Huijia Health's nFOPT® non-invasive optical fiber physiological monitoring technology and new generative AI smart care system provide 24-hour safety and health management solutions for all ages from infants to the elderly. nFOPT® technology utilizes the total reflection principle of optical fiber data transmission, has no electromagnetic wave interference, and has high sensitivity and high precision physiological sensing capabilities. Combined with the generative AI system, it can actively and intelligently monitor various physiological indicators, including respiration, heart rate, pulse, sleep status, cough frequency, blood pressure, etc., and can be applied to smart cushions (such as clothing, pillows, mattresses, cushions), Smart assistive devices, smart care systems, smart driving and other scenarios.

VISION's Air Quality Detector II has been integrated into the Environmental Protection Administration's Air Quality Monitoring Network as a key sensor. Building upon practical experience and feedback, its enhanced design is adaptable for various outdoor scenarios with modular expansion capabilities. The device features PM2.5 sensing technology derived from ITRI, setting it apart as the sole Taiwanese-designed and manufactured air quality monitor in the network. Noteworthy for its exceptional sensing performance, the Air Quality Detector II employs LTE communication technology and GPS positioning. With a visually appealing design, remote maintenance management, and convenient inspection capabilities, this device serves multiple functions. Its distinctive blue and white color scheme seamlessly blends with the environment, providing a unique and aesthetically pleasing alternative to the conventional appearance of air quality monitors.

Leveraging on-site equipment, process steps, control settings, and product status, we enable abnormality detection, scheduling simulations, root cause analysis, troubleshooting, and actionable scheduling suggestions. Virtual measurement enhances MES data quality, boosting SPC alarm accuracy to 95%. By integrating MLOps and AutoML, we connect factory MES, SPC, WAT, and scheduling data to deliver precise abnormality predictions up to 8 hours in advance.

AIGym is a retraining equipment supplier that provides AI intelligent technology and digital weight modules. Through intuitive exercise measurements to create personalized exercise models, scientific training programs can be easily completed by users, and data comparisons can be made to track the results. Provides eccentric, general and ageing training modules, suitable for gyms, corporations, rehabilitation units and community centers. Rental or purchase options are available, and the software modules meet different needs.

According to the design in a 20-foot container, the noimal output power can be selected by 100/150/200 or 250 kW (optional), and the total capacity of Lithium-ion battery is 272 ~ 408 kWh (optional). The whole system, including battery, PCS and other auxiliary system. This solution can be integrated with Grid, solar PV system, Diesel genset to form a regional power system (microgrid).

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.

【Taipei Main Station Navigator APP】 Taipei Main Station covers four major railway systems: Taiwan Railway, Taiwan High Speed Rail, Taipei Metro and Taoyuan Metro. The complexity of the internal route has led Taipei Main Station to be dubbed the largest underground maze in Taiwan. Not only are outsiders prone to getting lost, but Taipeiers are not even aware of it. We launched Taipei Navi App to private the function of Indoor navigation, tasty food and shopping, intelligent parking, and push notification. It's easy for people to know where they are and find their destination 【Taipei Songshan Airport】 The Airport Terminal Building is 61,000 square meters. Managers take time to confirm position and execution status when assigning tasks and events. With this Staff Positioning and Dispatching System, managers can assign work. Employees see the location and content and can report the status of completion on the system. The manager can check whether the employee actually arrived at the given location.

The Administration for Digital Industries(ADI) is actively promoting the use of data from Civil IoT Taiwan and integrating it with other public and private sector data to develop data-driven applications and IoT solutions with commercial value. ADI assists businesses in securing international orders, achieving a total of NT$12.6 billion in revenue.

Provide EOC at all levels to disaster management, notification and response, task dispatch, evacuation and shelter, resource scheduling, search and rescue, analysis Statistics, etc. after a disaster occur. Strengthen the collaborative response and dispatch capabilities of central and local governments in large-scale disasters. Minimize the damages caused by disasters. Disaster prevention and relief data are presented in visual charts, showing the latest disaster situation in the disaster area, and can include a variety of warning messages, disaster information, monitoring information, disaster response information and other graphics layers. The graphical interface decision-making dashboard can display important commands Decision-making auxiliary information is provided to commanders at all levels for reference, and then they can issue disaster relief response instructions according to different situations, strengthen the effective use and management of resources, and increase disaster relief energy.

With the rapid growth of electric vehicles (EVs) in Taiwan, the demand for charging infrastructure is increasing. However, most developments focus on high-power DC fast charging systems, which face significant challenges. Complicated feeder application procedures and imbalances in urban power grids make it difficult to deploy medium and large-scale charging stations. While AC chargers (up to 22kW) are easier to install, they fail to meet the need for fast and efficient charging. To address these challenges, GREAT introduces the "Charging Silk Road" solution. This innovative system combines advanced energy storage systems (ESS) with fast charging equipment to enable independent power supply and reduce reliance on the public grid. By integrating renewable energy sources such as solar and wind power, the system maximizes energy efficiency and sustainability. Additionally, the solution features Vehicle-to-Grid (V2G) capability, allowing stored energy to be fed back to the grid during peak demand or emergencies, supporting grid stability. Through a distributed, multi-site deployment model based on independent microgrids, GREAT’s Charging Silk Road creates a flexible, resilient, and efficient green charging network, accelerating the transition to net-zero carbon emissions.

By leveraging AI digital twin models, we predict KPI performance to support decision-making for backup machine selection, enabling energy savings and operational efficiency. Explainable AI is used to analyze the parameters influencing KPI, revealing that air compressor efficiency is strongly correlated with its shaft bearings, highlighting the importance of predictive maintenance for these components. Additionally, an intuitive dashboard visualizes the relationship between demand and supply data, facilitating real-time decision-making and responsiveness. With the integration of LLM Agents, users can communicate seamlessly with digital twins through natural language, enhancing usability and interaction efficiency.

The Central Weather Administration (CWA) is the government agency responsible for earthquake monitoring and reporting in Taiwan. In order to reduce fatalities and property losses caused by earthquakes, a high-density real-time seismic network and the advanced earthquake early warning (EEW) system have been implemented and in operation. Currently, the CWA EEW system can issue the warning message in about 10 seconds after a strong earthquake occurring in or very near Taiwan. Starting from 2021, in order to further enhance the benefit of EEW to the practical earthquake hazard mitigation, the CWA has begun to develop the customized urban EEW system. By means of introducing the on-site earthquake early warning methods, and developing the artificial intelligence models. The systems, particularly for the big urban shallow earthquake, aim to further shorten the time of EEW to 7 seconds. That means it can give warning to the areas about 25 km away from the epicenter, and more people could be alerted before the destructive shaking.