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Distinguished Professor, Department of Computer Science and Information Engineering

Medical Image Processing, Machine Learning / Neural Networks, Infant Crying Detection and Recognition, Automated Optical Inspection (AOI), Digital Image Processing, Face detection and recognition, Intelligent Vision Inspection, Wafer Defect Inspection

Distinguished Professor, Department of Computer Science and Information Engineering

Microprocessor System, Embedded Network System, Internet of Things Application, Intelligent Manufacturing and Robotic Control

Professor, Department of Computer Science and Information Engineering

Robot Path Planning, Embedded Systems, Computer Architecture, Specializing in The Integration of Robots and Embedded Systems to Provide Machine Vision, Artificial Intelligence, Real-Time Services and Other Functions

Associate Professor, Department of Computer Science and Information Engineering

Image Processing, Machine Learning, Machine Vision, Natural Language Processing

Associate Professor, Department of Computer Science and Information Engineering

Hyperspectral Signal and Image Processing, Machine Learning, Data Compression, Information Theory, Estimation and Detection Theory, Pattern Recognition

Associate Professor, Department of Computer Science and Information Engineering

E-Health Information, Wearable Computing, Embedded System Design, Computer Network, Ekg and Gait Analysis

Assistant Professor, Department of Computer Science and Information Engineering

Wireless Communication Network, Internet of Things, Mobile Computing, AI in Autonomous Drone, AI in Self, Driving Vehicle, Computational Intelligence

Assistant Professor, Department of Computer Science and Information Engineering

Optimum Design, Artificial Intelligence, Graph Theory

Distinguished Professor, Department of Electrical Engineering

Automated Optical Inspection (AOI), Computer Vision System Design and Application, Digital Image Processing, Digital Signal Processing

Professor, Department of Electrical Engineering

Digital System Design, Measurement Signal Processing, Variable Sampling Control, Energy Conversion

Professor, Department of Electrical Engineering

Digital Signal Processing, Visual Signal Processing, Medical Image Processing

Associate Professor, Department of Electrical Engineering

Embedded Processor Firmware, Real-time Operating System Integration, Computer Vision and Graphics Applications

Distinguished Professor, Department of Electronic Engineering

Very Large-Scale Integrated Circuit Design, Computer-Aided Circuit Design, Embedded Software Design

Distinguished Professor, Department of Electronic Engineering

H.264 / H.265 Video Coding Algorithm and Chip Design, Digital and Stereoscopic TV Processing Algorithm and Chip Design

Assistant Professor, Department of Electronic Engineering

Fpga, Embedded System, Video Compression, Auto Panoramic Image System, DSP VLSI Design

Distinguished Professor, Department of Information Management

Data Mining, Data Analysis, Database System, Data Retrieval, Artificial Intelligence, Machine Learning, Cluster Computing

Assistant Professor, Department of Information Management

Smart Medical Decision Support, Big Data Analysis, Algorithms, Network Service Quality

Distinguished Professor, Department of Industrial Design

Industrial Design, Compatibility Design, Ergonomics Design, Automated Structural Design

Professor, Department of Cultural Heritage Conservation

Artefact Recognition Analysis, Artefact Cleaning Technology, Artefact Material History

Professor, Department of Business Administration

Science and Technology Innovation, Science and Technology Industry Analysis and Competition Strategy, Industry Value Chain Integration, Electronic Enterprise and E-commerce, Business Process Reengineering, Industrial Consultation


Name of Technology	Character Recognition and Defect Detection of Metal
Developer of Technology	Department of Computer Science and Information Engineering Yi-Lung Lin, Associate Professor
Summary of Technology	Casting Since the metal surface is easy to reflect light, it is not easy to identify using traditional optical character detection. This technology is used to identify the characters of several English/number symbols on the control valve. The symbols include numbers 0 to 9 and English letters A to Z. The technology that has been used includes image preprocessing, deep convolution layer, time series layer, and connectionist temporal classification layer. Considering the field application, it is designed as a lightweight model running on the mobile phone, and a set of character optical detection systems that integrate computer vision. This technology reduces the identification time and improves the accuracy of identification, which is convenient for subsequent application in product inventory management and production history filing after production is completed. Technical characteristic: 1. Digital management of products traceability 2. Reducing the working hours for field personnel to input data 3. Increasing the efficiency of the assembly and shipping process 4. Improving the production history system


Name of Technology	Application of Hyperspectral Smart Agriculture in Food Safety Taking Eggs as Examples
Developer of Technology	Department of Computer Science and Information Engineering Shih-Yu Chen, Associate Professor
Summary of Technology	This technology uses Snapshot to take a hyperspectral image of egg defects and uses a line scan camera to take a picture of egg freshness. The obtained hyperspectral image is processed and trained using an AI depth learning algorithm. In terms of prediction speed, this technology can achieve real-time identification (3.23 ms/image), and the overall accuracy of the two classifications can reach more than 95%. Technical characteristic: 1. Non-invasive: Using the non-invasive hyperspectral camera 2. Real-time: Conducting real-time analysis by AI depth learning algorithm. Accuracy can reach more than 95% and the prediction time of one image only takes 3.23ms. 3. Economize manpower: Saving the manpower of traditional manual sections. 4. Increase production: Long-running


Name of Technology	AI-based Textile Defect Inspection
Developer of Technology	Department of Computer Science and Information Engineering Chien-Chou Lin, Professor
Summary of Technology	Generative adversarial network model is first built and defect identification/location network is used to find the location of defects. The degree of automation of textile defect detection technology is significantly upgraded. An algorithm is designed to improve the accuracy of identification. The defects are located in the images and the corresponding locations of the textile and defects are recorded in real time for later examination and review. This technique is also useful in ensuring the areas of textile with defects are avoided during the cutting process. An identification accuracy of higher than 95% is achieved.


Name of Technology	Industry 4.0 Connected Intelligent Ball Screw
Developer of Technology	Department of Computer Science and Information Engineering Ching-Lung Chang, Professor
Summary of Technology	The performance of ball screws can be effectively monitored by integrating the hybrid sensor for vibration and temperature with algorithm. The user is able to monitor the condition of each work station in real time on remote web pages. Based on the results, timely maintenance can be arranged to avoid unexpected shutdown. Other benefits include high equipment availability and energy efficiency. Intelligent ball screw diagnosis: Vibration alert – Warning signals are triggered as abnormal vibration occurs in order to ensure the normal operation of the ball screws. The incident records are logged as well. Lifetime pre-diagnosis – The online pre-diagnosis enables ball screw health monitoring, facilitates maintenance arrangement and avoids emergency shutdown. Intelligent lubrication – The automatic lubrication ensures worker safety and avoids shutdown of ball screws caused by insufficient lubrication. This condition-based lubrication approach is cost effective and environmentally friendly.


Name of Technology	Agricultural Land Survey Aiot Application and Vehicle Integrated System
Developer of Technology	Bachelor Program in Interdisciplinary Studies Wang Szu-Hong, Project-Appointed Assistant Professor


Name of Technology	Vehicle Surround View Display System with Free Viewing Function (AVM)
Developer of Technology	Department of Electronic Engineering Ching-Lung Su, Associate Professor
Summary of Technology	4 optical wide-angle fisheye lenses are used to provide a panoramic view of the vehicle, showing high-resolution images of the entire vehicle from above and at any angle of view. When parking and reversing, obstacles around the vehicle can be inspected without blind spot, improving safety and protection.


Name of Technology	Indoors Gait Positioning System (Indoors GPS)
Developer of Technology	Department of Computer Science and Information Engineering Wen-Fong Wang, Associate Professor
Summary of Technology	The indoors GPS applies the cell-phone built-in sensors (accelerometer, magnetometer, gyroscope, etc.) with dead reckoning algorithms to locate users, who stay in a complex indoors separated space. This novel idea has been developed in a cell-phone user APP. This app has the functions of service initialization, indoor navigation, check-in, and more.


Name of Technology	Mowing robot
Developer of Technology	Department of Computer Science and Information Engineering Ching-Lung Chang, Professor
Summary of Technology	Embedded platform, combined with RTK-GPS, 9-axis sensor and the Kalman filter, PID control, ultrasonic sensing, image recognition and other technologies. Create a mowing robot with precise autonomous mowing and obstacle avoidance capability. Relevant technologies can be transferred to achieve automatic mowing and save manpower. It can be applied to lawn finishing in campus, parks, homes, etc.
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Name of Technology	Intelligent Trash Can Featuring Patrolling and Surveillance
Developer of Technology	Department of Electrical Engineering Chian-C. Ho, Associate Professor
Summary of Technology	Traditional trash cans are usually installed in the out-of-the-way dead corners so that people are often littering the trash anywhere because of urgency or inconvenience. This work “Intelligent Trash Can Featuring Patrolling and Surveillance” can collect the trach proactively from people so as to improve the effectiveness and efficiency of cleaning service. On the other hand, traditional security surveillance cameras are usually installed on the ceiling corners or crossways so that there are still many dead spaces and security loopholes to be monitored. This work can patrol and inspect proactively so as to improve the effectiveness and efficiency of surveillance service.

Members

Intelligent Recognition Industry Service Research Center

No. 123, Section 3, University Road, Douliu City, Yunlin County (3F-AI304/AI305,Dream Hub Building) Tel: 05-5342601 ext. 2776-2780 05-5524539

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No. 123, Section 3, University Road, Douliu City, Yunlin County (3F-AI304/AI305,Dream Hub Building)
Tel: 05-5342601 ext. 2776-2780　05-5524539