Centrally manage all signing tasks online, allowing you to keep track of the contract signing process at any time. The JGB system supports mobile phones and computers, so you can sign contracts anytime and anywhere, eliminating the need for paper signings and making work time allocation more efficient. In compliance with Taiwan's "Electronic Signature Law", contract signing will strictly ensure the legal validity and security of electronic signatures.

AI智慧辦公室文件助手: 使用最先進的AI文件識別技術, 建立自動化文件處理系統, AI小幫手可替需求人員搜尋或自動生成所需要的文件。

Through 5G’s features of high speed, low latency and big connection, the 5G enterprise private network can provide highly secure and reliable network services. In order to avoid the outflow of sensitive data, enterprises can transmit data on this internal network to keep it isolated from the public network. Currently, this is being used in manufacturing, medical, transportation, and in other industries which also have demanding network quality requirements. Chunghwa Telecom’s self- developed "5G intelligent A +" private network service adopts independent core network technology and can connect to both the N78 public network and the N79 private network at the same time. It also has a number of world-leading exclusive functions, including the ability to determine whether data can enter the enterprise intranet based on conditions such as serial number, IP, and website address. It can also adjust bandwidth and data access rights based on person, time, and location. Combined with cloud and a variety of other application services, the CHT 5G private network enables enterprises to establish a complete network solution.

The imbalance of agricultural production, sales and labor shortage have always been a huge challenge for farmers. The independent 5G private network with O-RAN effectively introduces affordable smart agricultural system through a leasing model, combining AI, unmanned vehicles, image recognition and other field information to upgrade smart farm production management system and increase the revenue.

With the rapid growth of electric vehicles (EVs) in Taiwan, the demand for charging infrastructure is increasing. However, most developments focus on high-power DC fast charging systems, which face significant challenges. Complicated feeder application procedures and imbalances in urban power grids make it difficult to deploy medium and large-scale charging stations. While AC chargers (up to 22kW) are easier to install, they fail to meet the need for fast and efficient charging. To address these challenges, GREAT introduces the "Charging Silk Road" solution. This innovative system combines advanced energy storage systems (ESS) with fast charging equipment to enable independent power supply and reduce reliance on the public grid. By integrating renewable energy sources such as solar and wind power, the system maximizes energy efficiency and sustainability. Additionally, the solution features Vehicle-to-Grid (V2G) capability, allowing stored energy to be fed back to the grid during peak demand or emergencies, supporting grid stability. Through a distributed, multi-site deployment model based on independent microgrids, GREAT’s Charging Silk Road creates a flexible, resilient, and efficient green charging network, accelerating the transition to net-zero carbon emissions.

This project uses the 阡陌(QianMo) System, which drawing the road profiles, markings, signs, and signals based on the road geography data as a universal base map, to integrate cross-domain digital applications. Besides, 阡陌(QianMo) System has developed an application on mobile phone, through this application, user can complete the case opening, drawing, conversation recording, and photo-taking operations on-site, and convert the accident cases into collision elements in accordance with time, vehicle type, direction, collision type, and number of casualties. On the other hands, when handling improvements for accident-prone intersections, 阡陌(QianMo) System can overlay the elements to implement the collision composition analysis serves as a scientific proof and visualization tool for researching traffic improvement measures and explaining accident risk hotspots. Besides, after the subsequent engineering improvements, user can provide revision of road geography data as the feedback. Through this mechanism, which can achieve a digital governance cycle. Finally, through 阡陌(QianMo) System , this project integrates cross-domain applications related to police administration, transportation bureau, and engineering industry to improve driving environments and prevent traffic accidents.

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.

The device incorporates cutting-edge Edge control technology and is compatible with various sensors. • Independent Operation The device can operate independently even when powered off or disconnected from the network, ensuring continuous operation and functionality. • Remote Firmware Update and Configuration It allows for remote updating of firmware and configuration settings over the network, enabling easy maintenance and optimization of the device. • Data Storage and Transmission The device is equipped with capabilities for storing and transmitting data, facilitating the collection and transfer of information as needed. • LED Indicators The device is equipped with LED indicators for power and communication status, providing visual feedback on its operational status. 1.Model No: AIOT-C01 Fog Detection Controller Application Scope: Mountain Roads, Railways, Highway, Airport, etc 2.Model No: AIOT-C02 Rainfall Detection Controller Application Scope: Mountainous Area Water Level and Rainfall.

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.

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 system can be connected to IoT devices in series, and the devices can be operated and monitored remotely. The corresponding data of the devices is available on the website to meet the living needs of tenants and landlords.

Vehicle Classification enables transportation authorities to automatically identify and classify vehicles according to their visual attributes. This technology finds widespread application in traffic management systems, toll collection, parking facilities, and security setups. FETCi leverages AI algorithms and machine learning methodologies to analyze video data collected through LiDAR technology. With an extensive database and repository, FETCi's AI AVC proficiently sorts vehicles into distinct categories, including cars, trucks, motorcycles, buses, and more. By integrating LiDAR technology with AI-powered image recognition, FETCi establishes a vehicle recognition system equipped with self-enhancement and deep learning capabilities.