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Hu Jiaqi,Qiao Zhaopeng,Fan Lunhe,Tang Yongqiang,Cao Liangzhi,Zu Tiejun,He Qingming,Li Zhifeng,Wang Sheng 한국원자력학회 2023 Nuclear Engineering and Technology Vol.55 No.4
MgF2 as a moderator material has been extensively used in the beam shaping assembly (BSA) that plays an important role in the boron neutron capture therapy (BNCT). Regarded as important for applications, the thermal neutron scattering data of MgF2 were calculated, based on the phonon expansion model. The structural properties of MgF2 were researched by the VASP code based on the ab-initio methods. The PHONOPY code was employed to calculate the phonon density of states. Furthermore, the NJOY code was used to calculate the thermal neutron scattering data of MgF2. The calculated inelastic cross sections plus absorption cross sections are in agreement with the available experimental data. The neutron transport in the BSA has been simulated by using a hybrid Monte-Carlo-Deterministic code NECP-MCX. The results indicated that compared with the calculation of the free gas model, the thermal neutron flux and epithermal neutron flux at the BSA exit port calculated by using the thermal neutron scattering data of MgF2 were reduced by 27.7% and 8.2%, respectively.
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The Mixed Kernel Function SVM-based Point Cloud Classification
Chao Chen,Xiaomin Li,Abdelkader Nasreddine Belkacem,Zhifeng Qiao,Enzeng Dong,Wenjun Tan,Duk Shin 한국정밀공학회 2019 International Journal of Precision Engineering and Vol.20 No.5
Measurement and detection of ground information by airborne Lidar are one of the hot topics in the field of intelligent sensing in recent years. This study proposes a new point cloud classification algorithm of Mixed Kernel Function SVM to distinguish different types of ground objects. Firstly, the combined features including the coordinate values, the RGB value, normalized elevation, standard deviation of elevation, and elevation difference of point cloud data were extracted. A mixed kernel function of Gauss and Polynomial was designed. Then, one-versus-rest SVM multiple classifiers was constructed. Finally, the feature of 3D point cloud data was employed to train the SVM classifiers. The overall classification accuracies of test data were 97.69% and 99.13% for two data sets, I and II respectively. In addition, the experimental results have showed that the performance of the proposed method with mixed kernel function SVM was better than standard SVM method with Gaussian kernel function and polynomial kernel function only, which demonstrates the effectiveness of the proposed method.
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