Analysis and Enlightenment of Virtual Simulation Practice Teaching in Civil Aviation Vocational Colleges

作者： Liang   Zhang   来源： International Journal of New Developments in Education 年份： 2023 文献类型 ： 期刊 关键词： virtual   practical   simulation   teaching   model   technology  

描述： This paper analyzes the significance of virtual simulation technology in practical teaching in civil aviation higher vocational colleges. It is the need to implement national and civil aviation bureau-level vocational education reforms and development plans, an important means to construct a teaching model of “combining virtual with real, virtual assisting reality, and virtual and real integration, and innovative practical training”, an important carrier to establish a practice teaching system with characteristic integration of “work, course, competition, and certification”, a practical means to align with job competency and curriculum reform to enhance the quality of talent development. It points out the problems existing in virtual simulation practical teaching in civil aviation higher vocational colleges, and finally puts forward the enlightenment of domestic virtual simulation practical training construction for virtual simulation practical teaching in civil aviation higher vocational education.

Analysis and Enlightenment of Virtual Simulation Practice Teaching in Civil Aviation Vocational Colleges

作者： Liang   Zhang   来源： International Journal of New Developments in Education 年份： 2023 文献类型 ： 期刊 关键词： virtual   practical   simulation   teaching   model   technology  

描述： This paper analyzes the significance of virtual simulation technology in practical teaching in civil aviation higher vocational colleges. It is the need to implement national and civil aviation bureau-level vocational education reforms and development plans, an important means to construct a teaching model of “combining virtual with real, virtual assisting reality, and virtual and real integration, and innovative practical training”, an important carrier to establish a practice teaching system with characteristic integration of “work, course, competition, and certification”, a practical means to align with job competency and curriculum reform to enhance the quality of talent development. It points out the problems existing in virtual simulation practical teaching in civil aviation higher vocational colleges, and finally puts forward the enlightenment of domestic virtual simulation practical training construction for virtual simulation practical teaching in civil aviation higher vocational education.

Research on Noncollinear Shear Wave Mixing Technology for Fatigue Crack Detection and Visualization of Aerospace Titanium Alloys

作者： Yuhua   Zhang       Silong   Quan       Yuezhong   Li   来源： Journal of Materials Engineering and Performance 年份： 2023 文献类型 ： 期刊 关键词： damage   visualization   alloy   shear   mixing   invisible   nonlinear   wave   fatigue   crack   noncollinear   Titanium   coefficient  

描述： In this study, we apply the noncollinear shear wave mixing technique to detect and visualize fatigue damage in aerospace titanium alloy components that are invisible to conventional methods. This technique exploits the nonlinear interaction between two columns of shear waves and fatigue damage in titanium alloy components, which generates a sum-frequency longitudinal wave. By adjusting the relative position and separation distance of the excitation transducers and the receiving transducer in the nonlinear mixing system, we can effectively control the mixing beam in the horizontal/vertical direction of the detection position. We test the nonlinear characteristics of 63 detection points near the fatigue crack. The experimental results show that the mixing nonlinear coefficient exhibits a “step” trend when the fatigue crack is inside the titanium alloy, and the “step” width increases with the crack length. The distribution of the mixing nonlinear coefficient can be used to effectively characterize the length of invisible fatigue crack in the vertical direction. Moreover, we can visualize the fatigue damage area near the invisible fatigue crack inside the metal component by using the contour map of the normalized mixing nonlinear coefficient of 63 detection points near the fatigue crack.

Research on Noncollinear Shear Wave Mixing Technology for Fatigue Crack Detection and Visualization of Aerospace Titanium Alloys

作者： Yuhua   Zhang       Silong   Quan       Yuezhong   Li   来源： Journal of Materials Engineering and Performance 年份： 2023 文献类型 ： 期刊 关键词： damage   visualization   alloy   shear   mixing   invisible   nonlinear   wave   fatigue   crack   noncollinear   Titanium   coefficient  

描述： In this study, we apply the noncollinear shear wave mixing technique to detect and visualize fatigue damage in aerospace titanium alloy components that are invisible to conventional methods. This technique exploits the nonlinear interaction between two columns of shear waves and fatigue damage in titanium alloy components, which generates a sum-frequency longitudinal wave. By adjusting the relative position and separation distance of the excitation transducers and the receiving transducer in the nonlinear mixing system, we can effectively control the mixing beam in the horizontal/vertical direction of the detection position. We test the nonlinear characteristics of 63 detection points near the fatigue crack. The experimental results show that the mixing nonlinear coefficient exhibits a “step” trend when the fatigue crack is inside the titanium alloy, and the “step” width increases with the crack length. The distribution of the mixing nonlinear coefficient can be used to effectively characterize the length of invisible fatigue crack in the vertical direction. Moreover, we can visualize the fatigue damage area near the invisible fatigue crack inside the metal component by using the contour map of the normalized mixing nonlinear coefficient of 63 detection points near the fatigue crack.

Estimating Soil Carbon Sequestration of Jatropha for Sustainable Aviation Fuel Pathway

作者： Zhang   Zongwei     Li   Junqi     Wang   Zihan     Liu   Haonan     Wei   Keheng   来源： Water, Air, & Soil Pollution 年份： 2023 文献类型 ： 期刊 关键词： emission   Oil   Aviation   use   sequestration   carbon   Fuel   Sustainable   Land   Jatropha  

描述： two methods for producing Jatropha oil–based aviation kerosene (pathway 1: oil residue used for

Estimating Soil Carbon Sequestration of Jatropha for Sustainable Aviation Fuel Pathway

作者： Zhang   Zongwei     Li   Junqi     Wang   Zihan     Liu   Haonan     Wei   Keheng   来源： Water, Air, & Soil Pollution 年份： 2023 文献类型 ： 期刊 关键词： emission   Oil   Aviation   use   sequestration   carbon   Fuel   Sustainable   Land   Jatropha  

描述： two methods for producing Jatropha oil–based aviation kerosene (pathway 1: oil residue used for

Self-Adaptive-Filling Deep Convolutional Neural Network Classification Method for Mountain Vegetation Type Based on High Spatial Resolution Aerial Images

作者： Shiou   Li     Xianyun   Fei     Peilong   Chen     Zhen   Wang     Yajun   Gao     Kai   Cheng     Huilong   Wang     Yuanzhi   Zhang   来源： Remote Sensing 年份： 2023 文献类型 ： 期刊 关键词： based   image   sensing   deep   images   vegetation   learning   type   classification   aerial   remote   mountain   high   Analysis   spatial   object  

描述： The composition and structure of mountain vegetation are complex and changeable, and thus urgently require the integration of Object-Based Image Analysis (OBIA) and Deep Convolutional Neural Networks (DCNNs). However, while integration technology studies are continuing to increase, there have been few studies that have carried out the classification of mountain vegetation by combining OBIA and DCNNs, for it is difficult to obtain enough samples to trigger the potential of DCNNs for mountain vegetation type classification, especially using high-spatial-resolution remote sensing images. To address this issue, we propose a self-adaptive-filling method (SAF) to incorporate the OBIA method to improve the performance of DCNNs in mountain vegetation type classification using high-spatial-resolution aerial images. Using this method, SAF technology was employed to produce enough regular sample data for DCNNs by filling the irregular objects created by image segmenting using interior adaptive pixel blocks. Meanwhile, non-sample segmented image objects were shaped into different regular rectangular blocks via SAF. Then, the classification result was defined by voting combining the DCNN performance. Compared to traditional OBIA methods, SAF generates more samples for the DCNN and fully utilizes every single pixel of the DCNN input. We design experiments to compare them with traditional OBIA and semantic segmentation methods, such as U-net, MACU-net, and SegNeXt. The results show that our SAF-DCNN outperforms traditional OBIA in terms of accuracy and it is similar to the accuracy of the best performing method in semantic segmentation. However, it reduces the common pretzel phenomenon of semantic segmentation (black and white noise generated in classification). Overall, the SAF-based OBIA using DCNNs, which is proposed in this paper, is superior to other commonly used methods for vegetation classification in mountainous areas.

Self-Adaptive-Filling Deep Convolutional Neural Network Classification Method for Mountain Vegetation Type Based on High Spatial Resolution Aerial Images

作者： Shiou   Li     Xianyun   Fei     Peilong   Chen     Zhen   Wang     Yajun   Gao     Kai   Cheng     Huilong   Wang     Yuanzhi   Zhang   来源： Remote Sensing 年份： 2023 文献类型 ： 期刊 关键词： based   image   sensing   deep   images   vegetation   learning   type   classification   aerial   remote   mountain   high   Analysis   spatial   object  

描述： The composition and structure of mountain vegetation are complex and changeable, and thus urgently require the integration of Object-Based Image Analysis (OBIA) and Deep Convolutional Neural Networks (DCNNs). However, while integration technology studies are continuing to increase, there have been few studies that have carried out the classification of mountain vegetation by combining OBIA and DCNNs, for it is difficult to obtain enough samples to trigger the potential of DCNNs for mountain vegetation type classification, especially using high-spatial-resolution remote sensing images. To address this issue, we propose a self-adaptive-filling method (SAF) to incorporate the OBIA method to improve the performance of DCNNs in mountain vegetation type classification using high-spatial-resolution aerial images. Using this method, SAF technology was employed to produce enough regular sample data for DCNNs by filling the irregular objects created by image segmenting using interior adaptive pixel blocks. Meanwhile, non-sample segmented image objects were shaped into different regular rectangular blocks via SAF. Then, the classification result was defined by voting combining the DCNN performance. Compared to traditional OBIA methods, SAF generates more samples for the DCNN and fully utilizes every single pixel of the DCNN input. We design experiments to compare them with traditional OBIA and semantic segmentation methods, such as U-net, MACU-net, and SegNeXt. The results show that our SAF-DCNN outperforms traditional OBIA in terms of accuracy and it is similar to the accuracy of the best performing method in semantic segmentation. However, it reduces the common pretzel phenomenon of semantic segmentation (black and white noise generated in classification). Overall, the SAF-based OBIA using DCNNs, which is proposed in this paper, is superior to other commonly used methods for vegetation classification in mountainous areas.