• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.154-155
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• 2023

오픈 소스인 YOLO(You Only Look Once) 객체 탐지 알고리즘이 공개된 이후, 산업 현장에서는 고성능 컴퓨터에서 벗어나 효율과 특수한 환경에 사용하기 위해 임베디드 시스템에 도입하고 있다. 그러나, NVIDIA의 Jetson nano의 경우, Pytorch의 YOLOv7 딥러닝 모델에 대한 추론이 진행되지 않는다. 따라서 제한적인 전력과 메모리, 연산능력 최적화 과정은 필수적이다. 본 논문은 NVIDIA의 임베디드 플랫폼 Jetson 계열의 Xavier NX, Orin AGX, Nano에서 딥러닝 모델을 적용하기 위한 최적화 과정과 플랫폼에서 다양한 크기의 YOLOv7의 PyTorch 모델들을 Tensor RT로 변환하여 FPS(Frames Per Second)를 측정 및 비교한다. 측정 결과를 통해, 각 임베디드 플랫폼에서 YOLOv7 모델의 추론은 Tensor RT는 Pytorch에서 약 4.1배 적은 FPS 변동성과 약 2.25배 정도의 FPS 속도향상을 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.595-596
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• 2023

스마트팩토리의 핵심 설비 기기인 협동로봇의 유지보수를 위해 다양한 센서 데이터를 활용한 딥러닝 기반 결함 진단 연구가 확대되고 있다. 하지만 협동로봇은 기계적 특성과 수행하는 작업의 다양성으로 인해 내부 센서 데이터의 복잡도가 매우 높아 고정적인 결함 진단 기법을 적용하기 어렵다. 따라서 본 논문은 협동로봇의 동작 패턴을 직관적이고 신속하게 인지할 수 있는 비전 기술을 활용하여, 동작 오류 진단을 기반으로 원인이 되는 조인트 결함 위치를 추적하는 딥러닝 기법을 제안한다.

• Journal of Convergence for Information Technology
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• v.12 no.1
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• pp.39-44
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• 2022

The purpose of this study is to extract the type, location, and absolute size of an object in an image using a deep learning algorithm, predict the relative distance between objects, and use this to detect contact between objects. To analyze the size ratio of objects, YOLO, a CNN-based object detection algorithm, is used. Through the YOLO algorithm, the absolute size and position of an object are extracted in the form of coordinates. The extraction result extracts the ratio between the size in the image and the actual size from the standard object-size list having the same object name and size stored in advance, and predicts the relative distance between the camera and the object in the image. Based on the predicted value, it detects whether the objects are in contact.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.435-441
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• 2024

In order for the large amount of collected data sets to be used as deep learning training data, sensitive personal information such as resident registration number and disease information must be changed or encrypted to prevent it from being exposed to hackers, and the data must be reconstructed to match the structure of the built deep learning model. Currently, these tasks are performed manually by experts, which takes a lot of time and money. To solve these problems, this paper proposes a technique that can automatically perform data processing tasks to protect personal information during the deep learning process. In the proposed technique, privacy protection tasks are performed based on data generalization and data reconstruction tasks are performed using circular queues. To verify the validity of the proposed technique, it was directly implemented using C language. As a result of the verification, it was confirmed that data generalization was performed normally and data reconstruction suitable for the deep learning model was performed properly.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.453-459
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• 2022

A decrease in fertility rates causes problems such as decrease in the working-age population, and has a significant impact on national policies. Currently, the Republic of Korea has a conscription system that imposes military service on all men over the age of 18. However, the transition to the volunteer miliatry system is emerging as a social issue due to the decrease in the fertility rate. In this paper, news articles and comments searched for through the keyword ' volunteer miliatry system' were collected to analyze the social perception of the volunteer miliatry system from 2018, when the fertility rate dropped to less than 1. Some of the collected comments were labeled, and emotional levels were calculated through deep learning models. Through this study, we found that awareness of recruitment system conversion did not increase as the decrease in the fertility rate, and it was confirmed that people's interest is gradually increasing.

• Journal of Convergence for Information Technology
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• v.11 no.12
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• pp.30-38
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• 2021

In this paper, we propose a method for extracting thermal data from thermal image and improving detection of heating equipment using the data. The main goal is to read the data in bytes from the thermal image file to extract the thermal data and the real image, and to apply the composite image obtained by synthesizing the image and data to the deep learning model to improve the detection accuracy of the heating facility. Data of KHNP was used for evaluation data, and Faster-RCNN is used as a learning model to compare and evaluate deep learning detection performance according to each data group. The proposed method improved on average by 0.17 compared to the existing method in average precision evaluation.As a result, this study attempted to combine national data-based thermal image data and deep learning detection to improve effective data utilization.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.156-163
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• 2021

Attention mechanisms have been used in deep learning-based computer vision systems, including single image super-resolution (SISR) networks. However, existing SISR networks with attention mechanism focused on real image super-resolution, so it is hard to know whether they are available for CG or VR images. In this paper, we attempt to apply a recent attention module, called balanced attention mechanism (BAM) module, to 12 state-of-the-art SISR networks, and then check whether the BAM module can achieve performance improvement in CG or VR image super-resolution. In our experiments, it has been confirmed that the performance improvement in CG or VR image super-resolution is limited and depends on data characteristics, size, and network type.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.459-463
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• 2024

Frequent closures among self-employed individuals lead to national economic losses. Given the high closure rates in the restaurant industry, predicting changes in this sector is crucial for business survival. While research on factors affecting restaurant industry survival is active, studies predicting commercial district changes are lacking. Thus, this study focuses on forecasting such alterations, designing a deep learning model for Seoul's administrative district commercial district changes. It collects 2023 and 2022 second-quarter variables related to these changes, converting yearly fluctuations into percentages for augmentation. The proposed deep learning model aims to predict commercial district changes. Future policies, considering this study, could support restaurant industry growth and economic development.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.719-719
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• 2022

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.557-559
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• 2022

최근 딥러닝의 기술발전으로 자연어 처리 분야에서 Q&A, 문장추천, 개체명 인식 등 다양한 연구가 진행 되고 있다. 딥러닝 기반 자연어 처리에서 좋은 성능을 보이는 트랜스포머 기반 BERT 모델의 성능향상에 대한 다양한 연구도 함께 진행되고 있다. 본 논문에서는 토픽모델인 잠재 디리클레 할당을 이용한 토픽별 지식그래프 분류와 입력문장의 토픽을 추론하는 방법으로 K-BERT 모델을 학습한다. 분류된 토픽 지식그래프와 추론된 토픽을 이용해 K-BERT 모델에서 대용량 지식그래프 사용의 효율적 방법을 제안한다.