An incident of wastewater treatment audit at a home electroplating factory was the driving force for Xie Wenbin, the general manager of WoLong Intelligence Environmental Company, to venture into the environmental engineering field Leaving behind the envied position of senior engineer at TSMC, he embarked on this entrepreneurial journey with a mission to create a better environment for Taiwan and future generations Facing erratic climate changes causing droughts and floods, Xie utilized artificial intelligence AI technology to become a pioneer in water resource protection Observations on Taiwan's rainfall trends over the past half-century indicate that due to intensifying climate change, the drought cycles in Taiwan have shortened from 17 years to between 3 and 5 years In April 2021, Taiwan experienced its worst water shortage in over half a century Subsequently, central and southern Taiwan suffered from floods caused by heavy rainfall, making water resource protection a critical issue in today's society Xie Wenbin, formerly a chief engineer at TSMC responsible for water treatment, achieved remarkable results in a water-saving event organized by the Water Resources Agency and Hsinchu Science Park in 2015, where TSMC ranked first During his tenure at the Environment and Development Foundation, he undertook the Industrial Bureau's project on enhancing water efficiency in industries, advising over 300 Taiwanese companies AI Adoption in Water Resource Applications Aims for Early Warning and Forecasting Goals Since its inception less than six months ago, WoLong Intelligence Environmental Company has focused on government projects and SMEs for IoT setups or AI intelligence adoption in wastewater and sewage treatment, aiming to achieve early warning and decision-making goals to enhance water resource efficiency There are three main themes of smart AIoT applications predictive and decision-making water treatment systems, optimization of water treatment operation protocols, and smart water treatment management platforms These are the core businesses of WoLong Intelligence Scope and Benefits of AIpoint Water Treatment AI Intelligence System Xie Wenbin noted that the Industrial Bureau supervises 66 wastewater treatment plants, plus tens of thousands of regulated wastewater treatment facilities commonly facing issues with their chemical coagulation systems due to inaccurate and untimely human control and feedback-based chemical dosing, leading to unstable water quality Large amounts of PAC and Polymer are used unnecessarily, producing a significant amount of sludge Current testing methods contain errors, and there is a need to establish a more comprehensive AI testing data database to support and assist in setting dosing standards The integration of big data with patented AI models enhances the efficiency and precision control of wastewater pollution prevention systems Adopting AIpoint Precision Dosing System Achieves Emission Reduction and System Longevity The adoption of WoLong Intelligence's AIpoint precision dosing intelligent system can achieve the following benefits Reduce manual operation Reduce the amount of chemicals used Reduce sludge production Reduce carbon emissions Save energy Extend system life System automatic prevention and maintenance Lower conductivity levels AI Water Resource Service Models and Processes Indeed, wastewater and sewage plants vary in their degree of digitalization Therefore, Xie Wenbin categorizes clients and sets up IoT devices and softwarehardware systems for those not yet digitalized for those already equipped with IoT, AI technologies are introduced to solve specific problems and enhance the efficiency of wastewater treatment 'Not all customer issues need to be resolved with AI, and we never use AI for the sake of using AI,' says Xie Wenbin, who assesses and implements based on the actual needs of the customers With his extensive experience in water treatment and the addition of AI experts, factories only need to provide water quality data for AI prediction and decision-making In practice, some factories have stringent cybersecurity requirements, so AIpoint's smart cloud platform can directly connect the data collection hardware to the factory site, and the data is not stored in the cloud The system automatically categorizes and filters algorithms to find the most suitable model, which is then connected to the system end to generate predictions and decisions Additionally, the cloud platform is secured with blockchain encryption, and the factory end follows the same steps for rapid integration Also, the backend monitoring system can assist in early warning for water treatment or water recovery systems, including sensor fault prevention, automatic protection, and retraining of backend models Projects typically last about three to six months, after which the model is adjusted based on the system's condition This part is offered as a subscription service combined with after-sales service WoLong Intelligence Environmental Company also sets short, medium, and long-term goals In the short term, it aims to maintain integrity and establish a strong brand identity in the medium term, it plans to build an ecosystem with hardware partners and collectively expand the market and ultimately, it hopes to export its services internationally Financially, Xie Wenbin is looking to actively seek angel investment, targeting NT20 million to achieve the company's goal of sustainable operation Founder and General Manager of WoLong Intelligence Environmental Company, Xie Wenbin「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

After the COVID-19 pandemic, the push for digital transformation using digital tools has accelerated across all industries However, for business owners, the question arises Is it worth implementing AI What benefits does it bring to the company In fact, there are many AutoML platforms currently available that help businesses speed up the introduction of AI and build AI models, simplifying the adoption of AI for companies Businesses face significant challenges in adopting AI, and automated machine learning platforms offer solutions Jia Heng Technology's General Manager Liang Baifeng stated that businesses face challenges such as scarcity of talent, data handling, timing of modeling, integration with production, technology mastery, and cost efficiency when adopting AI Nevertheless, not every process needs to incorporate AI technology What businesses need are AI custom solutions that meet business requirements Thus, AutoML is a core tool for businesses applying AI technology Previously, to build 100 AI models, 100 modeling experts were needed With AutoML, only a few data scientists are required to build 100 models Once AI models are established, they can be integrated into business production processes Thus, complex application scenarios can be addressed through highly customized modeling to meet client demands In the process of enterprise AI adoption, it used to rely heavily on AI experts, but in the future, it will be driven by industry experts, focusing on solving real business application scenarios as the key to success Liang Baifeng thinks there are four key phases One, Scenario Selection Deciding whether machine learning is the right approach for solving the problem Two, Data Preparation Data is just material Choosing the 'right' and 'effective' data is crucial Three, Model Building Focus on the efficiency of model design, a combination of multiple models is necessary to solve problems Four, Production Integration The model meets the restrictions of production while maintaining flexibility based on production conditions To address the issues of diverse business scenarios, high implementation hurdles, long cycle times, and high costs faced by traditional AI model design, it is essential to utilize AutoML technology to create an automated platform, effectively resolving the developmental and implementation challenges of AI DarwinML's Four Core Technologies help enterprises start from scratch in model design Developed by Jia Heng Technology, DarwinML is an AutoML platform for designing AI machine learning models based on genetic evolution theory DarwinML uses an evolutionary approach to automatically design and optimize machine learning and deep learning models, featuring excellent capabilities in model generation and hyperparameter optimization, starting from 'zero' to design models automatically The four core technologies of DarwinML are described as follows One, Model Gene Bank Collects a large number of algorithms and basic modules that can be applied to Deep Learning, Machine Learning, and Data Feature Extraction Two, Auto-evolution Algorithm Utilizes genetic algorithms, model interpretative statistical methods, and reinforcement learning techniques In the continuous model evolution, it enhances model quality Three, Complete Model Lifecycle Management Uses DarwinML and Darwin Inference to build a closed ecosystem for model generation, use, and re-optimization DarwinML significantly shortens the modeling time, and efficiency is markedly improved In the traditional model design process, originally from data feature extraction, model design, model training to parameter adjustment, it took AI engineers 3-6 months to manually model However, using DarwinML for automatic modeling can shorten it to 3-7 days, significantly reducing time and markedly improving efficiency DarwinML can automatically generate models and rule sets based on objectives, with modules possessing self-evolving capabilities Its core technologies include machinedeep learningmodel gene banks, model evolutionary design algorithms, and big data parallel computing technology, among others, yielding significant benefits such as One, Data organization, data labeling, and data cleaning are semi-automated, reducing dependency on the workload and volume of labels by 40 Two, Machine learning modeling time is reduced to minutes, with a modeling capacity 5-10 higher than traditional modeling Three, Deep learning modeling time is reduced to hours, achieving a standard consistent with the industry's best models but more straightforward and faster This article is organized from selected content of the 'AI Engineering Online Meetup'「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

What is AutoML automated machine learning and how is it different from ML traditional data analysis It needs further clarification first Traditional machine learning must go through data cleaning, data pre-processing, feature engineering, feature selection, algorithm selection, model establishment, model training, parameter adjustment, and then evaluation results to produce model applications During the process, if there is a problem with the parameters, the algorithm must be re-selected, the model must be re-established, etc, and the process must be repeated hundreds of times If new information becomes available, all steps must be repeated Through automated machine learning, the output process of model application only needs to go through the automation of four major steps data cleaning, feature engineering, data modeling and model evaluation to achieve model application Even if new data needs to be collected, it can be achieved through Automated machine learning is achieved, saving time and effort Comparison between ML and AutoML Source Action Bagel Co, Ltd AutoML is a program that can automate the time-consuming and repetitive work in machine learning model development This allows small and medium-sized enterprises that relatively lack AI talents to create their own customized machine learning models In recent years, major international companies have rushed into this market, including Cloud AutoML released by Google in 2018, and AutoPilot launched by cloud computing leader AWS in 2019 AutoML has become a standard feature of mainstream learning services, from web-based interfaces to free Program development and workflow visual management, etc, service development is becoming more and more diversified MoBagel is a professional team composed of top data scientists, engineers, and product project managers The team members come from prestigious universities around the world, including Stanford, Berkeley, Oxford, and National Taiwan University in the United States They also have experience in Selected to participate in Silicon Valley's well-known accelerator 500 Startup, selected to participate in Japan's SoftBank Innovation Program, and also won a name in Nokia's Open Innovation Challenge Mobile Bagel Decanter AI platform shortens the analysis project from two months to two days Mobile specializes in data science and machine learning technology In 2016, it developed the automated machine learning analysis tool Decanter AI So far, it has helped more than 100 companies introduce AI into important decisions, and the analysis project has been shortened from two months to Two days The fields served include retail, telecommunications, manufacturing, finance and other industries Lin Yushen, deputy general manager of Action Bagel Co, Ltd, said that Decanter AI makes AI as simple and efficient as Excel, which can improve enterprise data analysis productivity Users do not need to have in-depth professional knowledge and experience Through a simple super-operating interface, they can perform automated machine learning for data analysis and prediction There are three simple steps to use Decanter AI Step 1 Organize the data into csv format Step 2 Upload to DecanterAI to set prediction goals Step 3 Decanter AI automatically models and obtains prediction results The deployment method can be in the public cloud or in the private cloud of the enterprise After the internal data is uploaded, it can be modeled and used DecanterAI uses three steps, simple and convenient The advantage of AutoML is that it can automatically train a large number of models, adjust parameters, produce the best model, and quickly deploy and import it After the new coronavirus COVID-19 epidemic, all walks of life are facing new market changes and must Transform digitally with fast and convenient digital tools In recent years, Action Bagel has continued to promote the optimization of the DecanterAI platform and establish industrial data modeling and analysis capabilities, and has produced substantial results For example, Chunghwa Telecom uses its platform to conduct blind tests on code-carrying customers and perform data analysis to effectively reduce user churn rates and improve customer retention rates As a leading domestic food manufacturer, due to the expiration date of drinks and the production and sales of the cold chain, it must be fully integrated to reduce inventory and loss problems After importing the DecanterAI platform, in addition to accurately predicting market demand, it can also accurately predict market demand based on expiration date data Analyzing production and distribution quantities can also help reduce warehousing and logistics costs AutoML industry has diverse and extensive applications and great potential for future development Action Bagel believes that AutoML has a wide range of industrial applications, including employee turnover prediction, production demand prediction and revenue performance prediction that are troubled by the manufacturing industry store passenger flow prediction, product replenishment prediction, membership prediction in the smart retail industry Promotional forecasting customer churn forecasting and potential customer list forecasting in the telecommunications industry accurate financial marketing, credit card fraud detection and insurance application quick review in the financial industry and even real estate price forecasting, power outage disaster forecasting, etc are all helpful To solve the operating difficulties of the industry and create new business models AutoML has diverse industrial applications, covering manufacturing, retail, finance and other industries Source Action Bagel Co, Ltd How much time and preparation does it take to import AutoML Lin Yushen said that in actual practice, the introduction process of automated machine learning enterprises includes four major stages 1 Preparation period Collaborate with enterprises to discuss business pain points, help define analysis propositions, and provide professional data science advice and optimal solutions, lasting about two weeks 2 Verification period Use a small-scale pilot project to quickly verify the analysis results to ensure proposition setting, data quality, analysis process, prediction technology, etc, as the basis for subsequent practical application and amplification It takes three weeks 3 Introduction period Support cloud or local product deployment according to enterprise needs Provide operation and maintenance teaching, Help Center, data analysis consulting, corporate training courses and other product introduction services, which will take more than one month 4 Application period Analysisdata teams can execute various AI projects through the product's common interface and implement them quickly The prediction engine can be connected through the API to develop application modules according to practical scenarios This is the final stage of application and is time-consuming and can take up to several months However, Action Bagel conducts a system integration project process with its SI partners The SI partners discuss business propositions and provide data sets, and then conduct data health checks and Baseline models Based on this, Action Bagel provides data diagnosis reports After confirming the pilot project proposition and producing a demand planning document, the project execution phase begins, with model establishment, optimization and analysis reports provided System integration with SI industry players, on the one hand, optimizes module development, and on the other hand, uses APIs to connect data sources and output prediction results, import them into the enterprise's field, and effectively solve the propositions faced by enterprises in digital transformation Looking forward to the future, Decanter AI platform will continue to develop various AI innovative application services, and cooperate with the upstream, midstream and downstream industries such as enterprise resource planning ERP, customer relationship management CRM, business analysis BI and e-commerce platforms EC and other partners maximize the benefits of the ecosystem through co-creation, sharing and altruism This article is derived from the selected content of "AI Engineering Online Small Gathering"「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

In light of the rapid development of Artificial Intelligence AI, InfuseAI has created PrimeHub, a one-stop AI deployment platform software, with the aim of lowering the barriers for AI adoption and assisting enterprises in a successful transformation InfuseAI Inc, founded in 2018 by senior developers from the Taiwanese open-source community, g0v's Chia-liang Kao, and KKTIX founder Liang-Bin Hsueh, specializes in PrimeHub, the AI deployment platform software InfuseAI's COO Liang-Bin Hsueh shares insights on MLOps implementation and application InfuseAI's COO, Liang-Bin Hsueh mentioned that AI project development is an iterative process During the AI product lifecycle from data collection, development, model deployment to monitoring etc, it contains many technical debts that prolong the project cycle, increase costs, and significantly reduce the benefits Furthermore, according to statistics, data scientists spend 65 of their work hours on tasks other than model development The realization of AI not only involves development but also includes maintenance and cross-team collaboration issues All these factors significantly slow down the speed of AI development to match the company's needs Liang-Bin Hsueh points out that the issues arising from the inability of AI development speed to keep up with enterprise needs can be specifically illustrated in the following two aspects Speed of AI model development In the past, it took 1 to 2 years to complete an AI project However, as enterprises adopt AI, the project and AI model numbers multiply Operational issues after AI model deployment The lifecycle of an AI model begins after deployment As data accumulates, AI models can be retrained to enhance performance However, as the number of models grows, operational issues and computational resource bottlenecks emerge Hence, the MLOps platform—PrimeHub, developed by InfuseAI, encompasses the processes from AI model development, training management, to operational deployment and monitoring, offering a one-stop platform service through a smooth automated AI workflow that enables true enterprise AI implementation In other words, the InfuseAI team continuously adds to PrimeHub Apps by integrating third-party application services, actively collaborating with more manufacturers to seamlessly integrate AI models into PrimeHub, and eagerly anticipates cooperation with more businesses focused on AI technology and SI partners to inspire more applications on the MLOps platform and further promote large-scale AI implementation Since its formation over three years ago, InfuseAI's clients include Taiwan AI Academy, ESUN Financial Holdings, Sinopac Financial Holdings, National Taiwan University Hospital, and Chi Mei Hospital Among these, InfuseAI works closely with Taiwan AI Academy to address various academic needs Teaching assistants at each branch only need to operate simply in the PrimeHub platform, where all management tasks are automatically completed Students in PrimeHub’s self-service platform establish a uniform pre-configured environment, allowing multiple deep learning calculations simultaneously, isolated by containers without interference Additionally, assistants can decide on the data to load based on the course progress, automatically loading course files and datasets when students launch the environment Yushan Financial Holdings began intensive integration into AI development in 2018, acquiring GPU computing resources They discovered the need for robust infrastructure to speed up operations amidst numerous individuals and projects concurrently They sought a management platform to assist with computing resource management and data authorization Liang-Bin Hsueh states that the PrimeHub platform aims to help enterprises scale AI development, reducing model deployment time from days to hours, further facilitated by APIs and APPs to automate and optimize workflows PrimeHub operates on a yearly subscription basis, promising ongoing optimization of the platform environment and the flexibility to offer customized services to different customers Currently, it offers three solutions PrimeHub Enterprise Edition, PrimeHub Deploy—a lightweight model deployment management plan, and PrimeHub Community Edition, allowing users to choose according to their needs This article is a curated selection from the "AI Engineering Online Meetup"「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

E-Hui Technology collaborates with the Institute for Information Industry to create an anomaly detection system In closed production lines where anomaly data is hard to collect, the biggest problem with AI image recognition is the paucity of training data and its rarity, making implementation very challenging E-Hui Technology, in collaboration with the Service Creation Institute of the Institute for Information Industry, has developed unsupervised learning technology, wherein the computer does not need to label specific items to recognize during its learning process It classifies based on the characteristics of the data For instance, in manufacturing, where defect detection is critical, the system first learns from the predominantly normal products on the production line When a few defective items are mixed in, the machine uses classification rules to screen out the defective items Similarly, if a consumer credit card transaction suddenly shows completely unusual characteristics that do not match typical patterns and habits, the machine autonomously detects this anomaly Due to its distinctiveness, this technology is applicable to unique sectors Using unsupervised deep learning algorithms for developing AI anomaly detection technology eliminates the need for collecting and labeling anomaly data, reducing the introduction period to 1-2 months The target market is traditional industries such as chemical and steel, which find it difficult to gather anomaly data This technology has a high tolerance for image quality, requiring only regular surveillance footage from the production line to train the model It can detect various types of anomalies and has a simple and fast implementation process The system includes components for image acquisition, training mechanisms, edge real-time computing hosts, and cloud inspection services, forming a complete anomaly detection solution that can be integrated with factory patrols, apps, and other reporting mechanisms 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

Originally believing that Taiwan was not fertile ground for the development of remote medical services, Xu Keyu, founder and CEO of the Health Networking Information Company, was driven by goodwill to build a remote medical collaboration platform for rural areas The easing of regulations and the isolation caused by the COVID-19 pandemic unexpectedly ignited the demand for remote medical care 'The wind has risen,' Xu Keyu, standing at the cusp of opportunity, sailed the winds, developed the 'Immediate Doctor View APP' and marked a new milestone in Taiwan’s remote medical services Xu Keyu lived in the United States for 20 years and was deeply involved in venture capital, boasting over 30 years of high-tech investment and management experience in Greater China and Silicon Valley Due to the vast geographic size of the US, accessing medical care is challenging, hence remote medical care is quite common, with home-care doctors conducting initial diagnostics and giving treatment advice over the phone From a charitable perspective, creating Taiwan's first remote medical platform After retiring and returning to Taiwan, Xu Keyu participated in rural medical volunteer activities with the Rotary Club Moved by the scarcity of medical resources in rural areas, where volunteer doctors pay for their own transportation and medication, and differ in time attendance, leading to repeated consultations and resource sharing issues Therefore, Xu Keyu hopes to promote remote medical services by establishing a cooperative platform, starting from a 'charitable' perspective Health Network Information Company's 'Immediate Doctor View' platform offers a system free of charge for volunteer doctors, with features including video recording, video calls, registration, and medical records Currently, over 200 doctors are stationed on the platform According to Xu Keyu, there are two models of remote medical services One is the Smart Hospital model, which is centered around hospitals primarily with the aim of treating diseases, incorporating clinics, pharmacies, cooperative medical labs, and even mobile device providers, with services provided to patients by the hospital integration The second model is patient-centered Internet medical treatment, where all medical-related facilities revolve around the patient The ownership of medical data and records belongs to the patient, not the hospital Doctors need authorization to access patient data, and patients can also revoke a doctor's authorization This approach can revamp the physician-patient dynamic, shifting authority back to the patients, also forcing doctors to improve overall medical quality by eliminating weaker elements Statistically, there are 58 rural health stations and 15 island health stations across Taiwan, with only fixed visiting family physicians stationed By using the system platform, only nurses need to operate retinal cameras and related portable diagnostic equipment to immediately get health reports Once the doctor in the remote location detects an issue, they can quickly arrange referrals, allowing for early detection and treatment, which saves on medical costs and secures health Starting from a charitable initiative, providing a field for verification of innovative business models in healthcare, the loosening of regulations brings more possibilities for remote medical care, turning Taiwan, once considered a 'remote medical desert' by Xu Keyu, into a place showing signs of developmental dawn In response to remote medical services, the Ministry of Health and Welfare officially issued the 'Telemedicine Treatment Measures' on May 11, 2018, relaxing the care targets and modes for remote medical care, which include follow-up for acute inpatients within three months, institutional residential long-term care residents with chronic prescriptions from a medical institution, integrated care related to family doctors, and foreign patients who are not covered by national health insurance but are planning to receive or have received treatment from local medical institutions, all of whom can benefit from remote medical services Remote medical services include health education, health promotion, disease prevention, and post-recovery treatments To protect public health, the Ministry of Health and Welfare will promote the 'Health Passbook' in the second quarter of 2020, aimed at returning the autonomy of health care and medical record data to the public, aligning with Xu Keyu’s philosophy The 'Immediate Doctor View APP' primarily targets the B2B market, focusing on corporate employee care According to a research report by the 104 Job Bank, each year over 100,000 managers resign to take care of elderly family members, causing corporate personnel turnover If this platform is utilized effectively, through triple video conferencing and full recording, one can accompany their parents for a doctor visit while in the office, reducing the chance of managerial resignations Additionally, with the introduction of remote medical care benefits in enterprises, whether on assignment or business trips, personnel can receive timely medical consultation and care, enhancing the company’s ability to retain talent Moreover, hospitals or clinics can also create VIP groups through the system platform, providing exclusive home care for VIP clients with significant assets, or for patients with severe conditions, offering appropriate medical resources at all times, reducing costs for hospitals and ensuring better care for patients Technology drives remote medical services, with AIIoT being crucial Xu Keyu states that technology is the driving force behind Taiwan's remote medical services, with AI and IoT playing key roles Here, 5G helps solve the fundamental network issues, and IoT devices collect personal physiological information including temperature, heart rate, pulse, and blood pressure These devices, in conjunction with AI, identify potential issues and provide guidance, assisting doctors in making remote disease diagnoses and offering precise recommendations The Immediate Doctor View APP system includes four modules 1 Communication module, including CTI features which automatically bring up client-related data such as medical history, personal information, and IP address upon phone connection 2 HIS module 3 Service support module 4 Online payment module This system has been providing remote medical services to rural areas, expatriates, international students, and travelers abroad since April 2019 Now, whether in rural areas or overseas, during the day or at midnight, patients can immediately access professional medical consultations through the mobile video feature Xu Keyu believes that the biggest challenge in entrepreneurship is to integrate various stakeholders in the overall ecosystem, including doctors, devices, and data He has to devise methods and strategies to persuade everyone to join the platform, initially providing resources for free, and plans to adopt innovative business models in the future, charging listing fees, thereby fostering mutual benefits among doctors, patients, corporations, and device providers on the platform, and building a more robust environment for Taiwan's remote medical services Immediate Doctor View APP development team Health Network Information Company Founder and CEO Xu Keyu「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

On average, one person is diagnosed with colorectal cancer every 37 minutes, making it the most prevalent type of cancer in the country for many years In its early stages, the disease is almost asymptomatic, and if there is lower gastrointestinal bleeding, it often goes unnoticed when mixed with feces Patients without regular screening habits can easily miss the optimal treatment window Unlike the existing iFOBT stool tests, Rui Ai Biotech has developed the world's first home-use 'Hemoglobin Sensor' referred to as 'Blood Gun', which can detect blood in the toilet bowl after defecation This helps users to interpret risks that are invisible to the naked eye, aiming not just to facilitate home health management, but also to improve the screening willingness and initiative of potential patients Patent King's Startup, Home-use Blood Gun Wins National Innovation Award The current hospital-based fecal occult blood test requires individuals to collect stool samples and go to the hospital for biochemical analysis This process lacks convenience and immediacy, reducing the public's willingness to undergo testing When the blood in a patient's stool solution is visible to the naked eye, it often indicates that the disease has progressed to stage II or III In light of this, the innovative research team from Hsinchu, Rui Ai Biotech, founded in 2017 by Dr Yan Shuo-Ting, a renowned 'Patent King' from Foxconn group, identified a gap in the current needs for occult blood testing Utilizing his expertise in semiconductor optics and proprietary algorithms, he led the team to develop the 'Hemoglobin Sensor' for home use, which not only attracted angel investments from the US but also garnered numerous patents and the 16th National Innovation Award in 2019, winning countless accolades nationally and internationally Photometric Occult Blood Sensing Technology A 10-second Test After Toilet Use The Hemoglobin Sensor is easy to use Employing optical sensing technology, users simply follow five steps after defecation to effectively detect early signs and initiate early treatment Step One After defecation, allow the feces to settle in the water for five minutes, then attach the specialized filter paper to the front probe of the sensor Step Two Press and hold the power button for three seconds to turn on the device Step Three Briefly press the sensing button to begin measurement, immerse the front probe in water for about 10-15 seconds Step Four After viewing the results displayed on the screen, push the filter paper into the water the paper is biodegradable and flush confidently Step Five Rinse the probe with clean water for 3-5 seconds and wipe with a tissue, place it back on the charging stand Blood Gun Usage Method Image Source Rui Ai Biotech Optics Combined with AI Improves Accuracy, Creating a Staple Home Product The main challenge in home-based occult blood testing is eliminating noise, identifying hemoglobin signals, and analyzing them There are currently home-based hemoglobin test papers available however, due to the difficulty in avoiding impurities at home which affects accuracy, and the relatively high cost of consumables, it has not yet become widespread in the market The photometric 'Hemoglobin Sensor' breaches these technological barriers, with its internal optical components utilizing a highly sensitive CMOS image sensor CIS, combined with an optical module minimized to the smallest possible size The proprietary algorithm analyzes and effectively avoids noise interference, accurately determining the results as negative no occult blood or positive occult blood present Blood Gun Spectral Sensing Technology Diagram Image Source Rui Ai Biotech The handheld 'Hemoglobin Sensor' has been crowdfunding on the platform ZecZec since February this year, reaching its target of one million New Taiwan dollars by the end of March It is also available for purchase on PChome Store Street httpswwwpcstorecomtwredeye Rui Ai Biotech hopes to make the Hemoglobin Sensor a household essential for testing, and is also committed to promoting the extended application of this patented technology, such as integrating it into smart toilets The product has already attracted attention from several domestic and international bathroom and health testing equipment manufacturers, presenting considerable potential business opportunities and marketability Rui Ai Biotech Team, fourth from the left is CEO Dr Yan Shuo-Ting Image Source Rui Ai Biotech Facebook Rui Ai Biotech Blood Gun Introduction Image Source Rui Ai Biotech This article is published with permission from Rui Ai Biotech on AI HUB「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」