60-year-old Mr Huang was admitted to the hospital due to a stroke After lying in the intensive care unit for two weeks, his condition suddenly took a turn for the worse After rescue, he was lucky enough to survive In fact, with the assistance of AI critical illness early warning technology, hospitals can detect signs and take timely and accurate medical measures 6-8 hours before a patient's heart stops, which can greatly reduce the chance of death in the hospital The deterioration of the condition is a process that evolves over time, and its subtle changes are by no means without context Previous research reports show that about 60 to 70 of inpatients who experience unexpected in-hospital cardiac arrest had symptoms 6 to 8 hours before their cardiac arrest, but only a quarter of them were recognized by clinical staff Detection and discovery, therefore, there is a need for a risk warning tool or system that can be used earlier and continuously to monitor the condition, alert medical staff to pay attention to subtle changes in the patient's condition at any time, and take timely and accurate intervention measures before the condition progresses to effectively reduce adverse events or the risk of serious adverse events Unexpected deterioration cannot be detected early Acute and severe patients often undergo unpredictable changes, and timely detection or prediction of potential acute and severe patients is an important issue The currently commonly used clinical assessment method is Modified Early Warning Score MEWS, which uses simple physiological parameter assessment including heartbeat, respiratory rate, systolic blood pressure, body temperature, urine output and state of consciousness to screen out high-risk patients, and has been proven to be predictive Patient clinical prognosis MEWS is a scoring mechanism with a single time point and a standardized formula However, the AI crisis warning system developed by Boxin Medical Electronics - Hospital Emergency and Critical Care Early Warning Index System EWS is designed to predict patient status with immediate response , collect the physiological data of patients over time for deep learning, find the best prediction model, and improve the overall accuracy Boxin Medical Electronics uses a big data analysis model to build an early warning system EWS, IoT Internet of Things and 5G communication technology, allowing medical staff to remotely monitor the physiological status of patients through communication equipment, and monitor emergency and severe cases quickly The patient's condition changes and the golden rescue period of 6-8 hours before cardiac arrest can be grasped After Boxin Medical Electronics introduces AI visual interpretation, unmanned operation can greatly reduce medical manpower The AI technology developed by Boxin Medical Electronics is the Gradient Boosting Ensemble Learning System GBELS to build an early warning system It is a learning-based EWS prediction algorithm developed by the company, which is an integrated learning Ensemble Learning and is classified as supervised learning, providing the following three functions 1 Early warning risk notification is used to analyze representative data using GBELS to provide an early risk score so that medical staff can conduct immediate clinical assessment and provide appropriate medical treatment 2 Reduce medical manpower Collect continuous physiological monitoring data, such as heartbeat, respiration, blood pressure and blood oxygen concentration, etc, to reduce the time for medical staff to write cases 3 Combine IOT logistics network and 5G communication technology to quickly transmit medical data such as monitoring parameters and imaging data, and assist medical staff to monitor changes in patients' condition remotely through communication equipment AI critical illness system monitoring to master the golden treatment period Boxin Medical Electronics stated that assessing the severity of the disease in acute and severe patients is a complex task, and patients often experience unpredictable changes Clinical medical staff often judge the condition based on their own clinical experience or intuition, which lacks science and objectivity, resulting in the inability to correctly identify and timely detect potentially acute and severe patients, resulting in or misdiagnosis leading to increased in-hospital mortality of patients The introduction of an AI early critical illness warning system can assist emergency and critical care medical staff to correctly predict the patient's condition and allow patients to receive the care they need immediately This can reduce the manpower arrangement of the emergency and critical care ward at the same time and reduce labor costs In addition, the easy-to-carry design will help the system be introduced into ambulances, home care and other places in the future, so that emergency patients can receive appropriate care earlier Other departments within the hospital can also develop new applications around this system, which can effectively accelerate the development and promotion of smart medical technology With the COVID-19 epidemic still raging in many countries around the world, this system can also help hospitals in various places to operate more effectively Caring for and monitoring the condition of critically ill patients In addition to AI critical illness warning, Boxin Medical Electronics has also developed AI image interpretation - Medical Physiological Monitor Life Cycle Compliance Testing AVS, which uses AI image interpretation technology to develop automated quality inspection of life support medical equipment The instrument solves the time-consuming problem of medical instrument testing It can reduce testing time by 70, increase the number of tests by 3 times, and effectively reduce labor costs by 50 At the same time, it is 100 compliant with regulatory requirements, and gradually solves the shortage of manpower and medical resources in the medical field , medical work overload and other issues It has now taken root in mainland China and is actively preparing for its launch in Europe It will develop towards the Japanese and American markets in the future Boxin Medical Electronics develops AI image interpretation-medical physiological monitor life cycle compliance testing AVS to solve the time-consuming problem of medical instrument testing and can reduce testing by 70 time At this stage, Boxin Medical's smart medical technology has been introduced into medical hospitals including Hsinchu MacKay, Changkei, Dongyuan General Hospital, Kaohsiung University of Technology Affiliated Hospital, Zhenxin Hospital, Hsintai Hospital, Taipei Medical University Affiliated Hospital, etc GE HealthcareInc, an internationally renowned medical materials manufacturer, and Mindray Medical, China's largest medical materials manufacturer, are both representative customers of Boxin Medical Electronics 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

Although satellite remote sensing images can make all surface objects visible, it still requires a lot of time and manpower to be truly applied to the industry In order to effectively solve the problems that customers face in digesting huge amounts of image data and eliminate technical obstacles for cross-domain users to process satellite remote sensing images, ThinkTron has developed an "AI Analysis Cloud Service Platform for Remote Sensing Big Data" as a new beginning for cross-domain AI applications for spatial information In recent years, in response to the impact of industrial globalization, Taiwan's agriculture has continued to transition towards technology-based and higher quality, improving the yield and quality of crops by solving problems, such as microclimate impacts and pest and disease control The demand of agriculture on images has expanded endlessly to accurately grasp the growing environment of crops In the early years when UAVs unmanned aerial vehicles were not yet popular, manual field surveys were the most basic but most labor-intensive work With the emergence of UAV drones, aerial photography operations might not be difficult, but the range that can be photographed is limited Furthermore, surveying expertise is required to accurately capture spatial information At this time, the use of satellite remote sensing data may break away from the past imagination of using image data Taiwan Space Agency TASA ODC data warehouse services In the past ten years, with the breakthrough of modern satellite remote sensing application technology, Digital Earth has become a new trend in global data collection Countries have developed data cube image storage technology, and the development of smart agriculture has become one of the largest image users Determining planting distribution is the first step in understanding crop yields Free satellite remote sensing images, powerful data warehousing support, and the team's robust image recognition technology are important supports for accelerating agricultural transformation Using satellite remote sensing image data can accelerate the development of smart agriculture However, in the past, it was difficult to extract large-area crop distribution through satellite remote sensing images, not to mention the cost If you wanted to use free information, you had to visit all websites of international space agencies, look through the wide variety of satellite specifications, and carefully evaluate the sensor specifications, image resolution, and revisit cycle After finding suitable images, you had to look at them one by one to filter the ones you need Next is downloading dozens of images that are often several hundreds of Megabytes MB each, which might exceed the capacity of your computer Also, after overcoming image access and preparing data, you must then start to confirm the downloaded image products and which bands you want, because the image you see is not just an image file jpg or png, but rather complex multi-spectral information, attribute fields and coordinate information It takes a lot of effort just to confirm the correct information Facing GIS software packages with complex functions is the start of another trouble The complex image pre-processing process and the inflexible machine learning package greatly reduce the efficiency of analyzing data After finally getting the results of crop identification, you might find that the best time for using map information may have already passed The above-mentioned complex and time-consuming satellite image processing problems are precisely the pain points of the market ThinkTron expanded from traditional machine learning to modern deep learning applications, and developed an "AI Analysis Cloud Service Platform for Remote Sensing Big Data" under the GeoAI framework, breaking through the constraints of details in the spatial information for customers Differences between the process before and after introducing the AI analysis cloud service platform ThinkTron said that Taiwan's ODC Open Data Cube system has been completed and began providing services after years of efforts from the Taiwan Space Agency TASA, formally becoming aligned with international trends The powerful warehousing technology allows users to easily capture and use image data of a specific time and spatial range according to their needs The warehouse stores multiple satellite image resources from international space agencies, including the ESA's Sentinel-1 one image every 6 days, Sentinel-2 one image every 6 days, USGS's Landsat-7 one image every 16 days, Landsat-8 one image every 16 days, and the domestic Formosat-2 one image every day and Formosat-5 one image every 2 days ThinkTron develops satellite image recognition tools based on Python Breaking free from the limitations of GIS Geographic Information System software packages, ThinkTron integrated GDAL Geospatial Data Abstraction Library based on Python, and considered computing efficiency and parallel processing when developing all tools required for satellite image processing and image recognition modeling, including coordinate system and data format conversion, grid and vector data interaction, and data intra-difference and normalization All of the tools are designed with AI applications in mind, and some commonly used tools are packaged into an open source package under the name TronGisPy to benefit the technical community ThinkTron utilized the team's understanding of satellite remote sensing images and the collected tagged data crop distribution maps to preset the image recognition modeling process, the required training data specifications, and dataset definitions This is imported into the machine learning LightGBM or deep learning CNN framework that was completed in advance, and the entire training process to be performed in the Web GIS interface, providing users with partial flexibility to freely filter images, confirm spatial and temporal ranges, select models, and adjust hyperparameters In addition to the operation of training models, it also provides historical models to output identification results, and finally displays the identification results of crop distribution on the Web GIS map In fact, agriculture is not the only industry that needs satellite remote sensing applications AI applications of spatial information have also appeared in various fields as companies in different industries aim to enhance their global competitiveness For example, surveying and mapping companies that have a large amount of map data can use the AI analysis cloud service platform to store map data while also accelerating the efficiency of digital mapping Under the severe global climate change and the risk of strong earthquakes, there is a wide variety industrial insurance, agricultural insurance, financial insurance, or disaster insurance are all inseparable from spatial information The use of remote sensing image recognition to understand insurance targets has long been an international trend AI Analysis Cloud Service Architecture for Remote Sensing Big Data

Green energy is the future trend and will surely lead to huge business opportunities in the future Wind power has been one of the green energy sources that have attracted global attention in recent years It will become an important force in my country's renewable energy and help Taiwan's power generation reach the goal of 20 by 2025 to improve Taiwan's energy independence As the number and power of domestic wind turbines wind turbines increases year by year, it is particularly important to ensure that the power storage equipment achieves safe, long-term performance, is not easily attenuated during charging and discharging, and is sustainable, low-carbon and environmentally friendly At the same time, the wind turbine equipment itself Health inspection, maintenance and repair have also become the focus of wind farm operators In order to meet the needs of wind farm customers, the green energy business unit of Hua Mo Industry has launched long-lasting energy storage all-vanadium redox flow battery electrolyte and wind turbine AI predictive operation and maintenance, providing 100 safety, long-term efficiency and reducing customer initial manufacturing costs cost-effective power energy storage equipment, and through AI predictive operation and maintenance services to help customers reduce power generation costs by 10 and save up to 30 in maintenance and warranty costs Hua Molybdenum Industry was established in 1998 The industry started by refining vanadium, molybdenum and rare metal elements and other products, and used them in high-end steel, professional chemicals and specialty chemicals industries, and vanadium is more like a steel-making Vitamins can increase the effectiveness of steelmaking Among them, vanadium and molybdenum related products are one of the company's main projects The company sees that the all-vanadium redox flow battery, which is 100 vanadium-based, will be a very promising mainstream green energy technology in terms of long-term energy storage in the future, and before 2010 The government has actively invited legal entities such as the Industrial Research Institute to conduct research on related component materials in solid-state batteries and all-vanadium batteries In addition, the Ministry of Economic Affairs expects renewable energy to account for 20 of power generation in 2025 and reach 15GW Based on the above Considering this, Hua Molybdenum Industry decided to devote all its efforts to research and invest in the technological development of self-developed all-vanadium redox flow battery electrolyte in 2017, in order to accelerate the compliance rate of renewable energy in 2025 Hua Molybdenum pointed out that "renewable energy power is relatively unstable, and Taiwan itself lacks lithium resources In lithium battery manufacturing, almost 80-90 of battery cells must rely on foreign procurement, and there is a lack of 100 domestic self-sufficient energy storage Resources and technology "Similarly, how does Taiwan overcome the problem of having no natural vanadium resources To this end, Hua Molybdenum Industry uses original technology to use waste catalysts from petrochemical industries such as CNPC refineries or Taishuo petrochemical processes Up to 10 of the vanadium ion content can be used to extract high-value vanadium resources, thereby producing Taiwan's 100 self-made all-vanadium redox flow battery electrolyte without being affected by resources, effectively achieving resource recycling Since 2017, Hua Molybdenum Industrial has successfully created all-vanadium flow electrolyte technology, and has successfully passed product verification by the Industrial Research Institute, the Nuclear Research Institute and many international manufacturers Taiwan’s power storage energy target is to reach 15GW in 2025 Its power distribution includes 500MW in Taipower’s automatic frequency regulation system, 500MW in E-dReg and 500MW in existing or newly built solar power plants For example, electricity consumption is mainly between 4 pm and 10 pm, which is the peak period for people's daily electricity consumption For this reason, the Energy Administration specifically requires Taipower to strengthen the upgrade of energy storage equipment, which has also driven the market's interest in all-vanadium redox flow batteries Energy storage system equipment is in high demand In addition, Taiwan's current total power reserve construction and contribution has not yet reached 100MW, and the gap from the 2025 target of 15GW of power storage is still more than 15 times Using all-vanadium redox flow batteries to successfully create 100 safe, low-carbon, environmentally friendly and long-lasting energy storage system equipment Compared with the short-term power storage of lithium batteries, the biggest advantage of all-vanadium redox flow batteries is that it is globally recognized as a long-term power reserve It can store energy for a long time up to 12 hours, which means that if it is charged for 12 hours, It can release power for 12 hours Compared with the electricity measurement method of general energy storage systems, which is daily electricity consumption power in kilowatts x time in hours, for all-vanadium redox flow batteries, power and hours are different Special design, the power is also called a stack, which is composed of four materials metal, polymer mold, carbon felt and graphite plate, and the power consumption time is calculated based on the amount of electrolyte in cubes Therefore, when the power electric push x the amount of electrolyte the daily electricity consumption of our all-vanadium redox flow battery for energy storage The product features of the all-vanadium redox flow battery energy storage system equipment include four major features safety, long-term performance, not easy to decay during charging and discharging, and sustainable, low-carbon and environmentally friendly The quality of the all-vanadium flow battery is 100 safe Since the electric energy is stored in the vanadium-containing electrolyte, it can avoid any flammable accidents caused by a fully charged energy storage system In terms of battery life, compared to the short battery life of lithium batteries, all-vanadium redox flow batteries can have a battery life of more than 20-25 years through changes in price Regarding the charge and discharge performance of energy storage, unlike lithium batteries which have a certain number of charge and discharge times 5000-600 times, there is no limit to the number of charge and discharge times of all-vanadium redox flow batteries Regarding zero carbon emissions, which is highly valued globally, unlike lithium batteries which have recycling issues, the electrolyte of the all-vanadium redox flow battery can be used permanently The material components of the stack are environmentally friendly and fully recyclable to create a truly sustainable and low-cost Carbon-friendly energy storage system Onshore wind turbine AI prediction smart operation and maintenance allows customers to reduce power generation costs by 10 and save maintenance and warranty costs by up to 30 Hua Molybdenum Industry not only improves the long-term power storage efficiency of renewable energy customers through all-vanadium redox flow battery energy storage system equipment and helps customers reduce initial purchase costs, but also uses AI smart operation and maintenance empirical calculations for offshore and onshore wind turbines Field demonstrations were drawn on Taipower's onshore wind farm, and we actively accumulated our own technical experience and energy in AI predictive operation and maintenance With the support of the AI HUB project of the Industrial Bureau of the Ministry of Economic Affairs, the cooperation site will focus on the Phase I wind farm of Taipower Corporation and provide smart operation data of wind turbines for more than 6 months for analysis The AI predictive operation and maintenance system for onshore wind turbines uses machine learning The main technology provider comes from ONYX Insight, a subsidiary of British Petroleum BP The company uses AI Hub analysis software technology to analyze the wind turbines faced by Taipower Pain point analysis, including power generation loss of road-based wind turbines and damage prediction of key components of land-based wind turbines such as gearboxes, pitch bearings under abnormal vibration three-dimensional vibration frequency or abnormal temperature, etc output Through this implementation, it can effectively help Taipower reduce power generation costs by 10, increase asset value by 12, and save up to 30 in maintenance and warranty costs In the past three years, ONYX Insight has successfully predicted and operated more than 20,000 offshore or onshore wind turbines around the world, accumulating extremely high AI model accuracy It is believed that the international partnership established with ONYX Insight will effectively guide and accelerate the green energy division of Hua Molybdenum Industry in its goal and layout to become an independent technology service provider for wind turbine AI predictive operation and maintenance Works with partner ONYX insight to provide customers with an AI predictive operation and maintenance system, including wind turbine power generation loss and damage prediction of key wind turbine components Building a solid foundation for domestic wind turbine operation and maintenance, using Taiwan as a base to expand to Southeast Asian wind farms The market output value of offshore wind turbine AI predictive operation and maintenance in Taiwan will exceed NT30 billion in the future, and the energy storage market has an output value of more than 100 billion US dollars globally In the future company vision, Hua Molybdenum Industrial hopes to become An independent technical service provider for vanadium flow battery electrolyte and wind turbine AI predictive operation and maintenance The long-term goal is to establish a local supply chain of vanadium flow battery electrolytes around the world by accumulating abundant technology and performance capital to supply industry needs nearby 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」