Our core product, AI-Stack, is a leading AI infrastructure management platform, essential for businesses adopting AI services. It integrates third-generation GPU partitioning, GPU aggregation, multi-node computing, a new user interface, multi-cloud management, open-source deep learning tools, and environment deployment features, helping enterprises tackle the challenges brought by fast-paced AI advancements.

Currently, the prognosis of critically ill patients in the intensive care unit (ICU) is often assessed using the APACHE or SOFA scoring systems to predict mortality rates. However, these systems have been in use for over 20 years, and the data they are based on is outdated. Today, clinical data is more abundant and precise, and advancements in machine analysis capabilities have made traditional scoring systems insufficient for meeting the demands of accurate predictions and personalized medicine. To address this, our team has developed a survival prediction device for critically ill patients. Using an AI algorithm model, it utilizes clinical data such as physiological, laboratory, and examination results to predict the patient's survival rates for the next 30, 60, and 90 days, providing a more accurate reference for prognosis assessment and medical resource allocation.

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.

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.

Openfind Ciao is an AI answer engine, designed with the principles of "simple, cost-effective, and controllable". It helps businesses and organizations easily implement generative AI that all employees can use with minimal effort, even within limited budgets and resources. Ciao leverage multiple AI models, including OpenAI and Gemini, so businesses don't need to purchase separate accounts. By implementing Openfind Ciao, businesses can enable employees to access the knowledge and information they need, saving valuable time, transforming workflows, and boosting productivity.

The EUP Fleet Management System integrates ESG management, providing visualized reports and big data analysis to accelerate communication between the supply chain and transporters, ensuring effective ESG management and achieving carbon reduction goals. Through AI big data analysis, it automatically provides green ratings and compares carbon emissions with industry peers, enhancing market competitiveness and brand image. Additionally, cloud-based, digital, and automated management enables enterprises to easily monitor fleet emissions and develop carbon reduction strategies.

Enables on-premise training of medical AI models, including deep-learning and generative AI machine learning, from healthcare providers' own data lakes while ensuring data privacy, ownership, robustness, and cost-effectiveness.

The technical solution of Foxtron Electric Bus combines domestic and international resources, each contributing its strengths： 1. It adopts Foxtron's self-developed VCU software, integrates and develops the EEA architecture for electric buses, and utilizes motors, batteries, and chassis suspension systems from international major manufacturers. In collaboration with domestic manufacturers, Foxtron designs and develops dedicated low-floor bus frames for chassis cooperation. 2. In the product design of electric buses, Foxtron identifies pain points in traditional buses and improves upon them. For example, traditional buses often have issues with rollover protection and numerous blind spots, as well as differential wheel rotation caused by wheelbase, leading to frequent traffic accidents due to visibility problems. To address these issues, Foxtron employs Computer-Aided Engineering (CAE) and NVH (Noise, Vibration, and Harshness) analysis to verify the design of various systems and components. This ensures the overall strength of the vehicle body and chassis, enhances passenger safety, and incorporates features such as panoramic image display and an electronic wide-angle rearview mirror with a blind spot detection system to solve driver blind spot issues. Additionally, to alert pedestrians and other road users to the risk of differential wheel rotation, Model T projects prominent light beams onto the ground during turns, marking the danger zone.

Develop by specific specifications of power companies

LanCore Net-Zero AIoT System can be applied to aquaculture industry, using IoT technology combined with mobile APP, which can perform aquaculture aerator control, frequency conversion, environmental monitoring and data collection. It has both smart farming and energy-saving benefits. The special DC/AC control structure can directly utilize solar energy (DC) and share it with the mains (AC), automatically switching, no inverter required, and easily realizing on-site renewable energy generation. This innovative technology leads industrial upgrading and realizes green digital transformation.

With our fiber sDAS, Wave-In effectively reduce mobile signal dead spots caused by insufficient base station coverage, buildings or electromagnetic signal interference. Due to the pandemic, Taiwanese Engineers weren’t able to assist with on-site installation and coverage testing, to solve this problem and improve efficiency, Wave-In utilized Mixed Reality for collaboration and remote assistance.

1. Economic line: outdoor bullet IP cam. Model No.: AH65C6D - Successful Case: Installed in "Taoyuan Shalun Industrial Zone" for offshore. - Sony Starvis™ sensor, 2MP, 5MP, 4K resolution available for selection - Different angle lens are available. Smart IR LED range is up to 30meter. - Edge AI including person detection as virtual fence, vehicle Detection - Aluminum die-cast chassis with stainless steel screws. IP68 certification and base on project requirements to provide salt corrosion resistant treatment on casing.