and power efficiency is a challenging aspect of developing a localization algorithm. In this paper, a new range‐free localization algorithm, called PCAL, is proposed using soft computing techniques. The proposed method utilizes hop‐count distances as the data to train and build a neural network. Before feeding the data into the neural network for the purpose of training, the dimensionality of data is reduced by principal component analysis algorithm. The performance of the proposed algorithm is evaluated using simulation. The obtained results show that the proposed algorithm has a better performance in contrast to other algorithms based on storage space, communication overhead, and localization accuracy. Furthermore, the effect of various parameters on the PCAL algorithm is studied.     

A 200 W broadband high power amplifier using hybrid power combining network and digital level control

In this article, a 2 to 6 GHz solid‐state power amplifier with 53 dBm output power has been analyzed, designed, and fabricated. To achieve a wideband high output power, we introduce a 16‐way hybrid power combiner based on microstrip planar binary and parallel structures. The simulation and measurement results of the proposed hybrid power combining network (PCN) show that the maximum power combining efficiency is around 86% with the insertion loss of around 0.6 to 1.5 dB and an isolation of 20 dB between the ports. Also, to compensate the output power variations due to the thermal and operating frequency changes across the bandwidth, a digital level control (DLC) unit utilizing an agile control algorithm is proposed which decreases the output power variations to 2% of the desired output power. A cooling heatsink fan system has been also designed in order to transfer the heat generated power to the air. The measured output power for the applied input continuous wave is higher than 52.5 dBm. In addition, the power added efficiency (PAE) is better than 15% across the wide portion of the bandwidth and the measured third‐order intermodulation is about 20 dBc (average).     

Multi-objective evolutionary optimization of polynomial neural networks for modelling and prediction of explosive cutting process

In this paper, evolutionary algorithms (EAs) are deployed for multi-objective Pareto optimal design of group method of data handling (GMDH)-type neural networks which have been used for modelling an explosive cutting process using some input–output experimental data. In this way, multi-objective EAs (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity-preserving mechanism are used for Pareto optimization of such GMDH-type neural networks. The important conflicting objectives of GMDH-type neural networks that are considered in this work are, namely, training error (TE), prediction error (PE), and number of neurons (N) of such neural networks. Different pairs of theses objective functions are selected for 2-objective optimization processes. Therefore, optimal Pareto fronts of such models are obtained in each case which exhibit the trade-off between the corresponding pair of conflicting objectives and, thus, provide different non-dominated optimal choices of GMDH-type neural networks models for explosive cutting process. Moreover, all the three objectives are considered in a 3-objective optimization process, which consequently leads to some more non-dominated choices of GMDH-type models representing the trade-offs among the training error, prediction error, and number of neurons (complexity of network), simultaneously. The overlay graphs of these Pareto fronts also reveal that the 3-objective results include those of the 2-objective results and, thus, provide more optimal choices for the multi-objective design of GMDH-type neural networks in terms of minimum training error, minimum prediction error, and minimum complexity.     

Improving the Mechanical Properties and Wear Resistance of a Commercial Pearlitic Rail Steel Using a Two-Step Heat Treatment

Metallurgical and Materials Transactions A - The possibility of improving the mechanical and wear performance of steel rails with conventional compositions, near-eutectoid and without special...     

Thermal-hydraulic and stress analysis of AP1000 reactor containment during LOCA in dry cooling mode

Some kinds of break in the reactor coolant system may cause the coolant to exit rapidly from the failure site,which leads to the loss of coolant accident (LOCA).In this paper,a stress analysis of an AP1000 reactor containment is performed in an LOCA,with the passive containment cooling system (PCCS) being available and not available for cooling the wall's containment.The variations in the mechanical properties of the wall's containment,including elastic modulus,strength,and stress,are analyzed using the ABAQUS code.A general two-phase model is applied for modeling thermal-hydraulic behavior inside the containment.Obtained pressure and temperature from thermal-hydraulic models are considered as boundary conditions of the ABAQUS code to obtain distributions of temperature and stress across steel shell of the containment in the accident.The results indicate that if the PCCS fails,the peak pressure inside the containment exceeds the design value.However,the stress would still be lower than the yield stress value,and no risk would threaten the integrity of the containment.     

Analytical and numerical investigation into the longitudinal vibration of uniform nanotubes

In recent years, prediction of the behaviors of micro and nanostructures is going to be a matter of increasing concern considering their developments and uses in various engineering fields. Since carbon nanotubes show the specific properties such as strength and special electrical behaviors, they have become the main subject in nanotechnology researches. On the grounds that the classical continuum theory cannot accurately predict the mechanical behavior of nanostructures, nonlocal elasticity theory is used to model the nanoscaled systems. In this paper, a nonlocal model for nanorods is developed, and it is used to model the carbon nanotubes with the aim of the investigating into their longitudinal vibration. Following the derivation of governing equation of nanorods and estimation of nondimensional frequencies, the effect of nonlocal parameter and the length of the nanotube on the obtained frequencies are studied. Furthermore, differential quadrature method, as a numerical solution technique, is used to study the effect of these parameters on estimated frequencies for both classical and nonlocal theories.     

An investigation into the vibration of harmonic drive systems

Harmonic drive systems are precise and specific transmission gear systems which are beneficial in terms of the high transmission ratio and almost zero backlash. These inherent and spectacular properties result in using this mechanism in robotic and space sciences where the precision and lightwieght play an important role. This paper presents a vibration analysis of harmonic drive systems using the shell theory. Equations of vibration for the flexspline and the circular spline of the system are derived and used to find the natural frequencies for both parts and, moreover, vibration response of the system under the operating condition is calculated. Also, obtained vibration equations are utilized to study the effects of different involved parameters such as the geometry of the flexspline and its gear tooth, eccentricity, and unbalancing on the vibrational behavior of the system.     

Metallurgical and Mechanical Characterization of Laser Spot Welded Low Carbon Steel Sheets

This paper aims at investigating metallurgical and mechanical characterization of low carbon steel laser spot welds. Microstructural examinations, microhardness tests and quasi‐static tensile‐shear tests were preformed. Mechanical properties of the welds were described in terms of peak load and failure mode. The effects of laser spot welding parameters including pulse frequency, laser energy, welding speed, pulse width and welded circle diameter, on low carbon steel laser spot weld performance were studied using the Taguchi design of experiment method. It was found that the effective laser pulse energy is the controlling factor in the determination of mechanical strength of laser spot welds.