• 한국인터넷방송통신학회논문지
• /
• 제10권6호
• /
• pp.107-111
• /
• 2010

차량 주행 중에 타이어에서 발생하는 초음파를 이용하여 손상 물질에 의한 타이어의 손상을 검출하는 방법에 대해 연구하였다. 손상이 있는 타이어는 회전 주기성이 있는 초음파 신호가 발생하므로 주기성을 검출하기 위해 포락선 검출 전처리 과정을 거친 후 자기상관함수를 사용하였다. 실험에서는 손상된 타이어의 1회전 시간과 자기상관함수를 이용해 구한 주기가 같은 것으로 나타났다. 이로 인해 타이어의 손상 유무를 분별할 수 있는 결과를 도출하였다.

• 한국자동차공학회논문집
• /
• 제11권2호
• /
• pp.1-7
• /
• 2003

This paper proposes a method to detect misfired cylinders by the exhaust pressure ascent rate. The misfire is generated by faults of electric system or faults of fuel delivery system. It is one of the abnormal combustions. Therefore, it increases the unburned hydrocarbon and the carbon monoxide and affects a bad influence to the 3-way catalyst. The misfire causes to decrease the power of the engine and increase the consumption of the fuel. Early detection and correction of the misfired cylinders can prevent these unusual phenomena. The misfired cylinders can be detected by the comparison of exhaust pressure ascent rate during each cycle. The exhaust pressure ascent rate is defined as pressure rise per time. Our experimental results showed that the proposed method is effective in the detection of the misfired cylinders on a gasoline engine regardless loads and revolutions of the engine.

• The Korean Journal of Ceramics
• /
• 제6권3호
• /
• pp.201-205
• /
• 2000

Fiber reinforced plastic (FRP) composites and ceramic matrix composites (CMC) which contain electrically conductive phases have been designed and fabricated to introduce the detection capability of damage/fracture detection into these materials. The composites were made electrically conductive by adding carbon and TiN particles into FRP and CMC, respectively. The resistance of the conductive FRP containing carbon particles showed almost linear response to strain and high sensitivity over a wide range of strains. After each load-unload cycle the FRP retained a residual resistance, which increased with applied maximum stress or strain. The FRP with carbon particles embedded in cement (mortar) specimens enabled micro-crack formation and propagation in the mortar to be detected in situ. The CMC materials exhibited not only sensitive response to the applied strain but also an increase in resistance with increasing number of load-unload cycles during cyclic load testing. These results show that it is possible to use these composites to detect and/or fracture in structural materials, which are required to monitor the healthiness or safety in industrial applications and public constructions.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제13권3호
• /
• pp.1722-1737
• /
• 2019

We developed an insider threat detection model to be used by organizations that repeat tasks at regular intervals. The model identifies the best combination of different feature selection algorithms, unsupervised learning algorithms, and standard scores. We derive a model specifically optimized for the organization by evaluating each combination in terms of accuracy, AUC (Area Under the Curve), and TPR (True Positive Rate). In order to validate this model, a four-year log was applied to the system handling sensitive information from public institutions. In the research target system, the user log was analyzed monthly based on the fact that the business process is processed at a cycle of one year, and the roles are determined for each person in charge. In order to classify the behavior of a user as abnormal, the standard scores of each organization were calculated and classified as abnormal when they exceeded certain thresholds. Using this method, we proposed an optimized model for the organization and verified it.

• 한국전기전자재료학회논문지
• /
• 제36권5호
• /
• pp.520-524
• /
• 2023

An algorithm was developed to detect and block serial arc currents using HPF. The AC series arc problem is that the load current is greater than the fault current and no leakage current occurs. As a solution, an arc detection method utilizing differences in high- frequency amplitudes was developed. HPT was applied to the load current and FFT was applied to eliminate low frequencies. An algorithm has been developed to detect arc waveforms when they exceed a certain value compared to the average of normal waveforms. Using one cycle of data, arc detection is faster and arc accidents are prevented.

• 대한원격탐사학회지
• /
• 제32권5호
• /
• pp.539-550
• /
• 2016

육상의 수체를 탐지하는 것은 홍수, 태풍, 지진해일과 같은 재해 모니터링에 있어 핵심적인 사항이며, 습지, 빙하 등 지표 수자원의 시 공간적 변화를 파악하는데 중요한 역할을 한다. 본 연구에서는 Kompsat-5 SAR 영상으로부터 육상의 수체를 탐지하기 위하여 임계값에 기반한 접근방법의 적용성을 분석하고, 다양한 임계값 설정 기법의 탐지 성능을 평가하였다. 또한 SAR 영상의 스펙클 필터링이 임계값 설정에 미치는 영향을 분석하였으며, 영상에서 수체가 차지하는 비율에 따른 탐지 성능의 변화에 대한 정량적인 평가를 수행하였다. 추가적으로 탐지 성능을 향상시키기 위해 히스토그램의 bimodality 검정과 majority filtering 처리를 활용하는 새로운 알고리즘을 제안하였다. 세종시 지역의 사례의 경우 제안된 알고리즘을 통해 최종적으로 약 96%의 탐지율과 0.3%의 오탐지율로 수체를 탐지할 수 있음을 보였다.

• 대한공간정보학회지
• /
• 제24권4호
• /
• pp.3-11
• /
• 2016

Sixty four percent of Korean territory consists of forest which is fragile for forest fire. However, it is difficult to detect the disaster-induced damages due to topographic complexity in mountainous areas and harsh weather conditions. For this reason, satellite imaging systems have been widely utilized to detect the damage caused by forest fire. In particular, ground vegetation condition can be estimated from multi-spectral satellite images and change detection technique has been used to detect forest fire damages. However, since Korea has clear four seasons, simple change detection technique has limitation. In this regard, this study applied the NDVI(normalized difference vegetation index) annual cycle modeling technique on time-series of Landsat images from 1991 to 2007 to analyze influence of forest fire of Yangyang-gun, Gangwon-do in 2005 on vegetation condition. The encouraging result was obtained when comparing the areas where forest fire occurs with non-damaged areas. The mean value of NDVI was decreased by 0.07 before and after the forest fire. On the other hand, annual variability of NDVI had been increasing and peak value of NDVI was stationary after the forest fire. It is interpreted that understory vegetation was seriously damaged from the forest fire occurred in 2005.

• 정보보호학회논문지
• /
• 제25권6호
• /
• pp.1435-1447
• /
• 2015

금융회사에서는 내부자에 의한 개인정보유출 방지 및 내부통제 강화를 위하여 E-DRM(Enterprise-Digital Right Management), 개인정보검색, DLP(Data Loss Prevention), 출력물보안, 인터넷 망 분리시스템, 개인정보모니터링 시스템 등의 보안 솔루션을 도입 운영하고 있다. 하지만 개인정보유출 사고는 계속해서 발생하고 있으며, 이 기종 보안 솔루션간의 독립적인 로그 체계로 인하여 개인정보문서의 회사 내부유통 및 외부반출 관련한 정합성 있는 유통경로의 연관 분석이 어렵다. 본 논문은 보안문서를 기반으로 하여 업무시스템 및 이 기종 보안 솔루션간의 로그를 유기적으로 정합성 있게 연관 분석할 수 있는 연결고리 체계 방안을 제시하고, 업무시스템을 통하여 개인PC에 생성되는 보안문서나 개인이 작성한 보안문서에 대한 Life-Cycle 관리방안 및 개인정보가 포함된 보안문서에 대한 유통경로 추적을 위한 효율적인 탐지 방안을 제안하고자 한다.

• 대한교통학회지
• /
• 제18권4호
• /
• pp.75-85
• /
• 2000

본 연구에서는 도시부도로 신호교차로의 대기행렬을 단기(one cycle ahead)예측함에 있어 단일검지체계에 기반을 둔 한 지점의 시계열적 패턴을 갖는 검지자료(detection data)를 학습자료로 구성할 경우와 통합차량검지체계하에 기반을 둔 시공간적 상관관계를 갖는 검지자료를 학습자료로 이용할 경우를 가정하여 이에 대한 인공신경망의 학습능력과 예측능력을 비교하였다. 연구결과는 도시부도로 신호교차로상에서 차량군(platoon)의 이동에 따라 발생되는 시공간적인 상관관계를 갖는 교통류변수 $\ulcorner$상류유입교통량(k-1)->통행시간(k-1)->대기행렬(k)->유출교통량(k)->대기행렬(k+1)$\lrcorner$를 인공신경망의 학습자료로 구성할 경우, 교통류 패턴의 학습능력이 뛰어난 것으로 밝혀졌다.

• 한국멀티미디어학회논문지
• /
• 제25권1호
• /
• pp.1-9
• /
• 2022

Ship detection in synthetic aperture radar (SAR) images is an important application in marine monitoring for the military and civilian domains. Over the past decade, object detection has achieved significant progress with the development of convolutional neural networks (CNNs) and lot of labeled databases. However, due to difficulty in collecting and labeling SAR images, it is still a challenging task to solve SAR ship detection CNNs. To overcome the problem, some methods have employed conventional data augmentation techniques such as flipping, cropping, and affine transformation, but it is insufficient to achieve robust performance to handle a wide variety of types of ships. In this paper, we present a novel and effective approach for deep SAR ship detection, that exploits label-rich Electro-Optical (EO) images. The proposed method consists of two components: a data augmentation network and a ship detection network. First, we train the data augmentation network based on conditional generative adversarial network (cGAN), which aims to generate additional SAR images from EO images. Since it is trained using unpaired EO and SAR images, we impose the cycle-consistency loss to preserve the structural information while translating the characteristics of the images. After training the data augmentation network, we leverage the augmented dataset constituted with real and translated SAR images to train the ship detection network. The experimental results include qualitative evaluation of the translated SAR images and the comparison of detection performance of the networks, trained with non-augmented and augmented dataset, which demonstrates the effectiveness of the proposed framework.