Significant temperature difference(300-77 K or even 4 K) can cause large deformations and displacements of the beam position monitors(BPMs),which affect BPMs measurement resolution or even cause their malfunction in c...Significant temperature difference(300-77 K or even 4 K) can cause large deformations and displacements of the beam position monitors(BPMs),which affect BPMs measurement resolution or even cause their malfunction in cryogenic situations.In this paper,to check the offset from the mechanical to electrical center in low temperature(77 K),Fourier's law and finite element method are used to simulate cryo-deformation.Laser tracker and micro-alignment telescope are employed in combined BPM calibration,installation and monitoring.The calibration error is<0.02 mm,and the installation and monitoring precision are 0.06 mm and 0.01 mm,respectively.The monitored cryo-deformation agrees well with the simulation results.These indicate that the combined alignment can improve performance of the BPM system.All these guaranteed the success of running the 9.55 MeV@2.14 mA cw protons.展开更多
China Initiative Accelerator Driven System (CIADS) proposed by the Chinese Academy of Sciences which is one of the twelve Major National Scientific and Technological Infrastructures has been approved by National Devel...China Initiative Accelerator Driven System (CIADS) proposed by the Chinese Academy of Sciences which is one of the twelve Major National Scientific and Technological Infrastructures has been approved by National Development and Reform Commission of China, and supposed to be the first principle prototype ADS experimental facility in the world.展开更多
The demo facility of china ADS injector II was installed in the tunnel in 2014. The on-site layout of demo facility is shown in the Fig. 1. The demo faclity mainly includes ECRIS, LEBT, RFQ acclerator, MEBT and TCM6. ...The demo facility of china ADS injector II was installed in the tunnel in 2014. The on-site layout of demo facility is shown in the Fig. 1. The demo faclity mainly includes ECRIS, LEBT, RFQ acclerator, MEBT and TCM6. The TCM6 is composed by six SC HWR cavity,six SC soleniods and five BPMs. The TCM6 will be the prototype for the future 2025 MeV CADS superconducting linac. The beam commissioning of CADS Injector II started in June 2015. It mainly consists of three steps.展开更多
By combining interactive graphic CAD and optimizing methods we have developed an automated optimum CAD system for railway location AORLCAD. Using the AORLCAD, the optimization of both horizontal and vertical alignm...By combining interactive graphic CAD and optimizing methods we have developed an automated optimum CAD system for railway location AORLCAD. Using the AORLCAD, the optimization of both horizontal and vertical alignment of railway can be realized by using automated optimization interactive graphic design alternatively. The result makes automated optimum technique for railway location more completed. The methods of interactive graphic design and optimum techniques used by AORLCAD are introduced in detail.展开更多
Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronar...Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image classification.Image denoising is done by a U-Net architecture that ensures effective image denoising.Attenuation correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of classification.Finally,a novel multi-scale diluted convolution(MSDC)network is proposed.It merges the features extracted in different scales and makes the model learn the features more efficiently.Three scales of filters with size 3×3 are used to extract features.All three steps are compared with state-of-the-art methods.The proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of 39.7.The proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.展开更多
Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere.Due to its ability to produce a detailed view of the soft tissues,including the s...Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere.Due to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine.The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases.It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation accuracy.This work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra S1.Pseudo-colour mask images were generated and used as ground truth for training the model.The work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley Data.The proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.展开更多
A Medium Energy Beam Transport line 1 (MEBT1) has been designed for Injector Scheme II of the China, ADS project. To match the beam from RFQ to Superconducting (SC) Half Wave Resonator (HWR) sections with emitta...A Medium Energy Beam Transport line 1 (MEBT1) has been designed for Injector Scheme II of the China, ADS project. To match the beam from RFQ to Superconducting (SC) Half Wave Resonator (HWR) sections with emittance preservation, the MEBT1 has been designed to be mechanically compact. Working at 162.5 MHz, the MEBT1 transports a 10 mA, 2.1 MeV proton beam using seven quadrupoles and two bunching cavities within 2.7 meters. Three collimators are placed between every two adjacent quadrupoles to collimate the beam halo. Design and construction of the MEBT1 are presented in this paper.展开更多
基金supported by the National Natural Science Foundation of China(No.11605262)
文摘Significant temperature difference(300-77 K or even 4 K) can cause large deformations and displacements of the beam position monitors(BPMs),which affect BPMs measurement resolution or even cause their malfunction in cryogenic situations.In this paper,to check the offset from the mechanical to electrical center in low temperature(77 K),Fourier's law and finite element method are used to simulate cryo-deformation.Laser tracker and micro-alignment telescope are employed in combined BPM calibration,installation and monitoring.The calibration error is<0.02 mm,and the installation and monitoring precision are 0.06 mm and 0.01 mm,respectively.The monitored cryo-deformation agrees well with the simulation results.These indicate that the combined alignment can improve performance of the BPM system.All these guaranteed the success of running the 9.55 MeV@2.14 mA cw protons.
文摘China Initiative Accelerator Driven System (CIADS) proposed by the Chinese Academy of Sciences which is one of the twelve Major National Scientific and Technological Infrastructures has been approved by National Development and Reform Commission of China, and supposed to be the first principle prototype ADS experimental facility in the world.
文摘The demo facility of china ADS injector II was installed in the tunnel in 2014. The on-site layout of demo facility is shown in the Fig. 1. The demo faclity mainly includes ECRIS, LEBT, RFQ acclerator, MEBT and TCM6. The TCM6 is composed by six SC HWR cavity,six SC soleniods and five BPMs. The TCM6 will be the prototype for the future 2025 MeV CADS superconducting linac. The beam commissioning of CADS Injector II started in June 2015. It mainly consists of three steps.
文摘By combining interactive graphic CAD and optimizing methods we have developed an automated optimum CAD system for railway location AORLCAD. Using the AORLCAD, the optimization of both horizontal and vertical alignment of railway can be realized by using automated optimization interactive graphic design alternatively. The result makes automated optimum technique for railway location more completed. The methods of interactive graphic design and optimum techniques used by AORLCAD are introduced in detail.
基金the Research Grant of Kwangwoon University in 2024.
文摘Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image classification.Image denoising is done by a U-Net architecture that ensures effective image denoising.Attenuation correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of classification.Finally,a novel multi-scale diluted convolution(MSDC)network is proposed.It merges the features extracted in different scales and makes the model learn the features more efficiently.Three scales of filters with size 3×3 are used to extract features.All three steps are compared with state-of-the-art methods.The proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of 39.7.The proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.
文摘Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere.Due to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine.The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases.It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation accuracy.This work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra S1.Pseudo-colour mask images were generated and used as ground truth for training the model.The work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley Data.The proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.
基金Supported by National Natural Science Foundation of China(11079001)
文摘A Medium Energy Beam Transport line 1 (MEBT1) has been designed for Injector Scheme II of the China, ADS project. To match the beam from RFQ to Superconducting (SC) Half Wave Resonator (HWR) sections with emittance preservation, the MEBT1 has been designed to be mechanically compact. Working at 162.5 MHz, the MEBT1 transports a 10 mA, 2.1 MeV proton beam using seven quadrupoles and two bunching cavities within 2.7 meters. Three collimators are placed between every two adjacent quadrupoles to collimate the beam halo. Design and construction of the MEBT1 are presented in this paper.