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.

Millilab provides non-invasive and contactless vital sign detection solutions. These solutions monitor a subject's heart rate and respiration rate while ensuring privacy. The results are visualized in real-time through a GUI (Graphical User Interface). Applications include smart home appliances, intruder detection, sleep quality analysis, and more.

Five Features 1. Remote Monitoring and Visualization 2. KPI and analytics of solar power & Comparison of equipment power generation: Check the health and aging of the power station.Display equipment power generation trend and equipment status. 3. Works Management System and Report of Works Statistics: Planned maintenance management, system dispatch, automatic scheduling, and analysis of maintenance deficiencies to improve and optimize. Analyze the data and give feedback to the maintenance team to improve the system availability and optimize personnel allocation. 4. Fault Detection & Customized Reports: With data processing and AI arithmetic, this system spontaneous alert, and detailed PV array condition monitoring. Regularly inspect the power generation and income of the power station. 5. Value Added Services: PV forecast in power dispatching and revenue estimation. Assess the power generation of the sites to assist financial analysis.

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.

Nearly one-third of the population in Yunlin County are first-tier industrial workers, mainly agricultural planting and fishery breeding. Through the inventory of various key program modules of scientific and technological agriculture and through service and product design, the six-tier digital system has been completed. It has been effectively improving the first-tier Industrial productivity and filling the gap in production instability, and receiving a solution for the lack of labor and the approval of the National Development Council Plan. This will also make Yunlin a place where cases of digital transformation and creation blooming. This service is introduced into multiple towns and villages in Yunlin County and 350 jobs have been created and about 320 million production value have been increased.The project won the 2022 “Smart City Innovation Application Awards".

Taichung City has deployed 1,220 air quality sensors, utilizing IoT and AI learning, along with a geographic information system to visually present real-time sensor monitoring data.By leveraging big data analysis, pollution hotspots and peak pollution periods are identified to assist environmental agencies in smart law enforcement. Additionally, cross-department collaboration is implemented for environmental governance, employing intelligent technology management for air quality control. The integration of unmanned aerial vehicles (UAVs) further enhances pollutant sampling efficiency.

In the past, human operation and judgment were required, but now technology assists people in handling water distribution work. Set up water level or flow sensing devices in important water supply channels and transmit the collected data to the information management platform. Using online information or mobile APPs, water stewards can instantly grasp the field water distribution to achieve the goals of saving manpower and accurately allocating water. Taking the research results of the Jianan Irrigation District as an example, water stewards management can save about 20 to 25% of water by allocating irrigation water. If it is combined with Smart irrigation system, it can save another 5% of water. Currently, Water Resources Department, MOEA continues to cooperate with National Science and Technology Council, Ministry of Agriculture to gradually expand to 3,000 hectares in the Jianan Irrigation District and 3,000 hectares in the Kaohsiung Irrigation District, and gradually promote Smart irrigation and water-saving management technology.

TextileGPT is an AI platform specifically designed for the textile industry, combining TNet's historical expertise and professional knowledge to accelerate product launches, reduce development costs, and strengthen knowledge transfer. Built on the TNet knowledge base, it provides intelligent dialogue capabilities enriched with industry-specific knowledge to address various challenges. The platform supports processing multiple data formats, including PDF, PPT, DOC, XLS, and web content, and features a user-friendly interface for quick adoption. TextileGPT aims to tackle issues like talent gaps and difficulties in knowledge inheritance, driving innovation and efficiency to enhance competitiveness, making it a key enabler for the textile industry's future development.

P-Waver Inc. is the global leader of AI on-site earthquake early warning system and structural safety monitoring system, providing earthquake early warning before earthquake, intelligent disaster mitigation measures, and structural safety monitoring after earthquake.

Carbon Star is a specialized greenhouse gas (GHG) inventory management platform developed by StarTrust Systems, offering enterprises extensive carbon management capabilities. The platform encompasses organizational greenhouse gas inventories (ISO 14064-1) and product carbon footprint inventories (ISO14067), featuring the most extensive functional modules to enable enterprises to effectively gather carbon emissions data throughout their operations. 1. The platform is equipped with calculation benchmarks for carbon emissions and the latest factor data, facilitating enterprises in promptly collecting and computing data. 2. It integrates comprehensive management functions, including approval workflows and reminders, to assist various departments within enterprises in effectively managing greenhouse gas emissions. 3. Departmental access is segregated to ensure confidentiality across departments, allowing cross-departmental retrieval and referencing of only designated data. This measure guarantees the confidentiality of information from both manufacturing facilities and product domains. 4. Carbon Star automatically generates reports and emission inventories swiftly, streamlining enterprise management and verification processes. 5. Data analysis results are presented through a dashboard interface, empowering enterprises to compare and analyze data efficiently.

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.

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.