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.

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.

Using world-class 5G and new technologies, FET builds a "5G telemedicine platform" to provide mobile, zero-day telemedicine services.In this year, 5G telemedicine services spread across 12 counties and 33 districts. During the Covid-19 period, we have also assisted many hospitals to remote clinical services and smoothly carry out routine medical treatment. Features * Real-time Telemedicine service to empower remote area medical resource * Support medical IoT devices * Highly system integration characteristic to hospitals * Newly introduced patient function to meet the demand during the epidemic * AI intelligent analysis to assist physicians in diagnosis * Full-time video recording to ensure doctor-patient relationship and information security * Support virtual NHI card

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.

Delta's smart city solutions are based on three main axes: smart park management, smart light poles, and community security monitoring: smart park management uses a cross-system city information integration platform that handles a wide range of data transmission, aggregation, visualisation, and analysis to help achieve the goals of smart city governance. Intelligent light poles provide low-carbon lighting services in the parks, and at the same time serve as information collection and transmission nodes for the intelligent Internet of Things, while security surveillance protects people and assets in the parks and the city from infringement. Delta's smart city solutions help customers achieve a better smart city with both technology and humanity.

As the response to pandemic and epidemic prevention effort, ARBOR combined “Infrared Thermal Imager technology” with “high performance medical panel PC”, and Recognition AI, to create LK403, Portable temperature and mask detection station.

The Track Safety Management System is primarily aimed at addressing safety concerns along the track. It provides real-time monitoring, statistics, and detection-triggered alerts for trackside environments including stations, level crossings, along the track, and slope areas. The goal of this management system is to establish early warning alerts and prevent accidents from causing damage. Through the intelligent AIOT recognition monitoring system, it detects and analyzes station pedestrian traffic, tracks pedestrians at level crossings, identifies passing vehicles and traffic flow, detects foreign objects at level crossings and activates alarms, monitors personnel lingering and debris along the track, conducts rockfall monitoring on slopes with a rain gauge, monitors the environmental conditions in equipment boxes, and oversees the operation status of level crossing equipment. The monitoring data and image information are transmitted to the management system platform for integrated management via fiber-optic networks, NBIOT, or mobile communication. Additionally, it provides central management personnel with real-time interpretation and remote control in case of anomalies.

With 1.4 GW of solar power resources, it provides a convenient platform for businesses and individuals to purchase renewable energy. By integrating AI technology and an optimized supply-demand matching mechanism, InteGreen helps users quickly find the best purchase plan. Its user-friendly interface allows users to easily follow renewable energy’s unstable dynamics.

We provide a solution for integrating Internet of Things and platform systems for electric vehicle rental operators and charging station operators. This solution allows flexible configuration of management platforms and user apps, and supports third-party payment system API integrations, to create a comprehensive shared vehicle rental and charging service.

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.

***E-Paper-like : it can retain the display even after power outage. **Battery + Solar : Power backup design **power saving up to 95% : Compared to traditional LCD and LED displays *24.5” Reflective Low-Power LCD : Visible under Intense Sunlight *Secure Access Module (SAM) : Zero Trust Architecture International Standards NIST SP 800-207

The diagnosis of ADHD is based on the core symptoms appearing in DSM-IV or V criteria. In addition to the criteria, some physicians use checklists to assist the diagnosis of ADHD. However, either symptoms or checklists are subjective, which leads to diagnostic problem. In this product, data from the gyroscope and accelerometer in a smart watch were used to analyze the movements of children with ADHD. The children with ADHD and controls wore the watches on their hands simultaneously in class and compared the data from recorders for analysis. This method can help us to establish a objective tool for diagnosis and evaluation of ADHD.