• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권1호
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• pp.269-286
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• 2018

Signal detection in symmetric alpha-stable ($S{\alpha}S$) distributed noise is a challenging problem. This paper proposes a detector based on a decreasing exponential function (DEF). The DEF detector can effectively suppress the impulsive noise and achieve good performance in the presence of $S{\alpha}S$ noise. The analytical expressions of the detection and false alarm probabilities of the DEF detector are derived, and the parameter optimization for the detector is discussed. A performance analysis shows that the DEF detector has much lower computational complexity than the Gaussian kernelized energy detector (GKED), and it performs better than the latter in $S{\alpha}S$ noise with small characteristic exponent values. In addition, the DEF detector outperforms the fractional lower order moment (FLOM)-based detector in $S{\alpha}S$ noise for most characteristic exponent values with the same order of magnitude of computational complexity.

• 대한안전경영과학회지
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• 제22권1호
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• pp.1-8
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• 2020

Maintenance of power distribution facilities is a significant subject in the power supplies. Fault caused by deterioration in power distribution facilities may damage the entire power distribution system. However, current methods of diagnosing power distribution facilities have been manually diagnosed by the human inspector, resulting in continuous pole accidents. In order to improve the existing diagnostic methods, a thermal image analysis model is proposed in this work. Using a thermal image technique in diagnosis field is emerging in the various engineering field due to its non-contact, safe, and highly reliable energy detection technology. Deep learning object detection algorithms are trained with thermal images of a power distribution facility in order to automatically analyze its irregular energy status, hereby efficiently preventing fault of the system. The detected object is diagnosed through a thermal intensity area analysis. The proposed model in this work resulted 82% of accuracy of detecting an actual distribution system by analyzing more than 16,000 images of its thermal images.

• 디지털콘텐츠학회 논문지
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• 제10권3호
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• pp.479-484
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• 2009

무선 네트워크 사용이 증가하면서 무선 네트워크의 보안 시스템의 중요성이 부각되고 있는 실정이다. MANET은 이동 노드만으로 구성되어 있기 때문에 공격이 발생해도 그에 대한 탐지나 대응이 어렵다. 그리고 노드들의 이동성 때문에 유선 네트워크 환경에서 사용하던 보안 시스템을 그대로 적용하기에는 어려움이 많다. 따라서 이러한 환경에서 공격자의 악의적인 공격으로부터 시스템을 보호하고 즉각적으로 대처해야만 한다. 본 논문에서는 악의적인 공격을 탐지하고 자원의 효율적 사용을 위해서 클러스터를 헤드를 이용한 침입탐지 시스템을 제안한다. 보다 정확한 침입탐지를 위해 규칙들의 집합을 정의하고 일치 여부를 판단하는 방법을 이용하였다. 제안한 방법의 성능 평가를 위해서 blackhole, message negligence, jamming 공격을 이용하였다.

• 제어로봇시스템학회논문지
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• 제21권8호
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• pp.742-747
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• 2015

The quality of steel plate products is determined by the number of defects and the process problems are estimated by shapes of defects. Therefore pinholes defects of cold rolled steel have to be controlled. In order to improve productivity and quality of products, within each production process, the product is inspected by an adequate inspection system individually in the lines of steelworks. Among a number of inspection systems, we focus on the pinholes detection system. In this paper, we propose an embedded system using FPGA which can detect pinholes defects. The proposed system is smaller and more flexible than a traditional system based on expensive frame grabbers and PC. In order to detect consecutive defects, FPGAs acquire two dimensional image and process the image in real time by using correlation of lines. The proposed pinholes detection algorithm decreases arithmetic operations of image processing and also we designed the hardware to shorten the data path between logics due to decreasing propagation delay. The experimental results show that the proposed embedded system detects the reliable number of pinholes in real time.

• 제어로봇시스템학회논문지
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• 제21권7호
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• pp.621-626
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• 2015

This paper presents a camera calibration method in order to estimate the lane detection and inter-vehicle distance estimation system for an automotive safety driving system. In order to implement the lane detection and vision-based inter-vehicle distance estimation to the embedded navigations or black box systems, it is necessary to consider the computation time and algorithm complexity. The process of camera calibration estimates the horizon, the position of the car's hood and the lane width for extraction of region of interest (ROI) from input image sequences. The precision of the calibration method is very important to the lane detection and inter-vehicle distance estimation. The proposed calibration method consists of three main steps: 1) horizon area determination; 2) estimation of the car's hood area; and 3) estimation of initial lane width. Various experimental results show the effectiveness of the proposed method.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.31-40
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• 2021

The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.

• 대한의생명과학회지
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• 제29권3호
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• pp.109-120
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• 2023

Recently, the explosive increase of carbapenemase-producing Enterobacterales (CPE) in the worldwide poses a serious threat. The purpose of this study is to investigate epidemiology, detection, and treatment of CPE. Three main carbapenemase are reported worldwide, which were KPC, NDM, and OXA-48-like. KPC type are mostly found in USA, China, Europe, and Latin America. NDM type are mostly found in South Asia. OXA-48-like are often seen in the Mediterranean and Northern Africa. In Korea, CPE have increased explosively since 2015. In 2021, 18,099 CPE were isolated, which were Klebsiella pneumoniae, Escherichia coli, and Enterobacter cloacae in order. The CPE genotype was distributed with KPC, NDM, OXA type in order. Phenotypic detection methods include carbapenemase production tests (CPT) and differential tests of CPE. CPTs include modified Hodge test, modified carbapenem inactivation method (mCIM), Carba NP test, among which mCIM is the most widely used due to easy accessibility and accuracy. A lot of genotypic methods are being done for quick results, and commercialized kits using multiplex real-time PCR and microarray are widely used. Colistin and tigecycline are used as the first line of CPE treatment and are used in combination with second line drugs such as meropenem and fosfomycin.

• 디지털콘텐츠학회 논문지
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• 제19권4호
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• pp.711-717
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• 2018

컴퓨터와 네트워크의 비약적인 발전은 다양한 정보 교환을 쉽게 하였다. 하지만, 그와 동시에 다양한 위험 요소를 발생시켜 악의적 목적을 가진 사용자와 그룹은 취약한 시스템을 대상으로 공격을 하고 있다. 침입탐지시스템은 네트워크 패킷 분석을 통해 악의적인 행위를 탐지한다. 하지만, 많은 양의 패킷을 짧은 시간 내에 처리해야 하는 부담이 있다. 따라서, 이 문제를 해결하기 위하여 우리는 User Level에서 동작하는 네트워크 침입탐지시스템의 탐지 성능 향상을 위해 Kernel Level에서 동작하는 시스템을 제안한다. 실제로, kernel level에서 동작하는 네트워크 침입탐지시스템을 구현함으로써 패킷 분석 및 탐지 성능을 향상함을 확인하였다.

• Transactions on Electrical and Electronic Materials
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• 제17권6호
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• pp.363-368
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• 2016

Rectangular shape detection plays an important role in many image recognition systems. However, it requires continued research for its improved performance. In this study, we propose a strong rectangular shape detection algorithm, which combines the canny edge and line detection algorithms based on the perpendicularity and parallelism of a rectangle. First, we use the canny edge detection algorithm in order to obtain an image edge map. We then find the edge of the contour by using the connected component and find each edge contour from the edge map by using a DP (douglas-peucker) algorithm, and convert the contour into a polyline segment by using a DP algorithm. Each of the segments is compared with each other to calculate parallelism, whether or not the segment intersects the perpendicularity intersecting corner necessary to detect the rectangular shape. Using the perpendicularity and the parallelism, the four best line segments are selected and whether a determined the rectangular shape about the combination. According to the result of the experiment, the proposed rectangular shape detection algorithm strongly showed the size, location, direction, and color of the various objects. In addition, the proposed algorithm is applied to the license plate detecting and it wants to show the strength of the results.

• Steel and Composite Structures
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• 제19권1호
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• pp.191-208
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• 2015

The sensitivity-based finite element model updating method has received increasing attention in damage detection of structures based on measured modal parameters. Finding an optimization technique with high efficiency and fast convergence is one of the key issues for model updating-based damage detection. A new simple and computationally efficient optimization algorithm is proposed and applied to damage detection by using finite element model updating. The proposed method combines the Gauss-Newton method with region truncation of each iterative step, in which not only the constraints are introduced instead of penalty functions, but also the searching steps are restricted in a controlled region. The developed algorithm is illustrated by a numerically simulated 25-bar truss structure, and the results have been compared and verified with those obtained from the trust region method. In order to investigate the reliability of the proposed method in damage detection of structures, the influence of the uncertainties coming from measured modal parameters on the statistical characteristics of detection result is investigated by Monte-Carlo simulation, and the probability of damage detection is estimated using the probabilistic method.