Academic literature on the topic 'Line-feature-based mapping'
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Journal articles on the topic "Line-feature-based mapping"
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An, Su-Yong, Jeong-Gwan Kang, Lae-Kyoung Lee, and Se-Young Oh. "Line Segment-Based Indoor Mapping with Salient Line Feature Extraction." Advanced Robotics 26, no. 5-6 (January 2012): 437–60. http://dx.doi.org/10.1163/156855311x617452.
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Li Yunduo, 李运舵, 车进 Che Jin, and 薛澄 Xue Cheng. "基于点线特征匹配的实时定位及地图重建方法." Laser & Optoelectronics Progress 59, no. 2 (2022): 0210003. http://dx.doi.org/10.3788/lop202259.0210003.
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Sun, Lu Ping, Bo Qian, and Yan Zhi Guan. "Mapping Matrix of Variable Cross-Section Roll Forming Based on Visual Detection." Advanced Materials Research 683 (April 2013): 797–800. http://dx.doi.org/10.4028/www.scientific.net/amr.683.797.
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An image mapping algorithm is proposed to improve the precision of flexible roll forming. The mapping algorithm based on visual feedback and Image analysis technology. On-line detection device realizes image acquisition of cross-section profile. After processing and analysis, geometric feature is extracted by Hough transform in the image. Geometric feature is transformed to matrix which is built a mapping relation with the original matrix. The mapping algorithm describes the displacement and rotary error in the process of flexible roll forming. Closed loop control is realized by feeding back the error to the control system. Experiments results show that the mapping algorithm is a good algorithm for on-line dimensional precision detection in sheet metal forming.
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Zhang, Tong, Chunjiang Liu, Jiaqi Li, Minghui Pang, and Mingang Wang. "A New Visual Inertial Simultaneous Localization and Mapping (SLAM) Algorithm Based on Point and Line Features." Drones 6, no. 1 (January 13, 2022): 23. http://dx.doi.org/10.3390/drones6010023.
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In view of traditional point-line feature visual inertial simultaneous localization and mapping (SLAM) system, which has weak performance in accuracy so that it cannot be processed in real time under the condition of weak indoor texture and light and shade change, this paper proposes an inertial SLAM method based on point-line vision for indoor weak texture and illumination. Firstly, based on Bilateral Filtering, we apply the Speeded Up Robust Features (SURF) point feature extraction and Fast Nearest neighbor (FLANN) algorithms to improve the robustness of point feature extraction result. Secondly, we establish a minimum density threshold and length suppression parameter selection strategy of line feature, and take the geometric constraint line feature matching into consideration to improve the efficiency of processing line feature. And the parameters and biases of visual inertia are initialized based on maximum posterior estimation method. Finally, the simulation experiments are compared with the traditional tightly-coupled monocular visual–inertial odometry using point and line features (PL-VIO) algorithm. The simulation results demonstrate that the proposed an inertial SLAM method based on point-line vision for indoor weak texture and illumination can be effectively operated in real time, and its positioning accuracy is 22% higher on average and 40% higher in the scenario that illumination changes and blurred image.
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Zhou, Fei, Limin Zhang, Chaolong Deng, and Xinyue Fan. "Improved Point-Line Feature Based Visual SLAM Method for Complex Environments." Sensors 21, no. 13 (July 5, 2021): 4604. http://dx.doi.org/10.3390/s21134604.
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Traditional visual simultaneous localization and mapping (SLAM) systems rely on point features to estimate camera trajectories. However, feature-based systems are usually not robust in complex environments such as weak textures or obvious brightness changes. To solve this problem, we used more environmental structure information by introducing line segments features and designed a monocular visual SLAM system. This system combines points and line segments to effectively make up for the shortcomings of traditional positioning based only on point features. First, ORB algorithm based on local adaptive threshold was proposed. Subsequently, we not only optimized the extracted line features, but also added a screening step before the traditional descriptor matching to combine the point features matching results with the line features matching. Finally, the weighting idea was introduced. When constructing the optimized cost function, we allocated weights reasonably according to the richness and dispersion of features. Our evaluation on publicly available datasets demonstrated that the improved point-line feature method is competitive with the state-of-the-art methods. In addition, the trajectory graph significantly reduced drift and loss, which proves that our system increases the robustness of SLAM.
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He, Xuan, Wang Gao, Chuanzhen Sheng, Ziteng Zhang, Shuguo Pan, Lijin Duan, Hui Zhang, and Xinyu Lu. "LiDAR-Visual-Inertial Odometry Based on Optimized Visual Point-Line Features." Remote Sensing 14, no. 3 (January 27, 2022): 622. http://dx.doi.org/10.3390/rs14030622.
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This study presents a LiDAR-Visual-Inertial Odometry (LVIO) based on optimized visual point-line features, which can effectively compensate for the limitations of a single sensor in real-time localization and mapping. Firstly, an improved line feature extraction in scale space and constraint matching strategy, using the least square method, is proposed to provide a richer visual feature for the front-end of LVIO. Secondly, multi-frame LiDAR point clouds were projected into the visual frame for feature depth correlation. Thirdly, the initial estimation results of Visual-Inertial Odometry (VIO) were carried out to optimize the scanning matching accuracy of LiDAR. Finally, a factor graph based on Bayesian network is proposed to build the LVIO fusion system, in which GNSS factor and loop factor are introduced to constrain LVIO globally. The evaluations on indoor and outdoor datasets show that the proposed algorithm is superior to other state-of-the-art algorithms in real-time efficiency, positioning accuracy, and mapping effect. Specifically, the average RMSE of absolute trajectory in the indoor environment is 0.075 m and that in the outdoor environment is 3.77 m. These experimental results can prove that the proposed algorithm can effectively solve the problem of line feature mismatching and the accumulated error of local sensors in mobile carrier positioning.
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Wang, Runzhi, Kaichang Di, Wenhui Wan, and Yongkang Wang. "Improved Point-Line Feature Based Visual SLAM Method for Indoor Scenes." Sensors 18, no. 10 (October 20, 2018): 3559. http://dx.doi.org/10.3390/s18103559.
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In the study of indoor simultaneous localization and mapping (SLAM) problems using a stereo camera, two types of primary features—point and line segments—have been widely used to calculate the pose of the camera. However, many feature-based SLAM systems are not robust when the camera moves sharply or turns too quickly. In this paper, an improved indoor visual SLAM method to better utilize the advantages of point and line segment features and achieve robust results in difficult environments is proposed. First, point and line segment features are automatically extracted and matched to build two kinds of projection models. Subsequently, for the optimization problem of line segment features, we add minimization of angle observation in addition to the traditional re-projection error of endpoints. Finally, our model of motion estimation, which is adaptive to the motion state of the camera, is applied to build a new combinational Hessian matrix and gradient vector for iterated pose estimation. Furthermore, our proposal has been tested on EuRoC MAV datasets and sequence images captured with our stereo camera. The experimental results demonstrate the effectiveness of our improved point-line feature based visual SLAM method in improving localization accuracy when the camera moves with rapid rotation or violent fluctuation.
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Wu, Jianfeng, Jian Xiong, and Hang Guo. "Improving robustness of line features for VIO in dynamic scene." Measurement Science and Technology 33, no. 6 (March 25, 2022): 065204. http://dx.doi.org/10.1088/1361-6501/ac547f.
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Abstract The point feature, whose dynamic robustness has been widely studied, dominates in the field of visual simultaneous localization and mapping (SLAM) or visual-inertial odometry (VIO). When discussing a dynamic scene, line features are not given enough attention. This paper proposes a pre-processing step for VIO to reduce the influence of lines upon dynamic objects on system robustness and merges it into a state-of-the-art optimization-based VIO pipeline. First, it is determined whether the line feature is a potential dynamic line based upon the result of the semantic segmentation, optical flow and re-projection error. Then, instead of filtering them out, the information matrixes of these line features in the optimization function is adjusted by a weight-based method according to their tracked size. A simulated challenged visual-inertial dataset is used to evaluate the proposed algorithm against other state-of-the-art methods. The results shows that proposed method can increase robustness to dynamic scenes and make the line-based map neater and more intuitive through avoiding the drawing of dynamic line features during the mapping procedure.
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Zhao, Qijian, Yanlong Cao, Ting Liu, Lifei Ren, and Jiangxin Yang. "Tolerance specification of the plane feature based on the axiomatic design." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 5 (May 25, 2018): 1481–92. http://dx.doi.org/10.1177/0954406218772001.
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Tolerance specification involves selecting tolerance types for functional or assembly features to control the variation of features. General methods tend to formulate a frame to specify all the features of part, while the specification methods or reasoning rules for specific feature (point, line, plane, cylinder, etc.) are less studied. This paper focuses on the tolerance-type selection of the plane feature. The theory of axiomatic design is introduced to select the tolerance type for the plane feature, and the problem is interpreted as a redundant decoupled design. To achieve the functional requirements, design parameters and constraints of physics domain are determined. The mapping rules, which are between design parameters and functional requirements, are generated based on the independent axiom. Considering the large number of solutions of the design, the constraints such as cost and inspection methods are introduced to reduce the number of solutions. The minimum information axiom is introduced for the optimum mapping rules and the tolerance types are selected by the optimum mapping rules for the plane feature. Finally, the specification process is concluded and demonstrated by means of an example.
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Bian, Jiang, Xiaolong Hui, Xiaoguang Zhao, and Min Tan. "A monocular vision–based perception approach for unmanned aerial vehicle close proximity transmission tower inspection." International Journal of Advanced Robotic Systems 16, no. 1 (January 1, 2019): 172988141882022. http://dx.doi.org/10.1177/1729881418820227.
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Employing unmanned aerial vehicles to conduct close proximity inspection of transmission tower is becoming increasingly common. This article aims to solve the two key problems of close proximity navigation—localizing tower and simultaneously estimating the unmanned aerial vehicle positions. To this end, we propose a novel monocular vision–based environmental perception approach and implement it in a hierarchical embedded unmanned aerial vehicle system. The proposed framework comprises tower localization and an improved point–line-based simultaneous localization and mapping framework consisting of feature matching, frame tracking, local mapping, loop closure, and nonlinear optimization. To enhance frame association, the prominent line feature of tower is heuristically extracted and matched followed by the intersections of lines are processed as the point feature. Then, the bundle adjustment optimization leverages the intersections of lines and the point-to-line distance to improve the accuracy of unmanned aerial vehicle localization. For tower localization, a transmission tower data set is created and a concise deep learning-based neural network is designed to perform real-time and accurate tower detection. Then, it is in combination with a keyframe-based semi-dense mapping to locate the tower with a clear line-shaped structure in 3-D space. Additionally, two reasonable paths are planned for the refined inspection. In experiments, the whole unmanned aerial vehicle system developed on Robot Operating System framework is evaluated along the paths both in a synthetic scene and in a real-world inspection environment. The final results show that the accuracy of unmanned aerial vehicle localization is improved, and the tower reconstruction is fast and clear. Based on our approach, the safe and autonomous unmanned aerial vehicle close proximity inspection of transmission tower can be realized.
Dissertations / Theses on the topic "Line-feature-based mapping"
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Seidl, Christoph. "Evolution in Feature-Oriented Model-Based Software Product Line Engineering." Master's thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-81200.
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Software Product Lines (SPLs) are a successful approach to software reuse in the large. Even though tools exist to create SPLs, their evolution is widely unexplored. Evolving an SPL manually is tedious and error-prone as it is hard to avoid unintended side-effects that may harm the consistency of the SPL. In this thesis, the conceptual basis of a system for the evolution of model-based SPLs is presented, which maintains consistency of models and feature mapping. As basis, a novel classification is introduced that distinguishes evolutions by their potential to harm the mapping of an SPL. Furthermore, multiple remapping operators are presented that can remedy the negative side-effects of an evolution. A set of evolutions is complemented with appropriate remapping operations for the use in SPLs. Finally, an implementation of the evolution system in the SPL tool FeatureMapper is provided to demonstrate the capabilities of the presented approach when co-evolving models and feature mapping of an SPL.
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Seidl, Christoph. "Evolution in Feature-Oriented Model-Based Software Product Line Engineering." Master's thesis, 2011. https://tud.qucosa.de/id/qucosa%3A25866.
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Software Product Lines (SPLs) are a successful approach to software reuse in the large. Even though tools exist to create SPLs, their evolution is widely unexplored. Evolving an SPL manually is tedious and error-prone as it is hard to avoid unintended side-effects that may harm the consistency of the SPL. In this thesis, the conceptual basis of a system for the evolution of model-based SPLs is presented, which maintains consistency of models and feature mapping. As basis, a novel classification is introduced that distinguishes evolutions by their potential to harm the mapping of an SPL. Furthermore, multiple remapping operators are presented that can remedy the negative side-effects of an evolution. A set of evolutions is complemented with appropriate remapping operations for the use in SPLs. Finally, an implementation of the evolution system in the SPL tool FeatureMapper is provided to demonstrate the capabilities of the presented approach when co-evolving models and feature mapping of an SPL.:1 Introduction
1.1 Motivation for Evolving Software Product Lines
1.2 Outline of the Thesis
2 Background and Scope
2.1 Concepts and Terminology
2.1.1 Software Product Lines
2.1.2 Model-Driven Software Development
2.1.3 FeatureMapper
2.2 Scope
2.3 Related Work
3 Evolution of Software Product Lines
3.1 Evolutions
3.1.1 Evolutions in the Problem Space
3.1.2 Evolutions in the Solution Space
3.2 Classification Systems for Evolutions
3.2.1 Classification by Behavior Preservation
3.2.2 Classification Systems in the Literature
3.2.3 Classification by Semantical Extent of Model Changes
3.3 Remapping Operations
3.3.1 Remapping in the Problem Space
3.3.2 Remapping in the Solution Space
3.4 Classification and Remapping of Evolutions
3.4.1 Classification and Remapping of Problem Space Evolutions
3.4.2 Classification and Remapping of Solution Space Evolutions
4 A Framework for Evolutions in FeatureMapper
4.1 Relevant Technology
4.1.1 Refactory
4.1.2 EMFText
4.2 Implementation
4.2.1 Implementation of the Evolutions System
4.2.2 Implementation of the Remapping System
4.2.3 Implementation of the User Interface System
4.2.4 Implementation of the Test Suite
4.3 Possibilities for Extension
4.3.1 Adding New Evolutions
4.3.2 Adapting Existing Evolutions
5 Example Project
5.1 Initial Situation in 2001
5.2 First Revision in 2006
5.2.1 Removing the Cassette Player
5.2.2 Adding an MP3 CD Player
5.2.3 Adding a Personal Navigation Device
5.2.4 Changing the Implementation of the UI Builder
5.2.5 Summary of the Changes of the First Revision in 2006
5.3 Second Revision in 2011
5.3.1 Creating a Multi-Format CD Player
5.3.2 Enhancing Voice Recognition to Control the Audio Player
5.3.3 Restructuring Personal Navigation Maps
5.3.4 Changing the Implementation of the CD Player
5.3.5 Summary of the Changes of the Second Revision in 2011
5.4 Conclusion of the Example Project
6 Conclusion
6.1 Summarized Findings
6.2 Limitations and Drawbacks
6.3 Possibilities for Future Work
6.4 Theoretical and Practical Contributions
A Object Remapping Specification (*.orspec)
A.1 Object Remapping Specification Model
A.2 Object Remapping Specification Syntax
B DocBooklet (*.docbooklet)
B.1 DocBooklet Model
B.2 DocBooklet Syntax
C NavMap (*.navmap)
C.1 NavMap Model
C.2 NavMap Syntax
List of Figures
List of Tables
List of Listings
Bibliography
Conference papers on the topic "Line-feature-based mapping"
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Huo, Xinyou, Liqian Dou, Hanchen Lu, Bailing Tian, and Miaomiao Du. "A Line/Plane Feature-based Lidar Inertial Odometry and Mapping." In 2019 Chinese Control Conference (CCC). IEEE, 2019. http://dx.doi.org/10.23919/chicc.2019.8866215.
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Gao, Qiang, Guangrui Wei, Yuehui Ji, Yu Song, Junjie Liu, and Ning Han. "Fast Simultaneous Localization and Mapping Algorithm with Point and Line Feature Based on Image Entropy." In 2022 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2022. http://dx.doi.org/10.1109/icma54519.2022.9856289.
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Song, Yu, Joris S. M. Vergeest, and Tjamme Wiegers. "Identifying Feature Handles of Freeform Shapes." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49438.
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Trends, ergonomics and engineering analysis post more challenges than ever to product shape designs, especially in the freeform area. In this paper, freeform feature handles are proposed for easing of difficulties in modifying an existing freeform shape. Considering the variations of curvature as the footprint of a freeform feature(s), curvature analysis is applied to find manipulators, e.g. handles, of a freeform feature(s) in the shape. For these, a Laplacian based pre-processing tool is proposed first to eliminate background noise of the shape. Then least square conformal mapping is applied to map the 3D geometry to a 2D polygon mesh with the minimum distortions of angle deformation and non-uniform scaling. By mapping the curvature of each vertex in the 3D shape to the 2D polygon mesh, a curvature raster image is created. With image processing tools, different levels of curvature changing are identified and marked as feature point(s) / line(s) / area(s) in the freeform shape. Following the definitions, the handles for those intrinsic freeform features are established by the user based on those feature items. Experiments were conducted on different types of shapes to verify the rightness of the proposed method. Different effects caused by different parameters are discussed as well.
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Ismail, Hesham, and Balakumar Balachandran. "A Comparison of Feature Extraction Algorithms Based on Sonar Sensor Data." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-62989.
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Simultaneous localization and mapping (SLAM) is a technique used to determine the location of a mobile vehicle in an unknown environment, while constructing a map of the unknown environment at the same time. Mobile platforms, which make use of SLAM algorithms, have industrial applications in autonomous maintenance, such as the inspection of flaws and defects in oil pipelines and storage tanks. An important component of SLAM is feature extraction, which is the process of detecting and extracting significant features such as corners, edges, and walls in an environment. Here, the use of sonars as sensors mounted on a mobile platform is examined, and a comparison of different algorithms currently in use is made and presented. This comparison is performed through a combination of experimental and numerical studies. The triangulation-based fusion algorithm is examined for point feature detection, and the standard Hough Transform and the triangulation Hough fusion (THF) are used for line detection. Comparisons are discussed and presented along with ongoing work.
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Leylek, Zafer, and A. J. Neely. "Development of a Blade Parametric Modeling Methodology for Design and Analysis of Computer Experiments." In ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-43109.
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This paper will present an enhanced parametric modeling technique for gas turbine stator and rotor blades. The enhanced blade parametric modeling system has been developed as part of a wider research program into global surrogate modeling of compressor and turbine aerodynamic performance using Design and Analysis of Computer Experiments (DACE) based techniques. The proposed method is based on a hybrid of geometric feature and Non-uniform Rational B-Spline (NURBS) based techniques. A base-line geometry is defined using the physical parameters and represented using NURBS curves and surfaces. A number of constraints are then imposed on the parametric model to ensure that DACE techniques can be effectively utilized. This is accomplished by mapping the geometric feature based parameters from the physical space to an alternative parametric space so that all feasible and numerically stable blade configurations can be represented using a unit hyper-cube. This method ensures a one-to-one mapping between the parametric sub-space and the geometric feature based system. The mapping is geometrically and numerically stable and does not produce ill-conditioned and unrealistic blade geometries. The development of the blade parametric modeling process allows the application of the complete suit of DACE tools and techniques. The method is valid for all axial blade profiles which include compressor and turbine stator and rotor blades.
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Jalink, J., A. Firiti, J. van den Biggelaar, A. Reverdy, and B. Lai. "Infrared Lock-In Thermography: From Localization of Low Power and Masked Defects to Absolute Temperature Mapping for Product Debug." In ISTFA 2019. ASM International, 2019. http://dx.doi.org/10.31399/asm.cp.istfa2019p0104.
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Abstract The application of IR-Lock-In Thermography (IRLIT) has been extended from 2D and 3D package fault isolation to on-die level analysis. In addition, the technique has become more sensitive allowing for detection of much lower dissipated power. In this paper, several fault localization cases covering PCB assemblies down to die level analysis are discussed using IR-LIT and absolute temperature mapping. Where possible, the analysis is complemented with physical defect verification. The fault isolation cases include an ultra-low power dissipation (<150 nW) and several case studies with high ohmic connections. For the latter a new method based on phase mapping is discussed allowing for 2D localization of thermally invisible defects. The method will be demonstrated on a test vehicle where phase data extracted from a visible feature of the device under test is studied. After this, a case study at die level is presented in an attempt to distinguish the phase information from two stacked M2-M3 metallization layers of the Back-End Of the Line (BEOL). Finally, temperature mapping results of a 5 micron wide aluminum feature in silicon-oxide is presented that is pushing the optical resolution of the tool.
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Zhang, Siyu, R. Ganesan, and T. S. Sankar. "Self-Organizing Neural Networks for Automated Machinery Monitoring Systems." In ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium collocated with the ASME 1995 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/cie1995-0831.
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Abstract Two fundamental problems that are frequently encountered in automated machinery monitoring and diagnostics are formulated into their corresponding mathematical problems of clustering and trend analysis. The need for and the efficiency of multiple-index based trend analysis, in both precisely evaluating the current conditions of a machine system using on-line vibration measurements and obtaining a reliable prediction about its future behaviour, are systematically brought out. Neural network solutions to these problems, particularly the solutions using Self-Organizing Maps (SOM) are sought. Statistical parameters of the on-line vibration signal such as peak-to-peak value, absolute mean value, crest factor etc., are used to form the data set depending on the machinery system being monitored and diagnosed. Self-organizing mapping algorithm is then employed to perform the clustering and feature extraction which takes as the input the multi-dimensional data set and provides as the output the condition of the machinery system. Associated one-layer neural network is developed during the process of SOM and the training of this network is performed in an unsupervised learning mode. A new efficient neural network algorithm that has been previously developed by the present authors for multiple-index based regression is adapted to perform the trend analysis of a machine system. Applications of the above neural network algorithms to the condition monitoring and life estimation of both a bearing system as well as a rotor system are fully demonstrated using real-life data.
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Baschuk, J. J., Alan West, and B. W. Leitch. "Small Scale Burst Facility: Experimental Investigation of Fluid-Structure Interaction in a PHWR." In ASME 2013 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/pvp2013-97422.
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The Pressurized Heavy Water Reactor (PHWR) is based on natural uranium fuel and heavy water moderator. A unique feature of the PHWR is the horizontal fuel channel that allows for on-line re-fuelling and fuel management. A fuel channel consists of two concentric tubes, each approximately 6 meters long. The inner tube, known as the pressure tube, contains the uranium fuel bundles and the pressurized (∼10 MPa) primary coolant. The outer tube, known as the calandria tube, separates the heavy water moderator (∼70°C) from the pressure tube (∼300°C). A potential accident scenario is the bursting of a fuel channel. The escaping hot fluid generates a pressure wave in the moderator, which would interact with the adjacent pressure/calandria tube assemblies and the outer containment calandria vessel, potentially damaging components within the reactor core. To improve the understanding of channel bursts and associated fluid structure interaction, a 1:6 scale reactor vessel test facility (Small Scale Burst Facility) was constructed at the Atomic Energy of Canada Ltd, Chalk River Laboratories. The test facility allows for the measurement of transient pressures, the development and collapse of the steam bubble created by the burst tube, and resultant response of the neighboring tubes and scaled calandria vessel. A single bursting tube, or a single tube bursting within an array of neighboring tubes, can be tested. The results from recent tests are presented, which include a three-dimensional map of the pressure pulse from a single, bursting tube. Future work will include 3-D mapping of near wall bursts and modeling the experiments using Arbitary Lagrangian Eulerian methods in the finite element program, LS-DYNA. This work is part of the development of a next generation modeling tool for fuel channel phenomena.
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Ertelt, Christoph, and Kristina Shea. "An Application of Shape Grammars to Planning for CNC Machining." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86827.
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Planning for Computerized Numerical Control (CNC) fabrication requires generation of process plans for the fabrication of parts that can be executed on CNC enabled machine tools. To create such plans, a large amount of domain specific knowledge is required to map the desired geometry of a part to a manufacturing process, thus decomposing design information into a set of feasible machining operations. Approaches to automate this planning process still rely heavily on human capabilities, such as planning and reasoning about geometry in relation to machining capabilities. In this paper, the authors present a new, shape grammar-based approach for automatically creating fabrication plans for CNC machining from a given part geometry. To avoid the use of static feature sets and their pre-defined mappings to machining operations, the method encodes knowledge of fundamental machine capabilities. A method for generating a vocabulary of removal volume shapes based on the available tool set and machine tool motions is defined in combination with a basic rule set for shape removal covering tool motion, removal volume calculation and CNC code generation. The use of shape grammars as a formalism enables systematic formulation of hard and soft constraints on spatial relations between the volume to be removed and the removal volume shape for a machining operation. The method is validated using an example of machining a simple part on a milling machine. Overall, the approach and method presented is an enabler for the creation of an autonomous fabrication system and CNC machine tools that are able to reason about part geometry in relation to available capabilities and carry out on-line planning for CNC fabrication.