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Special Cases

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2021.12
【2021 Application Example】 HRT Technology Improves Production Efficiency by 20% Through AOI Detection of Defects in VCSEL Packaging

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

2021-12-05
【2021 Application Example】 AI Analysis Cloud Service Platform for Remote Sensing Big Data Enables the Smooth Application of Satellite Remote Sensing Images

Although satellite remote sensing images can make all surface objects visible, it still requires a lot of time and manpower to be truly applied to the industry In order to effectively solve the problems that customers face in digesting huge amounts of image data and eliminate technical obstacles for cross-domain users to process satellite remote sensing images, ThinkTron has developed an "AI Analysis Cloud Service Platform for Remote Sensing Big Data" as a new beginning for cross-domain AI applications for spatial information In recent years, in response to the impact of industrial globalization, Taiwan's agriculture has continued to transition towards technology-based and higher quality, improving the yield and quality of crops by solving problems, such as microclimate impacts and pest and disease control The demand of agriculture on images has expanded endlessly to accurately grasp the growing environment of crops In the early years when UAVs unmanned aerial vehicles were not yet popular, manual field surveys were the most basic but most labor-intensive work With the emergence of UAV drones, aerial photography operations might not be difficult, but the range that can be photographed is limited Furthermore, surveying expertise is required to accurately capture spatial information At this time, the use of satellite remote sensing data may break away from the past imagination of using image data Taiwan Space Agency TASA ODC data warehouse services In the past ten years, with the breakthrough of modern satellite remote sensing application technology, Digital Earth has become a new trend in global data collection Countries have developed data cube image storage technology, and the development of smart agriculture has become one of the largest image users Determining planting distribution is the first step in understanding crop yields Free satellite remote sensing images, powerful data warehousing support, and the team's robust image recognition technology are important supports for accelerating agricultural transformation Using satellite remote sensing image data can accelerate the development of smart agriculture However, in the past, it was difficult to extract large-area crop distribution through satellite remote sensing images, not to mention the cost If you wanted to use free information, you had to visit all websites of international space agencies, look through the wide variety of satellite specifications, and carefully evaluate the sensor specifications, image resolution, and revisit cycle After finding suitable images, you had to look at them one by one to filter the ones you need Next is downloading dozens of images that are often several hundreds of Megabytes MB each, which might exceed the capacity of your computer Also, after overcoming image access and preparing data, you must then start to confirm the downloaded image products and which bands you want, because the image you see is not just an image file jpg or png, but rather complex multi-spectral information, attribute fields and coordinate information It takes a lot of effort just to confirm the correct information Facing GIS software packages with complex functions is the start of another trouble The complex image pre-processing process and the inflexible machine learning package greatly reduce the efficiency of analyzing data After finally getting the results of crop identification, you might find that the best time for using map information may have already passed The above-mentioned complex and time-consuming satellite image processing problems are precisely the pain points of the market ThinkTron expanded from traditional machine learning to modern deep learning applications, and developed an "AI Analysis Cloud Service Platform for Remote Sensing Big Data" under the GeoAI framework, breaking through the constraints of details in the spatial information for customers Differences between the process before and after introducing the AI analysis cloud service platform ThinkTron said that Taiwan's ODC Open Data Cube system has been completed and began providing services after years of efforts from the Taiwan Space Agency TASA, formally becoming aligned with international trends The powerful warehousing technology allows users to easily capture and use image data of a specific time and spatial range according to their needs The warehouse stores multiple satellite image resources from international space agencies, including the ESA's Sentinel-1 one image every 6 days, Sentinel-2 one image every 6 days, USGS's Landsat-7 one image every 16 days, Landsat-8 one image every 16 days, and the domestic Formosat-2 one image every day and Formosat-5 one image every 2 days ThinkTron develops satellite image recognition tools based on Python Breaking free from the limitations of GIS Geographic Information System software packages, ThinkTron integrated GDAL Geospatial Data Abstraction Library based on Python, and considered computing efficiency and parallel processing when developing all tools required for satellite image processing and image recognition modeling, including coordinate system and data format conversion, grid and vector data interaction, and data intra-difference and normalization All of the tools are designed with AI applications in mind, and some commonly used tools are packaged into an open source package under the name TronGisPy to benefit the technical community ThinkTron utilized the team's understanding of satellite remote sensing images and the collected tagged data crop distribution maps to preset the image recognition modeling process, the required training data specifications, and dataset definitions This is imported into the machine learning LightGBM or deep learning CNN framework that was completed in advance, and the entire training process to be performed in the Web GIS interface, providing users with partial flexibility to freely filter images, confirm spatial and temporal ranges, select models, and adjust hyperparameters In addition to the operation of training models, it also provides historical models to output identification results, and finally displays the identification results of crop distribution on the Web GIS map In fact, agriculture is not the only industry that needs satellite remote sensing applications AI applications of spatial information have also appeared in various fields as companies in different industries aim to enhance their global competitiveness For example, surveying and mapping companies that have a large amount of map data can use the AI analysis cloud service platform to store map data while also accelerating the efficiency of digital mapping Under the severe global climate change and the risk of strong earthquakes, there is a wide variety industrial insurance, agricultural insurance, financial insurance, or disaster insurance are all inseparable from spatial information The use of remote sensing image recognition to understand insurance targets has long been an international trend AI Analysis Cloud Service Architecture for Remote Sensing Big Data

2021-11-28
【2021 Application Example】 AI Complements the Disruption of Traditional Industry Experience: Production Forecast Analysis in Plastic Recycling Process

As the number of veteran craftsmen in traditional industries diminishes In Taiwan, SMEs have always played a central role in Taiwan's industry, accompanying Taiwan through various 'economic miracles' periods But as time progresses, the old masters gradually became elderly craftsmen Coupled with the phenomenon of fewer children and changes in the overall industrial structure, fewer and fewer of the new generation are willing to enter traditional industries Now, it can be observed that the main combination on most SMEs' operational fields is formed by 'elder craftsmen' together with 'foreign workers' These experienced craftsmen, who act as living dictionaries of field experience, suffer from a lack of successors to continue the tradition, leading to a growing difficulty in sustaining on-site experience transfer in traditional industries The limits of traditional hands-on process optimization are in sight Located in Tainan Baoan Industrial District, 'Tangxian Company' was established in 1972, initially manufacturing high-quality weaving equipment It possesses the capability to manufacture machinery, and in recent years it has actively developed environmentally friendly plastic recycling equipment in response to international green energy, recycling, and environmental protection demands Ultimately, they have successfully developed low energy consumption, low waste, high purity, and high output recycling granulators with a sleek and efficient machine design supplemented by advanced intelligent control technology Tangxian Company's self-developed plastic recycling granulator equipment However, in the production process of plastic recycling, when faced with hundreds of material types and dozens of process temperatures, speed settings, what is faced is thousands of possible parameter combinations Previously, the adjustment of various production process conditions was reliant on the on-site staff the experience of the craftsmen Thus, during the transition of production of different incoming materials such as PET, PP, PE, a significant amount of raw materials would be wasted during the trial phase The professional information gap in traditional industries Tangxian Company recognizes the importance of data In the past, although process parameters were recorded, due to a lack of data capabilities at the time, it was primarily in paper form, manually written down by the operating staff, accumulating a large amount of paper data However, this also meant a lack of scientifically accurate and detailed information available for real-time reference and adjustment Process parameters logbook, records the state of about a dozen machines and production figures hourly In quality control as well, due to a lack of control over the quality of output and monitoring and feedback mechanisms for unit time production, it becomes difficult to predict the profit conditions of each batch Production management can only estimate and average cost and productivity changes over the process from the outcomes, without being able to objectively and timely restore the production conditions to reasonableness or make clearer adjustments when facing quality abnormalities Site reality left image shows recycled scraps right image shows pellet production Taiwanese manufacturers possess strong machinery manufacturing capabilities, and many modern machines now have data capabilities, recording real-time status and information via IoT But is the infrastructure of the factory's on-site and information systems ready yet When the Old Master Meets AI With government referral, Tangxian Company partnered with a Taiwanese data science company, working together to integrate AI services and optimize internal processes using AI They started with a medium-sized plastic recycling production line within the factory as a trial field After establishing a successful benchmark, this model was expanded to larger plastic recycling machinery within the factory to continue verification and application Initially, both parties converted the past handwritten paper data into digital format using OCR supplemented by manual correction Tangxian Company also worked with the supplier of the human-machine interface of the machinery to integrate the control panel and parameter data into the factory's database, allowing real-time monitoring of machine status and eliminating the complexities and potential errors of manual transcription Panel of plastic recycling granulation machine, showing current process temperatures, speeds, and power usage Meanwhile, the Taiwanese data science company further modeled dozens of parameter data through AI, conducting scenario analysis to simulate various production possibilities under environmental parameters and material inputs, identifying key characteristic parameters and providing parameter adjustment recommendations to decrease costs during the trial phase Applying data analysis to traditional industry machinery processes After the old master receives the raw materials, they only need to enter the relevant material characteristic parameters, and the system automatically generates recommended process parameters After small adjustments by the old master, they proceed with the trial production of the material, effectively reducing the waste of materials, water, electricity, and manpower caused by incorrect attempts Moreover, Tangxian Company has proactively deployed the concept of 'production pedigree' in the plastic recycling process, allowing the batch's raw materials and process parameters to be accessed by scanning a QRCode Production and sales pedigree of plastic recycling pelletizing Taiwan's SMEs have strong machinery capabilities, just waiting for the 'east wind' of data From industrial revolutions 20 to 30, even 40, many Taiwanese SMEs face challenges in transitions not just in upgrading machinery, but after investing in modern equipment and generating data, they do not know how to utilize it effectively It is impractical for these manufacturers to develop a specialized data analysis department on their own meanwhile, Taiwan also has many innovative teams with strong software capabilities in AI and data analysis, possessing the technology but lacking the field and data Therefore, if the traditional industries of Taiwan could be fully integrated with the innovative teams in AI and data analysis, it would not only address the current challenges of manpower and experience transfer faced by traditional industries but also advance Taiwan's development and application of AI significantly「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

2021-01-21
【2021 Application Example】 Fongyu Uses AI Knowledge-based Fish Farming to Effectively Increase Aquatic Production by 10%

Fisheries is an important industry in an island economy However, the fish farming industry has faced severe challenges in recent years, including climate change, labor shortage, and rising costs In particular, nearly 110,000 workers in agriculture will retire due to old age over the next 10 years For this reason, the need for aquaculture to move towards smart farming is becoming increasingly urgent Founded in 2014, Fongyu Corp Ltd has developed a unique eco-friendly farming model based on its own fish farming It uses AI knowledge-based fish farming to effectively increase aquatic product production by 10, and reduced labor cost by 15 The word "Fongyu" has a profound meaning "Fong" represents good mountains and "Yu" represents good water, and is the hope that companies will allow Taiwan to always have good mountains and good waterIt is also a homophone for "having a full figure," expressing the hope that products will give consumers a full and healthy body and mind The founder of the company, Liu Chien-Shen, has been through the difficult entrepreneurial journey of becoming an apprentice in fish farming, raising funds, renting fish farms, establishing a fish farming company, building a brand, and expanding sales Labor shortage and aging workers are hidden worries in the fish farming industry Currently, fish farms in Taiwan are still mainly traditional fish farms, and farming techniques are still passed down through word-of-mouth In addition, the labor shortage and average age of workers exceeding 60 years old has made it impossible to effectively stably improve productivity and yield This farming method makes it difficult to prevent and control diseases, and greatly increases the possibility of excessive use of drugs, environmental pollution, and water quality and ecological damage, creating a vicious cycle that lowers the quality of fish farming In addition, 651 of workers in Taiwan's fish farming industry are inadequately skilled With limited support from IoT sensors, traditional fish farmers still mainly rely on their own experience and knowledge for water quality management, feeding, and disease detection Fish farming management relies heavily on the ability of individual fishermen Once experienced workers retire, the industry will not only face the issue of succession, but also the difficult of stably supplying a certain amount of harvest that meets quality standards This may cause a dilemma for the entire industry from fish farming to sales In order to improve the pain point of inability to pass on experience in fish farming, and at the same time create a "digital" foundation for fish farming, the top priority must be to collect farming behavior data and develop AI services as an important starting point Fishery digital twin technology helps fishermen transition to smart farming With the assistance of the Institute for Information Technology III, Fongyu implemented the "fishery digital twin" technology to dynamically adjust the farming schedule In other words, the fish farming schedule is adjusted according to the species, habits, and variables of the fish The use of AI in fish farming not only effectively increase aquatic production by 10, but also reduced labor cost by 15 In terms of specific methods, we first digitalized the fish ponds, feed, and decision-making behavior for each species, such as sea bass and Taiwan tilapia, and recorded the seasonal temperature changes from releasing seedlings to harvesting, all of which were digitalized, gradually recording the experience and methods of experienced workers into a rich database Based on the recorded data, we analyzed the compound variables to find the best farming behavior and generate a dynamic farming schedule The records for each pool provide data on workers' experience However, fish farming behavior generally relies on rules of thumb Even experienced fish farmers cannot ensure that they will find the best solution Therefore, new methods are proposed to solve this issue That is, "to determine the best fish farming behavior by predicting the interaction with water quality and past data on feeding, and evaluating fish farming behavior based on water quality and fish farming," and provide fishermen with the most intuitive recommendations through daily schedules To continue optimizing the dynamic fish farming calendar on a rolling basis, iterations of the model will be developed through the three-step cycle 1 Input the current fish farming calendar into the model 2 The model predicts the future environment 3 Shortcomings of the fish farming calendar are corrected based on the future environment to obtain a new version of the fish farming calendar In the process, the experience of aquaculture experts is used to establish the causal relationship between fish farming behavior and the environment The establishment of a dynamic fish farming process and technology-based fish farming recommendation services provide a traceable and detailed fish farming process It is one of the few technologies that can digitalize fish farming Fishermen can quickly and easily record their daily behaviors to build knowledge without taking up too much time, but in the long run it can reduce labor cost by 15 and increase output and revenue by an average of 10 Smart fish farming has achieved outstanding results, reducing labor cost by 15 and increasing output by 10 At the same time, the fish farming calendar can also be extended to different aquatic species, such as white shrimp, milkfish, clams, and Taiwan tilapia, to produce fish farming schedules for ponds with different specifications, and the harvested aquatic species can be traced according to different specifications, establishing vertically integrated services for safe food products Fongyu's main products are divided into two categories One is aquaculture modules, including fry, feed, materials and probiotics, production planning and processes, and monitoring, which can be sold separately or exported as modules The high-quality aquatic products produced by Fongyu have repeatedly won awards Figure Fongyursquos official website The other category is high-quality aquatic products, including seabass fillets, seabass balls, oil-free seabass balls, seabass dumplings, and seabass soup The products have won various awards, including the top ten souvenirs in Pingtung in 2017, "Barramundi Fillet" won the 2017 Eatender of the Council of Agriculture COA, "Oil-Free Barramundi Fillet" won the 2018 Eatender Gold Food Award of the COA, and "Dumplings of Barramundi" and "Barramundi Broth" won the 2019 Eatender of the COA The consecutive awards represent that the "quality" of Fongyursquos aquatic products can be seen and eaten with peace of mind In addition, Fongyu has exclusive fingerlings that meet international needs, such as Pure seawater cultured tilapia fingerlings and seawater Taiwan tilapia fingerlings from selective breeding FY-01 are items that aquaculture companies in many countries are looking forward to The company also has aquaculture modules, disease monitoring tools, and feeding materials designed in accordance with the environment, in order to provide customers with more stable income

2021-09-28

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2024 Practical Issues: Using Artificial Intelligence to Combat Social Engineering Emails

Industry AI Application Services Industry Pain Points As hacker techniques evolve, the complexity of social engineering attacks increases Attackers often exploit human weaknesses through disguised emails, links, or attachments, luring users into clicking or responding inadvertently, thereby stealing confidential information These attack methods not only increase the risk of corporate data leakage but also intensify the challenges of protection mechanisms Reliance on traditional security tools alone is no longer effective Therefore, enterprises need to adopt more advanced technologies and risk control measures to address such increasingly complex threats Benefits of Introducing AI Through precise data analysis and pattern recognition, AI systems can quickly detect potential abnormal behaviors and proactively prevent attacks before they occur This technology can automatically adjust protection strategies according to emerging attack methods, thus significantly reducing risk Moreover, the system is highly adaptable, able to adjust to ever-changing cybersecurity threats, providing more accurate protection and ensuring timely prevention of various cybersecurity attacks, enhancing the speed and accuracy of overall protection Additionally, this technology can serve as a powerful tool to prevent security vulnerabilities Through rule setting and learning mechanisms, the system can gradually enhance its defensive capabilities As the system operates over time, it automatically learns and analyzes new social engineering methods Based on these learning outcomes, the system can establish a more robust detection model, thereby more effectively identifying and intercepting unlawful attacks This continuous learning mode enables the system to constantly adapt to new attack strategies, providing a stable and robust support for the company's cybersecurity frontline, further reducing the likelihood of risks and ensuring long-term stability of corporate cybersecurity Common AI Technologies Bayesian classifiers, Support Vector Machines, Logistic Regression Deep learning, frameworks such as、。BERT、TextRNN。 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-11-15」

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113th Year Practical Topics: Land Survey Image Recognition

Industry Artificial Intelligence Application Services Industry Pain Points Due to the frequent changes in the use of land and buildings, traditional manpower auditing work is difficult to keep up with changes in a timely manner This not only requires a large amount of manpower and financial investment, but is also limited by the complexity of the terrain and inspection efficiency, leading to delays in updating relevant data, affecting policy formation and management Additionally, this approach is also difficult to rapidly respond to changes in land use in large areas, further increasing management and monitoring challenges, making it impossible to achieve high-efficiency, accurate land use surveys and tracking AI Benefits With advanced image recognition capability, it is possible to automatically identify various objects in images, such as buildings or concrete surfaces, and provide detailed related information, such as the area of land occupied by objects The application of this technology significantly enhances the efficiency of agricultural and building inspections, especially in situations with limited resources or complex terrain, helping to save a substantial amount of execution costs Through automated image recognition and data analysis, the inspection work becomes more accurate and efficient, reducing the burden on human resources Moreover, this technology can significantly enhance the accuracy of land taxation, assisting government agencies in quickly identifying potentially illegal land use situations The system can automatically detect potential anomalies in images and compare them with existing land use data, rapidly identifying non-compliant usage methods Such applications not only enhance regulatory efficiency but also strengthen the transparency of land management, helping to achieve more comprehensive land management goals, ensuring rational use of land resources, and reducing occurrences of illegal use Common AI Technologies Deep Learning, frameworks such asYOLO、Faster R-CNN、SSD、Mask R-CNN、RetinaNet、EfficientDet。 Generative Artificial Intelligence, such asOpenAIofGPT、AnthropicofClaudeetc 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-12-02」

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Practical Issues for 2024: Document Writing Assistant

Industry Artificial Intelligence Application Services Industry Pain Points As business volume document volume continues to increase, many employees face an excessive workload, especially needing to spend significant time on writing, reviewing, and revising documents This not only prolongs the workflow but may also lead to reduced efficiency and even an increase in errors due to excessive busyness If documents cannot be processed timely, it affects the overall progress of work and lowers team morale Therefore, effectively reducing the burden of document writing has become a pressing issue for the industry AI Benefits Using artificial intelligence technology can automatically apply formatting and language standards to assist in writing various types of documents, ensuring compliance with established standards and reducing the time needed for manual formatting Such systems not only enhance accuracy but also expedite the document output, effectively preventing errors or omissions that could occur in traditional manual writing processes For instance, when a user begins drafting a document, the system will automatically insert the appropriate format and check language standards, making the whole process smoother and more precise, thereby improving work efficiency Additionally, the system can automatically generate an initial draft of the document based on the content provided by the user, adhering to standardized formats and terms, further simplifying the tedious manual process This feature greatly reduces the need for user revisions, allowing them to focus more on content refinement The high-quality draft generated automatically serves as a starting point for users to make fine-tuning adjustments as needed without starting from scratch This not only shortens the time for document writing but also ensures that each produced document meets standard regulations, reducing further modification and review needs, and thereby enhancing overall work efficiency and quality Common AI Technologies Generative Artificial Intelligence, such asOpenAIandGPT、AnthropicofClaudeetc 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-11-23」

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2024 Practical Issue: AI Assisted Topic Collection

Industry AI Application Services Industry Pain Points Rapid changes in public sentiment on social platforms, significant events, and local news are wide-ranging and fast It's challenging for manpower to comprehensively grasp every topic, especially needing systematic categorization and filtering Manual processing of sentiment collection can lead to misjudgments or delays due to complicated sources or a large amount of information, making it difficult to track hotspots in real time This leads to reactions that are not timely or accurate enough, affecting policy decisions or the grasp of public attention, especially with diverse media sources and dynamic social movements AI Integration Benefits By specifying time segments and conditions, the system can quickly filter content related to particular legislators, party groups, administrative regions, or specific demographics, and automatically generate concise summaries This capability allows decision-makers to quickly master key information, reducing the time needed for manual data organization At the same time, the system can filter information based on different age groups, genders, or other concerns, ensuring the collected data is more diverse This filtering ability enhances the grasp of diverse groups and needs, allowing policymakers to respond more accurately to the needs of people at all levels Additionally, the system can automatically organize and generate reference materials for governance, covering a wide range of public perspectives and thereby enhancing the inclusiveness and accuracy of governance Common AI Technologies Generative Artificial Intelligence, such asOpenAIofGPT、AnthropicofClaudeetc Enhanced Search Retrieval。 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-12-02」

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113-year Practical Issues: Authentic Data Center Video Monitoring Manager

Industry Artificial Intelligence Application Services Industry Pain Points Due to limited human resources at the agency, workloads cannot be effectively allocated, resulting in a frequent accumulation of video files that are difficult to analyze or process This situation not only affects the timely utilization of video information but may also cause delays in processing or incomplete analysis of critical data The lack of sufficient manpower for manual operations prevents the potential value of the video files from being realized, making it impossible to provide the necessary decision-making basis or support the agency's business development needs AI Integration Benefits Using artificial intelligence technology to learn about agency operations,ISMSData center regulations and immediate analysis of video content to detect any violations by personnel Such technology can instantaneously identify any potential issues, ensuring that data center operations comply with all safety standards and significantly reducing the errors or oversights that manual monitoring could cause Additionally, the system can periodically produce video analysis reports, providing concrete data support to help managers enhance their monitoring of the data center These reports not only serve as reference materials for management improvement but also ensure the continuous enhancement of physical security in the data center This automated monitoring method enables the system to significantly boost management efficiency, especially when facing complex daily monitoring tasks, thus reducing reliance on manual inspections The system can automatically detect anomalies based on learned regulations and immediately notify relevant personnel for handling, ensuring issues are discovered and resolved at the earliest stage Moreover, the technology continuously learns and adapts to new standards, further enhancing the accuracy and efficiency of data center management and ensuring the ongoing improvement of the security monitoring process Common AI Technologies Deep learning, frameworks such asYOLO、Faster R-CNN、SSD、Mask R-CNN、RetinaNet、EfficientDet。 Generative Artificial Intelligence, for exampleOpenAIofGPT、AnthropicofClaudeetc 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-12-02」

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2024 Key Topics: Integration of AI in Smart Customer Service to Humanize Systems

Industry AI Application Services Industry Pain Points When users inquire about the same issues through different methods, systems might have difficulty interpreting the correct intentions, leading to incomplete answers or incorrect information This not only decreases user satisfaction but may also increase the workload on customer service departments Future challenges include enhancing system understanding of semantics and improving responses to diverse question expressions to boost service efficiency If systems fail to filter vast amounts of data adequately, incorrect information may end up in the knowledge base, affecting the accuracy of responses This confusion can lead to user frustration and even complaints To remedy this issue, there should be an increase in automated data filtering and verification mechanisms to ensure that responses are accurate and timely The process of organizing customer service queries is time-consuming and often fails to respond promptly to governmental policy changes, particularly during wide-scale dialogues and directive transmissions Future improvements should include boosting automation efficiency, enabling systems to rapidly understand and summarize conversation highlights to ensure customer service systems are effective in dealing with directives and user queries AI Adoption Benefits Smart customer service integration with artificial intelligence technology and system humanization significantly enhances training efficiency Through automated learning, the system can quickly master substantial customer interaction data and self-improve, not only increasing the accuracy of customer service responses but also significantly reducing costs associated with staff training As such automated learning functions allow the system to more accurately address varying customer demands, ongoing updates to the database will continuously strengthen the system's competitiveness and responsiveness For instance, when customers pose complex questions, smart customer service can rapidly provide precise answers based on accumulated data, reducing the need for human intervention while enhancing overall service quality Moreover, the system can rapidly generate a diverse range of QampA content, including extending questions and conceptualizing synonyms, addressing the time-consuming and cumbersome problem of manually setting up QampAs Through such automated generation capabilities, smart customer service's response range is broader, and the system remains highly flexible, capable of instant responses to various customer inquiries Such features not only lessen the workload on customer service staff but also enhance the system's scalability In the future, there is likely to be fully automated problem extension and resolution mechanisms, further optimizing overall service processes Most importantly, through the use of human-like language processing technology, smart customer service provides users with a more natural and fluid interaction experience, reducing feelings of mechanization Such humanized design enhances user interaction experiences, making them feel more valued and thereby increasing their trust and satisfaction with smart customer service For example, when users inquire about municipal services, smart customer service can respond in a natural and emotionally engaging language, making citizens feel more genuinely interacted with This not only enhances the affability of the service but also strengthens the public's willingness to use municipal services Common AI Technologies Generative Artificial Intelligence, such asOpenAItheGPT、AnthropictheClaudeetc 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-12-02」

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2024 Practical Issue: Urban Open Data and Generative AI Applications

Industry Sector AI Application Services Industry Pain Points The number of currently available urban data sets is vast,700The currently available urban datasets are numerous, however, finding suitable data for analysis and application is not an easy task These data often vary in format, covering different areas, and without appropriate technical support, it is difficult to rapidly integrate and analyze effectively Companies or governmental departments using this data frequently encounter challenges in filtering and unclear application scenarios, consequently affecting the precision and timeliness of urban development decisions Despite a large amount of open data from central governments, the lack of integration technology and platforms makes it challenging to enhance data usability Businesses struggle to transform these data into commercial solutions with real-world application value AI Integration Benefits Publicly provided data covers a wide range of areas and holds substantial application value, but due to the lack of effective integration, many enterprises and organizations cannot fully utilize their potential when developing new services Through the application of generative artificial intelligence technology, the scope of these data applications can be significantly expanded, providing more flexible and diversified value-added services, allowing businesses to uncover new commercial opportunities For instance, by leveraging automated data processing and analysis techniques, firms can more easily extract valuable information from vast open data sources, leading to the development of innovative products and services Moreover, by integrating multi-source data and conducting real-time analysis, these technologies provide powerful decision support to organizations facing market changes or policy adjustments, enhancing overall operational efficiency and enabling faster, cost-effective development of innovative applications In smart city operations, for instance, the use of generative AI to analyze urban open data can offer more precise traffic management solutions and environmental monitoring reports, further driving the intelligent development of urban operations Common AI Technologies Generative Artificial Intelligence, such asOpenAIincludingGPT、AnthropicothersClaudeetc 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-12-02」

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2024 Practical Issue: Utilizing Government Open Data and Implementing Generative AI Applications to Formulate Investment Attraction Strategies

Industry AI Application Services Industry Industry Pain Points In urban development, when formulating investment attraction strategies, it is necessary to analyze the industrial distribution, development positioning, and transportation convenience of each industrial zone However, rapid changes in global industry trends and the large number of profit-making enterprises within cities significantly increase the difficulty of analysis Simultaneously considering industrial development and urban industry energy linkage not only consumes time but also complicates rapid strategy adjustments to align with new technological trends Introduction of AI Benefits By utilizing open data, the system can effectively analyze the urban industrial distribution and combine international trends with technological development to provide a comprehensive and forward-looking investment strategy for government agencies These strategies, crafted based on the city's industrial strengths and essential capacities such as talent and technology, provide targeted policy-making support to attract businesses and investors that meet municipal needs Furthermore, this technology ensures that strategies stay abreast of the latest market dynamics, maintaining the city's advantage in a competitive market Data analytics technology not only shortens the time for data organization and decision-making but also accommodates market changes with dynamic adjustments, enhancing investment efficiency further For example, the system can automatically update the city's investment strategies based on varying industry needs and market fluctuations, ensuring each decision promptly reflects the latest trends and demands Such technological applications not only strengthen the government's decision-making capabilities but also enhance the overall competitiveness of the city, securing a favorable position in the global industrial chain, attracting more investment opportunities, and promoting long-term economic development Common AI Technologies Generative Artificial Intelligence, such asOpenAIofGPT、AnthropicofClaudeetc 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-11-23」

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2024 Real-World Issue: Virtual Reality Showrooms

Industry Artificial Intelligence Application Services Industry Pain Points In many traditional showrooms, there is a lack of sales professionals who can immediately understand customer needs and tailor their offerings accordingly, especially in integrating virtual and real environments Many companies are unable to provide a seamless transition from physical to virtual showrooms, resulting in incomplete customer experiences, incapability to maintain brand continuity and consistency at various touchpoints, which negatively impacts customer purchase decisions and satisfaction In multilingual markets, the inability to use fluent and professional multilingualism to clearly explain product features and design advantages limits the ability of businesses to effectively reach a broader international customer base, lacking personalized interaction that makes customers feel alienated and makes it difficult to build strong brand loyalty and unforgettable user experiences during the sales process AI Integration Benefits Through the intelligent virtual expert system, the system can instantly simulate professional sales personnel, providing precise and personalized sales recommendations based on each customer's needs This system not only dynamically adjusts content recommendations but also effectively reduces reliance on human resources, especially during busy times or when it is not possible to timely arrange sales staff Virtual experts can provide high-level services on the spot, ensuring that customers receive the information they need and enhancing overall satisfaction This approach not only enhances sales efficiency but also increases customer identification with and trust in the brand through intelligent services, thereby fostering brand loyalty Additionally, the system possesses powerful context tracking capabilities, enabling it to understand and respond to customer needs in real-time By dynamically analyzing the conversational context, the system can adjust its product recommendations or service presentations based on customer reactions, avoiding overly standardized responses This level of personalized interaction not only enhances professionalism but also increases purchase intent, allowing customers to feel the brand's dedication and thoughtful service during the presentation process At the same time, the system also supports multiple languages and can introduce products in a humorous and flexible interactive style, making the product presentation more attractive This feature allows companies to overcome language and cultural barriers, particularly in the international market or multinational exhibitions, helping brands to attract customers from different cultural backgrounds This extends beyond single-language formal communication, using diverse interactive methods in a relaxed atmosphere to deepen customers' understanding of the product and enhance their willingness to purchase Common AI Technologies Generative Artificial Intelligence, such asOpenAIofGPT、AnthropicofClaudeetc 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-12-02」

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113-year Practical Issue: Implementing Generative AI Voice Customer Service in Transportation Information Platforms to Optimize User Experience

Industry AI Application Services Industry Industry Pain Points The current pain points of transportation data platforms include insufficient data sources, single service offerings that cannot meet the diverse needs of different users Additionally, many platforms have non-intuitive interfaces that confuse users during operation Especially in text input, it's not convenient, and the lack of web accessibility design tailored to different user needs makes it difficult for some people to use easily Moreover, the lack of public transport information often prevents passengers from getting comprehensive transit data in real time, affecting their travel experience AI Implementation Benefits Through data analysis, the system can grasp the changes in peak and off-peak periods based on passengers' travel plans and timing, and provide the best transportation strategy recommendations This feature allows passengers to make wiser choices during congested sections and times, reducing congestion and unnecessary waiting times Automatic scheduling of the optimal travel time for passengers not only helps to divert traffic but also enhances the efficiency of overall traffic management, making urban transit operations smoother Furthermore, the system can accurately identify the needs of each user and provide query services through interactive voice communication Whether it's inquiries about transfer recommendations, route information, or traffic conditions, the system responds instantly to user queries, providing clear and accurate answers This variety of transportation services allows users to more easily plan and adjust their travel routes, thereby meeting different travel needs and enhancing the overall travel experience Common AI Technologies Generative Artificial Intelligence, such asOpenAIsuch asGPT、Anthropicsuch asClaudeetc retrieval-enhanced generation。 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-12-02」

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Practical Topics for 2024: Cybersecurity Intelligence Experts

Industry AI Applications Services Industry Industry Pain Points Due to a shortage of personnel, the transition of cybersecurity intelligence tasks often faces difficulties This scenario may result in delays in transmitting crucial security information or threat analysis, thereby increasing the risks faced by the organization AI Implementation Benefits By leveraging artificial intelligence technology, the system can respond in real-time to various user queries regarding cybersecurity intelligence This reduces the need for manual processing and significantly enhances processing speed and efficiency The auto-reply feature for common questions ensures no crucial intelligence is missed during handover, providing timely and precise cybersecurity advice Such automated response systems can assist cyber security teams to manage daily intelligence tasks more swiftly, thereby enhancing the overall system security and reducing potential vulnerabilities Moreover, the system possesses strong data analysis capabilities, able to handle large volumes of cybersecurity data and extract key information The system learns from historical data to predict potential risks and detect possible threats in advance, which is highly valuable for the cybersecurity team Such analysis helps security experts better manage risk dynamics and make preemptive decisions, thus reducing the occurrence of attacks When the system detects potential threats, it automatically alerts the security team to take defensive measures, significantly enhancing the proactivity and accuracy of security defenses, ensuring the enterprise can effectively respond to various potential security challenges Common AI Technologies Generative Artificial Intelligence, such asOpenAIofGPT、AnthropicofClaudeetc 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-11-15」

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2023 Key Topic: Integration of Artificial Intelligence in Government Agency Call Services

Industry Artificial Intelligence Application Services Industry Pain Points Currently, voice announcements in government agencies still require manual recording When the content needs to be changed, re-recording is necessary This not only is time-consuming but also may lead to inconsistencies in voice quality due to changes in personnel, affecting overall service quality Frequent recording demands complicate system maintenance and increase labor costs, which are significant pain points in the operational efficiency of government institutions Call center staff often need to look up government department duties, but due to untimely or incomplete data updates, mistakes in call transfers are common This not only delays resolving public needs but also adds extra burden on staff Such situations lead to interruptions in processes and further reduce the service efficiency of government departments, greatly diminishing public satisfaction with government services The turnover rate in call centers is relatively high New staff often lack sufficient familiarity with the duties of government departments, making it difficult to grasp the complete content of the job in a short time, leading to long inquiry times and imprecise answers This not only increases the time cost of handling calls but also may lead to public dissatisfaction, further lowering the overall service quality of government departments Benefits of AI Integration The application of artificial intelligence in government agency call services significantly enhances the immediacy and efficiency of information delivery By converting text directly into virtual speech, the system does not rely on specific recording personnel or facilities, greatly reducing the cost and time required for recording This feature allows the system to quickly generate high-quality voice content and announce the latest news or critical information to the public in real-time, especially important during emergencies or rapid changes Whether it's urgent announcements or policy updates, the system ensures that information is accurately and swiftly conveyed to the public, thus enhancing the effectiveness of government information dissemination Artificial intelligence systems can also immediately answer simple questions raised by the public, covering common business consultations This function not only significantly shortens the waiting time for the public but also effectively reduces the volume of calls, allowing operators to focus on handling more complex issues Through such intelligent services, government agencies can improve overall service quality while reducing the workload of call center departments, making the service more efficient and of higher quality, ensuring that the public's needs are met promptly and accurately By integrating voice recognition technology, the system can instantly analyze public voice needs and connect with city directories and knowledge bases, automatically routing calls to the appropriate business handler When the system cannot accurately recognize voice demands, calls are automatically directed to operators, and the dialogue history with the public is displayed to avoid repetitive explanations This integration not only reduces the workload on manual call handling but also significantly enhances the efficiency of call processing, making the services faster and more accurate1999知識庫連接，將通話自動轉接至正確的業務承辦人，確保問題能夠迅速得到解決。當系統無法準確辨識語音需求時，通話會自動轉接至話務人員，並同時顯示與民眾的對話紀錄，避免民眾重複說明，提升整體服務的流暢度與精確性。這樣的應用不僅減少了人工接聽電話的負擔，也有效提高了話務處理的效率，使得話務服務更加迅速且精準。 Common AI Technologies Generative Artificial Intelligence, such asOpenAIofGPT、AnthropicofClaudeetc Deep Learning, structures such asTacotron 2、WaveNet。 Text-to-Speech Technologies, includingandandand、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。、、、。Google Text-to-Speech、Amazon Polly、IBM Watson Text to Speech、Microsoft Azure Text to Speech。 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-12-02」

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2024 Practical Issue: AI-Driven Interactive Drug Prevention Promotion with Citizens

Industry AI Application Services Industry Pain Points Current drug prevention promotions largely depend on physical activities such as seminars, booths, or periodic events This approach lacks systematization and interactivity, fails to cover online learning or engaging tools, restricting self-learning and limiting outreach only to participants In non-working hours, like nights or holidays, public sectors struggle to provide immediate counseling This results in a lack of timely help for those in need, reducing the effectiveness of drug prevention measures AI Integration Benefits By integrating drug prevention knowledge, we create an unrestricted interactive platform to convey information everywhere, anytime Through this tech, the public can engage in real-time learning about the dangers of drugs, thus enhancing their awareness This platform not only widens prevention scope but also strengthens educational impacts, allowing rapid access to information and deepening the reach of drug prevention strategies—effectively broadening governmental outreach and increasing the efficacy of the overall prevention initiatives Furthermore, through automation, this platform also offers basic drug prevention consultancy services Even during non-working hours, it can instantly respond to public inquiries, ensuring constant support and guidance This automation significantly reduces dependence on manpower while providing people with ongoing assistance any time they need, enhancing the timeliness and flexibility of prevention efforts and extending government service coverage Common AI Technologies Generative Artificial Intelligence, such asOpenAIofGPT、AnthropicofClaudeetc 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-11-23」

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Practical Issues in 2024: Budget Management Assistant for Business Units

Industry AI Application Services Industry Pain Points Business units often face staffing shortages, especially in budget management When personnel responsible for budget management resign or hand over their duties, it may lead to lost data or difficulties in getting up to speed, thereby affecting the efficiency of budget control Moreover, budget management involves many items without a systematic record, those taking over can spend a lot of time organizing data or finding historical records Especially during annual budget reviews or allocations, these challenging handovers can cause delays in budget allocation or execution, further impacting the operational efficiency of the business AI Implementation Benefits The system possesses the ability to instantly organize and summarize accounting data, enabling complex budget item analysis to be automated, thus saving extensive manual effort For example, when users need to inquire about the status of a specific budget item, the system can quickly retrieve relevant information, providing real-time responses and making the budget usage transparent Additionally, the system can generate budget reports automatically, allowing detailed comparisons of historical data and current expenses, helping decision-makers quickly grasp the budget execution status and avoiding errors or oversights due to manual operations This automation not only enhances efficiency in budget management but also ensures transparency and accuracy in budget usage Furthermore, the system can provide users with precise analysis of account usage during the budget management process This not only helps users better understand the flow of funds but also enables the management to promptly comprehend the efficiency of resource utilization, thereby making more accurate decisions The system can automatically organize all types of budget item data based on user needs and provide real-time responses to complex queries Such features considerably reduce data processing time, enhancing the workflow's efficiency and accuracy For example, if an anomaly in budget expenditures arises, the system will immediately alert users to review and adjust, thus avoiding the risk of misallocating resources Common AI Technologies Generative Artificial Intelligence,OpenAIofGPT、AnthropicofClaudeamong others 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-11-15」

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2024 Practical Topic: Deepfake Video Artificial Intelligence Detection Model

Industry Artificial Intelligence Application Services Industry Pain Points Today's image and video production technology has developed highly, making deepfake videos increasingly realistic and difficult for ordinary people to distinguish between real and fake For businesses or media, failing to properly identify these deepfake videos may lead to the spread of misinformation, further affecting credibility and business operations This problem requires an effective solution that can detect deepfake content in real time and prevent its further dissemination, especially in industries involving finance, news, and government agencies where the authenticity of content is crucial to public trust Benefits of Introducing AI The system can effectively identify unnatural facial expressions, image textures, and other subtleties in videos, details that are often hard to detect by human eyes Using artificial intelligence technology, the system can automatically detect potential forged videos This function is undeniably a highly valuable tool for investigative agencies, helping them in subsequent tracking and investigation, significantly improving the detection efficiency, and reducing reliance on human resources Many scam tactics often utilize deepfake technology to create seemingly real videos, making it easier for victims to be deceived Therefore, the application is not limited to investigative work it can also serve as an important tool for fraud awareness education, helping the public recognize and guard against scam tactics Through real-time educational outreach using examples of deepfake videos, the system can help the public more intuitively understand how to identify false videos, which is crucial for enhancing societal security awareness Common AI Technologies Convolutional Neural Networks, Recurrent Neural Networks Technologies, like Microsoft'sVideo Authenticator。 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-11-15」

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