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【2020 Application Example】 AI Address Parsing, No More Hitting Walls in Searching for Coordinates

Empower addresses with spatial coordinates to help drive the 'Open Data' policy

In recent years, the government has been promoting 'Open Data' hoping that the openness of data will facilitate inter-agency data flow, enhance administrative efficiency, meet public needs, and strengthen public oversight of the government. Among them, transportation data is closely related to daily life, often reported by the public with the incidents specifying obvious local landmarks or addresses; there have also been public feedback about the traffic reports on police radio that lacked actual coordinates. Introducing these addresses, which were originally without spatial attributes, into the geographical coordinate system is one step toward 'Smart Spatial Decision Making'.

However, unstructured addresses, without manual intervention to improve the inconsistency of address formats, do not yield high location accuracy, necessitating an improvement in data quality and usability to unlock the potential applications of open data. This further aids in policy promotion and widespread application to different sectors including tourism, employment, birth and adoption.

Unregulated and diverse writing styles of addresses lead to low location accuracy

Address Locator is jointly developed by SongXu Information Co., Ltd. and YanDing Intelligent Co., Ltd. GOLiFE as a 'stand-alone address locating software' providing single or batch address location services. To imbue address data with spatial attributes, the core technology of Address Locator involves 'Address Parsing' and 'Address Location' in two stages. Initially, 'Address Parsing' distributes the addresses aimed for positioning according to administrative region hierarchy keywords: province/city, township/district, village, road/street, alley, lane, number; subsequently, 'Address Location' matches the split addresses with the parent address to obtain the location level and corresponding coordinates.

However, in the actual business integration process, since address sources are maintained separately by different authorities, a lack of consistent standards remains a common issue. Problems include: special characters (at address examples in specific regions), omitted administrative units, repetitive administrative hierarchical keywords, special street-alley segments, mismatch in Chinese numericals vs. Arabic numerals, and non-current addresses leading to complex address formats that are difficult to accurately split.

Establishing an address tokenization model, achieving precise location alignment!

To effectively handle various messy address formats and alleviate the difficulties in location alignment for the existing Address Locator, AI and Natural Language Processing technologies are implemented for 'Address Normalization' and a 'Chinese Tokenization Tool' to optimize existing address location capability. 'Address Normalization' addresses the issues of missing keywords, variant character forms, and missing administrative areas; whereas 'Chinese Tokenization Tool' helps resolve 'split errors' caused by special address formats, preventing unsuccessful positioning.

Successful address parsing through AI tokenization technology

▲ Successful address parsing through AI tokenization technology

In the past, while handling address location services, manual preprocessing for data standardization was required, hence it was not solely marketed as a product, but included in project plans that offered address location services. However, after incorporating address normalization and AI tokenization technology, it has become a complete product, significantly reducing the time users spend on manual adjustments and achieving the intended location accuracy. Furthermore, the AI-enhanced Address Locator is now introduced on the SongXu Information Co. Ltd. website, including product descriptions and official listings.

After four months of testing and modifications, AI technology was successfully incorporated into the existing address location product. From selecting the tokenization tools, building the corpus, training the model, and interfacing with product features, to complete test planning, collection from 'Government Data Open Platform' and 'Taichung City Government Data Open Platform,' including over 62 datasets and more than 300,000 addresses, achieving a complete match rate of 90.08% and a fuzzy match rate of 98%, greatly surpassing the original product in match rates and processing time!

To promote AI technology applications in the information services sector, the AI-enhanced address location service is positioned as a new solution and showcased on the SongXu company website; starting from product function introductions, explaining address regularization methods and address location features; subsequently, guiding potential customers to envision applicable scenarios including: decision analytics, precision marketing, and other applications. The product will aid various sectors’ data by assigning spatial information to addresses, delving into the context and trends of data in two-dimensional space.

Address Location Solution

▲ Address Location Solution

Providing spatial coordinates for attractions, intersections, and points of interest

Successful development and implementation of AI-enhanced products in companies focused on smart transportation systems in the domestic market revealed that, while effectively solving address location issues, they also recognized that descriptions of spatial information, beyond addresses inclusive. During their progress, integrating AI more broadly into 'Entity Recognition' is set to be an important future application not limited to address location. In an era of information overload, collecting data is straightforward; identifying keywords of interest is key. Future development directions aim to optimize these products and create more business opportunities!

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

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【導入案例】維繫遊艇王國美譽 嘉信遊艇導入國內第一套FRP複材超音波智慧檢測
Maintaining the reputation of the “Kingdom of Yachts” - Kha Shing Enterprise introduces the first domestic FRP ultrasonic smart inspection of composite materials

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 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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 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【導入案例】赫銳特科技VCSEL封裝元件瑕疵導入AOI檢測 提升產能效率20
HRT Technology Improves Production Efficiency by 20% Through AOI Detection of Defects in VCSEL Packaging

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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 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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