Oil Painting Art Communication System Based on Artificial Intelligence Optimization Algorithm

2021 ◽  
pp. 1101-1110
Author(s):  
Nan Gao ◽  
Liya Fu
Keyword(s):  
Artificial Intelligence ◽  
Optimization Algorithm ◽  
Communication System ◽  
Oil Painting

scholarly journals Atmospheric refractivity estimation from AIS signal power using the quantum-behaved particle swarm optimization algorithm

2019 ◽  
Vol 11 (1) ◽  
pp. 542-548
Author(s):  
Wenlong Tang ◽  
Hao Cha ◽  
Min Wei ◽  
Bin Tian ◽  
Xichuang Ren
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Communication System ◽  
Particle Swarm Optimization Algorithm ◽  
Estimation Method ◽  
Particle Swarm ◽  
Automatic Identification ◽  
Identification System ◽  
Signal Power ◽  
Swarm Optimization

Abstract This paper proposes a new refractivity profile estimation method based on the use of AIS signal power and quantum-behaved particle swarm optimization (QPSO) algorithm to solve the inverse problem. Automatic identification system (AIS) is a maritime navigation safety communication system that operates in the very high frequency mobile band and was developed primarily for collision avoidance. Since AIS is a one-way communication system which does not need to consider the target echo signal, it can estimate the atmospheric refractivity profile more accurately. Estimating atmospheric refractivity profiles from AIS signal power is a complex nonlinear optimization problem, the QPSO algorithm is adopted to search for the optimal solution from various refractivity parameters, and the inversion results are compared with those of the particle swarm optimization algorithm to validate the superiority of the QPSO algorithm. In order to test the anti-noise ability of the QPSO algorithm, the synthetic AIS signal power with different Gaussian noise levels is utilized to invert the surface-based duct. Simulation results indicate that the QPSO algorithm can invert the surface-based duct using AIS signal power accurately, which verify the feasibility of the new atmospheric refractivity estimation method based on the automatic identification system.

The Location of Electric Vehicle Charging Stations based on FRLM with Robust Optimization

2019 ◽  
Vol 33 (08) ◽  
pp. 1959027 ◽  
Author(s):  
Jing-min Wang ◽  
Yan Liu ◽  
Yi-fei Yang ◽  
Wei Cai ◽  
Dong-xuan Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Robust Optimization ◽  
Electric Vehicle ◽  
Optimization Algorithm ◽  
Site Selection ◽  
Queuing Theory ◽  
Location Model ◽  
Electric Vehicle Charging ◽  
Charging Stations ◽  
Charging Demand

It is very important for the application of artificial intelligence to accurately and quickly help the electric vehicles to find matching charging facilities. The site selection for electric vehicle charging station (EVCS) is a new field of artificial intelligence application, using artificial intelligence to analyze the current complex urban electric vehicle driving path, and then determining the location of charging stations. This paper proposes a novel hybrid model to decide the location of EVCS. First of all, this paper carries out the flow-refueling location model (FRLM) based on path requirement to determine the site selection of EVCS. Secondly, robust optimization algorithm is used to resolve the location model considering the uncertainty of charging demand. Then, queuing theory, which takes the charging load as a constraint in the location model, is integrated into the model. Last, but not the least, a case is conducted to verify the validity of the proposed model when dealing with location problem. As a result of the above analysis, it is effective to apply robust optimization algorithm and to determine the location of EVCSs effectively when charging demand generated on the path is uncertain. At the same time, queuing theory can help to determine the optimal number of EVCSs effectively, and reduce the cost of building EVCSs.

A MPR optimization algorithm for FSO communication system with star topology

2015 ◽  
Vol 356 ◽  
pp. 147-154 ◽  
Author(s):  
Linlin Zhao ◽  
Xuefen Chi ◽  
Peng Li ◽  
Lin Guan
Keyword(s):  
Optimization Algorithm ◽  
Communication System ◽  
Star Topology

scholarly journals Deep Loving - The Friend of Deep Learning

2020 ◽  
pp. 51-53
Author(s):  
Satish Gajawada ◽  
Hassan M. H. Mustafa
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Literature Review ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Definition Of

Artificial Intelligence and Deep Learning are good fields of research. Recently, the brother of Artificial Intelligence titled "Artificial Satisfaction" was introduced in literature [10]. In this article, we coin the term “Deep Loving”. After the publication of this article, "Deep Loving" will be considered as the friend of Deep Learning. Proposing a new field is different from proposing a new algorithm. In this paper, we strongly focus on defining and introducing "Deep Loving Field" to Research Scientists across the globe. The future of the "Deep Loving" field is predicted by showing few future opportunities in this new field. The definition of Deep Learning is shown followed by a literature review of the "Deep Loving" field. The World's First Deep Loving Algorithm (WFDLA) is designed and implemented in this work by adding Deep Loving concepts to Particle Swarm Optimization Algorithm. Results obtained by WFDLA are compared with the PSO algorithm.

Diabetes Diagnosis System Based on Support Vector Machines Trained by Vortex Optimization Algorithm

2018 ◽  
pp. 203-218 ◽  
Author(s):  
Sadi Fuat Cankaya ◽  
Ibrahim Arda Cankaya ◽  
Tuncay Yigit ◽  
Arif Koyun
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Support Vector Machines ◽  
Optimization Algorithm ◽  
Intelligent Systems ◽  
Support Vector ◽  
Diabetes Diagnosis ◽  
Diagnosis System ◽  
Vector Machines ◽  
Expert Support

Artificial intelligence is widely enrolled in different types of real-world problems. In this context, developing diagnosis-based systems is one of the most popular research interests. Considering medical service purposes, using such systems has enabled doctors and other individuals taking roles in medical services to take instant, efficient expert support from computers. One cannot deny that intelligent systems are able to make diagnosis over any type of disease. That just depends on decision-making infrastructure of the formed intelligent diagnosis system. In the context of the explanations, this chapter introduces a diagnosis system formed by support vector machines (SVM) trained by vortex optimization algorithm (VOA). As a continuation of previously done works, the research considered here aims to diagnose diabetes. The chapter briefly gives information about details of the system and findings reached after using the developed system.

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