• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.583-598
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• 2022

As human-based tunnel inspections are affected by the subjective judgment of the inspector, making continuous history management difficult. There is a lot of deep learning-based automatic crack detection research recently. However, the large public crack datasets used in most studies differ significantly from those in tunnels. Also, additional work is required to build sophisticated crack labels in current tunnel evaluation. Therefore, we present a method to improve crack detection performance by inputting existing datasets into a deep learning model. We evaluate and compare the performance of deep learning models trained by combining existing tunnel datasets, high-quality tunnel datasets, and public crack datasets. As a result, DeepLabv3+ with Cross-Entropy loss function performed best when trained on both public datasets, patchwise classification, and oversampled tunnel datasets. In the future, we expect to contribute to establishing a plan to efficiently utilize the tunnel image acquisition system's data for deep learning model learning.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.859-865
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• 2018

A construction of infrastructures and base station on the moon could be undertaken by linking with the regions where construction materials and energy could be supplied on site. It is necessary to detect craters on the lunar surface and gather their topological information in advance, which forms permanent shaded regions (PSR) in which rich ice deposits might be available. In this study, an effective method for automatic detection of lunar craters on the moon surface is taken into consideration by employing a latest version of deep-learning algorithm. A training of a deep-learning algorithm is performed by involving the still images of 90000 taken from the LRO orbiter on operation by NASA and the label data involving position and size of partly craters shown in each image. the Faster RCNN algorithm, which is a latest version of deep-learning algorithms, is applied for a deep-learning training. The trained deep-learning code was used for automatic detection of craters which had not been trained. As results, it is shown that a lot of erroneous information for crater's positions and sizes labelled by NASA has been automatically revised and many other craters not labelled has been detected. Therefore, it could be possible to automatically produce regional maps of crater density and topological information on the moon which could be changed through time and should be highly valuable in engineering consideration for lunar construction.

• Annual Conference of KIPS
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• 2022.05a
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• pp.333-336
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• 2022

2021년에 발표된 재활용 가능 자원의 분리수거 관한 정부 지침으로 투명 페트병은 유색 플라스틱과 구분되어 분리배출하는 것이 의무화되었다. 그러나 제도가 시행된 지 반년이 지났지만, 혼합 배출률이 크게 줄지 않았고 아파트에서는 미화원들이 일일이 투명 페트병을 분리하고 있는 등 주민들의 불편함은 커지고 있다. 본 논문에서는 기존 분리수거장에 쉽게 설치 가능한 보급형 페트병 분리수거 시스템을 개발하여 분리수거 효율성을 높이고자 한다. 우리는 AlexNet, GoogleNet 알고리즘을 이용하여 딥러닝 모델을 이용하고 자체 제작한 데이터셋으로 학습시켜 하드웨어에 적용함으로써 보급형 페트병 분리수거 시스템을 설계하였다.

• Annual Conference of KIPS
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• 2022.11a
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• pp.547-549
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• 2022

최근 소비경제의 폭발적 성장과 더불어 쓰레기로 인해 우리 생활 주변은 물론 해양까지 환경오염이 점점 심각해지고 있다. 그에 따른 재활용 시스템의 필요성이 높아지고 있으며, 지속 가능한 발전을 위해 세계적으로 환경을 위한 연구가 진행되고 있다. 본 논문에서는 딥러닝 기반의 AI 기술을 적극적으로 활용하여 분리수거가 아닌, 페트병을 재활용하는 과정을 집중적으로 개선한다. 이를 통하여, 페트병이 원인인 환경오염을 해결할 뿐만 아니라 고급 재활용 원료를 생산할 수 있게 하여 경제적인 효과도 얻을 수 있는 시스템을 제안한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.285-286
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• 2024

본 연구에서는 현대 사회의 환경 보호 관심 상승과 소비자들의 재활용 참여 욕구를 고려하여 YOLOv5를 활용한 무인 공병 환급기를 개발하였다. 이 시스템은 정확한 물체 감지와 금액 추정 알고리즘을 결합하여 사용자가 간편하게 공병을 반환하고 적절한 보상을 받을 수 있는 효과적인 시스템을 제공한다. 프로젝트의 성공은 재활용 참여율 증가와 지속 가능한 소비 문화 형성에 기여할 것으로 기대된다.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.291-298
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• 2020

Sign language was designed for the deaf and dumb people to allow them to communicate with others and connect to the society. However, sign language is uncommon to the rest of the society. The unresolved communication barrier had eventually isolated deaf and dumb people from the society. Hence, this study focused on design and implementation of a wearable sign language interpreter. 6 inertial measurement unit (IMU) were placed on back of hand palm and each fingertips to capture hand and finger movements and orientations. Total of 28 proposed word-based American Sign Language were collected during the experiment, while 156 features were extracted from the collected data for classification. With the used of the long short-term memory (LSTM) algorithm, this system achieved up to 99.89% of accuracy. The high accuracy system performance indicated that this proposed system has a great potential to serve the deaf and dumb communities and resolve the communication gap.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.197-202
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• 2021

Biometric technology using finger veins is receiving a lot of attention due to its high security, convenience and accuracy. And the recent development of deep learning technology has improved the processing speed and accuracy for authentication. However, the training data is a subset of real data not in a certain order or method and the results are not constant. so the amount of data and the complexity of the artificial neural network must be considered. In this paper, the deep learning model of Inception-Resnet-v2 was used to improve the high accuracy of the finger vein recognizer and the performance of the authentication system, We compared and analyzed the performance of the deep learning model of DenseNet-201. The simulations used data from MMCBNU_6000 of Jeonbuk National University and finger vein images taken directly. There is no preprocessing for the image in the finger vein authentication system, and the results are checked through EER.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.467-473
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• 2020

Recently, the number of traffic accidents has also increased with the increase in the penetration rate of cars in Korea. In particular, not only inter-vehicle accidents but also human accidents near crosswalks are increasing, so that more attention to traffic safety around crosswalks are required. In this paper, we propose a system for predicting the safety level around the crosswalk by recognizing an approaching vehicle and estimating the speed of the vehicle using NVIDIA Jetson Nano-class edge devices. To this end, various machine learning models are trained with the information obtained from deep learning-based vehicle detection to predict the degree of risk according to the speed of an approaching vehicle. Finally, based on experiments using actual driving images and web simulation, the performance and the feasibility of the proposed system are validated.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1949-1955
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• 2021

With the recent COVID-19 situation, countries are seriously feeling the need for medical personnel and their technologies. PDepending on the aging society, the number of medical staff is actually decreasing, and in order to solve this problem, research is needed to replace the part that does not require high expertise among actual medical practices performed by doctors. This paper describes and proposes actual research methods related to unmanned medical systems that use various deep learning image processing-based technologies to check the recovery status applicable to rehabilitation areas where medical staff should face patients directly. The proposed method replaces passive calculations such as a protractor or a method of drawing a line in a photograph, which is the method used for actual motion comparison. Since it is performed in real time, it helps to diagnose quickly, and it is easy for medical staff to provide necessary information because data on the degree of match of motion performance can be checked.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.9-17
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• 2023

In this paper, we propose a one-class vibration anomaly detection system for bearing defect diagnosis. In order to reduce the economic and time loss caused by bearing failure, an accurate defect diagnosis system is essential, and deep learning-based defect diagnosis systems are widely studied to solve the problem. However, it is difficult to obtain abnormal data in the actual data collection environment for deep learning learning, which causes data bias. Therefore, a one-class classification method using only normal data is used. As a general method, the characteristics of vibration data are extracted by learning the compression and restoration process through AutoEncoder. Anomaly detection is performed by learning a one-class classifier with the extracted features. However, this method cannot efficiently extract the characteristics of the vibration data because it does not consider the frequency characteristics of the vibration data. To solve this problem, we propose an AutoEncoder model that considers the frequency characteristics of vibration data. As for classification performance, accuracy 0.910, precision 1.0, recall 0.820, and f1-score 0.901 were obtained. The network design considering the vibration characteristics confirmed better performance than existing methods.