• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.133-142
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• 2021

With the recent advancements of deep learning, companies such as smart home, healthcare, and intelligent transportation systems are utilizing its functionality to provide high-quality services for vehicle detection, emergency situation detection, and controlling energy consumption. To provide reliable services in such sensitive systems, deep learning models are required to have high accuracy. In order to develop a deep learning model for analyzing previously mentioned services, developers should utilize the state of the art deep learning models that have already been verified for higher accuracy. The developers can verify the accuracy of the referenced model by validating the model on the dataset. For this validation, the developer needs structural information to document and apply deep learning models, including metadata such as learning dataset, network architecture, and development environments. In this paper, we propose a description language that represents the network architecture of the deep learning model along with its metadata that are necessary to develop a deep learning model. Through the proposed description language, developers can easily verify the accuracy of the referenced deep learning model. Our experiments demonstrate the application scenario of a deep learning description document that focuses on the license plate recognition for the detection of illegally parked vehicles.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.9-21
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• 2022

In this paper, we developed a 100% detection system for entering and leaving vehicles by improving the detection rate of existing detection cameras based on the LiDAR sensor, which is one of the core technologies of the 4th industrial revolution. Since the currently operating parking lot depends only on the recognition rate of the license plate number of about 98%, there are various problems such as inconsistency in the entry/exit count, inability to make a reservation in advance due to inaccurate information provision, and inconsistency in real-time parking information. Parking status information should be managed with 100% accuracy, and for this, we built a parking lot entrance/exit detection system using LIDAR. When a parking system is developed by applying the LIDAR sensor, which is mainly used to detect vehicles and objects in autonomous vehicles, it is possible to improve the accuracy of vehicle entry/exit information and the reliability of the entry/exit count with the detected sensing information. The resolution of LIDAR was guaranteed to be 100%, and it was possible to implement so that the sum of entering (+) and exiting (-) vehicles in the parking lot was 0. As a result of testing with 3,000 actual parking lot entrances and exits, the accuracy of entering and exiting parking vehicles was 100%.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.1-9
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• 2024

This study proposes a multi-scale template matching technique with image pyramids (TMI) to measure structural dynamic displacement using a vision sensor under atmospheric turbulence conditions and evaluates its displacement measurement performance. To evaluate displacement measurement performance according to distance, the three-story shear structure was designed, and an FHD camera was prepared to measure structural response. The initial measurement distance was set at 10m, and increased with an increment of 10m up to 40m. The atmospheric disturbance was generated using a heating plate under indoor illuminance condition, and the image was distorted by the optical turbulence. Through preliminary experiments, the feasibility of displacement measurement of the feature point-based displacement measurement method and the proposed method during atmospheric disturbances were compared and verified, and the verification results showed a low measurement error rate of the proposed method. As a result of evaluating displacement measurement performance in an atmospheric disturbance environment, there was no significant difference in displacement measurement performance for TMI using an artificial target depending on the presence or absence of atmospheric disturbance. However, when natural targets were used, RMSE increased significantly at shooting distances of 20 m or more, showing the operating limitations of the proposed technique. This indicates that the resolution of the natural target decreases as the shooting distance increases, and image distortion due to atmospheric disturbance causes errors in template image estimation, resulting in a high displacement measurement error.

• Journal of Bio-Environment Control
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• v.28 no.2
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• pp.117-125
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• 2019

This study compared the temperature and humidity measured by an aspirated radiation shield (ARS), the accuracy of which has been recently verified, and those measured by a system developed by the parent company (Company A) to investigate and improve the performance of the developed system. The results are as follows. Overall, the two-plate system had a lower radiation shielding effect than the one-plate system but showed better performance results when excluding the effect of strawberry vegetation on the systems. The overall maximum temperature ranges measured by company A's system and the ARS were $20.5{\sim}53.3^{\circ}C$ and $17.8{\sim}44.1^{\circ}C$, respectively. Thus, the maximum temperature measured by company A's system was $2.7{\sim}9.2^{\circ}C$ higher, and the maximum daily temperature difference was approximately $12.2^{\circ}C$. The overall average temperature measured by company A's system and the ARS was $12.4{\sim}38.6^{\circ}C$ and $11.8{\sim}32.7^{\circ}C$, respectively. Thus, the overall average temperature measured by company A's system was $0.6{\sim}5.9^{\circ}C$ higher, and the maximum daily temperature difference was approximately $6.7^{\circ}C$. The overall minimum temperature ranges measured by company A's system and the ARS were $4.2{\sim}28.6^{\circ}C$ and $2.9{\sim}26.4^{\circ}C$, respectively. Thus, the minimum temperature measured by company A's system was $1.3{\sim}2.2^{\circ}C$ higher, and the minimum daily temperature difference was approximately $2.9^{\circ}C$. In addition, the overall relative humidity ranges measured by company A's system and the ARS were 52.9~93.3% and 55.3~96.5%, respectively. Thus, company A's system showed a 2.4~3.2% lower relative humidity range than the ARS. However, there was a day when the relative humidity measured by company A's system was 18.0% lower than that measured by the ARS at maximum. In conclusion, there were differences in the relative humidity measured by the two company's devices, as in the temperature, although the differences were insignificant.