• Title/Summary/Keyword: 능동 모델

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Non-Prior Training Active Feature Model-Based Object Tracking for Real-Time Surveillance Systems (실시간 감시 시스템을 위한 사전 무학습 능동 특징점 모델 기반 객체 추적)

  • 김상진;신정호;이성원;백준기
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.5
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    • pp.23-34
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    • 2004
  • In this paper we propose a feature point tracking algorithm using optical flow under non-prior taming active feature model (NPT-AFM). The proposed algorithm mainly focuses on analysis non-rigid objects[1], and provides real-time, robust tracking by NPT-AFM. NPT-AFM algorithm can be divided into two steps: (i) localization of an object-of-interest and (ii) prediction and correction of the object position by utilizing the inter-frame information. The localization step was realized by using a modified Shi-Tomasi's feature tracking algoriam[2] after motion-based segmentation. In the prediction-correction step, given feature points are continuously tracked by using optical flow method[3] and if a feature point cannot be properly tracked, temporal and spatial prediction schemes can be employed for that point until it becomes uncovered again. Feature points inside an object are estimated instead of its shape boundary, and are updated an element of the training set for AFH Experimental results, show that the proposed NPT-AFM-based algerian can robustly track non-rigid objects in real-time.

Image Segmentation of Lung Parenchyma using Improved Deformable Model on Chest Computed Tomography (개선된 가변형 능동모델을 이용한 흉부 컴퓨터단층영상에서 폐 실질의 분할)

  • Kim, Chang-Soo;Choi, Seok-Yoon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.13 no.10
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    • pp.2163-2170
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    • 2009
  • We present an automated, energy minimized-based method for Lung parenchyma segmenting Chest Computed Tomography(CT) datasets. Deformable model is used for energy minimized segmentation. Quantitative knowledge including expected volume, shape of Chest CT provides more feature constrain to diagnosis or surgery operation planning. Segmentation subdivides an lung image into its consistent regions or objects. Depends on energy-minimizing, the level detail image of subdivision is carried. Segmentation should stop when the objects or region of interest in an application have been detected. The deformable model that has attracted the most attention to date is popularly known as snakes. Snakes or deformable contour models represent a special case of the general multidimensional deformable model theory. This is used extensively in computer vision and image processing applications, particularly to locate object boundaries, in the mean time a new type of external force for deformable models, called gradient vector flow(GVF) was introduced by Xu. Our proposed algorithm of deformable model is new external energy of GVF for exact segmentation. In this paper, Clinical material for experiments shows better results of proposal algorithm in Lung parenchyma segmentation on Chest CT.

Intrusion Situation Classification Model for Intelligent Intrusion Awareness (지능적인 침입 인지를 위한 침입 상황 분류 모델)

  • Hwang, Yoon-Cheol;Mun, Hyung-Jin
    • Journal of Convergence for Information Technology
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    • v.9 no.3
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    • pp.134-139
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    • 2019
  • As the development of modern society progresses rapidly, the technologies of society as a whole are progressing and becoming more advanced. Especially in the field of security, more sophisticated and intelligent attacks are being created. Meanwhile, damaging situations are becoming several times larger than before Therefore, it is necessary to re-classify and enhance the existing classification system. It is required to minimize the intrusion damage by actively responding to intelligent intrusions by applying this classification scheme to currently operating intrusion detection systems. In this paper, we analyze the intrusion type caused by intelligent attack We propose a new classification scheme for intrusion situations to guarantee the service safety, reliability, and availability of the target system, We use this classification model to lay the foundations for the design and implementation of a smart intrusion cognitive system capable of early detection of intrusion, the damages caused by intrusion, and more collections active response.

Human Pose Matching Using Skeleton-type Active Shape Models (뼈대-구조 능동형태모델을 이용한 사람의 자세 정합)

  • Jang, Chang-Hyuk
    • Journal of KIISE:Software and Applications
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    • v.36 no.12
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    • pp.996-1008
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    • 2009
  • This paper proposes a novel approach for the model-based pose matching of a human body using Active Shape Models. To improve the processing time of model creation and registration, we use a skeleton-type model instead of the conventional silhouette-based models. The skeleton model defines feature information that is used to match the human pose. Images used to make the model are for 600 human bodies, and the model has 17 landmarks which indicate the body junction and key features of a human pose. When applying primary Active Shape Models to the skeleton-type model in the matching process, a problem may occur in the proximal joints of the arm and leg due to the color variations on a human body and the insufficient information for the fore-rear directions of profile normals. This problem is solved by using the background subtraction information of a body region in the input image and adding a 4-directions feature of the profile normal in the proximal parts of the arm and leg. In the matching process, the maximum iteration is less than 30 times. As a result, the execution time is quite fast, and was observed to be less than 0.03 sec in an experiment.

Implementation of saccadic eye movement system with saliency map model (Saliency map 모델을 갖는 도약 안구 시각 시스템의 구현)

  • Cho, Jun-Ki;Lee, Min-Ho;Shin, Jang-Kyoo;Koh, Kwang-Sik
    • Journal of Sensor Science and Technology
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    • v.10 no.1
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    • pp.52-61
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    • 2001
  • We propose a new saccadic eye movement system with visual selective attention. Saliency map models generate the scan pathways in a natural scene, of which the output makes an attended location. Saccadic eye movement model is used for producing the target trajectories to move the attended locations very rapidly. To categorize human saccadic eye movement, saccadic eye movement model was divided into three parts, each of which was then individually modeled using different neural networks to reflect a principal functionality of brain structures related with the saccadic eye movement in our brain. Based on the proposed saliency map models and the saccadic eye movement model, an active vision system using a CCD type camera and BLDC motor was developed and demonstrated with experimental results.

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Hybrid Control Strategies for Seismic Protection of Benchmark Cable-Stayed Bridges (지진하중을 받는 벤치마크 사장교를 위한 복합제어 기법)

  • Park, Kyu-Sik;Jung, Hyung-Jo;Lee, Chong-Heon;Lee, In-Won
    • Proceedings of the Earthquake Engineering Society of Korea Conference
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    • 2002.03a
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    • pp.435-442
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    • 2002
  • 본 연구에서는 사장교의 제어기법 개발을 위한 구조물로 제공되는 벤치마크(benchmark) 사장교에 대해 복합제어 기법을 적용하였다. 이 벤치마크 문제에서는 2003년 완공 예정으로 미국 Missouri 주에 건설 중인 Cape Girardeau 교를 대상 구조물로 고려하였다. Cape Girardeau 교는 New Madrid 지진구역에 위치하고, Mississippi 강을 횡단하는 주요 교량이라는 점 때문에 설계단계에서부터 내진 문제에 대하여 자세하게 고려되었다. 상세 설계도면을 기반으로 하여 교량의 전체적인 거동 특성을 정확하게 나타낼 수 있는 3차원 모델이 만들어졌고, 사장교의 제어 성능에 관련된 평가 기준이 수립되었다. 본 연구에서 사용한 복합제어 기법이란 지진하중으로 인해 구조물에 발생되는 하중을 줄이기 위한 수동제어 기법과 상판변위와 같은 구조물의 응답을 추가적으로 제어하기 위한 능동제어 기법이 결합된 제어방법이다. 수동제어 장치로는 현재 일반적으로 많이 사용되고 있는 납고무받침(lead rubber bearing)을 사용하였다. 능동제어 방법에는 $H_2$/LQG 제어 알고리듬(algorithm)을 사용하였다. 수치해석 결과 제안방법의 성능은 수동제어 방법에 비해 매우 효과적이며, 능동제어 방법에 비해서는 좀더 좋은 제어성능을 나타내었다. 또한, 복합제어 방법은 수동제어 부분 때문에 능동제어 방법에 비해 좀더 신뢰할 수 있는 제어 방법이다. 따라서 제안된 제어방법은 지진하중을 받는 사장교의 제어를 위해 효과적으로 사용될 수 있다.

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Development of Semi-automatic Construction Tool for Named Entity Dictionary based on Active Learning (능동 학습 기법을 활용한 개체명 사전 반자동 구축 도구 개발)

  • Yun, Bo-Hyun;Oh, Hyo-Jung
    • The Journal of Korean Association of Computer Education
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    • v.18 no.6
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    • pp.81-88
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    • 2015
  • Along with advent of Web 3.0 era and advanced technologies of IoT(Internet of Things), massive amounts of information are generated. Reflecting this trend, this paper developed a semi-automatic construction tool for named entity dictionary based on active learning. Our proposed method chose error candidates to verify among the preliminary results using initial trained model and re-trained the model for correctly labeled data by user. We adopt active learning approach for minimizing human effort utilized metadata features of Wikipedia. Based on experimental results using our tool, we show that 68.6% errors were automatically corrected.

An Adaptive Active Noise Cancelling Model Using M-Channel Subband QMF Filter Banks (M-채널 서브밴드 QMF 필터뱅크를 이용한 적응 능동소음제거 모델)

  • 허영대;권기룡;문광석
    • Journal of Korea Multimedia Society
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    • v.2 no.1
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    • pp.30-37
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    • 1999
  • A wideband active noise cancelling system involves adaptive filters with hundreds of taps. The computational burden required with these long adaptive filters. This paper presents active noise cancelling system using M-channel QMF filter banks in which the adaptive weights are computed in subbands. The analysis and synthesis filter banks use cosine-modulated pseudo QMF filters. The reference signal for on-line identification of error path transfer characteristics is used to difference signal between the output of adaptive filters and the output of lowpass subband filters. The proposed adaptive subband filter bank suggests robust active noise cancelling system retaining the computational complexity and convergence speed advantaged of subband processing.

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Design of active combustion control system using DSP (DSP를 이용한 연소불안정 능동 제어장치 설계)

  • Park, Ik-Soo;Park, Joo-Won;Choi, Ho-Jin;Hwang, Yong-Seok;Jin, Yoo-In;Yoon, Hyun-Gull
    • Proceedings of the Korean Society of Propulsion Engineers Conference
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    • 2009.05a
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    • pp.128-132
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    • 2009
  • Digital control system for active combustion control of lab-scale combustor equipped with secondary fuel injection is designed. Controllability with adaptive law is revealed with the Cambridge Combustor model and the requirement for control system is derived. The input and output requirements of frequency estimator and fuel supply actuator for the adaptive control law is verified with cold tests. The system can be used as digital based active combustion controller having 150Hz combustion instability.

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Active Sonar Target Recognition Using Fractional Fourier Transform (Fractional Fourier 변환을 이용한 능동소나 표적 인식)

  • Seok, Jongwon;Kim, Taehwan;Bae, Geon-Seong
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.17 no.11
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    • pp.2505-2511
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    • 2013
  • Many studies in detection and classification of the targets in the underwater environments have been conducted for military purposes, as well as for non-military purpose. Due to the complicated characteristics of underwater acoustic signal reflecting multipath environments and spatio-temporal varying characteristics, active sonar target classification technique has been considered as a difficult technique. And it has difficulties in collecting actual underwater data. In this paper, we synthesized active target echoes based on ray tracing algorithm using target model having 3-dimensional highlight distribution. Then, Fractional Fourier transform was applied to synthesized target echoes to extract feature vector. Recognition experiment was performed using neural network classifier.