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【2020 Application Example】 Nuclear Power Plant Calling It Quits: Elevating Importance of Smart Safety Management

Plant safety is a crucial aspect of industrial security. Currently, many surveillance cameras are used in conjunction with manual monitoring by security personnel to provide information. However, manual monitoring has its limitations. Implementing an AI system to assist in detecting abnormal behaviors and facial recognition can significantly aid security personnel by covering blind spots in manual monitoring.

Located in Shimen District, New Taipei City, the Jinshan Nuclear Power Plant is nestled between mountains and the sea, boasting picturesque scenery. However, this first nuclear power plant in Taiwan is entering its decommissioning phase and will soon become a part of history. With the decommissioning process underway, numerous external contractors will be entering and exiting the complex, complicating access management. The need for continual safety monitoring of external construction to ensure nuclear safety is critical. Additionally, although the Lungmen Nuclear Power Plant is currently mothballed, it still contains sensitive areas and requires a reduction in staff presence, thus prompting an urgent demand for smarter safety management.

With assistance from the Taiwan Nuclear Level Industrial Development Association, the AI team at the Institute for Information Industry aims to tackle the issues of safety and occupational safety at the Jinshan Nuclear Power Plant with minimal staffing. Based on interviews, the technology needs identified for AI implementation at the plant include personnel access control and safety monitoring of personnel and the plant area.

Facial Recognition AI Solves Two Major Challenges: Personnel Access Control and Plant Safety Monitoring

For personnel access control, a facial recognition system is deployed at the nuclear power plant. Utilizing the uniqueness of human faces and AI's high recognition rate, the effectiveness of the plant's personnel access control is enhanced. In terms of personnel operations and plant safety, an abnormal behavior detection system is also deployed. This system utilizes AI to recognize abnormal or dangerous behaviors from the postures of individuals captured by surveillance cameras, promptly providing feedback to safety personnel for action.

Selected by the Institute for Information Industry, the solution from Wantech Intelligent Sensing (abbreviated as Wantech) focuses on developing facial and posture recognition functionalities. After several discussions with Wantech, Google's Facenet and Posenet algorithms were chosen for implementation. Facenet, requiring only 128 dimensions per face image, achieves optimal performance with just a few photos, making it particularly suitable for building industrial-grade facial recognition systems. Posenet, used for motion detection, transforms data via a Data Processing Unit (DPU) into a format suitable for machine learning algorithms—Support Vector Machine (SVM)—for binary classification of human postures into falling or not falling categories.

Utilizing Visual Pages for Clear Management Interfaces

The user interfaces for both systems are implemented using Python's web framework Flask, which provides web services adaptable across different operating systems, achieving a cross-platform purpose. The Glasses App is developed using Unity to access web data.

In recent years, advancements in AI technology have increasingly incorporated facial recognition into safety management. The unique characteristics of facial features eliminate the risks associated with RFID forgery and offer higher accuracy compared to other biometric recognitions (fingerprints, voiceprints), complete objectivity devoid of personal bias, easy system setup and maintenance, and fully automated operations requiring no additional manpower. Undoubtedly, incorporating facial recognition into safety management systems can significantly enhance the safety factor of the plant while reducing management complexities.

Body Posture Recognition Operating in the Laboratory

▲ Body Posture Recognition Operating in the Laboratory

Taiwan has four nuclear power plants, bearing significant management costs. Continued implementation of AI technology solutions can not only reduce labor costs but also significantly enhance the effectiveness of safety management.

「Translated content is generated by ChatGPT and is for reference only. Translation date:2024-05-19」

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【導入案例】挺進智慧物流50 新竹物流醫材配送班表超高效率
Advancing to Smart Logistics 5.0: Hsinchu Logistics Delivers Medical Materials with Ultra-High Efficiency

After incorporating AI technology, traditional logistics companies have seen significant improvements in transportation efficiency and reductions in transportation costs, especially in the transfer of medical materials which involves timely service and rights of hospitals and patients The implementation of intelligent logistics can save medical material businesses the cost of constructing GDP warehouses and other expenses up to millions A major domestic logistics leader, Hsinchu Transport HCT, owns a fleet of 3,500 vehicles and a storage area of 60,000 square meters, providing customized logistics solutions including logistics, commerce, finance, information, distribution, storage, and processing The company handles up to 580,000 parcels per day, with a maximum capacity reaching 900,000 parcels, making the enhancement of transshipment efficiency crucial for HCT Medical materials transportation at hospitals need optimization of current operational processes and enhancements in 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data coordination to improve the accuracy and efficiency of product shipments, enhancing operational quality and management benefits at the demand side At the same time, some businesses are also investing in the standardization and systematization of internal operational processes, thus enhancing operational efficiency and quality In the freight logistics sector, logistics companies' warehouse staff need to expend labor to control different logistics shipment operations If they often receive emergency task notifications for shipments to medical facilities, they usually depend on small regional logistics providers to provide customized delivery services Although this improves delivery times, it does not allow for integrated informational services The new GDP regulations for medical materials require suppliers to undergo GDP compliance certification Therefore, Hsinchu Transport, assisted by the Ministry of Economic Affairs' AI coaching program, not only extends existing logistics 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time Furthermore, daily cargo conditions size, destination vary, and due to these fluctuating and distinct demands, the deployment of tractor heads and containers changes accordingly Under these circumstances, Hsinchu Transport relies on past experiences to schedule departures at each satellite depot and adjusts daily according to the cargo needs Due to the reliance on empirical scheduling, it is often difficult to consider all variables and considerations, leaving room for improvement in the current departure schedules The cargo delivery planning inherently constitutes an NP-Hard problem, difficult to solve with traditional analytical methods Hsinchu Transport, in collaboration with Singular Infinity, utilizes the Simulated Annealing SA algorithm to find solutions The new logistic service introduced by Hsinchu Transport is 'GDP Container Shift Planning' This planning involves estimating future volumes of medical materials between stations and scheduling container truck shifts 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content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

【導入案例】AI嘛會煮咖啡 無人烘豆機靠AI 精準設點與培養忠實客群
AI Can Make Coffee! Autonomous Coffee Roasters Relying on AI for Precise Location Setting and Cultivating Loyal Customers

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to two major issues one is the appropriateness of customer flow and machine placement locations which still rely on manual analysis the second is penetrating the market of mid to high-end coffee lovers accurately AI resolves two major challenges for autonomous coffee roasters suitable placement and cultivating a loyal customer base To tackle these issues and help autonomous coffee roasters quickly break into the market, Raysharp Electronics intends to implement AI for people flow counting analysis and unfamiliar face recognition These technologies aim to calculate the crowd size at potential roaster locations and classify consumers by gender and age for more precise market analysis They also provide multiple choices for the roasting of raw coffee beans, offering a more customized service tailored to the needs and tastes of professional coffee aficionados with a pack of 'high-quality roasted beans' Since 2018, the rise of unmanned stores has been mainly due to owners wanting to reduce 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roasters in high-traffic areas, owners can use cameras to capture the crowd and assess whether the machine location has an adequate customer base, quickly analyzing whether to reposition the machines, and more easily targeting the best locations for middle and high-end coffee lovers The unmanned coffee roaster features a professional roasting mode interface, providing options based on the origin and variety of coffee beans, their roasting methods light, medium, deep, and related temperature, wind speed, and timing settings If improvement needs arise during the process, engineers can adjust firmware parameters and also assist in integration with the owner's ordering system Staff members briefly describe the operation of the autonomous coffee roaster 'Black Gold' penetrates deeper into coffee shops, science parks, and commercial buildings through AI This autonomous coffee roaster targets coffee connoisseurs and can be placed in middle to high-end coffee shops to roast more customized coffee beans than those available in bulk stores Upon completing a batch of coffee beans, it immediately provides them to professional technicians within the coffee shops for grinding and manual brewing The remaining roasted beans can also be taken home for brewing and enjoyment It also adds value to coffee shops by better understanding consumer preferences for coffee beans and launching more customer-attracting drink promotions and appropriate inventory management In addition to coffee shops, the autonomous coffee roaster can also utilize AI-based people counting analysis to precisely set up near scientific parks and commercial buildings, offering high-quality coffee beans for office brewing to internal employees with high coffee consumption needs Furthermore, implementing a physical membership system can initiate coffee bean purchase promotions or periodic payment incentives, thus attracting new clients and cultivating existing customer loyalty and retention The operation interface of the smart autonomous coffee roaster「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

這是一張圖片。 This is a picture.
Realizing the dream of unmanned stores, Magpie Life is building the future of the smartphone industry

"The DNA of Magpie Life is not limited to vending machines We believe that vending machines combine technology, access, and humanities to bring us exciting results" This is a sentence on the official website of Magpie Life Let the vending machines bring To live a pleasant life and build a considerate, technological and sustainable future for the smartphone industry is also the original intention of Magpie Life Founded in 2018, Magpie Life launched Taiwan’s first private-brand mobile payment scan code sensor 4 months after its establishment, completing the consumption experience through screen touch The Magpie U1 smart vending machine manages the POS system and gathers data in the background, allowing consumers to synchronize with the world's new retail pace and experience a new retail consumption experience of purchasing convenience, checkout security, visual entertainment, and improved logistics replenishment efficiency Traditional vending machines lack information visibility and AI 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demand convenient life, food consumption patterns value dining experiencesimple and fast, and are equipped with mobile phone-connected ordering models, and hot drinks and Fresh food delivery is the focus of two major trends The location, items sold, consumption methods and multiple payment methods are the focus of market growth for smart vending machines In terms of convenience, Taiwanese consumers still prefer to purchase vending machine food near stations, airports, schools, and businesses in business districts Various payment methods are also gaining more support from consumers, indicating that in the future, automatic Vending machines can be developed in two directions diversified items and diversified payment methods AI sales forecast technology integrates back-end management to achieve precise marketing purposesDue to the wide variety of products, it is difficult to know the performance of products under different factors such as season, market conditions , promotional activities, etc, it is easy to cause out-of-stock or over-inventory situations Magpie Life has specially developed "AI sales forecasting technology" and integrated it into the back-end management system, hoping to lock in customer purchasing preferences and intentions through data analysis In order to achieve the purpose of precise marketing, make accurate business decisions and effectively allocate limited resources The introduction of AI systems can achieve the three major goals of precise marketing, inventory management and supply chain management This system is a replenishment decision-making aid designed specifically for supply chain managers It uses AI to predict future sales demand, helping companies effectively optimize production capacity, inventory and distribution strategies Its overall system architecture includes1 Data exploratory analysis function Provides automatic value filling, automatic coding and automatic feature screening functions for missing values in the data 2 Modeling function 1 Provides model training functions for two types of prediction problems regression Regression and time series Time Series Forecast nbsp2 Supports Auto ML automatic modeling, and the best model is recommended by the system Integrated models can also be established to improve model accuracy nbsp3 Supports multiple algorithm types Random Forest, XGBoost, GBM and other algorithms nbsp4 Supports a variety of time series models exponential smoothing, ARIMA, ARIMAX, intermittent demand, dynamic multiple regression and other models nbsp5 Supports a variety of model evaluation indicators R, MAE, MSE, RMSE, Deviance, AUC, Lift top 1, Misclassification and other indicators nbsp6 Supports automatic cutting of training data sets and Holdout verification data sets, and can manually adjust the ratio nbsp7 Supports automatic model ensemble learning Stacked Ensemble, balancing function learning Balancing Classes, and Early Stopping nbsp8 Supports the creation of multiple models at 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optimize the operational value chain and improve efficiency, and strengthen the core competitiveness of enterprises 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」