scholarly journals Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network

2021 ◽  
Vol 11 (19) ◽  
pp. 8943
Author(s):  
Rudy Alexis Guejia Burbano ◽  
Giovanni Petrone ◽  
Patrizio Manganiello
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Environmental Conditions ◽  
A Priori ◽  
The Other ◽  
Photovoltaic Panel ◽  
Pv Panel ◽  
Artificial Neural ◽  
The Difference ◽  
Artificial Neural Network Ann

In this paper, an artificial neural network (ANN) is used for isolating faults and degradation phenomena occurring in photovoltaic (PV) panels. In the literature, it is well known that the values of the single diode model (SDM) associated to the PV source are strictly related to degradation phenomena and their variation is an indicator of panel degradation. On the other hand, the values of parameters that allow to identify the degraded conditions are not known a priori because they can be different from panel to panel and are strongly dependent on environmental conditions, PV technology and the manufacturing process. For these reasons, to correctly detect the presence of degradation, the effect of environmental conditions and fabrication processes must be properly filtered out. The approach proposed in this paper exploits the intrinsic capability of ANN to map in its architecture two effects: (1) the non-linear relations existing among the SDM parameters and the environmental conditions, and (2) the effect of the degradation phenomena on the I-V curves and, consequently, on the SDM parameters. The ANN architecture is composed of two stages that are trained separately: one for predicting the SDM parameters under the hypothesis of healthy operation and the other one for degraded condition. The variation of each parameter, calculated as the difference of the output of the two ANN stages, will give a direct identification of the type of degradation that is occurring on the PV panel. The method was initially tested by using the experimental I-V curves provided by the NREL database, where the degradation was introduced artificially, later tested by using some degraded experimental I-V curves.

Evaluation of the Use of Artificial Neural Networks in Solar Energy

2021 ◽  
pp. 182-210
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Set Theory ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Pv System ◽  
Solar Insolation ◽  
Pv Panel ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial neural network (ANN) is initially used to forecast the solar insolation level and followed by the particle swarm optimisation (PSO) to optimise the power generation of the PV system based on the solar insolation level, cell temperature, efficiency of PV panel, and output voltage requirements. Genetic algorithm is a general-purpose optimization algorithm that is distinguished from conventional optimization techniques by the use of concepts of population genetics to guide the optimization search. Tabu search algorithm is a conceptually simple and an elegant iterative technique for finding good solutions to optimization problems. Simulated annealing algorithms appeared as a promising heuristic algorithm for handling the combinatorial optimization problems. Fuzzy logic algorithms set theory can be considered as a generation of the classical set theory. The artificial neural network (ANN)-based solar insolation forecast has shown satisfactory results with minimal error, and the generated PV power can be optimised significantly with the aids of the PSO algorithm.

scholarly journals An Artificial Neural Network Based Solution Scheme for Periodic Computational Homogenization of Electrostatic Problems

2019 ◽  
Vol 24 (2) ◽  
pp. 40 ◽  
Author(s):  
Felix Selim Göküzüm ◽  
Lu Trong Khiem Nguyen ◽  
Marc-André Keip
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Trial Function ◽  
A Priori ◽  
Three Dimensional ◽  
Internal Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Core Idea ◽  
Electrostatic Problems

The present work addresses a solution algorithm for homogenization problems based on an artificial neural network (ANN) discretization. The core idea is the construction of trial functions through ANNs that fulfill a priori the periodic boundary conditions of the microscopic problem. A global potential serves as an objective function, which by construction of the trial function can be optimized without constraints. The aim of the new approach is to reduce the number of unknowns as ANNs are able to fit complicated functions with a relatively small number of internal parameters. We investigate the viability of the scheme on the basis of one-, two- and three-dimensional microstructure problems. Further, global and piecewise-defined approaches for constructing the trial function are discussed and compared to finite element (FE) and fast Fourier transform (FFT) based simulations.

scholarly journals Artificial Neural Network for the Prediction of Tyrosine-Based Sorting Signal Recognition by Adaptor Complexes

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Debarati Mukherjee ◽  
Claudia B. Hanna ◽  
R. Claudio Aguilar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
A Priori ◽  
Sorting Signals ◽  
Cytosolic Domains ◽  
Intracellular Compartments ◽  
Distinct Sequence ◽  
Signal Specificity ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Sorting of transmembrane proteins to various intracellular compartments depends on specific signals present within their cytosolic domains. Among these sorting signals, the tyrosine-based motif (YXXØ) is one of the best characterized and is recognized byμ-subunits of the four clathrin-associated adaptor complexes (AP-1 to AP-4). Despite their overlap in specificity, eachμ-subunit has a distinct sequence preference dependent on the nature of the X-residues. Moreover, combinations of these residues exert cooperative or inhibitory effects towards interaction with the various APs. This complexity makes it impossible to predicta priori, the specificity of a given tyrosine-signal for a particularμ-subunit. Here, we describe the results obtained with a computational approach based on the Artificial Neural Network (ANN) paradigm that addresses the issue of tyrosine-signal specificity, enabling the prediction of YXXØ-μinteractions with accuracies over 90%. Therefore, this approach constitutes a powerful tool to help predict mechanisms of intracellular protein sorting.

scholarly journals Usefulness of an artificial neural network for a beginner to achieve similar interpretations to an expert when examining myocardial perfusion images

2020 ◽  
Author(s):  
Akiyo Chiba ◽  
Takashi Kudo ◽  
Reiko Ideguchi ◽  
Altay Myssaev ◽  
Seiji Koga ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Blood Flow ◽  
Myocardial Perfusion Imaging ◽  
Myocardial Perfusion ◽  
Stress Myocardial Perfusion Imaging ◽  
Stress Myocardial Perfusion ◽  
Artificial Neural ◽  
The Difference ◽  
Artificial Neural Network Ann

Abstract Background: This study examined whether using an artificial neural network (ANN) helps beginners in diagnostic cardiac imaging to achieve similar results to experts when interpreting stress myocardial perfusion imaging (MPI).Methods: 138 patients underwent stress MPI with Tc-labeled agents. An expert and a beginner interpreted stress/rest MPI with or without the ANN and the results were compared. The myocardium was divided into 5 regions (the apex; septum; anterior; lateral, and inferior regions), and the degree of myocardial blood flow was evaluated from 0 to 4, and SSS, SRS, and SDS were calculated. The ANN effect, defined as the difference in each of these scores between with and without the ANN, was calculated to investigate the influence of ANN on the interpreters' performance. Results: The ANN effect was smaller for the expert than for the beginner. Conclusions: When interpreting MPI, beginners may achieve similar results to experts by using an ANN. Thus, interpreting MPI with ANN may be useful for beginners.

scholarly journals Usefulness of an artificial neural network for a beginner to achieve similar interpretations to an expert when examining myocardial perfusion images

2021 ◽  
Author(s):  
A. Chiba ◽  
T. Kudo ◽  
R. Ideguchi ◽  
M. Altay ◽  
S. Koga ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Myocardial Perfusion ◽  
Stress Myocardial Perfusion Imaging ◽  
Stress Myocardial Perfusion ◽  
Defect Score ◽  
Ischemia Group ◽  
Artificial Neural ◽  
The Difference ◽  
Artificial Neural Network Ann

AbstractThis study examined whether using an artificial neural network (ANN) helps beginners in diagnostic cardiac imaging to achieve similar results to experts when interpreting stress myocardial perfusion imaging (MPI). One hundred and thirty-eight patients underwent stress MPI with Tc-labeled agents. An expert and a beginner interpreted stress/rest MPI with or without the ANN and the results were compared. The myocardium was divided into 5 regions (the apex; septum; anterior; lateral, and inferior regions), and the defect score of myocardial blood flow was evaluated from 0 to 4, and SSS, SRS, and SDS were calculated. The ANN effect, defined as the difference in each of these scores between with and without the ANN, was calculated to investigate the influence of ANN on the interpreters' performance. We classified 2 groups (insignificant perfusion group and significant perfusion group) and compared them. In the same way, classified 2 groups (insignificant ischemia group and significant ischemia group) and compared them. Besides, we classified 2 groups (normal vessels group and multi-vessels group) and compared them. The ANN effect was smaller for the expert than for the beginner. Besides, the ANN effect for insignificant perfusion group, insignificant ischemia group and multi-vessels group were smaller for the expert than for the beginner. On the other hand, the ANN effect for significant perfusion group, significant ischemia group and normal vessels group were no significant. When interpreting MPI, beginners may achieve similar results to experts by using an ANN. Thus, interpreting MPI with ANN may be useful for beginners. Furthermore, when beginners interpret insignificant perfusion group, insignificant ischemia group and multi-vessel group, beginners may achieve similar results to experts by using an ANN.

scholarly journals Automotive Start–Stop Engine Based on Fingerprint Recognition System

2019 ◽  
Vol 130 ◽  
pp. 01022
Author(s):  
Pranoko Rivandi ◽  
Astuti Winda ◽  
Dewanto Satrio ◽  
Mahmud Iwan Solihin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Recognition System ◽  
Security System ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Minutiae Extraction ◽  
Artificial Neural ◽  
The Difference ◽  
Artificial Neural Network Ann

Automated vehicle security system plays an important rule in nowadays advance automotive technology. One of the methods which can be applied for a security system is based on biometric identification system. Fingerprint recognition is one of the biometric systems that can be applied to the security system. In this work, fingerprint recognition system to start the motorcycle engine is developed. The fingerprint of the owner and other authorized persons will be stored into the database, then while the time of starting the engine of the vehicle, the fingerprint will be validated with the database. The minutiae extraction method is applied to find the difference of fingerprint each other after turn the image into grayscale and thinning. After the extraction, the next step is finding the ridge edge and bifurcation. The result of the image will be used as input to the Artificial Neural Network (ANN) to recognize authorized person only. The experiment of fingerprint recognition system shows that automatic start-stop engine using fingerprint recognition system based minutiae extraction and Artificial Neural Network (ANN) has accuracy 100 % and 100 %, respectively.

scholarly journals A Novel User Classification Method for Femtocell Network by Using Affinity Propagation Algorithm and Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Afaz Uddin Ahmed ◽  
Mohammad Tariqul Islam ◽  
Mahamod Ismail ◽  
Salehin Kibria ◽  
Haslina Arshad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Affinity Propagation ◽  
Training Time ◽  
User Classification ◽  
Training Samples ◽  
Femtocell Network ◽  
Artificial Neural ◽  
The Difference ◽  
Artificial Neural Network Ann

An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation.

Estimation of kiwifruit yield by leaf nutrients concentration and artificial neural network

2020 ◽  
Vol 158 (3) ◽  
pp. 185-193
Author(s):  
Ali Mohammadi Torkashvand ◽  
Afsoon Ahmadipour ◽  
Amin Mousavi Khaneghah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Algorithm ◽  
Proposed Model ◽  
Ca Model ◽  
Artificial Neural ◽  
The Difference ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Different Parts

AbstractThere is a fundamental concern regarding the prediction of kiwifruit yield based on the concentration of nutrients in the leaf (2–3 months before fruits harvesting). For this purpose, the current study was designed to employ an artificial neural network (ANN) to evaluate the kiwi yield of Hayward cultivar. In this regard, 31 kiwi orchards (6–7 years old) in different parts of Rudsar, Guilan Province, Iran, with 101 plots (three trees in every plot) were selected. The complete leaves of branches with fruits were harvested, and the concentration of nitrogen, potassium, calcium, and magnesium measured. After fruit harvesting in late November, the fruit yield of each plot was evaluated along with the fresh and dry weights of the fruit. The ANN analyses were carried out using a multi-layer perceptron with the Langburge-Marquardt training algorithm. Using calcium (Ca) as input data (Ca-model) was more accurate than using nitrogen (N-model). The maximum R2 and the lowest root mean square error was obtained when all nutrients and related ratios were considered as input variables. Since the difference between the proposed model and the model fitted by the calcium variable (Ca-model) was only about 6%, the Ca-model is recommended.

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