As water resources deplete and environmental protection needs increase, wastewater treatment plants have increasingly adopted AI technology to assist in monitoring and warning systems Zhongxin行's integration of big data and AI technology has opened up new possibilities in the environmental industry In the future, besides boosting the technological momentum of the wastewater treatment industry, it can also be promoted to other industries to foster technological and economic development Founded in year 1980 as Zhongxin Engineering later renamed to Zhongxin行 Company Limited, it is one of the largest and most technically equipped environmental companies in the domestic operation and maintenance field Zhongxin行's achievements in the operation and maintenance of sewer systems span across Taiwan, including science parks, industrial zones, international airports, schools, collective housing, national parks, and factories Introduction of AI systems in wastewater plants Precisely reduces medication addition times and lowers the risk of penalties for water quality violations At the wastewater treatment plant in Hsinchu Science Park, Zhongxin行 introduced the 'AOMBR Carbon Source and Aeration Intelligent Enhancement Control System Development,' which accurately predicts air volume control and reduces medication times, thus lowering the risk of hefty fines Zhongxin行 points out that with the vigorous development of advanced industries and increasingly strict effluent standards, a slight misalignment in equipment control can lead to major discrepancies in water quality In recent years, many wastewater treatment facilities have incorporated automatic control functions, yet onsite conditions often deviate slightly from theoretical expectations, causing situations where good treatment technologies must continuously adapt and adjust to achieve effective effluent water quality control 'The better the quality of the effluent, the greater the pressure on the operators This is the biggest pain point for Zhongxin行,' said a senior manager candidly Regular water quality testing and equipment maintenance ensure that effluent water stays below legal standards This means that operators need to be on top of equipment and water quality conditions daily If there are sudden anomalies in influent water quality or equipment malfunctions, linked issues can lead to pollution Therefore, besides performing regular maintenance and testing, it is critical to constantly monitor the dashboard to ensure system stability, consuming both manpower and mental energy Zhongxin行's on-site operators work 24-hour shifts, constantly monitoring effluent water quality Combined with laboratory water testing and analysis, if the wastewater treatment values do not meet requirements, they face both administrative and contractual fines from environmental agencies and granting authorities, which also create significant psychological pressure on the employees Over the years, Zhongxin行 has built up a vast database of water quality information and invaluable experience passed down among employees, allowing a comprehensive understanding of the entire system's operational characteristics Moreover, by analyzing equipment or water quality data for key signals, problems in the treatment units can be pinpointed If AI technology could be adopted to replace manual inspections of wastewater sources and generate pre-warning signals for systematic assessment, it would significantly alleviate the pressure on staff Response time reduced from 8 hours to 4 hours, saving half the time By implementing 'AOMBR Carbon Source and Aeration Intelligent Enhancement Control System Development,' Zhongxin行 utilizes accumulated wastewater data along with verbal recounts of operator experiences on-site With the support of AI technology and environmental engineering principles, key parameters in the biological treatment unit such as carbon source dosages and aeration can be effectively controlled Through the AI transformation of wastewater treatment, a balance is achieved among pollutant removal, microbial growth, equipment energy conservation, and operation economization, achieving rationalized control parameters Carbon source and aeration parameter adjustment steps range from data collection, model training to prediction verification In the long run, incorporating historical data calculations, AI can operate within known boundary conditions, not only recording past water quality and equipment operational characteristics far more accurately, but also developing predictive models to find optimal solutions that offer the best results in terms of chemical use, energy saving, reduced greenhouse gas emissions, and pollutant removal According to Zhongxin行's estimates, originally due to human parameter adjustments leading to errors, controlling response time would take about 8 hours With the introduction of AI technology, not only can measurement errors be reduced, but also the control response time can be shortened to 4 hours, saving around half the time This enhancement increases the turnover rate of personnel and effectively reduces the risks of penalties due to operator errors and thus markedly reducing the pressure on employees Dashboard digital display panel illustration「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

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

The Kaohsiung-based Kha Shing Enterprise Co, Ltd was established over 40 years ago, and is Taiwan's largest customized yacht company with customers all over America, Europe, Asia, and Australia, earning Taiwan the reputation of the "Kingdom of Yachts" Current FRP hull inspection still relies on traditional methods, such as visual inspection and knocking sounds, which is time-consuming and labor-intensive Kha Shing has applied PAUT array ultrasonic inspection to hull FRP composite materials for the first time, and combined it with AI to interpret ultrasound images, develop complete intelligent solutions, and create emerging markets for inspection companies Kha Shing Enterprise Co, Ltd was formerly Kha Shing Wood Industry Co, Ltd, and was a factory specializing in wood import in Kaohsiung Linhai Industrial Park when it was first established It began to design, manufacture, and sell yachts in 1977 After the second-generation successor of the company, President Kung Chun-Hao entered the company, he made a breakthrough in the previous manufacturing model that relied mainly on the skills of master craftsmen, introduced digital manufacturing to accelerate shipbuilding, and began to make larger yachts, ranking in the top 20 manufacturers worldwide among manufacturers of large yachts over 24 feet It also set a record of delivering 94 yachts within one year, earning Taiwan the reputation of "Kingdom of Yachts" Defect detection ensures yacht quality, using AI to replace humans to achieve higher efficiency Defect detection is very important to ensuring yacht quality At present, the yacht industry still uses very traditional defect detection methods The hull structure is usually made by hand lay-up or the vacuum infusion process, using visual inspection or knocking and the frequency of the sound to determine defects It requires time-consuming manual inspection If there are any defects, they must be reworked and repaired, and a gel coat subsequently sprayed The hull must be constructed in sections to facilitate inspection For large yachts over 24 meters long, construction in sections is very time-consuming and labor-intensive To shorten the time of the yacht manufacturing process, Kha Shing Enterprise will first carry out the gel coating process for the hull, and then perform the hand lay-on process The hull manufacturing process has two types of composite material test specimen structures In terms of 54-foot yacht hulls, the hull contains gel coat, core material, fiber and resin, and the total thickness is about 32cmplusmn01cm, which is twice the total thickness of FRP hull without core material of about 16cmplusmn01cm Defects such as incomplete impregnation of glass fiber or residual air bubbles between glass fiber and resin occasionally occur during the manufacturing process The types of defects include insufficient resin, voids, and delamination Once defects occur, the supply of hull materials will be insufficient and yacht delivery will be delayed Schematic diagram of types of FRP hull In order to solve this problem, Kha Shing Enterprise has engaged in technical cooperated with the metal materials industry and the AI technology industry, combining the ultrasonic inspection expertise of the metal materials industry with AI technologies developed by the AI technology industry in recent years to help solve issues of Kha Shing Enterprise with defect detection The method uses PAUT on the composite material structure of yachts, conducts FRP ultrasonic evaluation to determine the thickness of the yacht hull and material properties, and evaluates the ultrasonic probe frequency applicable to the hull structure based on professional ultrasonic experience After testing, a frequency of 5MHz and a probe width of 45mm can successfully find the location and size of defects in the simulated defect test specimen The three parties jointly found defect detection solutions from array ultrasonic evaluation, AI technology model development, and actual application in yachts The image inspected is an ultrasound image The image displays different colors based on the ultrasonic feedback signal An AI model that automatically identifies defective parts is established through the YOLO algorithm If the amount of abnormal data collected is insufficient for training, the CNN-based Autoencoder algorithm is used to collect normal image data for training and construct an AI model for abnormality detection The object detection YOLO model is trained by inputting image data marked as having defects, while the abnormality detection model is trained by inputting image data without defects Simulated defective specimen corresponding to PAUT results Defect detection by and AI system can shorten the construction period by 15 months and speed up determination by 50 After the development of this AI system is completed, it will be validated on actual 54-foot yachts of Kha Shing Enterprise, and can effectively resolve issues with defects The application of AI technology in ultrasonic inspection for intelligent determination is expected to accelerate determination by approximately 50, and will also shortens the construction period by 15 months, effectively improving the speed and quality of the yacht manufacturing process As Taiwan develops larger and more refined yachts, it will create opportunities for industry optimization and transformation, as well as opportunities for the development of key technologies The application of an AI ultrasonic inspection solution for composite materials is the first of its kind in the yacht industry, and is expected to attract more yacht manufacturers with inspection needs The AI ultrasonic inspection solution for composite materials has three major competitive advantages 1 Professional inspection experience and digital database to facilitate process management and analysis 2 Automatic AI determination and identification quickly identifies defects and provides immediate feedback to process engineers 3 High-efficiency process inspection provides defect repair recommendations, reduces damage rate, and improves the strength and quality of composite materials The application of AI technology can optimize the yacht manufacturing process, reduce manual inspection, create added value through the application of AI in Taiwanrsquos yacht industry, increase international purchase orders, and allow Taiwan yachts to continue to enjoy a good reputation in the world Furthermore, this business model has also spread to fields of application related to composite materials, increasing cross-sector market usage It is estimated to contribute approximately NT14 to NT2 billion in economic benefits to Taiwan's equipment maintenance and non-destructive testing market