The section shape of an assembled magnetic medium is the most important structural parameter of a high gradient magnetic separator, which directly affects the induction distribution and magnetic field gradient of the ...The section shape of an assembled magnetic medium is the most important structural parameter of a high gradient magnetic separator, which directly affects the induction distribution and magnetic field gradient of the magnetic separator. In this study, equilateral triangle, square, hexagonal, octagon, dodecagon, and round shape sections of the assembled magnetic medium are chosen to study their influence on magnetic field distribution characteristics using the ANSYS analysis. This paper utilizes a single assembled magnetic medium to understand the relationship between the geometry of the assembled magnetic medium and its magnetic field distribution characteristics. The results show that high magnetic field,regional field, magnetic field gradient, and magnetic force formed by the different sections of the assembled magnetic medium in the same background magnetic field reduce in turn based on the triangle,square, hexagonal, octagon, dodecagon, and round. Based on the magnetic field characteristics analytic results, the magnetic separation tests of the ilmenite are carried out. The results indicate that the section shape of the toothed plate compared with the section shape of cylinder can improve the recovery of ilmenite up to 45% in the same magnetizing current condition of 2A, which is consistent with magnetic field characteristics analysis of different assembled magnetic medium section shapes.展开更多
During the last two decades signicant work has been reported in the eld of cursive language’s recognition especially,in the Arabic,the Urdu and the Persian languages.The unavailability of such work in the Pashto lang...During the last two decades signicant work has been reported in the eld of cursive language’s recognition especially,in the Arabic,the Urdu and the Persian languages.The unavailability of such work in the Pashto language is because of:the absence of a standard database and of signicant research work that ultimately acts as a big barrier for the research community.The slight change in the Pashto characters’shape is an additional challenge for researchers.This paper presents an efcient OCR system for the handwritten Pashto characters based on multi-class enabled support vector machine using manifold feature extraction techniques.These feature extraction techniques include,tools such as zoning feature extractor,discrete cosine transform,discrete wavelet transform,and Gabor lters and histogram of oriented gradients.A hybrid feature map is developed by combining the manifold feature maps.This research work is performed by developing a medium-sized dataset of handwritten Pashto characters that encapsulate 200 handwritten samples for each 44 characters in the Pashto language.Recognition results are generated for the proposed model based on a manifold and hybrid feature map.An overall accuracy rates of 63.30%,65.13%,68.55%,68.28%,67.02%and 83%are generated based on a zoning technique,HoGs,Gabor lter,DCT,DWT and hybrid feature maps respectively.Applicability of the proposed model is also tested by comparing its results with a convolution neural network model.The convolution neural network-based model generated an accuracy rate of 81.02%smaller than the multi-class support vector machine.The highest accuracy rate of 83%for the multi-class SVM model based on a hybrid feature map reects the applicability of the proposed model.展开更多
To improve the recognition accuracy of off-line handwritten Tibetan characters the local gradient direction histograms based on the wavelet transform are proposed as the recognition features.First for a Tibetan charac...To improve the recognition accuracy of off-line handwritten Tibetan characters the local gradient direction histograms based on the wavelet transform are proposed as the recognition features.First for a Tibetan character sample image the first level approximation component of the Haar wavelet transform is calculated.Secondly the approximation component is partitioned into several equal-sized zones. Finally the gradient direction histograms of each zone are calculated and the local direction histograms of the approximation component are considered as the features of the character sample image.The proposed method is tested on the recently developed off-line Tibetan handwritten character sample database.The experimental results demonstrate the effectiveness and efficiency of the proposed feature extraction method.Furthermore compared with the detail components the approximation component contributes more to the recognition accuracy.展开更多
基金provided by the Postdoctoral Science Foundation of China(No.2013M542076)the self-determined and innovative research funds of WUT(No.2014-IV-069)the Ministry of Science and Technology of China(No.2011BAB05B01)
文摘The section shape of an assembled magnetic medium is the most important structural parameter of a high gradient magnetic separator, which directly affects the induction distribution and magnetic field gradient of the magnetic separator. In this study, equilateral triangle, square, hexagonal, octagon, dodecagon, and round shape sections of the assembled magnetic medium are chosen to study their influence on magnetic field distribution characteristics using the ANSYS analysis. This paper utilizes a single assembled magnetic medium to understand the relationship between the geometry of the assembled magnetic medium and its magnetic field distribution characteristics. The results show that high magnetic field,regional field, magnetic field gradient, and magnetic force formed by the different sections of the assembled magnetic medium in the same background magnetic field reduce in turn based on the triangle,square, hexagonal, octagon, dodecagon, and round. Based on the magnetic field characteristics analytic results, the magnetic separation tests of the ilmenite are carried out. The results indicate that the section shape of the toothed plate compared with the section shape of cylinder can improve the recovery of ilmenite up to 45% in the same magnetizing current condition of 2A, which is consistent with magnetic field characteristics analysis of different assembled magnetic medium section shapes.
基金funded by Qatar University Internal Grant under Grant No.IRCC-2020-009.The ndings achieved herein are solely the responsibility of the authors。
文摘During the last two decades signicant work has been reported in the eld of cursive language’s recognition especially,in the Arabic,the Urdu and the Persian languages.The unavailability of such work in the Pashto language is because of:the absence of a standard database and of signicant research work that ultimately acts as a big barrier for the research community.The slight change in the Pashto characters’shape is an additional challenge for researchers.This paper presents an efcient OCR system for the handwritten Pashto characters based on multi-class enabled support vector machine using manifold feature extraction techniques.These feature extraction techniques include,tools such as zoning feature extractor,discrete cosine transform,discrete wavelet transform,and Gabor lters and histogram of oriented gradients.A hybrid feature map is developed by combining the manifold feature maps.This research work is performed by developing a medium-sized dataset of handwritten Pashto characters that encapsulate 200 handwritten samples for each 44 characters in the Pashto language.Recognition results are generated for the proposed model based on a manifold and hybrid feature map.An overall accuracy rates of 63.30%,65.13%,68.55%,68.28%,67.02%and 83%are generated based on a zoning technique,HoGs,Gabor lter,DCT,DWT and hybrid feature maps respectively.Applicability of the proposed model is also tested by comparing its results with a convolution neural network model.The convolution neural network-based model generated an accuracy rate of 81.02%smaller than the multi-class support vector machine.The highest accuracy rate of 83%for the multi-class SVM model based on a hybrid feature map reects the applicability of the proposed model.
基金The National Natural Science Foundation of China(No.60963016)the National Social Science Foundation of China(No.17BXW037)
文摘To improve the recognition accuracy of off-line handwritten Tibetan characters the local gradient direction histograms based on the wavelet transform are proposed as the recognition features.First for a Tibetan character sample image the first level approximation component of the Haar wavelet transform is calculated.Secondly the approximation component is partitioned into several equal-sized zones. Finally the gradient direction histograms of each zone are calculated and the local direction histograms of the approximation component are considered as the features of the character sample image.The proposed method is tested on the recently developed off-line Tibetan handwritten character sample database.The experimental results demonstrate the effectiveness and efficiency of the proposed feature extraction method.Furthermore compared with the detail components the approximation component contributes more to the recognition accuracy.
