Optimization of Micro Compressor Blades Using CFD Analysis and Artificial Intelligence

2003 ◽  
Author(s):  
Akay A. Islek
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Design Method ◽  
Navier Stokes ◽  
Cfd Analysis ◽  
Compressor Blades ◽  
Artificial Neural Network Ann ◽  
Robust Design Method

This paper describes a robust design method for turbo machinery components. The method which uses a Navier Stokes (NS) solver, an Artificial Neural Network (ANN) and a Genetic Algorithm (GA) is applied to optimize radial micro compressor blades. Higher efficiency, less divergence from the design mass flow and smoother Mach number distributions are considered as objectives of the optimization.

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

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.

An Integrated RS and ANN Design Method for Product Agile Customization

2010 ◽  
Vol 458 ◽  
pp. 212-217 ◽  
Author(s):  
Chang Feng Yuan ◽  
Wan Lei Wang ◽  
Yan Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Process ◽  
Rough Set ◽  
Design Method ◽  
Roll Plate ◽  
Ann Models ◽  
Effective Decision ◽  
Artificial Neural Network Ann ◽  
Novel Design

Product agile customization design is an effective technological measure to win the customers and improve development efficiency. It needs designer to determine product structure quickly according to customer’s customized requirements. In this paper, a novel design method of product agile customization is presented by integrating rough set (RS) theory and artificial neural network (ANN) in the design process. In the method, design demands are reduced so as to form effective decision conditions by applying RS, and on that basis ANN models between design demands of different design stages and corresponding product structures are established so as to determine product structural styles quickly by applying ANN. Finally, this method is applied to the general schematic design process of a roll plate machine’s customization, and its validity is verified.

Application of Neural Network Based on Genetic Algorithm to Predict Hypotension Events during Spinal Anesthesia

2012 ◽  
Vol 542-543 ◽  
pp. 1394-1397
Author(s):  
Yu Qiu ◽  
Hai Jun Dai
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Global Optimization ◽  
General Anesthesia ◽  
Bp Neural Network ◽  
Simulation Experiment ◽  
Optimal Parameters ◽  
Accuracy Requirement ◽  
Artificial Neural Network Ann

An accurate and efficient artificial neural network (ANN) based genetic algorithm (GA) is presented for predicting hypotension during general anesthesia. The genetic algorithm global optimization characteristics are used to optimize the BP neural network weights, and learning samples are trained and modeled by BP neural network with optimal parameters. The simulation experiment is carried out with MATLAB. The result indicated that the model forecasting results are close with the actual results and meet the accuracy requirement to General Anesthesia.

Optimization Waiting Time at Berthing Area of Port Container Terminal with Hybrid Genetic Algorithm (GA) and Artificial Neural Network (ANN)

2014 ◽  
Vol 902 ◽  
pp. 431-436 ◽  
Author(s):  
A. Shahpanah ◽  
S. Poursafary ◽  
S. Shariatmadari ◽  
A. Gholamkhasi ◽  
S.M. Zahraee
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Waiting Time ◽  
Container Terminal ◽  
Hybrid Approach ◽  
Simulation Software ◽  
Optimum Number ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

A queuing network model related to arrival, departure and berthing process of ships at port container terminal is presented in this paper. The important datas collected from PTP port container terminal located at Malaysia. Based on the case study the model was built with using Arena 13.5 simulation software. Especially this study proposes a hybrid approach consisting of Genetic algorithm (GA), Artificial Neural Network (ANN) to find the the optimum number of equipments at berthing area of port container terminal. The input data that used in ANN obtained from Arena results. The main goal of this study is reduced waiting time of each ship at port container terminal, and Based on the result the optimum waiting time 50 will be achieved.

scholarly journals Deriving Rules for Forecasting Air Carrier Financial Stress and Insolvency: A Genetic Algorithm Approach

2010 ◽  
Author(s):  
Sergio Davalos ◽  
Richard Gritta ◽  
Bahram Adrangi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Local Optima ◽  
Air Carrier ◽  
Genetic Algorithm Approach ◽  
Genetic Algorithm Model

Statistical and artificial intelligence methods have successfully classified organizational solvency, but are limited in terms of generalization, knowledge on how a conclusion was reached, convergence to a local optima, or inconsistent results. Issues such as dimensionality reduction and feature selection can also affect a model's performance. This research explores the use of the genetic algorithm that has the advantages of the artificial neural network but without its limitations. The genetic algorithm model resulted in a set of easy to understand, if-then rules that were used to assess U.S. air carrier solvency with a 94% accuracy.

scholarly journals Mitigating Economic Denial of Sustainability (EDoS) in Cloud Environment using Genetic Algorithm and Artificial Neural Network

2019 ◽  
Vol 8 (10) ◽  
pp. 3415-3421
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Virtual Machines ◽  
Fitness Function ◽  
Medium Size ◽  
Cloud Environment ◽  
Edos Attacks ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Economic Denial of Sustainability (EDoS) is a latest threat in the cloud environment in which EDoS attackers continually request huge number of resources that includes virtual machines, virtual security devices, virtual networking devices, databases and so on to slowly exploit illegal traffic to trigger cloud-based scaling capabilities. As a result, the targeted cloud ends with a consumer bill that could lead to bankruptcy. This paper proposes an intelligent reactive approach that utilizes Genetic Algorithm and Artificial Neural Network (GANN) for classification of cloud server consumer to minimize the effect of EDoS attacks and will be beneficial to small and medium size organizations. EDoS attack encounters the illegal traffic so the work is progressed into two phases: Artificial Neural Network (ANN) is used to determine affected path and to detect suspected service provider out of the detected affected route which further consist of training and testing phase. The properties of every server are optimized by using an appropriate fitness function of Genetic Algorithm (GA) based on energy consumption of server. ANN considered these properties to train the system to distinguish between the genuine overwhelmed server and EDoS attack affected server. The experimental results show that the proposed Genetic and Artificial Neural Network (GANN) algorithm performs better compared to existing Fuzzy Entropy and Lion Neural Learner (FLNL) technique with values of precision, recall and f-measure are increased by 3.37%, 10.26% and 6.93% respectively.

scholarly journals Applications of Artificial Intelligence Techniques to Enhance Sustainability of Industry 4.0: Design of an Artificial Neural Network Model as Dynamic Behavior Optimizer of Robotic Arms

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Aydin Azizi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Industrial Robots ◽  
Optimal Parameters ◽  
Ann Model ◽  
Robotic Arms ◽  
Revolute Joints ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Industrial robots have a great impact on increasing the productivity and reducing the time of the manufacturing process. To serve this purpose, in the past decade, many researchers have concentrated to optimize robotic models utilizing artificial intelligence (AI) techniques. Gimbal joints because of their adjustable mechanical advantages have been investigated as a replacement for traditional revolute joints, especially when they are supposed to have tiny motions. In this research, the genetic algorithm (GA), a well-known evolutionary technique, has been adopted to find optimal parameters of the gimbal joints. Since adopting the GA is a time-consuming process, an artificial neural network (ANN) architecture has been proposed to model the behavior of the GA. The result shows that the proposed ANN model can be used instead of the complex and time-consuming GA in the process of finding the optimal parameters of the gimbal joint.

Sign in / Sign up

Export Citation Format

Share Document