Zobrazeno 1 - 10 of 17 pro vyhledávání: '"Neelam Turk"'
1
Akademický článek
A novel fault diagnostic system for rolling element bearings using deep transfer learning on bispectrum contour maps
Autor: Chhaya Grover, Neelam Turk
Publikováno v: Engineering Science and Technology, an International Journal, Vol 31, Iss , Pp 101049- (2022)
Transfer learning using Convolution Neural Networks (CNNs) has improved the state of the art results in many research studies. Rolling element bearing fault diagnosis is a domain that has been researched extensively using different data mining and ma
Externí odkaz: https://doaj.org/article/af14aac2ee364ce19c41b164243cbc68
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2
Akademický článek
Optimal Statistical Feature Subset Selection for Bearing Fault Detection and Severity Estimation
Autor: Chhaya Grover, Neelam Turk
Publikováno v: Shock and Vibration, Vol 2020 (2020)
The performance of bearing fault detection systems based on machine learning techniques largely depends on the selected features. Hence, selection of an ideal number of dominant features from a comprehensive list of features is needed to decrease the
Externí odkaz: https://doaj.org/article/aa13c3196c8d4dd6a59b27db8afcfba0
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6
Performance Analysis of Different Restoration Techniques for Degraded 3D Cervical Spine Mri Image
Autor: Sonam Khera, Neha Rajput, Sangeeta Kamboj, Neelam Turk
Publikováno v: Biomedical and Pharmacology Journal. 14:853-867
In the proposed work, noise models such as Salt & Pepper, Gaussian and Poisson are considered in order to corrupt the image.Image restoration is still challenging task to recover an original image using a degradation and restoration model. In the pap
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b4b0587e427085136a1706791a7ba8ae
https://doi.org/10.13005/bpj/2188
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7
A novel fault diagnostic system for rolling element bearings using deep transfer learning on bispectrum contour maps
Autor: Neelam Turk, Chhaya Grover
Publikováno v: Engineering Science and Technology, an International Journal, Vol 31, Iss, Pp 101049-(2022)
Transfer learning using Convolution Neural Networks (CNNs) has improved the state of the art results in many research studies. Rolling element bearing fault diagnosis is a domain that has been researched extensively using different data mining and ma
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::760a4e63b3228bd0f461b72ea99a31dd
http://www.sciencedirect.com/science/article/pii/S2215098621001701
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9
MQTT protocol employing IOT based home safety system with ABE encryption
Autor: Sonam Khera, Neelam Turk, Vatsal Gupta
Publikováno v: Multimedia Tools and Applications. 80:2931-2949
This project aims towards the usage of MQTT (Message queueing telemetry transport) protocol in IoT (Internet of things) along with adopting a feasible means of encrypting the message transfers in applications. The lightweight nature of MQTT protocol
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6e207990f2f65d080909f5d9ea96bafc
https://doi.org/10.1007/s11042-020-09750-4
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10
Rolling Element Bearing Fault Diagnosis using Empirical Mode Decomposition and Hjorth Parameters
Autor: Neelam Turk, Chhaya Grover
Publikováno v: Procedia Computer Science. 167:1484-1494
The condition of rolling element bearing can be monitored by analyzing vibration signatures of machines at regular intervals. Many complex and computationally intensive bearing fault diagnosis schemes have been proposed in the past, extracting variou
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c222db88b123d836e54754955b23cf0f
https://doi.org/10.1016/j.procs.2020.03.359
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