발간년도 : [2023]
| 논문정보 |
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| 논문명(한글) |
[Vol.18, No.1] An Empirical Study on the Improvement of Data Recognition Process in Big Data Based Tagging System |
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| 논문투고자 |
Sung-Jin Jang |
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| 논문내용 |
Automatic Identification & Data Capture System AIDC is a technology that identifies, verifies, records, communicates and stores information of individual items. AIDC can be used in a variety of advanced fields based on the Fourth Industrial Revolution and is used as a core technology for process automation in the industrial sector. Existing methods for collecting and recognizing data use barcode, scan function terminal, label function code, and RF frequency spectrum. The passive tag of the commonly used automatic recognition and data acquisition technology uses the power derived from the electromagnetic field of the RFID reader to transmit the data back to the reader. A reader can cause a problem of recognition performance by creating a tag collision phenomenon in order to recognize a large number of tags coming into the recognition range at the same time. When a large number of tags that come within the scope of the reader's recognition attempt to recognize at the same time, a tag collision occurs and as a result, accurate recognition is not possible. There are many ways to improve the efficiency of the system in tag recognition technology, and if the recognition process is complicated, a large number of cost parts are generated. In order to apply to the 4th industrial revolution based technology, the recognition algorithm of the existing method is simulated by increasing the number of tags and frames using the big data analysis technique As a result, the existing prediction method that can find the number of tags that can be guessed is quantified and predicted through big data analysis. |
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