• 제목/요약/키워드: Into Depth

검색결과 4,251건 처리시간 0.04초

수중운동체의 자동 수심조절 장치 설계 (A design of auto-depth controller for underwater vehicle)

  • 정연태;이영섭
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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    • pp.527-532
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    • 1993
  • Generally the method of depth controlling is classified into buoyancy control and thrust control. In this study, we employed thrust control system. And mathematical modeling and computer simulation are performed in order to design auto depth control system for underwater vehicle. Consequently, the specifications of components are determined, and the performance of system is analyzed.

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Safety assessment of an underground tunnel subjected to missile impact using numerical simulations

  • Thai, Duc-Kien;Nguyen, Duy-Liem;Pham, Thanh-Tung;Pham, Thai-Hoan
    • Computers and Concrete
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    • 제27권1호
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    • pp.1-12
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    • 2021
  • This work presents a safety assessment of an underground tunnel subjected to a ballistic missile attack employing the numerical approach. For the impact simulation, a box shaped reinforced concrete (RC) structure with a cross section dimension of 8.0×10.0 m under a soil layer that was attacked by a SCUD missile was modeled using finite element (FE) software LS-DYNA. SCUD missile is one of a series of tactical ballistic missiles developed by Soviet Union during the Cold War, which is adopted for a short-range ballistic missile. The developed FE simulation for the penetration depth of the missile impacting into the soil structure was verified from the well-known formula of the penetration prediction. The soil-structure interaction, the soil type, and the impact missile velocity effects on the penetration depth of the missile into the different soil types were investigated. The safety assessment of the underground tunnel was performed with regard to the different depths of the underground tunnel. For each missile velocity and soil type, a specific depth called the unsafe depth was obtained from the analysis results. The structure beneath the soil beyond this depth remains safe. The unsafe depth was found to be increased with the increasing missile velocity.

깊이 센서를 이용한 등고선 레이어 생성 및 모델링 방법 (A Method for Generation of Contour lines and 3D Modeling using Depth Sensor)

  • 정훈조;이동은
    • 디지털산업정보학회논문지
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    • 제12권1호
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    • pp.27-33
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    • 2016
  • In this study we propose a method for 3D landform reconstruction and object modeling method by generating contour lines on the map using a depth sensor which abstracts characteristics of geological layers from the depth map. Unlike the common visual camera, the depth-sensor is not affected by the intensity of illumination, and therefore a more robust contour and object can be extracted. The algorithm suggested in this paper first abstracts the characteristics of each geological layer from the depth map image and rearranges it into the proper order, then creates contour lines using the Bezier curve. Using the created contour lines, 3D images are reconstructed through rendering by mapping RGB images of the visual camera. Experimental results show that the proposed method using depth sensor can reconstruct contour map and 3D modeling in real-time. The generation of the contours with depth data is more efficient and economical in terms of the quality and accuracy.

Depth Map Coding Using Histogram-Based Segmentation and Depth Range Updating

  • Lin, Chunyu;Zhao, Yao;Xiao, Jimin;Tillo, Tammam
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제9권3호
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    • pp.1121-1139
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    • 2015
  • In texture-plus-depth format, depth map compression is an important task. Different from normal texture images, depth maps have less texture information, while contain many homogeneous regions separated by sharp edges. This feature will be employed to form an efficient depth map coding scheme in this paper. Firstly, the histogram of the depth map will be analyzed to find an appropriate threshold that segments the depth map into the foreground and background regions, allowing the edge between these two kinds of regions to be obtained. Secondly, the two regions will be encoded through rate distortion optimization with a shape adaptive wavelet transform, while the edges are lossless encoded with JBIG2. Finally, a depth-updating algorithm based on the threshold and the depth range is applied to enhance the quality of the decoded depth maps. Experimental results demonstrate the effective performance on both the depth map quality and the synthesized view quality.

깊이맵 변환을 이용한 3D 입체감 개선 방법 (Improvement of 3D Stereoscopic Perception Using Depth Map Transformation)

  • 장성은;정다운;서주하;김만배
    • 방송공학회논문지
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    • 제16권6호
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    • pp.916-926
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    • 2011
  • 3D 영화 콘텐츠처럼 고해상도로 만들어진 입체영상은 저해상도 영상에서는 동일한 3D 깊이감을 느낄 수가 없다는 단점이 있다. 본 논문에서는 공간적 복잡도을 이용하여 깊이맵을 변환하여 입체감을 개선할 수 있는 방법을 제안한다. 제안 방법에서는 깊이맵의 히스토그램을 분석한 후에, 깊이맵을 다수의 깊이평면으로 분할한 다음에 깊이 평면을 변환한다. 마지막으로 변환된 깊이평면을 통합하여 최종 깊이맵을 생성한다. 주관적 평가 실험에서는 입체감 개선 및 이에 따라 발생하는 시각적 피로도를 검증하였다. 실험에서는 입체감의 개선이 증명되었으며, 영상 해상도가 낮아져도, 입체감은 유지되는 것을 보여주었다. 또한 얻어진 입체영상의 시각적 피로도 검증을 통해서 생성된 입체영상은 피로도가 증가하지 않는 것을 보여주었다.

다중시점 환경에서의 슈퍼픽셀 세그먼테이션 기반 깊이 영상 개선 알고리즘 (Depth Map Correction Algorithm based on Segmentation in Multi-view Systems)

  • 정우경;한종기
    • 방송공학회논문지
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    • 제25권6호
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    • pp.954-964
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    • 2020
  • 실감형 미디어에서 현실감을 느끼게 하는 가장 중요한 요소는 깊이 정보이다. 따라서 고품질의 실감형 미디어를 제작하기 위해서는 고품질의 깊이 정보를 획득하는 것이 필수적이다. 본 논문에서는 고품질의 깊이 정보를 획득하기 위하여 다중 시점 환경에서 깊이 지도를 개선하기 위하여 깊이 지도를 여러 개의 세그먼트로 분할 및 다중 시점간의 관계를 고려하는 알고리즘을 제안한다. 제안된 알고리즘은 슈퍼픽셀 세그먼테이션 기법을 사용하여 기준 시점의 깊이 지도를 여러 세그먼트로 나누고, 각 세그먼트를 인접 시점으로 투영한다. 이후 투영된 세그먼트의 정보를 이용하여 인접 시점의 깊이 지도를 평면 추정을 이용하여 개선한 후, 기준 시점으로 역투영된다. 여러 개의 인접 시점에 대해 이 과정을 반복하여 개선된 인접 시점들의 값들과 기준 시점의 초기 깊이 지도를 가중치 합으로 갱신하여 깊이 지도를 개선한다. 기존 다중 시점 스테레오 비전 알고리즘에 제안된 알고리즘을 적용한 시뮬레이션을 통해 제안된 알고리즘의 결과가 주관적 및 객관적으로 기존 알고리즘을 능가하는 것을 보인다.

내진설계기준의 지반분류체계 및 설계응답스펙트럼 개선을 위한 연구 - (II) 제안 (Site Classification and Design Response Spectra for Seismic Code Provisions - (II) Proposal)

  • 조형익;;김동수
    • 한국지진공학회논문집
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    • 제20권4호
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    • pp.245-256
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    • 2016
  • In the companion paper (I - Database and Site Response Analyses), site-specific response analyses were performed at more than 300 domestic sites. In this study, a new site classification system and design response spectra are proposed using results of the site-specific response analyses. Depth to bedrock (H) and average shear wave velocity of soil above the bedrock ($V_{S,Soil}$) were adopted as parameters to classify the sites into sub-categories because these two factors mostly affect site amplification, especially for shallow bedrock region. The 20 m of depth to bedrock was selected as the initial parameter for site classification based on the trend of site coefficients obtained from the site-specific response analyses. The sites having less than 20 m of depth to bedrock (H1 sites) are sub-divided into two site classes using 260 m/s of $V_{S,Soil}$ while the sites having greater than 20 m of depth to bedrock (H2 sites) are sub-divided into two site classes at $V_{S,Soil}$ equal to 180 m/s. The integration interval of 0.4 ~ 1.5 sec period range was adopted to calculate the long-period site coefficients ($F_v$) for reflecting the amplification characteristics of Korean geological condition. In addition, the frequency distribution of depth to bedrock reported for Korean sites was also considered in calculating the site coefficients for H2 sites to incorporate sites having greater than 30 m of depth to bedrock. The relationships between the site coefficients and rock shaking intensity were proposed and then subsequently compared with the site coefficients of similar site classes suggested in other codes.

거리센서 계측기반 이동물체의 인식 알고리즘 (vehicle Control Algorithm based on Depth Sensor Measurement System)

  • 김종만;김영민
    • 한국전기전자재료학회:학술대회논문집
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    • 한국전기전자재료학회 2008년도 춘계학술대회 논문집 센서 박막재료연구회 및 광주 전남지부
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    • pp.6-9
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    • 2008
  • A 3D depth measurement system is proposed for mobile vehicles. Depth measurement system which is composed of a rotating mirror, a line laser and mono-camera is employed to detect depth, where the laser light is reflected by the mirror and projected to- the scene objects whose locations are to be determined. The obtained depth information is converted into an image. Such depth images of the road region represent even and plane while that of off-road region is irregular or textured. Road region is detected employing a simple spatial differentiation technique to detect the plain textured area. Identification results of the diverse situation of Non-linear trail are included in this paper.

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Depth Up-Sampling via Pixel-Classifying and Joint Bilateral Filtering

  • Ren, Yannan;Liu, Ju;Yuan, Hui;Xiao, Yifan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제12권7호
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    • pp.3217-3238
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    • 2018
  • In this paper, a depth image up-sampling method is put forward by using pixel classifying and jointed bilateral filtering. By analyzing the edge maps originated from the high-resolution color image and low-resolution depth map respectively, pixels in up-sampled depth maps can be classified into four categories: edge points, edge-neighbor points, texture points and smooth points. First, joint bilateral up-sampling (JBU) method is used to generate an initial up-sampling depth image. Then, for each pixel category, different refinement methods are employed to modify the initial up-sampling depth image. Experimental results show that the proposed algorithm can reduce the blurring artifact with lower bad pixel rate (BPR).

Deep Learning Based Monocular Depth Estimation: Survey

  • Lee, Chungkeun;Shim, Dongseok;Kim, H. Jin
    • Journal of Positioning, Navigation, and Timing
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    • 제10권4호
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    • pp.297-305
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    • 2021
  • Monocular depth estimation helps the robot to understand the surrounding environments in 3D. Especially, deep-learning-based monocular depth estimation has been widely researched, because it may overcome the scale ambiguity problem, which is a main issue in classical methods. Those learning based methods can be mainly divided into three parts: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning trains the network from dense ground-truth depth information, unsupervised one trains it from images sequences and semi-supervised one trains it from stereo images and sparse ground-truth depth. We describe the basics of each method, and then explain the recent research efforts to enhance the depth estimation performance.