• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.23-30
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• 2021

An Automated Industry and Occupation Coding System assigns statistical classification code to the enormous amount of natural language data collected from people who write about their industry and occupation. Unlike previous studies that applied information retrieval, we propose a system that does not need an index database and gives proper code regardless of the level of classification. Also, we show our model, which utilized KoBERT that achieves high performance in natural language downstream tasks with deep learning, outperforms baseline. Our method achieves 95.65%, 91.51%, and 97.66% in Occupation/Industry Code Classification of Population and Housing Census, and Industry Code Classification of Census on Basic Characteristics of Establishments. Moreover, we also demonstrate future improvements through error analysis in the respect of data and modeling.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1278-1282
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• 2009

In apartment type of housing, if resident's vehicle is registered in central control office and RFID TAG is issued, identification can be recognized from the time of entrance into parking lot and intelligent parking guide system can be activated based on the residents' profile. Parking Guide System leads a vehicle to the available parking space which is closest to the entrance gate of the vehicle's owner. And when residents forget where they parked their cars, they can query to the Parking Guide and Management System and get responses about the location. For the correct operation of this system, it is necessary to find out where the residents' cars have parked in real time and which lot is available for parking of other cars. RFID is very fancy solution for this system. RFID reader gathers the ID information in RFID TAGs in parked cars and updates the DB up to date. But, when non-residents' cars are parked inside apartment, RFID reader cannot identify them nor know the exact empty/occupied status of parking spaces because they don't react to RFID reader's query. So for the exact detection of empty/occupied status, we suggest the combined use of ultrasonic sensors and RFID. We designed a tree topology with intermediate data aggregators. The depth of tree is normally more than 3 from root (central office) to leaves (individual parking lots). The depth of 2 in tree topology brings about the bottleneck in communication and maintenance. We also designed the information fields used in RFID networks and Sensor Networks.