Design of Neural-Network-Based Mutual Coupling Compensators for Printed Planar Antenna Arrays

PurposeThis study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.Design/methodology/approachA data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.FindingsAmong five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, “machine speed” and “fabric width” are determined as the most important variables by using these tools. Then, optimum values for “machine speed” and “fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.Originality/valueAddressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.

Electromagnetic machine learning for estimation and mitigation of mutual coupling in strongly coupled arrays

Over the years, drone usage have become an increasing part of the ever-connected society that we are currently living in. Its usages have proliferated beyond the military sector to various commercial and consumer activities such as package delivery, disaster relief, agriculture and filming. With the rise of Wi-Fi controlled drone. Wi-Fi controlled drone has increased its popularity for personal use due to its affordability, and the ease of operating the drone through smart-devices like mobile phone, tablets and computers. As such, this increases the likelihood of drone presence in various environments, especially in critical government infrastructure, leading to various privacy and security concern by the authorities and the public with malicious intent. Therefore, various signature-based methodology of drone detection has emerged such as the visual and Radio Frequency (RF) signature-based detection method. Visual signature-based detection relies on camera capture and image processing but this is an expensive approach. Whereas, RF signature- based detection relies on the identification of the emission of RF signal by the drone. However, since most commercial electronics devices were built based on Wi-Fi technology, the differentiation of the RF signals transmitted between a drone or a standard Wi-Fi device in a crowded Wi-Fi environment such as a school campus or city area is an challenging task. In this paper, we propose a novel machine learning approach that leverages on the identified unique signatures of Wi-Fi devices in terms of Radio Frequency (RF) and network packets measurement to differentiate the presence of Wi-Fi drone and standard Wi-Fi devices in an urban setting. Furthermore, we also carried out a meticulous pre-processing procedure and a better training scheme of using Stratified K-Fold Cross-Validation (SKFCV), to enhance the richness in the data signature and fully exploit the permutation of the data during training respectively for better performance of the ML models. Two supervised classification Machine Learning (ML) models, namely the Logistic Regression (LR), and Artificial Neural Network (ANN) were applied using the joint data measurements to identify the presence of drone in dense Wi-Fi environment. The experimental results have shown that the proposed novel ML approach of using both RF and network measurement signatures coupled with the pre-processing and training methodology on LR and ANN ML models have outperformed the traditional RF signature-based drone detection ML accuracy results by 15.1% and 21.63% respectively in a crowded Wi-Fi environment.

Major League Baseball (MLB) is the highest level of professional baseball in the world and accounts for some of the most popular international sporting events. Many scholars have conducted research on predicting the outcome of MLB matches. The accuracy in predicting the results of baseball games is low. Therefore, deep learning and machine learning methods were used to build models for predicting the outcomes (win/loss) of MLB matches and investigate the differences between the models in terms of their performance. The match data of 30 teams during the 2019 MLB season with only the starting pitcher or with all pitchers in the pitcher category were collected to compare the prediction accuracy. A one-dimensional convolutional neural network (1DCNN), a traditional machine learning artificial neural network (ANN), and a support vector machine (SVM) were used to predict match outcomes with fivefold cross-validation to evaluate model performance. The highest prediction accuracies were 93.4%, 93.91%, and 93.90% with the 1DCNN, ANN, SVM models, respectively, before feature selection; after feature selection, the highest accuracies obtained were 94.18% and 94.16% with the ANN and SVM models, respectively. The prediction results obtained with the three models were similar, and the prediction accuracies were much higher than those obtained in related studies. Moreover, a 1DCNN was used for the first time for predicting the outcome of MLB matches, and it achieved a prediction accuracy similar to that achieved by machine learning methods.

Machine Learning Can Accurately Predict Overnight Stay, Readmission, and 30-Day Complications Following Anterior Cruciate Ligament Reconstruction

The main problems in phased array antenna are mutual coupling and ground effects that destroys the designed radiation pattern. In this paper these undesired effects is compensated over 5 dB for mutual coupling effect and about 10 dB for both mutual coupling and ground effects in wide frequency band 30-88 MHz.

Far-field pattern synthesis of small antenna arrays is a nontrivial problem. Typically radiation patterns are required to exhibit low sidelobe levels, whilst maintaining the ability to scan the main beam in space. Mutual coupling is a pertinent problem when the number of antenna elements is small, often precluding the use of conventional tapering algorithms. In this paper a technique previously employed to compensate for mutual coupling in linear arrays is extended to include small planar antenna arrays. The method consists of the calculation of the coupling coefficients between the array elements, which are then formed into a matrix and used to compensate for the effects of mutual coupling. It is then shown that this method is not suitable for all planar array configurations and a new technique is developed which allows the calculation of coupling matrices for these element configurations. Measured data from a small planar antenna array is presented, with the outlined methods employed to produce low sidelobe sum and difference beams.

ABSTRACTThis paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.

In the modern world, condition monitoring is crucial to the predictive maintenance of machinery. Gearboxes are widely used in machineries and auto motives to achieve the variable speeds. The major problem in gearbox is catastrophic failure due to heavy loads, corrosion and erosion, results in economic loss and creates high safety risks. So, it is necessary to provide condition monitoring technique to detect and diagnose failures, to achieve cost benefits to industry. The main purpose of this study to use Machine Learning (ML) algorithms and Artificial Neural Network (ANN) which are very powerful and reliable tool for fault detection and its most important attribute is its ability to efficiently detect non-stationary, non-periodic, transient features of the vibration signal. To do the vibration study, an experimental setup was created, and various faults were induced faults of various kinds that usually occurred in the gearbox. The gear in the gear train was subjected to vibration analysis which was captured via a sensor. Signal processing was carried out using MATLAB Toolbox. To automatically identify the flaws in the helical gearbox, an artificial neural network (ANN) and several machines learning methods, including KNN, decision tree, random forest, and SMV, were trained by creating a database from the experiment conducted. The outcomes showed potential in accurately classifying the faults. The results show that ANN has the highest accuracy of 99.6% with a 6.5662 seconds computational time while SVM has the lowest accuracy of 96% among them along with the highest computational time of 21.324 seconds.

Because the maximum radiation direction of the end-fire array antenna points to the axis of the array and the small element spacing required to suppress the grating lobes, the mutual coupling effect between the arrays is more serious than that of the broadside array. The article first established the end-fire array antenna pattern model and clutter signal model with the effect of mutual coupling. The clutter signal model includes two cases, ideal mutual coupling effect and mutual coupling error. On this basis, we take a ten-element uniform linear array using CST Microwave Studio to validate our model. Then we analyze the clutter distribution characteristics of the end-fire array airborne radar and the effect of mismatch on the spatial steering vector. Finally, an adaptive mutual coupling compensation method based on covariance matrix reconstruction is proposed. In general, clutter suppression methods with end-fire array take no account of the mutual coupling error changing over time, but the proposed method first adaptively estimates the mutual coupling error based on the airborne radar echo data and uses the estimated normalized impedance matrix to compensate the spatial steering vector, then combines the system parameters to construct the clutter covariance matrix. The simulations indicate that this method accurately estimate the impedance matrix, and it eliminates the clutter power reduction and spatial steering vector mismatch caused by mutual coupling error. Moreover, it also solves the serious non-stationary clutter problem of the end-fire array airborne radar.

In this paper a novel method for computing the radiation pattern and also the input impedance of a planar rectangular micro-strip antenna array with consideration of mutual coupling and mutual impedance is presented. Each two triangles with a common edge make an Edge element or RWG. Any such edge element is considered as an infinitesimal dipole. The electrical currents over each dipole can be calculated by solving an electric field integral equation (EFIE), using the method of moments. Using the resulting surface currents and considering the mutual coupling effect between the elements, the radiation pattern of the whole array is obtained employing a new method, which we call it “active element pattern based on RWG edge element”. Moreover, using the above mentioned model for the edge elements and also considering the mutual impedance between the elements, the input impedance of the array is now calculated.

This communication presents for the first time a 16-element planar X-band bidirectional antenna array for beamforming applications. The planar (2D) antenna array is comprised of planar polygon slot antenna elements that utilize a metamaterial inspired reactive coupling element. Simulation and measurement results are presented for both a single antenna element and a 16-element planar array, which resides on a Liquid Crystal Polymer (LCP) substrate. Successful comparison between measured and simulated results verifies that the single antenna element achieves a 10 dB return loss bandwidth of 17.5% and the planar antenna array achieves a 10 dB return loss bandwidth of 25% both at a center frequency of approximately 11 GHz. The measured gain of the planar antenna array is more than 14.5 dBi with an efficiency of 65%. The overall size of the planar antenna array is 3.3λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sub> × 3.6λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sub> × 0.0093λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sub> at 11 GHz.

While molecular dynamics (MD) is a very useful computational method for atomistic simulations, modeling the interatomic interactions for reliable MD simulations of real materials has been a long-standing challenge. In 2007, Behler and Parrinello first proposed and demonstrated an artificial neural network machine learning (ANN-ML) scheme, opening a new paradigm for developing accurate and efficient interatomic potentials for reliable MD simulation studies of the thermodynamics and kinetics of materials. In this paper, we show that an accurate and transferable ANN-ML interatomic potential can be developed for MD simulations of the La-Si-P system. The crucial role of training data in the ML potential development is discussed. The developed ANN-ML potential accurately describes not only the energy vs volume curves for all the known elemental, binary, and ternary crystalline structures in the La-Si-P system but also the structures of La-Si-P liquids with various compositions. Using the developed ANN-ML potential, the melting temperatures of several crystalline phases in the La-Si-P system are predicted by the coexistence of solid-liquid phases from MD simulations. While the ANN-ML model systematically underestimates the melting temperatures of these phases, the overall trend agrees with experiment. The developed ANN-ML potential is also applied to study the nucleation and growth of LaP as a function of different relative concentrations of Si and P in the La-Si-P liquid, and the obtained results are consistent with experimental observations.

The performance of an adaptive antenna array is strongly affected by the electromagnetic characteristics of the array. The mutual coupling effect degrades the performance significantly, especially when the inter element spacing is decreased. A method to eliminate the effect of mutual coupling was proposed by R.S. Adve and T.K. Sarkar (see AP-S Int. Symp. Digest, p.1164-67, 1996). The method of moments (MOM) is used to model the mutual coupling effect on the weights and on the final directivity pattern of the adaptive array. Using an example of a two-element half wavelength adaptive dipole array, it is shown that the mutual coupling between the elements of an adaptive array causes significant degradation in the performance of the array and thus should not be neglected. It is demonstrated that, using the algorithm proposed by Adve and Sarkar, the effects of mutual coupling in adaptive arrays can be eliminated successfully.

Machine Learning (ML) is used to learn a system. One of the purposes of this automatic learning is the construction of new computational models. ML shows success in various areas such as reference systems, brain-computer interface, robotics, and chemistry. Recently, Perturbation Theory (PT) operators and ML techniques have been combined to create powerful PTML (PT+ML) models, which are applied to complex biological systems in predicting drug-protein interaction. As for those target proteins involved in the dopamine pathway, nanotechnology, material science, etc. The PTML models add the values ​​of the operators to the values ​​of . Therefore, we need to calculate the values ​​of the PTOs (Perturbation Theory Operators) in the data processing step. This allows us to carry out a process of merging information with variables and conditions from different sources. Moving Averages (MA), multi-condition MA (MMA), double MA, covariance operators, etc., are some examples of useful PTOs. Then, we can use Multiple Linear Regression (MLR), Linear Discriminant Analysis (LDA), or other linear ML techniques to find the PTML model. In non-linear cases, we can fit PTML models using Artificial Neural Networks (ANN), Support Vector Machines (SVM), Classification Trees, and other ML methods. One of the important applications of ANN is found in the study of chemical reactions as an alternative to classical regression and classification techniques. Therefore, in this master's thesis, PTML-ANN models are developed with the intention of visualizing possible improvements in classical techniques. In this context, this term is presented in a generic way. ANNs are inspired by the biological neural networks of the human brain. They are made up of elements that behave in a similar way to the biological neuron in its most common functions. The ANN aside from "resembling" the brain present a series of characteristics of the brain. For example, ANNs learn from experience, generalize from previous examples to new examples, and abstract the main characteristics of a data series. With which these networks function as interconnected neurons that create stimuli. ANN-type network is considered to be a complex biomolecular network consisting of nodes and edges. These networks are formed by "neurons", where each of them is a function, which will take a certain amount of data and provide an output response. The ANN presents three different types of functions which are: &bull; Input function: It is the sum function obtained by multiplying the input and output data by their weights. &bull; Excitation or activation function: This will take as previous input / output data. The most common types of functions depending on the input / output will be the threshold (sigmoid) or hyperbolic tangent. &bull; Transfer functions: It is the function used to close. The value provided by the trigger functions is the input to the ANN network.