At present,the research on ferroelectric photovoltaic materials mainly focuses on photoelectric detection.In the context of the rapid development of the Internet of Things(IoT),it is particularly important to use smal...At present,the research on ferroelectric photovoltaic materials mainly focuses on photoelectric detection.In the context of the rapid development of the Internet of Things(IoT),it is particularly important to use smaller thin-film devices as sensors.In this work,an indium tin oxide/bismuth ferrite(BFO)/lanthanum nickelate device has been fabricated on an F-doped tin oxide glass substrate using the sol–gel method.The sensor can continuously output photoelectric signals with little environmental impact.Compared to other types of sensors,this photoelectric sensor has an ultra-low response time of 1.25 ms and ultra-high sensitivity.Furthermore,a material recognition system based on a BFO sensor is developed.It can effectively identify eight kinds of materials that are difficult for human eyes to distinguish.This provides new ideas and methods for developing the IoT in material identification.展开更多
As-built building information model （BIM） is an urgent need of the architecture, engineering, construction and facilities management （AEC/FM） community. However, its creation procedure is still labor-intensive and...As-built building information model （BIM） is an urgent need of the architecture, engineering, construction and facilities management （AEC/FM） community. However, its creation procedure is still labor-intensive and far from maturity. Taking advantage of prevalence of digital cameras and the development of advanced computer vision technology, the paper proposes to reconstruct a building facade and recognize its surface materials from images taken from various points of view. These can serve as initial steps towards automatic generation of as-built BIM. Specifically, 3D point clouds are generated from multiple images using structure from motion method and then segmented into planar components, which are further recognized as different structural components through knowledge based reasoning. Windows are detected through a multilayered complementary strategy by combining detection results from every semantic layer. A novel machine learning based 3D material recognition strategy is presented. Binary classifiers are trained through support vector machines. Material type at a given 3D location is predicted by all its corresponding 2D feature points. Experimental results from three existing buildings validate the proposed system.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52072041)the Beijing Natural Science Foundation(Grant No.JQ21007)the University of Chinese Academy of Sciences(Grant No.Y8540XX2D2).
文摘At present,the research on ferroelectric photovoltaic materials mainly focuses on photoelectric detection.In the context of the rapid development of the Internet of Things(IoT),it is particularly important to use smaller thin-film devices as sensors.In this work,an indium tin oxide/bismuth ferrite(BFO)/lanthanum nickelate device has been fabricated on an F-doped tin oxide glass substrate using the sol–gel method.The sensor can continuously output photoelectric signals with little environmental impact.Compared to other types of sensors,this photoelectric sensor has an ultra-low response time of 1.25 ms and ultra-high sensitivity.Furthermore,a material recognition system based on a BFO sensor is developed.It can effectively identify eight kinds of materials that are difficult for human eyes to distinguish.This provides new ideas and methods for developing the IoT in material identification.
基金supported by National Natural Science Foundation of China(No.51208425)Research Foundation of Northwestern Polytechnical University(No.JCY20130127)
文摘As-built building information model （BIM） is an urgent need of the architecture, engineering, construction and facilities management （AEC/FM） community. However, its creation procedure is still labor-intensive and far from maturity. Taking advantage of prevalence of digital cameras and the development of advanced computer vision technology, the paper proposes to reconstruct a building facade and recognize its surface materials from images taken from various points of view. These can serve as initial steps towards automatic generation of as-built BIM. Specifically, 3D point clouds are generated from multiple images using structure from motion method and then segmented into planar components, which are further recognized as different structural components through knowledge based reasoning. Windows are detected through a multilayered complementary strategy by combining detection results from every semantic layer. A novel machine learning based 3D material recognition strategy is presented. Binary classifiers are trained through support vector machines. Material type at a given 3D location is predicted by all its corresponding 2D feature points. Experimental results from three existing buildings validate the proposed system.
