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Artificial Intelligence and Computational Methods in the Modeling of Complex Systems

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

Deadline for manuscript submissions: closed (21 December 2020) | Viewed by 60267

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Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland
Interests: modeling; adsorption chillers; CFB boilers; oxy-fuel combustion; CLC; CaL; biomass; machine learning; artificial neural networks; fuzzy logic; genetic algorithms
Special Issues, Collections and Topics in MDPI journals

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Faculty of Safety Engineering, VŠB-Technical University of Ostrava, 700 13 Ostrava, Czechia
Interests: systematic approach to risk management in security, relationships and connections; types of hazards-physical, chemical, biological; hazard identification, scenario building, deterministic and probabilistic approach, qualitative, semi-quantitative and quantitative methods of risk analysis; organizational and technical barriers to risks, their effectiveness; the human factor, physical security

Special Issue Information

Dear Colleague,

Since heat transfer processes are irreversible, some entropy accomplished by exergy destruction is generated. These irreversibilities should be reduced to increase engine performance. One of the ways leading to an increase in a system’s efficiency is its analysis and optimization via modeling. This Special Issue aims to bring together research related to the modeling of complex systems. Original research articles, as well as review articles, with a particular focus on (but not limited to) optimization by artificial intelligence algorithms, are welcomed.

Dr. Jaroslaw Krzywanski
Dr. Marcin Sosnowsk
Dr. Radomír Ščurek
Guest Editor

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

  • complex systems
  • computing
  • machine learning
  • artificial intelligence
  • optimization
  • energy systems
  • renewable energy
  • energy policy

Published Papers (20 papers)

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Editorial

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5 pages, 204 KiB  
Editorial
Artificial Intelligence and Computational Methods in the Modeling of Complex Systems
by Marcin Sosnowski, Jaroslaw Krzywanski and Radomír Ščurek
Entropy 2021, 23(5), 586; https://doi.org/10.3390/e23050586 - 10 May 2021
Cited by 10 | Viewed by 1825
Abstract
Based on the increased attention, the Special Issue aims to investigate the modeling of complex systems using artificial intelligence and computational methods [...] Full article

Research

Jump to: Editorial

17 pages, 416 KiB  
Article
Expectation-Maximization Algorithm for the Calibration of Complex Simulator Using a Gaussian Process Emulator
by Yun Am Seo and Jeong-Soo Park
Entropy 2021, 23(1), 53; https://doi.org/10.3390/e23010053 - 31 Dec 2020
Cited by 2 | Viewed by 2183
Abstract
The approximated non-linear least squares (ALS) tunes or calibrates the computer model by minimizing the squared error between the computer output and real observations by using an emulator such as a Gaussian process (GP) model. A potential defect of the ALS method is [...] Read more.
The approximated non-linear least squares (ALS) tunes or calibrates the computer model by minimizing the squared error between the computer output and real observations by using an emulator such as a Gaussian process (GP) model. A potential defect of the ALS method is that the emulator is constructed once and it is no longer re-built. An iterative method is proposed in this study to address this difficulty. In the proposed method, the tuning parameters of the simulation model are calculated by the conditional expectation (E-step), whereas the GP parameters are updated by the maximum likelihood estimation (M-step). These EM-steps are alternately repeated until convergence by using both computer and experimental data. For comparative purposes, another iterative method (the max-min algorithm) and a likelihood-based method are considered. Five toy models are tested for a comparative analysis of these methods. According to the toy model study, both the variance and bias of the estimates obtained from the proposed EM algorithm are smaller than those from the existing calibration methods. Finally, the application to a nuclear fusion simulator is demonstrated. Full article
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14 pages, 2346 KiB  
Article
Artificial Intelligence for Modeling Real Estate Price Using Call Detail Records and Hybrid Machine Learning Approach
by Gergo Pinter, Amir Mosavi and Imre Felde
Entropy 2020, 22(12), 1421; https://doi.org/10.3390/e22121421 - 16 Dec 2020
Cited by 25 | Viewed by 4858
Abstract
Advancement of accurate models for predicting real estate price is of utmost importance for urban development and several critical economic functions. Due to the significant uncertainties and dynamic variables, modeling real estate has been studied as complex systems. In this study, a novel [...] Read more.
Advancement of accurate models for predicting real estate price is of utmost importance for urban development and several critical economic functions. Due to the significant uncertainties and dynamic variables, modeling real estate has been studied as complex systems. In this study, a novel machine learning method is proposed to tackle real estate modeling complexity. Call detail records (CDR) provides excellent opportunities for in-depth investigation of the mobility characterization. This study explores the CDR potential for predicting the real estate price with the aid of artificial intelligence (AI). Several essential mobility entropy factors, including dweller entropy, dweller gyration, workers’ entropy, worker gyration, dwellers’ work distance, and workers’ home distance, are used as input variables. The prediction model is developed using the machine learning method of multi-layered perceptron (MLP) trained with the evolutionary algorithm of particle swarm optimization (PSO). Model performance is evaluated using mean square error (MSE), sustainability index (SI), and Willmott’s index (WI). The proposed model showed promising results revealing that the workers’ entropy and the dwellers’ work distances directly influence the real estate price. However, the dweller gyration, dweller entropy, workers’ gyration, and the workers’ home had a minimum effect on the price. Furthermore, it is shown that the flow of activities and entropy of mobility are often associated with the regions with lower real estate prices. Full article
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14 pages, 2936 KiB  
Article
Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction
by Randall Claywell, Laszlo Nadai, Imre Felde, Sina Ardabili and Amirhosein Mosavi
Entropy 2020, 22(11), 1192; https://doi.org/10.3390/e22111192 - 22 Oct 2020
Cited by 22 | Viewed by 2912
Abstract
The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models [...] Read more.
The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures. Full article
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15 pages, 5973 KiB  
Article
Breaking of the Trade-Off Principle between Computational Universality and Efficiency by Asynchronous Updating
by Yukio-Pegio Gunji and Daisuke Uragami
Entropy 2020, 22(9), 1049; https://doi.org/10.3390/e22091049 - 19 Sep 2020
Cited by 6 | Viewed by 2661
Abstract
Although natural and bioinspired computing has developed significantly, the relationship between the computational universality and efficiency beyond the Turing machine has not been studied in detail. Here, we investigate how asynchronous updating can contribute to the universal and efficient computation in cellular automata [...] Read more.
Although natural and bioinspired computing has developed significantly, the relationship between the computational universality and efficiency beyond the Turing machine has not been studied in detail. Here, we investigate how asynchronous updating can contribute to the universal and efficient computation in cellular automata (CA). First, we define the computational universality and efficiency in CA and show that there is a trade-off relation between the universality and efficiency in CA implemented in synchronous updating. Second, we introduce asynchronous updating in CA and show that asynchronous updating can break the trade-off found in synchronous updating. Our finding spells out the significance of asynchronous updating or the timing of computation in robust and efficient computation. Full article
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13 pages, 3232 KiB  
Article
Analytic Hierarchy Process (AHP)-Based Aggregation Mechanism for Resilience Measurement: NATO Aggregated Resilience Decision Support Model
by Jan Hodicky, Gökhan Özkan, Hilmi Özdemir, Petr Stodola, Jan Drozd and Wayne Buck
Entropy 2020, 22(9), 1037; https://doi.org/10.3390/e22091037 - 16 Sep 2020
Cited by 7 | Viewed by 3107
Abstract
Resilience is a complex system that represents dynamic behaviours through its complicated structure with various nodes, interrelations, and information flows. Like other international organizations NATO has also been dealing with the measurement of this complex phenomenon in order to have a comprehensive understanding [...] Read more.
Resilience is a complex system that represents dynamic behaviours through its complicated structure with various nodes, interrelations, and information flows. Like other international organizations NATO has also been dealing with the measurement of this complex phenomenon in order to have a comprehensive understanding of the civil environment and its impact on military operations. With this ultimate purpose, NATO had developed and executed a prototype model with the system dynamics modelling and simulation paradigm. NATO has created an aggregated resilience model as an upgrade of the prototype one, as discussed within this study. The structure of the model, aggregation mechanism and shock parametrization methodologies used in the development of the model comprise the scope of this study. Analytic Hierarchy Process (AHP), which is a multi-criteria decision-making technique is the methodology that is used for the development of the aggregation mechanism. The main idea of selecting the AHP methodology is its power and usefulness in mitigating bias in the decision-making process, its capability to increase the number of what-if scenarios to be created, and its contribution to the quality of causal explanations with the granularity it provides. The parametrized strategic shock input page, AHP-based weighted resilience and risk parameters input pages, one more country insertion to the model, and the decision support system page enhance the capacity of the prototype model. As part of the model, the decision support system page stands out as the strategic level cockpit where the colour codes give a clear idea at first about the overall situational picture and country-wise resilience and risk status. At the validation workshop, users not only validated the model but also discussed further development opportunities, such as adding more strategic shocks into the model and introduction of new parameters that will be determined by a big data analysis on relevant open source databases. The developed model has the potential to inspire high-level decision-makers dealing with resilience management in other international organizations, such as the United Nations. Full article
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20 pages, 6291 KiB  
Article
Control of Pumps of Water Supply Network under Hydraulic and Energy Optimisation Using Artificial Intelligence
by Jan Studziński and Andrzej Ziółkowski
Entropy 2020, 22(9), 1014; https://doi.org/10.3390/e22091014 - 11 Sep 2020
Cited by 9 | Viewed by 2759
Abstract
This article presents several algorithms for controlling water supply system pumps. The aim of having control is the hydraulic optimisation of the network, i.e., ensuring the desired pressure in its recipient nodes, and minimising energy costs of network operation. These two tasks belong [...] Read more.
This article presents several algorithms for controlling water supply system pumps. The aim of having control is the hydraulic optimisation of the network, i.e., ensuring the desired pressure in its recipient nodes, and minimising energy costs of network operation. These two tasks belong to the key issues related to the management and operation of water supply networks, apart from the reduction in water losses caused by network failures and ensuring proper water quality. The presented algorithms have been implemented in an Information and Communications Technology (ICT) system developed at the Systems Research Institute of the Polish Academy of Sciences (IBS PAN) and implemented in the waterworks GPW S.A. in Katowice/Poland. Full article
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19 pages, 1041 KiB  
Article
Generalized Nonlinear Least Squares Method for the Calibration of Complex Computer Code Using a Gaussian Process Surrogate
by Youngsaeng Lee and Jeong-Soo Park
Entropy 2020, 22(9), 985; https://doi.org/10.3390/e22090985 - 4 Sep 2020
Cited by 4 | Viewed by 2374
Abstract
The approximated nonlinear least squares (ALS) method has been used for the estimation of unknown parameters in the complex computer code which is very time-consuming to execute. The ALS calibrates or tunes the computer code by minimizing the squared difference between real observations [...] Read more.
The approximated nonlinear least squares (ALS) method has been used for the estimation of unknown parameters in the complex computer code which is very time-consuming to execute. The ALS calibrates or tunes the computer code by minimizing the squared difference between real observations and computer output using a surrogate such as a Gaussian process model. When the differences (residuals) are correlated or heteroscedastic, the ALS may result in a distorted code tuning with a large variance of estimation. Another potential drawback of the ALS is that it does not take into account the uncertainty in the approximation of the computer model by a surrogate. To address these problems, we propose a generalized ALS (GALS) by constructing the covariance matrix of residuals. The inverse of the covariance matrix is multiplied to the residuals, and it is minimized with respect to the tuning parameters. In addition, we consider an iterative version for the GALS, which is called as the max-minG algorithm. In this algorithm, the parameters are re-estimated and updated by the maximum likelihood estimation and the GALS, by using both computer and experimental data repeatedly until convergence. Moreover, the iteratively re-weighted ALS method (IRWALS) was considered for a comparison purpose. Five test functions in different conditions are examined for a comparative analysis of the four methods. Based on the test function study, we find that both the bias and variance of estimates obtained from the proposed methods (the GALS and the max-minG) are smaller than those from the ALS and the IRWALS methods. Especially, the max-minG works better than others including the GALS for the relatively complex test functions. Lastly, an application to a nuclear fusion simulator is illustrated and it is shown that the abnormal pattern of residuals in the ALS can be resolved by the proposed methods. Full article
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33 pages, 6540 KiB  
Article
A Comprehensive Three-Dimensional Analysis of a Large-Scale Multi-Fuel CFB Boiler Burning Coal and Syngas. Part 1. The CFD Model of a Large-Scale Multi-Fuel CFB Combustion
by Jaroslaw Krzywanski, Karol Sztekler, Mateusz Szubel, Tomasz Siwek, Wojciech Nowak and Łukasz Mika
Entropy 2020, 22(9), 964; https://doi.org/10.3390/e22090964 - 31 Aug 2020
Cited by 23 | Viewed by 3935
Abstract
The paper is focused on the idea of multi-fuel combustion in a large-scale circulating fluidized bed (CFB) boiler. The article discusses the concept of simultaneous coal and syngas combustion. A comprehensive three-dimensional computational fluid dynamics (CFD) model is developed, which allows us to [...] Read more.
The paper is focused on the idea of multi-fuel combustion in a large-scale circulating fluidized bed (CFB) boiler. The article discusses the concept of simultaneous coal and syngas combustion. A comprehensive three-dimensional computational fluid dynamics (CFD) model is developed, which allows us to describe complex phenomena that occur in the combustion chamber of the CFB boiler burning coal and syngas produced from coal sludge. Full article
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26 pages, 1788 KiB  
Article
Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem
by Petr Stodola, Karel Michenka, Jan Nohel and Marian Rybanský
Entropy 2020, 22(8), 884; https://doi.org/10.3390/e22080884 - 12 Aug 2020
Cited by 39 | Viewed by 4598
Abstract
The dynamic traveling salesman problem (DTSP) falls under the category of combinatorial dynamic optimization problems. The DTSP is composed of a primary TSP sub-problem and a series of TSP iterations; each iteration is created by changing the previous iteration. In this article, a [...] Read more.
The dynamic traveling salesman problem (DTSP) falls under the category of combinatorial dynamic optimization problems. The DTSP is composed of a primary TSP sub-problem and a series of TSP iterations; each iteration is created by changing the previous iteration. In this article, a novel hybrid metaheuristic algorithm is proposed for the DTSP. This algorithm combines two metaheuristic principles, specifically ant colony optimization (ACO) and simulated annealing (SA). Moreover, the algorithm exploits knowledge about the dynamic changes by transferring the information gathered in previous iterations in the form of a pheromone matrix. The significance of the hybridization, as well as the use of knowledge about the dynamic environment, is examined and validated on benchmark instances including small, medium, and large DTSP problems. The results are compared to the four other state-of-the-art metaheuristic approaches with the conclusion that they are significantly outperformed by the proposed algorithm. Furthermore, the behavior of the algorithm is analyzed from various points of view (including, for example, convergence speed to local optimum, progress of population diversity during optimization, and time dependence and computational complexity). Full article
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30 pages, 16913 KiB  
Article
A Comprehensive, Three-Dimensional Analysis of a Large-Scale, Multi-Fuel, CFB Boiler Burning Coal and Syngas. Part 2. Numerical Simulations of Coal and Syngas Co-Combustion
by Jaroslaw Krzywanski, Karol Sztekler, Mateusz Szubel, Tomasz Siwek, Wojciech Nowak and Łukasz Mika
Entropy 2020, 22(8), 856; https://doi.org/10.3390/e22080856 - 31 Jul 2020
Cited by 21 | Viewed by 2960
Abstract
This paper presents the results of numerical computations for a large-scale OFz-425 CFB (circulating fluidized bed) boiler utilizing coal and syngas. Four different operating scenarios are considered, including the reference variant, corresponding to the conventional, mono-combustion of bituminous coal, and three tests involving [...] Read more.
This paper presents the results of numerical computations for a large-scale OFz-425 CFB (circulating fluidized bed) boiler utilizing coal and syngas. Four different operating scenarios are considered, including the reference variant, corresponding to the conventional, mono-combustion of bituminous coal, and three tests involving replacement of secondary air and part of the coal stream with syngas fed by start-up burners. Pressure, gas velocity, temperature, and carbon dioxide distribution in the combustion chamber are discussed in the paper. The results indicate that the syngas supply leads to an increase in local temperature and carbon dioxide concentrations. The proposed concept is not advisable as it may lead to frequent emergency stops of the CFB boiler. Full article
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24 pages, 4930 KiB  
Article
A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles
by Amjad J. Humaidi, Ibraheem Kasim Ibraheem, Ahmad Taher Azar and Musaab E. Sadiq
Entropy 2020, 22(7), 723; https://doi.org/10.3390/e22070723 - 30 Jun 2020
Cited by 46 | Viewed by 4191
Abstract
This paper suggests a new control design based on the concept of Synergetic Control theory for controlling a one-link robot arm actuated by Pneumatic artificial muscles (PAMs) in opposing bicep/tricep positions. The synergetic control design is first established based on known system parameters. [...] Read more.
This paper suggests a new control design based on the concept of Synergetic Control theory for controlling a one-link robot arm actuated by Pneumatic artificial muscles (PAMs) in opposing bicep/tricep positions. The synergetic control design is first established based on known system parameters. However, in real PAM-actuated systems, the uncertainties are inherited features in their parameters and hence an adaptive synergetic control algorithm is proposed and synthesized for a PAM-actuated robot arm subjected to perturbation in its parameters. The adaptive synergetic laws are developed to estimate the uncertainties and to guarantee the asymptotic stability of the adaptive synergetic controlled PAM-actuated system. The work has also presented an improvement in the performance of proposed synergetic controllers (classical and adaptive) by applying a modern optimization technique based on Particle Swarm Optimization (PSO) to tune their design parameters towards optimal dynamic performance. The effectiveness of the proposed classical and adaptive synergetic controllers has been verified via computer simulation and it has been shown that the adaptive controller could cope with uncertainties and keep the controlled system stable. The proposed optimal Adaptive Synergetic Controller (ASC) has been validated with a previous adaptive controller with the same robot structure and actuation, and it has been shown that the optimal ASC outperforms its opponent in terms of tracking speed and error. Full article
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14 pages, 1391 KiB  
Article
Optimization of the Casualties’ Treatment Process: Blended Military Experiment
by Jan Hodický, Dalibor Procházka, Roman Jersák, Petr Stodola and Jan Drozd
Entropy 2020, 22(6), 706; https://doi.org/10.3390/e22060706 - 25 Jun 2020
Cited by 3 | Viewed by 2647
Abstract
At the battalion level, NATO ROLE1 medical treatment command focuses on the provision of primary health care being the very first physician and higher medical equipment intervention for casualty treatments. ROLE1 has paramount importance in casualty reductions, representing a complex system in current [...] Read more.
At the battalion level, NATO ROLE1 medical treatment command focuses on the provision of primary health care being the very first physician and higher medical equipment intervention for casualty treatments. ROLE1 has paramount importance in casualty reductions, representing a complex system in current operations. This study deals with an experiment on the optimization of ROLE1 according to the key parameters of the numbers of physicians, the number of ambulances and the distance between ROLE1 and the current battlefield. The very first step in this study is to design and implement a model of current battlefield casualties. The model uses friction data generated from an already executed computer assisted exercise (CAX) while employing a constructive simulation to produce offense and defense scenarios on the flow of casualties. The next step in the study is to design and implement a model representing the transportation to ROLE1, its structure and behavior. The deterministic model of ROLE1, employing a system dynamics simulation paradigm, uses the previously generated casualty flows as the inputs representing human decision-making processes through the recorder CAX events. A factorial experimental design for the ROLE1 model revealed the recommended variants of the ROLE1 structure for both offensive and defensive operations. The overall recommendation is for the internal structure of ROLE1 to have three ambulances and three physicians for any kind of current operation and any distance between ROLE1 and the current battlefield within the limit of 20 min. This study provides novelty in the methodology of casualty estimations involving human decision-making factors as well as the optimization of medical treatment processes through experimentation with the process model. Full article
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18 pages, 4532 KiB  
Article
The Role of Gravity in the Evolution of the Concentration Field in the Electrochemical Membrane Cell
by Kornelia M. Batko, Andrzej Ślęzak and Wioletta M. Bajdur
Entropy 2020, 22(6), 680; https://doi.org/10.3390/e22060680 - 18 Jun 2020
Cited by 6 | Viewed by 2018
Abstract
The subject of the study was the osmotic volume transport of aqueous CuSO4 and/or ethanol solutions through a selective cellulose acetate membrane (Nephrophan). The effect of concentration of solution components, concentration polarization of solutions and configuration of the membrane system on the [...] Read more.
The subject of the study was the osmotic volume transport of aqueous CuSO4 and/or ethanol solutions through a selective cellulose acetate membrane (Nephrophan). The effect of concentration of solution components, concentration polarization of solutions and configuration of the membrane system on the value of the volume osmotic flux ( J v i r ) in a single-membrane system in which the polymer membrane located in the horizontal plane was examined. The investigations were carried out under mechanical stirring conditions of the solutions and after it was turned off. Based on the obtained measurement results J v i r , the effects of concentration polarization, convection polarization, asymmetry and amplification of the volume osmotic flux and the thickness of the concentration boundary layers were calculated. Osmotic entropy production was also calculated for solution homogeneity and concentration polarization conditions. Using the thickness of the concentration boundary layers, critical values of the Rayleigh concentration number ( R C r ), i.e., the switch, were estimated between two states: convective (with higher J v i r ) and non-convective (with lower J v i r ). The operation of this switch indicates the regulatory role of earthly gravity in relation to membrane transport. Full article
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19 pages, 836 KiB  
Article
LSSVR Model of G-L Mixed Noise-Characteristic with Its Applications
by Shiguang Zhang, Ting Zhou, Lin Sun, Wei Wang and Baofang Chang
Entropy 2020, 22(6), 629; https://doi.org/10.3390/e22060629 - 6 Jun 2020
Cited by 3 | Viewed by 1931
Abstract
Due to the complexity of wind speed, it has been reported that mixed-noise models, constituted by multiple noise distributions, perform better than single-noise models. However, most existing regression models suppose that the noise distribution is single. Therefore, we study the Least square [...] Read more.
Due to the complexity of wind speed, it has been reported that mixed-noise models, constituted by multiple noise distributions, perform better than single-noise models. However, most existing regression models suppose that the noise distribution is single. Therefore, we study the Least square S V R of the Gaussian–Laplacian mixed homoscedastic ( G L M L S S V R ) and heteroscedastic noise ( G L M H L S S V R ) for complicated or unknown noise distributions. The ALM technique is used to solve model G L M L S S V R . G L M L S S V R is used to predict short-term wind speed with historical data. The prediction results indicate that the presented model is superior to the single-noise model, and has fine performance. Full article
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17 pages, 4051 KiB  
Article
Non-Linear Regression Modelling to Estimate the Global Warming Potential of a Newspaper
by Alexis Lozano, Pedro Cabrera and Ana M. Blanco-Marigorta
Entropy 2020, 22(5), 590; https://doi.org/10.3390/e22050590 - 25 May 2020
Cited by 4 | Viewed by 3420
Abstract
Technological innovations are not enough by themselves to achieve social and environmental sustainability in companies. Sustainable development aims to determine the environmental impact of a product and the hidden price of products and services through the concept of radical transparency. This means that [...] Read more.
Technological innovations are not enough by themselves to achieve social and environmental sustainability in companies. Sustainable development aims to determine the environmental impact of a product and the hidden price of products and services through the concept of radical transparency. This means that companies should show and disclose the impact on the environment of any good or service. This way, the consumer can choose in a transparent manner, not only for the price. The use of the eco-label as a European eco-label, which bases its criteria on life cycle assessment, could provide an indicator of corporate social responsibility for a given product. However, it does not give a full guarantee that the product was obtained in a sustainable manner. The aim of this work is to provide a way of calculating the value of the environmental impacts of an industrial product, under different operating conditions, so that each company can provide detailed information on the impacts of its products, information that can form part of its "green product sheet". As a case study, the daily production of a newspaper, printed by coldset, has been chosen. Each process involved in production was configured with raw material and energy consumption information from production plants, manufacturer data and existing databases. Four non-linear regression models have been trained to estimate the impact of a newspaper’s circulation from five input variables (pages, grammage, height, paper type, and print run) with 5508 data samples each. These non-linear regression models were trained using the Levenberg–Marquardt nonlinear least squares algorithm. The mean absolute percentage errors (MAPE) obtained by all the non-linear regression models tested were less than 5%. Through the proposed correlations, it is possible to obtain a score that reports on the impact of the product for different operating conditions and several types of raw materials. Ecolabelling can be further developed by incorporating a scoring system for the impact caused by the product or process, using a standardised impact methodology. Full article
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12 pages, 978 KiB  
Article
Comparison of the Probabilistic Ant Colony Optimization Algorithm and Some Iteration Method in Application for Solving the Inverse Problem on Model With the Caputo Type Fractional Derivative
by Rafał Brociek, Agata Chmielowska and Damian Słota
Entropy 2020, 22(5), 555; https://doi.org/10.3390/e22050555 - 15 May 2020
Cited by 12 | Viewed by 2070
Abstract
This paper presents the algorithms for solving the inverse problems on models with the fractional derivative. The presented algorithm is based on the Real Ant Colony Optimization algorithm. In this paper, the examples of the algorithm application for the inverse heat conduction problem [...] Read more.
This paper presents the algorithms for solving the inverse problems on models with the fractional derivative. The presented algorithm is based on the Real Ant Colony Optimization algorithm. In this paper, the examples of the algorithm application for the inverse heat conduction problem on the model with the fractional derivative of the Caputo type is also presented. Based on those examples, the authors are comparing the proposed algorithm with the iteration method presented in the paper: Zhang, Z. An undetermined coefficient problem for a fractional diffusion equation. Inverse Probl. 2016, 32. Full article
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12 pages, 3459 KiB  
Article
Complex Extreme Sea Levels Prediction Analysis: Karachi Coast Case Study
by Faisal Ahmed Khan, Tariq Masood Ali Khan, Ali Najah Ahmed, Haitham Abdulmohsin Afan, Mohsen Sherif, Ahmed Sefelnasr and Ahmed El-Shafie
Entropy 2020, 22(5), 549; https://doi.org/10.3390/e22050549 - 14 May 2020
Cited by 10 | Viewed by 3535
Abstract
In this study, the analysis of the extreme sea level was carried out by using 10 years (2007–2016) of hourly tide gauge data of Karachi port station along the Pakistan coast. Observations revealed that the magnitudes of the tides usually exceeded the storm [...] Read more.
In this study, the analysis of the extreme sea level was carried out by using 10 years (2007–2016) of hourly tide gauge data of Karachi port station along the Pakistan coast. Observations revealed that the magnitudes of the tides usually exceeded the storm surges at this station. The main observation for this duration and the subsequent analysis showed that in June 2007 a tropical Cyclone “Yemyin” hit the Pakistan coast. The joint probability method (JPM) and the annual maximum method (AMM) were used for statistical analysis to find out the return periods of different extreme sea levels. According to the achieved results, the AMM and JPM methods erre compatible with each other for the Karachi coast and remained well within the range of 95% confidence. For the JPM method, the highest astronomical tide (HAT) of the Karachi coast was considered as the threshold and the sea levels above it were considered extreme sea levels. The 10 annual observed sea level maxima, in the recent past, showed an increasing trend for extreme sea levels. In the study period, the increment rates of 3.6 mm/year and 2.1 mm/year were observed for mean sea level and extreme sea level, respectively, along the Karachi coast. Tidal analysis, for the Karachi tide gauge data, showed less dependency of the extreme sea levels on the non-tidal residuals. By applying the Merrifield criteria of mean annual maximum water level ratio, it was found that the Karachi coast was tidally dominated and the non-tidal residual contribution was just 10%. The examination of the highest water level event (13 June 2014) during the study period, further favored the tidal dominance as compared to the non-tidal component along the Karachi coast. Full article
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20 pages, 5294 KiB  
Article
Reference Evapotranspiration Modeling Using New Heuristic Methods
by Rana Muhammad Adnan, Zhihuan Chen, Xiaohui Yuan, Ozgur Kisi, Ahmed El-Shafie, Alban Kuriqi and Misbah Ikram
Entropy 2020, 22(5), 547; https://doi.org/10.3390/e22050547 - 13 May 2020
Cited by 32 | Viewed by 3074
Abstract
The study investigates the potential of two new machine learning methods, least-square support vector regression with a gravitational search algorithm (LSSVR-GSA) and the dynamic evolving neural-fuzzy inference system (DENFIS), for modeling reference evapotranspiration (ETo) using limited data. The results of the new methods [...] Read more.
The study investigates the potential of two new machine learning methods, least-square support vector regression with a gravitational search algorithm (LSSVR-GSA) and the dynamic evolving neural-fuzzy inference system (DENFIS), for modeling reference evapotranspiration (ETo) using limited data. The results of the new methods are compared with the M5 model tree (M5RT) approach. Previous values of temperature data and extraterrestrial radiation information obtained from three stations, in China, are used as inputs to the models. The estimation exactness of the models is measured by three statistics: root mean square error, mean absolute error, and determination coefficient. According to the results, the temperature or extraterrestrial radiation-based LSSVR-GSA models perform superiorly to the DENFIS and M5RT models in terms of estimating monthly ETo. However, in some cases, a slight difference was found between the LSSVR-GSA and DENFIS methods. The results indicate that better prediction accuracy may be obtained using only extraterrestrial radiation information for all three methods. The prediction accuracy of the models is not generally improved by including periodicity information in the inputs. Using optimum air temperature and extraterrestrial radiation inputs together generally does not increase the accuracy of the applied methods in the estimation of monthly ETo. Full article
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11 pages, 1775 KiB  
Article
Simulation of S-Entropy Production during the Transport of Non-Electrolyte Solutions in the Double-Membrane System
by Andrzej Ślęzak, Wioletta M. Bajdur, Kornelia M. Batko and Radomir Šcurek
Entropy 2020, 22(4), 463; https://doi.org/10.3390/e22040463 - 18 Apr 2020
Cited by 2 | Viewed by 1929
Abstract
Using the classical Kedem–Katchalsky’ membrane transport theory, a mathematical model was developed and the original concentration volume flux (Jv), solute flux (Js) characteristics, and S-entropy production by Jv, [...] Read more.
Using the classical Kedem–Katchalsky’ membrane transport theory, a mathematical model was developed and the original concentration volume flux (Jv), solute flux (Js) characteristics, and S-entropy production by Jv, ( ( ψ S ) J v ) and by Js ( ( ψ S ) J s ) in a double-membrane system were simulated. In this system, M1 and Mr membranes separated the l, m, and r compartments containing homogeneous solutions of one non-electrolytic substance. The compartment m consists of the infinitesimal layer of solution and its volume fulfills the condition Vm → 0. The volume of compartments l and r fulfills the condition Vl = Vr → ∞. At the initial moment, the concentrations of the solution in the cell satisfy the condition Cl < Cm < Cr. Based on this model, for fixed values of transport parameters of membranes (i.e., the reflection (σl, σr), hydraulic permeability (Lpl, Lpr), and solute permeability (ωl, ωr) coefficients), the original dependencies Cm = f(ClCr), Jv = f(ClCr), Js = f(ClCr), ( Ψ S ) J v = f(ClCr), ( Ψ S ) J s = f(ClCr), Rv = f(ClCr), and Rs = f(ClCr) were calculated. Each of the obtained features was specially arranged as a pair of parabola, hyperbola, or other complex curves. Full article
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