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4th International Symposium on Instrumentation, control, Artificial Intelligence, and Robotics, ICA-SYMP 2025

作者： Maneeloke, Benjaset Boonto, Sudchai Sanposh, Peerayot Tipsuwan, Yodyium Chinthaned, Natthawut Tanaprasitpattana, Warit Kasetsart University Dept. of Electrical Engineering Bangkok Thailand King Mongkut's University of Technology Thonburi Dept. of Control Systems and Instrumentation Engineering Bangkok Thailand Kasetsart University Dept. of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331505400

This paper presents a novel methodology for closed-loop system identification of unstable nonlinear systems using the Koopman operator with Extended Dynamic Mode Decomposition with control (EDMDc). The study highlights the critical role of selecting appropriate observable functions to develop accurate and efficient Koopman models. We demonstrate that the resulting Koopman models exhibit excellent fitting and validation properties and retain the stabilizability of the original nonlinear systems. These models are verified through time and frequency responses under closed-loop control using a Linear Quadratic Regulator (LQR), confirming their effectiveness. Future work will extend this framework to more complex systems and incorporate machine learning techniques to refine the selection of observable functions. This approach aims further to enhance the adaptability and robustness of Koopman-based control strategies. © 2025 IEEE.

关键词： Dynamic mode decomposition

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Advancing Environmental Sustainability: Renewable Energy Transition, Carbon Footprint Reduction, and Emission control Innovations 22

Advancing Environmental Sustainability: Renewable Energy Tra...

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22nd International Learning and Technology Conference, L and T 2025

作者： Hussein, Aziza I. Alghamdi, Roba W. Al-Amoudi, Amal Abdullah Altahawi, Omar Hisham Kabbaja, Narjisse Mabrook, Mohamed Mourad Lotfy, Mostafa Wageh Effat University Electrical & Computer Eng. Dept. Jeddah Saudi Arabia Beni-Suef University Faculty of Navigation Science & Space Technology Space Communication Dept. Beni-Suef Egypt Beni-Suef University Faculty of Technology and Education Dept. of Process Control Technology Beni-Suef Egypt

ISBN: (纸本)9798331532215

Combating climate change and promoting environmental sustainability necessitate a dedicated emphasis on renewable energy sources and lowering carbon footprints. Shifting from fossil fuels to renewable resources, such as solar, wind, biomass, and geothermal energy, is imperative. Equally important is enhancing energy efficiency across industries, transportation, buildings, and household appliances to reduce overall energy consumption and greenhouse gas emissions. Technological advancements, such as improved energy storage and smart grid systems, are essential for boosting the efficiency and dependability of renewable energy sources. Strategies to lower carbon footprints include transitioning to clean transportation, optimizing land use, reducing waste, recycling, promoting sustainable agriculture, and implementing carbon offset initiatives. Public education, corporate responsibility, and personal environmental awareness play vital roles in supporting carbon reduction. By combining sustainable energy practices with carbon reduction measures, we can collectively work towards curbing climate change and fostering a sustainable future. Additionally, utilized techniques to address the problem of high air pollution during vehicle cold starts. We modeled catalytic converter dynamics in MATLAB Simulink, using simplifying assumptions. One significant assumption involved treating the catalytic converter as a Continuous Stirred-Tank Reactor (CSTR), which allowed us to model its temperature uniformly, without accounting for temperature changes along its length. This approach is aimed at saving lives and reducing costs by effectively minimizing air pollution. © 2025 IEEE.

关键词： Clean energy

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Enhancing 6G-Satellite Network Integration with Artificial Intelligence: A Future Communication Paradigm 22

Enhancing 6G-Satellite Network Integration with Artificial I...

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22nd International Learning and Technology Conference, L and T 2025

作者： Salah, Ibrahim Hussein, Aziza I. Wageh Lotfy, Mostafa Mabrook, Mohamed Mourad Almaaqal University College of Engineering Control and Computer Department Basrah61001 Iraq Effat University College of Engineering Energy and Technology Research Center Saudi Arabia Faculty of Technology and Education Beni-Suef University Dept. of Process Control Technology Beni-Suef Egypt Beni-Suef University Faculty of Navigation Science & Space Technology Space Communication Dept. Beni-Suef Egypt

ISBN: (纸本)9798331532215

The integration of 6G networks and satellite communications is set to revolutionize global connectivity, offering seamless coverage across terrestrial and non-terrestrial environments. Artificial Intelligence (AI) is essential for improving this integration, addressing challenges such as dynamic resource management, latency reduction, and network optimization. AI techniques like machine learning, deep learning, and reinforcement learning offer innovative solutions to handle the complexities of 6G-satellite networks. These advancements promise to significantly improve network efficiency, enhance data transmission reliability, and ensure seamless connectivity across different areas. Potential use cases include smart cities, autonomous vehicles, and Internet of Things (IoT) applications, where AI-driven 6G-satellite integration will be crucial. The proposed AI-enhanced 6G-satellite framework not only addresses current challenges but also lays the groundwork for a resilient, scalable, and globally interconnected communication infrastructure, offering a promising future. © 2025 IEEE.

关键词： Satellite communication systems

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Multi-Point Positional Insertion Tuning for Small Object Detection

Multi-Point Positional Insertion Tuning for Small Object Det...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kanoko Goto Takumi Karasawa Takumi Hirose Rei Kawakami Nakamasa Inoue Dept. of Computer Science Institute of Science Tokyo Tokyo Japan Dept. of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Small object detection aims to localize and classify small objects within images. With recent advances in large-scale vision-language pretraining, finetuning pretrained object detection models has emerged as a promising approach. However, finetuning large models is computationally and memory expensive. To address this issue, this paper introduces multi-point positional insertion (MPI) tuning, a parameter-efficient finetuning (PEFT) method for small object detection. Specifically, MPI incorporates multiple positional embeddings into a frozen pretrained model, enabling the efficient detection of small objects by providing precise positional information to latent features. Through experiments, we demonstrated the effectiveness of the proposed method on the SODA-D dataset. MPI performed comparably to conventional PEFT methods, including CoOp and VPT, while significantly reducing the number of parameters that need to be tuned.

关键词： Computational modeling Object detection Signal processing Feature extraction Encoding Acoustics Speech processing Tuning

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Transfer and Bayesian Learning Approaches for Land Use and Land Cover Classification

Transfer and Bayesian Learning Approaches for Land Use and L...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Zainab Malik Aisha Sir Elkhatem Gokhan Bilgin Dept. of Computer Engineering Yildiz Technical University Istanbul Turkey Dept. of Control and Automation Engineering Yildiz Technical University Istanbul Turkey

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Land use and land cover (LULC) classification is essential for understanding the impact of both human activities and natural processes on the Earth’s surface. This classification plays a critical role in deforestation, urban planning, and damage assessment. However, traditional machine learning techniques often face challenges in achieving consistent accuracy in diverse datasets. In this study, we implement transfer learning and Bayesian learning approaches to enhance the accuracy and robustness of LULC classification. Transfer learning leverages knowledge from previous classification problems, while Bayesian learning addresses uncertainties in the classification process. Using the Remote Sensing Image Classification Benchmark (RSI-CB128) dataset, the study evaluates the performance of Bayesian Convolutional Neural Networks (CNN), Mobile Neural Networks (MobileNet), Inception Neural Networks (InceptionNet), Densely Connected Convolutional Networks (DenseNet) and Efficient Neural Networks (EfficientNet). The study also investigates the impact of varying the Bayesian layers on model performance, finding that a medium number of layers optimally captures model uncertainty. The results indicate that the transfer learning models, particularly MobileNet, achieved the highest accuracy of 97. 48% compared to 90. 33% for Bayesian CNN. These findings suggest that transfer learning techniques are highly effective for LULC classification, providing a reliable method for practical applications in remote sensing.

关键词： Deforestation Accuracy Uncertainty Computational modeling Transfer learning Neural networks Urban planning Land surface Bayes methods Convolutional neural networks

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SB-AGT: A stochastic beam search-enhanced attention-based Gumbel tree framework for drug-drug interaction extraction from biomedical literature

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computers in Biology and Medicine 2025年 189卷 110011-110011页

作者： Saad, Eman Kishk, Sherif Ali-Eldin, Amr Saleh, Ahmed I. Communication and Information Technology Center Mansoura University Egypt Electronics and Communication Engineering Dept. Mansoura University Egypt Computer Engineering & Control Systems Dept. Mansoura University Egypt

Drug-Drug Interactions (DDI) and Chemical-Protein Interactions (CPI) detection are crucial for patient safety, as unidentified interactions may lead to severe Adverse Drug Reactions (ADRs). While extensive DDI and CPI information exists within biomedical literature, manual extraction by experts proves time-intensive and resource-demanding. This study presents SB-AGT: Stochastic Beam Search-enhanced Attention-based Gumbel Tree an innovative approach to automated DDI and CPI extraction through an attention-based architecture incorporating a modified Gumbel-Tree method with stochastic beam search optimization. SB-AGT leverages multi-head attention mechanisms and enhanced latent tree structures to effectively capture complex syntactic features and drug relationship patterns. Also, the methodology includes comprehensive preprocessing protocols and addresses dataset imbalance through hybrid sampling techniques. Experimental validation on the DDIExtraction 2013 and CHEMPROT datasets demonstrates the model's effectiveness, achieving precision, surpassing traditional approaches and showing competitive performance compared to contemporary pre-trained models. Through parametric analysis, we establish optimal beam search configurations that maximize the model's extraction accuracy. © 2025 Elsevier Ltd

关键词： Stochastic models

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Enhanced deep learning framework for real-time pain assessment using multi-modal fusion of facial features and video streams

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Engineering Applications of Artificial Intelligence 2025年 154卷

作者： El-Ghaish, Hany Al-Basiouny, Mohamed Yousry Alshewimy, Mahmoud A.M. Tanta University Faculty of Engineering Computer and Control Engineering Dept. Tanta Egypt

This paper introduces an enhanced deep learning multimodal framework for real-time pain assessment that addresses the limitations of the single-mode approaches. Specifically, time-related disadvantages and its poor temporal analysis, noted in recent studies. The proposed framework fuses VGG16 (Visual Geometry Group 16-layer network), a pre-trained convolutional neural network (CNN), with a bidirectional long short-term memory network (Bi-LSTM) augmented with attention mechanisms. VGG16 has a proven ability to capture fine-grained spatial information in facial expressions to extract powerful spatial features, whereas the Bi-LSTM augmented with attention mechanisms because of its computational efficiency and concisely captures temporal features. Moreover, adaptive boosting has used seeking generalization across a broad base of settings and Gradient-weighted Class Activation Mapping (Grad-CAM) to provide comprehensible and interpretable visual explanations of the decision-making processes of the framework. Extensive evaluations were conducted on diverse popular benchmark datasets: BioVid, UNBC-McMaster, and MIntPAIN. Our framework achieves an AUC (the Area under the Receiver Operating Characteristic Curve) of 0.99 and 99% cross-validation accuracy, corresponding to a 12.5% improvement in accuracy and a 20% reduction in false positives relative to state-of-the-art convolutional neural network–long short-term memory (CNN–LSTM) models. Real-time deployment on an NVIDIA Jetson Nano yields 120 ms (ms) inference per frame with less than 1% accuracy loss, demonstrating its feasibility for edge-based telemedicine. Combined with explainability via Grad-CAM and privacy-preserving vision-only inputs, this framework offers an ethical, scalable solution for nonverbal pain detection. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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GFT: Gradient Focal Transformer

arXiv

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arXiv 2025年

作者： Kriuk, Boris Gill, Simranjit Kaur Aslam, Shoaib Fakhrutdinov, Amir Hong Kong University of Science and Technology Dept. of Computer Science & Engineering Hong Kong University of Westminster Dept. of Computer Science & Engineering United Kingdom University of Engineering & Technology Lahore Dept. of Mechanical Mechatronics & Control Eng Pakistan Shanghai Jiao Tong University Dept. of Mechanical Engineering China

Fine-Grained Image Classification (FGIC) remains a complex task in computer vision, as it requires models to distinguish between categories with subtle localized visual differences. Well-studied CNN-based models, while strong in local feature extraction, often fail to capture the global context required for fine-grained recognition, while more recent ViT-backboned models address FGIC with attention-driven mechanisms but lack the ability to adaptively focus on truly discriminative regions. TransFG and other ViT-based extensions introduced part-aware token selection to enhance attention localization, yet they still struggle with computational efficiency, attention region selection flexibility, and detail-focus narrative in complex environments. This paper introduces GFT (Gradient Focal Transformer), a new ViT-derived framework created for FGIC tasks. GFT integrates the Gradient Attention Learning Alignment (GALA) mechanism to dynamically prioritize class-discriminative features by analyzing attention gradient flow. Coupled with a Progressive Patch Selection (PPS) strategy, the model progressively filters out less informative regions, reducing computational overhead while enhancing sensitivity to fine details. GFT achieves SOTA accuracy on FGVC Aircraft, Food-101, and COCO datasets with 93M parameters, outperforming ViT-based advanced FGIC models in efficiency. By bridging global context and localized detail extraction, GFT sets a new benchmark in fine-grained recognition, offering interpretable solutions for real-world deployment scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep learning

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Optimization Scheduling of Multi-Energy Microgrid Based on the QUBO Quantum Computing Model 19th

Optimization Scheduling of Multi-Energy Microgrid Based on t...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Wang, Baonan Wang, Hui Zhang, Dan College of Computer Science and Technology Shanghai University of Electric Power Shanghai China State Key Laboratory of Power System Operation and Control Dept. of Electrical Engineering Tsinghua University Beijing China Institute of Logistic Science and Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9789819608966

Current optimization methods for microgrid scheduling face issues such as insufficient precision in energy distribution, high operational costs, and inefficiency. In response to these challenges, an optimization scheduling method for multi-energy microgrids based on the QUBO (Quadratic Unconstrained Binary Optimization) quantum computing model is proposed in this paper, alongside a discretization approach based on a grid partitioning strategy. To validate the feasibility of addressing microgrid optimization scheduling problems in a quantum computing environment, a QUBO model is constructed and simulation studies are conducted in the qbsolv environment provided by D-Wave. The results indicate that operational costs are significantly reduced with an increase in the number of grids, particularly a reduction of 4.52% when the number of grids increases from five to ten. This method not only effectively enhances the precision of electricity supply and energy utilization efficiency but also reduces unnecessary electrical waste and economic costs. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Microgrids

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Securing Mobile Robotic Networks Against Replacement Attack

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IEEE Transactions on Automatic control 2025年

作者： Li, Yushan He, Jianping Chen, Cailian Guan, Xinping Cai, Lin Shanghai Jiao Tong University Dept. of Automation China Ministry of Education of China The Key Laboratory of System Control and Information Processing China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China University of Victoria Dept. of Electrical and Computer Engineering BC Canada

The security of mobile robotic networks (MRNs) has been an active research topic in recent years. This paper aims to secure the ubiquitous formation control of MRNs against the replacement attack, where an external robot can replace a formation robot by compromising the communication and physically interfering with the victim simultaneously. To counter this advanced attack, the novel idea of this work is to leverage the physical proximity of the formation shape and the interaction topology among robots for defense design. First, from the physical proximity perspective, we propose the convex neighbor polygon (CNP) to capture the geometric characteristic of the formation robots, and design a CNP-based security mechanism for the robots during the replacement attack. Then, from the interaction perspective, we introduce the indirect controllability to characterize the possibility that the attacker leverages the interaction between robots to deactivate the CNP mechanism, and establish the conditions regarding the topology structure and the attack input to counter the replacement. Finally, based on the obtained conditions, we demonstrate how to design the initiatory topology among the formation robots to enhance the defense performance. Comparative simulations verify the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Topology

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