Our AI server features several advanced patented functionalities: • Ensure the Privacy of Local Data: Patented trusted computing protection mechanisms ensure data security and privacy. • Protect Local AI Models from Theft: Prevents locally deployed AI models from being stolen, safeguarding corporate intellectual property. • Specialized LLM Compression Technology: Efficiently processes large AI models, enhancing performance and reducing resource requirements. Enables the use of NVIDIA A-series GPUs in place of H-series GPUs, significantly reducing hardware costs. • Reduce Enterprise Deployment Costs and Energy Consumption: For example, when training a 13B model, GPU requirements can be reduced from 7 H100 GPUs to 2 A6000 GPUs, significantly lowering costs and energy consumption. Our solution not only ensures the security and privacy of corporate data and AI models but also reduces the costs and energy consumption associated with AI deployment, making it an ideal choice for enterprise users.

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.

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.

In order to save the use of natural resources, implement resource recycling and reuse, reduce processing costs and reduce environmental load. We use the recycling processing technology to transfer the remaining cutting materials of the ALC body, as well as industrial by-products and domestic waste resources (waste glass) back to the ALC raw material for use. Actual testing of the product shows that the reuse technology is feasible, the quality is stable, and the efficiency of waste reduction and carbon reduction is high. Now It has obtained the environmental protection label from the Ministry of Environment and the green building materials label from the Ministry of Interior.

The Smart Dust Sensor technology is an innovative solution for integrating IoT networks into air quality monitoring. Developed collaboratively by the National Science and The National Science and Technology Council (NSTC), in collaboration with Taiwan Instrument Research Institute (TIRI) and Taiwan Semiconductor Research Institute (TSRI) of NARLabs, and leading academic teams in Taiwan, this technology has successfully miniaturized gas sensors while achieving low power consumption and high sensitivity. Its core features include exceptional sensitivity and stability, enabling real-time monitoring of multiple gases. Moreover, the fully domestically developed technology reduces dependence on imports and offers cost-effective sensor modules, driving the widespread adoption of air quality monitoring devices. The Smart Dust Sensor has undergone extensive field testing and third-party certifications across various applications. These include environmental monitoring stations (e.g., Taichung Dali Station), public recreational areas (e.g., odor monitoring in public restrooms), and industrial settings (e.g., semiconductor facilities and wastewater treatment plants). With its proven accuracy, reliability, and traceable data, the technology plays a key role in advancing air quality monitoring solutions, facilitating the development of smart cities, and contributing to improved environmental management.

MyGuardianAi Pro (Model:XC-211) is the world's first technology-leading product with precise fall prevention and detection accuracy. The fall detection accuracy reaches 99%, and the fall prediction accuracy reaches more than 90%. The related AI patented technology is also used in the prediction of Parkinson's disease among the elderly. MyGuardian Ai Pro adds fall prediction features to the latest patented fall system, which includes 1. The introduction of new sensors, 2. The development of innovative fall prevention algorithms, 3. The introduction of deep AI, 4. Integration of mobile apps and cloud computing, which can It reduces the risk of falls by up to 45% and is a portable emergency help device designed for the elderly to use when going out at home.

Applying our technologies portfolio to IoT system, we expand the diverse solutions with hardware, software and cloud services to custom reliable and cost-effective applications to incubate smart parks.

Integrate dynamic (static) digital map data and network map services, also improve the efficiency of public construction review.

Smart Parking System: Smart Parking Meter System, Smart Roadside Parking Charge Management Integrated System

Through Huijia Health's globally exclusive nFOPT® optical fiber monitoring technology, we have developed new products and solutions ESG AI Box (LiBox) that can be used in Industry 4.0. We plan to enter the semiconductor, petrochemical, energy, In the global market of industries such as electronic technology and machine tools, we provide ESG AI carbon reduction solutions to solve the dual-axis needs of industrial digital transformation and net-zero carbon reduction.

Acer ITS self-develop smart parking meter system provides vehicle detection, license plate recognition, auto-ticketing, multi-payment functions to achieve the goal of payment automation for roadside parking. It also provides instant parking space information so that drivers can see the parking availability through mobile APPs to avoid circling in the city that causing traffic jam and higher carbon emissions. It collects parking information through auto-ticketing system, and feeds in big data platform to produce accurate turnover rate and usage rate. These data are the base of policy making for parking authorities. Applications with automation, intellectualization and mobilization provide drivers with nice parking experiences, and enhance parking management performance for parking authorities. Let smart parking become new smart city indicator for satisfied people and high performance government.

Our team has built predictive models based on whole transcriptome data from patients with advanced metastatic prostate cancer, focusing on two different mechanisms of action: androgen receptor antagonists and androgen synthesis inhibitors. These two types of drugs essentially offer only one chance of selection, as resistance to one renders the other ineffective. Using weighted gene correlation network analysis, we identified several gene modules correlated to specific drug responses. Then, we combined several machine-learning algorithms and constructed classification models for different drug mechanisms. Our goal is to use detection services to determine the most effective drug for patients before administration.