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Composite neural learning-based adaptive actuator failure compensation control for full-state constrained autonomous surface vehicle

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Neural Computing and Applications 2025年第8期37卷 6369-6381页

作者： Song, Shuai Jiang, Yu Song, Xiaona Stojanovic, Vladimir School of Information Engineering Henan University of Science and Technology Kaiyuan Avenu Luoyang471023 China Faculty of Mechanical and Civil Engineering University of Kragujevac Kraljevo36000 Serbia

This article studies composite neural learning-based adaptive failure compensation control issues for the autonomous surface vehicle with full-state constraints. Initially, the control strategy solve the problems of computational complexity and state constraints and eliminate the negative effect of filter error on tracking performance by integrating with the command-filtered backstepping technique and barrier Lyapunov functions. Then, a composite neural learning framework is established, where the effect caused by approximation error on tracking accuracy can be efficiently reduced by constructing the serial-parallel estimation model to obtain the estimations of the system states. Furthermore, an adaptive resilient trajectory tracking controller is designed, which can ensure that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded satisfying the preset constraints even if the expected actuator faults occur suddenly. Finally, the feasibility and superiority of the designed control strategy are clarified by simulation results. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Backstepping

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Artificial intelligence-inspired comprehensive framework for desertification prediction

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International Journal of Cognitive Computing in engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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Neuroadaptive Containment Control for Nonlinear Multiagent Systems With Input Saturation: An Event-Triggered Communication Approach

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3163-3173页

作者： Wang, Xin Zhang, Sen Li, Huaqing Zhang, Wei Li, Hongyi Huang, Tingwen Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this article, a neuroadaptive event-triggered containment control strategy combined with the dynamic surface control (DSC) approach is proposed for nonlinear multiagent systems (MASs) with input saturation. Based on the event-triggered communication mechanisms, the updates of neural network weight and controllers are implemented solely under triggering conditions of violation, which markedly reduces unnecessary communication resources and minimizes inefficient control costs compared with the traditional control method. Radial basis function neural networks (RBF NNs) are employed to handle the nonlinear uncertainties of MASs. Simultaneously, an adaptive compensatory mechanism is incorporated within the backstepping process to address the nonlinear effect posed by input saturation. Additionally, we demonstrate the system stability by extending the Lyapunov theorem to jump and continuous scenarios while excluding Zeno behavior, realizing that all followers can enter the convex hull constructed by leaders. Finally, the effectiveness of the proposed methodology is verified through application simulations. © 2013 IEEE.

关键词： Radial basis function networks

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Performance *** Comparative Analysis of Multimodal Bilinear Pooling Fusion Approaches for Deep Learning-Based Visual Arabic-Question Answering Systems

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Computer Modeling in engineering & Sciences 2025年第4期143卷 373-411页

作者： Sarah M.Kamel Mai A.Fadel Lamiaa Elrefaei Shimaa I.Hassan Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer and Systems Engineering Faculty of Engineering and TechnologyBadr University in Cairo(BUC)Cairo11829Egypt Communication Systems Engineering Department Faculty of EngineeringBenha National UniversityObour11846QalyubiaEgypt

Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.

关键词： Arabic-VQA deep learning-based VQA deep multimodal information fusion multimodal representation learning VQA of yes/no questions VQA model complexity VQA model performance performance-complexity trade-off

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Parameter Adaptation of Enhanced Ant Colony System for Water Quality Rules Classification

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International Journal of Advanced Computer Science and Applications 2025年第3期16卷 716-723页

作者： Nasir, Husna Jamal Abdul Munif, Mohd Mizan Ahmad, Muhammad Imran Chow, Tan Shie Ku-Mahamud, Ku Ruhana Abdullah, Abu Hassan Faculty of Electronic Engineering and Technology Universiti Malaysia Perlis Perlis Malaysia Universiti Malaysia Perlis Perlis Malaysia Faculty of Business Management and Information Technology Universiti Muhammadiyah Malaysia Perlis Malaysia School of Computing Universiti Utara Malaysia Sintok Kedah Malaysia Faculty of Electrical Engineering & Technology Universiti Malaysia Perlis Perlis Malaysia

Water quality monitoring in aquaculture involves classifying and analyzing the collected data to assess the water quality that is appropriate for breeding, rearing and harvesting aquatic organisms. Systematic data classification is essential when it comes to managing large amounts of data that are continuously sensed in real time and have various attributes in each instance of a sequence. Ant Colony System (ACS) has been employed in optimizing the data classification in smart aquaculture, where the majority of the research focuses on enhancing the classification procedure using predetermined parameters within a specified range. Nevertheless, this approach does not guarantee ideal performance. This paper enhances the ACS algorithm by introducing the Enhanced Ant Colony System-Rule Classification (EACS-RC) algorithm, which improves rule construction by integrating pheromone and heuristic values while incorporating advanced pheromone update techniques. The optimal parameter values to be used by the proposed algorithm are obtained from parameter adaptation experiments in which different values within the defined range were applied to obtain the optimal value for each parameter. Experiments were performed on the Kiribati water quality dataset and the results of the EACS-RC algorithm were evaluated against the AntMiner and AGI-AntMiner algorithms. Based on the results, the proposed algorithm outperforms the benchmark algorithms in classification accuracy and processing time. The output of this study can be adopted by the other ACS variants to achieve optimal performance for data classification in smart aquaculture. © (2025), (Science and information Organization). All rights reserved.

关键词： Aquaculture

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Multi-UAVs Path Planning Based on Genetic Algorithm in Campus Emergency Response 24

Multi-UAVs Path Planning Based on Genetic Algorithm in Campu...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Zhong, Yangjun Faculty of Information Engineering Jiangxi University of Science and Technology Jiangx Ganzhou China

ISBN: (纸本)9798400710865

University campuses frequently face complex emergencies such as natural disasters, medical emergencies, and safety threats, posing significant challenges to the design of emergency response systems. Given the rapid advancement of drone technology and its efficiency in dynamic environments, this study proposes a genetic algorithm-based multi-Unmanned Aerial Vehicles (UAVs) path planning method to enhance the response efficiency to campus emergencies. Initially, we divide the campus environment into a series of spatial grids, thereby transforming the continuous path planning problem into a discrete optimization problem. By defining time-varying weights for critical areas, an objective function is designed to balance response time and area coverage. The individual encoding is represented by a sequence of path nodes to accommodate the cooperative operation of multi-UAVs systems. The selection, crossover, and mutation operations of genetic algorithms are adapted to suit this specific problem, ensuring global search capability under multiple constraints. Simulation experiments in a real campus environment validate the significant performance enhancement of the algorithm in response time, path optimization, and coverage effectiveness. This study provides a theoretical foundation and practical support for the effective deployment of multi-UAV cooperative systems, improving the intelligence level of campus security management. © 2024 Copyright held by the owner/author(s).

关键词： Motion planning

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Element relational graph-augmented multi-granularity contextualized encoding for document-level event role filler extraction

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Frontiers of Computer Science 2025年第2期19卷 137-138页

作者： Enchang ZHU Zhengtao YU Yuxin HUANG Shengxiang GAO Yantuan XIAN Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming 650500China

1 Introduction Document-level Role Filler Extraction aims to identify those spans of text that denote the role fillers for each event described in the document[1].Despite achieving certain accomplishments,existing methods are still not effective due to the following two issues:(1)there are difficulties in contextual modeling of long text,which requires modeling and understanding coherence and connections across sentences and paragraphs;(2)there usually ignore the explicit dependency relationships between event elements displayed in long *** this end,we propose a novel graph-augmented approach for document-level event role filler extraction,named element relational graph-augmented multi-granularity contextualized encoder(ERGM),whose main idea is to effectively enhance the model's capabilities in capturing deep semantic information of events in long texts and modeling dependency relationships among event elements by incorporating the Event elements relational ***,this method first constructs the structural graph by extracting elements from the source document.

关键词： contextual spite semantic

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Leveraging Quantum Communication with 6G Network: Optimize the Model for Secured Communications 1

Leveraging Quantum Communication with 6G Network: Optimize t...

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1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kumar Gautam, Anupam Kumar Rajendran, Naveen Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Aerospace Engineering Karnataka India

ISBN: (纸本)9798331518578

A broad-ranging consciousness-raising movement of mathematical ideas is dawning accompanying the onset of 6G science, that promises ubiquitous and flowing relations. Many visualize the inclusion of quantum computing (QC) into specific networks as a potential responsible for the complex computational and safety needs concerning this revolution. In light of 6G science, this paper delves further into the likely functions and results of QC. Totally classification of some (AP15) fault-finding QC uses in 6G science into four main classifications established in the literature review. Speaking accompanying experts engaged rooted these results even further;they uncovered best practices guide individual of the emphasize fault-finding applications. Researchers in addition to manufacturing experts alike grant permission to benefit from the research's judgments, which determine a firm basis for including the detracting uses of QC within 6G Wi-Fi electronics and profitable arrangements for implementing it. © 2025 IEEE.

关键词： Wireless local area networks (WLAN)

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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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