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Intelligent Tools and Applications in Engineering and Mathematics
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Intelligent Tools and Applications in Engineering and Mathematics

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (31 January 2020) | Viewed by 76488

Special Issue Editors

Prof. Dr. Fernando Morgado-Dias

E-Mail Website
Guest Editor
Madeira Interactive Technologies Institute and ITI/Larsys, Universidade da Madeira, 9000-390 Funchal, Portugal
Interests: artificial neural networks; artificial intelligence; sleep monitoring; FPGA; digital hardware; modeling; renewable and energy policy
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio G. Ravelo-Garcia

E-Mail Website
Guest Editor
1. ITI/Larsys/Madeira Interactive Technologies Institute, 9020-105 Funchal, Portugal
2. Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain
Interests: environmental data analysis; biomedical signal processing; nonlinear signal analysis; data mining; sensor-based systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. João Cabral

E-Mail Website
Guest Editor
Faculdade de Ciências e Tecnologia, University of Azores, 9500-321 Ponta Delgada, Portugal
Interests: rational maps iteration; dynamical systems; mathematical modeling; environment dynamics of mathematics methodology and teaching
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is focused on “Intelligent Tools and Applications in Engineering and Mathematics”. Currently, many new applications and tools used in the engineering and mathematical fields are based on some form of intelligence, be it artificial intelligence, reasoning, empirical-based experience, or learning of some form. We have noted this trend in many different fields and applications and with the current Special Issue we want to invite authors to show the latest developments in this field. This Special Issues will cover, but is not limited to, the following areas and topics:

  • Complex Systems: self-organization, chaos and nonlinear dynamics, simplicity and complexity, networks, symmetry breaking, similarity;
  • Computing: cloud computing, pattern recognition, hardware for prototyping;
  • Machine Learning: artificial intelligence, neural networks, cybernetics, robotics, man–machine interfaces;
  • Bio-Medical Applications: intelligent systems, bio-inspired algorithms, bioinformatics, biomedical circuits and systems;
  • Energy: energy economics, smart grids, intelligent energy management, intelligent energy systems, load forecasting, modeling and simulation in energy and sustainability, non-intrusive load monitoring.

Prof. Dr. Morgado Dias
Prof. Dr. Antonio G. Ravelo-Garcia
Prof. Dr. João Cabral
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent systems
  • machine learning
  • bio-medical applications
  • intelligent energy systems
  • computing
  • complex systems

Published Papers (17 papers)

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23 pages, 1647 KiB  
Article
Emotional State Recognition from Peripheral Physiological Signals Using Fused Nonlinear Features and Team-Collaboration Identification Strategy
by Lizheng Pan, Zeming Yin, Shigang She and Aiguo Song
Entropy 2020, 22(5), 511; https://0-doi-org.brum.beds.ac.uk/10.3390/e22050511 - 30 Apr 2020
Cited by 18 | Viewed by 2979
Abstract
Emotion recognition realizing human inner perception has a very important application prospect in human-computer interaction. In order to improve the accuracy of emotion recognition, a novel method combining fused nonlinear features and team-collaboration identification strategy was proposed for emotion recognition using physiological signals. [...] Read more.
Emotion recognition realizing human inner perception has a very important application prospect in human-computer interaction. In order to improve the accuracy of emotion recognition, a novel method combining fused nonlinear features and team-collaboration identification strategy was proposed for emotion recognition using physiological signals. Four nonlinear features, namely approximate entropy (ApEn), sample entropy (SaEn), fuzzy entropy (FuEn) and wavelet packet entropy (WpEn) are employed to reflect emotional states deeply with each type of physiological signal. Then the features of different physiological signals are fused to represent the emotional states from multiple perspectives. Each classifier has its own advantages and disadvantages. In order to make full use of the advantages of other classifiers and avoid the limitation of single classifier, the team-collaboration model is built and the team-collaboration decision-making mechanism is designed according to the proposed team-collaboration identification strategy which is based on the fusion of support vector machine (SVM), decision tree (DT) and extreme learning machine (ELM). Through analysis, SVM is selected as the main classifier with DT and ELM as auxiliary classifiers. According to the designed decision-making mechanism, the proposed team-collaboration identification strategy can effectively employ different classification methods to make decision based on the characteristics of the samples through SVM classification. For samples which are easy to be identified by SVM, SVM directly determines the identification results, whereas SVM-DT-ELM collaboratively determines the identification results, which can effectively utilize the characteristics of each classifier and improve the classification accuracy. The effectiveness and universality of the proposed method are verified by Augsburg database and database for emotion analysis using physiological (DEAP) signals. The experimental results uniformly indicated that the proposed method combining fused nonlinear features and team-collaboration identification strategy presents better performance than the existing methods. Full article
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
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22 pages, 17436 KiB  
Article
Gaussian Curvature Entropy for Curved Surface Shape Generation
by Akihiro Okano, Taishi Matsumoto and Takeo Kato
Entropy 2020, 22(3), 353; https://0-doi-org.brum.beds.ac.uk/10.3390/e22030353 - 18 Mar 2020
Cited by 9 | Viewed by 4652
Abstract
The overall shape features that emerge from combinations of shape elements, such as “complexity” and “order”, are important in designing shapes of industrial products. However, controlling the features of shapes is difficult and depends on the experience and intuition of designers. Among these [...] Read more.
The overall shape features that emerge from combinations of shape elements, such as “complexity” and “order”, are important in designing shapes of industrial products. However, controlling the features of shapes is difficult and depends on the experience and intuition of designers. Among these features, “complexity” is said to have an influence on the “beauty” and “preference” of shapes. This research proposed a Gaussian curvature entropy as a “complexity” index of a curved surface shape. The proposed index is calculated based on Gaussian curvature, which is obtained by the sampling and quantization of a curved surface shape and validated by the sensory evaluation experiment while using two types of sample shapes. The result indicates the correspondence of the index to perceived “complexity” (the determination coefficient is greater than 0.8). Additionally, this research constructed a shape generation method that was based on the index as a car design supporting apparatus, in which the designers can refer many shapes generated by controlling “complexity”. The applicability of the proposed method was confirmed by the experiment while using the generated shapes. Full article
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
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17 pages, 1745 KiB  
Article
Path Planning of Pattern Transfer Based on Dual-Operator and a Dual-Population Ant Colony Algorithm for Digital Mask Projection Lithography
by Yingzhi Wang, Tailin Han, Xu Jiang, Yuhan Yan and Hong Liu
Entropy 2020, 22(3), 295; https://0-doi-org.brum.beds.ac.uk/10.3390/e22030295 - 03 Mar 2020
Cited by 4 | Viewed by 2869
Abstract
In the process of digital micromirror device (DMD) digital mask projection lithography, the lithography efficiency will be enhanced greatly by path planning of pattern transfer. This paper proposes a new dual operator and dual population ant colony (DODPACO) algorithm. Firstly, load operators and [...] Read more.
In the process of digital micromirror device (DMD) digital mask projection lithography, the lithography efficiency will be enhanced greatly by path planning of pattern transfer. This paper proposes a new dual operator and dual population ant colony (DODPACO) algorithm. Firstly, load operators and feedback operators are used to update the local and global pheromones in the white ant colony, and the feedback operator is used in the yellow ant colony. The concept of information entropy is used to regulate the number of yellow and white ant colonies adaptively. Secondly, take eight groups of large-scale data in TSPLIB as examples to compare with two classical ACO and six improved ACO algorithms; the results show that the DODPACO algorithm is superior in solving large-scale events in terms of solution quality and convergence speed. Thirdly, take PCB production as an example to verify the time saved after path planning; the DODPACO algorithm is used for path planning, which saves 34.3% of time compared with no path planning, and is about 1% shorter than the suboptimal algorithm. The DODPACO algorithm is applicable to the path planning of pattern transfer in DMD digital mask projection lithography and other digital mask lithography. Full article
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
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18 pages, 5048 KiB  
Article
Machine Learning Photovoltaic String Analyzer
by Sandy Rodrigues, Gerhard Mütter, Helena Geirinhas Ramos and F. Morgado-Dias
Entropy 2020, 22(2), 205; https://0-doi-org.brum.beds.ac.uk/10.3390/e22020205 - 11 Feb 2020
Cited by 5 | Viewed by 2285
Abstract
Photovoltaic (PV) system energy production is non-linear because it is influenced by the random nature of weather conditions. The use of machine learning techniques to model the PV system energy production is recommended since there is no known way to deal well with [...] Read more.
Photovoltaic (PV) system energy production is non-linear because it is influenced by the random nature of weather conditions. The use of machine learning techniques to model the PV system energy production is recommended since there is no known way to deal well with non-linear data. In order to detect PV system faults, the machine learning models should provide accurate outputs. The aim of this work is to accurately predict the DC energy of six PV strings of a utility-scale PV system and to accurately detect PV string faults by benchmarking the results of four machine learning methodologies known to improve the accuracy of the machine learning models, such as the data mining methodology, machine learning technique benchmarking methodology, hybrid methodology, and the ensemble methodology. A new hybrid methodology is proposed in this work which combines the use of a fuzzy system and the use of a machine learning system containing five different trained machine learning models, such as the regression tree, artificial neural networks, multi-gene genetic programming, Gaussian process, and support vector machines for regression. The results showed that the hybrid methodology provided the most accurate machine learning predictions of the PV string DC energy, and consequently the PV string fault detection is successful. Full article
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
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14 pages, 3356 KiB  
Article
Deep Residual Learning for Nonlinear Regression
by Dongwei Chen, Fei Hu, Guokui Nian and Tiantian Yang
Entropy 2020, 22(2), 193; https://0-doi-org.brum.beds.ac.uk/10.3390/e22020193 - 07 Feb 2020
Cited by 55 | Viewed by 12695
Abstract
Deep learning plays a key role in the recent developments of machine learning. This paper develops a deep residual neural network (ResNet) for the regression of nonlinear functions. Convolutional layers and pooling layers are replaced by fully connected layers in the residual block. [...] Read more.
Deep learning plays a key role in the recent developments of machine learning. This paper develops a deep residual neural network (ResNet) for the regression of nonlinear functions. Convolutional layers and pooling layers are replaced by fully connected layers in the residual block. To evaluate the new regression model, we train and test neural networks with different depths and widths on simulated data, and we find the optimal parameters. We perform multiple numerical tests of the optimal regression model on multiple simulated data, and the results show that the new regression model behaves well on simulated data. Comparisons are also made between the optimal residual regression and other linear as well as nonlinear approximation techniques, such as lasso regression, decision tree, and support vector machine. The optimal residual regression model has better approximation capacity compared to the other models. Finally, the residual regression is applied into the prediction of a relative humidity series in the real world. Our study indicates that the residual regression model is stable and applicable in practice. Full article
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
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22 pages, 3915 KiB  
Article
Dynamic Analysis and Intelligent Control Strategy for the Internal Thermal Control Fluid Loop of Scientific Experimental Racks in Space Stations
by Ben-Yuan Cai, Hui-Yi Wei, Yun-Ze Li, Yuan-Yuan Lou and Tong Li
Entropy 2020, 22(1), 72; https://0-doi-org.brum.beds.ac.uk/10.3390/e22010072 - 06 Jan 2020
Cited by 5 | Viewed by 2915
Abstract
Scientific experimental racks are an indispensable supporter in space stations for experiments with regard to meeting different temperature and humidity requirements. The diversity of experiments brings enormous challenges to the thermal control system of racks. This paper presents an indirect coupling thermal control [...] Read more.
Scientific experimental racks are an indispensable supporter in space stations for experiments with regard to meeting different temperature and humidity requirements. The diversity of experiments brings enormous challenges to the thermal control system of racks. This paper presents an indirect coupling thermal control single-phase fluid loop system for scientific experimental racks, along with fuzzy incremental control strategies. A dynamic model of the thermal control system is built, and three control strategies for it, with different inputs and outputs, are simulated. A comparison of the calculated results showed that pump speed and outlet temperature of the cold plate branch are, respectively, the best choice for the control variable and controlled variable in the controller. It showed that an indirect coupling thermal control fluid loop system with a fuzzy incremental controller is feasible for the thermal control of scientific experimental racks in space stations. Full article
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
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17 pages, 3396 KiB  
Article
A Portable Wireless Device for Cyclic Alternating Pattern Estimation from an EEG Monopolar Derivation
by Fábio Mendonça, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias and Antonio G. Ravelo-García
Entropy 2019, 21(12), 1203; https://0-doi-org.brum.beds.ac.uk/10.3390/e21121203 - 07 Dec 2019
Cited by 17 | Viewed by 3327
Abstract
Quality of sleep can be assessed by analyzing the cyclic alternating pattern, a long-lasting periodic activity that is composed of two alternate electroencephalogram patterns, which is considered to be a marker of sleep instability. Experts usually score this pattern through a visual examination [...] Read more.
Quality of sleep can be assessed by analyzing the cyclic alternating pattern, a long-lasting periodic activity that is composed of two alternate electroencephalogram patterns, which is considered to be a marker of sleep instability. Experts usually score this pattern through a visual examination of each one-second epoch of an electroencephalogram signal, a repetitive and time-consuming task that is prone to errors. To address these issues, a home monitoring device was developed for automatic scoring of the cyclic alternating pattern by analyzing the signal from one electroencephalogram derivation. Three classifiers, specifically, two recurrent networks (long short-term memory and gated recurrent unit) and one one-dimension convolutional neural network, were developed and tested to determine which was more suitable for the cyclic alternating pattern phase’s classification. It was verified that the network based on the long short-term memory attained the best results with an average accuracy, sensitivity, specificity and area under the receiver operating characteristic curve of, respectively, 76%, 75%, 77% and 0.752. The classified epochs were then fed to a finite state machine to determine the cyclic alternating pattern cycles and the performance metrics were 76%, 71%, 84% and 0.778, respectively. The performance achieved is in the higher bound of the experts’ expected agreement range and considerably higher than the inter-scorer agreement of multiple experts, implying the usability of the device developed for clinical analysis. Full article
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
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18 pages, 588 KiB  
Article
Application of Data Mining for the Prediction of Mortality and Occurrence of Complications for Gastric Cancer Patients
by Cristiana Neto, Maria Brito, Vítor Lopes, Hugo Peixoto, António Abelha and José Machado
Entropy 2019, 21(12), 1163; https://0-doi-org.brum.beds.ac.uk/10.3390/e21121163 - 28 Nov 2019
Cited by 29 | Viewed by 4485
Abstract
The development of malign cells that can grow in any part of the stomach, known as gastric cancer, is one of the most common causes of death worldwide. In order to increase the survival rate in patients with this condition, it is essential [...] Read more.
The development of malign cells that can grow in any part of the stomach, known as gastric cancer, is one of the most common causes of death worldwide. In order to increase the survival rate in patients with this condition, it is essential to improve the decision-making process leading to a better and more efficient selection of treatment strategies. Nowadays, with the large amount of information present in hospital institutions, it is possible to use data mining algorithms to improve the healthcare delivery. Thus, this study, using the CRISP methodology, aims to predict not only the mortality associated with this disease, but also the occurrence of any complication following surgery. A set of classification models were tested and compared in order to improve the prediction accuracy. The study showed that, on one hand, the J48 algorithm using oversampling is the best technique to predict the mortality in gastric cancer patients, with an accuracy of approximately 74%. On the other hand, the rain forest algorithm using oversampling presents the best results when predicting the possible occurrence of complications among gastric cancer patients after their in-hospital stays, with an accuracy of approximately 83%. Full article
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
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27 pages, 384 KiB  
Article
On the Approximated Reachability of a Class of Time-Varying Nonlinear Dynamic Systems Based on Their Linearized Behavior about the Equilibria: Applications to Epidemic Models
by Manuel De la Sen
Entropy 2019, 21(11), 1045; https://0-doi-org.brum.beds.ac.uk/10.3390/e21111045 - 26 Oct 2019
Cited by 5 | Viewed by 1906
Abstract
This paper formulates the properties of point reachability and approximate point reachability of either a targeted state or output values in a general dynamic system which possess a linear time-varying dynamics with respect to a given reference nominal one and, eventually, an unknown [...] Read more.
This paper formulates the properties of point reachability and approximate point reachability of either a targeted state or output values in a general dynamic system which possess a linear time-varying dynamics with respect to a given reference nominal one and, eventually, an unknown structured nonlinear dynamics. Such a dynamics is upper-bounded by a function of the state and input. The results are obtained for the case when the time-invariant nominal dynamics is perfectly known while its time-varying deviations together with the nonlinear dynamics are not precisely known and also for the case when only the nonlinear dynamics is not precisely known. Either the controllability gramian of the nominal linearized system with constant linear parameterization or that of the current linearized system (which includes the time-varying linear dynamics) are assumed to be non-singular. Also, some further results are obtained for the case when the control input is eventually saturated and for the case when the controllability gramians of the linear parts are singular. Examples of the derived theoretical results for some epidemic models are also discussed. Full article
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
30 pages, 16996 KiB  
Article
Fuzzy Coordination Control Strategy and Thermohydraulic Dynamics Modeling of a Natural Gas Heating System for In Situ Soil Thermal Remediation
by Zhuang-Zhuang Zhai, Li-Man Yang, Yun-Ze Li, Hai-Feng Jiang, Yuan Ye, Tian-Tian Li, En-Hui Li and Tong Li
Entropy 2019, 21(10), 971; https://0-doi-org.brum.beds.ac.uk/10.3390/e21100971 - 05 Oct 2019
Cited by 5 | Viewed by 2554
Abstract
Soil contamination remains a global problem. Among the different kinds of remediation technologies, in situ soil thermal remediation has attracted great attention in the environmental field, representing a potential remedial alternative for contaminated soils. Soils need to be heated to a high temperature [...] Read more.
Soil contamination remains a global problem. Among the different kinds of remediation technologies, in situ soil thermal remediation has attracted great attention in the environmental field, representing a potential remedial alternative for contaminated soils. Soils need to be heated to a high temperature in thermal remediation, which requires a large amount of energy. For the natural gas heating system in thermal remediation, a fuzzy coordination control strategy and thermohydraulic dynamics model have been proposed in this paper. In order to demonstrate the superiority of the strategy, the other three traditional control strategies are introduced. Analysis of the temperature rise and energy consumption of soils under different control strategies were conducted. The results showed that the energy consumption of fuzzy coordination control strategy is reduced by 33.9% compared to that of the traditional control strategy I, constant natural gas flow and excess air ratio. Further, compared to the traditional control strategy II, constant excess air ratio and desired outlet temperature of wells, the strategy proposed can reduce energy consumption by 48.7%. The results illustrate the superiority of the fuzzy coordination control strategy, and the strategy can greatly reduce energy consumption, thereby reducing the cost of in situ soil thermal remediation. Full article
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
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11 pages, 3115 KiB  
Article
Prediction of the Superficial Heat Source Parameters for TIG Heating Process Using FEM and ANN Modeling
by Joanna Wróbel and Adam Kulawik
Entropy 2019, 21(10), 954; https://0-doi-org.brum.beds.ac.uk/10.3390/e21100954 - 29 Sep 2019
Cited by 2 | Viewed by 2119
Abstract
The basic problem of the numerical model’s quenching process is establishing the characteristics of the boundary conditions. The existing descriptions of the boundary conditions, which represent the parameters of equipment used in heat treatment processes, do not accurately reflect the actual process conditions. [...] Read more.
The basic problem of the numerical model’s quenching process is establishing the characteristics of the boundary conditions. The existing descriptions of the boundary conditions, which represent the parameters of equipment used in heat treatment processes, do not accurately reflect the actual process conditions. In the present study, the method of choice for superficial heat source parameters for TIG (tungsten inert gas) heating is modeled using artificial neural networks (ANN) and the finite element method (FEM). A comparison of the calculations obtained from the numerical model of non-steady state heat transfer with the results of the experimental studies is presented. The possibility of using ANN to compute the parameters of the boundary conditions for the heating treatment is analyzed. A multilayer feed-forward backpropagation network is developed and trained using value of temperature in the selected nodes obtained from numerical simulation. Full article
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
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22 pages, 7806 KiB  
Article
Survey Assessment for Decision Support Using Self-Organizing Maps Profile Characterization with an Odds and Cluster Heat Map: Application to Children’s Perception of Urban School Environments
by Francisco Javier Abarca-Alvarez, Francisco Sergio Campos-Sánchez and Rubén Mora-Esteban
Entropy 2019, 21(9), 916; https://0-doi-org.brum.beds.ac.uk/10.3390/e21090916 - 19 Sep 2019
Cited by 5 | Viewed by 4721
Abstract
The interpretation of opinion and satisfaction surveys based exclusively on statistical analysis often faces difficulties due to the nature of the information and the requirements of the available statistical methods. These difficulties include the concurrence of categorical information with answers based on Likert [...] Read more.
The interpretation of opinion and satisfaction surveys based exclusively on statistical analysis often faces difficulties due to the nature of the information and the requirements of the available statistical methods. These difficulties include the concurrence of categorical information with answers based on Likert scales with only a few levels, or the distancing of the necessary heuristic approach of the decision support system (DSS). The artificial neural network used for data analysis, called Kohonen or self-organizing maps (SOM), although rarely used for survey analysis, has been applied in many fields, facilitating the graphical representation and the simple interpretation of high-dimensionality data. This clustering method, based on unsupervised learning, also allows obtaining profiles of respondents without the need to provide additional information for the creation of these clusters. In this work, we propose the identification of profiles using SOM for evaluating opinion surveys. Subsequently, non-parametric chi-square tests were first conducted to contrast whether answer was independent of each profile found, and in the case of statistical significance (p ≤ 0.05), the odds ratio was evaluated as an indicator of the effect size of such dependence. Finally, all results were displayed in an odds and cluster heat map so that they could be easily interpreted and used to make decisions regarding the survey results. The methodology was applied to the analysis of a survey based on forms administered to children (N = 459) about their perception of the urban environment close to their school, obtaining relevant results, facilitating results interpretation, and providing support to the decision-process. Full article
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
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13 pages, 420 KiB  
Article
Spectral Quasi-Linearization Method for Non-Darcy Porous Medium with Convective Boundary Condition
by R. A. Alharbey, Hiranmoy Mondal and Ramandeep Behl
Entropy 2019, 21(9), 838; https://0-doi-org.brum.beds.ac.uk/10.3390/e21090838 - 26 Aug 2019
Cited by 14 | Viewed by 3127
Abstract
The boundary layer micropolar fluid over a horizontal plate embedded in a non-Darcy porous medium is investigated in this study. This paper is solely focused on contributions oriented towards the application of micropolar fluid flow over a stretching sheet. The prime equations are [...] Read more.
The boundary layer micropolar fluid over a horizontal plate embedded in a non-Darcy porous medium is investigated in this study. This paper is solely focused on contributions oriented towards the application of micropolar fluid flow over a stretching sheet. The prime equations are renewed to ordinary differential equations with the assistance of similarity transformation; they are then subsequently solved numerically using the spectral quasi-linearization method (SQLM) for direct Taylor series expansions that can be applied to non-linear terms in order to linearize them. The spectral collocation approach is then applied to solve the resulting linearized system of equations. The paper acquires realistic numerical explanations for rapidly convergent solutions using the spectral quasi-linearization method. Convergence of the numerical solutions was monitored using the residual error of the PDEs. The validity of our model is established using error analysis. The impact of different geometric parameters on angular velocity, temperature, and entropy generation numbers are presented in graphs. The results show that the entropy generation number decelerates with an increase in Reynolds number and Brinkmann number. The velocity profile increases with the increasing material parameter. The results indicate that the fluid angular velocity decreases throughout the boundary layer for increasing values of the material parameter. Full article
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
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16 pages, 4145 KiB  
Article
Deep Reinforcement Learning-Based Traffic Signal Control Using High-Resolution Event-Based Data
by Song Wang, Xu Xie, Kedi Huang, Junjie Zeng and Zimin Cai
Entropy 2019, 21(8), 744; https://0-doi-org.brum.beds.ac.uk/10.3390/e21080744 - 29 Jul 2019
Cited by 26 | Viewed by 5825
Abstract
Reinforcement learning (RL)-based traffic signal control has been proven to have great potential in alleviating traffic congestion. The state definition, which is a key element in RL-based traffic signal control, plays a vital role. However, the data used for state definition in the [...] Read more.
Reinforcement learning (RL)-based traffic signal control has been proven to have great potential in alleviating traffic congestion. The state definition, which is a key element in RL-based traffic signal control, plays a vital role. However, the data used for state definition in the literature are either coarse or difficult to measure directly using the prevailing detection systems for signal control. This paper proposes a deep reinforcement learning-based traffic signal control method which uses high-resolution event-based data, aiming to achieve cost-effective and efficient adaptive traffic signal control. High-resolution event-based data, which records the time when each vehicle-detector actuation/de-actuation event occurs, is informative and can be collected directly from vehicle-actuated detectors (e.g., inductive loops) with current technologies. Given the event-based data, deep learning techniques are employed to automatically extract useful features for traffic signal control. The proposed method is benchmarked with two commonly used traffic signal control strategies, i.e., the fixed-time control strategy and the actuated control strategy, and experimental results reveal that the proposed method significantly outperforms the commonly used control strategies. Full article
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
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21 pages, 835 KiB  
Article
An Effective Approach for Reliability-Based Sensitivity Analysis with the Principle of Maximum Entropy and Fractional Moments
by Xufang Zhang, Jiankai Liu, Ying Yan and Mahesh Pandey
Entropy 2019, 21(7), 649; https://0-doi-org.brum.beds.ac.uk/10.3390/e21070649 - 01 Jul 2019
Cited by 11 | Viewed by 3044
Abstract
The reliability-based sensitivity analysis requires to recursively evaluate a multivariate structural model for many failure probability levels. This is in general a computationally intensive task due to irregular integrations used to define the structural failure probability. In this regard, the performance function is [...] Read more.
The reliability-based sensitivity analysis requires to recursively evaluate a multivariate structural model for many failure probability levels. This is in general a computationally intensive task due to irregular integrations used to define the structural failure probability. In this regard, the performance function is first approximated by using the multiplicative dimensional reduction method in this paper, and an approximation for the reliability-based sensitivity index is derived based on the principle of maximum entropy and the fractional moment. Three examples in the literature are presented to examine the performance of this entropy-based approach against the brute-force Monte-Carlo simulation method. Results have shown that the multiplicative dimensional reduction based entropy approach is rather efficient and able to provide reliability estimation results for the reliability-based sensitivity analysis of a multivariate structural model. Full article
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
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24 pages, 7876 KiB  
Article
A Thermoelectric-Heat-Pump Employed Active Control Strategy for the Dynamic Cooling Ability Distribution of Liquid Cooling System for the Space Station’s Main Power-Cell-Arrays
by Hui-Juan Xu, Ji-Xiang Wang, Yun-Ze Li, Yan-Jun Bi and Li-Jun Gao
Entropy 2019, 21(6), 578; https://0-doi-org.brum.beds.ac.uk/10.3390/e21060578 - 10 Jun 2019
Cited by 9 | Viewed by 3151
Abstract
A proper operating temperature range and an acceptable temperature uniformity are extremely essential for the efficient and safe operation of the Li-ion battery array, which is an important power source of space stations. The single-phase fluid loop is one of the effective approaches [...] Read more.
A proper operating temperature range and an acceptable temperature uniformity are extremely essential for the efficient and safe operation of the Li-ion battery array, which is an important power source of space stations. The single-phase fluid loop is one of the effective approaches for the thermal management of the battery. Due to the limitation that once the structure of the cold plate (CP) is determined, it is difficult to adjust the cooling ability of different locations of the CP dynamically, this may lead to a large temperature difference of the battery array that is attached to the different locations of the CP. This paper presents a micro-channel CP integrated with a thermoelectric heat pump (THP) in order to achieve the dynamic adjustment of the cooling ability of different locations of the CP. The THP functions to balance the heat transfer within the CP, which transports the heat of the high-temperature region to the low-temperature region by regulating the THP current, where a better temperature uniformity of the CP can be achieved. A lumped-parameter model for the proposed system is established to examine the effects of the thermal load and electric current on the dynamic thermal characteristics. In addition, three different thermal control algorithms (basic PID, fuzzy-PID, and BP-PID) are explored to examine the CP’s temperature uniformity performance by adapting the electric current of the THP. The results demonstrate that the temperature difference of the focused CP can be declined by 1.8 K with the assistance of the THP. The proposed fuzzy-PID controller and BP-PID controller present much better performances than that provided by the basic PID controller in terms of overshoot, response time, and steady state error. Such an innovative arrangement will enhance the CP’s dynamic cooling ability distribution effectively, and thus improve the temperature uniformity and operating reliability of the Li-ion space battery array further. Full article
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
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Review

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36 pages, 1140 KiB  
Review
Particle Swarm Optimisation: A Historical Review Up to the Current Developments
by Diogo Freitas, Luiz Guerreiro Lopes and Fernando Morgado-Dias
Entropy 2020, 22(3), 362; https://0-doi-org.brum.beds.ac.uk/10.3390/e22030362 - 21 Mar 2020
Cited by 135 | Viewed by 12985
Abstract
The Particle Swarm Optimisation (PSO) algorithm was inspired by the social and biological behaviour of bird flocks searching for food sources. In this nature-based algorithm, individuals are referred to as particles and fly through the search space seeking for the global best position [...] Read more.
The Particle Swarm Optimisation (PSO) algorithm was inspired by the social and biological behaviour of bird flocks searching for food sources. In this nature-based algorithm, individuals are referred to as particles and fly through the search space seeking for the global best position that minimises (or maximises) a given problem. Today, PSO is one of the most well-known and widely used swarm intelligence algorithms and metaheuristic techniques, because of its simplicity and ability to be used in a wide range of applications. However, in-depth studies of the algorithm have led to the detection and identification of a number of problems with it, especially convergence problems and performance issues. Consequently, a myriad of variants, enhancements and extensions to the original version of the algorithm, developed and introduced in the mid-1990s, have been proposed, especially in the last two decades. In this article, a systematic literature review about those variants and improvements is made, which also covers the hybridisation and parallelisation of the algorithm and its extensions to other classes of optimisation problems, taking into consideration the most important ones. These approaches and improvements are appropriately summarised, organised and presented, in order to allow and facilitate the identification of the most appropriate PSO variant for a particular application. Full article
(This article belongs to the Special Issue Intelligent Tools and Applications in Engineering and Mathematics)
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