進階篩選

Technical category
技術研發單位
    • AI農情調查之UAV群眾協作平台

      FutureTech AI農情調查之UAV群眾協作平台

      "The Crowdsourcing UAV Platform for AI Agricultural Survey(AIAS) provides automatic mosaicing serviceis equipped with four breakthrough technologies, including (1)massive images gridㄒding, (2)parallel computing technology, (3)UAV task specification standardization,(4)UAV task matchmaking. AIAS platform is committed to creating aerial UBER crowdsourcing services in various aspects, such as (1)crop distribution surveys, (2)large-scale lodging surveys, (3)agricultural insurance investigation, (4)surveys on illegal use of farmland,(5)surveys on the subsidy of fallow."
    • Real-time identification of crop losses using UAV imagery

      AI & IOT Application FutureTech Real-time identification of crop losses using UAV imagery

      This technology integrates 1000+ times of UAV imaging experiences with labeled rice lodging images for training. A rice lodging recognition model using deep learning reaches 90 accuracy. The recognition model can be deployed in a microcomputer mounted on UAVs to implement edge computing. While taking aerial images, the inference can be completedreveal lodging areadamage level in-time.
    • Extreme small object identification for UAV monitoring

      AI & IOT Application Innotech Expo Extreme small object identification for UAV monitoring

      We develop the new neural network architecture to improve execution speedaccuracy. The result shows that our AI detection system can run on a microcomputer in real-time, meanwhile its accuracy 90. We put that microcomputer on a dronereturn the result to smartphone App in real-time.
    • 自主無人機巡檢系統

      FutureTech 自主無人機巡檢系統

      With our autonomous UAV systems, we can effectively reduce many high-riskhigh-cost inspections which require high manpower, such as inspections of dams, river, coastline patrols, etc Operators only need to setuplaunch the system to allow the UAV to complete the designated tasks independently. The UAV will fly autonomously through visual navigation,will detect, record, measuremark the specified objects accordingly, while updating information to the satellite aerial image using spatial positioning technology, so that stakeholders can have a more comprehensive understanding on overall situation.
    • Air/ground cooperation for optimal rice harvesting model

      AI & IOT Application FutureTech Air/ground cooperation for optimal rice harvesting model

      The Air/Ground cooperation for optimal rice harvesting model is established to provide a visual harvesting decision service on a cloud platform. Drones and mobile devices are employing to estimate grain moisture and forecast the variation of harvest moisture content (HMC) in the coming days by huge amounts of imagery data, deep learning algorithms, and weather forecasts. This model can benefit in several aspects, such as setting an accurate and comprehensive optimal harvest schedule, reducing the cost of agricultural apparatus and barn ovens, ensuring the rice quality, and maximizing farmers' benefits. The potential value of the model practice could be more than a billion in Taiwan.
    • 風力及磁力驅動之壓電獵能系統研究

      FutureTech 風力及磁力驅動之壓電獵能系統研究

      This study proposes a double elastic steel sheet vibration energy harvester system (DESS VEH), where the vibration generated by the deformation and/or clap of two magnetic excited elastic steel sheets is assisted by a piezo-patch to generate electric energy. The proposed model can be applied on an unmanned helicopterany other multi-rotor drones. The downstream flows from the rotors are collecteddriving a wind-turbine to excite the vibration steel sheet(s) systemgenerating electric power.
    • 5D智慧城市─SmartES平台

      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.
    • 5D Smart City─SmartES Platform

      National Applied Research Laboratories FutureTech 5D Smart City─SmartES Platform

      NCREE has originally developed 5D digital space—on the basis of 3D city models and connections of different kinds of sensors around the world—is an online to offline virtual space with a combination of rising 5G technology advantages. Collecting and reorganizing various 3D cartographic data with Building Information Modeling (BIM), satellite imagery, UAV 3D modeling with high-resolution cameras, LiDar point cloud data, etc., can increase the diversity of the building and landscape to make the city more realistic. 5D+5G smart city platform speeds up all aspects of value-add applications of smart cities, including Intelligent Transportation System (ITS), Smart Energy (SE), and Ambient Intelligent (AmI). We expect that the 5D smart city becomes the digital twin of a real city.
    • 無人機自動飛航送餐服務

      FutureTech 無人機自動飛航送餐服務

      Drone delivery is a popularemerging application at present. However, existing drone delivery systems can only deliver to outdoor open spaces via GPS,cannot directly to the interior of recipient's building. In the era of covid-19 pandemic, we aim to reduce human contactpropose a drone delivery system that can deliver packages to the doorstepthe interior of buildings,to achieve fully automatic control of the drone by developing visual positioning technique.
    • 用於智慧生活的靜態與動態視覺關鍵技術

      FutureTech 用於智慧生活的靜態與動態視覺關鍵技術

      Dynamic vision sensors have been investigated to report motion-only images for moving object recognition. Less but essential information helps post-process recognition algorithm reduces computationimproves accuracy. Implement low powerlow latency deep Learning chip based on neuromorphic Intelligence. The neuromorphic obstacle detection algorithm integrates visualproprioceptive signals. The algorithm is characterized by its efficiencylow power consumption. We possess the next-generation in-memory computing AI chipsnext-generation UAV key softwarehardware technology
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