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

作者： Lara-Benítez, Pedro Carranza-García, Manuel Martínez-Álvarez, Francisco Riquelme, José C. Division of Computer Science University of Sevilla Seville41012 Spain Data Science & Big Data Lab Pablo de Olavide University Seville41013 Spain

ISBN: (纸本)9783030578015

Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, their performance in real-time data streaming scenarios is a research area that has not yet been fully addressed. Nevertheless, there have been recent efforts to adapt complex deep learning models for streaming tasks by reducing their processing rate. the design of the asynchronous dual-pipeline deep learning framework allows to predict over incoming instances and update the model simultaneously using two separate layers. the aim of this work is to assess the performance of different types of deep architectures for data streaming classification using this framework. We evaluate models such as multi-layer perceptrons, recurrent, convolutional and temporal convolutional neural networks over several time-series datasets that are simulated as streams. the obtained results indicate that convolutional architectures achieve a higher performance in terms of accuracy and efficiency. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Active Learning for Road Lane Landmark Inventory with Random Forest in Highly Uncontrolled LiDAR Intensity Based Image 15th

Active Learning for Road Lane Landmark Inventory with Random...

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

作者： Izquierdo, Asier Lopez-Guede, Jose Manuel Airestudio Geoinformation Technologies Scoop Albert Einstein Kalea 44 E6 Oficina 8 Vitoria-Gasteiz01510 Spain Nieves Cano 12 Vitoria-Gasteiz01006 Spain San Sebastián Spain

ISBN: (纸本)9783030578015

Road landmark inventory is becoming an important industry for the maintenance of transport infrastructures among others. Several commercial sensors are available witch include LiDAR sensors allowing to capture up to 1.5 million data point per second. We obtain an intensity based image from the LiDAR point cloud intensity. the landmark detection is posed as a two class classification problem that may be solved by some standard approaches, for example, Random Forest (RF). Besides model parameter selection, a central problem is the construction of the labeled dataset due to human labor cost and the highly uncontrolled conditions of the data capture. We propose an open ended Active Learning approach with a human operator in the loop who can start the Active Learning process when detection quality is degraded by the change in data condition in order to achieve adaptation to them. As an additional contribution, we have assessed the ability of Active Learning to overcome the issues raised by highly class imbalanced dataset, reaching a True Pixel Ratio value of 0.98. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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Categorization of CoAP DoS Attack Based on One-Class Boundary Methods 18th

Categorization of CoAP DoS Attack Based on One-Class Bound...

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

作者： Timiraos, Míriam Michelena, Álvaro Díaz-Longueira, Antonio Jove, Esteban Aveleira-Mata, Jose García-Rodiguez, Isaías Bayón-Gutiérrez, Martín Alaiz-MoretÓn, Héctor Calvo-Rolle, José Luis University of A Coruña CTC CITIC Department of Industrial Engineering Calle Mendizábal s/n A Coruña Ferrol15403 Spain Fundación Instituto Tecnológico de Galicia Department of Water Technologies National Technological Center Cantón Grande 9 Planta 3 C.P. 15003 A Coruña Spain . MIC. Campus de Vegazana s/n León24071 Spain University of León Department of Electrical and Systems Engineering Campus de Vegazana s/n León24071 Spain

ISBN: (纸本)9783031425288

Denial of Service (DoS) attack over Internet of things (IoT) is among the most prevalent cyber threat, their complex behavior makes very expensive the use of Datagram Transport Layer Security (DTLS) for securing purposes. DoS attack exploits specific protocol features, causing disruptions and remaining undetected by legitimate components. this paper introduces a set of one-class boundary methods such as Approximate Convex Hull, K-Nearest Neighborhood and One-Class Support Vector Machine for developing a categorization model in order to prevent IoT DoS attacks over the CoAP (Constrained Application Protocol) environments. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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Laboratory Evaluation and Neural Network Modeling of Treated Macau Marine Clay 1

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6th GeoChina international conference on Civil and Transportation Infrastructures: From Engineering to Smart and Green Life Cycle Solutions, GeoChina 2021

作者： Wang, Yuanhang Yan, Wayne Ieong, Jacqueline Lok, thomas M. H. Department of Civil and Environmental Engineering University of Macau China

ISBN: (数字)9783030798543

ISBN: (纸本)9783030798536

the marine clay is generally regarded as poor materials for urban applications because of its high settlement and instability. In this study, the soft marine clay is mixing with cementitious material such as cement and lime in different ratios, so that the geotechnical characteristics of blends improved. A series of laboratory experiments are conducted to verify the enhanced performance of treated marine clay. the optimum moisture content (OMC) along with maximum dry unit weight obtained from Standard Proctor compaction test and Harvard miniature test. Unconsolidated undrained test (UUT) and unconfined compression test (UCT) are implemented to find out the compressive strength of samples. Blends selected from the dry side of the compaction curve own larger compressive strength than the others, which means an appropriate amount of water is enough for blends mixing. the results of laboratory tests are utilized to establish neural network models to predict engineering properties such as, compressive strength, optimum moisture content, and maximum dry density of treated marine clay. the correlation between the additive contents as well as curing days and compressive strength has been approved according to the test results. the properties such as maximum dry density, water contents, and additive contents are used as inputs to predict the compressive strengths. the predicted consequences of neural networks are well fitted with laboratory test results. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Compressive strength

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Machine Learning Based System for Detecting Battery State-of-Health 18th

Machine Learning Based System for Detecting Battery State-o...

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

作者： Michelena, Álvaro Díaz-Longueira, Antonio Timiraos, Míriam Zayas-Gato, Francisco Quintián, Héctor Fernández, Natalia Prieto Alaiz-Moretón, Héctor Calvo-Rolle, José Luis García-Ordás, María Teresa University of A Coruña CTC CITIC Department of Industrial Engineering Calle Mendizábal s/n Ferrol A Coruña15403 Spain Fundación Instituto Tecnológico de Galicia Department of Water Technologies National Technological Center Cantón Grande 9 Planta 3 A CoruñaC. P. 15003 Spain University of León Department of Electrical and Systems Engineering Campus de Vegazana s/n León24071 Spain

ISBN: (纸本)9783031425288

In different fields, such as electric mobility, the increasing use of small consumer electronics devices, and the development of renewable energy alternatives, energy storage systems have become crucial to technological advancement. Batteries are among the most frequently used techniques for storing electrical energy. However, it is essential to be aware of the operating status of their load cells to achieve proper and secure operation. this study compares the efficacy of various supervised classification techniques for categorizing the operating status of a Lithium Iron Phosphate - LiFePO4 (LFP) load cell using voltage values obtained from the battery. Each method was assessed using a real dataset, and excellent results were achieved, with accuracy values of up to 90%. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Iron compounds

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Unsupervised Data Analysis of Direct Numerical Simulation of a Turbulent Flame via Local Principal Component Analysis and Procustes Analysis 15th

Unsupervised Data Analysis of Direct Numerical Simulation of...

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

作者： D’Alessio, Giuseppe Attili, Antonio Cuoci, Alberto Pitsch, Heinz Parente, Alessandro Aero-Thermo-Mechanics Laboratory Université Libre de Bruxelles Bruxelles Belgium CRECK Modeling Lab Department of Chemistry Materials and Chemical Engineering Politecnico di Milano Piazza Leonardo da Vinci 32 Milano20133 Italy Université Libre de Bruxelles and Vrije Universiteit Brussel Brussels Belgium Institute for Combustion Technology RWTH Aachen University Aachen52056 Germany

ISBN: (纸本)9783030578015

Direct Numerical Simulations (DNS) of reacting flows provide high-fidelity data for combustion model reduction and validation, although their interpretation is not always straightforward because of the massive amount of information and the data high-dimensionality. In this work, a completely unsupervised algorithm for data analysis is investigated on a data-set obtained from a temporally-evolving DNS simulation of a reacting n-heptane jet in air. the proposed algorithm combines the Local Principal Component Analysis (LPCA) clustering algorithm with a variables selection algorithm via dimensionality reduction and Procustes Analysis. Unlike other data-analysis algorithms, it requires null or limited user expertise as all of its steps are unsupervised and solely entrusted to mathematical objective functions, without any hyperparameter tuning step required. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Numerical models

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Comparative Study of Wastewater Treatment Plant Feature Selection for COD Prediction 18th

Comparative Study of Wastewater Treatment Plant Feature Sel...

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

作者： Timiraos, Míriam Díaz-Longueira, Antonio Michelena, Álvaro Zayas-Gato, Francisco Casteleiro-Roca, José-Luis Jove, Esteban Quintián, Héctor Fontenla-Romero, Óscar Calvo-Rolle, José Luis University of A Coruña CTC CITIC Department of Industrial Engineering Calle Mendizábal s/n Campus de Esteiro Ferrol15471 Spain Fundación Instituto Tecnológico de Galicia Department of Water Technologies National Technological Center Cantón Grande 9 Planta 3 A Coruña15003 Spain

ISBN: (纸本)9783031425288

the focus of this study is on the instrumentation used in wastewater treatment stations, with the ultimate goal of creating virtual sensors in the future. In this stage, a specific correlation case is being examined to determine the appropriate and sufficient variables needed to predict chemical oxygen demand (COD) in a wastewater treatment plant. Initially, four methods are used to select the features with all the variables being monitored. this is followed by applying three regression techniques to evaluate the performance of the previously selected variables. All cases yielded a satisfactory prediction of COD, which indicates that virtual sensors can be implemented in the future with a high degree of predictability and effectiveness. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Demand Control Ventilation Strategy by Tracing the Radon Concentration in Smart Buildings 15th

Demand Control Ventilation Strategy by Tracing the Radon Con...

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

作者： Casado-Vara, Roberto García-Retuerta, David Bartolomé, Alvaro Jove, Esteban Calvo-Rolle, Jose Luis Rey, Angel Martin-del Corchado, Juan M. BISITE Research Group University of Salamanca Salamanca37008 Spain Department of Industrial Engineering University of A Coruña Ferrol A Coruña Spain Department of Applied Mathematics University of Salamanca Calle del Parque 2 Salamanca37008 Spain Department of Applied Mathematics Institute of Fundamental Physics and Mathematics University of Salamanca Calle del Parque 2 Salamanca37008 Spain Air Institute Calle Segunda 4 Salamanca37188 Spain

ISBN: (纸本)9783030578015

Ensuring air quality should be a mandatory premise in every building, since if not, its occupants are on high risk. In fact, Radon pollutants are stated to be the second main cause among all lung cancer patients in the United States. Radon is a noble gas which seeps up through the ground and accumulates there, making it hard to be identified. A proper ventilation system needs to be installed on industrial plants so that the Radon exhaled from building materials is properly dispelled, ensuring fresh, quality air. In order to keep a proper air quality level in smart buildings, a control ventilation strategy should be defined so that the exhaled Radon is ensured to be dispelled keeping the indoor air quality high. In the proposed paper, the diffusion-advecntion method has been studied in order to propose a solution on Radon concentration tracing on smart buildings ventilation system. Diffusion-advecntion is a mathematical method that will determine whether Radon will propagate or not, based on the concentration of Radon, the diffusion constant and the advecntion velocity of the indoor air, which can lead to a recommendation for the smart building ventilation system to be activated or not, respectively. In this paper a new ventilation strategy for smart buildings based on the Diffusion-advecntion equation has been proposed to improve air quality. the results of this new ventilation strategy have been tested in a real case study in a smart building in the city of Salamanca. the main outcome of this new strategy is the improvement in response times of the current systems. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Radon

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international Joint conference on 8th international conference on soft computing models in industrial and environmental applications, SOCO 2013, 6th international conference on Computational Intelligence in Security for Information Systems, CISIS 2013 and 4th international conference on European Transnational Education, ICEUTE 2013

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international Joint conference on 8th international conference on soft computing models in industrial and environmental applications, SOCO 2013, 6th international conference on Computational Intelligence in Security for Information Systems, CISIS 2013 and 4th international conference on European Transnational Education, ICEUTE 2013

ISBN: (纸本)9783319018539

the proceedings contain 76 papers. the special focus in this conference is on Evolutionary computation and optimization, Intelligent systems, applications and classification and clustering methods. the topics include: Parsimonious support vector machines modelling for set points in industrial processes based on genetic algorithm optimization;a multi-level filling heuristic for the multi-objective container loading problem;multiple choice strategy based pso algorithm with chaotic decision making – a preliminary study;optimization of solar integration in combined cycle gas turbines (ISCC);empirical study of the sensitivity of CACLA to sub-optimal parameter setting in learning feedback controllers;optimization design in wind farm distribution network;intelligent model to obtain initial and final conduction angle of a diode in a halfwave rectifier with a capacitor filter;the application of metamodels based on soft computing to reproduce the behaviour of bolted lap joints in steel structures;bayesian networks for greenhouse temperature control;enhanced image segmentation using quality threshold clustering for surface defect categorisation in high precision automotive castings;orthogonalmatching pursuit based classifier for premature ventricular contraction detection and text classification techniques in oil industry applications.

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2023年 749 LNNS卷 v-vi页

作者： Rolle, José Luis Calvo de Pisón, Francisco Javier Martínez Bringas, Pablo García García, Hilde Pérez Álvarez, Francisco Martínez Lora, Alicia Troncoso Herrero, Álvaro Quintián, Héctor Corchado, Emilio Department of Industrial Engineering University of A Coruña A Coruña Spain Department of Mechanical Engineering University of La Rioja Logroño Spain Faculty of Engineering University of Deusto Bilbao Spain School of Industrial Computer University of Leon León Spain Data Science and Big Data Lab Pablo de Olavide University Seville Spain Applied Computational Intelligence University of Burgos Burgos Spain Faculty of Science University of Salamanca Salamanca Spain

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