A class of network optimization methods for planar grid generation

2005 ◽  
Vol 52 (4) ◽  
pp. 363-379 ◽  
Author(s):  
N. Egidi ◽  
P. Maponi
Keyword(s):  
Network Optimization ◽  
Grid Generation ◽  
Optimization Methods ◽  
Planar Grid

scholarly journals Edge Network Optimization Based on AI Techniques: A Survey

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2830
Author(s):  
Mitra Pooyandeh ◽  
Insoo Sohn
Keyword(s):  
Network Optimization ◽  
Optimization Methods ◽  
The Internet ◽  
Security Issue ◽  
Edge Network ◽  
Iot Devices ◽  
High Level ◽  
Cloud Servers ◽  
The Internet Of Things

The network edge is becoming a new solution for reducing latency and saving bandwidth in the Internet of Things (IoT) network. The goal of the network edge is to move computation from cloud servers to the edge of the network near the IoT devices. The network edge, which needs to make smart decisions with a high level of response time, needs intelligence processing based on artificial intelligence (AI). AI is becoming a key component in many edge devices, including cars, drones, robots, and smart IoT devices. This paper describes the role of AI in a network edge. Moreover, this paper elaborates and discusses the optimization methods for an edge network based on AI techniques. Finally, the paper considers the security issue as a major concern and prospective approaches to solving this issue in an edge network.

Variational Planar Grid Generation

2020 ◽  
pp. 119-142
Author(s):  
Patrick Knupp ◽  
Stanly Steinberg
Keyword(s):  
Grid Generation ◽  
Planar Grid

scholarly journals Risk-Based Path Planning Optimization Methods for UAVs Over Inhabited Areas

2015 ◽  
Author(s):  
Eliot Rudnick-Cohen ◽  
Jeffrey W. Herrmann ◽  
Shapour Azarm
Keyword(s):  
Risk Assessment ◽  
Path Planning ◽  
Network Optimization ◽  
Computation Time ◽  
Optimization Methods ◽  
Flight Path ◽  
Low Risk ◽  
Optimization Approach ◽  
Local Approach ◽  
Solution Quality

Operating unmanned aerial vehicles (UAVs) over inhabited areas requires mitigating the risk to persons on the ground. Because the risk depends upon the flight path, UAV operators need approaches (techniques) that can find low-risk flight paths between the mission’s start and finish points. In some areas, the flight paths with the lowest risk are excessively long and indirect because the least-populated areas are too remote. Thus, UAV operators are concerned about the tradeoff between risk and flight time. Although there exist approaches for assessing the risks associated with UAV operations, existing risk-based path planning approaches have considered other risk measures (besides the risk to persons on the ground) or simplified the risk assessment calculation. This paper presents a risk assessment technique and bi-objective optimization methods to find low-risk and time (flight path) solutions and computational experiments to evaluate the relative performance of the methods (their computation time and solution quality). The methods were a network optimization approach that constructed a graph for the problem and used that to generate initial solutions that were then improved by a local approach and a greedy approach and a fourth method that did not use the network solutions. The approaches that improved the solutions generated by the network optimization step performed better than the optimization approach that did not use the network solutions.

The Computer Network Optimization Model Based on Neural Network Algorithm Research

2013 ◽  
Vol 798-799 ◽  
pp. 545-548
Author(s):  
Xun Wang ◽  
Jie Rong
Keyword(s):  
Neural Network ◽  
Network Optimization ◽  
Optimization Model ◽  
Computer Network ◽  
Hopfield Neural Network ◽  
Optimization Methods ◽  
Information Storage ◽  
Distributed Information ◽  
Network Algorithm ◽  
Neural Network Algorithm

The speed of development of the computer network is an urgent need to comprehensively improve and optimize the overall performance of the network. Neural network algorithm has a massively parallel processing and distributed information storage, Hopfield neural network showed a unique advantage in the associative memory and optimization based on the neural network algorithm for computer network optimization model of Hopfield neural network theory and reality computer network, modern optimization methods, it is combined.

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