Artificial neural network to predict the effects of coating parameters on layer thickness of chromium carbonitride coating on pre-nitrided steels

Chromium carbonitride coatings were formed on plain carbon and alloy steels by pre-nitrocarburizing, followed by thermoreactive deposition and diffusion in a salt bath below 700 °C. In the present study, an artificial neural network-based model (ANNs) was developed to predict the layer thickness of pre-nitrided steels. Seventeen parameters affecting the layer thickness were considered as inputs, including the pre-nitriding time, salt bath compositions ratio, salt bath aging time, ferrochromium particle size, ferrochromium weight percent, salt bath temperature, coating time, and different chemical compositions of steels. The network was then trained to predict the layer thickness amounts as outputs. A 2-feed-forward back-propagation network was developed and trained using experimental data form literatures. Five steels were investigated. The effects of coating parameters on the layer thickness of steels were modeled by ANNs as well. The predicted values are in very good agreement with the measured ones indicating that the developed model is very accurate and has the great ability for predicting the layer thickness.     

ANN-based prediction of ferrite fraction in continuous cooling of microalloyed steels

In the present study, an artificial neural networks-based model was developed to predict the ferrite fraction of microalloyed steels during continuous cooling. Fourteen parameters affecting the ferrite fraction were considered as inputs, including the cooling rate, initial austenite grain size, and different chemical compositions. The network was then trained to predict the ferrite fraction amounts as outputs. A multilayer feed-forward back-propagation network was developed and trained using experimental data form literatures. The predicted values are in very good agreement with the measured ones indicating that the developed model is very accurate and has the great ability for predicting the ferrite fraction.     

Study the Output Characteristics of a 90 kJ Filippove-Type Plasma Focus

The output characteristics of a Filippove-Type plasma focus “Dena” (288 μF, 25 kV, 90 kJ) is numerically investigated by considering the voltage, current, current derivative, and maximum current as a function of capacitor bank energy in the constant Argon gas pressure and compared to the experiment. It is shown that increase on the bank energy leads to the increment on the maximum current and decrement on the pinch time.     

Linear Decoupled and Quasi-Decoupled Space–Time Codes

Space-time transmit diversity results in coupling of transmitted symbols across different antennas, which increases the complexity of maximum-likelihood decoding. Symbol coupling can be completely or partially avoided if the space-time code (STC) satisfies specific decoupling conditions; examples of such codes are orthogonal space-time block codes and quasi-orthogonal codes. In this letter, we study decoupling conditions for a linear full-diversity STC. Quasi-decoupled codes are proposed as a partially decoupled full-diversity STC of any rate for any number of transmit antennas with minimum decoding delay. By optimizing the coding gain of quasi-decoupled codes, it is shown that quasi-orthogonal codes have competitive performance with respect to the Alamouti code, and the more-recent threaded algebraic space-time (TAST) codes and ABBA codes. A general full-diversity decoupling condition is considered, and the general solution to this case, which also encompasses previously known orthogonal STCs, is derived     

Effect of the contention window size on performance and fairness of the IEEE 802.11 standard

IEEE 802.11 is a widely used standard for MAC and PHY layers of WLANs. Unfortunately, the access methods offered in this standard cannot support QoS (Quality of Service) for real-time traffics. Using multimedia applications over WLANs is increasing and, on the other hand, it seems that the access methods employed in this standard causes high variations in delay or jitter and wastes bandwidth due to collisions. There are many methods to enable DCF—basic access method in 802.11—with service differentiation and QoS. The difficulty in majority of these methods is unfair bandwidth allocation among low and high priority traffics at high loads resulting starvation for low priority traffics. In this paper, we modify the way that the CW (Contention Window) size is calculated after a successful transmission and study the effect of the CW size on performance and fairness. Results of our simulations show that the performance of DCF with this modification is better, specially, for traffics in which throughput is the most important parameter. Besides, this method provides better fairness among low and high priority traffics. We also employ a scheme to enable 802.11 with service differentiation which grants dynamic priority to low priority traffics to prevent starvation, specially, in high loads.     

An extended version of sectional MinHash method for near-duplicate detection

The Journal of Supercomputing - Identifying near-duplicate data can be applied to any type of content and has been widely used for increasing search engines' efficiency, detecting plagiarism or...     

Synthesis,spectral characterization,properties and structure of dinuclear rhenium complex containing bis[4-(2-pyridylmethyleneamino)phenyl]methane ligand

The synthesis, characterization, properties and crystal structure of {(bis[4-(2-pyridylmethyleneamino)phenyl]methane)[Re(CO)₃Br]₂} complex are reported. The ¹H NMR data for this complex shows downfield shift of the bis-diimine ligand protons upon coordination to the Re center. The molecular structure of the dinuclear rhenium(I) complex exhibits distorted octahedral geometries around the each metal center with one Br and facial arrangement of three CO and a cis-bonded diimine ligands. The bis-diimine ligand, twists via a half-turn around the metal–metal axis. Electrochemical investigation reveals two oxidation waves and two irreversible reduction waves.     

THERMO-HYDRAULIC BEHAVIOR MODELING OF PASSIVE HEAT TRANSFER ENHANCEMENT TECHNIQUES USING A SOFT COMPUTING APPROACH

The main aim of this research is to demonstrate effectiveness of soft computing techniques in thermo-hydraulic behavior modeling of passive heat transfer enhancement (HTE) techniques. An artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), two effective modeling methods, have been used to model Nusselt numbers and friction factors of wire coil and twisted tape inserts in various flow regimes. The experimental data sets were utilized for training and validation of these models, and their results were compared with the corresponding correlations. The mean relative error (MRE) between the predicted results and experimental data of ANN and ANFIS models were found to be less than 3% and 1.5% for thermo-hydraulic behavior modeling of wire coil and twisted tape inserts, respectively. Depending on model complexity, performance of both ANN and ANFIS models was found to be superior to that of the corresponding power-law regressions. Hence, application of the soft computing approach to predict the performance of thermal systems in engineering applications is recommended.     

Phase‐only synthesis of planar arrays using autocorrelation matching method

Autocorrelation matching method is proposed for phase‐only synthesis of power pattern of planar antenna arrays. This method is based on equating the autocorrelation coefficients of a planar array having a specified amplitude of excitations to those of a conventionally designed planar array. The effectiveness of the proposed method is verified by synthesis of pencil‐beam and flat‐top patterns.