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Maneuvering Angle Rigid Formations With Global Convergence Guarantees

IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1464-1475页

作者： Liangming Chen Zhiyun Lin Hector Garcia de Marina Zhiyong Sun Mir Feroskhan School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Computer Architecture and Technology and also with CITICUniversity of Granada18014 GranadaSpain Department of Electrical Engineering Eindhoven University of Technology5600 MB Eindhoventhe Netherlands

Angle rigid multi-agent formations can simultaneously undergo translational,rotational,and scaling maneuvering,therefore combining the maneuvering capabilities of both distance and bearing rigid ***,maneuvering angle rigid formations in 2D or 3D with global convergence guarantees is shown to be a challenging problem in the existing literature even when relative position measurements are *** by angle-induced linear equations in 2D triangles and 3D tetrahedra,this paper aims to solve this challenging problem in both 2D and3D under a leader-follower *** the 2D case where the leaders have constant velocities,by using local relative position and velocity measurements,a formation maneuvering law is designed for the followers governed by double-integrator *** the leaders have time-varying velocities,a sliding mode formation maneuvering law is proposed by using the same *** the 3D case,to establish an angle-induced linear equation for each tetrahedron,we assume that all the followers'coordinate frames share a common Z ***,a formation maneuvering law is proposed for the followers to globally maneuver Z-weakly angle rigid formations in *** extension to Lagrangian agent dynamics and the construction of the desired rigid formations by using the minimum number of angle constraints are also *** examples are provided to validate the effectiveness of the proposed algorithms.

关键词： Index Terms—Angle rigid formations formation control formation maneuvering global convergence multi-agent systems

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New Evolutionary Selection Operators for Snake Optimizer 14th

New Evolutionary Selection Operators for Snake Optimizer

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14th International Joint Conference on Computational Intelligence, IJCCI 2022

作者： Khurma, Ruba Abu Alazab, Moutaz Merelo, J.J. Castillo, Pedro A. Department of Computer Science Al-Ahliyya Amman University Amman Jordan Department of Artificial Intelligence Al-Balqa University Al-Salt Jordan Department of Computer Architecture and Computer Technology ETSIIT CITIC University of Granada Granada Spain

ISBN: (纸本)9789897586118

Evolutionary algorithms (EA) adopt a Darwinian theory which is known as "survival of the fittest". Snake Optimizer (SO) is a recently developed swarm algorithm that inherits the selection principle in its structure. This is applied by selecting the fittest solutions and using them in deriving new solutions for the next iterations of the algorithm. However, this makes the algorithm biased towards the highly fitted solutions in the search space, which affects the diversity of the SO algorithm. This paper proposes new selection operators to be integrated with the SO algorithm and replaces the global best operator. Four SO variations are investigated by individually integrating four different selection operators: SO-roulettewheel, SO-tournament, SO-linearrank, and SO-exponentialrank. The performance of the proposed SO variations is evaluated. The experiments show that the selection operators have a great influence on the performance of the SO algorithm. Finally, a parameter analysis of the SO variations is investigated. Copyright © 2022 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Evolutionary algorithms

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Energy-Time Profiling for Machine Learning Methods to EEG Classification 1st

Energy-Time Profiling for Machine Learning Methods to EEG Cl...

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1st International Conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： Gómez-López, Juan Carlos Escobar, Juan José González, Jesús Gil-Montoya, Francisco Ortega, Julio Burmester, Mike Damas, Miguel Department of Computer Architecture and Technology CITIC University of Granada Granada Spain Department of Engineering University of Almería Almería Spain Department of Computer Science Florida State University Tallahassee United States

ISBN: (纸本)9783030881627

The Electroencephalography discipline studies a type of signals called Electroencephalograms (EEGs), which represent the electrical activity of different parts of the brain. EEGs are composed of a massive number of features that could be used to create an intelligent recognition system. Nevertheless, the high number of available features difficult the correct classification of the signals as most of them do not contain relevant information. The use of Feature Selection (FS) techniques allows the reduction of the number of features by extracting a reduced but powerful feature subset. Afterwards, the problem can be addressed by using both the reduced features along with classification methods. In this sense, this work proposes a comparison of five supervised classifiers to solve an Electroencephalography problem in the context of Motor Imagery-based BCI tasks. The predictive models are evaluated in terms of classification rate and performance (execution time and energy consumption), with the idea of determining which alternative offers the best trade-off among all the objectives. © 2021, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Recurrent Neural Networks and Efficiency in High-Dimensional EEG Classification 1st

Recurrent Neural Networks and Efficiency in High-Dimensional...

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1st International Conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： León, Javier Escobar, Juan José González, Jesús Ortega, Julio Arrabal-Campos, Francisco Manuel Ortiz, Andrés Damas, Miguel Department of Computer Architecture and Technology CITIC University of Granada Granada Spain Department of Engineering University of Almería Almería Spain Department of Communications Engineering University of Málaga Málaga Spain

ISBN: (纸本)9783030881627

The performance of neural networks has granted deep learning a place at the forefront of machine learning in the last decade. Although these models are computationally intensive, their advantage is recognized in a wide array of applications. Nonetheless, the large amount of learnable parameters in neural networks can be a disadvantage for small and high-dimensional datasets. In this context, the relationship between solution quality and model complexity gains significance due to overfitting concerns. This is precisely the case of Electroencephalography (EEG), where the data consists in relatively few patterns obtained from multiple electrode readings. In this paper, we take a look at two widely-known types of Recurrent Neural Networks (RNNs), namely Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), which are evaluated on three EEG Motor Imagery (MI) datasets by measuring not only classification accuracy, but also time and energy consumption. Although the results are not entirely competitive with the state-of-the-art in the datasets considered, the interaction that occurs between the size of the networks and their accuracy provides us with valuable insight about network structure design in the context of the curse of dimensionality. © 2021, Springer Nature Switzerland AG.

关键词： Electroencephalography

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Modelling Brain Connectivity Networks by Graph Embedding for Dyslexia Diagnosis 1st

Modelling Brain Connectivity Networks by Graph Embedding for...

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1st International Conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： Formoso, Marco A. Ortiz, Andrés Martínez-Murcia, Francisco J. Gallego-Molina, Nicolás Luque, Juan L. Escobar, Juan J. Montoya, Francisco G. Department of Communications Engineering University of Málaga Malaga Spain Department of Signal Theory Telematic and Communications University of Granada Granada Spain Granada Spain Department of Developmental Psychology University of Málaga Malaga Spain Department of Computer Architecture And Technology CITIC University Of Granada Granada Spain Department of Engineering University of Almeria Almeria Spain

ISBN: (纸本)9783030881627

Several methods have been developed to extract information from electroencephalograms (EEG). One of them is Phase-Amplitude Coupling (PAC) which is a type of Cross-Frequency Coupling (CFC) method, consisting in measure the synchronization of phase and amplitude for the different EEG bands and electrodes. This provides information regarding brain areas that are synchronously activated, and eventually, a marker of functional connectivity between these areas. In this work, intra and inter electrode PAC is computed obtaining the relationship among different electrodes used in EEG. The connectivity information is then treated as a graph in which the different nodes are the electrodes and the edges PAC values between them. These structures are embedded to create a feature vector that can be further used to classify multichannel EEG samples. The proposed method has been applied to classified EEG samples acquired using specific auditory stimuli in a task designed for dyslexia disorder diagnosis in seven years old children EEG’s. The proposed method provides AUC values up to 0.73 and allows selecting the most discriminant electrodes and EEG bands. © 2021, Springer Nature Switzerland AG.

关键词： Electrodes

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Energy-aware load balancing of parallel evolutionary algorithms with heavy fitness functions in heterogeneous CPU-GPU architectures

Energy-aware load balancing of parallel evolutionary algorit...

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作者： Escobar, Juan José Ortega, Julio Díaz, Antonio Francisco González, Jesús Damas, Miguel Department of Computer Architecture and Technology CITIC University of Granada Spain

By means of the availability of mechanisms such as Dynamic Voltage and Frequency Scaling (DVFS) and heterogeneous architectures including processors with different power consumption profiles, it is possible to devise scheduling algorithms that are aware of both runtime and energy consumption in parallel programs. In this paper, we propose and evaluate a multi-objective (more specifically, a bi-objective) approach to distribute the workload among the processing cores in a given heterogeneous parallel CPU-GPU architecture. The aim of this distribution may be either to save energy without increasing the running time or to reach a trade-off among time and energy consumption. The parallel programs considered here are master-worker evolutionary algorithms where the evaluation of the fitness function for the individuals in the population demands the most part of the computing time. As many useful bioinformatics and data mining applications exhibit this kind of parallel profile, the proposed energy-aware approach for workload scheduling could be frequently applied. © 2018 John Wiley & Sons, Ltd.

关键词： Energy utilization

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Wideband Gap-Waveguide Phase Shifter Based on a Glide-Symmetric Ridge

TechRxiv

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TechRxiv 2021年

作者： Palomares-Caballero, Ángel Megías, Carlos Molero, Carlos Alex-Amor, Antonio Padilla, Pablo Granada18071 Spain The Department of Computer Architecture and Technology CITIC ETSIIT University of Granada Granada18071 Spain The Information Technologies Department Universidad CEU San Pablo Madrid28003 Spain

This letter presents a gap-waveguide phase shifter based on ridged unit cells with glide-symmetric configuration. The proposed unit cell design provides a higher stable phase shift compared to a conventional ridged unit cell whose ridge height and waveguide width are tuned to achieve a stable phase shift. Through the insertion of glide-symmetric holes with semicircle base in the ridged waveguide, a stable phase shift in a wide frequency range is achieved. Depending on the radii of the holes, the operating frequency range of the phase shifter can be tuned. A 90o phase shifter in millimeter-wave range is designed and manufactured to validate the analysis. The experimental results reveal a 85o± 5o phase shift from 33 GHz to 43 GHz (26.3% bandwidth). In this bandwidth, the reflection coefficient is below -10 dB with a maximum insertion loss of 0.7 dB. © 2021, CC BY-NC-SA.

关键词： Phase shifters

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Corrigendum to "Electrical coupling regulated by GABAergic nucleo-olivary afferent fibres facilitates cerebellar sensory-motor adaptation" [Neural Netw. 155 (2022) 422-438]

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Neural networks : the official journal of the International Neural Network Society 2023年 165卷 676页

作者： Niceto R Luque Francisco Naveros Ignacio Abadía Eduardo Ros Angelo Arleo Sorbonne Université INSERM CNRS Institut de la Vision 17 rue Moreau F-75012 Paris France Department of Computer Architecture and Technology CITIC-University of Granada 2 Calle Periodista Rafael Gómez Montero ES 18014 Granada Spain. Electronic address: nluque@ugr.es. Department of Computer Architecture and Technology CITIC-University of Granada 2 Calle Periodista Rafael Gómez Montero ES 18014 Granada Spain Department of Neuroscience Baylor College of Medicine Houston TX USA. Department of Computer Architecture and Technology CITIC-University of Granada 2 Calle Periodista Rafael Gómez Montero ES 18014 Granada Spain. Sorbonne Université INSERM CNRS Institut de la Vision 17 rue Moreau F-75012 Paris France.

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The influence of input data standardization methods on the prediction accuracy of genetic programming generated classifiers 10th

The influence of input data standardization methods on the p...

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10th International Joint Conference on Computational Intelligence, IJCCI 2018

作者： Al Shorman, Amaal R. Faris, Hossam Castillo, Pedro A. Merelo, J.J. Al-Madi, Nailah Business Information Technology Department King Abdullah II School for Information Technology University of Jordan Amman Jordan Department of Computer Architecture and Computer Technology ETSIIT and CITIC University of Granada Granada Spain Computer Science Department Princess Sumaya University for Technology Amman Jordan

ISBN: (纸本)9789897583278

Genetic programming (GP) is a powerful classification technique. It is interpretable and it can dynamically build very complex expressions that maximize or minimize some fitness functions. It has a capacity to model very complex problems in the area of Machine Learning, Data Mining and Pattern Recognition. Nevertheless, GP has a high computational complexity time. On the other side, data standardization is one of the most important pre-processing steps in machine learning. The purpose of this step is to unify the scale of all input features to have equal contribution to the model. The objective of this paper is to investigate the influence of input data standardization methods on GP, and how it affects its prediction accuracy. Six different methods of input data standardization were checked in order to determine which one allows to achieve the most accurate result with lowest computational cost. The simulations have been implemented on ten benchmarked datasets with three different scenarios (varying the population size and number of generations). The results showed that the computational efficiency of GP is highly enhanced when coupled with some standardization methods, specifically Min-Max method for scenario I and Vector method for scenario II, and scenario III. Whereas, Manhattan and Z-Score methods had the worst results for all three scenarios. Copyright 2018 by SCITEPRESS – Science and technology Publications, Lda. All rights reserved.

关键词： Genetic programming

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Forecasting business failure in highly imbalanced distribution based on delay line reservoir 26

Forecasting business failure in highly imbalanced distributi...

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26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018

作者： Rodan, A. Castillo, P.A. Faris, H. Al-Zoubi, Ala M. Mora, A.M. Jawazneh, H. Higher Colleges of Technology United Arab Emirates King Abdullah II School for Information Technology University of Jordan Jordan Department of Computer Architecture and Technology ETSIIT CITIC University of Granada Spain Spain

ISBN: (纸本)9782875870476

Bankruptcy is a critical financial problem that affects a high number of companies around the world. Thus, in recent years an increasing number of researchers have tried to solve it by applying different machine-learning models as powerful tools for the different economical agents related to the company. In this work, we propose the use of a simple deterministic delay line reservoir (DLR) state space by combining it with three popular classification algorithms (J48, k-NN, and MLP) as an efficient and accurate solution to the bankruptcy prediction problem. The proposed approach is evaluated on a real world dataset collected from Spanish companies. Obtained results show that the proposed models have a higher predictive ability than traditional classification approaches (without DLR reservoir state), resulting in a suitable and efficient alternative approach to solve this complex problem. © ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

关键词： Neural networks

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