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共 177 条记录，以下是71-80 订阅

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Mobile Architecture for Forest Fire Simulation Using PhyFire-HDWind Model 14th

Mobile Architecture for Forest Fire Simulation Using PhyFire...

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14th international conference on soft computing models in industrial and environmental applications (SOCO)

作者： Hernandez, Alejandro Alvarez, David Isabel Asensio, M. Rodriguez, Sara Univ Salamanca BISITE Digital Innovat Hub Salamanca Spain Univ Salamanca SINUMCC Numer Simulat & Sci Computat Salamanca Spain

ISBN: (纸本)9783030200558;9783030200541

this article presents the design and implementation of a new visualization system for mobile platforms for the PhyFire-HDWind fire simulation model, called AppPhyFire. It proposes a mobile computing infrastructure, based on ArcGIS Server and REST architecture, which improves the user experience in actions associated with the fire simulation process. the PhyFire-HDWind model, of which the system presented here forms part, is a forest fire propagation simulation tool developed by the SINUMCC research group of the University of Salamanca, based on two own simplified physical models, the PhyFire physical fire propagation model, and the HDWind high definition wind field model, resolved using efficient numerical and computational tools and parallel computing, allowing simulation times shorter than the real time fire propagation, integrated into a Geographical Information System, and accessible through a server by the AppPhyFire. the system presented in this article allows a quick visualization of simulations results in mobile devices. this work presents the detailed operation of the system and its phases of operation.

关键词： Simulation Mobile architectures Prediction models PhyFire HDWind

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Comparative Study of Regression models Applied to the Prediction of Energy Generated by a Micro Wind Turbine 18th

Comparative Study of Regression Models Applied to the Pre...

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Proceedings of the 18th international conference on soft computing models in industrial and environmental applications, SOCO 2023

作者： Díaz-Longueira, Antonio Timiraos, Míriam Michelena, Álvaro Zayas-Gato, Francisco Casteleiro-Roca, José-Luis Jove, Esteban Quintián, Héctor Simić, Dragan Calvo-Rolle, José Luis University of A Coruña CTC CITIC Department of Industrial Engineering Calle Mendizabal s/n Ferrol A Coruña15471 Spain University of Novi Sad Faculty of Technical Sciences Trg Dositeja Obradovica 6 Novi Sad106314 Serbia

ISBN: (纸本)9783031425288

To create an efficient electricity system that includes renewable energy generation, able to respond correctly to the variability of power generation, it is essential to establish future production. For this reason, in this work, the performance of different regression models when predicting the energy produced by a small wind turbine based on meteorological variables is compared, seeking the best predictions. Four methods are evaluated: polynomial, bayesian, support vector machine, and artificial neural network. Several metrics are used to compare the models, such as Mean Absolute Error, Root Mean Squared Error, Mean Squared Error, Median Absolute Error and Coefficient of Determination, along with hypothesis testing. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Small-Wind Turbine Power Generation Prediction from Atmospheric Variables Based on Intelligent Techniques 15th

Small-Wind Turbine Power Generation Prediction from Atmosphe...

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15th international conference on soft computing models in industrial and environmental applications, SOCO 2020

作者： Baruque, Bruno Jove, Esteban Porras, Santiago Calvo-Rolle, José Luis Departmento de Ingeniería Informática University of Burgos Avd. de Cantabria s/n Burgos09006 Spain Departamento de Ingeniería Industrial University of A Coruña Avda. 19 de febrero s/n FerrolA Coruña15495 Spain Departamento de Economía Aplicada University of Burgos Plaza Infanta Doña Elena s/n Burgos09001 Spain

ISBN: (纸本)9783030578015

the present research work deals the model creation obtaining for power generation prediction of a small-wind turbine, based on the atmospheric variables of its location. For testing purposes, a real dataset has been obtained of a bio-climate house located in Sotavento Experimental Wind Farm in the north of Spain. A deep study of the system and atmospheric variables has been performed. then, some different regression techniques have been tested for accomplishing prediction, obtaining excellent results. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Wind power

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Inconsistency Detection on Data Communication Standards Using Information Extraction Techniques: the ABP Case 14th

Inconsistency Detection on Data Communication Standards Usin...

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14th international conference on soft computing models in industrial and environmental applications (SOCO)

作者： Leon, Sonia Antonio Rodriguez-Mondejar, Jose Puente, Cristina Univ Las Palmas Gran Canaria Las Palmas Gran Canaria Spain Comillas Pontifical Univ Madrid Spain

ISBN: (纸本)9783030200558;9783030200541

the present research aims mainly, at establishing an error tolerant procedure that extracts information from Natural Language (NL) Communication Standard Documents along with storing error knowledge. the error knowledge will contain information about the detected errors and inconsistencies as well as the actions taken to solve them. It will act as a key tool for solving the detected errors at various levels of the procedure. As a particular scope, the searching of errors and inconsistencies will be based on comparing results from two NLP tools, parsing and chunking. Information Extraction (IE) technics, aided by some specific-developed heuristic algorithms, are used. the approach has been applied to two different-written texts describing the Alternating Bit Protocol (ABP). A Semantic Net is automatically extracted. the error knowledge provides information to the user about what fragments of the text contained inconsistent structures or words and how they were or not solved. the implemented algorithm solved inconsistencies related to words tagged differently by the NLP tools and showed other errors due to the use of complex syntactic structures. Specific metrics were extracted that permitted identify some features of the texts.

关键词： Natural language processing Information Extraction Heuristic algorithm industrial communication standard Syntactical patterns Setting chunking Semantic Network NLP tools Error tolerant process

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Hybrid Genetic Algorithms and Tour Construction and Improvement Algorithms Used for Optimizing the Traveling Salesman Problem 15th

Hybrid Genetic Algorithms and Tour Construction and Improvem...

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15th international conference on soft computing models in industrial and environmental applications, SOCO 2020

作者： Ilin, Vladimir Simić, Dragan Simić, Svetislav D. Simić, Svetlana Faculty of Technical Sciences University of Novi Sad Trg Dositeja Obradovića 6 Novi Sad21000 Serbia Faculty of Medicine University of Novi Sad Hajduk Veljkova 1–9 Novi Sad21000 Serbia

ISBN: (纸本)9783030578015

the traveling salesman problem (TSP) aims at finding the shortest tour that passes through each vertex in a given graph exactly once. To address TSP, many exact and approximate algorithms have been proposed. In this paper, we propose three new algorithms for TSP based on a genetic algorithm (GA) and an order crossover operator. In the first algorithm, a generic version of a GA with random population is introduced. In the second algorithm, after the random population is introduced, the selected parents are improved with a 2-OPT algorithm and processed further with a GA. Finally, in the third algorithm, the initial solutions are obtained with a nearest neighbor algorithm (NNA) and a nearest insertion algorithm (NIA);afterwards they are improved with a 2-OPT and processed further with a GA. Our approach differs from previous papers for using a GA for TSP in two ways. First, every successive generation of individuals is generated based primarily on 4 best parents from the previous generation regardless the number of individuals in each population. Second, we have proposed the new hybridization between GA, NNA, NIA and 2-OPT. the overall results demonstrate that the proposed GAs offer promising results, particularly for large-sized instances. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Deep Learning and Metaheuristic for Multivariate Time-Series Forecasting 18th

Deep Learning and Metaheuristic for Multivariate Time-Seri...

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Proceedings of the 18th international conference on soft computing models in industrial and environmental applications, SOCO 2023

作者： Zito, Francesco Cutello, Vincenzo Pavone, Mario Department of Mathematics and Computer Science University of Catania v.le Andrea Doria 6 Catania95125 Italy

ISBN: (纸本)9783031425288

Time series forecasting is a widely used statistical technique that use past data to predict future values of variables. Its applications span across various fields, including finance, economics, and marketing. Multivariate time series forecasting, which involves two or more variables, is more complex than univariate time series forecasting and to address this complexity, neural networks are commonly used. However, the selection of an appropriate forecasting method is contingent upon the specific characteristics of the data. this paper proposes a new methodology that addresses such an issue and applies it to climate forecasting. the use of time series forecasting in climate forecasting has the potential to enhance our understanding of climate change and its impacts on various aspects of human life. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Climate change

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Deep Learning in Modeling Energy Cost of Buildings in the Public Sector 14th

Deep Learning in Modeling Energy Cost of Buildings in the Pu...

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14th international conference on soft computing models in industrial and environmental applications (SOCO)

作者： Zekie-Susac, Marijana Knezevic, Marinela Scitovski, Rudolf Univ Josip Juraj Strossmayer Osijek Fac Econ Trg Ljudevita Gaja 7 Osijek 31000 Croatia Univ Josip Juraj Strossmayer Osijek Dept Math Trg Ljudevita Gaja 6 Osijek 31000 Croatia

ISBN: (纸本)9783030200558;9783030200541

the cost of energy consumed in educational, health, public administration, military, and other types of public buildings constitutes a substantial proportion of the total expenditure of the public sector. Due to a large number of attributes that influence the energy cost of a building, most of the models developed in the literature use only a subset of predictors, often neglect occupational data, and do not exploit enough the potential of deep learning methods. In this paper a real data from Croatian public sector is used including constructional, energetic, geographical, occupational and other attributes. Algorithms for data preprocessing and for deep learning modelling procedure are suggested. the number of hidden units in the deep neural network is optimized by a cross-validation procedure, while the sigmoid activation function was tested with Adam optimization algorithm. the feature selection was conducted using the recursive feature elimination method with a regression random forest kernel. the aims were to identify the subset of relevant predictors of energy cost in public buildings that could assist decision makers in determining the priority of reconstruction measures as well as to test the potential of deep learning in predicting the yearly energy cost. the results have shown that the deep learning network with three hidden layers was the most successful in predicting energy cost using the wrapper-based method of feature extraction. the selection of features confirms the importance of occupational data, as well as heating, cooling, electricity lightning, and constructional attributes for estimating the total energy cost. those predictors can be used in decision making on allocating resources in public buildings reconstructions. the model implementation could improve public sector energy efficiency, save costs and contribute to the concepts of smart buildings and smart cities.

关键词： Energy cost Deep learning Neural networks Public sector

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A Hybrid Approach for Short-Term NO2 Forecasting: Case Study of Bay of Algeciras (Spain) 14th

A Hybrid Approach for Short-Term NO2 Forecasting: Case Study...

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14th international conference on soft computing models in industrial and environmental applications (SOCO)

作者： Van Roode, Steffanie Jesus Ruiz-Aguilar, Juan Gonzalez-Enrique, Javier Turias, Ignacio J. Univ Cadiz Polytech Sch Engn Intelligent Modelling Syst Res Grp Algeciras 11202 Spain Univ Cadiz Polytech Sch Engn Dept Comp Sci Engn Algeciras 11202 Spain Univ Cadiz Polytech Sch Engn Dept Civil & Ind Engn Algeciras 11202 Spain

ISBN: (纸本)9783030200558;9783030200541

A hybrid model is proposed in this research in order to forecast concentration values of NO2 with one-hour prediction horizon in the air quality monitoring network of the Bay of Algeciras area (Spain). Air pollution is an important environmental problem these days and it requires control. However, it is not an easy task. the main problem is that air pollution data series are nonlineal and non-stationary. thus, techniques based on regression and simple models are not able to entirely capture the phenomenon behaviour. A LASSO-ANN hybrid model is proposed. the first step has been to predict the linear part of the time-series performing a least absolute shrinkage and selection operator (LASSO) model. Later, an artificial neural network (ANN) model has been performed to predict the residual sequence, the unexplained part of the LASSO model. the chaotic residual behaviour has been smoothed using an autoregressive moving window and applying the window median. the last step has been to aggregate the predicted NO2 value and its predicted residual. the model has been validated and tested using cross-validation based on R correlation coefficient, MSE, MAE and d index of agreement, and also Friedman test and LSD test. In addition, the proposed approach has been compared to a simple ANN model. the results reveal that hybrid model presents a better performance than a multiple linear regression and also a simple ANN model. the main purpose is to develop a forecasting model capable of capturing the non-linear information of the variable and increase the accuracy of the outputs.

关键词： Air pollution forecast Artificial neural networks Hybrid models LASSO

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Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering 15th

Discovering Spatio-Temporal Patterns in Precision Agricultur...

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15th international conference on soft computing models in industrial and environmental applications, SOCO 2020

作者： Melgar-García, Laura Godinho, Maria Teresa Espada, Rita Gutiérrez-Avilés, David Brito, Isabel Sofia Martínez-Álvarez, Francisco Troncoso, Alicia Rubio-Escudero, Cristina Data Science & Big Data Lab Pablo de Olavide University Seville41013 Spain Department of Mathematical and Physical Sciences Polytechnic Institute of Beja Beja Portugal Center for Mathematics Fundamental Applications and Operations Research University of Lisboa Lisbon Portugal Associação dos Agricultores do Baixo Alentejo Beja Portugal Department of Engineering Polytechnic Institute of Beja Beja Portugal Instituto de Desenvolvimiento de Novas Tecnologias - Centre of Technology and Systems Lisbon Portugal Department of Computer Languages and Systems University of Seville Seville Spain

ISBN: (纸本)9783030578015

Agriculture has undergone some very important changes over the last few decades. the emergence and evolution of precision agriculture has allowed to move from the uniform site management to the site-specific management, with both economic and environmental advantages. However, to be implemented effectively, site-specific management requires within-field spatial variability to be well-known and characterized. In this paper, an algorithm that delineates within-field management zones in a maize plantation is introduced. the algorithm, based on triclustering, mines clusters from temporal remote sensing data. Data from maize crops in Alentejo, Portugal, have been used to assess the suitability of applying triclustering to discover patterns over time, that may eventually help farmers to improve their harvests. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Precision agriculture

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Forecasting Greenhouse Temperature Using Machine Learning models: Optimizing Crop Production in Andalucia 18th

Forecasting Greenhouse Temperature Using Machine Learning Mo...

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Proceedings of the 18th international conference on soft computing models in industrial and environmental applications, SOCO 2023

作者： Vega-Márquez, Belén Pardo-Martínez, Juan Villegas-Oliva, María del Mar Riquelme, José C. Department of Computer Languages and Systems University of Seville Seville Spain Greenhouse Technical Department Novagric Murcia Spain Greenhouse Technology Area Tecnova Foundation Almería Spain

ISBN: (纸本)9783031425288

Successful crop production in greenhouses is determined mainly by not only internal and external climatic variables but also by the characteristics of the greenhouse itself. One of the most important variables is the internal temperature, as it greatly determines whether or not the crop will succeed. the objective of this work is to use machine learning models capable of giving a short-term forecast of the indoor temperature of greenhouses in the future. this model will be able to guide growers in determining the best location for new greenhouses, as they will be able to see what the future indoor temperature will be based on the other predictors. the main challenge of this work has been that we have not used the past of the variable to predict, but only the past of the predictor variables. the results obtained show that the model is capable of predicting indoor temperature with significant precision, suggesting that the proposed methodology may be useful to optimize the selection process of greenhouse locations. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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