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RISS 인기검색어
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- 저자 : 한석민 ( Seokmin Han ) (검색결과 9 건)
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Deep Convolution Neural Networks 이용하여 결함 검출을 위한 결함이 있는 철도선로표면 디지털영상 재 생성
김현호 ( Hyeonho Kim ),한석민 ( Seokmin Han ) 한국인터넷정보학회 2020 인터넷정보학회논문지 Vol.21 No.6
This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails [14], so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images [15, 16]. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN) [1]. Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network [2], which is based on Fully Convolutional Network (FCN) [3]. To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.
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딥러닝 기술을 활용한 멀웨어 분류를 위한 이미지화 기법
김형겸 ( Hyeonggyeom Kim ),한석민 ( Seokmin Han ),이수철 ( Suchul Lee ),이준락 ( Jun-rak Lee ) 한국인터넷정보학회 2018 인터넷정보학회논문지 Vol.19 No.5
According to Symantec's Internet Security Threat Report(2018), Internet security threats such as Cryptojackings, Ransomwares, and Mobile malwares are rapidly increasing and diversifying. It means that detection of malwares requires not only the detection accuracy but also versatility. In the past, malware detection technology focused on qualitative performance due to the problems such as encryption and obfuscation. However, nowadays, considering the diversity of malware, versatility is required in detecting various malwares. Additionally the optimization is required in terms of computing power for detecting malware. In this paper, we present Stream Order(SO)-CNN and Incremental Coordinate(IC)-CNN, which are malware detection schemes using CNN(Convolutional Neural Network) that effectively detect intelligent and diversified malwares. The proposed methods visualize each malware binary file onto a fixed sized image. The visualized malware binaries are learned through GoogLeNet to form a deep learning model. Our model detects and classifies malwares. The proposed method reveals better performance than the conventional method.
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심층학습을 이용한 Railroad defect detection기법 분석 및 현황
한석민 韓國交通大學校 2022 한국교통대학교 논문집 Vol.57 No.-
Train is one of the most popular forms of public transportation. Therefore, unexpected accidents and delays are considered serious for railways, which make maintenance process essential. Currently, the inspection of the rail and the fasteners on the railway track is mainly operated by railway staff. Computer vision based methods are now being employed to detect the defects of rails and inspect the railroad condition, so that the high cost of the inspection by railroad investigation staff and low efficiency could be alleviated. Automated defect detection and segmentation can help investigators find rail defects. In this paper, the researches that applied computer vision based deep learning method to railroad defect detection and inspection have been reviewed, and the current trend and the direction of those researches were discussed.
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철도시설 상태기반정비 및 안전을 위한 인공지능 적용 현황과 분석
한석민 韓國交通大學校 2024 한국교통대학교 논문집 Vol.59 No.-
Artificial intelligence is now being applied across a wide range of industries, and the railway industry is no exception. Railway companies are exploring ways to leverage AI to enhance their maintenance and operations, particularly in reducing the manual labor involved in inspecting the condition of railway infrastructure. This proactive approach, known as Condition-Based Maintenance (CBM), aims to maintain assets before problems arise. While autonomous train operation has already been extensively studied, this paper focuses on the application of AI for inspecting railway structures in the context of CBM. It will also discuss potential future directions for AI in the railway industry.
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한석민 한국교통대학교 2021 한국교통대학교 논문집 Vol.56 No.-
In Deep Learning method, it is well known that it requires large amount of data to train the deep neural network. What makes the learning process more difficult is that it also requires the annotation of each data to fully train the neural network, which indicates that experts should provide the annotation through time-consuming labeling process. To alleviate the problem of time-consuming labeling process, some methods have been suggested such as weak-supervised method, one-shot learning, self-supervised, suggestive learning, and so on. In this manuscript, the researches that apply the suggestive learning are analyzed and its possible future direction of the research is suggested.
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