Search results for: Sancho Salcedo-Sanz

Items from 1 to 20 out of 69 results

article

Human-induced force reconstruction using a non-linear electrodynamic shaker applying an iterative neural network algorithm

César Peláez-Rodríguez, Álvaro Magdaleno, Sancho Salcedo-Sanz, Antolín Lorenzana

Bulletin of the Polish Academy of Sciences. Technical Sciences > 2023 > Vol. 71, nr 3 > art. no. e144615

An iterative neural network framework is proposed in this paper for the human-induced Ground Reaction Forces (GRF) replication with an inertial electrodynamic mass actuator (APS 400). This is a first approach to the systematization of dynamic load tests on structures in a purely objective, repeatable and pedestrian-independent basis. Therefore, an inversion-free offline algorithm based on Machine...

article

A Coral Reefs Optimization algorithm with substrate layer for robust Wi-Fi channel assignment

Carlos Camacho-Gómez, Ivan Marsa-Maestre, Jose Manuel Gimenez-Guzman, Sancho Salcedo-Sanz

Soft Computing > 2019 > 23 > 23 > 12621- 12640

In this paper, we tackle a problem of frequency assignment in Wi-Fi networks with a novel evolutionary-type algorithm. In this version of the problem, we consider the interferences originated by the access points, and also by the clients and all the 11 available channels in the 2.4 GHz Wi-Fi frequency band. The proposed evolutionary-type algorithm is the Coral Reefs Optimization approach with substrate...

article

Dynamical memetization in coral reef optimization algorithms for optimal time series approximation

Antonio M. Durán-Rosal, Pedro A. Gutiérrez, Sancho Salcedo-Sanz, César Hervás-Martínez

Progress in Artificial Intelligence > 2019 > 8 > 2 > 253-262

The huge amount of data chronologically collected in short periods of time by different devices and technologies is an important challenge in the analysis of times series. This problem has produced the development of new automatic techniques to reduce the number of points in the resulting time series, in order to facilitate their processing and analysis. In this paper, we propose a new modification...

article

Quantifying flood events in Bangladesh with a daily-step flood monitoring index based on the concept of daily effective precipitation

Ravinesh C. Deo, Jan F. Adamowski, Khaleda Begum, Sancho Salcedo-Sanz, more

Theoretical and Applied Climatology > 2019 > 137 > 1-2 > 1201-1215

Bangladesh, located in the Bay of Bengal, is a developing nation that is prone to devastating flood events that cause loss of lives and several other forms of humanatarian disasters. Identifying, developing and validating new scientific techniques that can be used for flood-risk warning and regular monitoring, including flood risk mitigation and adaptation, can help reduce the catastrophic effect...

article

A Multi-Objective Genetic Algorithm for overlapping community detection based on edge encoding

Gema Bello-Orgaz, Sancho Salcedo-Sanz, David Camacho

Information Sciences > 2018 > 462 > C > 290-314

The Community Detection Problem (CDP) in Social Networks has been widely studied from different areas such as Data Mining, Graph Theory Physics, or Social Network Analysis, among others. This problem tries to divide a graph into different groups of nodes (communities), according to the graph topology. A partition is a division of the graph where each node belongs to only one community. However, a...

article

A statistically-driven Coral Reef Optimization algorithm for optimal size reduction of time series

Antonio M. Durán-Rosal, Pedro A. Gutiérrez, Sancho Salcedo-Sanz, César Hervás-Martínez

Applied Soft Computing > 2018 > 63 > C > 139-153

This paper is focused on reducing the number of elements in time series with minimum information loss, with specific applications on time series segmentation. A modification of the coral reefs optimization metaheuristic (CRO) is proposed for this purpose, which is called statistical CRO (SCRO), where the main parameters of the algorithm are adjusted based on the mean and standard deviation associated...

article

Spatio-temporal analysis of wind resource in the Iberian Peninsula with data-coupled clustering

Mihaela I. Chidean, Antonio J. Caamaño, Julio Ramiro-Bargueño, Carlos Casanova-Mateo, more

Renewable and Sustainable Energy Reviews > 2018 > 81 > P2 > 2684-2694

In this paper a spatio-temporal analysis of wind power resource in the Iberian Peninsula is presented. The study uses the Second-Order Data-Coupled Clustering (SODCC) algorithm over reanalysis data in the for the period 1979 – 2014. Several characteristics of the method are detailed, such as the data-coupled clustering approach of SODCC, that ensures the non-singularity of the signal subspace within...

article

Influence of overhead on LTE downlink performance: a comprehensive model

Amir M. Ahmadzadeh, Lucas Cuadra, Miguel A. Arco-Vega, J. Antonio Portilla-Figueras, more

Telecommunication Systems > 2018 > 67 > 3 > 485-517

Overhead resource elements (REs) in Long Term Evolution (LTE) networks are used for some control, signaling and synchronization tasks at both the Physical level and Media Access Control sub-level. Accurately computing all the overhead REs is necessary to achieve an efficient system design, which is difficult because LTE is a complex standard that contains a large number of implementation flexibilities...

chapter

Coral Reef Optimization for intensity-based medical image registration

Enrique Bermejo, Manuel Chica, Sancho Salcedo Sanz, Oscar Cordon

2017 IEEE Congress on Evolutionary Computation (CEC) > 533 - 540

2017 IEEE Congress on Evolutionary Computation (CEC)

Image registration (IR) is an extended and important problem in computer vision. It involves the transformation of different sets of image data having a shared content into a common coordinate system. Specifically, we will deal with the 3D intensity-based medical IR problem where the intensity distribution of the images is considered, one of the most complex and time consuming variants. The limitations...

article

Hybridizing Cartesian Genetic Programming and Harmony Search for adaptive feature construction in supervised learning problems

Andoni Elola, Javier Del Ser, Miren Nekane Bilbao, Cristina Perfecto, more

Applied Soft Computing > 2017 > 52 > C > 760-770

The advent of the so-called Big Data paradigm has motivated a flurry of research aimed at enhancing machine learning models by following very diverse approaches. In this context this work focuses on the automatic construction of features in supervised learning problems, which differs from the conventional selection of features in that new characteristics with enhanced predictive power are inferred...

article

Efficient prediction of total column ozone based on support vector regression algorithms, numerical models and Suomi-satellite data

Leo Carro-Calvo, Carlos Casanova-Mateo, Julia Sanz-Justo, José Luis Casanova-Roque, more

Atmosfera > 2017 > 30 > 1 > 1-10

This paper proposes a novel prediction method for Total Column Ozone (TCO), based on the combination of Support Vector Regression (SVR) algorithms and different predictive variables coming from satellite data (Suomi National Polar-orbiting Partnership satellite), numerical models (Global Forecasting System model, GFS) and direct measurements. Data from satellite consists of temperature and humidity...

article

A feature selection method for author identification in interactive communications based on supervised learning and language typicality

Esther Villar-Rodriguez, Javier Del Ser, Miren Nekane Bilbao, Sancho Salcedo-Sanz

Engineering Applications of Artificial Intelligence > 2016 > 56 > C > 175-184

Authorship attribution, conceived as the identification of the origin of a text between different authors, has been a very active area of research in the scientific community mainly supported by advances in Natural Language Processing (NLP), machine learning and Computational Intelligence. This paradigm has been mostly addressed from a literary perspective, aiming at identifying the stylometric features...

chapter

Estimating the Concentration of Nitrates in Water Samples Using PSO and VNS Approaches

Pablo López-Espí, Sancho Salcedo-Sanz, Á. M. Pérez-Bellido, Emilio G. Ortiz-García, more

Lecture Notes in Computer Science > Applications of Evolutionary Computing > EvoENVIRONMENT Contributions > 132-141

In this paper we present a study of the application of a Particle Swarm Optimization (PSO) and a Variable Neighborhood Search (VNS) algorithms to the estimation of the concentration of nitrates in water. Our study starts from the definition a model for the Ultra-violet spectrophotometry transmittance curves of water samples with nitrate content. This model consists in a mixture of polynomial, Fermi...

article

A novel Grouping Coral Reefs Optimization algorithm for optimal mobile network deployment problems under electromagnetic pollution and capacity control criteria

Sancho Salcedo-Sanz, Pilar García-Díaz, Javier Del Ser, Miren Nekane Bilbao, more

Expert Systems with Applications > 2016 > 55 > C > 388-402

This paper proposes a novel optimization algorithm for grouping problems, the Grouping Coral Reefs Optimization algorithm, and describes its application to a Mobile Network Deployment Problem (MNDP) under four optimization criteria. These criteria include economical cost and coverage, and also electromagnetic pollution control and capacity constraints imposed at the base stations controllers, which...

chapter

A novel adaptive density-based ACO algorithm with minimal encoding redundancy for clustering problems

Esther Villar-Rodriguez, Antonio Gonzalez-Pardo, Javier Del Ser, Miren Nekane Bilbao, more

2016 IEEE Congress on Evolutionary Computation (CEC) > 3139 - 3145

2016 IEEE Congress on Evolutionary Computation (CEC)

In the so-called Big Data paradigm descriptive analytics are widely conceived as techniques and models aimed at discovering knowledge within unlabeled datasets (e.g. patterns, similarities, etc) of utmost help for subsequent predictive and prescriptive methods. One of these techniques is clustering, which hinges on different multi-dimensional measures of similarity between unsupervised data instances...

chapter

Solving the Delay-Constrained Capacitated Minimum Spanning Tree Problem Using a Dandelion-Encoded Evolutionary Algorithm

Ángel M. Pérez-Bellido, Sancho Salcedo-Sanz, Emilio G. Ortiz-García, Antonio Portilla-Figueras, more

Lecture Notes in Computer Science > Simulated Evolution and Learning > Evolutionary Optimisation > 151-160

The Delay-Constrained Capacitated Minimum Spanning Tree (DC-CMST) is a recently proposed problem which arises in the design of the topology of communications networks. The DC-CMST proposes the joint optimization of the network topology in terms of the traffic capacity and its mean time delay. In this paper, an evolutionary algorithm which uses Dandelion-encoding is proposed to solve the problem. The...

chapter

An Agent System for Bandwidth Allocation in Reservation-Based Networks Using Evolutionary Computing and Vickrey Auctions

Ángel M. Pérez-Bellido, Sancho Salcedo-Sanz, José A. Portilla-Figueras, Emilio G. Ortíz-García, more

Lecture Notes in Computer Science > Agent and Multi-Agent Systems: Technologies and Applications > Main Track: Agents for Network Management > 476-485

This paper presents an agent system for bandwidth allocation in reservation-based networks, using Vickrey auctions and an evolutionary algorithm. Our evolutionary approach performs the bandwidth allocation to agents which bid for networks resources by means of willingness-to-pay functions. It also calculates in a parallel way the price to be paid to the network’s owner. We tackle the case of one-link,...

chapter

Nature-Inspired Algorithms for the Optimization of Optical Reference Signals

Sancho Salcedo-Sanz, José Saez-Landete, Manuel Rosa-Zurera

Lecture Notes in Computer Science > Parallel Problem Solving from Nature - PPSN IX > Applications > 282-291

This paper presents two nature-inspired approaches to the design of Zero Reference Codes (ZRC) for optical applications, both in one and two dimensions. Specifically we present a genetic algorithm and a simulated annealing hybridized with a restricted search operator to cope with the problem constraints. Extensive experiments have shown that nature-inspired approaches proposed can improve the results...

chapter

An Evolution of Geometric Structures Algorithm for the Automatic Classification of HRR Radar Targets

Leopoldo Carro-Calvo, Sancho Salcedo-Sanz, Roberto Gil-Pita, Antonio Portilla-Figueras, more

Lecture Notes in Computer Science > Intelligent Data Engineering and Automated Learning - IDEAL 2007 > Neural-evolutionary Fusion Algorithms and Their Applications > 1151-1159

This paper presents a novel approach to solve multiclass classification problems using pure evolutionary techniques. The proposed approach is called Evolution of Geometric Structures algorithm, and consists in the evolution of several geometric structures such as hypercubes, hyperspheres, hyperoctahedrons, etc. to obtain a first division of the samples space, which will be re-evolved in a second step...

chapter

A Novel Grouping Heuristic Algorithm for the Switch Location Problem Based on a Hybrid Dual Harmony Search Technique

Sergio Gil-Lopez, Itziar Landa-Torres, Javier Ser, Sancho Salcedo-Sanz, more

Lecture Notes in Computer Science > Advances in Computational Intelligence > Mathematical and Theoretical Methods in Computational Intelligence > 17-24

This manuscript proposes a novel iterative approach for the so-called Switch Location Problem (SLP) based on the hybridization of a group-encoded Harmony Search combinatorial heuristic (GHS) with local search and repair methods. Our contribution over other avantgarde techniques lies on the dual application of the GHS operators over both the assignment and the grouping parts of the encoded solutions...

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INFONA - science communication portal

Search results for: Sancho Salcedo-Sanz

Human-induced force reconstruction using a non-linear electrodynamic shaker applying an iterative neural network algorithm

A Coral Reefs Optimization algorithm with substrate layer for robust Wi-Fi channel assignment

Dynamical memetization in coral reef optimization algorithms for optimal time series approximation

Quantifying flood events in Bangladesh with a daily-step flood monitoring index based on the concept of daily effective precipitation

A Multi-Objective Genetic Algorithm for overlapping community detection based on edge encoding

A statistically-driven Coral Reef Optimization algorithm for optimal size reduction of time series

Spatio-temporal analysis of wind resource in the Iberian Peninsula with data-coupled clustering

Influence of overhead on LTE downlink performance: a comprehensive model

Coral Reef Optimization for intensity-based medical image registration

Hybridizing Cartesian Genetic Programming and Harmony Search for adaptive feature construction in supervised learning problems

Efficient prediction of total column ozone based on support vector regression algorithms, numerical models and Suomi-satellite data

A feature selection method for author identification in interactive communications based on supervised learning and language typicality

Estimating the Concentration of Nitrates in Water Samples Using PSO and VNS Approaches

A novel Grouping Coral Reefs Optimization algorithm for optimal mobile network deployment problems under electromagnetic pollution and capacity control criteria

A novel adaptive density-based ACO algorithm with minimal encoding redundancy for clustering problems

Solving the Delay-Constrained Capacitated Minimum Spanning Tree Problem Using a Dandelion-Encoded Evolutionary Algorithm

An Agent System for Bandwidth Allocation in Reservation-Based Networks Using Evolutionary Computing and Vickrey Auctions

Nature-Inspired Algorithms for the Optimization of Optical Reference Signals

An Evolution of Geometric Structures Algorithm for the Automatic Classification of HRR Radar Targets

A Novel Grouping Heuristic Algorithm for the Switch Location Problem Based on a Hybrid Dual Harmony Search Technique

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