Knowledge management solution receives another level! Integrating Generative AI, Search Engine, and NLP technologies, AI Search for KM produces precise and customized responses, offering users a better experience in knowledge search and QA services. We integrate Generative AI and intelligent Search Engine for knowledge management, assisting in the digital transformation of enterprises and enhancing productivity. Solving pain points encountered by technicians and aligning with user application needs.

iCue Smart Care Assistant iCue is more than a non-contact smart sensing mat—it’s a comprehensive intelligent care solution. -Smart Sensing Mat: Installed under any mattress, it continuously monitors physiological data like respiratory rate, heart rate, body movements, and bed exits without direct contact. -Real-Time Alerts: Detects critical events such as nighttime bed exits or prolonged immobility, instantly notifying caregivers to prevent falls and pressure ulcers. -AI-Driven Insights: Generates daily and weekly reports, analyzing health trends and providing data-driven care recommendations. -Professional Support: Offers expert nursing consultation to help users and institutions interpret data and optimize care plans. -Cross-Platform Integration: Sends real-time notifications to mobile and desktop devices, ensuring caregivers and family members stay informed anytime, anywhere.

Embracing sustainability, Slimca Here introduces an ultra-thin rechargeable system compatible with USB-C. No need for wasteful disposal when the power runs out – simply recharge with ease using a standard USB-C cable.

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.

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.

Advanced machine learning algorithms process massive amounts of data collected from a building's HVAC system in real time. This technology enables a comprehensive analysis of various factors, including energy usage patterns, occupancy rates, outdoor climate conditions, and more, resulting in highly accurate HVAC system operational optimization. Our technology offers greater energy savings than traditional BMS solutions and is able to save energy while maintaining indoor comfort, allowing for quick and seamless integration into existing HVAC systems without the need for costly and time-consuming hardware upgrades.

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).

“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.

NU1, for office buildings with mobile access control system, bringing down 70% cost on wire deployment and 80% energy saving while no need for host PC from the very beginning.

Through Sensor Node, including water level sensor, rain gauge, data recorder and LPWAN transmission module, the system provides the information factors including water level , rainfall, inundation height inside and outside the water gate. Then transmit data to the back end system. The members in emergency center could do the best practices based on these crucial information

K430 is the new addition to ARBOR’s rugged handheld lineup, this Android based handheld device has keypad with numeric keys, built-in 1D/2D barcode scanner, and is equipped with a large 4850mAh battery, that will easily last for the entire working shift, suitable for warehousing, inventory, delivery and more.

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.