China
Africa
Explore chapters and articles related to this topic
Orienteering and Coverage
Published in Yasmina Bestaoui Sebbane, Multi-UAV Planning and Task Allocation, 2020
Delaunay triangulation can be constructed to discover the topological properties of a network. Delaunay triangulation is an important data structure in computational geometry, which satisfies the empty circle property: for each side in Delaunay triangulation, a circle passing through the end points of this side can be determined without enclosing other points [219]. The proposed method of detecting and localizing coverage holes consists of four phases, which are as follows: Detection of coverage holes: Every node detects whether coverage holes exist around it based on a hole detection algorithm.Merging of coverage holes: A merging method of holes can be provided to present the global view of a coverage hole by indicating the location and shape of an isolated coverage hole.Size estimation of local coverage holes: The inscribed empty circles are used for the estimation of the size of every local coverage hole.Tree description: For each isolated coverage hole, line segments are used to connect the centers of each pair of inscribed empty circles. If a separated tree can be recognized, the corresponding coverage hole that contains the tree can be exclusively determined.
Frameless Network Architecture for User-Centric 5G Radio Access Networks
Published in Hrishikesh Venkatarman, Ramona Trestian, 5G Radio Access Networks: Centralized RAN, Cloud-RAN, and Virtualization of Small Cells, 2017
Xiaodong Xu, Zhao Sun, Jiaxiang Liu
In order to achieve EE optimization for CP/UP adaptation, the first step aims at constructing a seamless deployment of the CP with minimum transmission power. The Voronoi diagram, a geometric structure in computational geometry, divides the space into a number of regions consisting of all the points closer to a specific site than to any other. As energy consumption is proportional to distance, the Voronoi diagram also defines regions where less energy consumption is required. In order to achieve better EE in the CP’s construction and adaptation, we construct a Voronoi coverage area for the Controlling-AEs. The Data-AEs located within the Voronoi coverage area are controlled by the corresponding Controlling-AE.
Two Dimensional (2D) Surface Model
Published in Ahmad Fikri Bin Abdullah, A Methodology for Processing Raw Lidar Data to Support Urban Flood Modelling Framework, 2020
A TIN is typically based on a Delaunay triangulation, but its utility can be limited by the selection of input data points i.e., well-chosen points need to be located such that they capture significant changes in surface form, such as topographical summits, breaks of slope, ridges, valley floors, pits, and cols. In mathematics and computational geometry, a Delaunay triangulation for a set Ρ of points in a plane is a triangulation DT(P) such that no point in Ρ is inside the circumcircle of any triangle in DT(P). Delaunay triangulations maximize the minimum angle of all the angles of the triangles in the triangulation; they tend to avoid skinny triangles (Mark et.al, 2008).
Reconstructing original design: Process planning for reverse engineering
Published in IISE Transactions, 2023
Zhaohui Geng, Arman Sabbaghi, Bopaya Bidanda
By implementing the point cloud generation algorithms, a large set of well-structured landmarks can be actively created. The following step is to register the configurations by removing the translational and rotational factors. Without loss of generality, we first focus on multiple scans of a single part. Let Xi be a configuration matrix for scan i of a part, containing all the 3-D k landmarks. In conventional landmark-based SSA, the object shape is defined by the remaining geometric information in Xi when translation, size, and rotation are removed. The majority of the SSA theories and methodologies are built on top of shape space (Dryden and Mardia, 2016). However, the accuracy of the dimensions, or size in SSA, is also an important quality factor of manufacturing processes. Therefore, the configuration matrices transformed to the size-and-shape space should contain all the geometric information except that the translational and rotational factors are removed. This process is also known as registration in other fields, such as computer vision and computational geometry.
Towards the next-generation GIS: a geometric algebra approach
Published in Annals of GIS, 2019
Linwang Yuan, Zhaoyuan Yu, Wen Luo
In summary, past research on next-generation GIS generally falls into two categories. The first is to reconstruct the representation and computation models based on abstract theories of geographical representation and spatial cognition; and the second aims to improve the representation, computation, and architecture of existing GIS using emerging computer techniques. It is noted that existing representation and computation methods of GIS draw from the different fields of geometry, cartography and computer graphics. Specifically, the reference framework of space representation takes a cartographic perspective; computations are based on Euclidean and computational geometry; and spatial data management and analysis borrow techniques from computer science. This mixed framework has limitations as there is often inconsistency in the expression of geographical semantics, representation of multi-scale spatiotemporal evolution processes, and modelling of geographical object interactions. Therefore, it is necessary to innovate GIS from the very foundation, that is, the underlying theory. It should be independent of the ever-changing computer technology and can support an uniform representation and computation of complex geographical objects and phenomena.
Optimal junction localization minimizing maximum miners’ evacuation distance in underground mining network
Published in Mining Technology, 2023
Zhixuan Shao, Maximilien Meyrieux, Mustafa Kumral
In many mining engineering cases, heuristics perform extensively because data are limited, and the exact values of many engineering characteristics are not accurate. However, for special cases where optimality is required, the research is deepened to find an optimal solution. The Welzl algorithm (Welzl 1991) is an exact algorithm that is capable of solving computational geometry problems such as the minimum covering circle problem. Welzl algorithm was used in various fields (Gärtner 1999). For instance, Berg et al. (2008) illustrated a scenario where a robotic arm could be positioned optimally to reach the dispersed objects in a plane by using the Welzl algorithm. Fonseca and Winter (2012) used the Welzl algorithm as a sub-method inside their algorithm to find minimum bounding volumes for proteins. The algorithm works based on the principle of recursivity. Admittedly, a recursive approach is able to provide solutions to a complex problem by calling upon instances of the same problem with lower complexity until obtaining a trivial case. As such, once the minimum covering circle cannot be determined, the Welzl algorithm will be recursively executed the subroutines using the same framework until a unique and correct minimum covering circle is left. Furthermore, what is also included in the logic of the Welzl algorithm is the random selection of points which makes it a randomized algorithm. It has been shown that the incorporation of a degree of randomness to the algorithm allows for obtaining a linear time complexity (Welzl 1991), which leads to the classification of the minimum circle problem in the category of ‘LP-type problem’ similar to many other computational geometry problems such as the smallest enclosing ellipsoid.