• Title/Summary/Keyword: Location Error

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Energy Based Source Location by Using Acoustic Emission for Damage Detection in Steel and Composite CNG Tank (금속 및 복합재 CNG 탱크에서의 손상 검출을 위한 음향방출 에너지 기반 위치표정 기술)

  • Kim, Il-Sik;Han, Byeong-Hee;Park, Choon-Su;Yoon, Dong-Jin
    • Journal of the Korean Society for Nondestructive Testing
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    • v.35 no.5
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    • pp.332-340
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    • 2015
  • Acoustic emission (AE) is an effective nondestructive test that uses transient elastic wave generated by the rapid release of energy within a material to detect any further growth or expansion of existing defects. Over the past decades, because of environmental issues, the use of compressed natural gas (CNG) as an alternative fuel for vehicles is increasing because of environmental issues. For this reason, the importance and necessity of detecting defects on a CNG fuel tank has also come to the fore. The conventional AE method used for source location is highly affected by the wave speed on the structure, and this creates problems in inspecting a composite CNG fuel tank. Because the speed and dispersion characteristics of the wave are different according to direction of structure and laminated layers. In this study, both the conventional AE method and the energy based contour map method were used for source location. This new method based on pre-acquired D/B was used for overcoming the limitation of damage localization in a composite CNG fuel tank specimen which consists of a steel liner cylinder overwrapped by GFRP. From the experimental results, it is observed that the damage localization is determined with a small error at all tested points by using the energy based contour map method, while there were a number of mis-locations or large errors at many tested points by using the conventional AE method. Therefore, the energy based contour map method used in this work is more suitable technology for inspecting composite structures.

A RSS-Based Localization for Multiple Modes using Bayesian Compressive Sensing with Path-Loss Estimation (전력 손실 지수 추정 기법과 베이지안 압축 센싱을 이용하는 수신신호 세기 기반의 위치 추정 기법)

  • Ahn, Tae-Joon;Koo, In-Soo
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.12 no.1
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    • pp.29-36
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    • 2012
  • In Wireless Sensor Network(WSN)s, the detection of precise location of each node is essential for utilizing sensing data acquired from sensor nodes effectively. Among various location methods, the received signal strength(RSS) based localization scheme is mostly preferable in many applications because it can be easily implemented without any additional hardware cost. Since a RSS-based localization scheme is mainly affected by radio channel or obstacles such as building and mountain between two nodes, the localization error can be inevitable. To enhance the accuracy of localization in RSS-based localization scheme, a number of RSS measurements are needed, which results in the energy consumption. In this paper, a RSS based localization using Bayesian Compressive Sensing(BSS) with path-loss exponent estimation is proposed to improve the accuracy of localization in the energy-efficient way. In the propose scheme, we can increase the adaptative, reliability and accuracy of localization by estimating the path-loss exponents between nodes, and further we can enhance the energy efficiency by the compressive sensing. Through the simulation, it is shown that the proposed scheme can enhance the location accuracy of multiple unknown nodes with fewer RSS measurements and is robust against the channel variation.

Co-registration of PET-CT Brain Images using a Gaussian Weighted Distance Map (가우시안 가중치 거리지도를 이용한 PET-CT 뇌 영상정합)

  • Lee, Ho;Hong, Helen;Shin, Yeong-Gil
    • Journal of KIISE:Software and Applications
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    • v.32 no.7
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    • pp.612-624
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    • 2005
  • In this paper, we propose a surface-based registration using a gaussian weighted distance map for PET-CT brain image fusion. Our method is composed of three main steps: the extraction of feature points, the generation of gaussian weighted distance map, and the measure of similarities based on weight. First, we segment head using the inverse region growing and remove noise segmented with head using region growing-based labeling in PET and CT images, respectively. And then, we extract the feature points of the head using sharpening filter. Second, a gaussian weighted distance map is generated from the feature points in CT images. Thus it leads feature points to robustly converge on the optimal location in a large geometrical displacement. Third, weight-based cross-correlation searches for the optimal location using a gaussian weighted distance map of CT images corresponding to the feature points extracted from PET images. In our experiment, we generate software phantom dataset for evaluating accuracy and robustness of our method, and use clinical dataset for computation time and visual inspection. The accuracy test is performed by evaluating root-mean-square-error using arbitrary transformed software phantom dataset. The robustness test is evaluated whether weight-based cross-correlation achieves maximum at optimal location in software phantom dataset with a large geometrical displacement and noise. Experimental results showed that our method gives more accuracy and robust convergence than the conventional surface-based registration.

Diagnosis of Rib Fracture Using Artificial Intelligence on Chest CT Images of Patients with Chest Trauma (외상 환자의 흉부 CT에서 인공지능을 이용한 갈비뼈 골절 진단)

  • Li Kaike;Riel Castro-Zunti;Seok-Beom Ko;Gong Yong Jin
    • Journal of the Korean Society of Radiology
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    • v.85 no.4
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    • pp.769-779
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    • 2024
  • Purpose To determine the pros and cons of an artificial intelligence (AI) model developed to diagnose acute rib fractures in chest CT images of patients with chest trauma. Materials and Methods A total of 1209 chest CT images (acute rib fracture [n = 1159], normal [n = 50]) were selected among patients with chest trauma. Among 1159 acute rib fracture CT images, 9 were randomly selected for AI model training. 150 acute rib fracture CT images and 50 normal ones were tested, and the remaining 1000 acute rib fracture CT images was internally verified. We investigated the diagnostic accuracy and errors of AI model for the presence and location of acute rib fractures. Results Sensitivity, specificity, positive and negative predictive values, and accuracy for diagnosing acute rib fractures in chest CT images were 93.3%, 94%, 97.9%, 82.5%, and 95.6% respectively. However, the accuracy of the location of acute rib fractures was low at 76% (760/1000). The cause of error in the diagnosis of acute rib fracture seemed to be a result of considering the scapula or clavicle that were in the same position (66%) or some ribs that were not recognized (34%). Conclusion The AI model for diagnosing acute rib fractures showed high accuracy in detecting the presence of acute rib fractures, but diagnosis of the exact location of rib fractures was limited.

The Development of Vehicle Counting System at Intersection Using Mean Shift (Mean Shift를 이용한 교차로 교통량 측정 시스템 개발)

  • Chun, In-Gook
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.7 no.3
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    • pp.38-47
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    • 2008
  • A vehicle counting system at intersection is designed and implemented using analyzing a video stream from a camera. To separate foreground image from background, we compare three different methods, among which Li's method is chosen. Blobs are extracted from the foreground image using connected component analysis and the blobs are tracked by a blob tracker, frame by frame. The primary tracker use only the size and location of blob in foreground image. If there is a collision between blobs, the mean-shift tracking algorithm based on color distribution of blob is used. The proposed system is tested using real video data at intersection. If some huristics is applied, the system shows a good detection rate and a low error rate.

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Self-localization of a Mobile Robot for Decreasing the Error and VRML Image Overlay (오차 감소를 위한 이동로봇 Self-Localization과 VRML 영상오버레이 기법)

  • Kwon Bang-Hyun;Shon Eun-Ho;Kim Young-Chul;Chong Kil-To
    • Journal of Institute of Control, Robotics and Systems
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    • v.12 no.4
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    • pp.389-394
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    • 2006
  • Inaccurate localization exposes a robot to many dangerous conditions. It could make a robot be moved to wrong direction or damaged by collision with surrounding obstacles. There are numerous approaches to self-localization, and there are different modalities as well (vision, laser range finders, ultrasonic sonars). Since sensor information is generally uncertain and contains noise, there are many researches to reduce the noise. But, the correctness is limited because most researches are based on statistical approach. The goal of our research is to measure more exact robot location by matching between built VRML 3D model and real vision image. To determine the position of mobile robot, landmark-localization technique has been applied. Landmarks are any detectable structure in the physical environment. Some use vertical lines, others use specially designed markers, In this paper, specially designed markers are used as landmarks. Given known focal length and a single image of three landmarks it is possible to compute the angular separation between the lines of sight of the landmarks. The image-processing and neural network pattern matching techniques are employed to recognize landmarks placed in a robot working environment. After self-localization, the 2D scene of the vision is overlaid with the VRML scene.

A Fast Vision-based Head Tracking Method for Interactive Stereoscopic Viewing

  • Putpuek, Narongsak;Chotikakamthorn, Nopporn
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.1102-1105
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    • 2004
  • In this paper, the problem of a viewer's head tracking in a desktop-based interactive stereoscopic display system is considered. A fast and low-cost approach to the problem is important for such a computing environment. The system under consideration utilizes a shuttle glass for stereoscopic display. The proposed method makes use of an image taken from a single low-cost video camera. By using a simple feature extraction algorithm, the obtained points corresponding to the image of the user-worn shuttle glass are used to estimate the glass center, its local 'yaw' angle, as measured with respect to the glass center, and its global 'yaw' angle as measured with respect to the camera location. With these estimations, the stereoscopic image synthetic program utilizes those values to interactively adjust the two-view stereoscopic image pair as displayed on a computer screen. The adjustment is carried out such that the so-obtained stereoscopic picture, when viewed from a current user position, provides a close-to-real perspective and depth perception. However, because the algorithm and device used are designed for fast computation, the estimation is typically not precise enough to provide a flicker-free interactive viewing. An error concealment method is thus proposed to alleviate the problem. This concealment method should be sufficient for applications that do not require a high degree of visual realism and interaction.

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A Background Initialization for Video Surveillance

  • Lim Kang Mo;Lee Se Yeun;Shin Chang Hoon;Kim Yoon Ho;Lee Joo Shin
    • Proceedings of the IEEK Conference
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    • 2004.08c
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    • pp.810-813
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    • 2004
  • In this paper, a background initialization for video surveillance proposed. The proposed algorithm is that the background images are sampled n frames during ${\Delta}t$ All Sampling frames are divided by $M{\times}N$ size block every frame. Average values of pixels for same location block of the sampling frames during ${\Delta}t$t are taken. then the maximum intensity $\alpha$ and the minimun intensity $\beta$ is obtained, respecticely. The intial by $M{\times}N$ size block, then average intensity $\eta$ of pixels for the block is obtained. If the average intensity $\eta$ is out of the initial range of the background image, it is decided the moving object image, and if the average intensity $\eta$ is included in the initial range of the background image. it is decided the background image. To examine the propriety of the proposed algorithm in this paper, the accuracy and robustness evaluation results for human and car in the indoor and outdoor enviroment. the error rate of the proposed method is less than the existing methods and the extraction rate of the proposed method is better than the existing methods.

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Simultaneous identification of damage in bridge under moving mass by Adjoint variable method

  • Mirzaee, Akbar;Abbasnia, Reza;Shayanfar, Mohsenali
    • Smart Structures and Systems
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    • v.21 no.4
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    • pp.449-467
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    • 2018
  • In this paper, a theoretical and numerical study on bridge simultaneous damage detection procedure for identifying both the system parameters and input excitation mass, are presented. This method is called 'Adjoint Variable Method' which is an iterative gradient-based model updating method based on the dynamic response sensitivity. The main advantage of proposed method is inclusion of an analytical method to augment the accuracy and speed of the solution. Moving mass is a model which takes into account the inertia effects of the vehicle. This interaction model is a time varying system and proposed method is capable of detecting damage in this variable system. Robustness of proposed method is illustrated by correctly detection of the location and extension of predetermined single, multiple and random damages in all ranges of speed and mass ratio of moving vehicle. A comparison study of common sensitivity and proposed method confirms its efficiency and performance improvement in sensitivity-based damage detection methods. Various sources of errors including the effects of measurement noise and initial assumption error in stability of method are also discussed.

Design of User Friendly KML Validation Tool based on OpenLayers (오픈레이어 기반 사용자 친화적 KML 검증도구 설계)

  • Kim, Jung-Ok;Kang, Ji-Hun
    • Journal of Cadastre & Land InformatiX
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    • v.44 no.1
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    • pp.165-177
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    • 2014
  • The KML verification tool supports people who want to produce the highest quality KML file. In other words, it validate that a given KML document is well-formed with respect to XML standard meaning, and conform not only to the KML schema and the specification. Then it's only to notify error code line. People who want to use the KML file written by others would like to know both whether the validity of that file and general summary of feature's location, shape, and number. In this study, we recommended the user-friendly KML validator using OpenLayers and reporting geometries and images of the KML file.