In 2017, the launch of the iPhone X made 3D sensor technology used in Face ID highly popular, which drove the development of VCSEL, a core component in the 3D sensor module In the detection of defects in incoming packaged VCSEL, the use of AI inference models can solve the industry's issue with low yield and improve reliability to 95 VCSEL technology currently can be used in many applications and various end consumer markets, including robots, mobile devices, surveillance, drones, and ARVR VCSELs are a good solution in applications that require high-speed modulation capabilities, such as cameras and biometrics VCSEL technology has a wide range ofnbsp applications, including in drones Pictured Zoyi Technology's Agricultural Drone VCSEL technology has a wide range of applications, AI technology assists in defect detection HRT Technology stated that the packaged VCSEL market is also facing strong price competition from competitors, and needs to further reduce costs and enhance product competitiveness One of the key problems is the replacement of glass lens with epoxy resin lens The production of traditional glass lenses has high yield, but the cost is higher than that of epoxy resin lenses Due to the cutting process of epoxy resin, the side wall of cutting lines can easily have rough edges, causing it to be oversized The release of stress caused by heat during the mounting process will directly cause the optical lens to break HRT Technology pointed out that the incoming inspection of VCSEL epoxy resin lenses is very important Under the constraints of packaging space, the space for fitting the package and optical lens is limited Moreover, the optical lenses will be confined to a metal frame If the dimensional tolerances are properly controlled, stress release due to heat during mounting can easily cause the optical lens to break, resulting in a yield loss of up to 10 in the VCSEL package reliability verification, resulting in an increase in production costs In order to solve the problems above, HRT Technology hopes to use AI to monitor the size and appearance defects of epoxy resin components in the VCSEL epoxy resin lens incoming stage, verifying whether their dimensions meet specifications, whether the cutting edges are smooth, and whether there are any defects in their appearance Since traditional incoming material inspection requires a rough visual inspection by humans to distinguish the quality The problem of image collection needs to be solved first to successfully collect image data Therefore, HRT Technology first developed an Automated Optical Inspection AOI device, which includes X, Y, Z three-axis motion, high-resolution cameras, and related control software to automatically record images After collecting the image data, opencv aligns the test image and a normal image to determine differences between the two images, and then pixel mapping is used to compare the pixel area to complete initial screening Manual labeling is carried out according to the image classification above, including samples that are normal, have defects in appearance, or have different shape characteristics, and then algorithm training and verification is carried out Residual neural network ResNet or other related algorithms are used for deep learning to identify the quality of lenses Implementation of AOI inspection improves production efficiency by 20 and above Comparing the differences before and after the implementation of AI image inspection, the incoming VCSEL lens inspection before implementation only involved manual inspection of the appearance The lens is packaged on the VCSEL package that has completed die bonding After passing the general light up test, the final reliability test high temperature reflow is performed Failed samples go into the rework process However, after the implementation of AOI inspection, it can screen defective lenses sooner and reduce the cost of subsequent materials input, it can also reduce the need for rework due to failure, improving yield to 95 and above in the reliability verification This is expected to help companies reduce production costs by 10 and increase production efficiency by 20 and above The difference before and after implementing AI image detection HRT Technology pointed out that this technology is an AI application developed based on tiny images It uses deep learning algorithms to identify defects in the images The trained network automatically classifies image data to predetermined categories Defect categories can be determined through reference images, so cumbersome programming is not required In the industrial machine vision environment, deep learning is mainly used for classification tasks in applications, such as inspection of industrial products or identification of parts In the future, with the development of IoT wearable devices and the trend of energy saving, the size of optoelectronic components will continue to shrink This technology can be applied to the detection of defects in the appearance of other tiny optoelectronic components in the future

With the increase in the elderly population, the incidence of various chronic diseases is rising daily Among these, heart failure is not only a silent killer it has a very long disease course with a high recurrence rate, leading to increased burden on healthcare personnel However, by using medically certified electrocardiography acoustics devices, coupled with AI predictive assessment of heart failure risk and remote care systems, diagnosis can be aided significantly, helping doctors make accurate diagnoses for subsequent patient medical care or referrals Heart failure has a lengthy course and medical expenditure is five times that of diabetes If you find yourself short of breath even with minimal movement, or if you wake up from sleep needing to sit up to feel comfortable, or if you have symptoms such as swollen lower limbs, anxiety, restlessness, fatigue, or a loss of appetite, be cautious These could be signs of heart failure According to statistics, there are about 60 million people with heart failure worldwide, with 5 million new cases every year In China, nearly 290 million people suffer from cardiovascular diseases, accounting for the second leading cause of death among urban residents around 12 million of these are heart failure patients, accounting for over 59 of cardiac-related deaths The disease course of heart failure is exceptionally long, and both its recurrence and rehospitalization rates are exceedingly high, resulting in medical costs that are twice that of hypertension and five times those of diabetes According to US research statistics, the 30-day mortality rates for patients with myocardial infarction and heart failure are respectively 166 and 111, and the rehospitalization rates within 30 days are 199 and 244 The symptoms of heart failure, because they are similar to those of other diseases such as chronic obstructive pulmonary disease and asthma, have an 185 misdiagnosis rate, which poses a challenging problem for healthcare institutions Leadtek, a major graphics card manufacturer, has been investing in the medical and healthcare sector since 2000 Following two heart attacks in 2011 and 2015 experienced by Chairman Lu Kunshan, Leadtek has focused on health big data, independently developing AI technology for heart failure recognition This AI application reads patients' electrocardiograms and phonocardiograms to perform anomaly detection and model prediction of heart failure risk, enabling early detection of disease symptoms Leadtek independently developed heart failure AI recognition technology to predict medical history and risk Leadtek's independently developed heart failure AI recognition technology has the following three judgment functions 1 Prediction of heart failure history Classifies electrocardiogram and phonocardiogram data into 'with hospitalization history of heart failure' and 'no history of heart failure' 2 Risk prediction of heart failure Provides a predictive risk value of heart failure occurrence based on the electrocardiogram and phonocardiogram data 3 Prediction of heart failure recurrence risk For patients with heart failure, it reads their phonocardiogram and electrocardiogram data, assessing the risk prediction of heart failure recurrence Leadtek states that the application of heart failure AI recognition technology can assist doctors in making more efficient and accurate diagnoses, facilitating subsequent medical treatment or referrals for patients As an instance, in studies of heart failure patients discharged from Taipei Veterans General Hospital, using the EMAT Electromechanical Activation Time index and SDI Systolic Dysfunction Index calculated by the synchronized electrocardiography-acoustic device as treatment guidelines resulted in a higher survival rate compared to those treated based on traditional symptoms This research has also been published in the authoritative international cardiology journal JACC, receiving recognition in the international market System manufacturers can apply heart failure AI recognition technology for other value-added applications Leadtek states that cooperating system manufacturers can choose to build their own heart failure AI risk prediction engine, uploading their system's electrocardiogram and phonocardiogram data to Leadtek's heart failure AI risk prediction engine, which then returns risk prediction values for integration by system manufacturers cooperating manufacturers as a value-added application input Not just for clinical use, the heart failure AI risk prediction engine can also be extended for use at home or in the workplace Additionally, this system can be extended to other applications, including One, hospital outpatient screening Doctors can use the electrocardiogram and phonocardiogram recorder along with the heart failure AI risk prediction model to conduct a 10-second rapid test in outpatient and emergency departments to assess a patient's cardiac history and heart failure risk Two, discharge risk assessment Doctors can use the electrocardiogram and phonocardiogram recorder along with the heart failure AI risk prediction model to assess the heart failure risk during a patient's hospital stay The test data can serve as a pre-discharge risk assessment and prognostic indicator Three, continuous home care Patients can use the electrocardiogram and phonocardiogram recorder, wearable electrocardiogram recorder, and transmit through a home transmission box gateway to measure electrocardiogram and phonocardiogram signals at home and upload them to the amor health cloud platform for heart failure AI risk prediction analysis Patients can manage their health autonomously via an APP, reviewing historical physiological trends disease management nurses can manage member health through the health management backend Web Four, home rehabilitation training Patients can wear a health bracelet to monitor activity, fatigue, circulation, and sleep, autonomously managing their health through the mobile APP and observing the risk of heart failure, engaging in exercise and rehabilitation training to aid in swift recovery The heart failure AI recognition technology system can also be extended to employee home care applications Additionally, in factories or offices, this system can also achieve employee health management goals, with applications including One, workplace safety units Provide employees with wearable electrocardiogram recorders before they start work duties Two, physiological monitoring for business executors While executing business duties or training, employees wear wearable electrocardiogram recorders for fatigue warnings, signaling whether physiological conditions allow continued execution of tasks Task segments can use data transmission boxes or apps to upload physiological monitoring information to the health management platform, assessing the heart failure risk for operations staff, with test data serving as an indicator for enterprise resource human units and public safety Three, workplace physiological monitoring center care The workplace physiological monitoring center can inspect and record employees' historicalphysiological trends through the health cloud platform Four, workplace nursing units Nursing units receiving instructions from the physiological monitoring center can provide health management advice based on employees' physiological trends nursing centers can manage employee health through the health management backend Web Five, employees can wear health bracelets to monitor activity, fatigue, circulation, and sleep, autonomously managing their health and observing the risk of heart failure through the mobile APP, engaging in exercise and rehabilitation training to aid in rapid recovery Workplace application of heart failure cloud care and big data center diagram「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

Watsons Taiwan, holding the leading position in physical chain drugstores in Taiwan, has continued to expand its digital transformation Since establishing Watsons' online store in 2014, apart from actively developing the e-commerce market, the company has significantly enhanced the online and offline OO omni-channel consumer experience by integrating Insider AI technology This integration utilizes extensive in-store sales data, consumer behavior analytics, and AI-driven personalized recommendations delivered at optimal times to increase conversion rates OO Online Plus Offline Boosts Customer Conversion Rate, Driving Business Growth Watsons Group, a global retail giant, has been deeply rooted in Taiwan for the past 30 years specializing in retail, store operation SOPs, and retail supply chain optimizations However, managing an e-commerce platform only began a few years ago Unlike the commonly discussed 'O2O' online to offline in retail, Watsons adopts 'OO', which is offline plus online Currently, about 20 of customers who order at Watsons' online store choose to pick up their goods at physical stores Proper service at these stores acts as a catalyst for converting online-originated customers into additional in-store revenues According to statistics, Watsons has nearly 6 million members with a substantial volume of transactions in physical retail outlets However, with over 12 million active app users and nearly 3 million app downloads, the level of member activation is still lacking By utilizing AI technology for data integration, such as providing optimized product recommendations through AI, Watsons could significantly enhance its customer conversion rate from offline to online consumption or guide online customers to in-store purchases, thereby driving business growth Homepage Personalized Recommendation Module Recommended for You Originally, Watsons used the e-commerce solution Hybris from the global system integrator SAP, which was more geared towards simple display and sales, lacking sufficient technical resources to handle enhancing the consumer experience Insider is a marketing technology martech company with offices in 25 cities globally, including a professional consultancy team in Taiwan that provides localized digital solutions Committed to optimizing digital marketing effectiveness with technology, Insider helps brands drive digital growth and is a partner to many domestic and global enterprises including Watsons, Carrefour, IKEA, Lenovo, Adidas, Sinyi Realty, and Singapore Airlines Insider has shown outstanding performance in improving customer conversion rates, repurchase rates, and advertising ROI through AI technology Watsons introduced Insider's AI algorithms primarily for enhancing customer experience, using AI's personalized and integrated marketing modules to elevate the customer interaction and improve e-commerce conversion rates Additionally, AI functionalities search for the right customers, expanding new customer groups and providing a superior shopping experience Page-specific Discount Code Copy Feature Recommended Based on Customer Behavior Insider has developed various technological modules that can be applied in different customer scenarios to enhance conversion rates Currently, Watsons' e-commerce websiteAPP utilizes different Insider modules, with some parts also tailored based on Watsons' unique attributes such as necessities repurchase, app navigation, and scratch card discounts, designing conversion kits or personalized recommendation modules for specific customer situations within Watsons Introduction of WebAPP Personalized Recommendation and Conversion Module Kits Effectively Increases Conversion Rates by 10 Watsons has already introduced the first four of the planned modules, with a full rollout of all five modules expected by 2021, aiming to enhance both online and offline cross-sales and thereby comprehensively improve Watsons’ overall e-commerce and retail performance 1 Web RecommendationConversion Suit 2 App RecommendationConversion Suit 3 InStory for eCommerce 4 Mobile App Template Store 5 Insider Architect Watsons has currently implemented the AT module, with completion expected by the end of 2021 Since partnering with Insider in 2020, Watsons has introduced WebAPP personalized recommendation and conversion module kits, effectively increasing transaction conversion rates by an average of over 10, with ROAS Return on Ad Spend averaging over 10 Watsons also hopes to integrate POS sales records into Insider's CDP Customer Data Platform to achieve a more optimized OO interaction mechanism and complete an all-channel consumer experience By combining Insider's AI technology, Watsons' self-operated official website, supplemented by extensive in-store sales data and member consumer behaviors, along with AI's personalized recommendations delivered at optimal points, the technology will significantly boost consumer transactions online and interactive opportunities in-store Utilizing new technologies in the competitive e-commerce sector allows Watsons to maintain a unique leadership position in the beautyhealth category in the consumers' minds「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」