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Mathematics
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Mathematical Applications of Complex Evidence Theory in Engineering
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Mathematical Applications of Complex Evidence Theory in Engineering

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 5521

Special Issue Editor

Prof. Dr. Fuyuan Xiao

grade E-Mail Website
Guest Editor
School of Big Data and Software Engineering, Chongqing University, Chongqing 400030, China
Interests: uncertain information modeling; multi-sensor information fusion; multi-level decision making; multi-attribute decision making; quantum decision

Special Issue Information

Dear Colleagues,

Benefiting from the characteristics of complex evidence theory in dealing with uncertainty, incompleteness, and ambiguity multisource information as well as its advantages in dealing with the uncertainty of the expression of data fluctuations in a specific time period, it has attractive application prospects in the field of artificial intelligence. Therefore, complex evidence theory is widely used in potential applications of medical diagnosis, quantum decision (prediction of interference effects in classification decision), multicriteria decision-making, and EEG data analysis. This special issue aims to promote the mathematical applications of complex evidence theory in engineering.

Prof. Dr. Fuyuan Xiao
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. Mathematics 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.

Keywords

  • uncertain information modeling and decision making
  • multisource information fusion
  • intelligent information processing
  • complex evidence theory for medical diagnosis
  • complex evidence theory for quantum decision
  • complex evidence theory for multicriteria decision-making
  • complex evidence theory for fault diagnosis
  • complex evidence theory for pattern classification

Published Papers (3 papers)

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Research

22 pages, 1135 KiB  
Article
Identifying Qualified Public Safety Education Venues Using the Dempster–Shafer Theory-Based PROMETHEE Method under Linguistic Environments
by Yiqian Zhang, Yutong Dai and Bo Liu
Mathematics 2023, 11(4), 1011; https://doi.org/10.3390/math11041011 - 16 Feb 2023
Cited by 1 | Viewed by 1089
Abstract
How to improve safety awareness is an important topic, and it is of great significance for the public to reduce losses in the face of disasters and crises. A public safety education venue is an important carrier to realize safety education, as it [...] Read more.
How to improve safety awareness is an important topic, and it is of great significance for the public to reduce losses in the face of disasters and crises. A public safety education venue is an important carrier to realize safety education, as it has the characteristics of professionalism, comprehensiveness, experience, interest, participation, and so on, arousing the enthusiasm of the public for learning. As a meaningful supplement to “formal safety education”, venue education has many advantages. However, there are problems in the current venue construction such as imperfect infrastructure, weak professionalism, poor service level, chaotic organizational structure, and low safety, which affect the effect of safety education. To evaluate safety education venues effectively, this study proposes an evidential PROMETHEE method under linguistic environments. The innovation of this study lies in the integration of various linguistic expressions into the Dempster–Shafer theory (DST) framework, realizing the free expression and choice of evaluation information. The results and contributions of this study are summarized as follows. First, a two-tier evaluation index system of public safety education venues including 18 sub-standards is constructed. Secondly, it sets up four levels of quality evaluation for public safety education venues. Third, the belief function is used to represent all kinds of linguistic information, so as to maximize the effect of linguistic information fusion. Fourthly, an evidential PROMETHEE model is proposed to rank the venues. Fifthly, a case study is presented to demonstrate the usage of the proposed method in detail, and the evaluation results are fully analyzed and discussed. The implications of this study are as follows. First of all, to enhance public safety education, people need to face the significance of experiential education venues. Second, experiential education venues can increase learners’ enthusiasm for learning. Thirdly, the evaluation index system provided in this paper can be used to guide the construction of appropriate education venues in cities. Fourthly, the method of linguistic information transformation based on DST is also applicable to other decision-making and evaluation problems. Finally, the evidential PROMETHEE method can not only evaluate the quality of education venues, but also be used to rank a group of alternative venues. Full article
(This article belongs to the Special Issue Mathematical Applications of Complex Evidence Theory in Engineering)
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17 pages, 2924 KiB  
Article
An Optimized Double-Nested Anti-Missile Force Deployment Based on the Deep Kuhn–Munkres Algorithm
by Wen Sun, Zeyang Cao, Gang Wang, Yafei Song and Xiangke Guo
Mathematics 2022, 10(23), 4627; https://doi.org/10.3390/math10234627 - 06 Dec 2022
Viewed by 2288
Abstract
In view of a complex multi-factor interaction relationship and high uncertainty of a battlefield environment in the anti-missile troop deployment, this paper analyzes the relationships between the defending stronghold, weapon system, incoming target, and ballistic missile. In addition, a double nested optimization architecture [...] Read more.
In view of a complex multi-factor interaction relationship and high uncertainty of a battlefield environment in the anti-missile troop deployment, this paper analyzes the relationships between the defending stronghold, weapon system, incoming target, and ballistic missile. In addition, a double nested optimization architecture is designed by combining deep learning hierarchy concept and hierarchical dimensionality reduction processing. Moreover, a deployment model based on the double nested optimization architecture is constructed with the interception arc length as an optimization goal and based on the basic deployment model, kill zone model, and cover zone model. Further, by combining the target full coverage adjustment criterion and depth-first search, a deep Kuhn–Munkres algorithm is proposed. The model is validated by simulations of typical scenes. The results verify the rationality and feasibility of the proposed model, high adaptability of the proposed algorithm. The research of this paper has important enlightenment and reference function for solving the force deployment optimization problems in uncertain battlefield environment. Full article
(This article belongs to the Special Issue Mathematical Applications of Complex Evidence Theory in Engineering)
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17 pages, 3706 KiB  
Article
User Authentication by Gait Data from Smartphone Sensors Using Hybrid Deep Learning Network
by Qian Cao, Fei Xu and Huiyong Li
Mathematics 2022, 10(13), 2283; https://doi.org/10.3390/math10132283 - 29 Jun 2022
Cited by 6 | Viewed by 1610
Abstract
User authentication and verification by gait data based on smartphones’ inertial sensors has gradually attracted increasing attention due to their compact size, portability and affordability. However, the existing approaches often require users to walk on a specific road at a normal walking speed [...] Read more.
User authentication and verification by gait data based on smartphones’ inertial sensors has gradually attracted increasing attention due to their compact size, portability and affordability. However, the existing approaches often require users to walk on a specific road at a normal walking speed to improve recognition accuracy. In order to recognize gaits under unconstrained conditions on where and how users walk, we proposed a Hybrid Deep Learning Network (HDLN), which combined the advantages of a long short-term memory (LSTM) network and a convolutional neural network (CNN) to reliably extract discriminative features from complex smartphone inertial data. The convergence layer of HDLN was optimized through a spatial pyramid pooling and attention mechanism. The former ensured that the gait features were extracted from more dimensions, and the latter ensured that only important gait information was processed while ignoring unimportant data. Furthermore, we developed an APP that can achieve real-time gait recognition. The experimental results showed that HDLN achieved better performance improvements than CNN, LSTM, DeepConvLSTM and CNN+LSTM by 1.9%, 2.8%, 2.0% and 1.3%, respectively. Furthermore, the experimental results indicated our model’s high scalability and strong suitability in real application scenes. Full article
(This article belongs to the Special Issue Mathematical Applications of Complex Evidence Theory in Engineering)
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