進階篩選

Technical category
技術研發單位
    • Occlusion resistant face detectionrecognition system

      AI & IOT Application Innotech Expo Occlusion resistant face detectionrecognition system

      The system, “Occluded Resistant Face DetectionRecognition System”, contains different scale detectors for calculating face locations. Additive angular margin loss is added into the training phase for achieving high efficiencyaccuracy. The system can achieves 80 accuracy under the 50 face occluded.
    • Personalized emotion sensing for spoken dialog interface

      AI & IOT Application FutureTech Personalized emotion sensing for spoken dialog interface

      The technology is an integrated solution incorporating the retrievable personality for multimodal emotion sensing for spoken services. This framework provides a real-world flexibility that enables the estimation of the target speaker emotion states without manual personalization.
    • Intelligent Image RecognitionAnalysis System for Small-sized Insect Pest

      AI & IOT Application FutureTech Intelligent Image RecognitionAnalysis System for Small-sized Insect Pest

      This study built an automatic insect pest image identification system based on tiny Yolov3 deep learning model. By optimizing the tiny Yolov3 detection model, images of insect pests on scanned sticky paper can be automatically identified. The system achieves a testing accuracy of 0.93, 0.90 for whitefliesthrips respectively.
    • 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.
    • 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.
    • Applying Machine Learning to User Mobility Type Identification for 5th Generation Mobile Networks

      AI & IOT Application FutureTech Applying Machine Learning to User Mobility Type Identification for 5th Generation Mobile Networks

      Due to the fast development of 5G networks, it is critical to identify users service types to allocate resources intelligently. Our technology focuses on users mobility type identification by extracting practical features from users cellular information. We proposed a system architecturehave collected 700-hour data with 150 GB. By using our dataother datasets in the world, we show that our technology can achieve 95 accuracy,reduce 16 energy consumption compared to traditional methods.
    • Zero Contact Detection-Facial Stroke, Heart Rate and Breath Detection Technology

      Precision Health Ecosystem FutureTech Zero Contact Detection-Facial Stroke, Heart Rate and Breath Detection Technology

      We use features such as asymmetric expression and crooked eyes to assess the risk of facial stroke. Observing the micro vibration of the head caused by the contraction of the heart, and develop a zero-contact facial heart rate and respiration rate detection technology in conjunction with the camera. The technology can accurately measure heart rate and respiration rate in real time, thereby reducing the risk of infection. This technology has obtained two ROC patents (M590433, I689285), two US patents (HEART RATE DETECTION METHOD AND DEVICE THEREOF,MOUTH AND NOSE OCCLUDED DETECTING METHOD AND SYSTEM THEREOF). The possibility of detecting strokes through AI machine learning methods is not only accurate, but also find out signs of stroke early to grasp the best time to seek medical treatment.
    • A Fall Detection System based on AI Edge Computing Technique

      AI & IOT Application Innotech Expo A Fall Detection System based on AI Edge Computing Technique

      In order to solve the undiscovered problem that caused by the fall ofelders, we develop a deep learning based systemnamed it 「SkyEye」,which includes our own sensor 「AI Falling Image Sensor」, a cloud sever that storespushes information about fall eventsa mobile app that communicates with users.
    • AI Video Analysis and Object Retrieval System

      National Applied Research Laboratories FutureTech AI Video Analysis and Object Retrieval System

      To resolve public safety and security issues, NCHC developed intelligent video analytics and retrieval system, which can integrate the attribute analysis and recognition of vehicles/pedestrians from CCTV surveillance cameras, utilize TWCC high-speed operation in developments such as AI image semantic analysis and video synopsis technique, to effectively and rapidly search for key targets(vehicles/pedestrians) and provide short clips of the targets’ moving path simultaneously, and as a result fulfill the quick search application service of technological surveillance and Investigation.
    • 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.
    • Speaker-aware Speech Enhancement

      AI & IOT Application FutureTech Speaker-aware Speech Enhancement

      The overall system first extracts embedded speaker identity features using a neural network model, then the deep neural network speech enhancement takes the augmented features as the input to generate the enhanced spectra. With the additional embedded features, the speech enhancement system can be guided to generate the optimal output corresponding to the speaker identity.
    • Intelligent Scalp Detection System

      Precision Health Ecosystem FutureTech Intelligent Scalp Detection System

      This system uses the innovative AIoT application to target annoying scalp maintenance problemsdevelops an intelligent scalp detectionmanagement system. The core of the technology is to use deep learning-based object detection models. Tens of thousands of microscopic image data sets that are labeledtrained for scalp symptoms. As a result, the scalp image recognition module is develop, such as dandruff, hair loss, oil, and inflammation, etc. Hence this system can provide scalp detection, and maintenance effectiveness tracking functions lead scalp maintenance services to a new level of intelligent management.
    • AI 2 Robot City

      AI & IOT Application FutureTech AI 2 Robot City

      ”AI2 Robot City” is a game-based learning kit for primary and secondary school students, which combines AI image-recognition teaching tool of MIT App Inventor and the computational thinking board game named as "Robot City." Through this learning kit, users will learn to make smart cars, create image-recognition models, write and perform mobile application. Users will learn to write a program to recognize the personal cards in the board game, and furthermore to control the smart cars with blue-tooth and compete in the computational thinking board game.
    • Intelligent image-guiding needle puncture

      Precision Health Ecosystem FutureTech Intelligent image-guiding needle puncture

      Optical coherence tomography using an optical probe with a diameter of 0.9 mm, combined with the 14-18 gauge needle, is used for clinical needle puncture. Using the real-time image obtained from the tip of the needle in the tissue, the needle position can be identified. Combined with artificial intelligence can achieve objective, accurate and automatic identification of the tissue, which has been successfully verified in anesthesia and laparoscopic surgery.
    • The Application of Intelligent Agricultural Control System on Orchard

      AI & IOT Application FutureTech The Application of Intelligent Agricultural Control System on Orchard

      This project integrated with industrial foresight technologies, including UAV, artificial intelligenceimage recognition, to collect real-time images, apply algorithm in evaluation, link the technology of IOT (Internet of Things)environment sensing,use unmanned vehicle to conduct controlling work.
    • Technology of anthropomorphic robotic armintelligent grasping

      Smart machinerynovel materials FutureTech Technology of anthropomorphic robotic armintelligent grasping

      Through patented wearable sensor fusion technology, human 3D motions can be captured in real-time which allow 7-DOF robotic armdexterous robotic hand to perform anthropomorphic motiongrasping action. Integrated with eye-in-hand visual module, the adaptive gripper can intelligently decide optimal grasping strategy to ensure success grasping of arbitrary shape of objects in smart manufacturing applications.
    • Artificial Intelligent 3D Sensing Image Processing System for Array Sensing Lidar

      Smart machinerynovel materials FutureTech Artificial Intelligent 3D Sensing Image Processing System for Array Sensing Lidar

      High-accuracy 3D sensingAI image processing system for constructing high-quality immersion 3D image for AR/VR. Chaotic Lidar with APD arrayTOF sensors supports millimeter-accuracyinterference-avoiding capability. High-performance CNN processor supports high-performancelow DRAM bandwidth computations for various image AI applications.
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