Spray cooling has proved its superior heat transfer performance in removing high heat flux for ground applications. However, the dissipation of vapor liquid mixture from the heat sur- face and the closed-loop circulat...Spray cooling has proved its superior heat transfer performance in removing high heat flux for ground applications. However, the dissipation of vapor liquid mixture from the heat sur- face and the closed-loop circulation of the coolant are two challenges in reduced or zero gravity space enviromnents. In this paper, an ejected spray cooling system for space closed-loop application was proposed and the negative pressure in the ejected condenser chamber was applied to sucking the two-phase mixture from the spray chamber. Its ground experimental setup was built and exper- imental investigations on the smooth circle heat surface with a diameter of 5 mm were conducted with distilled water as the coolant spraying from a nozzle of 0.51 mm orifice diameter at the inlet temperatures of 69.2 ℃ and 78.2 ℃ under the conditions of heat flux ranging from 69.76 W/cm2 to 311.45 W/cm2, volume flow through the spray nozzle varying from 11,22 L：h to 15.76 L·h. Work performance of the spray nozzle and heat transfer performance of the spray cooling system were analyzed; results show that this ejected spray cooling system has a good heat transfer performance and provides valid foundation for space closed-loop application in the near future.展开更多
同时定位与建图(Simultaneous localization and mapping,SLAM)能够在未知环境中构建地图并为机器人提供定位信息,是移动机器人领域重要研究方向之一.当前,大多数SLAM算法在静态环境中有较好的表现,但是在车辆和行人等运动物体较多的环...同时定位与建图(Simultaneous localization and mapping,SLAM)能够在未知环境中构建地图并为机器人提供定位信息,是移动机器人领域重要研究方向之一.当前,大多数SLAM算法在静态环境中有较好的表现,但是在车辆和行人等运动物体较多的环境中,广泛存在的动态点使激光点云前后帧的配准精度不高,降低了动态场景下定位和建图的准确性.针对激光点云中存在动态点的问题,本文对SLAM的前端特征提取及后端回环检测模块分别进行改进,以去除动态点,提升SLAM在动态环境下的性能.针对SLAM前端,提出了一种分步的地面分割方法,依据点云高度信息完成地面点粗提取以矫正点云,再使用随机采样一致性方法对矫正后的点云进行精细的地面分割,最后根据高度阈值采用种子生长聚类方法提取非地面动态点,并进行特征提取与配准;针对SLAM后端,使用点云描述子替代传统方法中基于空间位置关系的回环检测方法,以减小累计误差、提高回环检测灵敏度.实验结果显示,本方法在M2DGR street_08序列数据集上较现有方法均方根误差最大降低29.8%,在KITTI04序列数据集上均方根误差最大降幅达42.7%,说明本方法能有效增强动态环境下SLAM系统的全局一致性与定位精度.展开更多
Isolated power converters have emerged as an active research topic in power integrated circuit(IC)design.Reflecting this growing interest,ISSCC 2025 has featured a dedicated session on"Isolated Power and Gate Dri...Isolated power converters have emerged as an active research topic in power integrated circuit(IC)design.Reflecting this growing interest,ISSCC 2025 has featured a dedicated session on"Isolated Power and Gate Drivers".These converters enable safe and reliable power delivery across voltage domains and are widely used in renewable energy,electric vehicles,and telecommunications.Galvanic isolation prevents surge currents and ground loop issues in harsh high-voltage environments.As demand grows for compact,efficient,and high–power-density solutions,fully integrated architectures featuring on-chip transformers are increasingly favored over traditional module-based designs,offering>5 kV isolation with a smaller footprint and lower system cost[1].展开更多
激光雷达同时定位与建图(LiDAR SLAM)技术通常适用于静态环境下,而在动态场景下,定位与建图效果会受到影响;同时,地面分割模块通常用作点云分类处理,然而地面欠分割问题会影响特征点的选择;并且,通常的框架只使用一种回环检测方法,这可...激光雷达同时定位与建图(LiDAR SLAM)技术通常适用于静态环境下,而在动态场景下,定位与建图效果会受到影响;同时,地面分割模块通常用作点云分类处理,然而地面欠分割问题会影响特征点的选择;并且,通常的框架只使用一种回环检测方法,这可能会导致漏检现象。针对上述问题,提出一种动态场景下基于地面分割与回环优化的LiDAR SLAM系统(GSLC-SLAM)。首先,利用lmnet对点云进行动态剔除,该算法将生成的距离图像与残差图像作为网络的输入,并通过SalsaNext网络预测出动态物体;其次,利用高效的gridestiamte算法进行地面分割,该算法利用不均匀网格划分的方法来减少网格的数量,从而保证分割的效率,并利用正交性、高度和平坦度这3个指标进一步筛选地面点;最后,使用由LinK3D(Linear Keypoints for Three Dimensions point cloud)描述子与BoW3D(Bag of Words for Three Dimensions point cloud)词袋构成的新回环检测方法检测回环,该方法利用边缘特征点生成描述子,使用类似于汉明距离的方式进行描述子匹配,并采用类似于词袋的方法构建BoW3D作为LinK3D描述子的数据库,从而对关键帧提取的描述子进行存储以及回环检测。在数据集KITTI(Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago)上的实验结果表明,在KITTI00、02与05序列中与Lego-Loam(Lightweight and ground-optimized LiDAR odometry and mapping)相比,GSLC-SLAM的均方根误差(RMSE)分别降低了5.8%,78.2%,12.5%;相较于F-LOAM(Fast LiDAR Odometry And Mapping),在KITTI00与05序列中GSLC-SLAM的RMSE分别降低了76.7%和53.8%,而在KITTI02序列中GSLC-SLAM表现不佳。经过验证可知,GSLC-SLAM可以有效减少动态物体的干扰、精确分割地面点并减少回环检测的漏检,进而使系统定位精度更高且更鲁棒。展开更多
基金supported by the National Natural Science Foundation of China(No.50506003)
文摘Spray cooling has proved its superior heat transfer performance in removing high heat flux for ground applications. However, the dissipation of vapor liquid mixture from the heat sur- face and the closed-loop circulation of the coolant are two challenges in reduced or zero gravity space enviromnents. In this paper, an ejected spray cooling system for space closed-loop application was proposed and the negative pressure in the ejected condenser chamber was applied to sucking the two-phase mixture from the spray chamber. Its ground experimental setup was built and exper- imental investigations on the smooth circle heat surface with a diameter of 5 mm were conducted with distilled water as the coolant spraying from a nozzle of 0.51 mm orifice diameter at the inlet temperatures of 69.2 ℃ and 78.2 ℃ under the conditions of heat flux ranging from 69.76 W/cm2 to 311.45 W/cm2, volume flow through the spray nozzle varying from 11,22 L：h to 15.76 L·h. Work performance of the spray nozzle and heat transfer performance of the spray cooling system were analyzed; results show that this ejected spray cooling system has a good heat transfer performance and provides valid foundation for space closed-loop application in the near future.
文摘同时定位与建图(Simultaneous localization and mapping,SLAM)能够在未知环境中构建地图并为机器人提供定位信息,是移动机器人领域重要研究方向之一.当前,大多数SLAM算法在静态环境中有较好的表现,但是在车辆和行人等运动物体较多的环境中,广泛存在的动态点使激光点云前后帧的配准精度不高,降低了动态场景下定位和建图的准确性.针对激光点云中存在动态点的问题,本文对SLAM的前端特征提取及后端回环检测模块分别进行改进,以去除动态点,提升SLAM在动态环境下的性能.针对SLAM前端,提出了一种分步的地面分割方法,依据点云高度信息完成地面点粗提取以矫正点云,再使用随机采样一致性方法对矫正后的点云进行精细的地面分割,最后根据高度阈值采用种子生长聚类方法提取非地面动态点,并进行特征提取与配准;针对SLAM后端,使用点云描述子替代传统方法中基于空间位置关系的回环检测方法,以减小累计误差、提高回环检测灵敏度.实验结果显示,本方法在M2DGR street_08序列数据集上较现有方法均方根误差最大降低29.8%,在KITTI04序列数据集上均方根误差最大降幅达42.7%,说明本方法能有效增强动态环境下SLAM系统的全局一致性与定位精度.
基金supported in part by the National Natural Science Foundation of China under Grant U23A20353.
文摘Isolated power converters have emerged as an active research topic in power integrated circuit(IC)design.Reflecting this growing interest,ISSCC 2025 has featured a dedicated session on"Isolated Power and Gate Drivers".These converters enable safe and reliable power delivery across voltage domains and are widely used in renewable energy,electric vehicles,and telecommunications.Galvanic isolation prevents surge currents and ground loop issues in harsh high-voltage environments.As demand grows for compact,efficient,and high–power-density solutions,fully integrated architectures featuring on-chip transformers are increasingly favored over traditional module-based designs,offering>5 kV isolation with a smaller footprint and lower system cost[1].
文摘激光雷达同时定位与建图(LiDAR SLAM)技术通常适用于静态环境下,而在动态场景下,定位与建图效果会受到影响;同时,地面分割模块通常用作点云分类处理,然而地面欠分割问题会影响特征点的选择;并且,通常的框架只使用一种回环检测方法,这可能会导致漏检现象。针对上述问题,提出一种动态场景下基于地面分割与回环优化的LiDAR SLAM系统(GSLC-SLAM)。首先,利用lmnet对点云进行动态剔除,该算法将生成的距离图像与残差图像作为网络的输入,并通过SalsaNext网络预测出动态物体;其次,利用高效的gridestiamte算法进行地面分割,该算法利用不均匀网格划分的方法来减少网格的数量,从而保证分割的效率,并利用正交性、高度和平坦度这3个指标进一步筛选地面点;最后,使用由LinK3D(Linear Keypoints for Three Dimensions point cloud)描述子与BoW3D(Bag of Words for Three Dimensions point cloud)词袋构成的新回环检测方法检测回环,该方法利用边缘特征点生成描述子,使用类似于汉明距离的方式进行描述子匹配,并采用类似于词袋的方法构建BoW3D作为LinK3D描述子的数据库,从而对关键帧提取的描述子进行存储以及回环检测。在数据集KITTI(Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago)上的实验结果表明,在KITTI00、02与05序列中与Lego-Loam(Lightweight and ground-optimized LiDAR odometry and mapping)相比,GSLC-SLAM的均方根误差(RMSE)分别降低了5.8%,78.2%,12.5%;相较于F-LOAM(Fast LiDAR Odometry And Mapping),在KITTI00与05序列中GSLC-SLAM的RMSE分别降低了76.7%和53.8%,而在KITTI02序列中GSLC-SLAM表现不佳。经过验证可知,GSLC-SLAM可以有效减少动态物体的干扰、精确分割地面点并减少回环检测的漏检,进而使系统定位精度更高且更鲁棒。
