• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.86-87
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• 2021

As non-road mobile pollutants such as construction equipment are emerging as the main cause of air pollutants emission, construction equipment regulations are gradually strengthening. Research was conducted by correcting the emission coefficient to calculate and predict air pollutant emissions of construction equipment, but it did not reflect site variables such as field and equipment conditions that affect actual emissions. This study derived an Artificial Neural Network emission prediction model based on the actual emission data of excavators and trucks measured at the site and proposed a platform to predict the emission of air pollutants at the site according to the working size and conditions. Through this, it is possible to establish an eco-friendly process plan using a model from the construction plan.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10b
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• pp.831-834
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• 2001

네트워크가 보편화되면서 사이버 공간을 이용한 테러가 전 세게적으로 발생하고 있다. IP Fragmentation은 이 기종 네트워크 환경에서 IP 패킷의 효율적인 전송을 보장해주고 있지만, 몇 가지 보안 문제점을 가지고 있다. 불법 침입자는 이러한 IP Fragmentation 취약점을 이용해 IP Spoofing, Ping of Death, ICMP 공격과 같은 공격 기술을 이용하여 시스템에 불법적으로 침입하거나 시스템의 정상적인 동작을 방해한다. 최근에는 IP Fragmentation을 이용한 서비스 거부공격 외에도 이를 이용하여 패킷 필터링 장비나 네트워크 기반의 침입탐지시스템을 우회한 수 있는 문제점이 대두되고 있다. 본 논문에서는 패킷 재조합 기능을 제공하고 있지 않은 일부 라우터나 침입차단시스템 그리고 네트워크 기반의 침입탐지시스템들에서 불법 사용자가 패킷을 다수의 데이터그램으로 분할하여 공격한 경우 이를 탐지하거나 차단하지 못하는 경우에 대비하여 실시간 접근 로그 파일을 생성하고, 시스템 관리자가 의사결정을 할 수 있도록 함과 동시에 시스템 스스로 대처한 수 있는 시스템을 구현하여 타당성을 검증하고 그에 따른 기대효과를 제시한다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.5
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• pp.515-528
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• 2020

Underground concrete structures are usually designed to be used for decades, but in recent years, many of them are nearing their original life expectancy. As a result, it is necessary to promptly inspect and repair the structure, since it can cause lost of fundamental functions and bring unexpected problems. Therefore, personnel-based inspections and repairs have been underway for maintenance of underground structures, but nowadays, objective inspection technologies have been actively developed through the fusion of deep learning and image process. In particular, various researches have been conducted on developing a concrete crack detection algorithm based on supervised learning. Most of these studies requires a large amount of image data, especially, label images. In order to secure those images, it takes a lot of time and labor in reality. To resolve this problem, we introduce a method to increase the accuracy of crack area detection, improved by 0.25% on average by applying adversarial learning in this paper. The adversarial learning consists of a segmentation neural network and a discriminator neural network, and it is an algorithm that improves recognition performance by generating a virtual label image in a competitive structure. In this study, an efficient deep neural network learning method was proposed using this method, and it is expected to be used for accurate crack detection in the future.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.1
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• pp.1-8
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• 2003

Various methods have been studying to maintain and apply the multimedia inform abruptly increasing over all social fields, in recent years. For retrieval of still images, we is implemented content-based image retrieval system in this paper that make possible to retrieve similar objects from image database after segmenting query object from background if user request query. Query image is processed median filtering to remove noise first and then object edge is detected it by canny edge detection. And query object is segmented from background by using convex hull. Similarity value can be obtained by means of histogram intersection with database image after securing color histogram from segmented image. Also segmented image is processed gray convert and wavelet transform to extract spacial gray distribution and texture feature. After that, Similarity value can be obtained by means of banded autocorrelogram and energy. Final similar image can be retrieved by adding upper similarity values that it make possible to not only robust in background but also better correct object retrieval by using object segmentation method.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1475-1490
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• 2021

Detecting oil spill area using statistical characteristics of SAR images has limitations in that classification algorithm is complicated and is greatly affected by outliers. To overcome these limitations, studies using neural networks to classify oil spills are recently investigated. However, the studies to evaluate whether the performance of model shows a consistent detection performance for various oil spill cases were insufficient. Therefore, in this study, two CNNs (Convolutional Neural Networks) with basic structures(Simple CNN and U-net) were used to discover whether there is a difference in detection performance according to the structure of CNN and distribution characteristics of oil spill. As a result, through the method proposed in this study, the Simple CNN with contracting path only detected oil spill with an F1 score of 86.24% and U-net, which has both contracting and expansive path showed an F1 score of 91.44%. Both models successfully detected oil spills, but detection performance of the U-net was higher than Simple CNN. Additionally, in order to compare the accuracy of models according to various oil spill cases, the cases were classified into four different categories according to the spatial distribution characteristics of the oil spill (presence of land near the oil spill area) and the clarity of border between oil and seawater. The Simple CNN had F1 score values of 85.71%, 87.43%, 86.50%, and 85.86% for each category, showing the maximum difference of 1.71%. In the case of U-net, the values for each category were 89.77%, 92.27%, 92.59%, and 92.66%, with the maximum difference of 2.90%. Such results indicate that neither model showed significant differences in detection performance by the characteristics of oil spill distribution. However, the difference in detection tendency was caused by the difference in the model structure and the oil spill distribution characteristics. In all four oil spill categories, the Simple CNN showed a tendency to overestimate the oil spill area and the U-net showed a tendency to underestimate it. These tendencies were emphasized when the border between oil and seawater was unclear.