Symmetry

16 pages, 7380 KiB

Open AccessArticle

Multi-Column Atrous Convolutional Neural Network for Counting Metro Passengers

by Jun Zhang, Gaoyi Zhu and Zhizhong Wang

Symmetry 2020, 12(4), 682; https://doi.org/10.3390/sym12040682 - 24 Apr 2020

Cited by 8 | Viewed by 3998

We propose a symmetric method of accurately estimating the number of metro passengers from an individual image. To this end, we developed a network for metro-passenger counting called MPCNet, which provides a data-driven and deep learning method of understanding highly congested scenes and [...] Read more.

We propose a symmetric method of accurately estimating the number of metro passengers from an individual image. To this end, we developed a network for metro-passenger counting called MPCNet, which provides a data-driven and deep learning method of understanding highly congested scenes and accurately estimating crowds, as well as presenting high-quality density maps. The proposed MPCNet is composed of two major components: A deep convolutional neural network (CNN) as the front end, for deep feature extraction; and a multi-column atrous CNN as the back-end, with atrous spatial pyramid pooling (ASPP) to deliver multi-scale reception fields. Existing crowd-counting datasets do not adequately cover all the challenging situations considered in our work. Therefore, we collected specific subway passenger video to compile and label a large new dataset that includes 346 images with 3475 annotated heads. We conducted extensive experiments with this and other datasets to verify the effectiveness of the proposed model. Our results demonstrate the excellent performance of the proposed MPCNet. Full article

(This article belongs to the Special Issue Recent Advances on Deep Learning for Safety and Security of Multimedia Data in the Critical Infrastructure)

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19 pages, 17360 KiB

Open AccessArticle

Fixing Acceleration and Image Resolution Issues of Nuclear Magnetic Resonance

by Krzysztof Malczewski

Symmetry 2020, 12(4), 681; https://doi.org/10.3390/sym12040681 - 24 Apr 2020

Viewed by 2502

Abstract

Lately, Magnetic Resonance scans have struggled with their own inherent limitations, such as spatial resolution as well as long examination times. A novel, rapid compressively-sensed magnetic resonance high-resolution image resolution algorithm is presented in this research paper. This technique addresses these two key [...] Read more.

Lately, Magnetic Resonance scans have struggled with their own inherent limitations, such as spatial resolution as well as long examination times. A novel, rapid compressively-sensed magnetic resonance high-resolution image resolution algorithm is presented in this research paper. This technique addresses these two key issues by employing a highly-sparse sampling scheme and super-resolution reconstruction (SRR) method. Due to highly challenging requirements for the accuracy of diagnostic images registration, the presented technique exploits image priors, deblurring, parallel imaging, and a deformable human body motion analysis. Clinical trials as well as a phantom-based study have been conducted. It has been proven that the proposed algorithm can enhance image spatial resolution and reduce motion artefacts and scan times. Full article

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18 pages, 438 KiB

Open AccessArticle

Robust Finite-Time Control of Linear System with Non-Differentiable Time-Varying Delay

by Wanwisa Puangmalai, Jirapong Puangmalai and Thaned Rojsiraphisal

Symmetry 2020, 12(4), 680; https://doi.org/10.3390/sym12040680 - 24 Apr 2020

Cited by 6 | Viewed by 2848

Abstract

Practical systems such as hybrid power systems are currently implemented around the world. In order to get the system to work properly, the systems usually require their behavior to be maintained or state values to stay within a certain threshold. However, it is [...] Read more.

Practical systems such as hybrid power systems are currently implemented around the world. In order to get the system to work properly, the systems usually require their behavior to be maintained or state values to stay within a certain threshold. However, it is difficult to form a perfect mathematical model for describing behavior of the practical systems since there may be some information (uncertainties) that is not observed. Thus, in this article, we studied the stability of an uncertain linear system with a non-differentiable time-varying delay. We constructed Lyapunov-Krasovskii functionals (LKFs) containing several symmetric positive definite matrices to obtain robust finite-time stability (RFTS) and stabilization (RFTU) of the uncertain linear system. With the controller and uncertainties in the considered system, there exist nonlinear terms occurring in the formulation process. Past research handled these nonlinear terms as new variables but this led to some difficulty from a computation point of view. Instead, we applied a novel approach via Cauchy-like matrix inequalities to handle these difficulties. In the end, we present three numerical simulations to show the effectiveness of our proposed theory. Full article

(This article belongs to the Special Issue Fixed Point Theory and Computational Analysis with Applications)

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32 pages, 8133 KiB

Open AccessArticle

Intelligent Clustering and Dynamic Incremental Learning to Generate Multi-Codebook Fuzzy Neural Network for Multi-Modal Data Classification

by Muhammad Anwar Ma’sum

Symmetry 2020, 12(4), 679; https://doi.org/10.3390/sym12040679 - 24 Apr 2020

Cited by 1 | Viewed by 3202

Abstract

Classification in multi-modal data is one of the challenges in the machine learning field. The multi-modal data need special treatment as its features are distributed in several areas. This study proposes multi-codebook fuzzy neural networks by using intelligent clustering and dynamic incremental learning [...] Read more.

Classification in multi-modal data is one of the challenges in the machine learning field. The multi-modal data need special treatment as its features are distributed in several areas. This study proposes multi-codebook fuzzy neural networks by using intelligent clustering and dynamic incremental learning for multi-modal data classification. In this study, we utilized intelligent K-means clustering based on anomalous patterns and intelligent K-means clustering based on histogram information. In this study, clustering is used to generate codebook candidates before the training process, while incremental learning is utilized when the condition to generate a new codebook is sufficient. The condition to generate a new codebook in incremental learning is based on the similarity of the winner class and other classes. The proposed method was evaluated in synthetic and benchmark datasets. The experiment results showed that the proposed multi-codebook fuzzy neural networks that use dynamic incremental learning have significant improvements compared to the original fuzzy neural networks. The improvements were 15.65%, 5.31% and 11.42% on the synthetic dataset, the benchmark dataset, and the average of all datasets, respectively, for incremental version 1. The incremental learning version 2 improved by 21.08% 4.63%, and 14.35% on the synthetic dataset, the benchmark dataset, and the average of all datasets, respectively. The multi-codebook fuzzy neural networks that use intelligent clustering also had significant improvements compared to the original fuzzy neural networks, achieving 23.90%, 2.10%, and 15.02% improvements on the synthetic dataset, the benchmark dataset, and the average of all datasets, respectively. Full article

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11 pages, 6674 KiB

Open AccessArticle

Substructure-Based Topology Optimization for Symmetric Hierarchical Lattice Structures

by Zijun Wu and Renbin Xiao

Symmetry 2020, 12(4), 678; https://doi.org/10.3390/sym12040678 - 24 Apr 2020

Cited by 1 | Viewed by 3594

Abstract

This work presents a topology optimization method for symmetric hierarchical lattice structures with substructuring. In this method, we define two types of symmetric lattice substructures, each of which contains many finite elements. By controlling the materials distribution of these elements, the configuration of [...] Read more.

This work presents a topology optimization method for symmetric hierarchical lattice structures with substructuring. In this method, we define two types of symmetric lattice substructures, each of which contains many finite elements. By controlling the materials distribution of these elements, the configuration of substructure can be changed. And then each substructure is condensed into a super-element. A surrogate model based on a series of super-elements can be built using the cubic B-spline interpolation. Here, the relative density of substructure is set as the design variable. The optimality criteria method is used for the updating of design variables on two scales. In the process of topology optimization, the symmetry of microstructure is determined by self-defined microstructure configuration, while the symmetry of macro structure is determined by boundary conditions. In this proposed method, because of the educing number of degree of freedoms on macrostructure, the proposed method has high efficiency in optimization. Numerical examples show that both the size and the number of substructures have essential influences on macro structure, indicating the effectiveness of the presented method. Full article

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12 pages, 4976 KiB

Open AccessFeature PaperArticle

A New Experimental Method for Determining the Thickness of Thin Surface Layers of Intensive Plastic Deformation Using Electron Backscatter Diffraction Data

by Alexander Smirnov, Evgeniya Smirnova and Sergey Alexandrov

Symmetry 2020, 12(4), 677; https://doi.org/10.3390/sym12040677 - 24 Apr 2020

Cited by 6 | Viewed by 2867

Abstract

It is, in general, essential to investigate correlations between the microstructure and properties of materials. Plastic deformation often localizes within thin layers. As a result, many material properties within such layers are very different from the properties in bulk. The present paper proposes [...] Read more.

It is, in general, essential to investigate correlations between the microstructure and properties of materials. Plastic deformation often localizes within thin layers. As a result, many material properties within such layers are very different from the properties in bulk. The present paper proposes a new method for determining the thickness of a thin surface layer of intensive plastic deformation in metallic materials. For various types of materials, such layers are often generated near frictional interfaces. The method is based on data obtained by Electron Backscatter Diffraction. The results obtained are compared with those obtained by an alternative method based on microhardness measurements. The new method allows for determining the layer thickness of several microns in specimens after grinding. In contrast, the measurement of microhardness does not reveal the presence of this layer. The grain-based and kernel-based types of algorithms are also adopted for determining the thickness of the layer. Data processed by the strain contouring and kernel average misorientation algorithms are given to illustrate this method. It is shown that these algorithms do not clearly detect the boundary between the layer of intensive plastic deformation and the bulk. As a result, these algorithms are unable to determine the thickness of the layer with high accuracy. Full article

(This article belongs to the Special Issue Materials Science: Synthesis, Structure, Properties)

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21 pages, 4446 KiB

Open AccessReview

Sixth Generation (6G) Wireless Networks: Vision, Research Activities, Challenges and Potential Solutions

by Mohammed H. Alsharif, Anabi Hilary Kelechi, Mahmoud A. Albreem, Shehzad Ashraf Chaudhry, M. Sultan Zia and Sunghwan Kim

Symmetry 2020, 12(4), 676; https://doi.org/10.3390/sym12040676 - 24 Apr 2020

Cited by 254 | Viewed by 22857

Abstract

The standardization activities of the fifth generation communications are clearly over and deployment has commenced globally. To sustain the competitive edge of wireless networks, industrial and academia synergy have begun to conceptualize the next generation of wireless communication systems (namely, sixth generation, (6G)) [...] Read more.

The standardization activities of the fifth generation communications are clearly over and deployment has commenced globally. To sustain the competitive edge of wireless networks, industrial and academia synergy have begun to conceptualize the next generation of wireless communication systems (namely, sixth generation, (6G)) aimed at laying the foundation for the stratification of the communication needs of the 2030s. In support of this vision, this study highlights the most promising lines of research from the recent literature in common directions for the 6G project. Its core contribution involves exploring the critical issues and key potential features of 6G communications, including: (i) vision and key features; (ii) challenges and potential solutions; and (iii) research activities. These controversial research topics were profoundly examined in relation to the motivation of their various sub-domains to achieve a precise, concrete, and concise conclusion. Thus, this article will contribute significantly to opening new horizons for future research directions. Full article

(This article belongs to the Special Issue Information Technologies and Electronics)

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2 pages, 146 KiB

Open AccessEditorial

EDITORIAL: Asymmetry Indexes, Behavioral Instability and the Characterization of Behavioral Patterns

by Cino Pertoldi, Sussie Pagh and Lars Arve Bach

Symmetry 2020, 12(4), 675; https://doi.org/10.3390/sym12040675 - 24 Apr 2020

Cited by 2 | Viewed by 2362

Abstract

A change in a behavior is often the first and fast reaction to an environmental (external) or physiological (internal) stimulus that animals (and plants) are exposed to [...] Full article

(This article belongs to the Special Issue Asymmetry Indexes, Behavioural Instability and the Characterization of Behavioural Patterns)

29 pages, 387 KiB

Open AccessArticle

Symmetry Classes and Matrix Representations of the 2D Flexoelectric Law

by Houssam Abdoul-Anziz, Nicolas Auffray and Boris Desmorat

Symmetry 2020, 12(4), 674; https://doi.org/10.3390/sym12040674 - 23 Apr 2020

Cited by 4 | Viewed by 2820

Abstract

We determine the different symmetry classes of bi-dimensional flexoelectric tensors. Using the harmonic decomposition method, we show that there are six symmetry classes. We also provide the matrix representations of the flexoelectric tensor and of the complete flexoelectric law, for each symmetry class. [...] Read more.

We determine the different symmetry classes of bi-dimensional flexoelectric tensors. Using the harmonic decomposition method, we show that there are six symmetry classes. We also provide the matrix representations of the flexoelectric tensor and of the complete flexoelectric law, for each symmetry class. Full article

(This article belongs to the Special Issue Recent Advances in the Study of Symmetry and Continuum Mechanics)

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15 pages, 511 KiB

Open AccessArticle

Exact Mechanical Hierarchy of Non-Linear Fractional-Order Hereditariness

by Gioacchino Alotta, Emanuela Bologna and Massimiliano Zingales

Symmetry 2020, 12(4), 673; https://doi.org/10.3390/sym12040673 - 23 Apr 2020

Cited by 3 | Viewed by 2503

Abstract

Non-local time evolution of material stress/strain is often referred to as material hereditariness. In this paper, the widely used non-linear approach to single integral time non-local mechanics named quasi-linear approach is proposed in the context of fractional differential calculus. The non-linear model of [...] Read more.

Non-local time evolution of material stress/strain is often referred to as material hereditariness. In this paper, the widely used non-linear approach to single integral time non-local mechanics named quasi-linear approach is proposed in the context of fractional differential calculus. The non-linear model of the springpot is defined in terms of a single integral with separable kernel endowed with a non-linear transform of the state variable that allows for the use of Boltzmann superposition. The model represents a self-similar hierarchy that allows for a time-invariance as the result of the application of the conservation laws at any resolution scale. It is shown that the non-linear springpot possess an equivalent mechanical hierarchy in terms of a functionally-graded elastic column resting on viscous dashpots with power-law decay of the material properties. Some numerical applications are reported to show the capabilities of the proposed model. Full article

(This article belongs to the Special Issue Time and Space Nonlocal Operators in Structural Mechanics)

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8 pages, 269 KiB

Open AccessArticle

Polarized Initial States of Primordial Gravitational Waves

by Sugumi Kanno and Jiro Soda

Symmetry 2020, 12(4), 672; https://doi.org/10.3390/sym12040672 - 23 Apr 2020

Cited by 4 | Viewed by 2188

Abstract

Polarizations of primordial gravitational waves can be relevant when considering an inflationary universe in modified gravity or when matter fields survive during inflation. Such polarizations have been discussed in the Bunch–Davies vacuum. Instead of taking into account the dynamical generation of polarizations of [...] Read more.

Polarizations of primordial gravitational waves can be relevant when considering an inflationary universe in modified gravity or when matter fields survive during inflation. Such polarizations have been discussed in the Bunch–Davies vacuum. Instead of taking into account the dynamical generation of polarizations of gravitational waves, in this paper, we consider polarized initial states constructed from

S U (2)

coherent states. We then evaluate the power spectrums of the primordial gravitational waves in the states. Full article

(This article belongs to the Special Issue Symmetry with Gravity and Particle Theories)

20 pages, 1477 KiB

Open AccessArticle

Symmetry for Multimedia-Aided Art Teaching Based on the Form of Animation Teaching Organization and Social Network

by Zheng Xie

Symmetry 2020, 12(4), 671; https://doi.org/10.3390/sym12040671 - 23 Apr 2020

Cited by 10 | Viewed by 5403

Abstract

Symmetries play a vital role in multimedia-aided art teaching activities. The relevant teaching systems designed with a social network, including the optimized teaching methods, are on the basis of symmetry principles. In order to study art teaching, from the perspective of the teaching [...] Read more.

Symmetries play a vital role in multimedia-aided art teaching activities. The relevant teaching systems designed with a social network, including the optimized teaching methods, are on the basis of symmetry principles. In order to study art teaching, from the perspective of the teaching organization form, combined with the survey method, multimedia-aided art classroom teaching was explained in detail. Based on the symmetrical thinking in art teaching, the multimedia-aided teaching mode of art classroom was discussed. The reasons for the misunderstanding of multimedia-aided art teaching were analyzed, and the core factors affecting the use of multimedia art teaching were found. In art teaching, more real pictures were shown aided by multimedia; students could experience the beauty of symmetrical things in real life and were guided to find the artistic characteristics of these kinds of graphics, analyze them, and summarize them. The results showed that this method enriched the art multimedia teaching theory and improved the efficiency of art teaching. The blind use of multimedia technology by teachers in art classroom teaching was avoided. Therefore, the method can develop individualized teaching, develop students’ potential, and cultivate innovative consciousness and practical ability. Full article

(This article belongs to the Special Issue Symmetries in Art, Nature, and Biomolecules)

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23 pages, 8795 KiB

Open AccessArticle

Can a Dynamic Reward–Penalty Mechanism Help the Implementation of Renewable Portfolio Standards under Information Asymmetry?

by Xing Xin

Symmetry 2020, 12(4), 670; https://doi.org/10.3390/sym12040670 - 23 Apr 2020

Cited by 9 | Viewed by 2867

Abstract

To further promote the low-carbon and sustainable development of China’s power industry, the Chinese government is vigorously introducing competition into power sales market. Simultaneously, On November 15, 2018, the National Development and Reform Commission issued the “Notice on Implementing the Renewable Portfolio Standards [...] Read more.

To further promote the low-carbon and sustainable development of China’s power industry, the Chinese government is vigorously introducing competition into power sales market. Simultaneously, On November 15, 2018, the National Development and Reform Commission issued the “Notice on Implementing the Renewable Portfolio Standards (Draft)” to propose the implementation of power sales side Renewable Portfolio Standards (RPS), which cannot be realized without an effective government regulation mechanism. However, information asymmetry and the limited rationality of the regulatory agencies and private power sales companies in the regulation process make the regulatory effect uncertain to the detriment of a sustainable regulation of the power industry. Thus, it is necessary to optimize the regulation mechanism of the RPS policy in China. We considered the competitive relationship between integrated power sales companies and independent power sales companies, and established an evolutionary game model based on a limited rationality. We also analyzed the implementation effects of the static reward penalty mechanism and dynamic reward penalty mechanism, respectively. The system dynamics (SD) simulation results showed that under the static reward penalty mechanism, there is no evolutionary stable equilibrium solution, and there will be volatility that exists in the evolution process. However, the dynamic reward penalty mechanism can effectively solve these problems. What is more, our results implied that governments should formulate appropriate RPS quotas, improve the green certificate trading mechanism, and take into account the market size of power sales while implementing RPS policy. Full article

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19 pages, 3388 KiB

Open AccessArticle

An Imbalanced Data Handling Framework for Industrial Big Data Using a Gaussian Process Regression-Based Generative Adversarial Network

by Eunseo Oh and Hyunsoo Lee

Symmetry 2020, 12(4), 669; https://doi.org/10.3390/sym12040669 - 23 Apr 2020

Cited by 16 | Viewed by 3635

Abstract

The developments in the fields of industrial Internet of Things (IIoT) and big data technologies have made it possible to collect a lot of meaningful industrial process and quality-based data. The gathered data are analyzed using contemporary statistical methods and machine learning techniques. [...] Read more.

The developments in the fields of industrial Internet of Things (IIoT) and big data technologies have made it possible to collect a lot of meaningful industrial process and quality-based data. The gathered data are analyzed using contemporary statistical methods and machine learning techniques. Then, the extracted knowledge can be used for predictive maintenance or prognostic health management. However, it is difficult to gather complete data due to several issues in IIoT, such as devices breaking down, running out of battery, or undergoing scheduled maintenance. Data with missing values are often ignored, as they may contain insufficient information from which to draw conclusions. In order to overcome these issues, we propose a novel, effective missing data handling mechanism for the concepts of symmetry principles. While other existing methods only attempt to estimate missing parts, the proposed method generates a whole set of data set using Gaussian process regression and a generative adversarial network. In order to prove the effectiveness of the proposed framework, we examine a real-world, industrial case involving an air pressure system (APS), where we use the proposed method to make quality predictions and compare the results with existing state-of-the-art estimation methods. Full article

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21 pages, 1656 KiB

Open AccessArticle

The Linguistic Interval-Valued Intuitionistic Fuzzy Aggregation Operators Based on Extended Hamacher T-Norm and S-Norm and Their Application

by Wei-Bo Zhu, Bin Shuai and Shi-Hang Zhang

Symmetry 2020, 12(4), 668; https://doi.org/10.3390/sym12040668 - 23 Apr 2020

Cited by 15 | Viewed by 2812

Abstract

Linguistic interval-valued intuitionistic fuzzy sets, as an extension of interval-valued intuitionistic fuzzy sets, have strong practical value in the management of complex uncertainty system with qualitative evaluation information. This study focuses on the development of several linguistic interval-valued intuitionistic fuzzy Hamacher (LIVIFH) aggregation [...] Read more.

Linguistic interval-valued intuitionistic fuzzy sets, as an extension of interval-valued intuitionistic fuzzy sets, have strong practical value in the management of complex uncertainty system with qualitative evaluation information. This study focuses on the development of several linguistic interval-valued intuitionistic fuzzy Hamacher (LIVIFH) aggregation operators based on the extended Hamacher t-norm and s-norm. First, the extended Hamacher t-norm and s-norm, which are applicable to linguistic information environment, are applied to define the linguistic interval-valued intuitionistic fuzzy Hamacher operational laws. Second, based on the proposed operational laws, this study defines the linguistic interval-valued intuitionistic fuzzy Hamacher weighted average (LIVIFHWA) operator and the linguistic interval-valued intuitionistic fuzzy Hamacher weighted geometric (LIVIFHWG) operator, and then investigates their properties. Furthermore, the degeneracy and monotonicity of the proposed operators with respect to the adjustable parameter are explored. Finally, a multiple attribute group decision-making (MAGDM) approach is developed based on the proposed LIVIFH aggregation operators, and then this approach is applied to a supplier selection problem. Parameter analysis indicates that the adjustable parameter in the proposed LIVIFH aggregation operators could reflect the attitudes of decision makers. The LIVIFHWA operator would be more appropriate to optimistic decision makers, and the LIVIFHWG operator to pessimistic decision makers. In addition, as the adjustable parameter increasing, both attitudes tend to be neutral. The proposed method is also compared with two other approaches to show its feasibility and efficiency. Full article

(This article belongs to the Special Issue Fuzzy Mathematics Applied to Science, Engineering and Sustainability Issues)

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8 pages, 419 KiB

Open AccessArticle

Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

by Wismaji Sadewo, Zuherman Rustam, Hamidah Hamidah and Alifah Roudhoh Chusmarsyah

Symmetry 2020, 12(4), 667; https://doi.org/10.3390/sym12040667 - 23 Apr 2020

Cited by 21 | Viewed by 4039

Abstract

Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic [...] Read more.

Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic cancer early. This paper proposes a machine learning approach with the twin support vector machine (TWSVM) method as a new approach to detecting pancreatic cancer early. TWSVM aims to find two symmetry planes such that each plane has a distance close to one data class and as far as possible from another data class. TWSVM is fast in building a model and has good generalizations. However, TWSVM requires kernel functions to operate in the feature space. The kernel functions commonly used are the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. This paper uses the TWSVM method with these kernels and compares the best kernel for use by TWSVM to detect pancreatic cancer early. In this paper, the TWSVM model with each kernel is evaluated using a 10-fold cross validation. The results obtained are that TWSVM based on the kernel is able to detect pancreatic cancer with good performance. However, the best kernel obtained is the RBF kernel, which produces an accuracy of 98%, a sensitivity of 97%, a specificity of 100%, and a running time of around 1.3408 s. Full article

(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)

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15 pages, 936 KiB

Open AccessArticle

Direct Power Compensation in AC Distribution Networks with SCES Systems via PI-PBC Approach

by Walter Gil-González, Federico Martin Serra, Oscar Danilo Montoya, Carlos Alberto Ramírez and César Orozco-Henao

Symmetry 2020, 12(4), 666; https://doi.org/10.3390/sym12040666 - 23 Apr 2020

Cited by 10 | Viewed by 2787

Abstract

Here, we explore the possibility of employing proportional-integral passivity-based control (PI-PBC) to support active and reactive power in alternating current (AC) distribution networks by using a supercapacitor energy storage system. A direct power control approach is proposed by taking advantage of the Park’s [...] Read more.

Here, we explore the possibility of employing proportional-integral passivity-based control (PI-PBC) to support active and reactive power in alternating current (AC) distribution networks by using a supercapacitor energy storage system. A direct power control approach is proposed by taking advantage of the Park’s reference frame transform direct and quadrature currents (

i_{d}

and

i_{q}

) into active and reactive powers (p and q). Based on the open-loop Hamiltonian model of the system, we propose a closed-loop PI-PBC controller that takes advantage of Lyapunov’s stability to design a global tracking controller. Numerical simulations in MATLAB/Simulink demonstrate the efficiency and robustness of the proposed controller, especially for parametric uncertainties. Full article

(This article belongs to the Special Issue Symmetry in Renewable Energy and Power Systems)

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10 pages, 1512 KiB

Open AccessArticle

Control of Robot Arm Motion Using Trapezoid Fuzzy Two-Degree-of-Freedom PID Algorithm

by Meng Bi

Symmetry 2020, 12(4), 665; https://doi.org/10.3390/sym12040665 - 23 Apr 2020

Cited by 24 | Viewed by 5679

Abstract

Symmetries play very important in the dynamics of robot systems. The relevant control of robot arm motion with fault diagnosis including the optimized fuzzy algorithm based on the error rate adjustment P, I, D value (Fuzzy PID algorithm) model relies on symmetry principles. [...] Read more.

Symmetries play very important in the dynamics of robot systems. The relevant control of robot arm motion with fault diagnosis including the optimized fuzzy algorithm based on the error rate adjustment P, I, D value (Fuzzy PID algorithm) model relies on symmetry principles. A robot is a kind of mechanical device that can program and perform certain operations and mobile tasks under automatic control. The manipulator is a very complex multi-input multi-output non-linear system and the main actuator of the robot. This paper focuses on the design of a control algorithm for a two-degree-of-freedom (2-DOF) manipulator. First, the mathematical model of a 2-DOF articulated manipulator is established, that is, the functional relationship between the input driving force vector and the output rotation angle vector of a 2-DOF manipulator. Then, a set of trajectory planning algorithms are designed by using gradient model control, which can calculate the trajectory of the end-effector of a 2-DOF manipulator according to the user’s task requirements. The experimental results verify the effectiveness of the proposed algorithm. Full article

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24 pages, 3443 KiB

Open AccessArticle

Fuzzy-Based Symmetrical Multi-Criteria Decision-Making Procedure for Evaluating the Impact of Harmful Factors of Healthcare Information Security

by Rajeev Kumar, Abhishek Kumar Pandey, Abdullah Baz, Hosam Alhakami, Wajdi Alhakami, Alka Agrawal and Raees Ahmad Khan

Symmetry 2020, 12(4), 664; https://doi.org/10.3390/sym12040664 - 22 Apr 2020

Cited by 42 | Viewed by 4389

Abstract

Growing concern about healthcare information security in the wake of alarmingly rising cyber-attacks is being given symmetrical priority by current researchers and cyber security experts. Intruders are penetrating symmetrical mechanisms of healthcare information security continuously. In the same league, the paper presents an [...] Read more.

Growing concern about healthcare information security in the wake of alarmingly rising cyber-attacks is being given symmetrical priority by current researchers and cyber security experts. Intruders are penetrating symmetrical mechanisms of healthcare information security continuously. In the same league, the paper presents an overview on the current situation of healthcare information and presents a layered model of healthcare information management in organizations. The paper also evaluates the various factors that have a key contribution in healthcare information security breaches through a hybrid fuzzy-based symmetrical methodology of AHP-TOPSIS. Furthermore, for assessing the effect of the calculated results, the authors have tested the results on local hospital software of Varanasi. Tested results of the factors are validated through the comparison and sensitivity analysis in this study. Tabulated results of the proposed study propose a symmetrical mechanism as the most conversant technique which can be employed by the experts and researchers for preparing security guidelines and strategies. Full article

(This article belongs to the Special Issue Fuzzy Mathematics Applied to Science, Engineering and Sustainability Issues)

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19 pages, 5567 KiB

Open AccessFeature PaperArticle

Symmetric MHD Channel Flow of Nonlocal Fractional Model of BTF Containing Hybrid Nanoparticles

by Muhammad Saqib, Sharidan Shafie, Ilyas Khan, Yu-Ming Chu and Kottakkaran Sooppy Nisar

Symmetry 2020, 12(4), 663; https://doi.org/10.3390/sym12040663 - 22 Apr 2020

Cited by 49 | Viewed by 3046

Abstract

A nonlocal fractional model of Brinkman type fluid (BTF) containing a hybrid nanostructure was examined. The magnetohydrodynamic (MHD) flow of the hybrid nanofluid was studied using the fractional calculus approach. Hybridized silver (Ag) and Titanium dioxide (TiO₂) nanoparticles were dissolved in [...] Read more.

A nonlocal fractional model of Brinkman type fluid (BTF) containing a hybrid nanostructure was examined. The magnetohydrodynamic (MHD) flow of the hybrid nanofluid was studied using the fractional calculus approach. Hybridized silver (Ag) and Titanium dioxide (TiO₂) nanoparticles were dissolved in base fluid water (H₂O) to form a hybrid nanofluid. The MHD free convection flow of the nanofluid (Ag-TiO₂-H₂O) was considered in a microchannel (flow with a bounded domain). The BTF model was generalized using a nonlocal Caputo-Fabrizio fractional operator (CFFO) without a singular kernel of order

α

with effective thermophysical properties. The governing equations of the model were subjected to physical initial and boundary conditions. The exact solutions for the nonlocal fractional model without a singular kernel were developed via the fractional Laplace transform technique. The fractional solutions were reduced to local solutions by limiting

α \to 1

. To understand the rheological behavior of the fluid, the obtained solutions were numerically computed and plotted on various graphs. Finally, the influence of pertinent parameters was physically studied. It was found that the solutions were general, reliable, realistic and fixable. For the fractional parameter, the velocity and temperature profiles showed a decreasing trend for a constant time. By setting the values of the fractional parameter, excellent agreement between the theoretical and experimental results could be attained. Full article

(This article belongs to the Special Issue Turbulence and Multiphase Flows and Symmetry)

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13 pages, 376 KiB

Open AccessReview

Dark Matter Candidates with Dark Energy Interiors Determined by Energy Conditions

by Irina Dymnikova

Symmetry 2020, 12(4), 662; https://doi.org/10.3390/sym12040662 - 22 Apr 2020

Cited by 7 | Viewed by 2418

Abstract

We outline the basic properties of regular black holes, their remnants and self-gravitating solitons G-lumps with the de Sitter and phantom interiors, which can be considered as heavy dark matter (DM) candidates generically related to a dark energy (DE). They are specified by [...] Read more.

We outline the basic properties of regular black holes, their remnants and self-gravitating solitons G-lumps with the de Sitter and phantom interiors, which can be considered as heavy dark matter (DM) candidates generically related to a dark energy (DE). They are specified by the condition

T_{t}^{t} = T_{r}^{r}

and described by regular solutions of the Kerr-Shild class. Solutions for spinning objects can be obtained from spherical solutions by the Newman-Janis algorithm. Basic feature of all spinning objects is the existence of the equatorial de Sitter vacuum disk in their deep interiors. Energy conditions distinguish two types of their interiors, preserving or violating the weak energy condition dependently on violation or satisfaction of the energy dominance condition for original spherical solutions. For the 2-nd type the weak energy condition is violated and the interior contains the phantom energy confined by an additional de Sitter vacuum surface. For spinning solitons G-lumps a phantom energy is not screened by horizons and influences their observational signatures, providing a source of information about the scale and properties of a phantom energy. Regular BH remnants and G-lumps can form graviatoms binding electrically charged particles. Their observational signature is the electromagnetic radiation with the frequencies depending on the energy scale of the interior de Sitter vacuum within the range available for observations. A nontrivial observational signature of all DM candidates with de Sitter interiors predicted by analysis of dynamical equations is the induced proton decay in an underground detector like IceCUBE, due to non-conservation of baryon and lepton numbers in their GUT scale false vacuum interiors. Full article

(This article belongs to the Special Issue Symmetries in the Universe)

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21 pages, 4034 KiB

Open AccessArticle

Image Zooming Based on Two Classes of C¹-Continuous Coons Patches Construction with Shape Parameters over Triangular Domain

by Yunyi Tang and Yuanpeng Zhu

Symmetry 2020, 12(4), 661; https://doi.org/10.3390/sym12040661 - 22 Apr 2020

Cited by 5 | Viewed by 2497

Abstract

Image interpolation is important in image zooming. To improve the quality of image zooming, in this work, we proposed a class of rational quadratic trigonometric Hermite functions with two shape parameters and two classes of

C^{1}

-continuous Coons patches constructions over a [...] Read more.

Image interpolation is important in image zooming. To improve the quality of image zooming, in this work, we proposed a class of rational quadratic trigonometric Hermite functions with two shape parameters and two classes of

C^{1}

-continuous Coons patches constructions over a triangular domain by improved side–side method and side–vertex method. Altering the values of shape parameters can adjust the interior shape of the triangular Coons patch without influencing the function values and partial derivatives of the boundaries. In order to deal with the problem of well-posedness in image zooming, we discussed symmetrical sufficient conditions for region control of shape parameters in the improved side–side method and side–vertex method. Some examples demonstrate the proposed methods are effective in surface design and digital image zooming.

C^{1}

-continuous Coons patches constructed by the proposed methods can interpolate to scattered 3D data. By up-sampling to the constructed interpolation surface, high-resolution images can be obtained. Image zooming experiment and analysis show that compared to bilinear, bicubic, iterative curvature-based interpolation (ICBI), novel edge orientation adaptive interpolation scheme for resolution enhancement of still images (NEDI), super-resolution using iterative Wiener filter based on nonlocal means (SR-NLM) and rational ball cubic B-spline (RBC), the proposed method can improve peak signal to noise ratio (PSNR) and structural similarity index (SSIM). Edge detection using Prewitt operator shows that the proposed method can better preserve sharp edges and textures in image zooming. The proposed methods can also improve the visual effect of the image, therefore it is efficient in computation for image zooming. Full article

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16 pages, 1961 KiB

Open AccessFeature PaperArticle

A Novel Learning Rate Schedule in Optimization for Neural Networks and It’s Convergence

by Jieun Park, Dokkyun Yi and Sangmin Ji

Symmetry 2020, 12(4), 660; https://doi.org/10.3390/sym12040660 - 22 Apr 2020

Cited by 30 | Viewed by 4728

Abstract

The process of machine learning is to find parameters that minimize the cost function constructed by learning the data. This is called optimization and the parameters at that time are called the optimal parameters in neural networks. In the process of finding the [...] Read more.

The process of machine learning is to find parameters that minimize the cost function constructed by learning the data. This is called optimization and the parameters at that time are called the optimal parameters in neural networks. In the process of finding the optimization, there were attempts to solve the symmetric optimization or initialize the parameters symmetrically. Furthermore, in order to obtain the optimal parameters, the existing methods have used methods in which the learning rate is decreased over the iteration time or is changed according to a certain ratio. These methods are a monotonically decreasing method at a constant rate according to the iteration time. Our idea is to make the learning rate changeable unlike the monotonically decreasing method. We introduce a method to find the optimal parameters which adaptively changes the learning rate according to the value of the cost function. Therefore, when the cost function is optimized, the learning is complete and the optimal parameters are obtained. This paper proves that the method ensures convergence to the optimal parameters. This means that our method achieves a minimum of the cost function (or effective learning). Numerical experiments demonstrate that learning is good effective when using the proposed learning rate schedule in various situations. Full article

(This article belongs to the Special Issue Advance in Nonlinear Analysis and Optimization)

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12 pages, 3309 KiB

Open AccessArticle

Optimization of Public Health Education Parameters for Controlling the Spread of HIV/AIDS Infection

by Mohammad Hossein Ostadzad, Salman Baroumand and Mohammad Reza Mahmoudi

Symmetry 2020, 12(4), 659; https://doi.org/10.3390/sym12040659 - 22 Apr 2020

Cited by 1 | Viewed by 2736

Abstract

Due to the prevalence of Human Immuno-deficiency Virus/Acquired Immuno-Deficiency Syndrome (HIV/AIDS) infection in society and the importance of preventing the spread of this disease, a mathematical model for sexual transmission of HIV/AIDS epidemic with asymptomatic and symptomatic phase and public health education is [...] Read more.

Due to the prevalence of Human Immuno-deficiency Virus/Acquired Immuno-Deficiency Syndrome (HIV/AIDS) infection in society and the importance of preventing the spread of this disease, a mathematical model for sexual transmission of HIV/AIDS epidemic with asymptomatic and symptomatic phase and public health education is stated as a symmetric system of differential equations in order to reduce the spread of this infectious disease. It is demonstrated that public health education has a considerable effect on the prevalence of the disease. Moreover, the cost of education is very high and for this reason, a cost-optimal control is applied to provide the best possible combination of the parameters corresponding to education in controlling the spread of the disease by means of the Genetic Algorithm (GA) and Simulated Annealing (SA). Full article

(This article belongs to the Special Issue Bioinformatics and Computational Biology)

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13 pages, 1569 KiB

Open AccessArticle

Similarities of Flow and Heat Transfer around a Circular Cylinder

by Hao Ma and Zhipeng Duan

Symmetry 2020, 12(4), 658; https://doi.org/10.3390/sym12040658 - 22 Apr 2020

Cited by 9 | Viewed by 4726

Abstract

Modeling fluid flows is a general procedure to handle engineering problems. Here we present a systematic study of the flow and heat transfer around a circular cylinder by introducing a new representative appropriate drag coefficient concept. We demonstrate that the new modified drag [...] Read more.

Modeling fluid flows is a general procedure to handle engineering problems. Here we present a systematic study of the flow and heat transfer around a circular cylinder by introducing a new representative appropriate drag coefficient concept. We demonstrate that the new modified drag coefficient may be a preferable dimensionless parameter to describe more appropriately the fluid flow physical behavior. A break in symmetry in the global structure of the entire flow field increases the difficulty of predicting heat and mass transfer behavior. A general simple drag model with high accuracy is further developed over the entire range of Reynolds numbers met in practice. In addition, we observe that there may exist an inherent relation between the drag and heat and mass transfer. A simple analogy model is established to predict heat transfer behavior from the cylinder drag data. This finding provides great insight into the underlying physical mechanism. Full article

(This article belongs to the Special Issue Aero/Hydrodynamics and Symmetry 2020)

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21 pages, 12441 KiB

Open AccessArticle

The Influence of Different Loads on the Footbridge Dynamic Parameters

by Artūras Kilikevičius, Darius Bačinskas, Jaroslaw Selech, Jonas Matijošius, Kristina Kilikevičienė, Darius Vainorius, Dariusz Ulbrich and Dawid Romek

Symmetry 2020, 12(4), 657; https://doi.org/10.3390/sym12040657 - 22 Apr 2020

Cited by 15 | Viewed by 3903

Abstract

Bringing together the experience and knowledge of engineers allowed building modern footbridges as very slender structures. This in turn has led to structural vibration problems, which is a direct consequence of slender structures. In some footbridges, this problem occurs when natural construction frequencies [...] Read more.

Bringing together the experience and knowledge of engineers allowed building modern footbridges as very slender structures. This in turn has led to structural vibration problems, which is a direct consequence of slender structures. In some footbridges, this problem occurs when natural construction frequencies are close to excitation frequencies. This requires a design methodology, which would ensure user safety and convenience of use of the footbridge in operation. Considering the aforementioned dynamic response, the analysis of the finite element model of a footbridge was conducted focusing on critical acceleration and deformation meanings. The model was based on the footbridge prototype located in Vilnius, Lithuania. Two different loading methods were developed to investigate the dynamic effects caused by people crossing a footbridge. The comparison of experimental and finite element model (FEM) results revealed that the footbridge in operation is within the limit values of comfort requirements in terms of its vibrations. Full article

(This article belongs to the Special Issue Symmetry in Mechanical Engineering Ⅱ)

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22 pages, 828 KiB

Open AccessFeature PaperArticle

A Filter and Nonmonotone Adaptive Trust Region Line Search Method for Unconstrained Optimization

by Quan Qu, Xianfeng Ding and Xinyi Wang

Symmetry 2020, 12(4), 656; https://doi.org/10.3390/sym12040656 - 21 Apr 2020

Cited by 3 | Viewed by 2686

Abstract

In this paper, a new nonmonotone adaptive trust region algorithm is proposed for unconstrained optimization by combining a multidimensional filter and the Goldstein-type line search technique. A modified trust region ratio is presented which results in more reasonable consistency between the accurate model [...] Read more.

In this paper, a new nonmonotone adaptive trust region algorithm is proposed for unconstrained optimization by combining a multidimensional filter and the Goldstein-type line search technique. A modified trust region ratio is presented which results in more reasonable consistency between the accurate model and the approximate model. When a trial step is rejected, we use a multidimensional filter to increase the likelihood that the trial step is accepted. If the trial step is still not successful with the filter, a nonmonotone Goldstein-type line search is used in the direction of the rejected trial step. The approximation of the Hessian matrix is updated by the modified Quasi-Newton formula (CBFGS). Under appropriate conditions, the proposed algorithm is globally convergent and superlinearly convergent. The new algorithm shows better performance in terms of the Dolan–Moré performance profile. Numerical results demonstrate the efficiency and robustness of the proposed algorithm for solving unconstrained optimization problems. Full article

(This article belongs to the Special Issue Advance in Nonlinear Analysis and Optimization)

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13 pages, 3904 KiB

Open AccessArticle

Numerical Modeling of Sloshing Frequencies in Tanks with Structure Using New Presented DQM-BEM Technique

by Zhenda Wei, Junwen Feng, Mohammad Ghalandari, Akbar Maleki and Zahra Abdelmalek

Symmetry 2020, 12(4), 655; https://doi.org/10.3390/sym12040655 - 21 Apr 2020

Cited by 4 | Viewed by 2806

Abstract

The sloshing behavior of systems is influenced by different factors related to the liquid level and tank specifications. Different approaches are applicable for the assessment of sloshing behavior in a tank. In this paper, a new numerical model based on the differential quadrature [...] Read more.

The sloshing behavior of systems is influenced by different factors related to the liquid level and tank specifications. Different approaches are applicable for the assessment of sloshing behavior in a tank. In this paper, a new numerical model based on the differential quadrature method and boundary element approaches is adopted to investigate the sloshing behavior of a tank with an elastic thin-walled beam. The model is developed based on small slope considerations of the free surface. The main assumption of fluid modeling is homogeneity, isotropy, inviscid, and only limited compressibility of the liquid. Indeed, the formulation is represented based on the reduced-order method and then is employed for simulating the coupling between structure and fluid in symmetric test cases. The results are verified with the ANSYS and literature for symmetric rigid structural walls and then the code is employed to study the behavior of fluid-structure interaction in a symmetric tank with new and efficient immersed structure. Full article

(This article belongs to the Special Issue Turbulence and Multiphase Flows and Symmetry)

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24 pages, 7324 KiB

Open AccessArticle

A Fast UTD-Based Method for the Analysis of Multiple Acoustic Diffraction over a Series of Obstacles with Arbitrary Modeling, Height and Spacing

by Domingo Pardo-Quiles and José-Víctor Rodríguez

Symmetry 2020, 12(4), 654; https://doi.org/10.3390/sym12040654 - 21 Apr 2020

Cited by 6 | Viewed by 2639

Abstract

A uniform theory of diffraction (UTD)-based method for analysis of the multiple diffraction of acoustic waves when considering a series of symmetric obstacles with arbitrary modeling, height and spacing is hereby presented. The method, which makes use of graph theory, funicular polygons and [...] Read more.

A uniform theory of diffraction (UTD)-based method for analysis of the multiple diffraction of acoustic waves when considering a series of symmetric obstacles with arbitrary modeling, height and spacing is hereby presented. The method, which makes use of graph theory, funicular polygons and Fresnel ellipsoids, proposes a novel approach by which only the relevant obstacles and paths of the scenario under study are considered, therefore simultaneously providing fast and accurate prediction of sound attenuation. The obstacles can be modeled either as knife edges, wedges, wide barriers or cylinders, with some other polygonal diffracting elements, such as doubly inclined, T- or Y-shaped barriers, also considered. In view of the obtained results, this method shows good agreement with previously published formulations and measurements whilst offering better computational efficiency, thus allowing for the consideration of a large number of obstacles. Full article

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23 pages, 1763 KiB

Open AccessFeature PaperArticle

AppCon: Mitigating Evasion Attacks to ML Cyber Detectors

by Giovanni Apruzzese, Mauro Andreolini, Mirco Marchetti, Vincenzo Giuseppe Colacino and Giacomo Russo

Symmetry 2020, 12(4), 653; https://doi.org/10.3390/sym12040653 - 21 Apr 2020

Cited by 16 | Viewed by 4518

Abstract

Adversarial attacks represent a critical issue that prevents the reliable integration of machine learning methods into cyber defense systems. Past work has shown that even proficient detectors are highly affected just by small perturbations to malicious samples, and that existing countermeasures are immature. [...] Read more.

Adversarial attacks represent a critical issue that prevents the reliable integration of machine learning methods into cyber defense systems. Past work has shown that even proficient detectors are highly affected just by small perturbations to malicious samples, and that existing countermeasures are immature. We address this problem by presenting AppCon, an original approach to harden intrusion detectors against adversarial evasion attacks. Our proposal leverages the integration of ensemble learning to realistic network environments, by combining layers of detectors devoted to monitor the behavior of the applications employed by the organization. Our proposal is validated through extensive experiments performed in heterogeneous network settings simulating botnet detection scenarios, and consider detectors based on distinct machine- and deep-learning algorithms. The results demonstrate the effectiveness of AppCon in mitigating the dangerous threat of adversarial attacks in over 75% of the considered evasion attempts, while not being affected by the limitations of existing countermeasures, such as performance degradation in non-adversarial settings. For these reasons, our proposal represents a valuable contribution to the development of more secure cyber defense platforms. Full article

(This article belongs to the Special Issue Recent Advances on Deep Learning for Safety and Security of Multimedia Data in the Critical Infrastructure)

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Symmetry, Volume 12, Issue 4 (April 2020) – 193 articles

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