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Formation Control of Heterogeneous Multi-Agent Systems Under Fixed and Switching Hierarchies

IEEE Access 2024年 12卷 97868-97882页

作者： Shamrooz Aslam, Muhammad Bilal, Hazrat Chang, Wen-Jer Yahya, Abid Anjum Badruddin, Irfan Kamangar, Sarfaraz Hussien, Mohamed China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou221116 China University of Science and Technology of China Department of Automation Hefei230026 China Department of Marine Engineering Keelung202 Taiwan Botswana International University of Science and Technology Department of Electrical Computer and Telecommunications Engineering Palapye Botswana King Khalid University College of Engineering Department of Mechanical Engineering Abha Saudi Arabia King Khalid University Faculty of Science Department of Chemistry Abha61413 Saudi Arabia

Modern information technology is driving the rapid development of the information and automation industry through the combination of computer technology, communication technology, and control technology. Inspired by nature's cluster movement, such as birds, people proposed the concept of Multi-Agent Systems (MASs). This paper deals with the formation control problem for a class of heterogeneous multi-agent systems with time-delays. First, the formation control of heterogeneous multi-agent systems with communication delay in fixed and switching topologies is investigated. A heterogeneous multi-agent system model with input delay and communication delay is established. Furthermore, based on this model, the formation control protocol is designed and the stability of the proposed control protocol in fixed and switched topologies is analyzed. The first- and second-order multi-agent control system was formulated for both fixed and switched topology, respectively. Then, using the new LMI toolbox, the feasibility of the parameters is obtained and the example is created by MATLAB. The simulation results show that the proposed formation control protocol with delay in this paper can make the systems converge to the expected value under fixed and switched hierarchies. © 2013 IEEE.

关键词： Topology

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A novel approach to enhance feature selection using linearity assessment with ordinary least squares regression for Alzheimer’s Disease stage classification

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Multimedia Tools and Applications 2024年第38期83卷 86059-86078页

作者： Mabrouk, Besma Bouattour, Nadia Mabrouki, Noura Sellami, Lamia Ben Hamida, Ahmed Advanced Technologies for Medicine and Signals ATMS Department of Electrical and Computer Engineering National Engineers School Sfax Tunisia Department of Neurology University Hospital Habib Bourguiba Sfax Tunisia Department of Psychiatry University Hospital Taher Sfar Mahdia Tunisia Department IS College of Computer Science King Khaled Universiy ‘KKU’ Abha Kingdom of Saudi Arabia Saudi Arabia

Diagnosing Alzheimer’s disease (AD) in its prodromal stage is a significantly crucial area of research. Approximately 50% of individuals within the well-known Mild Cognitive Impairment (MCI) cohort are estimated to progress to AD, and the factors influencing conversion remain unknown. Gaining insights into the disease evolution can enhance support strategies and potentially slow down the pathology. Utilizing the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, our objective is to construct a framework for distinguishing between Normal Controls (NC) and different stages of Alzheimer’s Disease (AD), encompassing Earlier Mild Cognitive Impairment (EMCI), Later Mild Cognitive Impairment (LMCI), and AD patients. In pursuit of this objective, we preprocessed Diffusion Tensor and Magnetic Resonance brain images from 237 subjects, generating corresponding brain connectivity maps. Notably, we introduce an innovative linearity assessment method that utilizes the Ordinary Least Squares (OLS) linear regression model to identify and select relevant features for classification. This approach effectively identifies features with strong linear relationships to the target variable. Our method’s superiority is demonstrated through a comparative analysis with the traditional SelectKBest approach. By integrating this feature selection strategy with a Logistic Regression model, our study achieves both efficient and highly accurate classification outcomes, highlighting the effectiveness of the proposed method. In a four-class classification scenario, the model attained an accuracy of 66%±0.06. In binary classification, the results were equally impressive, with an area under the curve of 0.68±0.10% for CN vs. EMCI discrimination, 99±0.02%for distinguishing LMCI from adjacent classes CN and EMCI, and 0.79%±0.08 for discriminating AD from healthy subjects. Additionally, the calculation of Pearson’s correlation coefficient has been employed to identify cortical regions affected b

关键词： Neurodegenerative diseases

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Exploring The Efficacy of Convolutional Neural Networks in Predicting Cardiovascular Disease Risk Factors from Biomedical Imaging Data

Exploring The Efficacy of Convolutional Neural Networks in P...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Muruguiyan, Akshara Dhasmana, Gulshan Al-Farouni, Mohammed Badhoutiya, Arti Singh, Balpreet Aswini, S. St. Joseph's College Of Engineering & Technology Thanjavur India Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Electrical Engineering Mathura India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This exploration investigates the adequacy of convolutional neural networks (CNNs) in foreseeing cardiovascular disease (CVD) risk factors utilizing biomedical imaging information. Utilizing a different dataset including X-ray, CT, and ultrasound pictures close by clinical metadata, four CNN structures (CNN, Reset, Dense Net, and VGGNet) were executed and assessed. DenseNet arose as the top-performing model, accomplishing an exactness of 88.1%, accuracy of 90.1%, review of 86.2%, and F1 score of 88.0%. These outcomes show the prevalent prescient exhibition of Dense Net in contrast with different structures. The review adds to the progression of cardiovascular gamble evaluation techniques, featuring the capability of CNN-based approaches for further developing clinical independent direction and patient results. Pushing ahead, future examination bearings incorporate the investigation of bigger and more different datasets, as well as improving model interpretability and coordinating multimodal imaging information and clinical biomarkers. © 2024 IEEE.

关键词： Risk assessment

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Deep Moore-Penrose Inverse Network with Refinement Strategy for One-class Classification

Deep Moore-Penrose Inverse Network with Refinement Strategy ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Gao, Junna Kong, Dehui Yin, Baocai Lin, Weisi Zhang, Wandong Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Western University Department of Electrical and Computer Engineering London Canada

ISBN: (纸本)9798350337020

Multilayer least-square (LS)-based one-class classification networks (MLS-OCNs) have gained great attention for the purpose of identifying anomalies and outliers. However, many MLS-OCNs encounter the issue of loosely connected feature coding because they use two separate mechanisms for feature encoding and final pattern recognition. This paper proposes a solution to this problem by introducing a multilayer algorithm called deep Moore-Penrose inverse network with refinement (DMPINR). In particular, DMPINR employs an end-to-end learning process based on the Moore-Penrose inverse (MPI) to identify optimal latent space and classify objects simultaneously. To enhance the robustness of representations, the DMPINR technique pulls back the residual error from the output layer to the hidden layers sequentially, recalculating the parameters of these hidden layers using MPI. The experimental results on ten popular OCC datasets demonstrate that the proposed approach outperforms many existing MLS-OCNs in G-Mean and F1 scores. © 2023 IEEE.

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Medivision - Advanced Medical Imaging and Visualization through AR/VR

MediVision - Advanced Medical Imaging and Visualization thro...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yoosaf, Salih Ziyad, Naeema University of Twente Faculty of Electrical Engineering Mathematics and Computer Science AE Enschede7500 Netherlands Wright State University Bernard D. Researcheris with the Department of Electrical Engineering DaytonOH45435 United States

ISBN: (纸本)9781665410205

Medivision revolutionizes medical imaging by integrating Augmented Reality (AR) and Virtual Reality (VR) with traditional 2D data. This platform transforms standard DICOM images into immersive 3D models, enhancing diagnostic accuracy and anatomical exploration. Using Unity 3D and advanced image processing, Medivision enables in-teractive visualization of complex conditions through intuitive gestures and labeling. It supports clinical diagnostics, treatment planning, and medical education by simulating procedures and disease progression. Medivision also promotes global healthcare accessibility, reduces diagnostic errors, and supports remote consultations, setting a new benchmark for accuracy and education in medical imaging. © 2024 IEEE.

关键词： Medical education

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Analysis and Assessment of Wind Energy Potential of Almukalla in Yemen

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computers, Materials & Continua 2022年第8期72卷 3113-3129页

作者： Murad A.A.Almekhlafi Fahd N.Al-Wesabi Majdy M.Eltahir Anwer Mustafa Hilal Amin M.El-Kustaban Abdelwahed Motwakel Ishfaq Yaseen Manar Ahmed Hamza Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Computer Science King Khalid UniversityMuhayelAseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayelAseerSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Electronic Department Faculty of EngineeringUniversity of Science and TechnologySana’aYemen

Energy is an essential element for any civilized country’s social and economic development,but the use of fossil fuels and nonrenewable energy forms has many negative impacts on the environment and the *** Republic of Yemen has very good potential to use renewable ***,we find few studies on renewable wind energy in *** the lack of a similar analysis for the coastal city,this research newly investigates wind energy’s potential near the Almukalla area by analyzing wind ***,evaluation,model identification,determination of available energy density,computing the capacity factors for several wind turbines and calculation of wind energy were extracted at three heights of 15,30,and *** wind speeds were obtained only for the currently available data of five recent years,2005–*** study involves a preliminary assessment of Almukalla’s wind energy potential to provide a primary base and useful insights for wind engineers and *** research aims to provide useful assessment of the potential of wind energy in Almukalla for developing wind energy and an efficient wind *** Weibull distribution shows a perfect approximation for estimating the intensity of Yemen’s wind *** on both theWeibullmodel and the results of the annual wind speed data analysis for the study site in Mukalla,the capacity factor for many turbines was also calculated,and the best suitable turbine was *** to the International Wind Energy Rating criteria,Almukalla falls under Category 7,which is,rated“Superb”most of the year.

关键词： Almukalla energy potential Rayleigh distribution Weibull distribution wind power density wind speed

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The Improved Mango Plant Detection Model Based on Attention Module Mechanism

The Improved Mango Plant Detection Model Based on Attention ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Sung, Wen-Tsai Tofik Isa, Indra Griha National Chin-Yi University of Technology Department of Electrical Engineering Taichung411030 Taiwan Graduate Institute Prospective Technology of Electrical Engineering and Computer Science National Chin-Vi University of Technology Taichung411030 Taiwan

ISBN: (纸本)9781665410205

Agriculture is one of the sources of income a region can rely on to support its economy. Traditional agriculture relies primarily on human performance and observation, resulting in greater production costs and, subsequently, higher selling prices. Artificial intelligence-based technology can be used to reduce production costs, increase productivity, and provide consumer convenience. An indicator that is easy to interpret in measuring the quality and optimization of plant growth is the visualization of the condition of the leaves. The artificial intelligence technique that can be implemented in this regard is the object detection model. However, the challenge is the complex, multi-object, and multi-intersection condition of the leaves, which causes the model to be less optimal in conducting classification and detection tasks regarding whether the leaf condition is good or not. A YOLOv7 model will be employed in order to detect leaf quality, whether in an 'optimal' or 'not optimal' condition. To enhance the model's performance by improving accuracy through feature extraction enhancement, YOLOv7 will be integrated with the attention module, called the convolutional block attention module (CBAM). The case study in this research is detecting a mango plant which is one of the plants that can provide a high economic impact and the object observed is the mango plant leaf. Several previous studies related to the implementation of attention modules in object detection include the improved pest-YOLO for real-time pest detection by combining YOLOv3 with efficient channel attention (ECA) and a transformer encoder. The ECA module and transformer encoder were integrated into the backbone and neck block systems of YOLO [1]. The lightweight YOLO model combined with SE-CSPGhostnet by improving the backbone block which employs squeeze-and-excitation networks (SENet) and a convolution technique consisting of regular convolution and ghost convolution [2]. There is a highlighted improvem

关键词： Cost reduction

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Modified TRFI Channel Estimation Scheme in OFDM-IM for 802.11p 23

Modified TRFI Channel Estimation Scheme in OFDM-IM for 802.1...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Zhang, Si-Yu Dong, Qing-Shang Zheng, Hui Zhang, Yue-Xia Shahrrava, Behnam School of Information and Communication Engineering Beijing Information Science and Technology University Beijing China University of Windsor Department of Electrical and Computer Engineering ON Canada

ISBN: (纸本)9798350325959

IEEE 802.11p is developed to support cooperative intelligent transport systems. In such systems, channel estimation is a challenging problem due to the channel's double dispersive nature. In this paper, a modified time domain reliable test frequency domain interpolation (M-TRFI) scheme for 802.11p orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems is proposed. Unlike OFDM, OFDM-IM can achieve better performance in vehicular communication environments. However, due to the in-active subcarriers, channel estimation for OFDM-IM is more difficult than OFDM. Inspired by the OFDM TRFI and the structure of OFDM-IM, the proposed M-TRFI utilizes preambles and least square (LS) methods to fill in the missing channel information on in-active subcarriers, reducing the serious performance degradation caused by the conventional TRFI used in OFDM-IM. Simulation results show that the proposed M-TRFI achieves a similar estimation performance as that of the OFDM TRFI. Moreover, the proposed M-TRFI significantly outperforms the existing channel estimation performance designed for OFDM-IM systems. © 2023 IEEE.

关键词： IEEE 802.11p standard index modulation (IM) least square (LS) Orthogonal frequency division multiplexing (OFDM) time domain reliable test frequency domain interpolation (TRFI) vehicular channels

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Machine learning assisted snort and zeek in detecting DDoS attacks in software-defined networking

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1627-1643页

作者： AbdulRaheem, Muyideen Oladipo, Idowu Dauda Imoize, Agbotiname Lucky Awotunde, Joseph Bamidele Lee, Cheng-Chi Balogun, Ghaniyyat Bolanle Adeoti, Joshua Oluwatobi Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin 240003 Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum 44801 Germany Research and Development Center for Physical Education Health and Information Technology Department of Library and Information Science Fu Jen Catholic University New Taipei City 24205 Taiwan Department of Computer Science and Information Engineering Asia University Taichung City 41354 Taiwan

A new network architecture called the Software-Defined Network (SDN) gives next-generation networks a more flexible and efficiently controlled network architecture. Using the programmable central controller design, network supervisors may easily supervise and manage the entire infrastructure. However, due to its centralized structure, SDN has been a target of various attack vectors. The most successful attack method against the SDN among these has been Distributed Denial of Service (DDoS). Therefore, this study proposes a snort and Zeek enabled with machine learning (ML) based model to classify the benign traffic from DDoS attack traffic. This study main contribution is the discovery of new features for DDoS attack detection, which made it difficult to distinguish authorized traffic from attack traffic when spread across so many points of origin. Using the ML-based enabled RYU controller with SNORT and ZEEK created fewer false positives and a smaller variety of true positives per attack than the existing methods. The processing time of ML-based enabled with SNORT and ZEEK on the real-time testbed is better contrasted to the existing methods. Using the open resource technologies offered a far better understanding of cyber safety and its benefits from the readily available programs to construct a solid network keeping an eye on the traffic. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Cybersecurity Distributed denial of service Intrusion detection systems Snort Software-defined networks Virtual machine Zeek

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Computational Fabrication and Assembly for In Situ Manufacturing

arXiv

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arXiv 2024年

作者： Nisser, Martin Eric William Department of Electrical Engineering and Computer Science United States

Fabrication today relies on disparate, large machines spread across industrial facilities. These are operated by domain experts to construct and assemble artefacts in sequential steps from large numbers of parts. This traditional, centralized mass manufacturing paradigm is characterized by large capital costs and inflexibility to changing needs, complex global supply chains hinged on economic and political stability, and waste and over-manufacturing of uniform artefacts that fail to meet the technical and personal needs of today’s diverse individuals and use cases. Today, these challenges are particularly severe at points of need, such as the space environment. The space environment is remote and unpredictable, and the ability to manufacture in situ offers unique opportunities to address new challenges as they arise. However, the challenges faced in space are often mirrored on Earth. In hospitals, disaster zones, low resource environments and laboratories, the ability to manufacture customized artefacts at points of need can significantly enhance our ability to respond rapidly to unforeseen events. In this thesis, I introduce digital fabrication platforms with co-developed hardware and software that draw on tools from robotics and human-computer interaction to automate manufacturing of customized artefacts at the point of need. Highlighting three research themes across fabrication machines, modular assembly, and programmable materials, the thesis will cover a digital fabrication platform for producing functional robots, a modular robotic platform for in-space assembly deployed in microgravity, and a method for programming magnetic material to selectively assemble. © 2024, CC BY-NC-ND.

关键词： Supply chains

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