概述了太空原位制造和修复技术(In-situ Fabrication and Repair,ISFR)的概念和特点,并结合该技术的发展背景,介绍了国内外以选择性激光烧结和电子束熔融技术为代表的太空原位制造、空间钎焊修复、太空基地建筑构建等方向发展现状及趋...概述了太空原位制造和修复技术(In-situ Fabrication and Repair,ISFR)的概念和特点,并结合该技术的发展背景,介绍了国内外以选择性激光烧结和电子束熔融技术为代表的太空原位制造、空间钎焊修复、太空基地建筑构建等方向发展现状及趋势。结合我国空间技术事业发展,提出充分借鉴国外相关技术成果,围绕主要应用材料,重点开展空间环境适应性和应用验证研究,并探索利用模拟月壤开展太空原位制造技术研究的设想与建议。展开更多
Biometric security systems based on facial characteristics face a challenging task due to variability in the intrapersonal facial appearance of subjects traced to factors such as pose, illumination, expression and agi...Biometric security systems based on facial characteristics face a challenging task due to variability in the intrapersonal facial appearance of subjects traced to factors such as pose, illumination, expression and aging. This paper innovates as it proposes a deep learning and set-based approach to face recognition subject to aging. The images for each subject taken at various times are treated as a single set, which is then compared to sets of images belonging to other subjects. Facial features are extracted using a convolutional neural network characteristic of deep learning. Our experimental results show that set-based recognition performs better than the singleton-based approach for both face identification and face verification. We also find that by using set-based recognition, it is easier to recognize older subjects from younger ones rather than younger subjects from older ones.展开更多
文摘概述了太空原位制造和修复技术(In-situ Fabrication and Repair,ISFR)的概念和特点,并结合该技术的发展背景,介绍了国内外以选择性激光烧结和电子束熔融技术为代表的太空原位制造、空间钎焊修复、太空基地建筑构建等方向发展现状及趋势。结合我国空间技术事业发展,提出充分借鉴国外相关技术成果,围绕主要应用材料,重点开展空间环境适应性和应用验证研究,并探索利用模拟月壤开展太空原位制造技术研究的设想与建议。
文摘Biometric security systems based on facial characteristics face a challenging task due to variability in the intrapersonal facial appearance of subjects traced to factors such as pose, illumination, expression and aging. This paper innovates as it proposes a deep learning and set-based approach to face recognition subject to aging. The images for each subject taken at various times are treated as a single set, which is then compared to sets of images belonging to other subjects. Facial features are extracted using a convolutional neural network characteristic of deep learning. Our experimental results show that set-based recognition performs better than the singleton-based approach for both face identification and face verification. We also find that by using set-based recognition, it is easier to recognize older subjects from younger ones rather than younger subjects from older ones.
