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Symmetry
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Artificial Intelligence, Adaptation and Symmetry/Asymmetry
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Artificial Intelligence, Adaptation and Symmetry/Asymmetry

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Mathematics".

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 6612

Special Issue Editor

Prof. Dr. Hongbin Ma

E-Mail Website
Guest Editor
Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China
Interests: robotics; image processing; machine learning; multi-agent system

Special Issue Information

Dear Colleagues,

Since the Dartmouth Summer School in 1956, artificial intelligence has become one of the most active cross-discipline research areas, attracting mathematicians, physicists, logicians, biologists, and so on. In fact, the dream of humankind to explore the order of nature and the capability of man-made machines can even be traced back to thousands of years ago. Through this process, recognizing the intrinsic features or principles of real-world or artificial systems has been a key problem which is still far from being completely resolved.

Adaptation and symmetry are two critical features for understanding the order and evolution of complex systems. As such, some interesting problems can be raised: How can AI work better with adaptation? Can AI understand symmetry or nonsymmetry? Is it possible to construct AI with a symmetric structure or block for certain real problems? What can be expected if we regard a human as an AI agent or vice versa? Does the relationship between AI and humans exhibit some degree of symmetry? Can a process of adaptation be designed or fused into AI for discovering order-like symmetry?

Hence, a Special Issue of the journal Symmetry is suggested for related academic discussions. Any papers addressing artificial intelligence, adaptation, and symmetry will be welcome, and cross-disciplinary discussions are especially sought-after for this Special Issue, such as unmanned systems, traffic systems, industrial systems, logistic systems, medical systems, internet, IoT, smart grids, communication networks, social networks, wargames or dynamic games, and so on. In particular, we believe that AI for science will be one growing trend in the future, and we hope that these discussions on adaptation and symmetry may introduce new ideas to the field of AI and may also help to explore new topics in the future.

Prof. Dr. Hongbin Ma
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. 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 2400 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

  • artificial intelligence
  • adaptation
  • learning
  • complex system
  • symmetry
  • asymmetry

Published Papers (4 papers)

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Research

17 pages, 754 KiB  
Article
Data-Driven Control Based on Information Concentration Estimator and Regularized Online Sequential Extreme Learning Machine
by Xiaofei Zhang, Hongbin Ma and Huaqing Zhang
Symmetry 2024, 16(1), 88; https://doi.org/10.3390/sym16010088 - 10 Jan 2024
Viewed by 615
Abstract
Due to the complexity of digital equipment and systems, it is quite difficult to obtain a precise mechanism model in practice. For an unknown discrete-time nonlinear system, in this paper, a semi-parametric model is used to describe this discrete-time nonlinear system, and this [...] Read more.
Due to the complexity of digital equipment and systems, it is quite difficult to obtain a precise mechanism model in practice. For an unknown discrete-time nonlinear system, in this paper, a semi-parametric model is used to describe this discrete-time nonlinear system, and this semi-parametric model contains a parametric uncertainty part and a nonparametric uncertainty part. Based on this semi-parametric model, a novel data-driven control algorithm based on an information concentration estimator and regularized online sequential extreme learning machine (ReOS-ELM) is designed. The information concentration estimator estimates the parametric uncertainty part; The training data of ReOS-ELM network is obtained, based on symmetry and information concentration estimator, then the training of ReOS-ELM network and the estimate of nonparametric uncertainty part using ReOS-ELM network are carried out online, successively. A stability analysis and three simulation examples were performed, and the simulation results show that the proposed data-driven control algorithm is effective in improving the control accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence, Adaptation and Symmetry/Asymmetry)
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19 pages, 4067 KiB  
Article
Industrial Machinery Components Classification: A Case of D-S Pooling
by Amina Batool, Yaping Dai, Hongbin Ma and Sijie Yin
Symmetry 2023, 15(4), 935; https://doi.org/10.3390/sym15040935 - 19 Apr 2023
Cited by 1 | Viewed by 2367
Abstract
Industries are increasingly shifting towards unmanned and intelligent systems that require efficient processing and monitoring of structures across various applications, ranging from machine manufacturing to waste disposal. In order to achieve the goal of intelligent processing, it is crucial to accurately classify and [...] Read more.
Industries are increasingly shifting towards unmanned and intelligent systems that require efficient processing and monitoring of structures across various applications, ranging from machine manufacturing to waste disposal. In order to achieve the goal of intelligent processing, it is crucial to accurately classify and differentiate various components and parts. However, existing studies have not focused on simultaneously classifying electro-mechanical machinery components. This poses a challenge as these components, including capacitors, transistors, ICs, inductors, springs, locating pins, washers, nuts, and bolts, exhibit high intra- and inter-class similarity, making their accurate classification a tedious task. Furthermore, many of these components have symmetrical shapes but are asymmetrical among different classes. To address these challenges, this article introduces a new double-single (D-S) pooling method that focuses on the higher resemblance of seventeen electro-mechanical component classifications with minimum trainable parameters and achieves maximum accuracy. The industrial machine component classification model (IMCCM) consists of two convolutional neural network (CNN) blocks designed with a D-S pooling method that facilitates the model to effectively highlight the differences for the higher similar classes, and one block of grey-level co-occurrence matrix (GLCM) to strengthen the classification outcome. The extracted fused features from these three blocks are then forwarded to the random forest classifier to distinguish components. The accuracy achieved by this proposed model is 98.15%—outperforming the existing state of the arts (SOTAs) models, and has 141,346 trainable parameters– hence, highly effective for industrial implementation. Full article
(This article belongs to the Special Issue Artificial Intelligence, Adaptation and Symmetry/Asymmetry)
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19 pages, 4650 KiB  
Article
EcReID: Enhancing Correlations from Skeleton for Occluded Person Re-Identification
by Minling Zhu and Huimin Zhou
Symmetry 2023, 15(4), 906; https://doi.org/10.3390/sym15040906 - 13 Apr 2023
Viewed by 1072
Abstract
Person re-identification is a challenging task due to the lack of person image information in occluded scenarios. The current methods for person re-identification only take into account global information, neglect local information, and are not responsive to changes in input. Additionally, these methods [...] Read more.
Person re-identification is a challenging task due to the lack of person image information in occluded scenarios. The current methods for person re-identification only take into account global information, neglect local information, and are not responsive to changes in input. Additionally, these methods do not address the issue of inaccurate joint detection caused by occlusion. In this paper, we propose an occluded person re-identification method based on a graph model and deformable method, which is able to simultaneously focus on global and local information and can flexibly adapt to local information and changes in the input, efficiently resolving issues such as occluded or incorrect joint information. Our method consists of three modules: the mutual help denoising module, inter-node aggregation and update module, and graph matching module. The mutual help denoising module acquires global features and person skeleton node features using a CNN backbone network and a pose estimation model, respectively. It uses symmetric deformable graph attention to obtain the local and global features of the joint points in different views, correcting the information of incorrect nodes and extracting favorable human features. The inter-node aggregation and update module employs deformable graph convolution operations to enhance the relations between the nodes in the same view, resulting in higher-order information. The graph matching module uses graph matching methods based on the human topology to obtain a more accurate similarity calculation for masked images. Experimental results on the Occluded-Duck and Occluded-REID datasets show that our proposed method achieves Rank-1 accuracies of 64.8% and 84.5%, respectively, outperforming current mainstream methods such as HOReID. Our method also achieves good results on the MARKET-1501 and DukeMTMC-ReID datasets. These results demonstrate that our proposed method can extract person features well and effectively improve the accuracy of person re-identification tasks. Full article
(This article belongs to the Special Issue Artificial Intelligence, Adaptation and Symmetry/Asymmetry)
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17 pages, 6285 KiB  
Article
A CFD Validation Effect of YP/PV from Laboratory-Formulated SBMDIF for Productive Transport Load to the Surface
by Dennis Delali Kwesi Wayo, Sonny Irawan, Mohd Zulkifli Bin Mohamad Noor, Foued Badrouchi, Javed Akbar Khan and Ugochukwu I. Duru
Symmetry 2022, 14(11), 2300; https://doi.org/10.3390/sym14112300 - 02 Nov 2022
Cited by 3 | Viewed by 1676
Abstract
Several technical factors contribute to the flow of cuttings from the wellbore to the surface of the well, some of which are fundamentally due to the speed and inclination of the drill pipe at different positions (concentric and eccentric), the efficacy of the [...] Read more.
Several technical factors contribute to the flow of cuttings from the wellbore to the surface of the well, some of which are fundamentally due to the speed and inclination of the drill pipe at different positions (concentric and eccentric), the efficacy of the drilling mud considers plastic viscosity (PV) and yield point (YP), the weight of the cuttings, and the deviation of the well. Moreover, these overlaying cutting beds breed destruction in the drilling operation, some of which cause stuck pipes, reducing the rate of rotation and penetration. This current study, while it addresses the apropos of artificial intelligence (AI) with symmetry, employs a three-dimensional computational fluid dynamic (CFD) simulation model to validate an effective synthetic-based mud-drilling and to investigate the potency of the muds’ flow behaviours for transporting cuttings. Furthermore, the study examines the ratio effects of YP/PV to attain the safe transport of cuttings based on the turbulence of solid-particle suspension from the drilling fluid and the cuttings, and its velocity–pressure influence in a vertical well under a concentric and eccentric position of the drilling pipe. The resulting CFD analysis explains that the YP/PV of SBM and OBM, which generated the required capacity to suspend the cuttings to the surface, are symmetric to the experimental results and hence, the position of the drill pipe at the concentric position in vertical wells required a lower rotational speed. A computational study of the synthetic-based mud and its potency of not damaging the wellbore under an eccentric drill pipe position can be further examined. Full article
(This article belongs to the Special Issue Artificial Intelligence, Adaptation and Symmetry/Asymmetry)
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