中尺度环流在复杂的天气环境下时常被大尺度环流掩盖,不能及时获取中尺度云团的时空密度,影响了中尺度云团三维流场可视化模拟效果,为了提升可视化模拟性能,提出基于四叉树算法(levels of detail,LOD)的中尺度云团三维流场可视化模拟分...中尺度环流在复杂的天气环境下时常被大尺度环流掩盖,不能及时获取中尺度云团的时空密度,影响了中尺度云团三维流场可视化模拟效果,为了提升可视化模拟性能,提出基于四叉树算法(levels of detail,LOD)的中尺度云团三维流场可视化模拟分析方法。分析了中尺度云团三维相对流场,从中获取了中尺度云团三维流场运动规律,并基于二维直方图取得中尺度云团三维流场特征;将粒子源与四叉树结构相结合,构成四叉树粒子系统,把取得的特征映射到系统内,利用该系统中的粒子完成中尺度云团三维流场的渲染绘制,从而实现中尺度云团三维流场的可视化模拟。实验结果表明,绘制帧率对比测试、可视化模拟速率对比测试和可视化模拟效果对比测试结果清晰度较高,可视化程度较高,实用性强、可靠性高。展开更多
起重机作为广泛应用的特种装备,运行危险性大,极易发生安全事故。为降低由操作不当引起的安全事故,国家高度重视起重机的安全培训工作。目前培训操作多为传统的示教式培训,培训成本高、效果差。虚拟现实技术具有沉浸式、交互性、多感知...起重机作为广泛应用的特种装备,运行危险性大,极易发生安全事故。为降低由操作不当引起的安全事故,国家高度重视起重机的安全培训工作。目前培训操作多为传统的示教式培训,培训成本高、效果差。虚拟现实技术具有沉浸式、交互性、多感知等优点。基于此,采用虚拟现实技术建立起重装备培训和考核系统,大幅提升工人培训效果。为还原起重机真实使用场景,采用逆向工程技术对起重机及起重机厂房进行建模;针对三维虚拟场景真实性以及模型数量巨大交互模型复用性差的问题,采用细节层次(levels of detail,LOD)模型构建起重装备几何模型,从而优化系统的真实感和实时性;利用Unity平台实现了起重装备的三维场景漫游、碰撞检测和快速导航;基于MySQL数据库,实时存储培训数据以及起重机重要参数数据;依据现有桥机平台对虚拟平台进行了验证,结果显示该起重机虚拟现实培训系统可以在降低培训成本的同时大幅提升了培训感官,实验效果良好。展开更多
本文论述了基于视点的连续 L o D算法在动态虚拟地形场景绘制中的应用。我们通过分析规则格网数字高程模型的规律性 ,推导了 3个与视点相关的判据来动态简化地形 :1图像空间误差 ;2视锥台相交性 ;3节点方向性。实验结果证明 ,该算法在...本文论述了基于视点的连续 L o D算法在动态虚拟地形场景绘制中的应用。我们通过分析规则格网数字高程模型的规律性 ,推导了 3个与视点相关的判据来动态简化地形 :1图像空间误差 ;2视锥台相交性 ;3节点方向性。实验结果证明 ,该算法在基本保持视觉效果不变的情况下 ,极大地提高了虚拟地形场景的绘制速度。展开更多
Traditional artificial neural networks （ANN） such as back-propagation neural networks （BPNN） provide good predictions of length-of-day （LOD）. However, the determination of network topology is difficult and time ...Traditional artificial neural networks （ANN） such as back-propagation neural networks （BPNN） provide good predictions of length-of-day （LOD）. However, the determination of network topology is difficult and time consuming. Therefore, we propose a new type of neural network, extreme learning machine （ELM）, to improve the efficiency of LOD predictions. Earth orientation parameters （EOP） C04 time-series provides daily values from International Earth Rotation and Reference Systems Service （IERS）, which serves as our database. First, the known predictable effects that can be described by functional models-such as the effects of solid earth, ocean tides, or seasonal atmospheric variations--are removed a priori from the C04 time-series. Only the residuals after the subtraction of a priori model from the observed LOD data （i.e., the irregular and quasi-periodic variations） are employed for training and predictions. The predicted LOD is the sum of a prior extrapolation model and the ELM predictions of the residuals. Different input patterns are discussed and compared to optimize the network solution. The prediction results are analyzed and compared with those obtained by other machine learning-based prediction methods, including BPNN, generalization regression neural networks （GRNN）, and adaptive network-based fuzzy inference systems （ANFIS）. It is shown that while achieving similar prediction accuracy, the developed method uses much less training time than other methods. Furthermore, to conduct a direct comparison with the existing prediction tech- niques, the mean-absolute-error （MAE） from the proposed method is compared with that from the EOP prediction comparison campaign （EOP PCC）. The results indicate that the accuracy of the proposed method is comparable with that of the former techniques. The implementation of the proposed method is simple.展开更多
文摘中尺度环流在复杂的天气环境下时常被大尺度环流掩盖,不能及时获取中尺度云团的时空密度,影响了中尺度云团三维流场可视化模拟效果,为了提升可视化模拟性能,提出基于四叉树算法(levels of detail,LOD)的中尺度云团三维流场可视化模拟分析方法。分析了中尺度云团三维相对流场,从中获取了中尺度云团三维流场运动规律,并基于二维直方图取得中尺度云团三维流场特征;将粒子源与四叉树结构相结合,构成四叉树粒子系统,把取得的特征映射到系统内,利用该系统中的粒子完成中尺度云团三维流场的渲染绘制,从而实现中尺度云团三维流场的可视化模拟。实验结果表明,绘制帧率对比测试、可视化模拟速率对比测试和可视化模拟效果对比测试结果清晰度较高,可视化程度较高,实用性强、可靠性高。
文摘起重机作为广泛应用的特种装备,运行危险性大,极易发生安全事故。为降低由操作不当引起的安全事故,国家高度重视起重机的安全培训工作。目前培训操作多为传统的示教式培训,培训成本高、效果差。虚拟现实技术具有沉浸式、交互性、多感知等优点。基于此,采用虚拟现实技术建立起重装备培训和考核系统,大幅提升工人培训效果。为还原起重机真实使用场景,采用逆向工程技术对起重机及起重机厂房进行建模;针对三维虚拟场景真实性以及模型数量巨大交互模型复用性差的问题,采用细节层次(levels of detail,LOD)模型构建起重装备几何模型,从而优化系统的真实感和实时性;利用Unity平台实现了起重装备的三维场景漫游、碰撞检测和快速导航;基于MySQL数据库,实时存储培训数据以及起重机重要参数数据;依据现有桥机平台对虚拟平台进行了验证,结果显示该起重机虚拟现实培训系统可以在降低培训成本的同时大幅提升了培训感官,实验效果良好。
基金supported by the West Light Foundation of the Chinese Academy of Sciences
文摘Traditional artificial neural networks （ANN） such as back-propagation neural networks （BPNN） provide good predictions of length-of-day （LOD）. However, the determination of network topology is difficult and time consuming. Therefore, we propose a new type of neural network, extreme learning machine （ELM）, to improve the efficiency of LOD predictions. Earth orientation parameters （EOP） C04 time-series provides daily values from International Earth Rotation and Reference Systems Service （IERS）, which serves as our database. First, the known predictable effects that can be described by functional models-such as the effects of solid earth, ocean tides, or seasonal atmospheric variations--are removed a priori from the C04 time-series. Only the residuals after the subtraction of a priori model from the observed LOD data （i.e., the irregular and quasi-periodic variations） are employed for training and predictions. The predicted LOD is the sum of a prior extrapolation model and the ELM predictions of the residuals. Different input patterns are discussed and compared to optimize the network solution. The prediction results are analyzed and compared with those obtained by other machine learning-based prediction methods, including BPNN, generalization regression neural networks （GRNN）, and adaptive network-based fuzzy inference systems （ANFIS）. It is shown that while achieving similar prediction accuracy, the developed method uses much less training time than other methods. Furthermore, to conduct a direct comparison with the existing prediction tech- niques, the mean-absolute-error （MAE） from the proposed method is compared with that from the EOP prediction comparison campaign （EOP PCC）. The results indicate that the accuracy of the proposed method is comparable with that of the former techniques. The implementation of the proposed method is simple.
