Robotiive is an AI Computer Vision powered Industrial automation (RPA) solution that builds the foundation of your smart factory. By automating manufacturing processes and workflows, upgrading your legacy equipment, and reducing human workload, it builds upon your existing systems and ensures nothing is left behind in the age of digital transformation.

The Ministry of Economic Affairs, Department of Industrial Technology has commissioned the Industrial Technology Research Institute's Center for Smart Sensing and System Technology to develop a "Composite Long-Term Air Quality Sensor." This sensor is capable of simultaneously measuring particulate matter (PM2.5), ozone (O3), carbon monoxide (CO), volatile organic compounds (VOC), and other air quality monitoring parameters. The sensor utilizes optical detection methods to detect concentrations of suspended particulate matter (PM2.5) and ozone (O3) in the air. Innovative key technologies, including dual-channel micro-particle sieving, micro-particle signal feature recognition, LED dynamic light source stability, and multi-environmental factor compensation, are employed to enhance the sensor's lifespan and accuracy. Furthermore, semiconductor and micro-electromechanical (MEMS) technologies are utilized to create a micro-heating chip. This chip, combined with gas nano-sensing materials and a single-chip sensing control circuit, converts the detected gas sensing resistance changes into carbon monoxide concentration values for the detection of carbon monoxide (CO), total volatile organic compounds (TVOC), and other harmful and odorous gas concentrations.

Millilab provides non-invasive and contactless vital sign detection solutions. These solutions monitor a subject's heart rate and respiration rate while ensuring privacy. The results are visualized in real-time through a GUI (Graphical User Interface). Applications include smart home appliances, intruder detection, sleep quality analysis, and more.

Develop by specific specifications of power companies

Most cold chain logistics vehicles currently use engine-driven compressors to power refrigeration units. This increases engine wear and fuel consumption, especially during idling when unloading. With global warming and stricter carbon reduction policies, retrofitting fuel-based cold chain vehicles has become urgent to reduce emissions and fuel costs. Our solution provides an independent power system that allows refrigeration/freezing units to operate without engine idling. It uses solar panels for supplemental energy and high-performance, safe LiFePO₄ batteries to ensure stable power. This setup reduces fuel use and lowers carbon emissions during stops. The system also offers emergency power support and features remote monitoring for real-time management and quick troubleshooting. Additionally, it is fully compatible with electric logistics vehicles, requiring no redesign. This makes it a sustainable, green solution that meets future low-carbon logistics needs.

A-MTK provides IP cam management tool named “IP Wizard III”for customers to manage a large number of IP cams quickly and easily. A-MTK provides following management functions: 1. Check the status and remaining capacity of the mSD card in the camera. 2. one-click to bring back various parameters of the camera: to facilitate backup and transfer to a new camera 3. Batch change IP cam image parameters, such as image compression, number of sheets, bitrate, etc. 4. batch change IP cam password: to facilitate regular password changes to meet information protection needs 5. A-MTK Camera can actively report log records, and at the same time provide CGI for the platform to capture log reports, and can be batch capture.

The system developed by this project provides a smart solution for station including digital fence system, platform pedestrian flow analysis, animal tracking expelled system, track water situation analysis system and platform digital information signage. The platform comprehensively analyses the image of around the station, train coordinates, passenger entry and exit, and water depth etc. Which can be used for follow up review and improvement of abnormal events such as track intrusion, crowd congestion and road flooding to achieve optimal train operation configuration and class spacing control. And provide abnormal event warning and troubleshooting and passenger intelligence information services. After the integration of multiple subsystem through the IOC platform, the system will automatically alert an alarm to the traffic control center and announce the train near the area to remind the driver to pay attention to the situation and enhance the safety of the train and the people or cars in the area when the detection system alarms.

The application of digital twin technology in building smart cities is becoming increasingly clear, serving as a crucial engine for driving urban transformation. In the development of smart cities in recent years, citizens can view the growth and development of the entire city, including population growth, new construction, and other major activities, in a 3D format, gradually emphasizing the concept of a virtual city. Digital twin technology can simulate disaster scenarios, including weather events and traffic accidents, assisting disaster response personnel in rapidly understanding the situation and optimizing the efficiency of disaster response. In recent years, with the advancement of aerial photography, simulation technology, and computing resources, The National Science and Technology Center for Disaster Reduction has leveraged advanced technology to create a mountainous flood inundation model system. This system enhances disaster warning and impact analysis, providing a comprehensive view of vulnerable settlements. It utilizes dynamic models and virtual-real integration, presenting alert information and disaster impacts through visual elements, contributing to informed decision-making in disaster situations.

ITTS Smart Logistics Solution-AI Video Control Center, from the perspective of practitioners, uses a smart management system to help the logistics company master the three major information of people, vehicles and parcel. The main features are: APP-based mobile control, Smart phones can provide anytime & anywhere control capability  Dynamics Video + image real-time monitoring, real-time conditions of vehicles moving and stopping are clearly visible  Visualization of data reports, clear operational information such as fuel consumption rate and delivery rate at a glance  AI intelligent interpretation, real-time reminder for driving violation judgment, reduce the violation rate, and provide a safe driving environment The accumulation of AI machine learning can be extended to several aspects in the future:  For drivers-through driving behavior interpretation, the driver's condition can be found immediately to prevent dangerous driving; automatic routing suggestions to help novice drivers quickly get started and reach the level of mature delivery  The city management can share real-time road conditions images or video, reflect road level and post-disaster road conditions, and street management capabilities  Provide scientific and technological law enforcement capabilities to localities-image recording of license plate recognition can assist police units in investigating cases

“Chia-E Smart Patrol Car” utilizes AI image recognition and mobile edge computing technology to automatically compare license plate recognition results with stolen vehicles’ blacklist in real-time. If a match is found, patrol officers will be notified immediately by voice warning, so that they can stop the vehicle for investigation. By implementing technology, “Chia-E Smart Patrol Car” can improve the efficiency of investigation, maintain public safety, and maximize the effectiveness of patrolling.

This exhibition is an atmospheric physical-chemical grid model for prediction of changes in air quality, utilizing data from the U.S. NCEP global weather forecast and regional air pollutant emissions. Local scientific investigation data has been used to adjust and calibrate the model and thereby largely improve the accuracy of the air quality forecast over Taiwan. The system is capable of simulating the changes in air quality over the next three days (72 hours) in Taiwan, with a temporal resolution of 1 hour and a spatial resolution of 3 km. The simulated data can be integrated with air quality data from the Civil IoT Taiwan, informing regulatory authorities the changes in air quality for the near future, and key information for formulation of control strategies

By applying environmental sensing IoT technology and mobile sensors, the coverage of sensing can be significantly enhanced. With the analysis through AI algorithms, it assists governments in identifying potential sources of pollution for regulatory actions and pollution reduction measures. Meanwhile, supplemented by generative AI innovation technology, it achieves smart environmental governance.