Dr. Jia Uddin
http://hdl.handle.net/10361/6999
2024-03-28T18:22:56Z
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Acoustic emission sensor network based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines
http://hdl.handle.net/10361/9576
Acoustic emission sensor network based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines
Islam, Md Rashedul; Uddin, Jia; Kim, Jongmyon
Reliable and efficient fault diagnosis of induction motors is an important issue in industrial environments. This paper proposes a method for reliable fault diagnosis of induction motors using signal processing of acoustic emission (AE) data, including Gabor filtering and the use of multiclass support vector machines (MCSVMs), where a ZigBee based wireless sensor network (WSN) model is used for efficiently transmitting AE signals to a diagnosis server. In the proposed fault diagnosis approach, the induction motor’s different state signals are acquired through proper placement of AE sensors. The AE data are sent to a server through the wireless sensor network and decomposed using discrete wavelet transformation (DWT). An appropriate band is then selected using the maximum energy ratio, and a one-dimensional (1D) Gabor filter with various frequencies and orientation angles is applied to reduce abnormalities and extract various statistical parameters for generating features. In addition, principal component analysis (PCA) is applied to the extracted features to select the most dominant feature dimensions. Finally, one-against-one multiclass support vector machines (OAA-MCSVMs) are used to classify multiple fault types of an induction motor, where each SVM individually trains with its own features to increase the fault classification accuracy of the induction motor. In experiments, the proposed approach achieved an average classification accuracy of 99.80%, outperforming conventional fault diagnosis models.
This article was published in the Ad-Hoc and Sensor Wireless Networks [© 2016 Old City Publishing, Inc.]
2016-01-01T00:00:00Z
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A novel parallel texture feature extraction method using log-gabor filter and singular value decomposition (SVD)
http://hdl.handle.net/10361/9519
A novel parallel texture feature extraction method using log-gabor filter and singular value decomposition (SVD)
Ratul, Md Aminur Rab; Raja, Sharif Ahmmad; Uddin, Jia
Texture feature extraction consolidated with texture feature detection and feature matching solves many typical problems of image processing and computer vision; such as, texture classification, pattern recognition, object detection, and image segmentation. Through this paper, a new method for texture feature extraction is presented which uses Log-Gabor Filter and Singular Value Decomposition (SVD) algorithm. In the proposed model, sample images are converted to gray level images. And then, to elicit suitable distinctive texture orientation, a 2D Log- Gabor filter with various frequencies and different edges disintegrated with the SVD employ on each converted gray level images. Finally, singular values of SVD used as feature vector for this texture feature extraction model. For training and testing of experimental datasets, Naive Bayes classifier has been used. The Log-Gabor and SVD based feature extraction shows improved performance by exhibiting higher classification accuracy for our tested dataset compare to conventional Gabor and SVD feature extraction method. Furthermore, in order to decrease the computational and time complexity, an NVIDIA GeForce GTX780 GPU is used to implement our proposed model in parallel. The GPU implementation of proposed model showed average 3X speedup for per image than conventional CPU implementation.
This conference paper was published in the ACM International Conference Proceeding Series [© 2017 ACM] and the definite version is available at : http://doi.org/10.1145/3036331.3036352 The Journal's website is at: https://dl.acm.org/citation.cfm?doid=3036331.3036352
2017-01-01T00:00:00Z
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A novel modified SFTA approach for feature extraction
http://hdl.handle.net/10361/9502
A novel modified SFTA approach for feature extraction
Hasan, Md Junayed; Uddin, Jia; Pinku, Subroto Nag
To increase the efficiency of conventional Segmentation Based Fractal Texture Analysis (SFTA), we propose a new approach on SFTA algorithm. We use an optimum multilevel thresholding hybrid method of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called HGAPSO with the optimization technique for classification based on grey level range to get more accurate output. Experimental results show that proposed approach exhibits average 2% higher classification accuracy than conventional SFTA for our tested dataset.
This conference paper was published in the IEEE Xplore [© 2017 IEEE] and the definite version is available at : http://doi.org/10.1109/CEEICT.2016.7873115 The Journal's website is at: http://ieeexplore.ieee.org/document/7873115/
0009-01-01T00:00:00Z
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An adaptive digital image watermarking scheme with PSO, DWT and XFCM
http://hdl.handle.net/10361/9498
An adaptive digital image watermarking scheme with PSO, DWT and XFCM
Mitashe, Mashruha Raquib; Habib, Ahnaf Rafid Bin; Razzaque, Anindita; Tanima, Ismat Ara; Uddin, Jia
In this paper, a novel adaptive digital image watermarking model based on modified Fuzzy C-means clustering is proposed. For watermark embedding process, we used Discrete Wavelet Transform (DWT). A segmentation technique XieBeni integrated Fuzzy C-means clustering (XFCM) is used to identify the segments of original image to expose suitable locations for embedding watermark. We also pre-processed the host image using Particle Swarm Optimization (PSO) to lend a hand to the clustering process. The goal is to focus on proper segmentation of the image so that the embedded watermark can withstand common image processing attacks and provide security to digital images. Several attacks were performed on the watermarked images and original watermark was extracted. Performance measures like PSNR, MSE, CC were computed to test the extracted watermarks with and without attacks. Experimental results show that the proposed scheme has performed well in terms of imperceptibility and robustness when compared to other watermarking models.
This article was published in the IEEE Xplore [© 2017 IEEE] and the definite version is available at : http://doi.org/10.1109/ICIVPR.2017.7890868 The Journal's website is at: http://ieeexplore.ieee.org/document/7890868/?reload=true
2017-02-01T00:00:00Z