@futureeducation.in
Proffesor and Head, Department of CSE-(Artificial Intelligence and Machine LEarning)
FUTURE INSTITUTE OF TECHNOLOGY
Ph.D., M.tech, B.Tech, B.Sc. (Physics Hons.)
Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing
Scopus Publications
Tiash Ghosh and Pradipta K. Banerjee
Springer Singapore
Debosmita Chakraborty, Ujjal Sur, and Pradipta Kumar Banerjee
IEEE
In recent times, integration of distributed energy resources with conventional power networks has been increased rapidly and with that several interlinking converters and power electronic devices are there. This increases the complexity of the system. In this paper, a fault classification technique based on random forest classifier has been proposed. As the random forest tree is an artificial intelligence tool, therefore, it is guaranteed the results obtained are of high accuracy value. The high accuracy in fault detection and classification is highly needed for a power system network to eradicate the fault from the system. This method has been tested over both transmission and distribution networks to show the efficacy of this proposed method, where the distribution network is a modified practical Indian distribution grid. Also a comparative study of this method with existing classification techniques like SVM, KNN and others has been done.
Tiash Ghosh and Pradipta K. Banerjee
Springer Singapore
Amith Achuthanunni, Ratul Kishore Saha, and Pradipta K. Banerjee
IEEE
Pradipta K. Banerjee and Asit K. Datta
Springer Science and Business Media LLC
Asit Kumar Datta, Madhura Datta, and Pradipta Kumar Banerjee
Chapman and Hall/CRC
Pradipta K. Banerjee and Asit K. Datta
Elsevier BV
Papia Banerjee, Pradipta K. Banerjee, and Asit K. Datta
Springer Berlin Heidelberg
Pradipta K. Banerjee and Asit K. Datta
Elsevier BV
Pradipta K. Banerjee and Asit K. Datta
Elsevier BV
Pradipta K. Banerjee and Asit K. Datta
Elsevier BV
Papia Banerjee and Pradipta K. Banerjee
IEEE
Phase eigen subspace based face recognition under varying lighting conditions is proposed. Universal subspace analysis is exploited in frequency domain and phase spectrum is extracted instead of using raw spatial data of face images. Improved results are obtained when simplified bi-directional associative memory neural network is used as classifier. The proposed scheme is experimented over two standard databases like YaleB and PIE and the promising recognition accuracy is achieved while comparing to standard subspace methods.
A. Basu, J. K. Chandra, P. K. Banerjee, S. Bhattacharyya, and A. K. Datta
IEEE
Pradipta K. Banerjee, Jayanta K. Chandra, and Asit K. Datta
Springer Berlin Heidelberg
Jayanta K. Chandra, Pradipta K. Banerjee, and Asit K. Datta
IEEE
In this paper a new approach for the detection of defects in woven fabric is presented where the singular value decomposition (SVD) method is used. SVD basically removes the interlaced grating structure of the waft and warp of the fabric leaving aside the defective part of the fabric. An intensity threshold value along with the module of definite size is considered for the binarization of the background free fabric image. Finally, for the removal of the noise from the binary fabric image the morphological opening operation with the suitable structuring element is performed. The technique is tested on 287 fabric samples consisting of five different types of defects in three types of woven fabrics from TILDA database. 94.08% success rate of detection of defects is achieved.
Pradipta K. Banerjee, Jayanta K. Chandra, and Asit K. Datta
Springer Berlin Heidelberg
Pradipta K. Banerjee, Jayanta K. Chandra, and Asit K. Datta
ACM Press
Jayanta K. Chandra, Pradipta K. Banerjee, and Asit K. Datta
Informa UK Limited
Basic morphological operations such as the erosion, dilation, opening, and closing often fail to detect various types of defects that may be present in woven fabric, mainly because of the heuristic selection of structuring element needed for these operations. In this paper, an artificial neural network (ANN) is utilized for the selection of structuring element, where ANN is trained by two pre‐assigned normalized numbers related to the warp and weft counts of the test fabric. The test gray fabric image is pre‐processed to remove noise and the interlaced grating structure of weft and warp and then converted to a binary image by thresholding. An intensity threshold value of the processed fabric image and the dimension of a sliding window needed for correlation operation are obtained from the trained ANN. Defects are detected after morphological reconstruction of the processed binary fabric image, where an ANN trained structuring element is used. The technique is tested on 317 samples for eight different types of defects in three types of plain woven fabrics from TILDA database and 92.8% success of detection is achieved.
Pradipta K. Banerjee, Jayanta K. Cahndra, and Asit K. Datta
Elsevier BV
Pradipta K. Banerjee, Jayanta K. Chandra, and Asit K. Datta
IEEE
Pradipta K. Banerjee and Asit K. Datta
Springer Science and Business Media LLC
Jayanta K. Chandra, Pradipta K. Banerjee, and Asit K. Datta
IEEE