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」

AI is thriving in healthcare services, where pharmacists in community pharmacies are essential for providing drug knowledge and pharmaceutical services However, these pharmacists often spend much time manually processing prescription entries into systems, which takes away from the time they could spend on drug education, medication effectiveness tracking, and other professional services How can AI help community pharmacies to support pharmacists Tedious, time-consuming, and repetitive tasks, and AI solutions Pharmacy operations are under threat from new market dynamics and limited profit-making modes, making digital upgrades challenging for single-pharmacist community pharmacies Pharmacists, taking on multiple roles to understand the health levels of community residents, face tedious, time-consuming, and highly repetitive tasks that hinder the quality of service and make it difficult to respond to customers non-stop all year round Smart Pharmacist Assistant Service Platform Enabled by Jiankangli Technology's smart pharmacist assistant service platform's system architecture, paired with the mobile application 'Smart Good Doctor' and the backend system 'Smart Good Pharmacist', along with the integration of external development feature resources 'OCR Prescription Recognition' and 'RPA Process Automation Training Module RPA library' Primarily applied in clinics and pharmacies at the primary healthcare level, this aims to solve various operational challenges and pain points It includes using digital technology to improve work efficiency, bridging the gap between the public and medical institutions, enhancing the medical relationship, achieving better operational and manpower benefits Additionally, it enhances medication safety for the public and improves their knowledge on medications, while also reducing the daily burden on pharmacists in pharmaceutical services Smart Pharmacist Assistant Project In the current stage, the Institute for Information Industry's team is guiding the integration of pharmacy information system vendors with AI startups, focusing on the development of intelligent prescription image recognition technologies, along with drug image recognition and smart drug scheduling reminder technologies as key research areas This has led to the implementation of practical deployments in 12 community pharmacies in Greater Taipei With the help of the Taiwan Young Pharmacists Association in promoting these technologies, over 100 community pharmacy proprietors have expressed interest in adopting such technologies Once the integration of these service platform systems is complete, it will become a model for promoting AI services in Taiwan's community pharmacy pharmaceutical services「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

A brilliant idea transforming AI facial recognition technology As artificial intelligence develops, more and more industries are embracing AI technology, even subtly entering into people's lives As most bakeries sell freshly made bread and pastries, which typically do not have barcodes, they rely on cashiers to visually identify each item and enter the type and price of the bread Thus, inspired by AI facial recognition technology, if such artificial intelligence could identify hundreds of types of bread, it could enhance checkout efficiency Diverse handmade breads delight customers but challenge clerks A local bakery has over 100 types of bread, regularly updating or adding new products, offering customers a variety of choices this poses a challenge for cashiers It takes two months to train a cashier, but even after they start, there's still a 5 to 10 error rate due to bread recognition mistakes each month, especially during peak checkout times after work, causing bottlenecks and further errors due to the stress on cashiers The difficulty in training cashiers and the lack of precision in the checkout process have long troubled businesses When baking meets artificial intelligence, it sparks a marvelous retail experience In typical bakeries, bread is sold 'naked' immediately after baking and then 'packaged' when it cools to room temperature Both methods require cashiers to recognize and remember the prices and undergo two months of training before they can work the cash register Even then, there is still a 5 to 10 error rate each month My Dee Bakery, with its extensive range of over 100 bread types, poses a significant challenge for cashiers Due to Yun Kui Technology Co, Ltd's expertise in developing iPad POS systems, which are designed to be simple, convenient, and easy to use, they allow businesses to check out efficiently and accurately Therefore, integrating the existing POS system with AI image recognition capabilities enables businesses to carry out transactions more efficiently and precisely AI bread recognition model operational schematic Image provided by Yun Kui Technology The execution can be simplified into eight steps, which include 1 Data collection Take bread image data at bakeries 2 Image annotation The image data is handed over to Mu Kesi Co, Ltd for manual annotation 3 AI modeling and training Managed by Mu Kesi, who adjusts AI models and training 4 iPad POS adjustment Simultaneous adjustments of the UI interface on the POS side and backend integration with the AI model 5 Start testing Once Mu Kesi reaches over 95 recognition accuracy with current data, formal integration testing begins 6 Real scene testing Move to the bakery to gather data and verify the correct recognition rates 7 Planning real scene application accessories When recognition accuracy exceeds 98, design accessories for on-site checkout, such as remote cameras and projection light sources 8 Official Application Integration with electronic receipts goes live POS machine AI bread recognition checkout process Start recognition - Recognition complete - Checkout - Confirm checkout, takes only 3 seconds Image provided by Yun Kui Technology AI bread recognition system, making multitasking easy After adding AI capabilities, not only can it save upfront training time and costs for bakery cashiers and reduce costs from recognition errors, but it can also speed up the checkout process and efficiency, increasing customer satisfaction This can later be promoted to various retail industries, expanding the new map of smart retail Before and after comparison chart of the bread checkout process with AI valuation Image provided by Yun Kui Technology「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」