The research and development cycle of advanced manufacturing processes traditionally requires a large investment of time and resources. Experiments can be expensive and are hence conducted on relatively small scales. This poses problems for typically data-hungry machine learning tools which could otherwise expedite the development cycle. We build upon prior work by applying conditional generative adversarial networks (GANs) to scanning electron microscope (SEM) imagery from an emerging manufacturing process, shear assisted processing and extrusion (ShAPE). We generate realistic images conditioned on temper and either experimental parameters or material properties. In doing so, we are able to integrate machine learning into the development cycle, by allowing a user to immediately visualize the microstructure that would arise from particular process parameters or properties. This work forms a technical backbone for a fundamentally new approach for understanding manufacturing processes in the absence of first-principle models. By characterizing microstructure from a topological perspective we are able to evaluate our models' ability to capture the breadth and diversity of experimental scanning electron microscope (SEM) samples. Our method is successful in capturing the visual and general microstructural features arising from the considered process, with analysis highlighting directions to further improve the topological realism of our synthetic imagery.
Designing streetscapes from street-view imagery using diffusion models
Department of Urban and Regional Planning, University of Florida（城市与区域规划系，佛罗里达大学）；Singapore-MIT Alliance for Research and Technology Centre (SMART)（新加坡-麻省理工联合研究中心（SMART））；Department of Landscape Architecture and Urban Planning, Texas A&M University（景观建筑与城市规划系，德克萨斯大学安德森分校）；Department of Agronomy, University of Florida（农业系，佛罗里达大学）
Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery
SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Wuhan University of Science and Technology（武汉科技大学）；The Hong Kong Polytechnic University（香港理工大学）；The University of Hong Kong（香港大学）
U-net super-neural segmentation and similarity calculation to realize vegetation change assessment in satellite imagery
University of Shanghai for Science and Technology（上海理工大学）；Indiana University Bloomington（印第安纳大学伯明顿分校）；Tianjin University（天津大学）；Suffolk University（萨福克大学）
