scholarly journals Does an Artificial Intelligence Perform Market Manipulation With Its Own Discretion? – A Genetic Algorithm Learns in an Artificial Market Simulation -

2020 ◽  
Author(s):  
Takanobu Mizuta
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Market Manipulation ◽  
Artificial Market ◽  
Market Simulation

scholarly journals Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tiziana Ciano ◽  
Massimiliano Ferrara ◽  
Meisam Babanezhad ◽  
Afrasyab Khan ◽  
Azam Marjani
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Velocity Profile ◽  
Fluid Velocity ◽  
Accurate Prediction ◽  
Inference System ◽  
Porous Tube ◽  
Intelligence Algorithm ◽  
Water Based ◽  
Artificial Intelligence Algorithm

AbstractThe heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

Knowledge Through Evolution

2008 ◽  
pp. 311-324
Author(s):  
Russell Beale ◽  
Andy Pryke
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Credit Card ◽  
Machine Learning Algorithms ◽  
Statistical Techniques ◽  
Dynamic Visualization ◽  
Telecommunications Network ◽  
Credit Card Data ◽  
Conventional Machine ◽  
Interactive 3D

This chapter argues that a knowledge discovery system should be interactive, should utilise the best in artificial intelligence (AI), evolutionary, and statistical techniques in deriving results, but should be able to trade accuracy for understanding. Further, it needs to provide a means for users to indicate what exactly constitutes “interesting”, as well as understanding suggestions output by the computer. One such system is Haiku, which combines interactive 3D dynamic visualization and genetic algorithm techniques, and enables users to visually explore features and evaluate explanations generated by the system. Three case studies are described which illustrate the effectiveness of the Haiku system, these being Australian credit card data, Boston area housing data, and company telecommunications network call patterns. We conclude that a combination of intuitive and knowledge-driven exploration, together with conventional machine learning algorithms, offers a much richer environment, which in turn can lead to a deeper understanding of the domain under study.

Image Recognition Technology Based on Artificial Intelligence

2013 ◽  
Vol 347-350 ◽  
pp. 3537-3540
Author(s):  
Hai Yun Lin ◽  
Yu Jiao Wang ◽  
Jian Chun Cai
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Semantic Feature ◽  
Current Image ◽  
Semantic Feature Extraction

In respect of the classification of current image retrieval technology and the existing issues, the paper put forward a method designed for image semantic feature extraction based on artificial intelligence. The new method has solved the tough problem of image semantic feature extraction, by fusing fuzzy logic, genetic algorithm and artificial neural network altogether, which greatly improved the efficiency and accuracy of image retrieval.

Steel Lazy Wave Riser Optimization Using Artificial Intelligence Tool

2020 ◽  
Author(s):  
Mayank Lal ◽  
Abhilash Sebastian ◽  
Feng Wang ◽  
Xiaohua Lu
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Design Process ◽  
Oil And Gas ◽  
Time Domain Analysis ◽  
Fatigue Performance ◽  
Wave Form ◽  
Steel Catenary Risers ◽  
Performance Drivers ◽  
Floating Platforms

Abstract Use of steel lazy wave risers has increased as oil and gas developments are happening in deeper waters or in parts of the world with no pipeline infrastructure. These developments utilize FPSO’s with offloading capabilities necessary for these developments. However, due to more severe motions compared to other floating platforms, traditional catenary form of risers are unsuitable for such developments and this is the reason Steel lazy wave risers (SLWR) are required. SLWRs have shown to have better strength and fatigue performance and lower tensions at the hang-off compared to steel catenary risers. A suitable Lazy-Wave form of the catenary riser is achieved by targeted placement of a custom configured buoyancy section. The strength and fatigue performance of steel lazy wave risers are governed by parameters such as length to start of this buoyancy section, length of the buoyancy section, hang-off angle and the buoyancy factor. Achieving these key performance drivers for a SLWR takes several iterations throughout the design process. In this paper, genetic algorithm which is an artificial intelligence optimization tool has been used to automate the generation of an optimized configuration of a steel lazy wave riser. This will enable the riser designer to speed up the riser design process to achieve the best location, coverage and size of the buoyancy section. The results that the genetic algorithm routine produces is compared to a parametric study of steel lazy wave risers varying the key performance drivers. The parametric analysis uses a regular wave time domain analysis and captures trends of change in strength and fatigue performance with change in steel lazy wave parameters.

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