• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1489-1497
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• 2018

Web application services have diversified. At the same time, research on intrusion detection is continuing due to the surge of cyber threats. Also, As a single-defense system evolves into multi-level security, we are responding to specific intrusions by correlating security events that have become vast. However, it is difficult to check the OS, service, web application type and version of the target system in real time, and intrusion detection events occurring in network-based security devices can not confirm vulnerability of the target system and success of the attack A blind spot can occur for threats that are not analyzed for problems and associativity. In this paper, we propose the validation of effectiveness for intrusion detection events using TF-IDF. The proposed scheme extracts the response traffics by mapping the response of the target system corresponding to the attack. Then, Response traffics are divided into lines and weights each line with an TF-IDF weight. we checked the valid intrusion detection events by sequentially examining the lines with high weights.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.118-126
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• 1996

In object analysis by image processing, finding lines plays a universal role. And these lines can be easily found by detecting ridges and valleys in digital gray scale images. In this paper, a new method of detecting ridges and valleys by using local min/max operations was presented. This method detects ridges and valleys of desired width by using erosion and dilation properties of local min/max operations, and requires no information of ridge or valley direction. Therefore the method is efficient and computationally simple in comparision with the conventional analytical method.

• Current Optics and Photonics
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• v.3 no.3
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• pp.204-209
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• 2019

In an optical interferometer that uses sinusoidal modulation and quadrature detection, the amplitude and offset of the interference signal vary with time, even without considering system noise. As a result, the circular Lissajous figure becomes elliptical, with wide lines. We propose and experimentally demonstrate a method for compensating quadrature detection error, based on digital signal processing to deal with scaling and fitting. In scaling, fluctuations in the amplitudes of in-phase and quadrature signals are compensated, and the scaled signals are fitted to a Lissajous unit circle. To do so, we scale the average fluctuation, remove the offset, and fit the ellipse to a unit circle. Our measurements of a target moving with uniform velocity show that we reduce quadrature detection error from 5 to 2 nanometers.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.2
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• pp.171-179
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• 2010

Effective monitoring and maintenance operation of towers, power lines and other defects to ensure high quality and reliability of electric power supplied to customers is becoming one of the most important tasks of today's power industry. One specific technology that has the potential to automate the entire surveillance process is unmanned aerial vehicles. In this paper, we propose a new power line extraction method using the directivity of a power line and Hough transform to detect efficiently power lines from thermal aerial images. In simulation results for several aerial images, the proposed method shows good performance in extracting power line detection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.9
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• pp.2203-2208
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• 2014

The Hough transform (HT), that is a typical algorithm for detecting lines in images, needs considerable computational costs and easily detects pseudo-lines on the real world images, because of the large amount of features generated by their complex background or noise. This paper proposes an improved HT that add a preprocessing to estimate the validity of features to the conventional HT. The feature estimation can remove a lot of inessential features for the line detection using a pattern of $3{\times}3$ block features. Experiments using various images show that the proposed algorithm saves computational costs by removing 14%~58% of features depending on images and besides it is superior to the conventional HT in valid line detection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2161-2166
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• 2015

This paper analyzes the straight line detection using the principal component analysis (PCA) and proposes its improved algorithm to which two new functions are added. The first function removes invalid pixels through the detected straight line and detects a line again. The second function detects lines from non-overlapped blocks, selects valid line candidates, and detects a valid line from pixels adjacent to each line candidate. The proposed algorithm detects a more accurate straight line with a low computation in comparison with the conventional algorithm in an image with somewhat refined lines.

• Journal of the Korea Society of Computer and Information
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• v.17 no.7
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• pp.67-76
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• 2012

This paper proposes an efficient method that helps a visually impaired person to detect a pavement borderline. A pedestrian is equipped with a camera so that the front view of a natural scene is captured. Our approach analyzes the captured image and detects the borderline of a pavement in a very robust manner. Our approach performs the task in two steps. In a first step, our approach detects a vanishing point and vanishing lines by applying an edge operator. The edge operator is designed to take a threshold value adaptively so that it can handle a dynamic environment robustly. The second step is to determine the borderlines of a pavement based on vanishing lines detected in the first step. It analyzes the vanishing lines to form VRays that confines the pavement only. The VRays segments out the pavement region in a radial manner. We compared our approach against Canny edge detector. Experimental results show that our approach detects borderlines of a pavement very accurately in various situations.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.598-608
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• 2023

To comprehensively grasp the dynamic changes in the coastal terrain and coastal erosion, it is imperative to incorporate temporal and spatial continuity through frequent and continuous monitoring. Recently, there has been a proliferation of research in coastal monitoring using remote sensing, accompanied by advancements in image monitoring and analysis technologies. Remote sensing, typically involves collection of images from aircraft or satellites from a distance, and offers distinct advantages in swiftly and accurately analyzing coastal terrain changes, leading to an escalating trend in its utilization. Remote satellite image-based coastal line detection involves defining measurable coastal lines from satellite images and extracting coastal lines by applying coastal line detection technology. Drawing from the various data sources surveyed in existing literature, this study has comprehensively analyzed encompassing the definition of coastal lines based on satellite images, current status of remote satellite imagery, existing research trends, and evolving landscape of technology for satellite image-based coastal line detection. Based on the results, research directions, on latest trends, practical techniques for ideal coastal line extraction, and enhanced integration with advanced digital monitoring were proposed. To effectively capture the changing trends and erosion levels across the entire Korean Peninsula in future, it is vital to move beyond localized monitoring and establish an active monitoring framework using digital monitoring, such as broad-scale satellite imagery. In light of these results, it is anticipated that the coastal line detection field will expedite the progression of ongoing research practices and analytical technologies.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.783-788
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• 2003

In this paper, we present a new approach to detect and recognize human face in the image from vision camera equipped on the mobile robot platform. Due to the mobility of camera platform, obtained facial image is small and pose-various. For this condition, new algorithm should cope with these constraints and can detect and recognize face in nearly real time. In detection step, ‘coarse to fine’ detection strategy is used. Firstly, region boundary including face is roughly located by dual ellipse templates of facial color and on this region, the locations of three main facial features- two eyes and mouth-are estimated. For this, simplified facial feature maps using characteristic chrominance are made out and candidate pixels are segmented as eye or mouth pixels group. These candidate facial features are verified whether the length and orientation of feature pairs are suitable for face geometry. In recognition step, pseudo-convex hull area of gray face image is defined which area includes feature triangle connecting two eyes and mouth. And random lattice line set are composed and laid on this convex hull area, and then 2D appearance of this area is represented. From these procedures, facial information of detected face is obtained and face DB images are similarly processed for each person class. Based on facial information of these areas, distance measure of match of lattice lines is calculated and face image is recognized using this measure as a classifier. This proposed detection and recognition algorithms overcome the constraints of previous approach [15], make real-time face detection and recognition possible, and guarantee the correct recognition irregardless of some pose variation of face. The usefulness at mobile robot application is demonstrated.

• Journal of Plant Biotechnology
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• v.47 no.4
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• pp.309-315
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• 2020

Advances in biotechnology have led to progress in crop genetic engineering to improve agricultural productivity. The use of genetically modified (GM) crops has increased, as have consumers' and regulators' concerns about the safety of GM crops to human health, and ecological biodiversity. As such, the identification of GM crops is a critical issue for developers and distributors, and their labeling is mandatory. Multiplex polymerase chain reaction (PCR) has been developed and its use validated for the detection and identification of GM crops in quarantine. Herein, we established a simultaneous detection method to identify four GM maize events. Event-specific primers were designed between the junction region of transgene and genome of four GM maize lines, namely 5307, DAS-40278-9, MON87460, and MON87427. To verify the efficiency and accuracy of the multiplex PCR we used specificity analysis, limit of detection evaluation, and mixed certified reference materials identification. The multiplex PCR method was applied to analyze 29 living, modified maize volunteers collected in South Korea in 2018 and 2019. We performed multiplex PCR analysis to identify events and confirmed the result by simplex PCR using each event-specific primer. As a result, rather than detecting each event individually, the simultaneous detection PCR method enabled the rapid analysis of 29 GM maize volunteers. Thus, the novel multiplex PCR method is applicable for living modified organism volunteer identification.