進階篩選

Technical category
    • (test)Application of inorganic nanofiber technology to promote the development of biotechnology

      Smart machinerynovel materials FutureTech (test)Application of inorganic nanofiber technology to promote the development of biotechnology

      Inorganic porous nanofibers with surfaceinterface defects are prepared through humidity-controlled electrospinninghigh-temperature annealing technology. Under the irradiation of light sources of different wavelengths (380~780 nm), the bound electrons stored in the valence band can be excited to the conduction band to form free electrons on the surface of the material, generating different intensities of microcurrents, light sensitivitymicrocurrent changes. Because the "inorganic nanofiber" technology has high uniquenesshigh product compatibility, it can be applied to a wide range of markets.
    • 5D智慧城市─SmartES平台

      AI & IOT Application FutureTech 5D智慧城市─SmartES平台

      NCREE has originally developed 5D digital space—on the basis of 3D city modelsconnections of different kinds of sensors around the world— is an online to offline virtual space with a combination of rising 5G technology advantages. Collectingreorganizing various 3D cartographic data with Building Information Modeling (BIM), satellite imagery, UAV 3D modeling, LiDar point cloud data, etc., can increase the diversity of the buildinglandscape. 5D+5G smart city platform would accelerate the 5D smart city to become the digital twin of a real city.
    • 基於深度學習之異常檢測

      FutureTech 基於深度學習之異常檢測

      "For video anomaly detection, we apply pretrained models to obtain the foregroundthe optical flow as ground truth. Then our model estimates the information by taking only a single frame as input. For human behaviors, we take the human poses as inputuse a GCN-based model to predict the future poses. Both the anomaly scores of these two works are given by the error of the estimation. For defect detection, our model takes patches of the image as inputlearns to extract features. The anomaly score of each patch is given by the distance between the patchall the training patches."