scholarly journals An Effective Antenna Array Diagnosis Method via Multivalued Neural Network Inverse Modeling Approach

2021 ◽  
Vol 10 (3) ◽  
pp. 58-70
Author(s):  
O. J. Famoriji ◽  
T. Shongwe
Keyword(s):  
Neural Network ◽  
Antenna Array ◽  
Radiation Pattern ◽  
Antenna Arrays ◽  
Inverse Modeling ◽  
Error Function ◽  
Training Sample ◽  
Inverse Model ◽  
Training Data ◽  
Inverse Modelling

Failure of element (s) in antenna arrays impair (s) symmetry and lead to unwanted distorted radiation pattern. The replacement of defective elements in aircraft antennas is a solution to the problem, but it remains a critical problem in space stations. In this paper, an antenna array diagnosis technique based on multivalued neural network (mNN) inverse modeling is proposed. Since inverse analytical input-to-output formulation is generally a challenging and important task in solving the inverse problem of array diagnosis, ANN is a compelling alternative, because it is trainable and learns from data in inverse modelling. The mNN technique proposed is an inverse modelling technique, which accommodates measurements for output model. This network takes radiation pattern samples with faults and matches it to the corresponding position or location of the faulty elements in that antenna array. In addition, we develop a new training error function, which focuses on the matching of each training sample by a value of our proposed inverse model, while the remaining values are free, and trained to match distorted radiation patterns. Thereby, mNN learns all training data by redirecting the faulty elements patterns into various values of the inverse model. Therefore, mNN is able to perform accurate array diagnosis in an automated and simpler manner.

scholarly journals Improved Empirical Identification of the Inverse Model of a Squeeze-Film Damper Bearing Based on a Recurrent Neural Network

2018 ◽  
Author(s):  
Ghaith Ghanim Al-Ghazal ◽  
Philip Bonello ◽  
Sergio G. Torres Cedillo
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Structural Model ◽  
Solution Process ◽  
Time History ◽  
Inverse Model ◽  
Training Data ◽  
Squeeze Film ◽  
Squeeze Film Damper ◽  
History Of

Most recently proposed techniques for inverse rotordynamic problems seek to identify the unbalance on a rotor using a known structural model and measurements from externally mounted sensors only. Such non-intrusive techniques are important for balancing rotors that cannot be accessed under operational conditions because of temperature or space restrictions. The presence of nonlinear bearings, like squeeze-film damper (SFD) bearings used in aero-engines, complicates the solution process of the inverse rotordynamic problem. In certain practical aero-engine configurations, the solution process requires a substitute for internal instrumentation to quantify the SFD journal vibration. This can be provided by an inverse model of the SFD bearing which outputs the time history of the relative vibration of the SFD journal relative to its housing, for a given input time history of the SFD force. This paper focuses on the inverse model of the SFD and presents an improved methodology for its identification via a Recurrent Neural Network (RNN) trained using experimental data from a purposely designed rig. The novel application of chirp excitation via two orthogonal shakers considerably improves both the quality of the training data and the efficiency of its generation, relative to an earlier preliminary work. Validation test results show that the RNNs can predict the journal displacement time history with reasonable accuracy. It is therefore expected that such an inverse SFD model would serve as a reliable component in the solution of the wider inverse problem of a rotordynamic system.

scholarly journals Inverse modeling of turbidity currents using an artificial neural network approach: verification for field application

2021 ◽  
Vol 9 (5) ◽  
pp. 1091-1109
Author(s):  
Hajime Naruse ◽  
Kento Nakao
Keyword(s):  
Neural Network ◽  
Inverse Modeling ◽  
Inverse Model ◽  
Turbidity Currents ◽  
Random Errors ◽  
Large Area ◽  
Initial Flow ◽  
Hydraulic Conditions ◽  
Input Parameters ◽  
Averaged Model

Abstract. Although in situ measurements in modern frequently occurring turbidity currents have been performed, the flow characteristics of turbidity currents that occur only once every 100 years and deposit turbidites over a large area have not yet been elucidated. In this study, we propose a method for estimating the paleo-hydraulic conditions of turbidity currents from ancient turbidites by using machine learning. In this method, we hypothesize that turbidity currents result from suspended sediment clouds that flow down a steep slope in a submarine canyon and into a gently sloping basin plain. Using inverse modeling, we reconstruct seven model input parameters including the initial flow depth, the sediment concentration, and the basin slope. A reasonable number (3500) of repetitions of numerical simulations using a one-dimensional layer-averaged model under various input parameters generates a dataset of the characteristic features of turbidites. This artificial dataset is then used for supervised training of a deep-learning neural network (NN) to produce an inverse model capable of estimating paleo-hydraulic conditions from data on the ancient turbidites. The performance of the inverse model is tested using independently generated datasets. Consequently, the NN successfully reconstructs the flow conditions of the test datasets. In addition, the proposed inverse model is quite robust to random errors in the input data. Judging from the results of subsampling tests, inversion of turbidity currents can be conducted if an individual turbidite can be correlated over 10 km at approximately 1 km intervals. These results suggest that the proposed method can sufficiently analyze field-scale turbidity currents.

A Comparison of Swarm-Based Optimization Algorithms in Linear Antenna Array Synthesis

2021 ◽  
Author(s):  
Ali Durmus ◽  
Rifat KURBAN ◽  
Ercan KARAKOSE
Keyword(s):  
Antenna Array ◽  
Radiation Pattern ◽  
Antenna Arrays ◽  
Heuristic Algorithms ◽  
Beam Width ◽  
Side Lobe ◽  
Side Lobe Level ◽  
Linear Antenna ◽  
Half Power ◽  
Antenna Array Synthesis

Abstract Today, the design of antenna arrays is very important in providing effective and efficient wireless communication. The purpose of antenna array synthesis is to obtain a radiation pattern with low side lobe level (SLL) at a desired half power beam width (HPBW) in far-field. The amplitude and position values ​​of the array elements can be optimized to obtain a radiation pattern with suppressed SLLs. In this paper swarm-based meta-heuristic algorithms such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Mayfly algorithm (MA) and Jellyfish Search (JS) algorithms are compared to realize optimal design of linear antenna arrays. Extensive experiments are conducted on designing 10, 16, 24 and 32-element linear arrays by determining the amplitude and positions. Experiments are repeated 30 times due to the random nature of swarm-based optimizers and statistical results show that performance of the novel algorithms, MA and JS, are better than well-known methods PSO and ABC.

scholarly journals Radiation Pattern Optimization by Loaded Impedance in the Base of Passive Element

2011 ◽  
Vol 62 (2) ◽  
pp. 113-116
Author(s):  
Marek Dvorský
Keyword(s):  
Antenna Array ◽  
Radiation Pattern ◽  
Antenna Arrays ◽  
Passive Element ◽  
Wave Antenna ◽  
Best Value ◽  
Passive Antenna

Radiation Pattern Optimization by Loaded Impedance in the Base of Passive ElementThis article deals with optimizing the radiation pattern by using two vertical middle wave antenna arrays. Optimization is achieved by adding an appropriate impedance into the base of the passive antenna radiator. The proposed solution deals with finding the best value of the loaded impedance. The final result is to achieve the most effective shape of the radiation pattern. The proposed method was finally approved by measurement on a real middle wave antenna array.

scholarly journals SIMULATION OF A FLEXIBLE LINEAR GRAPHENE ANTENNA ARRAY ON A PAPER SUBSTRATE

2021 ◽  
Vol 8 ◽  
pp. 307-313
Author(s):  
Alexander G. Cherevko ◽  
Yury V. Morgachev
Keyword(s):  
Antenna Array ◽  
Radiation Pattern ◽  
Antenna Arrays ◽  
Dipole Antenna ◽  
Paper Substrate ◽  
Final Shape ◽  
Flexible Antenna ◽  
Folded Dipole

The article presents an analysis of flexible graphene antenna arrays, which has shown the promise of using a folded dipole antenna as an element of such array. The structure of the flexible folded dipole operating at a frequency of 5,8 GHz on a photo-paper substrate is considered. Simulation yields a gain of 2,53 dBi with a final efficiency of 75% and | S 11| -31,82 dB. The influence of bending on the final shape of the radiation pattern is considered, as well as the value and position of the minimum of the | S 11|. The gain of a linear three-element graphene flexible antenna array based on a folded flexible dipole is 5,78 dBi.

scholarly journals Radiation Pattern Synthesis of the Coupled Almost Periodic Antenna Arrays Using Artificial Neural Network ANN Model

2022 ◽  
Author(s):  
Hamdi Bilel ◽  
Aguili Taoufik
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radiation Pattern ◽  
Antenna Arrays ◽  
Almost Periodic ◽  
Ann Model ◽  
Early Stopping ◽  
Pattern Synthesis ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper proposes a radiation pattern synthesis of the almost periodic antenna arrays including mutual coupling effects (that extracted by the Floquet analysis according to our previous work), which principally has a high directivity and large bandwidth. For modeling the given structures, the moment method combined with the Generalized Equivalent Circuit (MoM-GEC) is proposed. The artificial neural network (ANN) as a powerful computational model has been successfully applied to the antenna array pattern synthesis. The results showed that the multilayer feedforward neural networks are rugged and can successfully and efficiently resolve various distinctive complex almost periodic antenna patterns (with different source amplitudes) (in particular, both periodic and randomly aperiodic structures are taken into account). However, the artificial neural network (ANN) is capable of quickly producing the synthesis results using generalization with the early stopping (ES) method. A significant time gain and memory consumption are achieved by using this given method to improve the generalization (called early stopping). To justify this work, several examples are developed and discussed.

scholarly journals Average radiation pattern digital antenna array and a method for reducing the average level of side lobes.

2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
E.N. Mishchenko ◽  
◽  
S.E. Mishchenko ◽  
N.V. Shatskiy ◽  
◽  
...  
Keyword(s):  
Antenna Array ◽  
Radiation Pattern ◽  
Antenna Arrays ◽  
Signal To Noise Ratio ◽  
Experimental Studies ◽  
Digital Processing ◽  
Average Level ◽  
Main Beam ◽  
Rounding Errors ◽  
Phase Calibration

n the development of the existing statistical theory of antennas, new analytical relations are obtained for estimating the average radiation pattern of a digital antenna array. These ratios take into account the variance of rounding errors of the weight coefficients, errors in the amplitude and phase calibration of the antenna, jitter noise and rounding errors of the signal at the output of the analog-to-digital converter. It is shown that most of the factors affect the average level of the side lobes, as in analog antenna arrays. However, the variances of the phase calibration errors and the jitter noise determine the contribution of the new term, which has symmetry in the angular coordinate and has an extremums in the direction of the main beam of the antenna array and in the direction that is mirrored relative to the main beam. It is established that the variances of rounding errors depend on the signal-to-noise ratio and, when performing numerical studies, should be estimated based on the results of experimental studies. It is shown that digital processing in a sliding window reduces the average level of the side lobes by separating the spectral components of the signal and noise.

scholarly journals Inverse modeling of turbidity currents using artificial neural network: verification for field application

2020 ◽  
Author(s):  
Hajime Naruse ◽  
Kento Nakao
Keyword(s):  
Neural Network ◽  
Inverse Modeling ◽  
Field Application ◽  
Inverse Model ◽  
Turbidity Currents ◽  
Random Errors ◽  
Large Area ◽  
Initial Flow ◽  
Hydraulic Conditions ◽  
Input Parameters

Abstract. Although in situ measurements observed on modern frequently occurring turbidity currents have been performed, the flow characteristics of turbidity currents that occur only once every hundreds of years and deposit turbidites over a large area have not yet been elucidated. In this study, we propose a method for estimating the paleo-hydraulic conditions of turbidity currents from ancient turbidites by using machine learning. In this method, we hypothesize that turbidity currents result from suspended sediment clouds that flow down a steep slope in a submarine canyon and into a gently sloping basin plain. Using inverse modeling, we reconstruct seven model input parameters including the initial flow depth, the sediment concentration and the basin slope. Repeated numerical simulation using one-dimensional shallow water equations under various input parameters generates a dataset of the characteristic features of turbidites. This artificial dataset is then used for supervised training of a deep learning neural network (NN) to produce an inverse model capable of estimating paleo-hydraulic conditions from data of the ancient turbidites. Only 3,500 datasets are needed to train this inverse model. The performance of the inverse model is tested using independently generated datasets. Consequently, the NN successfully reconstructs the flow conditions of the test datasets. In addition, the proposed inverse model is quite robust to random errors in the input data. Judging from the results of subsampling tests, inversion of turbidity currents can be conducted if an individual turbidite can be correlated over 10 km at approximately 1 km intervals. These results suggest that the proposed method can sufficiently analyze field-scale turbidity currents.

scholarly journals Decoupling of Multifrequency Dipole Antenna Arrays for Microwave Imaging Applications

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
E. Saenz ◽  
K. Guven ◽  
E. Ozbay ◽  
I. Ederra ◽  
R. Gonzalo
Keyword(s):  
Antenna Array ◽  
Radiation Pattern ◽  
Antenna Arrays ◽  
Mutual Coupling ◽  
Near Field ◽  
Field Measurements ◽  
Dipole Antenna ◽  
Far Field ◽  
Radiated Power ◽  
Near Field Scanning

The mutual coupling between elements of a multifrequency dipole antenna array is experimentally investigated byS-parameter measurements and planar near-field scanning of the radiated field. A multifrequency array with six dipoles is analyzed. In order to reduce the coupling between dipoles, a planar metasurface is placed atop the array acting as superstrate. The mutual coupling of the antenna elements in the absence and presence of the superstrate is presented comparatively. Between 3 and 20 dB mutual coupling reduction is achieved when the superstrate is used. By scanning the field radiated by the antennas and far-field measurements of the radiation pattern, it is observed that the superstrate confines the radiated power, increases the boresight radiation, and reduces the endfire radiation.

Optimum design of linear and circular antenna arrays using equilibrium optimization algorithm

2021 ◽  
pp. 1-12
Author(s):  
Ali Durmus ◽  
Rifat Kurban
Keyword(s):  
Antenna Array ◽  
Radiation Pattern ◽  
Antenna Arrays ◽  
Optimization Algorithm ◽  
Communication Systems ◽  
Control Volume ◽  
Optimization Technique ◽  
Beam Width ◽  
Side Lobe ◽  
Side Lobe Level

Abstract In this paper, equilibrium optimization algorithm (EOA), which is a novel optimization algorithm, is applied to synthesize symmetrical linear antenna array and non-uniform circular antenna array (CAA). The main purpose of antenna array synthesis is to achieve a radiation pattern with low maximum side lobe level (MSL) and narrow half-power beam width (HPBW) in far-field. The low MSL here is an important parameter to reduce interference from other communication systems operating in the same frequency band. A narrow HPBW is needed to achieve high directionality in antenna radiation patterns. Entering the literature as a novel optimization technique, EOA optimally determined the amplitude and position values of the array elements to obtain a radiation pattern with a low MSL and narrow HPBW. The EOA is inspired by models of the control volume mass balance used to predict equilibrium as well as dynamic states. To demonstrate the flexibility and performance of the proposed algorithm, 10-element, 16-element and 24-element linear arrays and eight-element, 10-element and 12-element CAAs are synthesized. The MSL and HPBW values of radiation pattern obtained with the EOA are very successful compared to the results of other optimization methods in the literature.

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