• Core-tech

    Deep Reinforcement Learning in Autonomous Miniature Car Racing

    This project develops a high-performance end-to-end reinforcement learning training platform for autonomous miniature car racing. With this platform, our team won the championship of Amazon DeepRacer, a world autonomous racing competition. In addition, by combining various reinforcement learning algorithms and frameworks, our self-developed autonomous racing platform can operate at a much higher speed, surpassing the performance of Amazon DeepRacer.
    reinforcement learning artificial intelligence autonomous driving auto racing AWS
  • Environment

    Development and application of marine exploration and ecological survey technologies under climate change

    The project is to establish an AUV system capable of performing underwater exploration and ecological surveys in various shallow water areas over a long period. This system can automate the collection, analysis, and recording of coral reef ecosystems' imaging, acoustic and hydrological data in designated areas.
    Artificial Intelligence Underwater Creatures Coral Underwater Ecological Survey Marine Conservation
  • Environment

    Argicultural Literature Reading Comprehension based on Question Generation

    With the maturity of deep learning technology, reading comprehension model (given an article and a question, the AI model automatically finds the answer to the question from the article) has become a key element in natural language applications. Such as knowledge extraction and knowledge graph construction can be solved through reading comprehension model. In this project, we investigate the employment of the reading comprehension model to build an agricultural knowledge graph from Taiwan agricultural literatures. One challenge, however, is that existing reading comprehension models are not tailored for agricultural literature and therefore cannot be used directly. In this project, we leverage the question generation technology as a mechanism for agricultural data augmentation, and then train the literature reading comprehension model in the agricultural field.
    Reading Comprehension Model Question Generation Argicultural Knowledge Graph
  • Core-tech

    Snippet Policy Network: Knee-Guided Neuroevolution for Multi-Lead ECG Early Classification

    We have proposed in this project the first time series classification technique that considers accuracy, earliness, and varied lengths simultaneously, containing a novel deep reinforcement learning framework and a new multi-objective optimization neural network algorithm. The proposed technique is fit for the problem of early classification of cardiovascular diseases based on ECG signals and shown to deliver the best performance in this area, holding the leading position worldwide.
    Early Classification Multi-objective Optimization Reinforcement Learning ECG Artificial Intelligence
  • Core-tech

    A comprehensive evaluation of self-supervised speech models - SUPERB

    Machines need annotations to learn, but human babies learn human languages with almost no annotations. Can machines do the same thing? To allow machines to learn human languages with only observations like human babies, a research team at Taiwan has partnered with the speech research groups in Meta, CMU, MIT, and JHU to develop a brand new self-supervised speech processing evaluation framework, Speech Processing Universal PERformance Benchmark (SUPERB).
    Self-supervised Learning
  • Core-tech

    Advanced Technologies for Designing Trustable AI Services

    This integrated research project follows the Taiwan's 2030 Science & Technology Vision and takes LOHAS community and inclusive technology as the major research direction. We aim to develop trustable AI technologies, and introduce them to future smart services. That will realize the development of human-centric smart technology, and strengthen the governance and application of emerging technologies. The integrated project consists of 7 sub-projects led by PIs from National Taiwan University, National Tsing-Hua Universiy and Academia Sinica and composed of top AI technological teams. These sub-projects are divided into 3 clusters, including machine learning (sub-projects 1 and 2), computer vision (sub-projects 3 and 4), and human-centric computing (sub-projects 5, 6 and 7). We will deal with the issues of bias, fairness, transparency, explainability, traceability, and so on, from the aspects of data collection, technology, and application landing. Each sub-project will implement specific smart services to reflect the benefits and practical applications of the developed technologies. The NTU Joint Research Center for AI Technology and All Vista Healthcare, an AI Innovation Research Center supported by MOST, is responsible for management, planning, and execution of the integrated research project. We will propose a plan that can be generalized and applied to the intelligent service industry.
    Computer Vision Human-Centric Computing Machine Learning Natural Language Processing Trustable AI
  • Core-tech

    Data Representation and Learning for Dialogue System

    The application of voice assistants is becoming more and more popular, however, due to the inefficiency of artificial intelligence-based technology, current products are mostly built by using rules-based methods. Therefore, in this project, we would like to propose some corresponding solutions for different components of the dialogue system to improve the data efficiency and work efficiency of each component.
    Dialogue system Automatic speech recognition Natural language understanding Natural language generation Text to speech
  • Semiconductor & Manufacturing

    Advanced Machine Tools Research Center

    Tool wear and health condition monitoring during the processing: The tool wear monitoring technology developed by our researching team is specifically designed to analyze whether the tool is broken, collapsed, etc., and to estimate the remaining useful life(RUL) of the tool according to the working conditions of mass processing. By acquiring the vibration signal data with three-axis accelerometers installed on the machine tool, this technology could determine whether the current tool cutting vibration has exceeded the safety range by plotting a control chart. Once it exceeds the safe range, the current tool processing state will be assumed as abnormal. It gives users a reference to replace the broken tools immediately to prevent continuing processing, which causes vast loss such as poor quality of workpieces. In addition, this technology allows users to build models for distinct working conditions to predict the RUL of tools. It could allow users to evaluate the current health condition of tools and schedule the time to change the tool.
    Tool wear and health condition monitoring during the processing Thermal compensation technique for machine tool spindle Bearing Condition Evaluation
  • Medicine & Healthcare


    Artificial Intelligence Automatic Sputum Microscopy System AIASMS: AIASMS can automatically shoot images from sputum smear samples and identify Mycobacterium tuberculosis(MTB). AIASMS can find the most suitable depth for shooting images and then automatically shoot images under the microscope through auto-focusing. These images will be stored in the cloud database. Meanwhile, AIASMS detect whether the sputum smear contains MTB. AIASMS can accurately label the location of the TB. The sensitivity and specificity can reach 90% and 99% respectively. We also cooperate with three hospitals in the northern, central, and southern regions of Taiwan to conduct blind testing, and the sensitivity can be more than 90%. AIASMS can also distinguish between MTB and Non-tuberculous mycobacteria (NTM). Finally, n the expansion of technology, we have extended the identification of the acid-resistant staining to the identification of gram staining, so that our system can widely identify a variety of bacteria.
    Artificial Intelligence Automatic Sputum Microscopy System AIASMS Image focusing method and Auto-focusing microscopic apparatus Auto-loading slide machine
  • Environment

    Intelligent Agricultural Cultivation Support System Integrating UAV Surveillance

    This research project aims to establish an intelligent agricultural cultivation support system, by integrating unmanned aerial vehicle (UAV) surveillanceartificial intelligent (AI) analytical techniques.
    Smart Agriculture Monitoring Agricultural Disaster Assessment Land Use Monitoring
  • Smart City

    O'Intelligent Inc.

    AI-enabled Service Assurance Platform for 5G Vertical Application: The platform can help customers quickly import 5G vertical applications by providing overall customized solutions, including equipment evaluation, network deployment, application importing, network maintenance, and operation optimization. The platform can bridge the gap between the telecom industries and the vertical application industries and provide a total solution for the industries to import 5G vertical applications.
    AI-enabled Service Assurance Platform for 5G Vertical Application O'Intelligent hybrid 5G/Wi-Fi private network management system
  • Service

    Computer Vision Research Center, National Yang-Ming Chiao-Tung university

    Development of AI Platform for Smart Drone - Intelligent Flight: Due to its high mobility and the ability to fly in the sky, the drone has inspired more and more innovative applications/services in recent years. The goal of this project is to resolve the problem of blindly flying an unmanned aerial vehicle (UAV, which a drone in our case) when it is out of human sight or the range of wireless communication, and three major research and development directions will be considered in this project. Three artificial intelligence (AI) technologies, namely, smart sensing, smart control, and smart simulation, are applied in this project. Smart sensing - a flight system is developed, which can avoid the obstacles, complete a flight mission, and land safely. Smart control - an intelligence flight control system and a light-weighted somatosensory vest are developed. Smart simulation - a cost-effective training system and a 3D model simplification method are designed.
    Development of AI Platform for Smart Drone - Intelligent Flight Smart UAV technologies list Depth Estimation via Spatiotemporal Correspondence