An indicatrix of a shortest path on a convex surface

This article makes the surface of airplane into quadrilateral gridding by using the method of discrete gridding generation, and calculates the data of gridding by using the classical Dijkstra algorithm (local algorithm) which can seek the shortest path from the start point and the end point. With that we can achieve diffraction ray tracing. This method can be used for any convex surface of the diffraction ray tracing.

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Dependence of the joint configuration on the dynamic immobility fulcrum

Russian Osteopathic Journal ◽

10.32885/2220-0975-2019-1-2-108-114 ◽

2019 ◽

pp. 108-114

Author(s):

A. D. Kozlov ◽

Yu. P. Potekhina

Keyword(s):

Kinetic Energy ◽

Convex Surface ◽

The Other ◽

Concave Surface ◽

Joint Configuration ◽

Articular Surfaces

Although joints with synovial cavities and articular surfaces are very variable, they all have one common peculiarity. In most cases, one of the articular surfaces is concave, whereas the other one is convex. During the formation of a joint, the epiphysis, which has less kinetic energy during the movements in the joint, forms a convex surface, whereas large kinetic energy forms the epiphysis with a concave surface. Basing on this concept, the analysis of the structure of the joints, allows to determine forces involved into their formation, and to identify the general patterns of the formation of the skeleton.

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The Expected Distance of Shortest Path-Based In-Trees along Spanning Root Mobility on Grid Graph

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1587/transfun.2019kep0003 ◽

2020 ◽

Vol E103.A (9) ◽

pp. 1071-1077

Author(s):

Yoshihiro KANEKO

Keyword(s):

Shortest Path ◽

Grid Graph

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TURBULENT HEAT TRANSFER MEASUREMENT OF A JET IMPINGING ON THE RIB-ROUGHENED CONVEX SURFACE

Proceeding of International Heat Transfer Conference 11 ◽

10.1615/ihtc11.2200 ◽

1998 ◽

Author(s):

Young Suk Chung ◽

Dae Hee Lee ◽

Joon Sik Lee

Keyword(s):

Heat Transfer ◽

Convex Surface ◽

Turbulent Heat Transfer ◽

Turbulent Heat ◽

Transfer Measurement ◽

Heat Transfer Measurement ◽

Rib Roughened

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The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

Indonesian Journal of Artificial Intelligence and Data Mining ◽

10.24014/ijaidm.v1i1.4600 ◽

2018 ◽

Vol 1 (1) ◽

pp. 15

Author(s):

Achmad Fanany Onnilita Gaffar ◽

Agusma Wajiansyah ◽

Supriadi Supriadi

Keyword(s):

Ant Colony Optimization ◽

Shortest Path ◽

Optimization Algorithm ◽

Optimization Problems ◽

Google Earth ◽

Ant Colony ◽

Food Sources ◽

Shortest Route ◽

Study Results ◽

Destination Point

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

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