• 제목/요약/키워드: Detection,

검색결과 36,909건 처리시간 0.049초

지능형 감시를 위한 객체추출 및 추적시스템 설계 및 구현 (A Study on the Object Extraction and Tracking System for Intelligent Surveillance)

  • 장태우;신용태;김종배
    • 한국통신학회논문지
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    • 제38B권7호
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    • pp.589-595
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    • 2013
  • 최근 보안 관제를 위한 인원부족 및 감시 능력의 한계로 자동화된 지능형 관제 시스템에 대한 요구가 증가하고 있다. 이 논문에서는 지능형 감시시스템의 구축을 위하여 자동화된 객체추출 및 추적 시스템, 그리고 이상행위를 인지하는 이상행위 검출 시스템을 설계하고 구현하였다. 각 모듈은 기존의 연구 결과를 바탕으로 실제 환경에서 적용되고 상용화가 가능하도록 알고리즘의 성능을 높였으며, 구현 후 다양한 테스트를 통해 그 성과를 검증하였다. 특히, 배회 또는 도주와 같은 이상행위의 경우 1초 이내에 검출할 수 있었다.

Spectral resolution evaluation by MCNP simulation for airborne alpha detection system with a collimator

  • Kim, Min Ji;Sung, Si Hyeong;Kim, Hee Reyoung
    • Nuclear Engineering and Technology
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    • 제53권4호
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    • pp.1311-1317
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    • 2021
  • In this study, an airborne alpha detection system, which consists of a passivated implanted planar silicon (PIPS) detector and an air filter, was developed. A collimator applied to the alpha detection system showed an enhancement in resolution and a degradation in detection efficiency. The resolution and detection efficiency were compared and analyzed to evaluate the performance of the collimator. Thus, the resolution was found to be more important than the efficiency as a determining factor of the detection system performance, from the viewpoint of radionuclide identification. The performance was evaluated on three properties of the collimator: hole shape, hole length, and the ratio between the hole and frame pitches. From the hole shape performance evaluation, a hexagonal collimator showed the highest resolution. Further, the collimator with a hole pitch of 14 mm was found to have the highest resolution while that with a frame pitch of 4-6 mm (i.e., 1.2-1.4 times longer than the hole pitch) showed the highest resolution.

Electrochemical Determination of As(III) at Nanoporous Gold Electrodes with Controlled Surface Area

  • Seo, Min Ji;Kastro, Kanido Camerun;Kim, Jongwon
    • 대한화학회지
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    • 제63권1호
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    • pp.45-50
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    • 2019
  • Because arsenic (As) is a chemical substance toxic to humans, there have been extensive investigations on the development of As detection methods. In this study, the electrochemical determination of As on nanoporous gold (NPG) electrodes was investigated using anodic stripping voltammetry. The electrochemical surface area of the NPG electrodes was controlled by changing the reaction times during the anodization of Au for NPG preparation, and its effect on the electrochemical behavior during As detection was examined. The detection efficiency of the NPG electrodes improved as the roughness factor of the NPG electrodes increased up to around 100. A further increase in the surface area of the NPG electrodes resulted in a decrease of the detection efficiency due to high background current levels. The most efficient As detection efficiency was obtained on the NPG electrodes prepared with an anodization time of 50 s. The effects of the detection parameters and of the Cu interference in As detection were investigated and the NPG electrode was compared to flat Au electrodes.

Temporal and spatial outlier detection in wireless sensor networks

  • Nguyen, Hoc Thai;Thai, Nguyen Huu
    • ETRI Journal
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    • 제41권4호
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    • pp.437-451
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    • 2019
  • Outlier detection techniques play an important role in enhancing the reliability of data communication in wireless sensor networks (WSNs). Considering the importance of outlier detection in WSNs, many outlier detection techniques have been proposed. Unfortunately, most of these techniques still have some potential limitations, that is, (a) high rate of false positives, (b) high time complexity, and (c) failure to detect outliers online. Moreover, these approaches mainly focus on either temporal outliers or spatial outliers. Therefore, this paper aims to introduce novel algorithms that successfully detect both temporal outliers and spatial outliers. Our contributions are twofold: (i) modifying the Hampel Identifier (HI) algorithm to achieve high accuracy identification rate in temporal outlier detection, (ii) combining the Gaussian process (GP) model and graph-based outlier detection technique to improve the performance of the algorithm in spatial outlier detection. The results demonstrate that our techniques outperform the state-of-the-art methods in terms of accuracy and work well with various data types.

Effect of Nanostructures of Au Electrodes on the Electrochemical Detection of As

  • Kastro, Kanido Camerun;Seo, Min Ji;Jeong, Hwakyeung;Kim, Jongwon
    • Journal of Electrochemical Science and Technology
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    • 제10권2호
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    • pp.206-213
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    • 2019
  • The development of simple methods for As detection has received great attention because As is a toxic chemical element causing environmental and health-related issues. In this work, the effect of nanostructures of Au electrodes on their electroanalytical performance during As detection was investigated. Different Au nanostructures with various surface morphologies such as nanoplate Au, nanospike Au, and dendritic Au structures were prepared, and their electrochemical behaviors toward square-wave anodic stripping voltammetric As detection were examined. The difference in intrinsic efficiency for As detection between nanostructured and flat Au electrodes was explained based on the crystallographic orientations of Au surfaces, as examined by the underpotential deposition of Pb. The most efficient As detection performance was obtained with nanoplate Au electrodes, and the effects of the pre-deposition time and interference on As detection of the nanoplate Au electrodes were also investigated.

An Intrusion Detection Method Based on Changes of Antibody Concentration in Immune Response

  • Zhang, Ruirui;Xiao, Xin
    • Journal of Information Processing Systems
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    • 제15권1호
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    • pp.137-150
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    • 2019
  • Although the research of immune-based anomaly detection technology has made some progress, there are still some defects which have not been solved, such as the loophole problem which leads to low detection rate and high false alarm rate, the exponential relationship between training cost of mature detectors and size of self-antigens. This paper proposed an intrusion detection method based on changes of antibody concentration in immune response to improve and solve existing problems of immune based anomaly detection technology. The method introduces blood relative and blood family to classify antibodies and antigens and simulate correlations between antibodies and antigens. Then, the method establishes dynamic evolution models of antigens and antibodies in intrusion detection. In addition, the method determines concentration changes of antibodies in the immune system drawing the experience of cloud model, and divides the risk levels to guide immune responses. Experimental results show that the method has better detection performance and adaptability than traditional methods.

프로파일 기반 다단계 공격 탐지 기법에 관한 연구 (A Study on Multi-level Attack Detection Technique based on Profile Table)

  • 양환석
    • 디지털산업정보학회논문지
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    • 제10권4호
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    • pp.89-96
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    • 2014
  • MANET has been applied to a wide variety of areas because it has advantages which can build a network quickly in a difficult situation to build a network. However, it is become a victim of malicious nodes because of characteristics such as mobility of nodes consisting MANET, limited resources, and the wireless network. Therefore, it is required to lightweight attack detection technique which can accurately detect attack without causing a large burden to the mobile node. In this paper, we propose a multistage attack detection techniques that attack detection takes place in routing phase and data transfer phase in order to increase the accuracy of attack detection. The proposed attack detection technique is composed of four modules at each stage in order to perform accurate attack detection. Flooding attack and packet discard or modify attacks is detected in the routing phase, and whether the attack by modification of data is detected in the data transfer phase. We assume that nodes have a public key and a private key in pairs in this paper.

딥러닝 기반 드론 검출 및 분류 (Deep Learning Based Drone Detection and Classification)

  • 이건영;경덕환;서기성
    • 전기학회논문지
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    • 제68권2호
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    • pp.359-363
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    • 2019
  • As commercial drones have been widely used, concerns for collision accidents with people and invading secured properties are emerging. The detection of drone is a challenging problem. The deep learning based object detection techniques for detecting drones have been applied, but limited to the specific cases such as detection of drones from bird and/or background. We have tried not only detection of drones, but classification of different drones with an end-to-end model. YOLOv2 is used as an object detection model. In order to supplement insufficient data by shooting drones, data augmentation from collected images is executed. Also transfer learning from ImageNet for YOLOv2 darknet framework is performed. The experimental results for drone detection with average IoU and recall are compared and analysed.

발열 감지, 안면 마스크 착용 검출, 전자출입명부 QR 코드 체킹을 지원하는 보급형 COVID-19 디지털 사이니지 플레이어 설계 및 구현 (Design and Implementation of Entry-level COVID-19 Digital Signage Player supporting Fever Detection, Face Mask Wearing Detection and KI-pass QR Code Checking)

  • 쩐꾸억바오후이;박상군;정선태
    • 한국멀티미디어학회논문지
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    • 제25권1호
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    • pp.10-28
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    • 2022
  • In this paper, we present an entry-level COVID-19 stand-alone digitial signage player (CoSiP) which performs not only conventional digital signage functionalities but also fever detection, face mask wearing detection, and KI-pass QR code checking. The overall design of CoSiP is proposed, and implementation of a temperature checking algorithm using a low cost thermal sensor is elaborately presented. Through experiments over datasets and against a developed CoSiP device, it is shown that the fever detection, face mask wearing detection, KI-pass QR code checking as well as signage functionalities of the proposed CoSiP work properly and reliably.

Keypoint Detection과 Annoy Tree를 사용한 2D Hand Pose Estimation (Fast Hand Pose Estimation with Keypoint Detection and Annoy Tree)

  • 이희재;강민혜
    • 한국컴퓨터정보학회:학술대회논문집
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    • 한국컴퓨터정보학회 2021년도 제63차 동계학술대회논문집 29권1호
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    • pp.277-278
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    • 2021
  • 최근 손동작 인식에 대한 연구들이 활발하다. 하지만 대부분 Depth 정보를 포함한3D 정보를 필요로 한다. 이는 기존 연구들이 Depth 카메라 없이는 동작하지 않는다는 한계점이 있다는 것을 의미한다. 본 프로젝트는 Depth 카메라를 사용하지 않고 2D 이미지에서 Hand Keypoint Detection을 통해 손동작 인식을 하는 방법론을 제안한다. 학습 데이터 셋으로 Facebook에서 제공하는 InterHand2.6M 데이터셋[1]을 사용한다. 제안 방법은 크게 두 단계로 진행된다. 첫째로, Object Detection으로 Hand Detection을 수행한다. 데이터 셋이 어두운 배경에서 촬영되어 실 사용 환경에서 Detection 성능이 나오지 않는 점을 해결하기 위한 이미지 합성 Augmentation 기법을 제안한다. 둘째로, Keypoint Detection으로 21개의 Hand Keypoint들을 얻는다. 실험을 통해 유의미한 벡터들을 생성한 뒤 Annoy (Approximate nearest neighbors Oh Yeah) Tree를 생성한다. 생성된 Annoy Tree들로 후처리 작업을 거친 뒤 최종 Pose Estimation을 완료한다. Annoy Tree를 사용한 Pose Estimation에서는 NN(Neural Network)을 사용한 것보다 빠르며 동등한 성능을 냈다.

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