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A Consumer Electronics-Enhanced UAV System for Agricultural Farm Tracking With Fuzzy SMO and Actuator Fault Detection Control Algorithms

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IEEE Transactions on Consumer Electronics 2025年

作者： Bilal, Hazrat Aslam, Muhammad Shamrooz Tian, Yibin Ullah, Inam Ayouni, Sarra Vasilakos, Athanasios V. College of Mechatronics and Control Engineering College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou2211106 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan Princess Nourah bint Abdulrahman University Department of Information Systems College of Computer and Information Sciences Saudi Arabia Grimstad Norway

The adoption of agricultural robots, or agrobots, has revolutionized modern farming operations, ranging from crop monitoring to automated harvesting, significantly boosting productivity. Motivated by the rapid advancements in agrobots and their integration into smart agricultural practices, this study proposes an autonomous trajectory tracking system for wheat farms using quadcopter UAVs. To address actuator fault detection, including stuck faults and partial loss of efficiency, a TSF-H∞-SMO (Takagi-Sugeno Fuzzy-based H∞ Sliding Mode Observer) fault detection framework is introduced. The approach initiates with the derivation of a TSF (Takagi-Sugeno Fuzzy) attitude control model that integrates an uncertainty term, constructed from the original nonlinear dynamics of the UAV and approximated through local linear models at four equilibrium positions. An actuator fault model is subsequently integrated to develop a comprehensive TSF-UAV model, accounting for actuator faults. The TSF-H∞-SMO is then designed using matrix coordinate transformation to enable precise fault detection. The fault detection capabilities of the TSF-H∞-SMO are evaluated through simulations on the TSF-UAV model under SISO (single-input single-output) actuator fault scenarios. The experimental results validate the proposed system, demonstrating its ability to detect a range of actuator faults accurately and promptly. The analysis reveals a proportional relationship between the amplitude of the state change and the severity of the fault, attributed to the interaction between system states and actuator flaps. This approach underscores the potential for deploying autonomous UAV-based fault detection and trajectory tracking systems in agricultural applications. Furthermore, integrating such advanced fault-tolerant control algorithms holds promise for consumer technology applications, where precision, reliability, and robustness are critical to enhancing system performance and operational efficiency. © 1

关键词： Agricultural robots

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of Things Applications

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Procedia Computer Science 2025年 252卷 231-240页

作者： Niharika Karne Chandra Shekar Ramagundam Ranga Rao Patnala Shashishekhar Ramagundam Sai Nitisha Tadiboina Sr. Principal Systems Engineer in C&RS Platform services DT Amtrak USA University of south alabama Department of electrical engineering Masters in electrical engineering Senior Software Engineer in Cloud platform services City Of Philadelphia Pennsylvania USA Vice president Neuberger Berman US Software Development & Engineering in Content Discovery & AI Comcast USA Troy University Montgomery Campus AL. Department of computer Science Masters In Computer Science Department of Information Technology GEICO - Chevy Chase Chevy Chase MD 20815 United States

An independent industrial system may transform into a connected network through the assistance of Industrial Internet of Things (IIoT). The deployed sensors in the IIoT maintain surveillance of the industrial machinery and equipment. As a result, safety and reliability emerge as the primary concerns in IIoT. This presents a variety of well-known and increasing issues related to the industrial system. The IIoT devices are exposed to a wide range of malware, threats, and assaults. To prevent the IIoT devices from malware effects, effective protection plans must be implemented. But adequate security mechanisms are not be incorporated in IIoT devices with limited resources. It is essential to ensure the accuracy and dependability of information gathered by IIoT devices. Decisions taken with incomplete or inaccurate data might be devastating. To overcome these difficulties deep learning with reinforcement learning for complex decision-making in industry applications is developed in this research work. In this developed model, an Adaptive Deep Reinforcement learning (ADRL)-based resource management is performed to reduce the operation cost associated with IIoT deployments. Energy efficiency is essential in IIoT ecosystem, particularly for the devices that run on batteries. Through dynamic resource allocation based on workload needs and energy limits, ADRL-based resource management optimizes the usage of energy. The reliability of the designed model is enhanced by fine-tuning the parameters from DRL using the Ship Rescue Optimization (SRO) algorithm. Thus, ADRL-based resource management systems make real-time decisions based on current environmental conditions and system requirements. This helps the IIoT systems to react quickly to change demands and optimize resource allocation. Finally, the experimental analysis is performed to find the success rate of the developed resource management system via various metrics. Throughout the validation, the statistical analysis of the

关键词： Resource Management Industrial Internet of Things Applications Complex Decision Making Adaptive Deep Reinforcement Learning Ship Rescue Optimization

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The Balance Model for V2X System with Variable Infrastructure

The Balance Model for V2X System with Variable Infrastructur...

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systems of Signals Generating and Processing in the Field of on Board Communications

作者： Pavel V. Plotnikov Gleb I. Tambovtsev Andrei G. Vladyko Faculty of Information Technology and Software Engineering The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint Petersburg Russia Faculty of Radioelectronic Systems and Robotics The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint Petersburg Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

This paper addresses the optimization problem and seeks a balance equation between key performance-influencing parameters in a Vehicle-to-Everything (V2X) network. A novel mathematical optimization model is proposed, focusing on the interaction between On-Board Units (OBUs) and both stationary and mobile Roadside Units (RSUs/mRSUs). The optimization problem is expressed through a multi-objective function considering critical parameters such as data transmission latency, energy consumption, and resource availability. The study demonstrates how RSU density and mobility impact key network performance indicators, including system stability and data loss percentage during peak hours. This work provides a structured analytical approach to optimizing Intelligent Transportation System (ITS) infrastructure, ensuring more reliable and efficient communication between vehicles.

关键词： Energy consumption Computational modeling Throughput Energy efficiency Data models Vehicle-to-everything Optimization Intelligent transportation systems Load modeling Edge computing

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Enhanced Student Stress Prediction in Higher Education Using a TriBranch CNN Framework

Enhanced Student Stress Prediction in Higher Education Using...

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Integrated Circuits and Communication systems (ICICACS), IEEE International Conference on

作者： Fateh Bahadur Kunwar Satyajee Srivastava R Dhivya Monika Gulati K. Amudha D Antony Arul Raj Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Udaipur India Department of Computer Science and Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India Department of Information Technology M.Kumarasamy College of Engineering Karur India MMIM Maharishi Markandeshwar (Deemed to be University) Mullana Ambala India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy India Department of Software Systems PSG College of Arts & Science Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Students' stress levels have a significant impact on their academic performance in higher education, thus it's important to identify signs of stress early on and intervene to improve academic performance. Machine learning has evolved into a powerful tool for stress prediction, allowing for proactive support of at-risk youngsters. In order to better anticipate college students' stress levels, this study presents an enhanced TriBranch CNN framework. In order to provide the best possible performance, the technique includes data preparation, feature extraction, and model training. The dimensionality and redundancy are reduced using Principal Component Analysis (PCA). After being trained and tested against other models, the Fully Conditional Temporal Bayesian Network (Fully CTBN) outperformed them all with an accuracy of 93.29%, beating both traditional CNN and FCN approaches. These findings demonstrate that the Fully CTBN approach is useful for identifying students who may be struggling academically and providing schools with the information they need to implement data-driven interventions that boost student well-being and performance.

关键词： Training Accuracy Redundancy Human factors Machine learning Mental health Convolutional neural networks Integrated circuit modeling Stress Principal component analysis

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A Comparative Analysis of Artificial Intelligence and Business Intelligence Using Big Data Analytics

A Comparative Analysis of Artificial Intelligence and Busine...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Bhagath Chandra Chowdari Marella Gopi Chand Vegineni Sireesha Addanki Ehsan Ellahi Arun Kumar K Rishabh Mandal Department of Financial Services Insights & Data Capgemini India Enrollment and Eligibility Team India System soft Technologies llc Information Technology India Department of Information Systems Cleveland State University Ohio United States Department of Computer Science and Engineering Tula's Institute Dehradun UK India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Business intelligence (BI) encompasses the tools and uses to gather, combine, examine, and display data about a specific firm. Using historical and current efficiency comparisons, these structures assist in formulating plans and choices to achieve better outcomes in the future. Additionally, it helps firms determine which business issues and industry trends require attention. It has several benefits, such as increasing responsibility and efficiency, but it carries some disadvantages, such as being expensive, complicated, and time-consuming to adopt. Using big data-driven approaches to optimize talent recruitment is essential for improved decisions in the cutthroat world of BI. The efficacy of many machine learning algorithms to increase recruiting performance and accuracy is compared in this investigation. For optimizing, cross-validating, and hyperparameter modification were employed. The Random Forest (RF) algorithm has the greatest accuracy (92.9%), trailed by the Gradient Boosting Classifying system ( $\mathbf{92.6 \%}$ ) and the neural network (NNs) models ( ${92}.7 \%$ ). These findings imply that sophisticated ML algorithms, in particular RF and NN, can greatly improve BI platforms' hiring procedures. Recruiters can benefit from this research's insightful recommendations for incorporating advanced ML methods into talent recruitment plans.

关键词： Radio frequency Machine learning algorithms Accuracy Artificial neural networks Big Data Market research Classification algorithms Business intelligence Random forests Recruitment

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A meta-reinforcement learning method for adaptive payload transportation with variations

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Neurocomputing 2025年 638卷

作者： Chen, Jingyu Ma, Ruidong Xu, Meng Candan, Fethi Mihaylova, Lyudmila Oyekan, John Institute of Software Chinese Academy of Sciences Beijing China Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom School of Information Technology & Management University of International Business and Economics Beijing China Department of Computer Science University of York York United Kingdom

The safe transport of cable-suspended payloads by a group of Unmanned Aerial Vehicles (UAVs) depends on their capacity to effectively respond to fluctuations in the dynamics caused by external variations, such as wind gusts. For group transportation with obstacles, internal variations, such as changes in formation, can also alter the space occupancy of the system related to collision detection. However, traditional adaptive learning methods are challenging to adapt to these two variations. In this paper, we present a learning-based method for collision-free dual-UAV–payload transportation in the presence of varied wind force and formation change. It consists of an adaptive trajectory tracking controller based on meta-model-based reinforcement learning with online adaptation and a novel correction policy, and a path planner that can sample collision-free goal states of the system for the controller based on the meta-collision predictor. The simulation results demonstrate that the proposed trajectory tracking controller outperforms state-of-the-art model-free, model-based, and variational inference methods in terms of payload tracking error reduction and robustness when dealing with the variations mentioned above. Specifically, the proposed controller reduces the average payload tracking error to less than 0.1 metres in most tasks without obstacles. Furthermore, by following the adapted paths generated by the path planner, the trajectory tracking controller can effectively track the payload while ensuring collision-free safety of the dual-UAV–payload system during navigation among obstacles. The success rate of the proposed method is more than 80% in all scenarios with obstacles. Our project website can be seen at https://***/view/meta-payload-fly/ and the source code is available at https://***/wawachen/Meta-load-fly. © 2025

关键词： Unmanned aerial vehicles (UAV)

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A novel facial expression recognition framework using deep learning based dynamic cross-domain dual attention network

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PeerJ Computer Science 2025年 11卷 e2866-e2866页

作者： Alzahrani, Ahmed Omar Alghamdi, Ahmed Mohammed Ashraf, M. Usman Ilyas, Iqra Sarwar, Nadeem Alzahrani, Abdulrahman Alarood, Alaa Abdul Salam Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Makkah Jeddah Saudi Arabia Department of Software Engineering College of Computer Science and Engineering University of Jeddah Makkah Jeddah Saudi Arabia Department of Computer Science Government College Women University Sialkot Punjab Sialkot Pakistan Department of Computer Science Bahria University Punjab Lahore Pakistan

Variations in domain targets have recently posed significant challenges for facial expression recognition tasks, primarily due to domain shifts. Current methods focus largely on global feature adoption to achieve domain-invariant learning;however, transferring local features across diverse domains remains an ongoing challenge. Additionally, during training on target datasets, these methods often suffer from reduced feature representation in the target domain due to insufficient discriminative supervision. To tackle these challenges, we propose a dynamic cross-domain dual attention network for facial expression recognition. Our model is specifically designed to learn domain-invariant features through separate modules for global and local adversarial learning. We also introduce a semantic-aware module to generate pseudo-labels, which computes semantic labels from both global and local features. We assess our model’s effectiveness through extensive experiments on the Real-world Affective Faces Database (RAF-DB), FER-PLUS, AffectNet, Expression in the Wild (ExpW), SFEW 2.0, and Japanese Female Facial Expression (JAFFE) datasets. The results demonstrate that our scheme outperforms the existing state-of-the-art methods by attaining recognition accuracies 93.18, 92.35, 82.13, 78.37, 72.47, 70.68 respectively. Copyright 2025 Alzahrani et al. Distributed under Creative Commons CC-BY 4.0

关键词： Deep learning

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A direct predictive DC-link voltage control with warm-starting iterative approach for three-level NPC BTB converter fed PMSG-based wind turbine systems

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Expert systems with Applications 2025年 283卷

作者： Ganesh, Mayilsamy Jeong, Jae Hoon Research Center for Wind Energy systems School of IT Information and Control Engineering Kunsan National University 588 Daehak-ro Gunsan-si Jeonbuk54150 Korea Republic of College of Computer and Software Kunsan National University 588 Daehak-ro Gunsan-si Jeonbuk54150 Korea Republic of

This article presents a direct predictive DC-link voltage control (DPVC) scheme for a three-level (3L) neutral point clamped (NPC) back-to-back (BTB) converter fed permanent magnet synchronous generator (PMSG)-based wind turbine system (WTS). The conventional 3L NPC BTB converter employs an external PI controller to regulate the DC-link voltage at the set point by generating power or current references for the cascaded finite control set (FCS) predictive control. The presented strategy incorporates the DC-link voltage regulation term into the power regulation term in the grid-side converter (GSC) cost function, identifying the optimum switching state that directly regulates the DC-link voltage at the set point. Through this, the required active power is transferred to the grid to maintain power balance, and the conventional cascaded control structure is eliminated. In addition, a warm-starting iterative approach (WSIA) is introduced to derive the switching states employed for each switching instant. This reduces computation in both the GSC and machine-side converter (MSC) control and inherently incorporates switching frequency optimization. The presented scheme is validated in 3 MW rated WTS simulations under various operating conditions, with comparative studies performed to demonstrate its significance. © 2025 Elsevier Ltd

关键词： Predictive control systems

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Embedded System Security Protocols: Latest Trends and Issues

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International Journal of High Speed Electronics and systems 2025年第3期34卷

作者： Han, Huimin Gulzar, Yonis Nizamani, Ahsan Ahmed Nizamani, Abdul Haseeb Ali, Sijjad Aslam Bhatti, Mughair Mechanical and Electrical Engineering College Hainan Vocational University of Science and Technology Haikou571126 China Department of Management Information Systems King Faisal University P.O. Box 380 Al-Ahsa31982 Saudi Arabia School of Computer Science and Engineering Central South University Changsha410083 China College of Computer Science and Software Engineering Shenzhen University Guandong Shenzhen China School of Geography Nanjing Normal University Jiangsu Nanjing China

Given the increasingly serious security threats faced by embedded systems, an analysis has been conducted on the characteristics and vulnerabilities of embedded systems. Detailed discussions are provided on security threats faced by embedded systems from two major aspects: hardware-based attacks such as side-channel attacks, hardware Trojans, fault injection, microprobes, and hardware reverse engineering, and software attacks aimed at damaging, tampering, and stealing. The analysis includes existing security measures both domestically and internationally, highlighting their shortcomings. Based on the concept of trusted computing, the paper offers future research suggestions from several perspectives, including theoretical research models, trusted measurement, trusted reporting, trusted sharing, and isolation of resources. Finally, an ideal comprehensive joint security enhancement scheme is proposed, and future research directions are discussed. © 2025 World Scientific Publishing Company.

关键词： Embedded software

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A multi-objective cuckoo search algorithm using generalized Lèvy flight and dissimilar egg identification for multispectral image thresholding

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Applied Soft Computing 2025年 175卷

作者： Pal, Ramen Roy, Pritam Mallick, Srijon Mukhopadhyay, Somnath Sarkar, Sunita Hinchey, Mike Department of Computer Science and Engineering Assam University Silchar Assam India Cognizant Technology Solutions India Pvt. Ltd. India Samsung Semiconductor India Research India Lero the SFI Research Centre for Software Department of Computer Science & Information Systems University of Limerick Limerick Ireland

Cuckoo Search (CS) stands as a highly efficient meta-heuristic optimization algorithm. Existing literature showcases the ability of CS in multi-objective scenarios, delineated by the three fundamental rules. However, the first rule of the algorithm incurs the cost of a generation to update a single nest, while the third rule necessitates hit-and-trial methods for parameter adjustment. To address these concerns, a multi-objective cuckoo search algorithm is proposed in this paper. The algorithm builds upon a generalized concept of Lèvy Flight for generating new solutions. Problem-specific, constraint-based strategies for identifying the best nest and dissimilar eggs are also introduced. The algorithm is further applied to solve multispectral remote sensing image thresholding problem. Prior studies have underscored the efficiency of entropy and clustering-based thresholding methods over other techniques. Nevertheless, most entropy-based approaches entail converting color images to grayscale before segmentation, potentially sacrificing crucial spectral information and consequently degrading segmentation algorithm's performance. To avoid these limitations, this research introduces an entropy-based thresholding method to segment a color image without converting it to grayscale. The experiments are carried out using very high resolution (VHR) and coarse resolution (CR) multispectral (MS) images from the satellite sensors Pl'eidas-1B and Sentinel-2b, respectively. The proposed methods undergo validation against four state-of-the-art techniques on benchmark functions and six clustering indexes, respectively. © 2025 Elsevier B.V.

关键词： Heuristic algorithms

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