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A machine vision system for Ball Grid Array Package Inspection

Journal of Shanghai Jiaotong university(Science) 2005年第2期10卷 139-142页

作者：夏年炯曹其新李杰 Research Inst. of Robotics Shanghai Jiaotong Univ. Shanghai 200030 China Center for Intelligent Maintenance Univ. of Wisconsin at Milwaukee Milwaukee WI 53211 USA

An optical inspection method of the Ball Grid Array package(BGA) was proposed by using a machine vision system. The developed machine vision system could get main critical factors for BGA quality evaluation, such as the height of solder ball, diameter, pitch and coplanarity. The experiment has proved that this system is available for BGA failure detection.

关键词： ball gird array machine vision system coplanarity image processing

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Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

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International Journal of Coal Science & Technology 2021年第4期8卷 737-755页

作者： Amit Kumar Gorai Simit Raval Ashok Kumar Patel Snehamoy Chatterjee Tarini Gautam Department of Mining Engineering National Institute of TechnologyRourkela 769008India School of Minerals and Energy Resources Engineering University of New South WalesSydneyNSW 2052Australia Department of Computer Science&Engineering C.V.Raman Global UniversityBhubaneswarOdisha 752054India Department of Geological and Mining Engineering and Science Michigan Technological UniversityHoughtonMI 49931USA

Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific ***,the current study aims to develop a machine vision system for automated coal *** model was calibrated using 80 image samples that are captured for different coal samples in different *** the images were captured in RGB color space and converted into five other color spaces(HSI,CMYK,Lab,xyz,Gray)for feature *** intensity component image of HSI color space was further transformed into four frequency components(discrete cosine transform,discrete wavelet transform,discrete Fourier transform,and Gabor filter)for the texture features extraction.A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model *** datasets of the optimized features were used as an input for the model,and their respective coal characteristics(analyzed in the laboratory)were used as outputs of the *** R-squared values were found to be 0.89,0.92,0.92,and 0.84,respectively,for fixed carbon,ash content,volatile matter,and moisture *** performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression,support vector regression,and radial basis neural network *** study demonstrates the potential of the machine vision system in automated coal characterization.

关键词： Coal characterization machine vision system Artificial neural network Gaussian process regression

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Application of artificial intelligence for separation of live and dead rainbow trout fish eggs

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Artificial Intelligence in Agriculture 2019年第1期1卷 27-34页

作者： Abbas Rohani Morteza Taki Ghasem Bahrami Department of Biosystems Engineering Faculty of AgricultureFerdowsi University of MashhadMashhadIran Department of Agricultural machinery and Mechanization Agricultural Sciences and Natural Resources University of KhuzestanMollasaniIran

In this study a visual machine technology-based intelligent system was developed and evaluated for separation and recognizing the alive and dead eggs of rainbow trout *** features derived from imagery processing of alive and dead eggs were used as the decision-making variables in the ***-layer Perceptron neural network(MLP)and Support Vector machine(SVM)models were used as the *** paired t-test,10 effective features were selected from 15 features for *** k-fold cross validation method was used for better evaluation the *** changing the size of the training data set from 80%to 20%,the classifier ability and stabilitywere *** results showed that in the training phase,all the mean values of the statistical indices forMLP and SVMclassificationswere complete for all categories(100%of the classification was predicted correctly).Also,in the test phase,the performance indicators of both classifiers were very satisfactory(the average accuracywas 99.45%).Therefore,it is possible to use both classifierswith certainty for separation the rainbow trout fish eggs.

关键词： Rainbow trout Fish egg K-fold cross validation machine vision system

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DEVELOPMENT OF A CNC MICRO-EDM machine

DEVELOPMENT OF A CNC MICRO-EDM MACHINE

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2006年中国机械工程学会年会暨中国工程院机械与运载工程学部首届年会

作者： GUO Rui*, ZHAO Wansheng, LI Gang *School of Mechanical Engineering, Harbin Institute of Technology, 92 West Da-Zhi Street, Harbin 150001, China School of Mechanical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China

Micro-EDM has been a key technology for micro-machining because of the high precision and good surface quality that it can achieve. However, a number of issues remain to be solved before micro-EDM can

关键词： Micro-EDM CNC system machine vision system Linux.

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