• International conference on construction engineering and project management
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• 2015.10a
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• pp.304-309
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• 2015

On construction sites, it is important to monitor the performance of construction equipment and workers to achieve successful construction project management; especially, vision-based detection methods have advantages for the real-time site data collection for safety and productivity analyses. Although many researchers developed vision-based detection methods with acceptable performance, there are still limitations to be addressed: 1) sensitiveness to the shape and appearance changes of moving objects in difference working postures, and 2) high computation time. To deal with the limitations, this paper proposes a detection algorithm of construction equipment based on Integral Channel Features. For validation, 16,850 frames of video streams were recorded and analyzed. The results showed that the proposed method worked in high performance in terms of accuracy and processing time. In conclusion, the developed method can help to understand useful site information including working pattern, working time and input manpower analyses.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.4
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• pp.819-825
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• 2012

Nowadays many phishing sites contain HTTP links to victim web-site's contents such as images, bulletin board etc. to make the phishing sites look more real and similar to the victim web-site. We introduce a real-time phishing site detection system which makes use of the characteristic that the phishing sites' URLs flow into the victim web-site via the HTTP referer header field when the phishing site is visited. The detection system is designed to adopt an out-of-path network configuration to minimize effect on the running system, and a phishing site source code analysis technique to alert administrators in real-time when phishing site is detected. The detection system was installed on a company's web-site which had been targeted for phishing. As result, the detection system detected 40 phishing sites in 6 days of test period.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2459-2472
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• 2022

With the increase in copyright content exports to overseas markets due to the recent globalization of the Korean culture, the added value of the Korean digital content market is increasing at a significant rate. As such, as the size of the copyright market increases, different piracy sites have emerged that generate profits by illegally distributing works without the permission of the copyright holders, resulting in direct and indirect damage to these copyright holders. The existing copyright detection methods used in public institutions for solving this problem are limited, while the piracy sites are ever-changing. Methods are being continuously developed to achieve better detection results. To this end, it is possible to detect the latest infringement site domain by detecting the infringement site domain that is constantly changed through the search engine. This paper proposes an improved piracy site detection method using a search engine to prevent the damage caused by piracy sites.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.2
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• pp.313-320
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• 2015

While various phishing attacks are getting to be increased in constant, their response methods still stay on the stage of responding after identifying an attack. To detect a phishing site ahead of an attack, a method has been suggested with utilizing the Referer header field of HTTP. However, it has a limitation to implement a traffic gathering system for each of prospective target hosts. This paper presents a unknown phishing site detection method in the Interior network environment. Whenever a user try to connect a phishing site, its traffic is pre-processed with considering of the characteristics of HTTP protocol and phishing site. The phishing site detection phase detects a suspicious site under phishing with analysing HTTP content. To validate the proposed method, some evaluations were conducted with 100 phishing URLs along with 100 normal URLs. The experimental results show that our method achieves higher phishing site detection rate than that of existing detection methods, as 66% detection rate for the phishing URLs, and 0% false negative rate for the normal URLs.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.55 no.11
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• pp.532-537
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• 2006

Lightning induced faults accounts for more than 66% at the transmission lines of KEPCO. The lightning causes damages to power system equipments including transmission line, the blackout of electricity and the electro-magnetic interference. Because of this reason, we need the real time lightning information for the optimal operation of power system. And, it is required to obtain and accumulate the lightning current parameters for the insulation design. In 2005, KEPRI constructed a lightning detection network, the KLDNet (i.e. Kepco Lightning Detection & Information System) and launched a lightning information service for KEPCO customers. It is intended to provide data service on the operation of transmission lines and collect lightning-related data, which is the most important factor regulating power system design and operation. The new system will replace LPATS, the old detection system, which has been operating since 1995 and is rapidly failing in terms of both detection performance and location accuracy. The purpose of this paper is to explain the work performed and the results of that work in performing a site survey of several locations. The purpose of the site survey is to find locations acceptable for the installation of a lightning location receiver in support of a Lightning detection system(LDS). A restriction was placed on the surveyed locations, as they must belong to the Korea Electric Power Company. This requirement was made to facilitate the communication needs of the LDS network. Total of 15 sites were evaluated as possible LDS sensor sites. Some of the sites were rejected for physical reasons and therefore no electrical testing was performed. Of the 15 sites, total of 10 sites were considered acceptable and 5 sites were rejected for various reason. In this paper, we would like to explain the site survey and detection efficiency for LDS.

• Journal of KIBIM
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• v.11 no.2
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• pp.24-32
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• 2021

Recently, starting with smart construction research, interest in technology that automates construction site management using artificial intelligence technology is increasing. In order to automate construction site management, it is necessary to recognize objects such as construction equipment or workers, and automatically analyze the relationship between them. For example, if the relationship between workers and construction equipment at a construction site can be known, various use cases of site management such as work productivity, equipment operation status monitoring, and safety management can be implemented. This study derives a real-time object detection platform architecture that is required when performing construction site management using deep learning technology, which has recently been increasingly used. To this end, deep learning models that support real-time object detection are investigated and analyzed. Based on this, a deep learning model development process required for real-time construction site object detection is defined. Based on the defined process, a prototype that learns and detects construction site objects is developed, and then platform development considerations and architecture are derived from the results.

• The Plant Pathology Journal
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• v.40 no.1
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• pp.40-47
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• 2024

Garlic can be infected by a variety of viruses, but mixed infections with leek yellow stripe virus, onion yellow dwarf virus, and allexiviruses are the most damaging, so an easy, inexpensive on-site method to simultaneously detect at least these three viruses with a certain degree of accuracy is needed to produce virus-free plants. The most common laboratory method for diagnosis is multiplex reverse transcription polymerase chain reaction (RT-PCR). However, allexiviruses are highly diverse even within the same species, making it difficult to design universal PCR primers for all garlic-growing regions in the world. To solve this problem, we developed an inexpensive on-site detection system for the three garlic viruses that uses a commercial mobile PCR device and a compact electrophoresis system with a blue light. In this system, virus-specific bands generated by electrophoresis can be identified by eye in real time because the PCR products are labeled with a fluorescent dye, FITC. Because the electrophoresis step might eventually be replaced with a lateral flow assay (LFA), we also demonstrated that a uniplex LFA can be used for virus detection; however, multiplexing and a significant cost reduction are needed before it can be used for on-site detection.

• Journal of the Korean Society of Safety
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• v.23 no.6
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• pp.164-170
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• 2008

This research was verified detection way of intercept vehicles and performance evaluation after system installation using image detector as detection way of ground installation. By image recognition algorithm was on the trace of moving orbit of violation vehicles for detection way of intercept vehicles. When moving orbit is located special site, utilized geometric image calibration and DC-notch filter. These are cognitive system of license plate by making signal. Then, Bright Evidence Detection and Dark Evidence Detection were applied to after mixing. It is applied to way of Backward tracking for detection way of intercept vehicles. After the field evaluation of developed system, it should be analyzed the more high than recognition rate of minimum standards 80%. It should rise in the estimation of the site applicability is highly from now.

• International Journal of Oral Biology
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• v.44 no.1
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• pp.8-13
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• 2019

Recently, the importance of on-site detection of pathogens has drawn attention in the field of molecular diagnostics. Unlike in a laboratory environment, on-site detection of pathogens is performed under limited resources. In this study, we tried to optimize the experimental conditions for on-site detection of pathogens using a combination of ultra-fast convection polymerase chain reaction (cPCR), which does not require regular electricity, and nucleic acid lateral flow (NALF) immunoassay. Salmonella species was used as the model pathogen. DNA was amplified within 21 minutes (equivalent to 30 cycles of polymerase chain reaction) using ultra-fast cPCR, and the amplified DNA was detected within approximately 5 minutes using NALF immunoassay with nucleic acid detection (NAD) cassettes. In order to avoid false-positive results with NAD cassettes, we reduced the primer concentration or ultra-fast cPCR run time. For singleplex ultra-fast cPCR, the primer concentration needed to be lowered to $3{\mu}M$ or the run time needed to be reduced to 14 minutes. For duplex ultra-fast cPCR, $2{\mu}M$ of each primer set needed to be used or the run time needed to be reduced to 14 minutes. Under the conditions optimized in this study, the combination of ultra-fast cPCR and NALF immunoassay can be applied to on-site detection of pathogens. The combination can be easily applied to the detection of oral pathogens.