• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.175-180
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• 2024

This paper investigates the effect of a deep learning-based system on data labeling task productivity by individuals with developmental disabilities. It was found that interventions, particularly those using AI, significantly improved productivity compared to self-serving task. AI interventions were notably more effective than job coach-led approaches. This research underscores the positive role of AI in enhancing task efficiency for those with developmental disabilities. This study is the first to apply AI technology to the data labeling tasks of individuals with developmental disabilities and highlighting deep learning's potential in vocational training and productivity enhancement for this group.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.451-457
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• 2018

We propose an automatic detection and classification system of sewer damage database based on artificial intelligence and deep learning. In order to optimize the performance, we implemented a robust system against various environmental variations such as illumination and shadow changes. In our proposed system, a crack detection and damage classification method using a deep learning based Convolutional Neural Network (CNN) is implemented. For optimal results, 9,941 CCTV images with $256{\times}256$ pixel resolution were used for machine learning on the damaged area based on the CNN model. As a result, the recognition rate of 98.76% was obtained. Total of 646 images of $720{\times}480$ pixel resolution were extracted from various sewage DB for performance evaluation. Proposed system presents the optimal recognition rate for the automatic detection and classification of damage in the sewer DB constructed in various environments.

• Annual Conference of KIPS
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• 2022.05a
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• pp.691-694
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• 2022

최근 고해상도 영상이 필요하게 되었으며, 저해상도 영상을 고해상도 영상으로 변환하는 딥러닝 기반의 초해상도 알고리즘에 대한 연구가 활발히 진행되고 있다. 그럼에도 불구하고 딥러닝 기반의 초해상도 알고리즘은 하드웨어의 한계로 인해 임베디드 시스템에서 실행시간이 느린 단점이 있다. 본 논문에서는 심층신경망 기반의 초해상도 알고리즘의 네트워크 구조를 제시하고 다양한 활성화 함수에 따른 화질 및 실행시간 성능을 분석한다. 실험 결과, 젯슨 나노보드의 다양한 활성화 함수 중 화질과 실행 시간의 관계에서 도출한 최적의 활성화 함수가 PReLU 함수임을 확인하였다.

• Annual Conference of KIPS
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• 2023.05a
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• pp.663-664
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• 2023

코로나 19 이후 트렌드에 민감한 MZ 세대가 패션을 선도하면서 다양한 패션이 출현하여 사람들의 선택지를 확장하고 있으며 패션에 관심을 갖고 의상을 구매하는 사례가 증가함에 따라 사람들은 자신을 돋보이게 해주는 의상을 선택하는데 많은 시간을 할애한다. 본 논문에서 개인의 피부 톤, 눈색, 머리색을 분석하여 추출한 퍼스널 컬러를 기반으로 염색된 개인 맞춤 의상을 제공하는 시스템을 제안한다. 기존에 염색공정 시스템의 한계점을 해결하기 위해 딥러닝 모델을 기반으로 원단 염색을 고도화하고 개인 맞춤형 의상 제작의 새로운 제안으로 의류산업에 변화를 주고자 한다. 향후 제안한 시스템의 현실적인 검증과 성능 평가가 필요하다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.495-496
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• 2023

최근 보급되고 있는 스크린 야구장을 많은 이용자가 단순한 타격만을 하고 피드백이 없이 일회성으로 이용하고 있고 이용자의 타격 자세를 평가해주는 기능을 제공하지 않고 있다. 부정확한 자세로 타격을 하게 되면 부상의 위험도 있고, 타격 실력도 향상될 수 없다. 따라서 이용자가 올바른 타격자세를 취할 수 있도록 자세를 평가 해주는 시스템이 필요하다. 본 논문에서는 구글의 미디어 파이프와 딥러닝 기술을 활용하여 타격 자세 영상을 인식하여 타격 자세를 평가해주는 시스템을 개발하였다. 제안한 시스템은 사전에 다양한 영상을 LSTM 알고리즘으로 학습하여 이용자의 타격자세를 4개 등급으로 평가해준다. 이를 활용하여 스크린 야구장에서 카메라만 설치하여 간단하게 사용 가능하며 이용자들이 타격 자세를 자체 평가할 수 있다.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.91-98
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• 2022

In this study, Pukyong National University's floating population survey and analysis were conducted using Raspberry Pie, an open source hardware, and object detection algorithms based on deep learning technology. After collecting images using Raspberry Pie, the person detection of the collected images using YOLO3's IMAGEAI and YOLOv5 models was performed, and Haar-like features and HOG models were used for accuracy comparison analysis. As a result of the analysis, the smallest floating population was observed due to the school anniversary. In general, the floating population at the entrance was larger than the floating population at the exit, and both the entrance and exit were found to be greatly affected by the school's anniversary and events.

• Smart Media Journal
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• v.12 no.5
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• pp.73-80
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• 2023

Recently, intelligent control systems are developing rapidly in various application fields, and methods for utilizing technologies such as deep learning, IoT, and cloud computing for intelligent control systems are being studied. An important technology in an intelligent control system is recognizing and tracking objects in images. However, existing multi-object tracking technology has problems in accuracy and speed. In this paper, a real-time intelligent control system was implemented using YOLO v5 and YOLO v6 based on a one-shot architecture that increases the accuracy of object tracking and enables fast and accurate tracking even when objects overlap each other or when there are many objects belonging to the same class. The experiment was evaluated by comparing YOLO v5 and YOLO v6. As a result of the experiment, the YOLO v6 model shows performance suitable for the intelligent control system.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.549-558
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• 2021

In order to keep infrastructures such as tunnels and underground facilities safe, cracks of concrete lining in tunnel should be detected by regular inspections. Since regular inspections are accomplished through manual efforts using maintenance lift vehicles, it brings about traffic jam, exposes works to dangerous circumstances, and deteriorates consistency of crack inspection data. This study aims to provide methodology to automatically extract cracks from tunnel concrete lining images generated by the existing tunnel image acquisition system. Specifically, we train a deep learning based semantic segmentation model with open dataset, and evaluate its performance with the dataset from the existing tunnel image acquisition system. In particular, we compare the model performance in case of using all of a public dataset, subset of the public dataset which are related to tunnel surfaces, and the tunnel-related subset with negative examples. As a result, the model trained using the tunnel-related subset with negative examples reached the best performance. In the future, we expect that this research can be used for planning efficient model training strategy for crack detection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.146-151
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• 2021

Currently, the demand for vehicles from one family is increasing in many countries at home and abroad, reducing the number of people on the vehicle and increasing the number of vehicles on the road. The multi-passenger lane system, which is available to solve the problem of traffic congestion, is being implemented. The system allows police to monitor fast-moving vehicles with their own eyes to crack down on illegal vehicles, which is less accurate and accompanied by the risk of accidents. To address these problems, applying deep learning object recognition techniques using images from road sites will solve the aforementioned problems. Therefore, in this paper, we compare and analyze the performance of existing deep learning models, select a deep learning model that can identify real-time vehicle occupants through video, and propose a vehicle occupancy detection algorithm that complements the object-ident model's problems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.107-116
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• 2022

Recently, research has been actively conducted on the technology of inspection facilities through image-based analysis assessment of human-inaccessible facilities. This research was conducted to study the conditions of deep learning-based imaging data on bridges and to develop an evaluation prototype program for bridges. To develop a deep learning-based bridge damage detection prototype, the Semantic Segmentation model, which enables damage detection and quantification among deep learning models, applied Mask-RCNN and constructed learning data 5,140 (including open-data) and labeling suitable for damage types. As a result of performance modeling verification, precision and reproduction rate analysis of concrete cracks, stripping/slapping, rebar exposure and paint stripping showed that the precision was 95.2 %, and the recall was 93.8 %. A 2^nd performance verification was performed on onsite data of crack concrete using damage rate of bridge members.