Special Issue "Selected Papers from IIKII 2021 Conferences"

A special issue of Symmetry (ISSN 2073-8994).

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Dr. Teen-­Hang Meen
E-Mail Website
Guest Editor
Department of Electronic Engineering, National Formosa University, Yunlin 632, Taiwan
Interests: IOT devices; photovoltaic devices; STEM education
Special Issues and Collections in MDPI journals
Prof. Dr. Charles Tijus
E-Mail Website
Guest Editor
Director of the Cognitions Humaine et Artificielle Laboratory, Professeur de Psychologie Cognitive – Université, Paris 8, France
Interests: internet of objects; data mining; brain–computer interaction
Special Issues and Collections in MDPI journals
Prof. Dr. Jih-Fu Tu
E-Mail Website
Guest Editor
Department of Electrical Engineering, Lunghwa University of Science and Technology, Taoyuan 333, Taiwan
Interests: human computer interaction; internet technologies; distributed processing systems

Special Issue Information

Dear Colleagues,

Symmetry in language refers to a sense of harmonious and beautiful proportion and balance. In mathematics, “symmetry” has a more precise definition, i.e., that an object is invariant to any of various transformations, including reflectionrotation or scaling. Mathematical symmetry may be observed with respect to the passage of time, as a spatial relationship, through geometric transformations, through other kinds of functional transformations, and as an aspect of abstract objects, theoretic models and even knowledge itself. Recently, symmetry theorems and simulations have been widely applied in engineering to improve the developments of new technologies.

In addition, the International Institute of Knowledge Innovation and Invention (IIKII, http://www.iikii.org) promotes the exchange of innovations and inventions and establishes a communication platform for international innovations and research. Tthis year, IIKII is cooperating with the IEEE Tainan Section Sensors Council to hold IEEE conferences such as IEEE ECBIOS 2021 (http://www.ecbios.asia), IEEE ICAIRC 2021 (http://www.icairc.asia), IEEE ICKII 2021 (http://www.ickii.org), and IEEE ECICE 2021 (http://www.ecice.asia). This Special Issue entitled “Selected Papers from IIKII 2021 Conferences” shall publish excellent papers from IIKII 2021 conferences, including symmetry in physics, chemistry, biology, mathematics, and computer science, etc. We invite investigators to contribute original research articles, as well as review articles, to this Special Issue. Potential topics include but are not limited to:

  • Physics: conservation laws, Noether’s theorem, spatial parity, charge parity, time parity, G-parity, standard model, internal symmetry, Lorentz symmetry, transformations, invariance, conservation, local and global symmetries, laws and symmetry, symmetry breaking, color symmetry, periodic and quasiperiodic crystals, time-reversal symmetry breaking, symmetry and complexity, Curie–Rosen symmetry principles, constants, biophysics, entropy, and indistinguishability;
  • Chemistry: crystal and crystallography, chiral molecules, chiral resolution and asymmetric synthesis, asymmetric induction, chiral auxiliaries and chiral catalysts, stereochemistry, diastereomers, stereogenic, stereoisomers (enantiomers, atropisomers, diastereomers), stability, mixing, and phase separation;
  • Biology:symmetry in biology, radial symmetry (tetramerism, pentamerism, etc.), diversity, preservation, sustainability, morphology, origin of life, and molecular evolution (homochirality);
  • Mathematics: invariance, transformation, group theory, Lie groups, chirality, achiral or amphichiral, helix and Möbius strip, knot theory, graph theory, isometry, plane of symmetry, skewness, vertex algebra, asymmetry, dissymmetry, nonsymmetry and antisymmetry, supergroups and nonlinear algebraic structures, supersymmetry and supergravity, strings and branes, integrability and geometry, information theory, Felix Klein’s Erlangen Program, and continuous symmetry;
  • Computer Science, Theory and Methods:computer-aided design, computational geometry, computer graphics, visualization, image compression, data compression, pattern recognition, diversity, similarity, and conservation and sustainability.
  • Symmetry and other scientific disciplines and engineering.

Prof. Dr. Teen-­Hang Meen
Prof. Dr. Charles Tijus
Prof. Dr. Jih-Fu Tu
Guest Editors

Manuscript Submission Information

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

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry 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 1800 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

  • Physics symmetry
  • Chemistry symmetry
  • Biology symmetry
  • Mathematics symmetry, computer science, theory, and methods
  • Symmetry and other scientific disciplines and engineering

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Clustering and Classification Based on Distributed Automatic Feature Engineering for Customer Segmentation
Symmetry 2021, 13(9), 1557; https://doi.org/10.3390/sym13091557 - 24 Aug 2021
Viewed by 252
Abstract
To beat competition and obtain valuable information, decision-makers must conduct in-depth machine learning or data mining for data analytics. Traditionally, clustering and classification are two common methods used in machine mining. For clustering, data are divided into various groups according to the similarity [...] Read more.
To beat competition and obtain valuable information, decision-makers must conduct in-depth machine learning or data mining for data analytics. Traditionally, clustering and classification are two common methods used in machine mining. For clustering, data are divided into various groups according to the similarity or common features. On the other hand, classification refers to building a model by given training data, where the target class or label is predicted for the test data. In recent years, many researchers focus on the hybrid of clustering and classification. These techniques have admirable achievements, but there is still room to ameliorate performances, such as distributed process. Therefore, we propose clustering and classification based on distributed automatic feature engineering (AFE) for customer segmentation in this paper. In the proposed algorithm, AFE uses artificial bee colony (ABC) to select valuable features of input data, and then RFM provides the basic data analytics. In AFE, it first initializes the number of cluster k. Moreover, the clustering methods of k-means, Wald method, and fuzzy c-means (FCM) are processed to cluster the examples in variant groups. Finally, the classification method of an improved fuzzy decision tree classifies the target data and generates decision rules for explaining the detail situations. AFE also determines the value of the split number in the improved fuzzy decision tree to increase classification accuracy. The proposed clustering and classification based on automatic feature engineering is distributed, performed in Apache Spark platform. The topic of this paper is about solving the problem of clustering and classification for machine learning. From the results, the corresponding classification accuracy outperforms other approaches. Moreover, we also provide useful strategies and decision rules from data analytics for decision-makers. Full article
(This article belongs to the Special Issue Selected Papers from IIKII 2021 Conferences)
Article
Identification and Machine Learning Prediction of Nonlinear Behavior in a Robotic Arm System
Symmetry 2021, 13(8), 1445; https://doi.org/10.3390/sym13081445 - 06 Aug 2021
Viewed by 304
Abstract
In this study, the subject of investigation was the dynamic double pendulum crank mechanism used in a robotic arm. The arm is driven by a DC motor though the crank system and connected to a fixed side with a mount that includes a [...] Read more.
In this study, the subject of investigation was the dynamic double pendulum crank mechanism used in a robotic arm. The arm is driven by a DC motor though the crank system and connected to a fixed side with a mount that includes a single spring and damping. Robotic arms are now widely used in industry, and the requirements for accuracy are stringent. There are many factors that can cause the induction of nonlinear or asymmetric behavior and even excite chaotic motion. In this study, bifurcation diagrams were used to analyze the dynamic response, including stable symmetric orbits and periodic and chaotic motions of the system under different damping and stiffness parameters. Behavior under different parameters was analyzed and verified by phase portraits, the maximum Lyapunov exponent, and Poincaré mapping. Firstly, to distinguish instability in the system, phase portraits and Poincaré maps were used for the identification of individual images, and the maximum Lyapunov exponents were used for prediction. GoogLeNet and ResNet-50 were used for image identification, and the results were compared using a convolutional neural network (CNN). This widens the convolutional layer and expands pooling to reduce network training time and thickening of the image; this deepens the network and strengthens performance. Secondly, the maximum Lyapunov exponent was used as the key index for the indication of chaos. Gaussian process regression (GPR) and the back propagation neural network (BPNN) were used with different amounts of data to quickly predict the maximum Lyapunov exponent under different parameters. The main finding of this study was that chaotic behavior occurs in the robotic arm system and can be more efficiently identified by ResNet-50 than by GoogLeNet; this was especially true for Poincaré map diagnosis. The results of GPR and BPNN model training on the three types of data show that GPR had a smaller error value, and the GPR-21 × 21 model was similar to the BPNN-51 × 51 model in terms of error and determination coefficient, showing that GPR prediction was better than that of BPNN. The results of this study allow the formation of a highly accurate prediction and identification model system for nonlinear and chaotic motion in robotic arms. Full article
(This article belongs to the Special Issue Selected Papers from IIKII 2021 Conferences)
Show Figures

Figure 1

Article
Different Object Functions for SWIPT Optimization by SADDE and APSO
Symmetry 2021, 13(8), 1340; https://doi.org/10.3390/sym13081340 - 24 Jul 2021
Viewed by 417
Abstract
Multiple objective function with beamforming techniques by algorithms have been studied for the Simultaneous Wireless Information and Power Transfer (SWIPT) technology at millimeter wave. Using the feed length to adjust the phase for different objects of SWIPT with Bit Error Rate (BER) and [...] Read more.
Multiple objective function with beamforming techniques by algorithms have been studied for the Simultaneous Wireless Information and Power Transfer (SWIPT) technology at millimeter wave. Using the feed length to adjust the phase for different objects of SWIPT with Bit Error Rate (BER) and Harvesting Power (HP) are investigated in the broadband communication. Symmetrical antenna array is useful for omni bearing beamforming adjustment with multiple receivers. Self-Adaptive Dynamic Differential Evolution (SADDE) and Asynchronous Particle Swarm Optimization (APSO) are used to optimize the feed length of the antenna array. Two different object functions are proposed in the paper. The first one is the weighting factor multiplying the constraint BER and HP plus HP. The second one is the constraint BER multiplying HP. Simulations show that the first object function is capable of optimizing the total harvesting power under the BER constraint and APSO can quickly converges quicker than SADDE. However, the weighting for the final object function requires a pretest in advance, whereas the second object function does not need to set the weighting case by case and the searching is more efficient than the first one. From the numerical results, the proposed criterion can achieve the SWIPT requirement. Thus, we can use the novel proposed criterion (the second criterion) to optimize the SWIPT problem without testing the weighting case by case. Full article
(This article belongs to the Special Issue Selected Papers from IIKII 2021 Conferences)
Show Figures

Figure 1

Article
A Secure Three-Factor Anonymous User Authentication Scheme for Internet of Things Environments
Symmetry 2021, 13(7), 1121; https://doi.org/10.3390/sym13071121 - 23 Jun 2021
Viewed by 384
Abstract
Internet of Things (IoT) is composed of various kinds of devices such as cars, electrical appliances, machines and sensors. With IoT technologies, devices can exchange information through the network, people are allowed to get information collected by devices without interacting with them, and [...] Read more.
Internet of Things (IoT) is composed of various kinds of devices such as cars, electrical appliances, machines and sensors. With IoT technologies, devices can exchange information through the network, people are allowed to get information collected by devices without interacting with them, and automatic operations for devices are realized. Because of the variety of IoT devices, some of them possess limited computational capability. On the other hand, data transmission in IoT networks is usually through a public channel. To ensure efficiency and security for IoT environments, Lee et al. proposed a three-factor authentication scheme with hash function and XOR operation. They claimed their scheme possessed superior properties and could resist common attacks. After analyzing their scheme, we find that their scheme is vulnerable to five flaws. In this paper, how these found flaws threaten Lee et al.’s scheme is shown in detail. Then, we propose an improvement to overcome the found flaws and preserve the advantages by employing ECC. Full article
(This article belongs to the Special Issue Selected Papers from IIKII 2021 Conferences)
Show Figures

Figure 1

Back to TopTop