Wideband Circularly Polarized Sequentially Rotated Microstrip Antenna Array with Sequential-Phase Feeding Network

A wideband 2×2 sequential-phase fed circularly polarized(CP)microstrip antenna array(MAA)with sequentially rotated elements is proposed.Firstly,the antenna element loaded with shorting pins is studied as compared to the traditional counterpart.Then,a feeding network is designed to be connected together in a sequential rotation manner,resulting in forming a four-port network to allow symmetrical positioning of CP microstrip patch elements.After that,straight strips are added to the feeding network in order to increase the distance and reduce coupling between array elements.They also play a role in improving the impedance matching of the array.Finally,the proposed antenna array is designed and simulated.The results are presented that its impedance bandwidth is about 36.3%at 24.1~34.8 GHz for|S_(11)|<10 dB and axial-ratio(AR)bandwidth is about 20.4%at 24.2~29.7 GHz for AR【3 dB.Besides,a peak gain of 11.1 dBic is simulated over the operating frequency range,which is significantly improved compared to the classic and existing works.

Near-infrared Spectral Detection of the Content of Soybean Fat Acids Based on Genetic Multilayer Feed forward Neural Network

In the paper, a method of building mathematic model employing genetic multilayer feed forward neural network is presented, and the quantitative relationship of chemical measured values and near-infrared spectral data is established. In the paper, quantitative mathematic model related chemical assayed values and near-infrared spectral data is established by means of genetic multilayer feed forward neural network, acquired near-infrared spectral data are taken as input of network with the content of five kinds of fat acids tested from chemical method as output, weight values of multilayer feed forward neural network are trained by genetic algorithms and detection model of neural network of soybean is built. A kind of multilayer feed forward neural network trained by genetic algorithms is designed in the paper. Through experiments, all the related coefficients of five fat acids can approach 0.9 which satisfies the preliminary test of soybean breeding.

Response Surface Methodology and Artificial Neural Network Methods Comparative Assessment for Fuel Rich and Fuel Lean Catalytic Combustion

Modeling, predictive and generalization capabilities of response surface methodology (RSM) and artificial neural network (ANN) have been performed to assess the thermal structure of the experimentally studied catalytic combustion of stabilized confined turbulent gaseous diffusion flames. The Pt/γAl2O3 and Pd/γAl2O3 disc burners were located in the combustion domain and the experiments were accomplished under both fuel-rich and fuel-lean conditions at a modified equivalence (fuel/air) ratio (ø) of 0.75 and 0.25, respectively. The thermal structure of these catalytic flames developed over the Pt and Pd disc burners was scrutinized via measuring the mean temperature profiles in the radial direction at different discrete axial locations along with the flames. The RSM and ANN methods investigated the effect of the two operating parameters namely (r), the radial distance from the center line of the flame, and (x), axial distance along with the flame over the disc, on the measured temperature of the flames and predicted the corresponding temperatures beside predicting the maximum temperature and the corresponding input process variables. A three-layered Feed Forward Neural Network was developed in conjugation with the hyperbolic tangent sigmoid (tansig) transfer function and an optimized topology of 2:10:1 (input neurons:hidden neurons:output neurons). Also the ANN method has been exploited to illustrate the effects of coded R and X input variables on the response in the three and two dimensions and to locate the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of & F_Ratio are 0.9181 - 0.9809 & 634.5 - 3528.8 for RSM method compared to 0.9857 - 0.9951 & 7636.4 - 24,028.4 for ANN method beside lower values for error analysis terms.

A Kind of Second-Order Learning Algorithm Based on Generalized Cost Criteria in Multi-Layer Feed-Forward Neural Networks

A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi layer feed forward neural networks, the second order back propagation recursive algorithm based generalized cost criteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second order convergent rate. The performance and application prospect are analyzed. Lots of simulation experiments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multiple algorithm. The algorithm and selection of networks parameters are significant and the performance is more excellent than BP algorithm and the second order learning algorithm that was given by Karayiannis.

Combined Signal Processing Based Techniques and Feed Forward Neural Networks for Pathological Voice Detection and Classification

This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks(FFNN).The important pathological voices such as Autism Spectrum Disorder(ASD)and Down Syndrome(DS)are considered for analysis.These pathological voices are known to manifest in different ways in the speech of children and adults.Therefore,it is possible to discriminate ASD and DS children from normal ones using the acoustic features extracted from the speech of these subjects.The important attributes hidden in the pathological voices are extracted by applying different signal processing techniques.In this work,three group of feature vectors such as perturbation measures,noise parameters and spectral-cepstral modeling are derived from the signals.The detection and classification is done by means of Feed For-ward Neural Network(FFNN)classifier trained with Scaled Conjugate Gradient(SCG)algorithm.The performance of the network is evaluated by finding various performance metrics and the the experimental results clearly demonstrate that the proposed method gives better performance compared with other methods discussed in the literature.

Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of data.The traffic control and data forwarding functions are decoupled in software-defined networking(SDN)and allow the network to be programmable.Each switch in SDN keeps track of forwarding information in a flow table.The SDN switches must search the flow table for the flow rules that match the packets to handle the incoming packets.Due to the obvious vast quantity of data in data centres,the capacity of the flow table restricts the data plane’s forwarding capabilities.So,the SDN must handle traffic from across the whole network.The flow table depends on Ternary Content Addressable Memorable Memory(TCAM)for storing and a quick search of regulations;it is restricted in capacity owing to its elevated cost and energy consumption.Whenever the flow table is abused and overflowing,the usual regulations cannot be executed quickly.In this case,we consider lowrate flow table overflowing that causes collision flow rules to be installed and consumes excessive existing flow table capacity by delivering packets that don’t fit the flow table at a low rate.This study introduces machine learning techniques for detecting and categorizing low-rate collision flows table in SDN,using Feed ForwardNeuralNetwork(FFNN),K-Means,and Decision Tree(DT).We generate two network topologies,Fat Tree and Simple Tree Topologies,with the Mininet simulator and coupled to the OpenDayLight(ODL)controller.The efficiency and efficacy of the suggested algorithms are assessed using several assessment indicators such as success rate query,propagation delay,overall dropped packets,energy consumption,bandwidth usage,latency rate,and throughput.The findings showed that the suggested technique to tackle the flow table congestion problem minimizes the number of flows while retaining the statistical consistency of the 5G network.By putting the proposed flow method and checking whether a packet may move from point A to point B without breaking certain regulations,the evaluation tool examines every flow against a set of criteria.The FFNN with DT and K-means algorithms obtain accuracies of 96.29%and 97.51%,respectively,in the identification of collision flows,according to the experimental outcome when associated with existing methods from the literature.

Optimal Design of Wireless Power Transmission Systems Using Antenna Arrays

Three design methods for wireless power transmission(WPT)systems using antenna arrays have been investigated.The three methods,corresponding to three common application scenarios of WPT systems,are based on the method of maximum power transmission efficiency(MMPTE)between two antenna arrays.They are unconstrained MMPTE,weighted MMPTE,and constrained MMPTE.To demonstrate the optimal design process with the three methods,a WPT system operating at 2.45 GHz is designed,simulated,and fabricated,in which the transmitting(Tx)array,consisting of 36 microstrip patch elements,is configured as a square and the receiving(Rx)array,consisting of 5 patch elements,is configured as an L shape.The power transmission efficiency(PTE)is then maximized for the three application scenarios,which yields the maximum possible PTEs and the optimized distributions of excitations for both Tx and Rx arrays.The feeding networks are then built based on the optimized distributions of excitations.Simulations and experiments reveal that the unconstrained MMPTE,which corresponds to the application scenario where no radiation pattern shaping is involved,yields the highest PTE.The next highest PTE belongs to the weighted MMPTE,where the power levels at all the receiving elements are imposed to be equal.The constrained MMPTE has the lowest PTE,corresponding to the scenario in which the radiated power pattern is assumed to be flat along with the Rx array.

Analysis of the Smart Player’s Impact on the Success of a Team Empowered with Machine LeAnalysis of the Smart Player’s Impact on the Success of a Team Empowered with Machine Learningarning

The innovation and development in data science have an impact in all trades of life.The commercialization of sport has encouraged players,coaches,and other concerns to use technology to be in better position than r their opponents.In the past,the focus was on improved training techniques for better physical performance.These days,sports analytics identify the patterns in the performance and highlight strengths and weaknesses of potential players.Sports analytics not only predict the performance of players in the near future but it also performs predictive modeling for a particular behavior of a player in the past.The impact of a smart player on the success of a team is always a big question mark before the start of a match.The fans always want to know performance analysis of these superstar players and they always are interested to get to know more about their favorite player and they always have high hopes from their favorite player.Machine learning(ML)based techniques help in predicting the performance of an individual player as well as for the whole team.The statistics are very vital and useful for management,fans,and expert analysis.In our proposed framework,the adaptive back propagation neural network(ABPNN)model is used for the prediction of a player’s performance.The data is collected from football websites,and the results are stored in the cloud for fast fetching of data.They can be retrieved anywhere in the world through cloud storage.The results are computed with 94%accuracy and the performance of the smart player is formulated for the success of a team.

Wideband and high-gain BeiDou antenna with a sequential feed network for satellite tracking

BeiDou-3 navigation satellite system was officially opened in 2020.While bringing high-performance services to people around the world,the navigation system requires well-designed BeiDou antennas.In this paper,we propose a wideband circularly polarized high-performance BeiDou antenna.The antenna realizes wideband circularly polarized radiation through a four-port sequential feed network,and the phase imbalance of the feed network from 1.05 to 1.80 GHz is less than 7°.The manufactured antenna demonstrates a return loss of more than 13 dB and an axial ratio<3 dB over the entire global navigation satellite system(GNSS)frequency band.The right-handed circular polarization(RHCP)gain of the proposed antenna is greater than 4 dB in the GNSS low-frequency band and can reach more than 7.1 dB in the high-frequency band.Dimension of the proposed antenna is 120 mm×120 mm×20 mm,i.e.,0.54λo×0.54λo×0.09λo,whereλo is the wavelength of the center frequency.The proposed antenna connected to a GNSS receiver has tracked 12 BeiDou satellites with C/N0 ratios of GNSS signals greater than 30 dB.Such a high-performance antenna provides a basis for high-quality positioning services.

An Efficient Hybrid Model Based on Modified Whale Optimization Algorithm and Multilayer Perceptron Neural Network for Medical Classification Problems

Feedforward Neural Network(FNN)is one of the most popular neural network models that is utilized to solve a wide range of nonlinear and complex problems.Several models such as stochastic gradient descent have been developed to train FNNs.However,they mainly suffer from falling into local optima leading to reduce the accuracy of FNNs.Moreover,the convergence speed of training process depends on the initial values of weights and biases in FNNs.Generally,these values are randomly determined by most of the training models.To deal with these issues,in this paper,we develop a novel evolutionary algorithm by modifying the original version of Whale Optimization Algorithm(WOA).To this end,a nonlinear function is introduced to improve the exploration and exploitation phases in the search process of WOA.Then,the modified WOA is applied to automatically obtain the initial values of weights and biases in FNN leading to reduce the probability of falling into local optima.In addition,the FNN model trained by the modified WOA is used to develop a classification approach for medical diagnosis problems.Ten medical diagnosis datasets are utilized to evaluate the efficiency of the proposed method.Also,four evaluation metrics including accuracy,AUC,specificity,and sensitivity are used in the experiments to compare the performance of classification models.The experimental results demonstrate that the proposed method is better than other competing classification models due to achieving higher values of accuracy,AUC,specificity,and sensitivity metrics for the used datasets.

STUDYING THE ABRASION BEHAVIOR OF RUBBERY MATERIALS WITH COMBINED DESIGN OF EXPERIMENT-ARTIFICIAL NEURAL NETWORK

In this study, an application of artificial neural network （ANN） has been presented in modeling and studying the effect of compounding variables on abrasion behavior of rubber formulations. Three case studies were carried out in which the experiment data were collected according to classical response surface designs. Besides developing the ANN models, we developed response surface methodology （RSM） to confirm the ANN predictions. A simple relation was employed for determination of relative importance of each variable according to ANN models. It was shown through these case studies that ANN models delivered very good data fitting and their simulating curves could help the researchers to better understand the abrasion behavior.

Artificial intelligence model for predicting geomechanical characteristics using easy-to-acquire offset logs without deploying logging tools

This study focuses on predicting acoustic and mechanical rock properties using random forest and feed forward neural network models to evaluate the likelihood of developing efficient ways of handling absence of rock properties at offset locations.The Random Forest algorithm was used for direct prediction of the sonic data without considering the depth range of the facies;while Feed forward Neural network was used to predict the sonic data with emphasis on the lithofacies depths.The accuracy of these approaches was used in choosing the best and the most robust model for predicting sonic data when estimating formation strength and mechnical properties.Acoustic log was predicted after training a combination of caliper log,gamma log,depth,density log and resistivity log from offset wells.5 hidden layers that accounts for the data structural complexities was included in the model architecture.A multilayer perceptron network was adopted for the Random forest algorithm to handle linear combinations of input data set.Diverse error computations were used to evaluate the performance of the model.Lastly,mechanical properties and sanding potential was evaluated using standard relations and appropriate depositional conditions.Random forest algorithm gave the best prediction accuracy of more than 96%,but the Feed forward network has the lower mean absolute error and mean squared error of 2.75 and 5.93 respectively.Generally,the predicted compressive and shear wave velocity show increase of values with depth,a behavior that is capable of identifying payzone characteristics.This was validated by the distinction seen within the 200 feet gas sand formation in the deeper portion of the studied well(9600e9800 feet).Potential failure portions of the wells,a common feature in the field,were inferred from the sanding potential computed using the predicted mechanical properties value.