Entropy

Research

22 pages, 3805 KiB

Open AccessArticle

Investigation on Identifying Implicit Learning Event from EEG Signal Using Multiscale Entropy and Artificial Bee Colony

by Chayapol Chaiyanan, Keiji Iramina and Boonserm Kaewkamnerdpong

Entropy 2021, 23(5), 617; https://doi.org/10.3390/e23050617 - 16 May 2021

Cited by 3 | Viewed by 2102

The way people learn will play an essential role in the sustainable development of the educational system for the future. Utilizing technology in the age of information and incorporating it into how people learn can produce better learners. Implicit learning is a type [...] Read more.

The way people learn will play an essential role in the sustainable development of the educational system for the future. Utilizing technology in the age of information and incorporating it into how people learn can produce better learners. Implicit learning is a type of learning of the underlying rules without consciously seeking or understanding the rules; it is commonly seen in small children while learning how to speak their native language without learning grammar. This research aims to introduce a processing system that can systematically identify the relationship between implicit learning events and their Encephalogram (EEG) signal characteristics. This study converted the EEG signal from participants while performing cognitive task experiments into Multiscale Entropy (MSE) data. Using MSE data from different frequency bands and channels as features, the system explored a wide range of classifiers and observed their performance to see how they classified the features related to participants’ performance. The Artificial Bee Colony (ABC) method was used for feature selection to improve the process to make the system more efficient. The results showed that the system could correctly identify the differences between participants’ performance using MSE data and the ABC method with 95% confidence. Full article

(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)

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11 pages, 300 KiB

Open AccessArticle

Electrical Power Diversification: An Approach Based on the Method of Maximum Entropy in the Mean

by Rafael Bautista, Henryk Gzyl, Enrique ter Horst and Germán Molina

Entropy 2021, 23(3), 281; https://doi.org/10.3390/e23030281 - 26 Feb 2021

Cited by 1 | Viewed by 1402

Abstract

Electrical energy is generated in different ways, each located at some specific geographical area, and with different impact on the environment. Different sectors require heterogeneous rates of energy delivery, due to economic requirements. An important problem to solve is to determine how much [...] Read more.

Electrical energy is generated in different ways, each located at some specific geographical area, and with different impact on the environment. Different sectors require heterogeneous rates of energy delivery, due to economic requirements. An important problem to solve is to determine how much energy must be sent from each supplier to satisfy each demand. Besides, the energy distribution process may have to satisfy ecological, technological, or economic cost constraints. Full article

(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)

12 pages, 4032 KiB

Open AccessEditor’s ChoiceArticle

On-Road Detection of Driver Fatigue and Drowsiness during Medium-Distance Journeys

by Luca Salvati, Matteo d’Amore, Anita Fiorentino, Arcangelo Pellegrino, Pasquale Sena and Francesco Villecco

Entropy 2021, 23(2), 135; https://doi.org/10.3390/e23020135 - 21 Jan 2021

Cited by 35 | Viewed by 3722

Abstract

Background: The detection of driver fatigue as a cause of sleepiness is a key technology capable of preventing fatal accidents. This research uses a fatigue-related sleepiness detection algorithm based on the analysis of the pulse rate variability generated by the heartbeat and [...] Read more.

Background: The detection of driver fatigue as a cause of sleepiness is a key technology capable of preventing fatal accidents. This research uses a fatigue-related sleepiness detection algorithm based on the analysis of the pulse rate variability generated by the heartbeat and validates the proposed method by comparing it with an objective indicator of sleepiness (PERCLOS). Methods: changes in alert conditions affect the autonomic nervous system (ANS) and therefore heart rate variability (HRV), modulated in the form of a wave and monitored to detect long-term changes in the driver’s condition using real-time control. Results: the performance of the algorithm was evaluated through an experiment carried out in a road vehicle. In this experiment, data was recorded by three participants during different driving sessions and their conditions of fatigue and sleepiness were documented on both a subjective and objective basis. The validation of the results through PERCLOS showed a 63% adherence to the experimental findings. Conclusions: the present study confirms the possibility of continuously monitoring the driver’s status through the detection of the activation/deactivation states of the ANS based on HRV. The proposed method can help prevent accidents caused by drowsiness while driving. Full article

(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)

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12 pages, 285 KiB

Open AccessArticle

Partial Exactness for the Penalty Function of Biconvex Programming

by Min Jiang, Zhiqing Meng and Rui Shen

Entropy 2021, 23(2), 132; https://doi.org/10.3390/e23020132 - 21 Jan 2021

Cited by 6 | Viewed by 1614

Abstract

Biconvex programming (or inequality constrained biconvex optimization) is an important model in solving many engineering optimization problems in areas like machine learning and signal and information processing. In this paper, the partial exactness of the partial optimum for the penalty function of biconvex [...] Read more.

Biconvex programming (or inequality constrained biconvex optimization) is an important model in solving many engineering optimization problems in areas like machine learning and signal and information processing. In this paper, the partial exactness of the partial optimum for the penalty function of biconvex programming is studied. The penalty function is partially exact if the partial Karush–Kuhn–Tucker (KKT) condition is true. The sufficient and necessary partially local stability condition used to determine whether the penalty function is partially exact for a partial optimum solution is also proven. Based on the penalty function, an algorithm is presented for finding a partial optimum solution to an inequality constrained biconvex optimization, and its convergence is proven under some conditions. Full article

(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)

22 pages, 2667 KiB

Open AccessArticle

The Application of Fractal Transform and Entropy for Improving Fault Tolerance and Load Balancing in Grid Computing Environments

by Murad B. Khorsheed, Qasim M. Zainel, Oday A. Hassen and Saad M. Darwish

Entropy 2020, 22(12), 1410; https://doi.org/10.3390/e22121410 - 15 Dec 2020

Cited by 3 | Viewed by 2108

Abstract

This paper applies the entropy-based fractal indexing scheme that enables the grid environment for fast indexing and querying. It addresses the issue of fault tolerance and load balancing-based fractal management to make computational grids more effective and reliable. A fractal dimension of a [...] Read more.

This paper applies the entropy-based fractal indexing scheme that enables the grid environment for fast indexing and querying. It addresses the issue of fault tolerance and load balancing-based fractal management to make computational grids more effective and reliable. A fractal dimension of a cloud of points gives an estimate of the intrinsic dimensionality of the data in that space. The main drawback of this technique is the long computing time. The main contribution of the suggested work is to investigate the effect of fractal transform by adding R-tree index structure-based entropy to existing grid computing models to obtain a balanced infrastructure with minimal fault. In this regard, the presented work is going to extend the commonly scheduling algorithms that are built based on the physical grid structure to a reduced logical network. The objective of this logical network is to reduce the searching in the grid paths according to arrival time rate and path’s bandwidth with respect to load balance and fault tolerance, respectively. Furthermore, an optimization searching technique is utilized to enhance the grid performance by investigating the optimum number of nodes extracted from the logical grid. The experimental results indicated that the proposed model has better execution time, throughput, makespan, latency, load balancing, and success rate. Full article

(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)

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30 pages, 1619 KiB

Open AccessArticle

Reconstructing Nonparametric Productivity Networks

by Moriah B. Bostian, Cinzia Daraio, Rolf Färe, Shawna Grosskopf, Maria Grazia Izzo, Luca Leuzzi, Giancarlo Ruocco and William L. Weber

Entropy 2020, 22(12), 1401; https://doi.org/10.3390/e22121401 - 11 Dec 2020

Cited by 5 | Viewed by 2259

Abstract

Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to [...] Read more.

Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes. We fill this gap by developing a general framework to infer the network structure in a Bayesian sense, in order to better understand the underlying relationships driving system performance. Our approach draws on recent advances in information science, machine learning and statistical inference from the physics of complex systems to estimate unobserved network linkages. To illustrate, we apply our framework to analyze the production of knowledge, via own and cross-disciplinary research, for a world-country panel of bibliometric data. We find significant interactions between related disciplinary research output, both in terms of quantity and quality. In the context of research productivity, our results on cross-disciplinary linkages could be used to better target research funding across disciplines and institutions. More generally, our framework for inferring the underlying network production technology could be applied to both public and private settings which entail spillovers, including intra- and inter-firm managerial decisions and public agency coordination. This framework also provides a systematic approach to model selection when the underlying network structure is unknown. Full article

(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)

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17 pages, 11242 KiB

Open AccessArticle

Noise Reduction in Spur Gear Systems

by Aurelio Liguori, Enrico Armentani, Alcide Bertocco, Andrea Formato, Arcangelo Pellegrino and Francesco Villecco

Entropy 2020, 22(11), 1306; https://doi.org/10.3390/e22111306 - 16 Nov 2020

Cited by 27 | Viewed by 3434

Abstract

This article lists some tips for reducing gear case noise. With this aim, a static analysis was carried out in order to describe how stresses resulting from meshing gears affect the acoustic emissions. Different parameters were taken into account, such as the friction, [...] Read more.

This article lists some tips for reducing gear case noise. With this aim, a static analysis was carried out in order to describe how stresses resulting from meshing gears affect the acoustic emissions. Different parameters were taken into account, such as the friction, material, and lubrication, in order to validate ideas from the literature and to make several comparisons. Furthermore, a coupled Eulerian–Lagrangian (CEL) analysis was performed, which was an innovative way of evaluating the sound pressure level of the aforementioned gears. Different parameters were considered again, such as the friction, lubrication, material, and rotational speed, in order to make different research comparisons. The analytical results agreed with those in the literature, both for the static analysis and CEL analysis—for example, it was shown that changing the material from steel to ductile iron improved the gear noise, while increasing the rotational speed or the friction increased the acoustic emissions. Regarding the CEL analysis, air was considered a perfect gas, but its viscosity or another state equation could have also been taken into account. Therefore, the above allowed us to state that research into these scientific fields will bring about reliable results. Full article

(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)

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14 pages, 3289 KiB

Open AccessArticle

A New Adaptive Entropy Portfolio Selection Model

by Ruidi Song and Yue Chan

Entropy 2020, 22(9), 951; https://doi.org/10.3390/e22090951 - 28 Aug 2020

Cited by 4 | Viewed by 2236

Abstract

In this paper, we propose an adaptive entropy model (AEM), which incorporates the entropy measurement and the adaptability into the conventional Markowitz’s mean-variance model (MVM). We evaluate the performance of AEM, based on several portfolio performance indicators using the five-year Shanghai Stock Exchange [...] Read more.

In this paper, we propose an adaptive entropy model (AEM), which incorporates the entropy measurement and the adaptability into the conventional Markowitz’s mean-variance model (MVM). We evaluate the performance of AEM, based on several portfolio performance indicators using the five-year Shanghai Stock Exchange 50 (SSE50) index constituent stocks data set. Our outcomes show, compared with the traditional portfolio selection model, that AEM tends to make our investments more decentralized and hence helps to neutralize unsystematic risks. Due to the existence of self-adaptation, AEM turns out to be more adaptable to market fluctuations and helps to maintain the balance between the decentralized and concentrated investments in order to meet investors’ expectations. Our model applies equally well to portfolio optimizations for other financial markets. Full article

(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)

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15 pages, 7034 KiB

Open AccessArticle

Modeling of Eddy Current Welding of Rail: Three-Dimensional Simulation

by Xiankun Sun, He Liu, Wanqing Song and Francesco Villecco

Entropy 2020, 22(9), 947; https://doi.org/10.3390/e22090947 - 28 Aug 2020

Cited by 19 | Viewed by 3077

Abstract

In this paper is given a three-dimensional numerical simulation of the eddy current welding of rails where the longitudinal two directions are not ignored. In fact, usually it is considered a model where, in the two-dimensional numerical simulation of rail heat treatment, the [...] Read more.

In this paper is given a three-dimensional numerical simulation of the eddy current welding of rails where the longitudinal two directions are not ignored. In fact, usually it is considered a model where, in the two-dimensional numerical simulation of rail heat treatment, the longitudinal directions are ignored for the magnetic induction strength and temperature, and only the axial calculation is performed. Therefore, we propose the electromagnetic-thermal coupled three-dimensional model of eddy current welding. The induced eddy current heat is obtained by adding the z-axis spatial angle to the two-dimensional electromagnetic-thermal, thus obtaining some new results by coupling the numerical simulation and computations of the electric field and magnetic induction intensity of the three-dimensional model. Moreover, we have considered the objective function into a weak formulation. The three-dimensional model is then meshed by the finite element method. The electromagnetic-thermal coupling has been numerically computed, and the parametric dependence to the eddy current heating process has been fully studied. Through the numerical simulation with different current densities, frequencies, and distances, the most suitable heat treatment process of U75V rail is obtained. Full article

(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)

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16 pages, 708 KiB

Open AccessEditor’s ChoiceArticle

An Efficient Method Based on Framelets for Solving Fractional Volterra Integral Equations

by Mutaz Mohammad, Alexander Trounev and Carlo Cattani

Entropy 2020, 22(8), 824; https://doi.org/10.3390/e22080824 - 28 Jul 2020

Cited by 11 | Viewed by 2663

Abstract

This paper is devoted to shedding some light on the advantages of using tight frame systems for solving some types of fractional Volterra integral equations (FVIEs) involved by the Caputo fractional order derivative. A tight frame or simply framelet, is a generalization of [...] Read more.

This paper is devoted to shedding some light on the advantages of using tight frame systems for solving some types of fractional Volterra integral equations (FVIEs) involved by the Caputo fractional order derivative. A tight frame or simply framelet, is a generalization of an orthonormal basis. A lot of applications are modeled by non-negative functions; taking this into account in this paper, we consider framelet systems generated using some refinable non-negative functions, namely, B-splines. The FVIEs we considered were reduced to a set of linear system of equations and were solved numerically based on a collocation discretization technique. We present many important examples of FVIEs for which accurate and efficient numerical solutions have been accomplished and the numerical results converge very rapidly to the exact ones. Full article

(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)

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