5 research outputs found
A Self-adaptive Fireworks Algorithm for Classification Problems
his work was supported in part by the National Natural Science Foundation of China under Grants 61403206 and 61771258, in part by the Natural Science Foundation of Jiangsu Province under Grants BK20141005 and BK20160910, in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 14KJB520025, in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions, in part by the Open Research Fund of Jiangsu Engineering Research Center of Communication and Network Technology, NJUPT, under Grant JSGCZX17001, and in part by the Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, under Contract SKL2017CP01.Peer reviewedPublisher PD
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Surface Impedance Measurements of Single Crystal MgB2 Films for Radiofrequency Superconductivity Applications
We report microstructure analyses and superconducting radiofrequency (SRF) measurements of large scale epitaxial MgB{sub 2} films. MgB{sub 2} films on 5 cm dia. sapphire disks were fabricated by a Hybrid Physical Chemical Vapor Deposition (HPCVD) technique. The electron-beam backscattering diffraction (EBSD) results suggest that the film is a single crystal complying with a MgB{sub 2}(0001) {parallel} Al{sub 2}O{sub 3}(0001) epitaxial relationship. The SRF properties of different film thicknesses (200 nm and 350 nm) were evaluated under different temperatures and applied fields at 7.4 GHz. A surface resistance of 9 {+-} 2 {mu}{Omega} has been observed at 2.2 K
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Dual output feature fusion networks for femoral segmentation and quantitative analysis of the knee joint
BackgroundMagnetic resonance imaging (MRI) is the preferred imaging modality for diagnosing knee disease. Segmentation of the knee MRI images is essential for subsequent quantification of clinical parameters and treatment planning for knee prosthesis replacement. However, the segmentation remains difficult due to individual differences in anatomy, the difficulty of obtaining accurate edges at lower resolutions, and the presence of speckle noise and artifacts in the images. In addition, radiologists must manually measure the knee's parameters which is a laborious and time-consuming process.PurposeAutomatic quantification of femoral morphological parameters can be of fundamental help in the design of prosthetic implants for the repair of the knee and the femur. Knowledge of knee femoral parameters can provide a basis for femoral repair of the knee, the design of fixation materials for femoral prostheses, and the replacement of prostheses.MethodsThis paper proposes a new deep network architecture to comprehensively address these challenges. A dual output model structure is proposed, with a high and low layer fusion extraction feature module designed to extract rich features through the cross-fusion mechanism. A multi-scale edge information extraction spatial feature module is also developed to address the boundary-blurring problem.ResultsBased on the precise automated segmentation results, 10 key clinical parameters were automatically measured for a knee femoral prosthesis replacement program. The correlation coefficients of the quantitative results of these parameters compared to manual results all achieved at least 0.92. The proposed method was extensively evaluated with MRIs of 78 patients’ knees, and it consistently outperformed other methods used for segmentation.ConclusionsThe automated quantization process produced comparable measurements to those manually obtained by radiologists. This paper demonstrates the viability of automatic knee MRI image segmentation and quantitative analysis with the proposed method. This provides data to support the accuracy of assessing the progression and biomechanical changes of osteoarthritis of the knee using an automated process, thus saving valuable time for the radiologists and surgeons.</p