Special Issue "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: 31 January 2020.

Special Issue Editors

Prof. Dr. Morgado Dias
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Guest Editor
University of Madeira, Campus da Penteada, 9000-390 Funchal, Portugal
Interests: artificial neural networks; artificial intelligence; sleep monitoring; FPGA; digital harwdare; modelling and renewable energy
Special Issues and Collections in MDPI journals
Prof. Dr. Antonio G. Ravelo-Garcia
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Guest Editor
Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria 35017, Spain
Interests: biomedical signal processing; nonlinear signal analysis; data mining; sensor based systems
Prof. Dr. João Cabral
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Guest Editor
University of Azores, Faculdade de Ciências e Tecnologia, 9500-321 Ponta Delgada, Portugal
Interests: rational maps iteration; dynamical systems; mathematical modeling; environment dynamics of mathematics methodology and teaching

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 papers will be 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 1600 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 (8 papers)

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Research

Open AccessArticle
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
Entropy 2019, 21(11), 1045; https://doi.org/10.3390/e21111045 - 26 Oct 2019
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)
Open AccessArticle
Fuzzy Coordination Control Strategy and Thermohydraulic Dynamics Modeling of a Natural Gas Heating System for In Situ Soil Thermal Remediation
Entropy 2019, 21(10), 971; https://doi.org/10.3390/e21100971 - 05 Oct 2019
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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Open AccessArticle
Prediction of the Superficial Heat Source Parameters for TIG Heating Process Using FEM and ANN Modeling
Entropy 2019, 21(10), 954; https://doi.org/10.3390/e21100954 - 29 Sep 2019
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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Open AccessArticle
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
Entropy 2019, 21(9), 916; https://doi.org/10.3390/e21090916 - 19 Sep 2019
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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Open AccessArticle
Spectral Quasi-Linearization Method for Non-Darcy Porous Medium with Convective Boundary Condition
Entropy 2019, 21(9), 838; https://doi.org/10.3390/e21090838 - 26 Aug 2019
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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Open AccessArticle
Deep Reinforcement Learning-Based Traffic Signal Control Using High-Resolution Event-Based Data
Entropy 2019, 21(8), 744; https://doi.org/10.3390/e21080744 - 29 Jul 2019
Cited by 1
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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Open AccessArticle
An Effective Approach for Reliability-Based Sensitivity Analysis with the Principle of Maximum Entropy and Fractional Moments
Entropy 2019, 21(7), 649; https://doi.org/10.3390/e21070649 - 01 Jul 2019
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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Open AccessArticle
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
Entropy 2019, 21(6), 578; https://doi.org/10.3390/e21060578 - 10 Jun 2019
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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