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Convective Cell Tracking through Deep Learning based Computer Vision: Python-Based Algorithm for Identification & Tracking of Convective Cells using Doppler Weather Radar Reflectivity Images - Tapa blanda
Sinopsis
The present study developed an autonomous algorithm for the Convective cell Identification and TRAcking (CITRA) using DWR reflectivity images. The CITRA algorithm is implemented in Python using Deep learning technique of Neural Networks. Optical Character Recognition is used in the present study through "Tesseract" which is an unsupervised Neural Network module based on LSTM which analyses the input dimensional pixel array/image and outputs high-level strings. The algorithm runs through the DWR reflectivity image pixel values and recognizes the intensities of the pixels (>=30 dB) and segregates convective cells along with other estimated cell properties such as centroid of the storm, the area covered, distance and direction from the radar centre. The performance of CITRA algorithm was tested on different convective storms and it could successfully identify and track them along with other physical properties of the convective cells. Further, we have demonstrated the potential application of CITRA algorithm on the evolution of convective cells detected within the radar range. Presently, CITRA algorithm takes only reflectivity images as a single input parameter.
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Convective Cell Tracking through Deep Learning based Computer Vision PythonBased Algorithm for Identification Tracking of Convective Cells using Doppler Weather Radar Reflectivity Images
Publicado por
LAP Lambert Academic Publishing, 2020
ISBN 10: 6203194816
ISBN 13: 9786203194814
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Convective Cell Tracking through Deep Learning based Computer Vision: Python-Based Algorithm for Identification & Tracking of Convective Cells using Doppler Weather Radar Reflectivity Images
Publicado por
LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6203194816
ISBN 13: 9786203194814
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Convective Cell Tracking through Deep Learning based Computer Vision PythonBased Algorithm for Identification Tracking of Convective Cells using Doppler Weather Radar Reflectivity Images
Publicado por
LAP Lambert Academic Publishing, 2020
ISBN 10: 6203194816
ISBN 13: 9786203194814
Nuevo
PAP
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Convective Cell Tracking through Deep Learning based Computer Vision: Python-Based Algorithm for Identification & Tracking of Convective Cells using Doppler Weather Radar Reflectivity Images
Publicado por
LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6203194816
ISBN 13: 9786203194814
Nuevo
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Convective Cell Tracking through Deep Learning based Computer Vision
Publicado por
LAP LAMBERT Academic Publishing Dez 2020, 2020
ISBN 10: 6203194816
ISBN 13: 9786203194814
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The present study developed an autonomous algorithm for the Convective cell Identification and TRAcking (CITRA) using DWR reflectivity images. The CITRA algorithm is implemented in Python using Deep learning technique of Neural Networks. Optical Character Recognition is used in the present study through 'Tesseract' which is an unsupervised Neural Network module based on LSTM which analyses the input dimensional pixel array/image and outputs high-level strings. The algorithm runs through the DWR reflectivity image pixel values and recognizes the intensities of the pixels (>=30 dB) and segregates convective cells along with other estimated cell properties such as centroid of the storm, the area covered, distance and direction from the radar centre. The performance of CITRA algorithm was tested on different convective storms and it could successfully identify and track them along with other physical properties of the convective cells. Further, we have demonstrated the potential application of CITRA algorithm on the evolution of convective cells detected within the radar range. Presently, CITRA algorithm takes only reflectivity images as a single input parameter. 76 pp. Englisch. Nº de ref. del artículo: 9786203194814
Cantidad disponible: 2 disponibles
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Convective Cell Tracking through Deep Learning based Computer Vision : Python-Based Algorithm for Identification & Tracking of Convective Cells using Doppler Weather Radar Reflectivity Images
Publicado por
LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6203194816
ISBN 13: 9786203194814
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The present study developed an autonomous algorithm for the Convective cell Identification and TRAcking (CITRA) using DWR reflectivity images. The CITRA algorithm is implemented in Python using Deep learning technique of Neural Networks. Optical Character Recognition is used in the present study through 'Tesseract' which is an unsupervised Neural Network module based on LSTM which analyses the input dimensional pixel array/image and outputs high-level strings. The algorithm runs through the DWR reflectivity image pixel values and recognizes the intensities of the pixels (>=30 dB) and segregates convective cells along with other estimated cell properties such as centroid of the storm, the area covered, distance and direction from the radar centre. The performance of CITRA algorithm was tested on different convective storms and it could successfully identify and track them along with other physical properties of the convective cells. Further, we have demonstrated the potential application of CITRA algorithm on the evolution of convective cells detected within the radar range. Presently, CITRA algorithm takes only reflectivity images as a single input parameter. Nº de ref. del artículo: 9786203194814
Cantidad disponible: 1 disponibles
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Convective Cell Tracking through Deep Learning based Computer Vision
Publicado por
LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6203194816
ISBN 13: 9786203194814
Nuevo
Tapa blanda
Librería: moluna, Greven, Alemania
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ranganayakulu S. V.S. V. Ranganayakulu is currently working as Dean (R&D), in Guru Nanak Institutions Technical Campus(Autonomous) and holds M.Sc (Physics) in Electronics as specialization, M.Phil (Physics) in the area of Liquid Crys. Nº de ref. del artículo: 452575528
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Convective Cell Tracking through Deep Learning based Computer Vision: Python-Based Algorithm for Identification & Tracking of Convective Cells using Doppler Weather Radar Reflectivity Images
Publicado por
LAP LAMBERT Academic Publishing 2020-12-15, 2020
ISBN 10: 6203194816
ISBN 13: 9786203194814
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Paperback. Condición: New. Nº de ref. del artículo: 6666-IUK-9786203194814
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Convective Cell Tracking through Deep Learning based Computer Vision: Python-Based Algorithm for Identification & Tracking of Convective Cells using Doppler Weather Radar Reflectivity Images
Publicado por
LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6203194816
ISBN 13: 9786203194814
Nuevo
Tapa blanda
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Convective Cell Tracking through Deep Learning based Computer Vision
Publicado por
LAP LAMBERT Academic Publishing Dez 2020, 2020
ISBN 10: 6203194816
ISBN 13: 9786203194814
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Neuware -The present study developed an autonomous algorithm for the Convective cell Identification and TRAcking (CITRA) using DWR reflectivity images. The CITRA algorithm is implemented in Python using Deep learning technique of Neural Networks. Optical Character Recognition is used in the present study through 'Tesseract' which is an unsupervised Neural Network module based on LSTM which analyses the input dimensional pixel array/image and outputs high-level strings. The algorithm runs through the DWR reflectivity image pixel values and recognizes the intensities of the pixels (>=30 dB) and segregates convective cells along with other estimated cell properties such as centroid of the storm, the area covered, distance and direction from the radar centre. The performance of CITRA algorithm was tested on different convective storms and it could successfully identify and track them along with other physical properties of the convective cells. Further, we have demonstrated the potential application of CITRA algorithm on the evolution of convective cells detected within the radar range. Presently, CITRA algorithm takes only reflectivity images as a single input parameter.Books on Demand GmbH, Überseering 33, 22297 Hamburg 76 pp. Englisch. Nº de ref. del artículo: 9786203194814
Cantidad disponible: 2 disponibles