IBM's supercomputer Watson can predict when employees are likely to resign, with an accuracy rate of 95, saving IBM up to 300 million a year in retaining employees Moreover, through cloud computing services and modernization, IBM has streamlined 30 of personnel costs, allowing the remaining employees to earn higher salaries and engage in more valuable work So, in Taiwan, how can we ensure that 'employees who stay can receive higher salaries and perform more valuable work' The key lies in the 'competence setting' for each position According to the 'iCAP Competency Development Application Platform' established by the Ministry of Labor's Workforce Development Agency, every position has its main responsibilities, work tasks, behavioral indicators, work outputs, knowledge, skills, and attitudes Only by establishing 'competency' for each position can enterprises effectively apply this in employee recruitment, education and training, and performance management Without this, not knowing what employees should do is like groping in the dark, which can pose risks to business operations Competency Benchmark Example Currently, on the 'iCAP Competency Development Application Platform', there are 872 established competency benchmarks, including 553 items completed by various ministries This includes 253 items from the Ministry of Labor and 66 items from the Ministry of Education If companies want to establish their own 'competency benchmarks', they need to search for reference materials on the 'iCAP Competency Development Application Platform' Suppose a company wants to recruit 'sales' personnel but doesn't know what 'sales personnel' should do they should first search for 'sales personnel' as shown in the figure below Searching for 'sales' on the 'iCAP Competency Development Application Platform' You can find that there are 18 types of sales personnel At this point, the company needs to go through each one, check, read, and organize into the 'competency benchmarks' they need however, if we search what should be a common position in any company, 'general affairs', the result is unexpectedly zero items Searching for 'general affairs' on the 'iCAP Competency Development Application Platform' As seen above, although the 'iCAP Competency Development Application Platform' established by the Ministry of Labor's Workforce Development Agency can solve some of the 'competency benchmarks' for positions, the division of labor within each company is different, and some positions might not be found on the 'iCAP Competency Development Application Platform' Secondly, in small and medium enterprises, there are often 'multi-skilled workers', meaning many job responsibilities are on a single employee For example, in small enterprises with less than 30 people, usually, accounting, general affairs, and HR are handled by the same person If you want to establish competency benchmarks for this person, you have to search separately for 'accounting', 'general affairs', and 'HR', and then integrate these three types of job competencies, which is often time-consuming and ineffective This 'Small and Medium Enterprises AI Competency Evaluation System' aims to let 'people fully utilize their capabilities', by introducing AI to more accurately establish basic competency standards for employees, and to track their competency performance at any time Competency models are all generated and adjusted manually, which is time-consuming A domestic exporter of screws, nuts, fasteners, etc, had all its competency models generated and adjusted manually The execution process was time-consuming and insufficient to meet company needs due to personnel changes, such as previously, Qiao Mai Enterprise had specialized 'production control personnel', but after this personnel resigned, this job had to be done by other employees, meaning other employees' competency models needed to be adjusted immediately Or if the company needed to set up a development department due to future development, but previously no one had relevant experience, not only did they not know how to select from within, but also did not understand how to describe on a recruitment website to find the talent they really wanted Besides, the CEO of this company has always been troubled by internal performance management Due to the lack of precise standards and systems to measure employee performance, the results of each performance assessment did not accurately reflect the true performance of the employees, forming assessment blind spots and unable to identify truly deserving employees Thus, it is hoped that with the AI competency evaluation system, the necessary competencies for the development department can be immediately clarified, as well as how recruitment and performance appraisals should be conducted, so as to effectively solve the pain of unclear responsibilities and inaccurate assessments within the company Thus, its benefits are significant AI Competency System Establishment X Deep Learning This 4-month HR field competency system project has a clear execution direction, but the introduction of explanatory models such as Seq2Seq, Deep Keyphrase Generation, Tf-IDF keyword extraction algorithms, and PageRank are new attempts in the HR field During the process, open-source big data architecture is used for natural language processing to complete Word2Vector and index, and inverted index to establish keyword weight and relevance Due to the inability to process like image data with continuous numbers, it is necessary to simplify the feature values with related keywords such as skills, knowledge, and job categories Basic steps are briefly described as follows 1 Establish a Propagation model using Google's long-used LTR mixed Pointwise recommendation engine 2 months 2 Establish a Back Propagation model 2 months, adjust the hyperparameters of the loss function 3 Adjust the hyperparameters of the CF model 4 Establish a human-machine collaboration mechanism to obtain more data to feed the Model 5 Repeat the above steps During the development process of the competency model, Lianhe Trend Co, Ltd and Weiguang International Information Co, Ltd held multiple discussions, believing that there are interconnections between competencies After establishing the knowledge graph, further upload the competency scale to the Neo4j graph database for processing complex relational data structures with excellent performance Currently, 500 competency scales have been uploaded to the Neo4j relationship analysis platform Using python for wor2vector natural language analysis In addition to describing a position with a tensor after word2vector, finding out the appearance of this position's knowledge graph, according to this knowledge graph, one can understand the relevance between different positions and the similarity performance of their dimensions Finally, this knowledge graph is used to establish the company's 'competency model' and train it with deep learning AI Competency Evaluation System Interface In the future, in addition to establishing their own competency models, companies can also be opened to end-users Individuals can analyze their own competency performance to understand their possibilities for job change and their market value, as well as identify skills needing enhancement If companies respond to this knowledge graph, they can develop cross-industry products in the future 1 Short-term Analyze the competency scales iCAP, iPAS published by the government with natural language and keyword models, and cooperate with unsupervised learning to establish 'Native Competency Base Unit Models' 2 Medium-term Tailor-made exclusive competency models for enterprises Based on the existing 'Native Competency Base Unit Models', experts use supervised learning to train the individual company's 'Distributed Derivative Competency Models' 3 Long-term Establish 'Reinforcement Learning' models, incorporating employee career cognition and planning Competency model recommendations, comparable to professional human resource consultants Through the dynamic learning of the competency knowledge graph through unsupervised learning, individual companies' competency models are quickly established Internal human resources personnel or external professional HR consultants can then use the generated competency models to assess and apply aspects of talent recruitment, competency inventory, performance management, and education and training The system will automatically suggest competencies to be strengthened according to the company's existing job structure, including related knowledge, skills, and attitudes Through the continuous introduction and training of data, the system learns the employer's actual view of the model for that profession and feeds back to the cloud competency scale, completing the dynamic learning of the knowledge graph through transfer learning In the future, it can be comparable to professional HR consultants, thereby rapidly assisting many cross-disciplinary or technologically diverse companies in training employee competencies「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

In response to current trends, digital learning and mobile educational materials have attracted widespread attention With rapid technological advancements, effectively nurturing professionals who can 'adapt to developmental changes' is a critical concern that many businesses continually consider Over recent years, various enterprises have progressively integrated 'digital learning' into employee training programs to enhance educational outcomes, thus bringing 'digital learning' and 'mobile educational materials' into the limelight Outsourced narration is costly and cannot handle large volumes of demand Differences in the digital educational material production process before and after the implementation of the AI voice synthesis system Strategic Breakthrough Corporation of Taiwan has assisted companies in converting many seminars, physical courses, and training events conducted by public sectors into digital materials in the past years However, during the conversion process, it required inviting teachers, finding and renting filming locations, and post-production of recordings and videos During recording, issues such as speakers' nervousness, discomfort in front of cameras, or mispronunciations might lead to poor recording quality or constant retakes Though there was an option to provide customer-specific educational material narration, the outsourcing costs were high and could not handle the demand efficiently Therefore, there was a hope to introduce AI speech synthesis technology and develop an 'Intelligent Voice Synthesis Module' to instantly convert text on slides into natural, human-like voice files, thus saving on narration costs Realistic Intelligent Voice Synthesis Module, providing a diversified selection of voices AI Voice Synthesis Module Illustration Strategic Corporation of Taiwan collaborated with the AI technology team, Magic Cube Digital Ltd, using Tacotron2 combined with WaveNet and Tacotron features Characters are embedded into Mel-scale spectrogram plots, then a modified WaveNet model acting as the vocoder synthesizes waveform in the time domain from these spectrograms, finally developing an MOS Mean Opinion Score for voice quality evaluation that approximates human-like intelligent voice synthesis modules This AI Intelligent Voice Synthesis Module, after being tested by testers using the MOS voice quality evaluation standard, received a score of 43, meeting the initial project target score of 421 and surpassing WaveNet's score of 408, thereby demonstrating exceptional effectiveness AI Intelligent Voice Synthesis Module, reducing costs and increasing profits, will effectively enhance Taiwan's digital learning industry environment Costs have been significantly reduced after the implementation of the AI voice system, and profits have increased relatively This AI Intelligent Voice Synthesis Module not only reduces the cost of producing digital educational materials but also solves the difficulties faced by Taiwan's industry, government, and academia in spreading digital educational materials It can effectively enhance the efficiency of customers in producing digital teaching materials, significantly reduce labor shortages, and cost structural risks, and improve profitability Strategic Corporation of Taiwan will also continue to develop the 'Intelligent Transcription Module' and introduce Robotic Process Automation RPA to replace the current manual processes, such as captioning, dubbing, and file conversion in the production of digital educational materials, assisting in the transformation and enhancement of the domestic digital learning industry「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

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

Of the hundreds of activities, which one is your favorite There is a wide variety of activities every day in Taiwan, including forums, exhibitions, lectures, and free experiences Event organizers need to use their own media event official website, Facebook, Instagram, event websites, and pay media for marketing, but often do not know where the target customer is, and cannot accurately estimate the number of attendees Records of people's participation in various activities are used through this "Automated Exhibition Matching System," and the data is analyzed to predict what type of activities users like It automatically matches activities with users to provide fast, easy, and accurate marketing and promotion methods There are many types of activity themes, marketing and advertising costs are high, but results are poor A domestic curation company is working with township offices and tourism service providers in the marketing of rural villages It organizes a variety of activities every year, such as agricultural product exhibitions, rural experiences, parent-child themed experience days, and agricultural specialty product marketing Due to the vastly different characteristics of participants in the activities, which have different themes, effective and accurate market cannot be carried out when promoting the activities, which can easily lead to a significant increase in marketing expenses and low matching rate For example, when the curator organizes an exhibition with 200 booths in the venue, the overall marketing cost is about NT800,000 to NT12 million, in which construction of the official website, marketing on the event website, and text message notifications account for about NT400,000 to NT600,000, but the event's matching rate is less than 20, and precision marketing aimed at the target customer group cannot be carried out After adopting the "Automated Exhibition Matching System," it can automatically select suitable customer groups for push notifications Depending on the scale of the event and the exhibition period, the system rental fee is only about NT200,000 to NT300,000, significantly reducing event promotion costs Precision smart marketing, distributing coupons to target groups The "Automated Exhibition Matching System" currently has an accuracy of 82 and can effectively screen target consumers In terms of module accuracy, large amounts of data and data that no longer contains noise will be used to further improve the accuracy in the future Coupons will be distributed to target groups, so that groups that receive the coupons will actually participate in the event After adding value through AI, the system can replace the manual random distribution of coupons without a specific target The AI module can automatically adjust the weights to more accurately lock on to the target group The weighted formula uses the CRM system of the curation company to output analysis of member behavior in the past In the second stage, the AI weighted formula will be used to find the best calculation formula for different activity categories through automatic correction Service Framework of the Automated Exhibition Matching System During the implementation period, Fengchun Technology was troubled by the problem of "the AI classifier module training and learning not finding the best solution" After discussions with AI engineers, the company found that a shortcoming of "back-propagation neural network" is that it only finds the "local" best solution rather than the "global" best result during learning The goal of improving accuracy can be achieved by increasing the number of training times and adjusting parameters Expand system functions, connect member databases, and conduct behavioral analysis The "Exhibition Event Matching System" mainly provides event organizers, independent curators and the public with event matching Next, the platform functions will be expanded for use by event participants, and the accuracy of the AI classifier will be improved In the future, an ocean culture exhibition will be organized with the ocean industry, and use this system to find ocean culture promoters, connecting and expanding member databases for behavioral analysis

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

Best practices in AIIoT implementation As our country enters the first year of 5G commercialization this year, the integration of the Internet of Things with artificial intelligence to transmit data with zero delay will enable everyone to effectively manage all data Toilet 'odor monitoring' has become the best platform A certain chain supermarket in the country has 47 stores nationwide and in recent years, as competition in the supermarket industry intensifies, some stores plan seating areas and toilets for customer use Currently, the average number of customer complaints about toilet cleanliness in a certain store of the chain supermarket is about 10 times per month, which is notably higher than other stores, thus they hope to solve the problem of high complaint rates through artificial intelligence Customers frequently complain about dirty toilets The toilets of a certain store of the chain supermarket are inspected at 12 PM and 6 PM daily, and cleaned during the night shift Customer service staff often receive complaints about the dirty and smelly toilet environment, causing the need to constantly deploy manpower for toilet maintenance To achieve a 100 odor-free toilet, it would be necessary to employ a cleaning staff member permanently present in the toilet, however, this solution is too costly and wastes manpower Through a collaboration between Guo Xing Information Co, Ltd and the chain supermarket, the National Taichung University of Science and Technology AI team was commissioned to address this vexing issue using IoT and AI technologies IoT Monitoring x AI Manpower Dispatch Guo Xing Information equipped the toilet door locks with IoT sensory devices, and installed 'odor sensors' and 'air temperature and humidity sensors' outside the cubicles By monitoring the behavior, frequency, and timing of door usage, it predicts the cleanliness of the cubicle If a person opens the toilet door and closes it quickly, and if more than three consecutive people exhibit the same behavior, it predicts that the cubicle is dirty enough to require cleaning In terms of manpower dispatch, the system predicts staffing needs based on user frequency, holidays, and festive events, dynamically adjusts manpower reserves, and calculates the minimum staffing needed to maintain toilet comfort Service architecture of the Smart Toilet Quality Monitoring Platform This 'Smart Toilet Quality Monitoring Platform' is installed in the open-area toilets of business premises, collecting data such as usage frequency, time, odor intensity, air temperature, and humidity, and transmitting it to the platform for AI data analysis This enables management to understand the real-time usage, frequency, and dirtiness of the toilets, providing alerts to dispatch cleaning staff and take responsive actions It also assists managers in environmental quality monitoring and dirty conditions predictive dispatching Through historical data analysis, it suggests dynamic manpower deployment during different time intervals for effective human resource management and utilization Smart toilet detection, reducing cleaning labor costs After field tests of the Enhanced AI Smart Toilet Monitoring Platform, the retailer found the real-time monitoring and alert features extremely practical and is willing to continue using them Regarding 'reducing the number of complaints', a one-month data validation showed a significant effect, while 'dynamic manpower dispatch' is still under evaluation and validation After a month of data evaluation, a noticeable improvement was found in 'monitoring toilet usage' and 'reducing complaints' After trial use by the retailer, they are also willing to continue using the system In the future, notifications will be made according to 'usage time' to prevent accidents within the cubicles There will also be future deployments and promotions priced at low, mid, and high levels「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

Combatting the epidemic is like fighting a fire With the increasing influx of returning residents, the pressure of Wuhan virus quarantine is mounting, consuming more time Shortening quarantine times will positively benefit disease prevention efforts At a hospital in Southern Taiwan, the 'Smart Medical Clinical Decision Support System' has helped reduce the time taken for high-risk patients from entry at the quarantine station to clinical decisions by doctors It originally took about 2 and a half hours, but now it's down to less than 30 minutes, increasing the quarantine efficiency by 5 to 6 times and significantly reducing the risk of cross-infection between medical personnel and patients, as well as the manpower needed for quarantine As waves of overseas students densely return to Taiwan, not only the Central Epidemic Command Center, but also various medical institutions are tightening up, closely monitoring every quarantined individual There are also concerns about the potential infection risks to colleagues, which is exhausting At this point, employing AI technology to enhance quarantine efficiency is indeed a great blessing for the medical units and the health of the nation AI-Assisted Medicine Multiplies, Becoming a Hero in Pandemic Control To combat the severe pandemic of novel coronavirus, the hospital has integrated various smart medical techs and developed the 'Smart Medical Clinical Decision Support System', raising quarantine efficiency by 5 to 6 times It has shortened the time taken for high-risk patients from entering the quarantine station to doctors making clinical decisions from 2 and a half hours to less than 30 minutes, effectively reducing the risk of cross-infections The 'Smart Medical Clinical Decision Support System' implemented by the hospital includes three components front-end automation of medical records, AI-assisted interpretation of chest X-rays for diagnosing pneumonia, and continuous updates of clinical decisions based on the latest epidemic data provided by the health department This significantly enhances the hospital's response and decision-making ability in quarantine and epidemic prevention, and greatly benefits Taiwan's anti-epidemic efforts through the multiplicative effect of AI-assisted medicine National Cheng Kung University collaborates with the hospital, using smart medical technology to enhance quarantine efficiency Photo source Official website In the aspect of medical record automation, existing medical institutions often use traditional paper or verbal reporting, which potentially increases the risk of contact infections among medical staff and patients The automated medical records system in this hospital allows patients to fill out their own medical history, including travel, occupation, contacts, and clustering, using tablet computers These records are uploaded to the electronic medical record system, enabling immediate access by medical staff to make clinical decisions Each tablet is disinfected with alcohol after every use, reducing the risk of cross-infection and enhancing the efficiency of the quarantine station The hospital's Wuhan pneumonia screening shows a sensitivity and accuracy of up to 80 and 90, respectively The 'Chest X-Ray AI Interpretation for Pneumonia System Model' developed by the hospital's Department of Radiology, with active participation from Professor Yong-Nian Sun's team at the College of Electrical Engineering and Computer Science Utilizing a tuberculosis X-ray AI auto-interpretation model developed by a previous AI biotech medical innovation research center project, it was adapted to the hospitals' pneumonia imaging data The collaboration between the parties ensured rapid completion Currently assisting in over 152 suspected Wuhan pneumonia screenings, sensitivities and accuracies of up to 80 and 90 have been achieved, respectively Moreover, for students conducting in-home quarantine at school dormitories, the university has adopted a smart monitoring approach with a 'Warm Heart Smart Bracelet' developed by a cross-disciplinary team, which continuously monitors quarantined individuals' body temperatures and heart rates as indicators for predicting symptoms When a rise in body temperature is detected, individuals can proactively confirm abnormal symptoms via a smartphone app and be prompted to seek medical attention Currently, bracelets are collected weekly and data is centrally uploaded to a cloud platform by the management staff for ongoing tracking, wholly enhancing the level of pandemic control internally and externally The university's cross-disciplinary team uses 'Warm Heart Smart Bracelets' to implement home quarantine policies effectively Photo source Official website「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

Fast fashion in clothing, small quantities, diverse styles, short delivery times The textile industry faces the impact of the fast fashion trend among clothing brands, affecting the entire supply chain Global brand channels are promoting zero inventory, short delivery periods, and small-scale customization Balancing production time, quality, and cost is challenging Often, there is a discrepancy between ODM predictions and actual demands from brand owners, causing issues in material management and excessive inventory costs Due to inaccurate demand forecasts from customers, it often leads to difficulties in material preparation Excessive materials can increase leftover stock, while insufficient materials may delay delivery This project aims to establish an AI-based material demand forecast model specifically for major domestic manufacturers AI calculates sales trends to further predict demand The advisory team collaborates with Shentong Information Technology to mainly use the LSTM algorithm for the AI foundation The goal is to predict the next sales cycle based on past sales records, utilizing simple regression to complex 'Time Series Analysis' in statistics Usually, a period's sales volume closely relates to the previous period's, unless there is a major event, in which case it would typically follow a pattern There are various patterns of sales volume forecasts, including revenue, profit, customer counts, park visits, sales numberamount, etc This will take the example of a factory's monthly shipment batches, using the LSTM model to predict the next month's shipment batches Material Demand Analysis Execution Framework This project plans to establish a customer-specific material demand AI prediction model During the planning phase, three different machine learning algorithms were used to prototype the AI model Logistic Regression Algorithm Gradient Boosting Algorithm Deep Learning Algorithm Material Demand AI Prediction Model Planning Demand forecast error reduced from a maximum of 70 to 35, significantly reducing inventory volumes This project estimates customer demands, required material types, supply sources, and customer delivery dates using machine learning to establish a primary material procurement prediction system It reduces the prediction error of demand from the top five international customers from a high of 70 to 35, significantly lessening the amount of inventory needed「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

Labor-intensive, prone to oversights and errors, low shipping efficiency A domestic hook-and-loop tape traditional manufacturing transformation and brand management company has expanded new markets and business opportunities through the e-commerce platform model This requires reliance on substantial labor for product listing, order organizing, inventory management, and shipment tracking This results in limited product varieties and quantities that can be handled, and manual data entry is often prone to oversights or errors, affecting shipping efficiency and customer satisfaction, which is critical for the competitive advantage of the business in e-commerce Internally, many operations rely heavily on repetitive tasks across various computer systems, web pages, emails, etc Currently listed on 15 e-commerce platforms, updating single e-commerce information alone requires 2-3 months over 200 items, making rapid expansion difficult limited by manpower, product information is not detailed enough, leading to doubts in e-commerce reviews, affecting orders and subsequent satisfaction Presently, orders are only confirmed once a day, leading to an information gap of up to 24 hours Annually, there are over ten thousand orders to process into shipment orders, typically accumulating for 15-30 days before once grouping deductions from inventory, resulting in always inaccurate stock levels Streamlined Client Interface, Accelerating Implementation Efficiency The mentoring team collaborates with Ruijing Engineering Technology to integrate AI and RPA technologies through a web-based architecture Robotic Process Automation RPA applications are not installed on the local desktop but are stored on a server and accessed only when needed by the user This technology, known as Thin Client, provides higher performance and security compared to the Thick Client, which requires downloading applications and data to the local desktop The Thin Client does not require downloads on the local machine RPA collaborative service features include Web Scraping Complex web data collection and arrangement Email manipulation Data analysis and disassembly of content and attachments Web operation Precise and rapid web operations or filling in specific fields Application operation Timed positioning operations of other window applications Data processing Data format conversion, decomposition, and reassembly File Exchange Management Timed file production, adddeletemodify, FTP uploaddownload Database operation Heterogeneous database data exchange, read or write to a specific DB Data recognition Fixed format field data processing screenshot, snapshot, alphanumeric text parsing and recognition Scheduling Can be timed, repeated, cross-process all the above processes Alert mechanism Email, Line Notification etc designated or broadcast notification Software Robot Technology Solution Execution Architecture AI software robots enhance the processing speed of orders, inventory management, and purchasing in manufacturing operations, developing automated services to avoid data duplication and input errors, and seamlessly integrating processes across systems operating 247 The war room panel facilitates statistical analysis and real-time sales conditions on each e-commerce platform, predicting and optimizing product inventory Direct Purchase Order Process Automation Robot E-commerce Information War Room Statistical Analysis Dashboard Software Zero Errors, Reducing Costs by 15 to 90 面對快速變化又競爭激烈的市場環境，更需要減少重複性、低產值的工作，將人力運用在更高價值的工作上。 Facing a rapidly changing and highly competitive market environment, it is essential to reduce repetitive, low-value tasks, focusing manpower on higher-value work RPA software robots are 15 times more efficient than indirect staff, also enhancing process quality to near-zero error rate execution quality, offering opportunities to reduce costs by 15 to 90 Since it doesn't require significant changes to existing workflows, businesses generally do not need to spend substantial manpower on retraining or adapting to new workflows, which contributes to a higher acceptance rate among businesses Even in software deployment, it only takes about 4-5 weeks to go live「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」