Special Cases

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26
2020.5
【2020 Application Example】 Automatic fruit screening system: A solution that uses neural networks, AI, and automation to improve fruit screening efficiency by 10 times, increase output value by NT$1.7 billion, and significantly improve quality with 93% accuracy

Taiwan is located in the subtropics and has a diverse geographic environment that is very suitable for growing fruit Bananas and pineapples were once extremely popular export commodities that we are proud of However, farmers in consuming countries gradually obtained the excellent seeds of Taiwanrsquos fruits, and were able to grow the same quality fruit but at a more affordable price, causing our fruit exports to face a major crisis At present, although Taiwan's fruits such as mango and guava still have certain competitive advantages, if they fail to make further progress compared with other countries, they will still encounter the same problem over time and cannot be ignored Fruit quality and brand value are the only ways for Taiwan's fruit industry to remain competitive internationally Fruit screening is the main link in fruit production and marketing that determines quality Currently, the industry is highly dependent on aging rural manpower, resulting in rising fruit screening costs due to labor shortage and making it extremely difficult to maintain stable yield Therefore, the automation of fruit screening work has become a very important and urgent issue Professor Chi-Chun Lee at the Department of Electrical Engineering of National Tsing Hua University led a team to develop an automatic fruit screening system that combines cameras, conveyor belts, and AI The system currently has an accuracy reaching 93 One production season can increase the output value of mango by NT17 billion With the gradual development of the AI system, the accuracy is expected to improve in the future, and the same system can also be applied to other fruits, further promoting traceable fruit and driving the technological upgrading of Taiwan's fruit industry Fruit screening relies heavily on scarce manpower, and the aging of the rural population makes the situation even worse Professor Chi-Chun Lee learned about the fruit industryrsquos dilemma from his classmate Yu alias, who had studied together in the United States Yu is the young second-generation successor of one of Taiwan's largest fruit import and export companies According to Yu's observations in the industry over numerous years, Taiwan's fruit production and export usually generated good profits at first, but after fruit farmers in the consuming countries obtained the seeds, they will often attempt to grow the fruit locally to reduce costs and obtain greater profits If Taiwanese fruits cannot surpass the products of fruit farmers in consuming countries in terms of quality or brand value, they will be eliminated because competitors' costs are indeed lower Fruit screening is used to divide fruits according to quality If they cannot pass the minimum specification, they will be discarded as waste products In practice, the work of screening fruits will be carried out by farmers' goods yards and distributor' packaging yards respectively However, if it is not properly handled by the collection freight yards and the packaging yards do not do a good job in sampling in the early stage, it will result in a loss for distributors and cause 30 of AA grade fruits to be eliminated This job relies heavily on experienced fruit screeners More experienced fruit screeners can not only control the quality and reduce the chance of fruit damage in the fruit screening process, but also have the ability to pick out about 10 more A grade fruits, which adds great value What worries the industry is that experienced fruit screeners are gradually decreasing due to the aging population in rural areas, making them a very rare resource Such rare human resources are often in high demand during busy farming periods Farmers or distributors who fail to hire experienced fruit screeners have to settle for less experienced one, taking on the risk of additional losses and paying greater costs The most unfortunate situation suffering a loss of 30 mentioned above Fruit screening is an important process in the later stages of fruit production when packaging and selling Failure to properly control quality will result in huge losses AI is very suitable for assisting in fruit screening, but it is difficult to obtain data sets After understanding Yu's difficulties, Professor Lee found that this was a problem that could be solved using AI - although fruit screening relies heavily on experienced fruit screeners, it is a highly repetitive task Handling repetitive tasks with a large amount of data has always been a strength of AI However, the first problem appeared even before research and development work started Which fruit do we start with First of all, a suitable fruit must reach a certain export volume, and the fruit must still have considerable room for growth For some fruits that lack international competitiveness, such as bananas and pineapples, companies no longer have the ability to invest more funds to purchase equipment, let alone sponsor RampD or assist the RampD team in experiments When you have an idea, you need to pick up the pace and put it into practice as soon as possible Therefore, Irwin mango, which still has a certain advantage in terms of scale, was selected as the first experimental subject of the automatic fruit screening systemThe first step after harvesting mangoes is to screen the fruits for the first time at the goods yard After the fruits are screened, they are sent to the packaging yard for fumigation and disinfection, and preparation for sale or loaded into containers for export However, exporters with a deeper understanding of the target market will have stricter quality requirements and will often screen the fruit again to ensure the quality of the fruit before fumigation at the packaging site Since employees at the goods yard are paid based on the number of mangoes screened rather than on the quality of the mangoes, they focus on quantity when working As a result, to ensure the quality of the selected fruits, the subsequent packaging factory has to screen the fruit again, increasing labor The solution seems simple and clear - A camera, machine conveyor belts for grading and sorting, and an AI that can distinguish the quality of mangoes from their appearance are all that are needed to achieve automatic fruit screening However, the hard part is how can AI distinguish the quality of mangoes Thatrsquos right, you must start by establishing a training data set In order to create the data set, Professor Lee's team established a website that allows anyone to upload photos of mangoes and rate them Once the data sets are refined, they can be used to train AI The fruit screening machine developed by Professor Lee's team uses AI image recognition to select the best looking mangoes The accuracy of the trained AI reaches 93, which can increase the output value by NT17 billion in one season In 2019, the assistance of the Industrial Development Bureau now the Industrial Development Administration of the Ministry of Economic Affairs and AI HUB accelerated the verification of the technology Professor Lee's team accumulated 100,000 entries of data during the 2-month empirical period, and the accuracy of the trained AI reached 93 This is far higher than the manual screening accuracy of 70, resulting in a clear difference in quality In terms of export value, the output value of mango is expected to be increased by NT17 billion in one season It can also reduce labor costs by NT1866 million and avoid the seasonal labor shortage problem mentioned above In addition, since it is no longer necessary to screen the fruit once at the goods yard and packaging yard each, it also reduces losses caused by human error in the fruit screening process When the technology becomes more mature, the same system can be applied to other fruits exported by Taiwan, such as wax apple and guava, in the future, taking Taiwan's fruit industry to the next level Since it is AI, accuracy can be improved through continuous training, and continuous adjustment of algorithms and cooperation with equipment manufacturers can significantly improve production capacity In addition, Professor Lee is also organizing the AI Cup competition with the sponsorship of manufacturers and the government, allowing more teams to use the same data set to continue to develop the algorithm, in hopes of facilitating further cooperation with companies that are interested Irwin mango grade identification system on AI HUB Professor Lee's team hopes to use the power of AI to achieve complete traceability of fruits from production to packaging and transportation, thereby increasing the brand value of Taiwan's fruits Besides hoping to allow Taiwan's fruits to seize a place in the fiercely competitive foreign markets, with high-quality supply, Taiwan's fruits can also shine internationally and become the pride of Taiwan Taiwan's fruits still have certain competitive advantages in the international market, but they also face competitive pressure from fruit farmers in consuming countries as they are exported Easily save NT1866 million per mango season and significantly improve quality nbsp nbsp nbsp nbsp nbsp nbsp

2020-05-26
【2022 Application Example】 USRROBOT's AI Lawn Mowing Robot Enters the Blue Ocean of Golf Market

An AI smart lawn mowing robot, resembling a vacuum robot, shuttles back and forth on the 30-hectare golf course lawn for weeding This robot, independently developed and designed by Taiwanese, is equipped with the world's first electronic fencing positioning technology which utilizes high-precision GPS integrated with cloud AI computation to determine the most efficient mowing paths, targeting the lucrative blue ocean market of golf courses This AI lawn mowing robot was developed by USRROBOT, a Taiwanese startup established in 2019 Chao-Cheng Chen, the president of USRROBOT, once served as the executive vice president of one of the top five ODM tech companies in Taiwan, and specializes in software and hardware integration When he served as the chairman of the Service Robot Alliance, he knew that the service robot industry was bound grow rapidly due to declining birth rates and the growingly severe labor shortage New demand - The horticulture market is large and the has rigid demand "To develop the core technology of service robots, we must find rigid demand Looking at European and American countries, there is a shortage of labor, but demand for horticulture has increased, and there has been a long-term shortage of 7-10 of horticultural workers" Under this strong "rigid demand," Chao-Cheng Chen established USRROBOT, and the company's first product is the AI lawn mowing robot In terms of overseas markets, the United States is the world's largest horticulture market, accounting for 30-40 of the global output value It is estimated that there are about 1 million horticulture workers, but they have been experiencing a labor shortage of 7-10 in recent years and have not been able to improve the situation The main reasons for labor shortage are Aging population and gardening is a labor-intensive job, so young people don't want to do it Unlike in Taiwan, European and American countries attach great importance to lawn maintenance and have expressly stipulated in the law that heavy fines will be imposed for failing to mow the lawn Therefore, the AI lawn mowing robot has considerable market development potential The introduction of AI multi-device collaborative mowing sensor technology is expected to reduce the burden of staff maintaining the golf course The AI lawn mowing robot developed by USRROBOT is currently in its second generation Domestic universities and well-known art museums are using the latest model M1, and it is also being used by some world-renowned high-tech companies and well-known universities in the United States The company is currently involved in negotiations for subsequent business cooperation USRROBOT stated that the professional RTK system currently used can reduce the original GPS positioning error from tens of meters to about 2 centimeters, allowing the robot to move accurately outdoors After setting the boundaries, it can be easily operated using the app New application - Implementation in golf courses solves the problem of labor aging and shortage Chao-Cheng Chen further explained that the National Land Surveying and Mapping Center is a RTK service provider RTK provides the error reference map of the positioning point USRROBOT can access the positioning error value of a specific position through 4G Internet access The AI algorithm of USRROBOT reduces the general 10-20 m error of GPS to 2 cm After positioning, USRROBOT then uses six-axis accelerator positioning, gyroscopes, and wheel differential sensing devices for software and hardware integration Only by matching the wheel's movement pattern and the terrain can accurate mowing path planning be achieved The AI lawn mowing robot, which is 62 cm wide, 84 cm long, 46 cm high, and weighs only 25 kg, can set the mowing boundaries in the cloud It can avoid pools and sand pits through settings, using AI algorithms to automatically calculate the optimal path It is able to mow approximately 150 ping of grass in one hour The battery can be used continuously for more than 6 hours The battery life is currently the highest in the world In addition to general gardening companies, with the assistance of the AI project team of the Industrial Development Bureau, Ministry of Economic Affairs, USRROBOT's AI lawn mowing robot has been applied to golf course lawn mowing A well-known golf course located in Taiping District, Taichung City currently has a staff of 5 people who are responsible for the lawn, planting maintenance, and other landscape maintenance of the entire 30-hectare course However, the average age of staff is as high as 55 years old, and the golf course has been unable to recruit new staff members for a long time In view of the aging staff and the shortage of manpower, the golf course hopes to mitigate the impact with AI technology, and is therefore using AI multi-device collaborative mowing sensor technology, in hopes of reducing the burden of staff maintaining the golf course New challenges - Expert systems are needed to overcome difficulties with different grass species "This AI lawn mowing robot has low noise, low pollution, low labor costs, and is waterproof and anti-theft In the lawn mowing process, it can identify and avoid obstacles through ultrasonic sensors while maintaining mowing quality, maintaining aesthetic and consistent grass length" Chao-Cheng Chen went on to say that the most important part about golf courses is that the grass pattern should be beautiful and free from diseases and pests Based on the site survey, golf courses are mainly divided into three major areas green, fairway and rough There is no problem using the current mowing robot to mow the rough area, and it can overcome slopes within 20 degreesThe short grass in the fairway area may only be two centimeters long, and the grass types are also different, so the cutterhead design needs to be modifiedAs for the grass in the green area, the grass must be mowed close to the ground and maintained in a consistent direction because it affects the putting speed Many factors will affect the green index, and this part requires more research and testing The AI lawn mowing robot can identify and avoid obstacles through ultrasonic sensors while maintaining mowing quality The AI smart lawn mowing robot has a built-in camera that can be used to detect the health condition of the lawn Chao-Cheng Chen said that in the future, an expert system will also be introduced for early determination of whether there are diseases, pests in the lawn or whether there is sufficient moisture, and provide lawn health data analysis to customers, so that they can take preventive and response measures sooner to reduce disaster losses Chao-Cheng Chen, who is also a good golfer himself, said that golf has developed well in Taiwan However, due to weather factors, such as rainy and humid climate and typhoons, Taiwan's golf courses have harder soil and more potholes compared with top tier golf courses overseas If AI lawn mowing robots are to be widely introduced into golf courses, there are still many difficulties that must be overcome However, Taiwan's difficult terrain creates a good testing ground Once Taiwan can overcome the many problems and successfully introduce the robot, it will be able to expand to overseas markets and seize new market opportunities in a blue ocean Chao-Cheng Chen, President of USRROBOT nbsp

2022-06-01
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【2021 Application Example】 Hamastar Technology Builds an AI Model Management Platform to Accelerate the Application of AI

Riding the AI hype train, financial service providers are using their solid foundation in the industry to not only transform themselves, but also assist their customers with transformation Hamastar Technology, which has been established for over two decades, has been developing AI technology and assisting industry customers with the implementation of AI in recent years Hamastar Technology believes that to implement a complete AI project, in addition to AI theoretical knowledge, data analysis, and model training capabilities, it is also necessary to develop APIs for data, establish databases, develop front-end RWD web pages, and even consider layout design and user experience based on customer needs These tasks create technical barriers for AI startups Even from the perspective of companies that have reached a certain scale, it is hard to accumulate technical experience and accelerate business growth due repeatedly investing manpower developing similar functions in each project Institutional customers still require high level of customization for AI Using the requirements of government Agency A implemented by Hamastar Technology as an example, users must control false information from specific channels The platform needs to provide data ingestion functions for training models and predictions, and can complete natural language processing NLP text classification model training and use When the model discovers false information, it needs to immediately notify responsible personnel through messaging software The need of Agency B is to use an AI model to automatically classify petitions and immediately provide information on past cases as reference for the petitioner or officer Although the project models are similar data ingestion, model prediction, warning notification, the required functions still need to be separately developed for individual projects, and existing programs and models cannot be reused to speed up the implementation of subsequent projects After in-depth discussion, Hamastar Technology found that pain points of enterprises implementing AI projects include high implementation costs and lengthy project schedules It is difficult for a single enterprise to simultaneously have data scientists, analysts, engineers, and designers Current projects are all focused on solving the needs of specific fields, and it is difficult to reuse the AI models in other fields of application At the same time, the tools are concentrated in AI projects and cannot provide customers with total solutions In other words, due to the "limited manpower," "restricted fields," and "insufficient tools" of AI service providers, the implementation of AI technology projects requires high costs or lengthy timelines These are common problems that companies urgently need to solve Therefore, if there is an AI model application service management platform, it will be able to solve the above difficulties and not only reduce costs, but also accelerate project implementation and provide customers with one-stop solutions AI model application service management platform assists in quickly completing projects Therefore, with the support of the AI project of the Industrial Development Bureau, Ministry of Economic Affairs, Hamastar Technology carried out the "AI Model Application Service Management Platform AISP RampD Project" and engaged in the RampD of AISP products The purpose is for AI service providers to complete the AI projects with twice the result using only half the effort The AISP provides one-stop AI solutions AI service providers can quickly assemble required functions, such as data API, model management, and model prediction result monitoring subscription through existing module functions of the AISP It also provides commonly used graphical tools to help companies quickly design interactive charts or dashboards required by users, effectively reducing the labor costs required to execute projects, shortening the solution POC or implementation time, and accelerating the implementation and diffusion of industry AI In terms of product business model, in the short term, the company will extensively invite IT service providers with expertise in the field of AI to work together, and use platform services to solve the AI implementation problems faced by requesting units in various field, gradually building trust in the platform brand In the mid-term, the company hopes to gradually expand the market based on its past success, and form strategic alliances with multiple IT service providers to solve more and wider problems in specialized fields and provide more solutions for units to choose from The platform combines field experts to jointly expand overseas markets In the long term, after establishing AI strategic alliances in various specialized fields, the platform will have a large number of AI solution experts for specialized fields After accumulating a large amount of successful project experience, Hamastar Technology hopes that the AISP will be able to work with experts companies to expand into the international market Harmastar Technology Co, Ltd was formed in 2000 by recruiting numerous senior professional managers and technical experts in related fields It is committed to software technology RampD and services, and aims to develop into an international software company, actively creating opportunities for international cooperation in the industry Under the excellent leadership of its first president, the company has rapidly grown into a major software company in Taiwan

2021-11-08
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【2020 Application Example】 Peeking into a Baozi to See How AI Reduces Scrap Rates by 50% and Boosts Production Efficiency by 60% for Frozen Foods

From production line to dining table, who oversees the hygiene management of what we eat In recent years, there has been a continuous stream of news reports concerning food safety, such as repackaging expired goods, and poisoning incidents at Hong Rui Zhen It's clear that people are increasingly concerned about the hygiene of their food However, due to various quality control methods in food processing, there are inherent risks The World Health Organization WHO has pointed out that unsafe food and water cause physical harm to 2 million people each year Hence, international markets demand that food processing companies must establish a traceability system for products This is why major domestic food processors also aim to set up a production traceability system to quickly trace back to problematic raw materials and initiate recall and destruction of problematic food Visible assurance, implementing production transparency A major domestic food manufacturer producing frozen food and instant meals has expanded its market presence to North America, New Zealand, Japan, etc They are also at the forefront in promoting food management domestically, having obtained certifications such as HACCP, ISO22000, ISO14001 Since food production is labor-intensive, it is prone to quality impacts caused by worker fatigue Additionally, the production lines often have unclear records of production quantities, processes, and timing This obscurity in traceability makes it difficult to track production information when defects occur, leading to food safety management gaps that result in the scrapping of entire batches To address this, the Production Development Center at National Sun Yat-sen University utilized its advisory resources to help the food manufacturer tackle food safety management challenges, planning the use of AI technology to collect production data and establish anti-fraud and traceability for food production Intelligent manufacturing boosts food safety Although the level of automation is not high in the processing of bakery products, the food plant in this case is keen to enhance the automation of its production lines and introduce smart manufacturing For businesses, a traceability system not only helps establish brand image and increase product and brand value, but also gives consumers peace of mind due to the transparency of production lines Therefore, the Production Development Center at National Sun Yat-sen University matched AI technology service providers, Hong Ge Technology, in the first phase to plan the introduction of data collection devices to link food work orders information, reducing human operational omissions and capturing real-time production information through dashboards to ensure the consistency of production stage information potentially affected by human factors Schematic for intelligent production line planning The second phase involves using deep learning during the dough fermentation stage to calculate size and volume, analyze the relationship between temperature, humidity, fermentation time, and product volume, and assess whether to introduce AOI foreign object detection after freezing as a second quality control step Schematic of AI-integrated quality control for finished products Food processing ID card, launching the AI-era of food safety tracing In Taiwan, the understanding and acceptance of production history by consumers is gradually improving From the supply of raw materials, processing, production, to distribution and sales, it is necessary to have complete control and provide transparent information Publicly disclosing the production history not only increases trust between enterprises and consumers, but also aligns Taiwan's food safety environment with international standards In 2020, the Production Development Center at National Sun Yat-sen University will assist enterprises with the adoption of advanced AI technology, documenting the entire data process from industry to dining table and supervising food production processes to successfully implement product tracing, prevention of adulteration, and the establishment of high standards for products, thus advancing food processing products to international standards「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

2020-03-12
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【2023#17】AI Defect Detection Intelligence - Reducing Process Energy Consumption through Smart Monitoring Solutions

Industry Manufacturing CCategory Electronic Components Manufacturing 26 subcategories Industry Pain Points Innovation Pressure Rapid technological changes and the emergence of new components and technologies necessitate ongoing RampD and innovation to keep pace with market demands and technological trends Intense Competition The global presence of numerous manufacturers and suppliers requires companies to maintain competitive advantages in technology, quality, cost, and delivery times Cost Pressure Producing high-quality electronic components requires substantial RampD, equipment, and production costs Fluctuating raw material prices and rising labor costs also pressurize businesses Supply Chain Management Managing a supply chain becomes complicated when it involves multiple countries and regions, including raw material suppliers, manufacturers, and distributors Human Resources and Skill Shortages The requirement for highly skilled and knowledgeable personnel often faces imbalances and shortages in talent and skills IntroductionAIBenefits Efficient Quality Control and Inspection Automated detection of defects and non-conformities enhances product consistency and quality levels, reducing waste and recovery costs Preventive Maintenance Monitoring the performance and state of manufacturing equipment to predict potential failures and preemptively conduct maintenance, reducing downtime and production interruptions, enhancing equipment utilization and efficiency Process Optimization Analyzing large volumes of data from the manufacturing process to identify optimal process parameters and workflows, boosting efficiency, lowering costs, and enhancing product quality Quality Traceability and Provenance Recording each component's manufacturing details and related data from raw materials to the final shipped product ensures traceable quality, enhancing brand reputation and competitive edge Demand Forecasting Analysis Analyzing extensive manufacturing and quality data to predict production conditions, product reliability, and demand trends, allowing businesses to make more accurate production planning and resource allocation, reducing inventory and production costs Common AI Technologies or Applications Support Vector Machine, Random Forest Artificial Intelligence such as Support Vector Machine and Random Forest are appropriate for analyzing data in surface mount technology components production, optimizing parameters like temperature to predict yield rates Support Vector Machine is a good choice for smaller data volumes, while Random Forest is better suited for larger data volumes Additionally, Random Forest has a technical advantage in identifying abnormal conditions in surface mount device equipment such as vibrations Random Forest, Convolutional Neural Network, Long Short-Term Memory Network Employing Artificial Intelligence such as Random Forest, Convolutional Neural Network, or Long Short-Term Memory Network can effectively identify failure modes and abnormal signals from monitoring data to predict possible failures, facilitating timely maintenance and repairs, reducing downtime and maintenance costs Convolutional Neural Network Using Artificial Intelligence like Convolutional Neural Network algorithms to analyze and process imagery and video data of surface mounted components in the manufacturing process, automatically identifying product defects 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

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【2023#16】Deep Learning and Intelligent Customer Service AI Technology Application Plan at Kaohsiung Howard Hotel

Industry Accommodation and Food Services IMajor Category Accommodation Industry Subcategory 55 Industry Pain Points High Seasonal Demand Fluctuations Certain periods may experience decreased customer base and performance decline during low season Intense Competition The presence of numerous hotels, inns, and restaurants in the market requires operators to continuously innovate and enhance service quality to maintain competitiveness Significant Human Resource Demand The industry generally faces challenges with human resource shortages and high turnover Recruiting and retaining excellent staff, as well as providing appropriate training and career development, are key challenges High Costs and Profit Pressure Price competition and rising costs, including rent and labor costs, put pressure on profit margins Digital Transformation Pressure The need to keep up with technology and digital transformation, introducing new technologies and systems to improve efficiency, service, and cater to digital demands of customers Online Reviews and Reputation Management Affected by social media, managing reputation and addressing negative reviews is a challenge, demanding proactive response and improvements in customer satisfaction IntroductionAIBenefits Increased Efficiency Automating many tedious processes such as reservation management, inventory control, and supply chain management, thereby increasing operational efficiency and cost-effectiveness Personalized Services Analyzing data to understand customer preferences and behavior patterns to offer personalized services and recommendations, thus enhancing customer satisfaction, repeat visits, and word-of-mouth effects Prediction and Demand Management Analyzing data and predicting demand trends to optimize inventory management, workforce scheduling, and pricing strategies, fulfilling customer needs and increasing revenue and profits Virtual Assistants and Chatbots Providing immediate customer support and interaction, enhancing customer satisfaction, saving on labor costs, and improving service efficiency Sentiment Analysis and Context Identification Analyzing customer reviews and social media content to identify emotional tendencies and contexts, understanding and responding to customer needs, improving service quality Monitoring and Preventive Maintenance Monitoring the status and performance of equipment, early detection of faults, and performing preventive maintenance to reduce downtime, improve asset performance, and save on maintenance costs Common AI Technologies or Applications Content Filtering, Collaborative Filtering Using AI such as content filtering or collaborative filtering algorithms to analyze customer preferences and behavior data, providing personalized recommendations and suggestions, increasing customer engagement, enhancing the consumer experience, and boosting customer loyalty Generative Artificial Intelligence Using natural language processing to analyze and understand customer text comments, social media, and online reviews, extracting sentiments, opinions, and keywords to help businesses rapidly identify and respond to customer needs utilizing generative AI like pretrained transformers for answering common questions, making suggestions, handling bookings, providing real-time customer interaction and support, saving labor costs, and enhancing customer experience Random Forests, Long Short-Term Memory Networks Using AI such as random forests or LSTM to analyze extensive sales data, predicting future product demands to better arrange inventory 卷積神經網路:使用人工智慧如卷積神經網路,可以識別和分析如食物、場景和設施等圖片和影片中的內容,用於菜單管理、餐點辨識、客房清潔檢查等項目,提高工作效率和品質控制。 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

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【2023#15】South Estate Precision Marketing & APP 2.0 Integration Project

Industry Accommodation and Food Services IMajor Category Accommodation Industry 55 Subcategory Industry Pain Points Significant seasonal demand fluctuations Some areas face reduced visitors and declining performance during the off-season Tourism experience and quality management Providing a high-quality tourism experience is a crucial goal for the industry, yet sometimes faces challenges with unstable service quality and customer complaints High demand for human resources Human resources are critical to the industry, but sometimes there is a shortage of workforce and insufficient skills, necessitating enhanced training and talent development Digital transformation pressure With the advancement of digitalization and technology, the industry needs to address challenges associated with digital transformation, online sales, marketing strategies, and technological innovation Benefits of AI Adoption Personalized services AI can analyze and understand the preferences, needs, and behavior patterns of visitors, thereby providing personalized travel recommendations and services, improving visitor satisfaction, increasing repeat visitation and loyalty, and facilitating business growth Smart interaction AI can act as an intelligent customer service assistant, such as virtual assistants or chatbots, answering visitor questions, providing immediate support, and customizing interactions based on visitor needs, enhancing the customer experience Accurate demand forecasting AI can analyze visitor data and market trends to forecast demand peaks and changes, helping to optimize inventory management, workforce allocation, and pricing strategy, thereby enhancing operational efficiency and revenue Customer feedback insights AI can analyze the reviews and emotional feedback of visitors to extract key information, quickly identify and respond to customer needs, improve service quality, and enhance customer satisfaction and reputation Enriched travel experiences AI can integrate technologies such as augmented reality and virtual reality to provide richer and more immersive travel experiences, attract visitors, add value to the travel offerings, and gain competitive advantages CommonAITechnologies or Applications Random Forest, K-means Clustering, ContentCollaborative Recommendations AI applications such as the Random Forest algorithm analyze large amounts of visitor data to learn and predict their preferences and behavior for demand forecasting AI like K-means clustering and contentcollaborative recommendation algorithms segregate customers, offering more accurate and personalized travel experiences and services Generative AI Using natural language processing techniques to analyze and understand visitor communications including website reviews, social media posts, coupled with AI like generative pre-trained transformers for automated dialogue responses, sentiment analysis, and opinion mining better understand and respond to visitor needs Convolutional Neural Networks Utilizing AI such as convolutional neural network algorithms to identify and analyze content in images and videos, such as visitor facial expressions and actions, to understand visitor reactions and experience, and to further improve service quality and travel arrangements 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

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【2023#13】Hanquan Intelligent Production Scheduling Engine

Industry Category Manufacturing CMajor Category Electronic Component Manufacturing Sub-category 26 Industry Pain Points Cost pressures and competition Fluctuations in raw material prices, increased labor costs, and equipment investments require companies to find ways to reduce costs and increase efficiency while maintaining product quality and competitiveness High supply chain complexity Multiple suppliers and regions make production scheduling management difficult, causing delays in delivery times and inefficient flow of goods and information Rapidly changing technology and market demands Companies need to continuously adjust their product mix and production capabilities to meet the market needs due to shorter product life cycles and rapid technological updates Benefits of AI Implementation Optimization of the supply chain AI can optimize material procurement, production planning, and inventory management It can also predict demand changes, optimize supplier selection and logistic routes, reduce inventory costs, minimize delivery delays, and enhance the efficiency and flexibility of the supply chain Demand forecasting AI can analyze market trends, consumer behavior, and competitive intelligence to predict market demand and trends This enables companies to plan for demand more accurately and make better production and planning decisions, reducing inventory risks and increasing efficiency and sales capability Automated production and quality control AI on production lines can monitor production status, detect defects, and adjust production parameters in real-time, increasing production efficiency, reducing defect rates, minimizing human error, and resource wastage Intelligent product design and RampD AI can be applied in product design and RampD, accelerating product design iterations, optimizing product structure and performance It also analyzes vast amounts of data and simulation results, assisting designers in making better decisions, reducing development time and costs, enhancing product innovation capability and competitiveness Common AI Techniques or Applications QLearning Using artificial intelligence such asQlearning to handle route optimization problems can improve decision-making accuracy and enhance the ability to make sequential decisions in uncertain environments Recurrent Neural Networks, Long Short-Term Memory Networks AI technologies like RNNs and LSTMs can effectively capture patterns and trends over time, which are very useful for predicting future order demands Random Forests, Support Vector Machines, Gradient Boosting AI technologies such as random forests, SVMs, and gradient boosting are suitable for predicting product sales trends, thereby more efficiently managing inventory 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

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【2023#14】Optimization Techniques for Mechanical Maintenance on Automation Production Lines: A Demonstration Project

Industry Type Manufacturing CMajor Category Electronic Component Manufacturing Subcategory 26 Industry Pain Points Cost pressures Fluctuations in raw material prices, rising labor costs, and increasing production costs Rapid technological change The need for continual updates and upgrades of production equipment and processes to meet market demands and improve product quality Challenges in global supply chain management Facing issues in transportation, logistics, and inventory management instability and disruptions in supply chains may cause delivery delays and production interruptions Digital transformation pressures The need to integrate data management, IoT applications, and artificial intelligence to enhance production efficiency and flexibility Shortage of human resources and skills The industry requires technically skilled workers and professionals, but faces a shortage of these resources There is a need to strengthen training and education systems to meet industry demands IntroductionAIBenefits Automated production Artificial intelligence enables automated production processes, reducing dependency on skilled workers For example, robots and automation systems can handle simple, repetitive, and hazardous tasks, relieving workers and enhancing production efficiency and quality Smart monitoring and maintenance AI can be utilized for monitoring and maintaining manufacturing equipment, offering real-time detection and prediction of equipment failures, facilitating proactive maintenance, and thereby reducing production disruptions and maintenance costs Effective management of resources and energy Artificial intelligence can optimize production scheduling, resource allocation, and energy use, cutting costs and waste, and providing accurate predictions and optimization suggestions through data analysis Data-driven decision-making AI can uncover hidden patterns and trends within extensive manufacturing data, offering decision support to optimize production processes, resource allocation, and supply chain management, improving efficiency and flexibility Skills training and knowledge transfer AI can assist in virtual training and knowledge management, helping train new workers and transfer knowledge from experienced masters, thereby bridging skill gaps and increasing the efficiency and quality of new employees' work Common AI Technologies or Applications Support Vector Machine AI technologies like Support Vector Machine algorithms can analyze manufacturing processes and related data to optimize production parametersschedules and resource allocation, thus improving equipment production efficiency Random Forest AI technologies such as random forest algorithms can analyze historical equipment data points such as temperature, pressure, and vibrations to create models and predict the likelihood and causes of equipment failures, aiding operators in planning maintenance Generative Artificial Intelligence Artificial intelligence technologies like generative pre-trained transformers can develop smart assistants providing real-time guidance and training, combining natural language processing to analyze, preserve, and transfer the knowledge of seasoned workers, accelerating the learning and problem-solving skills of newcomers Convolutional Neural Networks and Q-Learning Utilizing AI technologies like convolutional neural networks and Q-learning to develop smart robots, executing simple, repetitive, and hazard-detection tasks, autonomously gathering data to study and optimize workflows 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

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[2023#12] Trial of AI Technology SaaS Model in Clinics to Enhance Medical Quality in Remote Areas

Industry Professional, Scientific and Technical Services MMajor Categories Other Professional, Scientific and Technical Services Subcategories Industry Pain Points Insufficient Medical Resources Some community clinics in certain areas face problems of insufficient medical resources, including a shortage of doctors and nurses, limited diagnostic equipment, and issues with the supply of medications and medical supplies, affecting the accessibility and quality of community medical services High Labor Costs Community clinic medical services, often being small facilities, face pressures related to the recruitment and retention of medical professionals Insufficient Information Systems Certain community clinics may suffer from inadequate information systems, for instance, difficulties in adopting and utilizing digitalization which limits the capability for information sharing and collaboration Difficult Collaboration Effective medical collaboration can enhance the efficiency of patient referrals and joint medical management, but the mechanisms and processes for cooperation may not be well established Financial Pressures Medical insurance policies may impact the operation and financials of community clinics AI Implementation Benefits Enhancing Diagnostic Precision and Efficiency AI in diagnostics can help doctors interpret medical images, analyze test results and medical records more accurately, thereby improving diagnostic precision and efficiency This aids in avoiding misdiagnoses, reducing unnecessary tests and referrals, thus saving time and resources Optimizing Medical Procedures and Resource Utilization AI can assist community clinics in optimizing medical processes and resource utilization, such as intelligent scheduling systems that schedule based on the characteristics of doctors and patients, reducing wait times and wastage, enhancing treatment efficiency Prevention and Health Management AI can analyze personal health data to forecast potential disease risks and offer personalized prevention and health management advice, aiding in early detection and prevention of chronic diseases, reducing medical burdens Automation and Smart Services AI can enable automated and smart medical services in community clinics, such as smart prescription systems that automatically generate prescriptions, reducing human errors, and robotic nursing assistants providing basic care, easing the workload of medical staff Data Analysis and Predictive Modeling AI can help community clinics better predict needs, optimize resource allocation, and make management decisions through data analysis and predictive modeling, improving operational efficiency and economic performance, reducing waste and costs CommonAITechnology or Applications Decision Trees In scenarios with clear rules and standards, AI such as decision tree algorithms can predict future scheduling needs based on factors like employee work time preferences and customer booking patterns Q-learning In response to continually changing demands and conditions, such as on-the-fly scheduling or changing customer needs, AI like Q-learning can learn optimal decision-making strategies through continuous trial and error Convolutional Neural Networks AI like convolutional neural networks employed in image analysis can automatically detect and examine medical images, assisting doctors in diagnosis and treatment planning Convolutional Neural Networks, Transformer-Based Bidirectional Encoder Representations AI such as convolutional neural networks along with transformer-based bidirectional encoder representations can automatically extract and organize information in medical document files such as medical records, test reports, and prescriptions, speeding up the document processing workflow, reducing human errors, and enhancing effective document retrieval and sharing Generative Artificial Intelligence Natural language processing technologies can understand and process human language, engaging in dialogues with patients Using generative AI like pretrained transformers, developers can create smart voice assistants and chatbots, allowing patients to interact with clinics through voice or text, providing basic medical consultation, answering questions, and offering health advice 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

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【2023#11】DeepBT Detector Advancements and Field Validation

Industry Healthcare and Social Work Services QMajor Category Healthcare Industry 86 Sub-categories Industry Pain Points Rising Medical Costs Continuous increases in the cost of medical services, medications, and equipment pose burdens on both patients and healthcare systems Imbalance of Medical Resources Insufficient medical facilities and professional staffing in rural areas Medical Data Privacy and Security Medical institutions need effective data protection and security measures to prevent misuse or breach of sensitive information Medical Quality and Safety Existence of medical errors and disputes Improvements in medical quality and safety could include enhancing diagnostic accuracy and treatment outcomes, fostering communication, training medical personnel, and strengthening the monitoring and reporting of medical errors AI Benefits Enhanced Diagnostic Accuracy and Efficiency AI can analyze and interpret vast amounts of medical imaging, lab results, and patient records, aiding doctors in making more accurate diagnoses, which improve patient treatment outcomes and reduce the risk of misdiagnosis Optimized Treatment Plans and Drug Development AI can identify disease patterns and treatment connections, optimizing treatment plans and drug development Improved Medical Efficiency AI can automate and optimize medical processes For example, medical service robots can enhance appointment efficiency, and intelligent monitoring systems can monitor patient status in real-time, reducing reliance on nursing staff Disease Prevention and Monitoring AI can leverage extensive data to identify potential disease risks and trends, assisting in early warning and preventative measures It can also offer personalized health advice based on patient data Reduced Medical Costs AI can help medical institutions reduce costs, such as automated document management and processing which cut down on labor expenses, intelligent drug management systems that decrease drug waste and inventory costs, and predictive analytics and risk assessments that facilitate efficient resource allocation, lowering unnecessary medical expenses Common AI Techniques or Applications Random Forest AI like the Random Forest algorithm can effectively handle data sets with high-dimensional features such as genomic data, performing excellently in identifying disease risk factors and predicting treatment responses K-means Clustering AI such as the K-means clustering algorithm can identify similar patterns and anomalies in patient groups, thereby aiding doctors in understanding disease heterogeneity and formulating more personalized treatment plans based on the specific characteristics of different patient groups Convolutional Neural Networks AI such as convolutional neural networks can automatically detect and analyze lesions in medical imaging, assisting in diagnosis and also applicable in tumor detection and building medical image databases Convolutional Neural Networks, Bidirectional Encoder Representations from Transformers BERT AI like convolutional neural networks and transformer-based BERT can process and analyze medical documents and records assist in communications and information extraction between doctors and patients Support Vector Machines, Principal Component Analysis AI like Support Vector Machine algorithms assist researchers in identifying key features in complex biological data, thereby speeding up the drug development process Principal Component Analysis helps researchers identify relationships and differences between samples, critical for understanding how drugs affect different biological molecules and pathways, and screening for drug candidates with potential therapeutic effects 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

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[Free registration] AI Application Competition and AI+ New Talent Selection Results Presentation on October 31

In 2023, the AI Application Competition and the AI New Talent Selection will jointly hold their results presentation and exhibition for the first time Exhibition areas will include four major themes medical biotechnology, commercial services, industrial manufacturing, and safety monitoring, bringing together 35 companies and nearly 50 innovative and explosive teams to gain an in-depth understanding of how AI can assist medical professionals in interpreting lesions, predicting the impact of drugs on diseases, and how to improve the quality of life of elderly people through semantic recognition, assist factories in establishing hazard warning systems, and drive innovation in industrial process development At the same time, an AI application pitch competition will be held on stage that day, and teams will be invited to share relevant technological breakthroughs We sincerely invite you to attend this year's dual competition results presentation Let us share how AI provides clever solutions to different challenges 【Event Details】 Date and time 1031 Tuesday 1000-1700 Event Location Songshan Cultural and Creative Park 2F Multi-Showcase Exhibition Hall 2F, No 133, Guangfu S Rd, Xinyi District, Taipei City Registration LinkhttpsseminarstcaorgtwD15r00565aspx 【Consultation contact person】 Ms Zhang 02 2570-63379925,AITalentaitalentsorgtw The organizer and executing organizer reserve the right to modify, supplement and cancel nbsp

2023AI新創青年共創黑客松DemoDay圖片2
Outstanding Teams Emerged in the "AI Innovation Youth Co-creation Hackathon"

For the first time this year, the "AI Innovation Youth Co-Creation Hackathon" was organized by the Administration for Digital industries ADI and implemented by the Institute for Information Technology III and the IAPS Accelerator of National Yang Ming Chiao Tung University NYCU The event invited juniors and above in college and people who recently graduated within the past two years to jointly explore and propose solutions that can actually solve market problems and social needs The solutions include six major themes ESG, AI, long-term care applications, automation, fraud prevention, and exercise and health Innovation mentors work with young students to inspire the young students' cross-domain communication, creativity, spirit of cooperation, and problem-solving abilities, and increases youth participation in the industry and forward-looking technologies This event attracted a total of 25 teams from colleges and universities across Taiwan to sign up After five workshops held in August, they worked hand in hand with innovation mentors to create innovative technology solutions that can actually solve market and social needs After being assessed by the industry, 14 teams were selected to participate in Demo Day held at the TTA performance space on September 27 The 14 teams participating in the Demo Day are as follows Uneven Road Shih Hsin University, Tumbler NYCU, Neural Engineering Laboratory Group R NYCU, YunTech Elderly YunTech, Diagnostica NYCU, IMAX makes people suffer Shih Chien University , I'm easy to fool National Taipei University, RRR Generations National United University, Laundry Room Stranger Chang Gung University, NYCU_Ravenclaw NYCU, Can you give me your hat Yuan Ze University, Ask the Magical Conch Chang Gung University, Black amp White Chung Yuan Christian University, Land Divers Aletheia University 14 innovative applications across six major fields, all with very interesting design concepts The innovation mentors assisted teams with developing innovating ideas into digital applications with potential for commercialization during the workshops The Demo Day event gathered together more than 50 guests from industry, government, and academia, as well as many students from Southeast Asia, which shows the importance attached by different sectors to the event In the end, four outstanding teams were selected by the 9 industry judges High distinction Neural Engineering Laboratory Group R Excellent NYCU_Ravenclaw Two honorable mentions Can you give me your hat Laundry Room Stranger After two months of intensive activities, the participating teams received recommendations from experts in industry and academia for practical applications and business models, which not only increased the maturity of the team's technical concept, but also enhanced the participants' sense of participation in industry and global trends We expect that these innovative technology applications will bring changes to society and create more value nbsp nbsp

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Ministry of Digital Affairs Administration for Digital Industries Announcement of the Second Batch of the AI Technical Service Capability Registration and Application Mechanism in 2023, Applications Now Being Accepted!!

The Ministry of Digital Affairs MODA Administration for Digital industries ADI guides the direction of research and development RampD of technology and application services by domestic digital service providers, and provides certifications of verified performance, so as to enhance the resilience R, integration I, security S, empowerment E, and competitiveness of the industry, and drive the growth of digital economy-related industriesThe AI technical service capability classification and registration mechanism was specially planned and established to take stock of domestic AI technical service capabilities through a credible classification and registration mechanism It compiles a map of the domestic AI industry, and assists information service providers with developing more intelligent products and services, in order to expand industrial services and scale, accelerate the application of AI in the industry, and increase industrial value and competitivenessThose that pass the AI technical service capability registration will gain greater creditability as an AI service provider and be connected to the AI HUB httpsaihuborgtw, matching supply and demand, assisting AI service providers with expanding business opportunities, and enhancing their competitiveness in the industry It can also be referenced for eligibility in future government-related guidance programs, and will be actively recommended to AI-related subsidy programs and venture capital platforms to assist enterprise development The Ministry of Digital Affairs Administration for Digital Industries now accepting applications for the second batch of AI technical service capability registration in 2023 and welcomes applicants nbsp I Definition of technical scope The definition of AI in the capability registration refers to the realization of simulated human cognition, machine autonomous inference, or knowledge work abilities in specific fields or general fields through new modeling methods, such as machine learning, deep learning, and neural networks This is used as the core business or integrated it into existing industries, software and hardware integration, or consulting service solutions Please note that traditional statistical techniques, such as regression analysis, are not within the scope of this capability registration II Eligibility Software and information service related institutions including for-profit institutions, non-profit institutions, and schools that completed business registration within the territory of the Republic of China in accordance with the law III Application period From now until 1800 on October 27, 2023 Friday IV Application method Please go to the link httpswwwcisatwAIloginindexphp before 1800 on October 27, 2023 Friday to apply for a user account and password, fill in basic information online, and download and fill out related documents Upload the electronic documents in 1 to 3 before the deadline that was announced I Application Form and Affidavit for the MODA ADI's 2023 AI Technical Service Capability Registration see Attachment 1 for the format II Proposal Form for the MODA ADI's 2023 AI Technical Service Capability Registration see Attachment 2 for the format III Required attachments of the proposal 1 A photocopy of the certificate of company registration or business registration issued by the central competent authority 2 The most recent tax bill for profit-seeking enterprise income tax, balance sheets, and statement of comprehensive income Or a photocopy of the approval notification for the declaration of income derived from professional practice 3 Resume of full-time employee 4 A photocopy of documents proving the performance of AI products or professional services Validity period of performance data 2021-2023 V Online Consultation Meeting To help companies understand the classification and key points of application for AI capability registration, an online consultation meeting Online Teams meeting the meeting link will be sent two days before the event will be held from 200 to 600 pm on October 18 Wednesday Registration linkClick here to register,10 minutes per company, one-on-one , QampA, welcome to sign up VI Consultation hotline 02-25533988 extension 385, Mr Yeh nbsp For attachments and more information, please seeLink

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【2023#06】Emergency Disaster Relief Material Dispatch and Replenishment Decision System

Industry Publishing, Media and ICT Industry JMain Category Information Services Industry 63Subcategory Industry Pain Points Material Storage and Replenishment In the event of a disaster, there is a need for a large amount of emergency relief materials, such as food, water sources, medical supplies, and rescue tools However, effectively storing and replenishing these materials while ensuring their timeliness and appropriateness is a challenge Determining the types and quantities of materials needed, as well as how to replenish them quickly after a disaster occurs, are problems that need to be addressed Materials Dispatch and Distribution After a disaster, effectively dispatching and distributing stored materials to affected areas is critical It is necessary to consider factors such as the needs of the disaster area, severity of the disaster situation, and transportation and communication conditions to ensure that materials are delivered quickly and prioritized for those most in need Risk Assessment and Forecasting The occurrence of natural disasters often has uncertainties, but through risk assessment and forecasting, preparation can be done in advance and material dispatch strategies can be optimized to deploy relief materials and human resources ahead of time, improving response capabilities Information Management and Coordination Disaster relief operations involve the participation of multiple organizations and institutions, including government departments, non-profit organizations, volunteers, etc Effective information management and coordination mechanisms can facilitate information sharing, task allocation, and collaborative coordination, ensuring an efficient operation of relief materials and maximum service coverage AI-Driven Benefits Increased Efficiency Artificial Intelligence can automate and optimize the processes for material dispatch and replenishment, rapidly analyze disaster situations and needs, and generate optimal material dispatch plans, improving the timeliness and accuracy of materials Additionally, AI can promote information sharing and collaboration among various organizations and institutions allowing different units to share real-time data and intelligence, participating together in the decision-making process for material dispatch and replenishment, thus enhancing the overall rescue efficiency Cost Reduction Using artificial intelligence to assess and predict the likelihood, intensity, and scope of disasters can avoid over-storage or waste at the same time, it can better plan and manage the storage and replenishment of supplies, making material dispatch more precise and effective, thereby reducing costs of manpower, materials, and transportation Optimized Resource Allocation Artificial intelligence can accurately calculate material needs of different disaster areas and demand points based on meteorological or geographical data, optimizing resource allocation to ensure that supplies are prioritized to areas and populations most in need, enhancing rescue effects while reducing resource wastage and losses Common AI Techniques or Applications Convolutional Neural Networks Utilizing artificial intelligence like convolutional neural networks to process and analyze satellite images of the disaster area, identifying damaged areas and routes, helps in assessing the situation in the disaster area and planning the allocation of rescue resources Long Short-Term Memory Networks Utilizing artificial intelligence like long short-term memory networks to analyze time-series data, such as historical disaster data and meteorological changes, to predict future trends in material needs, thus optimizing the allocation plans for materials and rescue personnel 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

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【2023#10】Defect determination of oil seal component elastomer after automatic truing machine

Industry Manufacturing C category Plastic products manufacturing industry 22Medium category Industrial Pain Points High labor costs and deficiencies Oil seal component refurbishment usually requires a large amount of labor, including disassembly, cleaning, inspection, repair, etc, resulting in high labor costs and possible labor shortages Low production efficiency The oil seal component refurbishment process can be tedious and time-consuming, and may require multiple steps and manual operations, making the refurbishment process inefficient and potentially limiting production capacity Quality control is difficult The quality of oil seal components is crucial to their performance and life During the refurbishment process, there may be human factors and inconsistencies, leading to quality control challenges Difficulties in data management and tracking Effective data management and tracking are required for the process providing traceability and quality assurance and results of oil seal component refurbishment, and a complete system is required to confirm the accuracy and reliability of the data Traceability Import AI benefits Improve detection efficiency and product quality The automatic truing machine combined with artificial intelligence can quickly and accurately detect defects in oil seal components including size discrepancies, surface defects, etc without the need for manual inspection one by one, greatly improving the efficiency of detection And reduce the production of defective products, thereby reducing the cost of after-sales repairs and returns Reduce labor costs The automated defect determination process reduces the need for manual participation, and companies can use valuable human resources for other more valuable tasks Optimize the production process Artificial intelligence can analyze the data in the production process and find potential improvement points to optimize the production process Improve customer satisfaction Artificial intelligence can improve detection efficiency and product quality, enhance customer confidence in products, and enhance customer satisfaction and loyalty Improving technological innovation capabilities Using artificial intelligence in the refurbishment process of oil seal components can continuously improve and optimize technology and improve the competitiveness and market position of products Common AI technologies or applications Support vector machine Using artificial intelligence such as support vector machine algorithms, extract and select the most representative features that can distinguish normal and defective oil seals Convolutional neural network Use artificial intelligence such as convolutional neural network algorithm to automatically learn features from pictures of oil seal components and perform defect detection and classification 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

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