airaTrack is a comprehensive personnel tracking system that utilizes AI algorithms developed by our team to extract features from both the "face and figure." It generates unique biometric IDs with a recognition accuracy of up to 99%, all without the need for registering facial data, addressing privacy concerns. Capable of cross-camera searches and matching 10,000 faces per second, it significantly enhances the efficiency of traditional manual matching processes and increases search efficiency and accuracy by 97% enabling automatic path tracking of personnel in the field. The system also generates investigation reports for evidential preservation.

Smart parking EV charger combines smart parking and charge service to provide total solution for roadside and off street parking. Integrates with license plate recognition system, it not only issue parking ticket, but also reduce occupation of fossil fuel vehicle and prolong parking with parking space barrier or other solutions. With smart card, the drivers do not require membership for charge and parking service.

The Autonomous AI Surveillance Drone is a major innovation in drone technology, combining multirotor and fixed-wing capabilities. Utilizing white noise principles to enhance lift and efficiency, it enables high-speed flight and wide-area coverage, making it ideal for disaster response, border patrol, and other critical missions. Its core AI algorithm ensures reliability through real-time error detection. If anomalies are detected, the drone activates a safety mode, automatically returning or landing to minimize risks and maintain mission continuity. The flight control system integrates AI chips and edge AI technology, enhancing processing speed while reducing energy consumption for extended, efficient operation. These advancements enable stable autonomous flight even in complex environments.

Our AI server features several advanced patented functionalities: • Ensure the Privacy of Local Data: Patented trusted computing protection mechanisms ensure data security and privacy. • Protect Local AI Models from Theft: Prevents locally deployed AI models from being stolen, safeguarding corporate intellectual property. • Specialized LLM Compression Technology: Efficiently processes large AI models, enhancing performance and reducing resource requirements. Enables the use of NVIDIA A-series GPUs in place of H-series GPUs, significantly reducing hardware costs. • Reduce Enterprise Deployment Costs and Energy Consumption: For example, when training a 13B model, GPU requirements can be reduced from 7 H100 GPUs to 2 A6000 GPUs, significantly lowering costs and energy consumption. Our solution not only ensures the security and privacy of corporate data and AI models but also reduces the costs and energy consumption associated with AI deployment, making it an ideal choice for enterprise users.

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.

-) Prognostics and Health Management -) AIR & Expert Assist -) AIR Design Builder -) AIR Quality Check -) AIR APPs in Cloud

ASUS Ultrasound Imaging System is a software based ultrasound solution enabling qualified and trained healthcare professionals to visualize anatomical structures and fluid. Easy-to use interface and handheld availability meet requirements of use in clinical purposes and home/ community healthcare environment. (1)Imaging Modes:B mode, M mode, Color Flow, Power Doppler, PW Doppler. (2)Probes: Linear, Convex, MicroConvex, Phased Array, Endocavity. (3)Battery continuously operating time up to 4 hours.

The need of video surveillance has increased with the issues of campus, public places security and intelligent transportation. What’s more, high-resolution image, identification, analysis and AIoT applications are getting popular than before. People are strict in the performance of transmission and computing. Internet communication technology has entered a new era as well. To achieve intelligent service, zMEC ’s platform as service through Kubernetes-based architecture which can not only integrate with the third-party but also expand different AIoT software application modules, such as identification and analysis, electronic fence. Besides, the technology of edge computing can reduce the demand of video and audio data for cloud center computing access and network bandwidth resources, so as to make up for the problem of traditional cloud computing delay and unsafe transmission link. It meets the need of high frequency bandwidth and low latency, and to satisfy the needs of information security and privacy protection.

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.

1. Integrating data of water quality and water consumption, the online system will automatically conduct continuous data monitoring, and can be applied for both clean water and sewage water measurement. 2. Real-time monitoring: Seize real-time water information to maintain the stability of water quality and instantly react when abnormal condition is detected, dramatically increasing management efficiency. 3. Smart Dosing Scenario: Integrating automatic dosing equipments with upper and lower alarm settings to ensure dynamic stability of water quality and decrease labor cost. 4. Data Analysis: Apply schedule planning to build up the trend of historical changes and to develop the situation management model. 5. Compliance with the regulations: Complete the full-time management mode, in compliance with the regulations of the competent authority and environmental protection standards. 6. The water quality sensor (specific model) is automatically cleaned without additional consumables and can be cleaned frequently.

NCREE has developed a Hybrid earthquake early warning system (EEWS) by integrating on-site EEWS from NCREE and regional EEWS from Central Weather Administration (CWA). Within a 10 to 100-kilometer radius from the earthquake epicenter in the local region, this system can provide 3 to 15 seconds of warning time. To achieve effective alerts with the available warning time, the utilization of a Hybrid EEWS cloud platform is crucial. This platform facilitates the transmission of alerts and the affected administrative regions to various industries. Industries, in turn, leverage these alerts to develop warning systems and automated control devices tailored to meet the specific requirements of different environments. This approach aims to minimize secondary damages resulting from earthquakes, transforming EEWS beyond being notifications received Presidential Alert on mobile phones. Instead, it offers a multi-faceted alerting and disaster reduction application system tailored to different environmental settings.