Please use this identifier to cite or link to this item:
https://accedacris.ulpgc.es/jspui/handle/10553/152181
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Quevedo Reina, Román | en_US |
| dc.contributor.author | Álamo Meneses, Guillermo Manuel | en_US |
| dc.contributor.author | Aznárez González, Juan José | en_US |
| dc.date.accessioned | 2025-11-20T20:31:59Z | - |
| dc.date.available | 2025-11-20T20:31:59Z | - |
| dc.date.issued | 2025 | en_US |
| dc.identifier | https://doi.org/10.5281/zenodo.15166284 | - |
| dc.identifier | oai:zenodo.org:15166284 | - |
| dc.identifier.uri | https://accedacris.ulpgc.es/jspui/handle/10553/152181 | - |
| dc.description | <div>This repository contains the model developed in the scientific article "Feasibility analysis of jacket support structures for offshore wind turbines employing a regression-based artificial neural network model" by Román Quevedo-Reina, Guillermo M. Álamo, Juan J. Aznárez.</div> <div>(https://doi.org/10.1016/j.compstruc.2025.108004)</div> <p>This is an enssemble model of 20 artificial neural networks with 74 neurons in the input layer, 4 hidden layers with 200 neurons per hidden layer, and 26 neuron in the output layer.</p> | - |
| dc.description | <div>The model is stored in the file "model.mat" and can be used by the included "class_model_regression" class.</div> <div> </div> <div> </div> <div><strong>Model Contents</strong></div> <div> </div> <div>The "model.mat" file contains a Matlab variable named "model", which has the following properties:</div> <ul> <li>dlnet: A collection of 20 artificial neural networks (ANNs) generated in the study.</li> <li>name_input: Names of the input variables for the model (described in the scientific article).</li> <li>name_output: Names of the partial checks predicted by the model (indicated in the scientific article and detailed in http://dx.doi.org/10.1016/j.oceaneng.2024.117802).</li> <li>mean_input: Mean values of each input variable, obtained from the training dataset. Used for normalization.</li> <li>std_input: Standard deviation of each input variable, obtained from the training dataset. Used for normalization.</li> <li>mean_output: Mean values of each output variable, obtained from the training dataset. Used for normalization.</li> <li>std_output: Standard deviation of each output variable, obtained from the training dataset. Used for normalization.</li> </ul> <div> </div> <div><strong>How to Use the Model</strong></div> <div> </div> <div><em>Prerequisites</em></div> <div>To use the model, you must have the Deep Learning Toolbox installed in Matlab.</div> <div> </div> <div><em>Evaluation Method</em></div> <div>The model can be evaluated by two different ways:</div> <div> </div> <div>1. Using the "eval_UF" method included in the "class_model_regression" for obtaining the predicted Utilization Factor. This method provides the following options:</div> <div> </div> <ul> <li> output: Specifies the output of the ensemble model. The options are: <ul> <li>"mean": The mean of the predictions of individual neural networks. (Default)</li> <li>"std": The standard deviation of the predictions of individual neural networks.</li> <li>"value": The specific value of the predictions of each individual networks.</li> <li>"both": The mean and the standard deviation of the predictions of individual neural networks, using the third dimension of the matrix.</li> </ul> </li> <li>scale: Specifies if the output's scale is transformed. The options are: <ul> <li>"log": The output of the model is the natural logarithm of the Utilization Factor. (Default)</li> <li>"natural": The output of the model is the Utilization Factor.</li> </ul> </li> <li>IDnet: Specifies which ANNs to use for the evaluation. The options are: <ul> <li>"all": Uses all ANNs embedded in the model. (Default)</li> <li>any number: Uses only the ANN at the specified position.</li> <li>any vector: Uses only the ANNs at the positions specified in the vector.</li> </ul> </li> </ul> <div> </div> <div>2. Using the "eval_prob" method included in the "class_model_regression" for obtaining the probability of feasibility. This method provides the following options:</div> <div> </div> <ul> <li>output: Specifies how the global feasibility is computed from the partial checks. <ul> <li>"global": Global feasibility is computed as the conjunction of independent events.</li> <li>"global_min": Global feasibility is equal to the minimum of the partial checks. (Default)</li> <li>"global_geomean": Global feasibility is the geometric mean of the partial checks.</li> <li>"partial": Returns the partial checks predictions.</li> </ul> </li> <li>ensembledvar: Specifies how the individual ANN predictions are agregated on partial checks predictions or on global feasibility prediction: <ul> <li>"partial": Ensembled is made on partial checks predictions. (Default)</li> <li>"global": Ensembled is made on global feasibility predictions.</li> </ul> </li> <li> IDnet: Specifies which ANNs to use for the evaluation. The options are: <ul> <li>"all": Uses all ANNs embedded in the model. (Default)</li> <li>any number: Uses only the ANN at the specified position.</li> <li>any vector: Uses only the ANNs at the positions specified in the vector.</li> </ul> </li> </ul> <div><em>Input Data Format</em></div> <div>Prepare your input as a matrix where:</div> <ul> <li>Each row represents a sample.</li> <li>The matrix must have 74 columns, corresponding to the 74 input variables, arranged in the same order as specified in name_input.</li> </ul> <div> </div> <div><em>Example</em></div> <div>Below is an example of how to load the model and use the eval method:</div> <div> </div> <div> </div> <div>% Load the model</div> <div>load('model.mat');</div> <div> </div> <div>% Prepare your input data (using case example)</div> <div>load("case_example.mat"); % Load case example</div> <div> inputData=example{:,:}; % Extract matrix of data</div> <div> </div> <div>% Evaluate the model (default)</div> <div>output_prob = model.eval_prob(inputData);</div> <div>output_UF = model.eval_UF(inputData);</div> <div> </div> <div>% Evaluate the model (specific options)</div> <div>output = model.eval_UF(inputData,'output','both','IDnet',1:10);</div> <p> </p> | - |
| dc.language | spa | en_US |
| dc.publisher | Zenodo | - |
| dc.rights | info:eu-repo/semantics/openAccess | - |
| dc.rights | Creative Commons Attribution 4.0 International | - |
| dc.rights | https://creativecommons.org/licenses/by/4.0/legalcode | - |
| dc.title | ANN Model presented in "Feasibility analysis of jacket support structures for Offshore Wind Turbines employing a regression-based Artificial Neural Network model" | en_US |
| dc.type | info:eu-repo/semantics/other | en_US |
| dc.identifier.supplement | https://doi.org/10.5281/zenodo.15166284 | - |
| dc.identifier.supplement | oai:zenodo.org:15166284 | - |
| item.grantfulltext | none | - |
| item.fulltext | Sin texto completo | - |
| crisitem.author.dept | GIR SIANI: Mecánica de los Medios Continuos y Estructuras | - |
| crisitem.author.dept | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
| crisitem.author.dept | Departamento de Ingeniería Civil | - |
| crisitem.author.dept | GIR SIANI: Mecánica de los Medios Continuos y Estructuras | - |
| crisitem.author.dept | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
| crisitem.author.dept | Departamento de Ingeniería Civil | - |
| crisitem.author.dept | GIR SIANI: Mecánica de los Medios Continuos y Estructuras | - |
| crisitem.author.dept | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
| crisitem.author.dept | Departamento de Ingeniería Civil | - |
| crisitem.author.orcid | 0000-0003-4228-0031 | - |
| crisitem.author.orcid | 0000-0001-5975-7145 | - |
| crisitem.author.orcid | 0000-0003-4576-7304 | - |
| crisitem.author.parentorg | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
| crisitem.author.parentorg | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
| crisitem.author.parentorg | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
| crisitem.author.fullName | Quevedo Reina, Román | - |
| crisitem.author.fullName | Álamo Meneses, Guillermo Manuel | - |
| crisitem.author.fullName | Aznárez González, Juan José | - |
| Appears in Collections: | Datasets ULPGC | |
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