Design and development of virtual instrument for fault diagnosis in fractal antenna array

Rani, Sunita; Sivia, Jagtar Singh

doi:10.1002/mmce.22026

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…Inequality (5) illustrates that for a given dataset, size l is fixed, and we can improve the generalization ability of the learning machine by controlling the VC dimension h . In fact, the right end of inequality ( 5) is the structural fault generalization of the SRM principle, which exists in the form of a real fault upper bound, i.e., the generalization bound.…”

Section: Computer Network Fault Minimization Principlementioning

confidence: 99%

“…Fault detection determines whether a fault exists in the network based on the signs of failure. Fault location, also known as root cause analysis or fault isolation or alarm/event correlation, is the process of deducing the location of a fault by analyzing the observed fault signs [5][6]. Testing, also known as fault confirmation, refers to confirming the fault location by further testing after fault localization [7][8].…”

Section: Introductionmentioning

confidence: 99%

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Application of machine learning based models in computer network data

Liu

2023

Applied Mathematics and Nonlinear Sciences

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Computer networks, as one of the indispensable infrastructures in today’s world, play an extremely important role in industrial production and daily life. This paper revolves around the intelligent diagnosis of computer network data faults using machine learning methods. Firstly, the support vector machine network fault minimization principle is proposed based on the machine learning model, which leads to under-learning or over-learning when the samples are limited, or there is noise. Then, a loss function is introduced for the under-learning or over-learning problem to ensure that the support vector machine can accurately achieve fault diagnosis. Finally, the evaluation index of computational network fault diagnosis is constructed for the experimental purpose, and four algorithms are selected as the experimental control group to analyze the data. It is obtained that SVM and ANN models have high DR and low FAR. Their DR is 87.9% and 84.5%, respectively, while their FAR is only 5.4%. This further validates the superiority of SVM in computer network data fault detection. This study possesses low training time complexity and can overcome the problem of uneven distribution of the number of faulty and normal samples in network fault diagnosis to some extent.

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Section: Computer Network Fault Minimization Principlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of machine learning based models in computer network data

Liu

2023

Applied Mathematics and Nonlinear Sciences

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“…In the proposed work virtual instrument has been designed in laboratory virtual instrument engineering workbench (LabVIEW) software which provides an engineering workbench that uses graphical language and displays the information about input and output parameters in the display window [32].…”

Section: Proposed Workmentioning

confidence: 99%

Development of virtual instrument model for parameter estimation of fractal antenna array

Rani¹,

Sivia²

2021

Int. J. Microw. Wireless Technol.

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This paper presents the parameter estimation of the fractal antenna array with the virtual instrument model designed in laboratory virtual instrument engineering workbench software. In this work resonant frequency, gain and voltage standing wave ratio have been used as an output parameter with the change in three input parameters such as radius of a circular patch, height of substrate, and dielectric constant of the material. Measured output parameters have been compared with neural network outputs and error has been represented in a graphical way for each output parameter of the antenna array. Along with output parameter estimation, a designing parameter such as radius of the circular patch has also been estimated with virtual instrument model and absolute error for radius has been shown in the display window of the designed model. The proposed antenna array has been fabricated and simulated results have been validated with measured results.

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“…This inevitably pushes antenna design for 5G devices to fit the ever-increasing requirements for greater bandwidth, more frequency bands, and superior interference immunity [17,18]. Furthermore, fault detection in antenna arrays and inverse scatteringbased non-linear problems need sophisticated yet cost-effective solutions, where Machine Learning (ML) can provide an edge over other techniques [19,20].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in Electromagnetics: Mini-Review

et al. 2021

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As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques. Although the accuracy of ML algorithms depends on the availability of sufficient data and expert handling of the model and hyperparameters, it is gradually becoming the desired solution when researchers are aiming for a cost-effective solution without excessive time consumption. In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing. It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture radar target recognition, and fault detection systems. It also provides limitations of this emerging field of study. The unique aspect of this work is that it surveys the state-of the art and recent advances in ML techniques as applied to EM.

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Design and development of virtual instrument for fault diagnosis in fractal antenna array

Cited by 7 publications

References 16 publications

Application of machine learning based models in computer network data

Application of machine learning based models in computer network data

Development of virtual instrument model for parameter estimation of fractal antenna array

Application of Machine Learning in Electromagnetics: Mini-Review

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