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Sensing and Control Technology in Multi-Agent Systems
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Sensing and Control Technology in Multi-Agent Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 3789

Special Issue Editors

Prof. Dr. Xin Wang

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China
Interests: information physical systems; DoS attacks; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kaibo Shi

E-Mail Website
Guest Editor
School of Information Science and Engineering, Chengdu University, Chengdu 610106, China
Interests: fractional-order neural networks; nonlinear systems; networked control systems; control theory and application of neural network
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of multi-agent systems (MASs) research is rapidly expanding. Sensing and control technology plays a crucial role in MASs, with research applications mainly focused on perception and state estimation, decision-making and control, communication and collaboration, simulation and optimization, among other factors. This technology enables MASs to perceive their environment, control actions, communicate and collaborate, and optimize performance, providing valuable support and assurance for the practical applications of MASs.

This Special Issue is dedicated to showcasing the latest research and developments in this field, with a focus on exploring the application potential of sensing and control technology in MASs. The topics covered in this Issue include, but are not limited to:

  • Control theory in artificial intelligence;
  • Consensus analysis of multi-agent systems;
  • State estimation;
  • Cyber attacks in communications;
  • Event-triggered protocol of networked control systems;
  • Security control of networked control systems.

Prof. Dr. Xin Wang
Prof. Dr. Kaibo Shi
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 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. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

21 pages, 2430 KiB  
Article
Context and Multi-Features-Based Vulnerability Detection: A Vulnerability Detection Frame Based on Context Slicing and Multi-Features
by Yulin Zhang, Yong Hu and Xiao Chen
Sensors 2024, 24(5), 1351; https://doi.org/10.3390/s24051351 - 20 Feb 2024
Viewed by 745
Abstract
With the increasing use of open-source libraries and secondary development, software projects face security vulnerabilities. Existing studies on source code vulnerability detection rely on natural language processing techniques, but they overlook the intricate dependencies in programming languages. To address this, we propose a [...] Read more.
With the increasing use of open-source libraries and secondary development, software projects face security vulnerabilities. Existing studies on source code vulnerability detection rely on natural language processing techniques, but they overlook the intricate dependencies in programming languages. To address this, we propose a framework called Context and Multi-Features-based Vulnerability Detection (CMFVD). CMFVD integrates source code graphs and textual sequences, using a novel slicing method called Context Slicing to capture contextual information. The framework combines graph convolutional networks (GCNs) and bidirectional gated recurrent units (BGRUs) with attention mechanisms to extract local semantic and syntactic information. Experimental results on Software Assurance Reference Datasets (SARDs) demonstrate CMFVD’s effectiveness, achieving the highest F1-score of 0.986 and outperforming other models. CMFVD offers a promising approach to identifying and rectifying security flaws in large-scale codebases. Full article
(This article belongs to the Special Issue Sensing and Control Technology in Multi-Agent Systems)
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17 pages, 2391 KiB  
Article
Malicious Traffic Identification with Self-Supervised Contrastive Learning
by Jin Yang, Xinyun Jiang, Gang Liang, Siyu Li and Zicheng Ma
Sensors 2023, 23(16), 7215; https://doi.org/10.3390/s23167215 - 17 Aug 2023
Cited by 3 | Viewed by 1456
Abstract
As the demand for Internet access increases, malicious traffic on the Internet has soared also. In view of the fact that the existing malicious-traffic-identification methods suffer from low accuracy, this paper proposes a malicious-traffic-identification method based on contrastive learning. The proposed method is [...] Read more.
As the demand for Internet access increases, malicious traffic on the Internet has soared also. In view of the fact that the existing malicious-traffic-identification methods suffer from low accuracy, this paper proposes a malicious-traffic-identification method based on contrastive learning. The proposed method is able to overcome the shortcomings of traditional methods that rely on labeled samples and is able to learn data feature representations carrying semantic information from unlabeled data, thus improving the model accuracy. In this paper, a new malicious traffic feature extraction model based on a Transformer is proposed. Employing a self-attention mechanism, the proposed feature extraction model can extract the bytes features of malicious traffic by performing calculations on the malicious traffic, thereby realizing the efficient identification of malicious traffic. In addition, a bidirectional GLSTM is introduced to extract the timing features of malicious traffic. The experimental results show that the proposed method is superior to the latest published methods in terms of accuracy and F1 score. Full article
(This article belongs to the Special Issue Sensing and Control Technology in Multi-Agent Systems)
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24 pages, 13251 KiB  
Article
Magnesium Ingot Stacking Segmentation Algorithm for Industrial Robot Based on the Correction of Image Overexposure Area
by Qiguang Li, Huazheng Zheng, Wensheng Wang and Chenggang Li
Sensors 2023, 23(15), 6809; https://doi.org/10.3390/s23156809 - 30 Jul 2023
Viewed by 859
Abstract
This paper proposes an adaptive threshold segmentation algorithm for the magnesium ingot stack based on image overexposure area correction (ATSIOAC), which solves the problem of mirror reflection on the surface of magnesium alloy ingots caused by external ambient light and auxiliary light sources. [...] Read more.
This paper proposes an adaptive threshold segmentation algorithm for the magnesium ingot stack based on image overexposure area correction (ATSIOAC), which solves the problem of mirror reflection on the surface of magnesium alloy ingots caused by external ambient light and auxiliary light sources. Firstly, considering the brightness and chromaticity information of the mapped image, we divide the exposure probability threshold into weak exposure and strong exposure regions. Secondly, the saturation difference between the magnesium ingot region and the background region is used to obtain a mask for the magnesium ingot region to eliminate interference from the image background. Then, the RGB average of adjacent pixels in the overexposed area is used as a reference to correct the colors of the strongly exposed and weakly exposed areas, respectively. Furthermore, in order to smoothly fuse the two corrected images, pixel weighted average (WA) is applied. Finally, the magnesium ingot sorting experimental device was constructed and the corrected top surface image of the ingot pile was segmented through ATSIOAC. The experimental results show that the overexposed area detection and correction algorithm proposed in this paper can effectively correct the color information in the overexposed area, and when segmenting ingot images, complete segmentation results of the top surface of the ingot pile can be obtained, effectively improving the accuracy of magnesium alloy ingot segmentation. The segmentation algorithm achieves a segmentation accuracy of 94.38%. Full article
(This article belongs to the Special Issue Sensing and Control Technology in Multi-Agent Systems)
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