In this paper, research has been conducted to increase the quantity of fiber produced in the enterprise by creating a sorting device for spun seeds, dividing them into fractions by geometric dimensions, and by re-ginn...In this paper, research has been conducted to increase the quantity of fiber produced in the enterprise by creating a sorting device for spun seeds, dividing them into fractions by geometric dimensions, and by re-ginning, separating those with long fibers. A new model was developed for geometric sorting of cotton seeds in the harvest, and experiments determined its effectiveness and the optimal values of the factors affecting the efficiency using mathematical modeling. Based on the results of the study, graphs of the influence of factors on device performance and on device efficiency were constructed.展开更多
The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,whi...The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.展开更多
Gas hydrate(GH)is an unconventional resource estimated at 1000-120,000 trillion m^(3)worldwide.Research on GH is ongoing to determine its geological and flow characteristics for commercial produc-tion.After two large-...Gas hydrate(GH)is an unconventional resource estimated at 1000-120,000 trillion m^(3)worldwide.Research on GH is ongoing to determine its geological and flow characteristics for commercial produc-tion.After two large-scale drilling expeditions to study the GH-bearing zone in the Ulleung Basin,the mineral composition of 488 sediment samples was analyzed using X-ray diffraction(XRD).Because the analysis is costly and dependent on experts,a machine learning model was developed to predict the mineral composition using XRD intensity profiles as input data.However,the model’s performance was limited because of improper preprocessing of the intensity profile.Because preprocessing was applied to each feature,the intensity trend was not preserved even though this factor is the most important when analyzing mineral composition.In this study,the profile was preprocessed for each sample using min-max scaling because relative intensity is critical for mineral analysis.For 49 test data among the 488 data,the convolutional neural network(CNN)model improved the average absolute error and coefficient of determination by 41%and 46%,respectively,than those of CNN model with feature-based pre-processing.This study confirms that combining preprocessing for each sample with CNN is the most efficient approach for analyzing XRD data.The developed model can be used for the compositional analysis of sediment samples from the Ulleung Basin and the Korea Plateau.In addition,the overall procedure can be applied to any XRD data of sediments worldwide.展开更多
Fe-rich intermetallic phases in recycled Al alloys often exhibit complex and 3D convoluted structures and morphologies.They are the common detrimental intermetallic phases to the mechanical properties of recycled Al a...Fe-rich intermetallic phases in recycled Al alloys often exhibit complex and 3D convoluted structures and morphologies.They are the common detrimental intermetallic phases to the mechanical properties of recycled Al alloys.In this study,we used synchrotron X-ray tomography to study the true 3D morphologies of the Ferich phases,Al_(2)Cu phases and casting defects in an ascast Al-5Cu-1.5Fe-1Si alloy.Machine learning-based image processing approach was used to recognize and segment the diff erent phases in the 3D tomography image stacks.In the studied condition,theβ-Al_(9)Fe_(2)Si_(2)andω-Al_(7)Cu_(2)Fe are found to be the main Fe-rich intermetallic phases.Theβ-Al_(9)Fe_(2)Si_(2)phases exhibit a spatially connected 3D network structure and morphology which in turn control the 3D spatial distribution of the Al_(2)Cu phases and the shrinkage cavities.The Al_(3)Fe phases formed at the early stage of solidification aff ect to a large extent the structure and morphology of the subsequently formed Fe-rich intermetallic phases.The machine learning method has been demonstrated as a powerful tool for processing big datasets in multidimensional imaging-based materials characterization work.展开更多
Silicon Carbide (SiC) machining by traditional methods with regards to its high hardness is not possible. Electro Discharge Machining, among non-traditional machining methods, is used for machining of SiC. The present...Silicon Carbide (SiC) machining by traditional methods with regards to its high hardness is not possible. Electro Discharge Machining, among non-traditional machining methods, is used for machining of SiC. The present work is aimed to optimize the surface roughness and material removal rate of electro discharge machining of SiC parameters simultaneously. As the output parameters are conflicting in nature, so there is no single combination of machining parameters, which provides the best machining performance. Artificial neural network (ANN) with back propagation algorithm is used to model the process. A multi-objective optimization method, non-dominating sorting genetic algorithm-II is used to optimize the process. Affects of three important input parameters of process viz., discharge current, pulse on time (Ton), pulse off time (Toff) on electric discharge machining of SiC are considered. Experiments have been conducted over a wide range of considered input parameters for training and verification of the model. Testing results demonstrate that the model is suitable for predicting the response parameters. A pareto-optimal set has been predicted in this work.展开更多
Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL...Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL samples were utilized for training the support vector machine(SVM)-,random forest(RF)-,and back propagation neural network(BPNN)-based models,respectively.Simultaneously,the machine learning model was embedded into genetic algorithm(GA)for parameter optimization to effectively predict uniaxial compressive strength(UCS)of CRL.Results indicate that the BPNN model with five hidden layers presents the best training effect in the data set of CRL.The SVM-based model shows a tendency to overfitting in the training set and poor generalization ability in the testing set.The RF-based model is suitable for training CRL samples with large data.Analysis of Pearson correlation coefficient matrix and the percentage increment method of performance metrics shows that the dry density,pore structure,and porosity of CRL are strongly correlated to UCS.However,the P-wave velocity is almost uncorrelated to the UCS,which is significantly distinct from the law for homogenous geomaterials.In addition,the pore tensor proposed in this paper can effectively reflect the pore structure of coral framework limestone(CFL)and coral boulder limestone(CBL),realizing the quantitative characterization of the heterogeneity and anisotropy of pore.The pore tensor provides a feasible idea to establish the relationship between pore structure and mechanical behavior of CRL.展开更多
Sensor-based ore sorting is a technology used to classify high-grade mineralized rocks from low-grade waste rocks to reduce operation costs.Many ore-sorting algorithms using color images have been proposed in the past...Sensor-based ore sorting is a technology used to classify high-grade mineralized rocks from low-grade waste rocks to reduce operation costs.Many ore-sorting algorithms using color images have been proposed in the past,but only some validate their results using mineral grades or optimize the algorithms to classify rocks in real-time.This paper presents an ore-sorting algorithm based on image processing and machine learning that is able to classify rocks from a gold and silver mine based on their grade.The algorithm is composed of four main stages:(1)image segmentation and partition,(2)color and texture feature extraction,(3)sub-image classification using neural networks,and(4)a voting system to determine the overall class of the rock.The algorithm was trained using images of rocks that a geologist manually classified according to their mineral content and then was validated using a different set of rocks analyzed in a laboratory to determine their gold and silver grades.The proposed method achieved a Matthews correlation coefficient of 0.961 points,higher than other classification algorithms based on support vector machines and convolutional neural networks,and a processing time under 44 ms,promising for real-time ore sorting applications.展开更多
A general prediction model for seven heavy metals was established using the heavy metal contents of 207soil samples measured by a portable X-ray fluorescence spectrometer(XRF)and six environmental factors as model cor...A general prediction model for seven heavy metals was established using the heavy metal contents of 207soil samples measured by a portable X-ray fluorescence spectrometer(XRF)and six environmental factors as model correction coefficients.The eXtreme Gradient Boosting(XGBoost)model was used to fit the relationship between the content of heavy metals and environment characteristics to evaluate the soil ecological risk of the smelting site.The results demonstrated that the generalized prediction model developed for Pb,Cd,and As was highly accurate with fitted coefficients(R^(2))values of 0.911,0.950,and 0.835,respectively.Topsoil presented the highest ecological risk,and there existed high potential ecological risk at some positions with different depths due to high mobility of Cd.Generally,the application of machine learning significantly increased the accuracy of pXRF measurements,and identified key environmental factors.The adapted potential ecological risk assessment emphasized the need to focus on Pb,Cd,and As in future site remediation efforts.展开更多
In this paper,an automated system and methodology for nondestructive sorting of apples are presented.Different from the traditional manual grading method,the automated,nondestructive sorting equipment can improve the ...In this paper,an automated system and methodology for nondestructive sorting of apples are presented.Different from the traditional manual grading method,the automated,nondestructive sorting equipment can improve the production efficiency and the grading speed and accuracy.Most popular apple quality detection and grading methods use two-dimensional(2D)machine vision detection based on a single charge-coupled device(CCD)camera detect the external quality.Our system integrates a 3D structured laser into an existing 2D sorting system,which provides the addition third dimension to detect the defects in apples by using the curvature of the structured light strips that are acquired from the optical system of the machine.The curvature of the structured light strip will show the defects in the apple surface.Other features such as color,texture,shape,size and 3D information all play key roles in determining the grade of an apple,which can be determined using a series of feature extraction methods.After feature extraction,a method based on principal component analysis(PCA)for data dimensionality reduction is applied to the system.Furthermore,a comprehensive classification method based on fuzzy neural network(FNN),which is a combination of knowledge-based and model-based method,is used in this paper as the classifier.Preliminary experiments are conducted to verity the feasibility and accuracy of the proposed sorting system.展开更多
According to the practical problems in eddy current sorting,the method and technology of eddy current hardness sorting based on LeastSquaresSupportVectorMachine(LS-SVM)are proposed based on the Xilinx Artix-7 FPGA in ...According to the practical problems in eddy current sorting,the method and technology of eddy current hardness sorting based on LeastSquaresSupportVectorMachine(LS-SVM)are proposed based on the Xilinx Artix-7 FPGA in this paper.The calculated sorting-hyperplane and designed sorting decision-making machine were used to sort different hardness of the vavles.The experimental results of the vavle sorting show that the sorting success rate can reach 100%under conditions that the number of test vavles is one quarter of the training vavles.The method and technology based on LS-SVM can solve the problems that the impedance feature value is nonlinear with the hardness value and variable sorting interval.It also proved that the LS-SVM algorithm has strong practical value in online eddy current sorting.展开更多
Several parallel sorting techniques on different architectures have been studied for many years. Due to the need for faster systems in today＇s world, parallelism can be used to accelerate applications. Nowadays, para...Several parallel sorting techniques on different architectures have been studied for many years. Due to the need for faster systems in today＇s world, parallelism can be used to accelerate applications. Nowadays, parallel operations are used to solve computer problems such as sort and search, which result in a reasonable speed. Sorting is one of the most important operations in computing world. The authors always try to find the best in different areas which the premier is speedup. In this paper, the authors issued a sort with O（logn） time complexity on PRAM EREW （Parallel Random Access Machine Exclusive Read Exclusive Write）. The algorithm is designed in a manner that keeps the tradeoff between the number of processor elements in the architecture and execution time. The simulation of the algorithm proves the theoretical analysis of the algorithm. The results of this research can be utilized in developing faster embedded systems. Sorting on Centralized Diamond （SOCD） algorithm is issued on the novel Centralized Diamond architecture which takes the advantages of Single Instruction Multiple Data （SIMD） architecture. This architecture and the sort on it are intuitive and optimal.展开更多
X-ray image has been widely used in many fields such as medical diagnosis,industrial inspection,and so on.Unfortunately,due to the physical characteristics of X-ray and imaging system,distortion of the projected image...X-ray image has been widely used in many fields such as medical diagnosis,industrial inspection,and so on.Unfortunately,due to the physical characteristics of X-ray and imaging system,distortion of the projected image will happen,which restrict the application of X-ray image,especially in high accuracy fields.Distortion correction can be performed using algorithms that can be classified as global or local according to the method used,both having specific advantages and disadvantages.In this paper,a new global method based on support vector regression（SVR）machine for distortion correction is proposed.In order to test the presented method,a calibration phantom is specially designed for this purpose.A comparison of the proposed method with the traditional global distortion correction techniques is performed.The experimental results show that the proposed correction method performs better than the traditional global one.展开更多
文摘In this paper, research has been conducted to increase the quantity of fiber produced in the enterprise by creating a sorting device for spun seeds, dividing them into fractions by geometric dimensions, and by re-ginning, separating those with long fibers. A new model was developed for geometric sorting of cotton seeds in the harvest, and experiments determined its effectiveness and the optimal values of the factors affecting the efficiency using mathematical modeling. Based on the results of the study, graphs of the influence of factors on device performance and on device efficiency were constructed.
文摘The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.
基金supported by the Gas Hydrate R&D Organization and the Korea Institute of Geoscience and Mineral Resources(KIGAM)(GP2021-010)supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2021R1C1C1004460)Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant funded by the Korean government(MOTIE)(20214000000500,Training Program of CCUS for Green Growth).
文摘Gas hydrate(GH)is an unconventional resource estimated at 1000-120,000 trillion m^(3)worldwide.Research on GH is ongoing to determine its geological and flow characteristics for commercial produc-tion.After two large-scale drilling expeditions to study the GH-bearing zone in the Ulleung Basin,the mineral composition of 488 sediment samples was analyzed using X-ray diffraction(XRD).Because the analysis is costly and dependent on experts,a machine learning model was developed to predict the mineral composition using XRD intensity profiles as input data.However,the model’s performance was limited because of improper preprocessing of the intensity profile.Because preprocessing was applied to each feature,the intensity trend was not preserved even though this factor is the most important when analyzing mineral composition.In this study,the profile was preprocessed for each sample using min-max scaling because relative intensity is critical for mineral analysis.For 49 test data among the 488 data,the convolutional neural network(CNN)model improved the average absolute error and coefficient of determination by 41%and 46%,respectively,than those of CNN model with feature-based pre-processing.This study confirms that combining preprocessing for each sample with CNN is the most efficient approach for analyzing XRD data.The developed model can be used for the compositional analysis of sediment samples from the Ulleung Basin and the Korea Plateau.In addition,the overall procedure can be applied to any XRD data of sediments worldwide.
基金supported by the National Natural Science Foundation of China(No.52004101)the Guangdong Province Science and Technology Plan(No.2017B090903005)the UK Engineering and Physical Sciences Research Council(Grant No.EP/L019965/1)。
文摘Fe-rich intermetallic phases in recycled Al alloys often exhibit complex and 3D convoluted structures and morphologies.They are the common detrimental intermetallic phases to the mechanical properties of recycled Al alloys.In this study,we used synchrotron X-ray tomography to study the true 3D morphologies of the Ferich phases,Al_(2)Cu phases and casting defects in an ascast Al-5Cu-1.5Fe-1Si alloy.Machine learning-based image processing approach was used to recognize and segment the diff erent phases in the 3D tomography image stacks.In the studied condition,theβ-Al_(9)Fe_(2)Si_(2)andω-Al_(7)Cu_(2)Fe are found to be the main Fe-rich intermetallic phases.Theβ-Al_(9)Fe_(2)Si_(2)phases exhibit a spatially connected 3D network structure and morphology which in turn control the 3D spatial distribution of the Al_(2)Cu phases and the shrinkage cavities.The Al_(3)Fe phases formed at the early stage of solidification aff ect to a large extent the structure and morphology of the subsequently formed Fe-rich intermetallic phases.The machine learning method has been demonstrated as a powerful tool for processing big datasets in multidimensional imaging-based materials characterization work.
文摘Silicon Carbide (SiC) machining by traditional methods with regards to its high hardness is not possible. Electro Discharge Machining, among non-traditional machining methods, is used for machining of SiC. The present work is aimed to optimize the surface roughness and material removal rate of electro discharge machining of SiC parameters simultaneously. As the output parameters are conflicting in nature, so there is no single combination of machining parameters, which provides the best machining performance. Artificial neural network (ANN) with back propagation algorithm is used to model the process. A multi-objective optimization method, non-dominating sorting genetic algorithm-II is used to optimize the process. Affects of three important input parameters of process viz., discharge current, pulse on time (Ton), pulse off time (Toff) on electric discharge machining of SiC are considered. Experiments have been conducted over a wide range of considered input parameters for training and verification of the model. Testing results demonstrate that the model is suitable for predicting the response parameters. A pareto-optimal set has been predicted in this work.
基金supported by the National Natural Science Foundation of China(Grant Nos.41877267 and 41877260)the Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA13010201).
文摘Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL samples were utilized for training the support vector machine(SVM)-,random forest(RF)-,and back propagation neural network(BPNN)-based models,respectively.Simultaneously,the machine learning model was embedded into genetic algorithm(GA)for parameter optimization to effectively predict uniaxial compressive strength(UCS)of CRL.Results indicate that the BPNN model with five hidden layers presents the best training effect in the data set of CRL.The SVM-based model shows a tendency to overfitting in the training set and poor generalization ability in the testing set.The RF-based model is suitable for training CRL samples with large data.Analysis of Pearson correlation coefficient matrix and the percentage increment method of performance metrics shows that the dry density,pore structure,and porosity of CRL are strongly correlated to UCS.However,the P-wave velocity is almost uncorrelated to the UCS,which is significantly distinct from the law for homogenous geomaterials.In addition,the pore tensor proposed in this paper can effectively reflect the pore structure of coral framework limestone(CFL)and coral boulder limestone(CBL),realizing the quantitative characterization of the heterogeneity and anisotropy of pore.The pore tensor provides a feasible idea to establish the relationship between pore structure and mechanical behavior of CRL.
文摘Sensor-based ore sorting is a technology used to classify high-grade mineralized rocks from low-grade waste rocks to reduce operation costs.Many ore-sorting algorithms using color images have been proposed in the past,but only some validate their results using mineral grades or optimize the algorithms to classify rocks in real-time.This paper presents an ore-sorting algorithm based on image processing and machine learning that is able to classify rocks from a gold and silver mine based on their grade.The algorithm is composed of four main stages:(1)image segmentation and partition,(2)color and texture feature extraction,(3)sub-image classification using neural networks,and(4)a voting system to determine the overall class of the rock.The algorithm was trained using images of rocks that a geologist manually classified according to their mineral content and then was validated using a different set of rocks analyzed in a laboratory to determine their gold and silver grades.The proposed method achieved a Matthews correlation coefficient of 0.961 points,higher than other classification algorithms based on support vector machines and convolutional neural networks,and a processing time under 44 ms,promising for real-time ore sorting applications.
基金financially supported from the National Key Research and Development Program of China(No.2019YFC1803601)the Fundamental Research Funds for the Central Universities of Central South University,China(No.2023ZZTS0801)+1 种基金the Postgraduate Innovative Project of Central South University,China(No.2023XQLH068)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(No.QL20230054)。
文摘A general prediction model for seven heavy metals was established using the heavy metal contents of 207soil samples measured by a portable X-ray fluorescence spectrometer(XRF)and six environmental factors as model correction coefficients.The eXtreme Gradient Boosting(XGBoost)model was used to fit the relationship between the content of heavy metals and environment characteristics to evaluate the soil ecological risk of the smelting site.The results demonstrated that the generalized prediction model developed for Pb,Cd,and As was highly accurate with fitted coefficients(R^(2))values of 0.911,0.950,and 0.835,respectively.Topsoil presented the highest ecological risk,and there existed high potential ecological risk at some positions with different depths due to high mobility of Cd.Generally,the application of machine learning significantly increased the accuracy of pXRF measurements,and identified key environmental factors.The adapted potential ecological risk assessment emphasized the need to focus on Pb,Cd,and As in future site remediation efforts.
文摘In this paper,an automated system and methodology for nondestructive sorting of apples are presented.Different from the traditional manual grading method,the automated,nondestructive sorting equipment can improve the production efficiency and the grading speed and accuracy.Most popular apple quality detection and grading methods use two-dimensional(2D)machine vision detection based on a single charge-coupled device(CCD)camera detect the external quality.Our system integrates a 3D structured laser into an existing 2D sorting system,which provides the addition third dimension to detect the defects in apples by using the curvature of the structured light strips that are acquired from the optical system of the machine.The curvature of the structured light strip will show the defects in the apple surface.Other features such as color,texture,shape,size and 3D information all play key roles in determining the grade of an apple,which can be determined using a series of feature extraction methods.After feature extraction,a method based on principal component analysis(PCA)for data dimensionality reduction is applied to the system.Furthermore,a comprehensive classification method based on fuzzy neural network(FNN),which is a combination of knowledge-based and model-based method,is used in this paper as the classifier.Preliminary experiments are conducted to verity the feasibility and accuracy of the proposed sorting system.
基金supported by a project of the National Natural Science Foundation(No.51865004)the Guizhou Science and Technology Department(No.QKH20161081,No.QKH20192881)
文摘According to the practical problems in eddy current sorting,the method and technology of eddy current hardness sorting based on LeastSquaresSupportVectorMachine(LS-SVM)are proposed based on the Xilinx Artix-7 FPGA in this paper.The calculated sorting-hyperplane and designed sorting decision-making machine were used to sort different hardness of the vavles.The experimental results of the vavle sorting show that the sorting success rate can reach 100%under conditions that the number of test vavles is one quarter of the training vavles.The method and technology based on LS-SVM can solve the problems that the impedance feature value is nonlinear with the hardness value and variable sorting interval.It also proved that the LS-SVM algorithm has strong practical value in online eddy current sorting.
文摘Several parallel sorting techniques on different architectures have been studied for many years. Due to the need for faster systems in today＇s world, parallelism can be used to accelerate applications. Nowadays, parallel operations are used to solve computer problems such as sort and search, which result in a reasonable speed. Sorting is one of the most important operations in computing world. The authors always try to find the best in different areas which the premier is speedup. In this paper, the authors issued a sort with O（logn） time complexity on PRAM EREW （Parallel Random Access Machine Exclusive Read Exclusive Write）. The algorithm is designed in a manner that keeps the tradeoff between the number of processor elements in the architecture and execution time. The simulation of the algorithm proves the theoretical analysis of the algorithm. The results of this research can be utilized in developing faster embedded systems. Sorting on Centralized Diamond （SOCD） algorithm is issued on the novel Centralized Diamond architecture which takes the advantages of Single Instruction Multiple Data （SIMD） architecture. This architecture and the sort on it are intuitive and optimal.
基金National Natural Science Foundation of China(No.61305118)
文摘X-ray image has been widely used in many fields such as medical diagnosis,industrial inspection,and so on.Unfortunately,due to the physical characteristics of X-ray and imaging system,distortion of the projected image will happen,which restrict the application of X-ray image,especially in high accuracy fields.Distortion correction can be performed using algorithms that can be classified as global or local according to the method used,both having specific advantages and disadvantages.In this paper,a new global method based on support vector regression（SVR）machine for distortion correction is proposed.In order to test the presented method,a calibration phantom is specially designed for this purpose.A comparison of the proposed method with the traditional global distortion correction techniques is performed.The experimental results show that the proposed correction method performs better than the traditional global one.
