scholarly journals Intelligent speed control of a Stirling engine using artificial neural network

2021 ◽  
Author(s):  
Mohammadreza Hojati ◽  
Ehsan Mansouri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Engine Speed ◽  
Combustion Engine ◽  
Mechanical Energy ◽  
Stirling Engine ◽  
Ambient Temperatures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
External Combustion

Abstract The Stirling engine is a kind of external combustion engine that is ideal for converting renewable energies to electricity or mechanical energy forms. Therefore, monitoring and controlling some of its parameters such as its speed is important. The purpose of this paper is to control a Stirling engine flywheel rotation speed using an intelligent parameter predictor. Since various parameters affect motor performance, determining the optimal value of them manually is impossible. Therefore, linking these parameters together and finding a relationship between them would be beneficial. Hence, using artificial intelligence (AI) to find a quick and efficient solution is particularly important. At the studied Stirling engine, speed, cold sink and ambient temperatures are defined as input parameters, and hot sink temperature is considered as the output parameter that should be calculated. To discover the relationship between inputs and output, an artificial neural network (ANN) is used. The results of this study showed that the use of ANNs can be significantly helpful in controlling engine speed.

Artificial neural network, ANN-PSO and ANN-ICA for modelling the Stirling engine

2015 ◽  
Vol 37 (5) ◽  
pp. 456-468 ◽  
Author(s):  
Somayeh Toghyani ◽  
Mohammad H. Ahmadi ◽  
Alibakhsh Kasaeian ◽  
Amir H. Mohammadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stirling Engine ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

scholarly journals Fuel consumption analysis in dynamic states of the engine with use of artificial neural network

2013 ◽  
Vol 155 (4) ◽  
pp. 16-25
Author(s):  
Piotr BERA
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuel Consumption ◽  
Engine Speed ◽  
Complex Problem ◽  
Dynamic Operating ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Ci Engines ◽  
Dynamic States

The paper presents the construction of fuel consumption dynamic characteristic determined with use of artificial neural network (ANN). The characteristic is based on the data obtained during measurements carried out on the engine dynamometer. The momentary fuel consumption as a non-linear function of two variable parameters, engine speed and torque, has been presented. Article discusses the way of determining such a characteristic which can be used for both SI and CI engines for entire range of engine speed and load. Described characteristic enables analysis of engine properties in dynamic states and allows computing mileage fuel consumption of the car with a given engine in combination with the specific transmission in any virtual driving cycle. An important advantage of the ANN method in case of general fuel consumption characteristic in dynamic operating states is that standard measurement equipment can be used and the solution to a complex problem is reduced to programming issues.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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