• 디지털산업정보학회논문지
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• 제16권1호
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• pp.67-78
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• 2020

Typical security solutions such as intrusion detection system are not suitable for detecting advanced persistent attack(APT), because they cannot draw the big picture from trivial events of security solutions. Researches on techniques for detecting multiple stage attacks by analyzing the correlations between security events or alerts are being actively conducted in academic field. However, these studies still use events from existing security system, and there is insufficient research on the structure of the entire security system suitable for advanced persistent attacks. In this paper, we propose an attack path and intention recognition system suitable for multiple stage attacks like advanced persistent attack detection. The proposed system defines the trace format and overall structure of the system that detects APT attacks based on the correlation and behavior analysis, and is designed with a structure of detection system using deep learning and big data technology, etc.

• 한국지구물리탐사학회:학술대회논문집
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• 한국지구물리탐사학회 2007년도 공동학술대회 논문집
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• pp.215-218
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• 2007

Tunnel detection is known to be one of the challenging problems in geophysical society. Among various geophysical methods, we tried to examine the applicability of electrical resistivity tomography (ERT) method to detect empty tunnel. In this study, we analyzed the ERT data acquired at the test site for tunnel detection. The inversion results have shown reasonable image of the tunnel although the resolution is quite poor. Moreover, we could obtain the three-dimensional attitude of tunnel through 3-D ERT imaging. Therefore, we expect that ERT can make contribution to the tunnel detection problem and further research effort such as fusion of geophysical methods will provide more reliable tunnel detection capability.

• 반도체디스플레이기술학회지
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• 제19권1호
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• pp.85-92
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• 2020

Object detection plays a critical role in the field of computer vision, and various researches have rapidly increased along with applying convolutional neural network and its modified structures since 2012. There are representative object detection algorithms, which are convolutional neural networks and YOLO. This paper presents two representative algorithm series, based on CNN and YOLO which solves the problem of CNN bounding box. We compare the performance of algorithm series in terms of accuracy, speed and cost. Compared with the latest advanced solution, YOLO v3 achieves a good trade-off between speed and accuracy.

• Bulletin of the Korean Chemical Society
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• 제33권11호
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• pp.3681-3685
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• 2012

We developed a simple and effective method for the SERS-based detection of protein-small molecule complexes and label-free proteins using avidin-induced silver aggregation. Upon excitation with light of the appropriate wavelength (633 and 532 nm), the aggregated silver nanoparticles generate a strong electric field that couples with the resonance of the molecules (atto610 and cytochrome c), increasing the characteristic signals of these molecules and resulting in sensitive detection. The detection limit of biotin with the proposed method is as low as 48 ng/mL. The most important aspect of this method is the induction of silver aggregation by a protein (avidin), which makes the silver more biocompatible. This technique is very useful for the detection of protein-small molecule complexes.

• 융합보안논문지
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• 제13권1호
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• pp.3-10
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• 2013

본 연구에서는 소프트웨어 제품 테스팅 과정에서 관측고장시간에 근거한 결함 발생률을 고려한 소프트웨어 신뢰성 모형에 대하여 연구 하였다. 신뢰성 분야에서 많이 사용되는 S-분포모형을 이용한 새로운 결함 확률을 추가한 문제를 제시하였다. 수명분포는 유한고장 비동질적인 포아송과정을 이용하였다 본 논문의 결함 발생률을 고려한 소프트웨어 고장 자료 분석에서는 고장 시간 자료를 적용하였으며 모수추정 방법은 최우추정법을 이용하여 결함 발생 확률에 대한 관계와 신뢰도를 추정 하였다.

• 해양환경안전학회:학술대회논문집
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• 해양환경안전학회 2007년도 춘계학술발표회
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• pp.127-132
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• 2007

From the 1978 Seasat synthetic aperture radar(SAR) to present systems, spaceborne SAR has demonstrated the capability to image the Earth's ocean and land features over broad areas, day and night, and under most weather conditions. The application of SAR for surveillance of commercial fishing grounds can aid in the detection of illegal fishing activities and provides more efficient use of limited aircraft or patrol craft resources. In the area of vessel traffic monitoring for commercial vessels, Vessel Traffic Service (VTS) which uses the ground-based radar system has some difficulties in detecting moving ships due to the limited detection range of about 10 miles. This paper introduces the field testing results of ship detection by RADARSAT SAR imagery, and proposes a new approach for a Vessel Monitoring System(VMS), including VTS, and SAR combination service.

• 센서학회지
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• 제18권3호
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• pp.190-196
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• 2009

In a conventional camera system, a target tracking system consists of a camera part and a image processing part. However, in the field of the real time image processing, the vision chip for edge detection which was made by imitating the algorithm of humanis retina is superior to the conventional digital image processing systems because the human retina uses the parallel information processing method. In this paper, we present a high speed target tracking system using the function of the CMOS vision chip for edge detection.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2014년도 추계학술발표대회
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• pp.879-881
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• 2014

A method is proposed to achieve improved number plate detection for mobile devices by applying a two-step convolutional neural network (CNN) approach. Supervised CNN-verified car detection is processed first. In the second step, we apply the detected car regions to the second CNN-verifier for number plate detection. Since mobile devices are limited in computing power, we propose a fast method to detect number plates. We expect to use in the field of intelligent transportation systems (ITS).

• 전력전자학회:학술대회논문집
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• 전력전자학회 2014년도 전력전자학술대회 논문집
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• pp.522-523
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• 2014

Analysis of faults in induction motors has become a major field of research due to importance of loss and damage reduction and maximum online performance of motors. There are several methods to analyze the faults in an induction motor from conventional Fourier transform to modern decision-making neural networks. Considering detectability of fault among all methods, a new fault detection solution has been proposed; it is called as frequency-domain Discrete Wavelet Transform (FD-DWT). In this method, the stator current is decomposed through series of low- and high-pass filters and consequently, the fault characteristics are more visible, because additional components have been reduced. The objective of this paper is early detection of input voltage unbalance in induction motor using wavelet transform in frequency domain. Experimental results show the effectiveness of the proposed method in early detection of faults.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1994년도 추계학술대회 논문집 학회본부
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• pp.405-407
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• 1994

In order to provide reliable power service and to prevent a potentail hazard and damage, it is important to detect high impedance fault in power distribution line. This paper presents a neural network based approach for the detection of high impedance faults. A time delay neural network has been selected and trained for the fault currents obtained from field experiments. Detection experiments have been performed with the data from four different high impedance surfaces. Experimental results indicated the feasibility of using TDNN for the detection of high impedance faults.