Taiwan is known as a manufacturing powerhouse, yet quality defect detection has always been a chronic sore point in production lines While AOI equipment is available to assist, most use fixed machinery which are limited by angles, resulting in less precise diagnostics and high false positive rates Massive Digital Engineering introduced an AOI intelligent robotic arm detection system that effectively reduces false positives and increases the accuracy of defect detection Generally, the yield rate of products affects the costs for enterprises and the return rate for customers The quality defect detection process in the manufacturing industry often necessitates a substantial amount of quality inspection labor Although there is AOI equipment to assist, these tools are mostly fixed detection machines Fixed cameras are easily limited by angles, resulting in less precise diagnostics and high false positive rates Thus, personnel need to re-screen and inspect afterwards, often manually visual inspection misses defects on average about 5, and can be as high as 20 Three major pain points in manufacturing quality detection Robotic Arm AOI with dynamic multi-angle inspection helps to solve these issues According to the practical understanding by Massive Digital Engineering, there are three main pain points in detecting product quality within the manufacturing industry Pain point one, manual inspection of product quality is prone to errors Currently, the manufacturing industry largely relies on human labor to inspect product appearance, but human judgment often entails errors, such as surface scratches, color differences, solder appearance, etc The error rate in defect judgment is high, and can only be inspected at the finished product stage, often leading to whole batch rejections and high costs in labor and production Pain point two, inability to quantify and record data from quality inspections Traditional manual inspections do not maintain inspection data, which makes it difficult to assign responsibility when quality disputes occur Moreover, high-end contract manufacturing orders from overseas brands often require traceability and corresponding defect records, which traditional human inspection methods struggle to meet Pain point three, limitations of traditional AOI visual inspection systems Current manufacturing uses AOI visual inspection systems, which due to the limitations of visual software technology, employ fixed cameras, fixed lighting, and single-angle operations This method may handle flat or linear-shaped products like rectangular or square items at a single inspection point However, it is more challenging to implement for products with complex shapes eg, irregular automotive parts, requiring multi-point and multi-degree inspections Massive Digital Engineering developed an AOI intelligent robotic arm detection system, effectively improving the accuracy of defect detection To address the pain points in quality inspection in manufacturing, Massive Digital Engineering initiated the concept of developing a multi-angle, movable inspection device, starting with the combination of two representative technologies in factory automation - robotic arms and machine vision By integrating robotic arms with AOI for dynamic multi-angle AI real-time quality inspection, the limitations of fixed inspection systems are addressed, and visual inspection techniques are enhanced by leveraging artificial intelligence, further elevating the sampling of images from flat to multi-dimensional and multi-angular Selected the automotive industry as the real-world testing ground to quickly respond to customer needs The AOI intelligent robotic arm detection system, utilizing AI technology including unsupervised learning, supervised learning, and semi-supervised learning, allows operators to use unsupervised deep learning techniques to learn about good products even when initial samples are incomplete or there are no defective samples, applying it in the visual inspection of automatic welding of car trusses This can solve issues of limited angles with fixed machinery before implementation, less precise diagnostics, and high false positive rates Automotive components are high in unit price and demand a stricter defect detection accuracy In industries that have adopted AI services, the automotive manufacturing sector was chosen as the real-world testing ground Massive Digital Engineering states that the automotive industry mainly consists of related component manufacturers and components typically have a higher unit price, hence requiring more in terms of quality inspection and yield rates, and demanding stricter accuracy Therefore, the automotive sector was chosen as the area for introduction By using a robotic arm combined with AI for dynamic multi-angle AOI visual real-time quality inspection, not only can the defect quality error rate of automotive components be improved, but the fixed-point AOI optical inspection can be enhanced to meet the measurement needs of most industries and finally, establishing a third-party system platform to build an integrated monitoring system platform, enabling immediate response and action when issues arise This system allows for recording and storing important data of products leaving the factory, serving as a basis for future digital production lines and virtual production At the same time, in the event of defects, it can immediately connect to Massive's MES monitoring system, quickly responding to the relevant manufacturing decision-making department, subsequently utilizing ERP systems for project management and reviews, effectively improving production efficiency and reducing production costs Helps to reduce communication costs and aims to become an industry standard In terms of industry integration, it provides a foundational standard for data continuity among upstream and downstream businesses, reducing communication costs within the supply chain Through certification of the contract manufacturers and brand owners, there is a chance to become the industry standard configuration Through the data database established by this project, operators can further optimize their supply chain management solutions using big data analysis Data Analysis, based on data, establish forecast planning, and utilizing technology to link upstream and downstream data of the supply chain, accurately controlling product quality In the future, when interfacing with European, American, and Japanese markets, which demand highly fine-tuned orders, operators can respond and integrate the industry supply chain Supply Chain more swiftly Ultimately, through the benchmark demonstration industry's field verification, such as with the automotive component manufacturing industry used as the benchmark demonstration field, by implementing the robotic arm combined with AI for dynamic multi-angle AOI visual real-time quality inspection system project, the supply chain connection between automotive contract manufacturers and OEMs can be optimized, becoming the industry standard Further seeking more AI teams to join the cross-industry development on the field collaboration platform, driving the overall ecosystem combining AI innovation with field application Self-driving vehicle developed by Massive Digital Engineering「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

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」