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6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Yousefi, Mohadeseh Naderahmadian, Yashar Beheshti, Soosan University of Guilan Department of Electrical and Computer Engineering Rasht Iran Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering Toronto Canada

ISBN: (纸本)9798331506520

The Generalized Adaptive Weighted Recursive Least Squares (GAWRLS) dictionary learning method has shown potential for unsupervised dictionary learning. This paper advances GAWRLS by incorporating classification error as an additional cost to enable supervised learning tasks and introduces the Label Consistency for online supervised dictionary learning in classification tasks. The new method is denoted as Label Consistent Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning (LC-GAWRLS). By incorporating both sparse representation error and classification error into the cost function, LC-GAWRLS enables simultaneous learning of the dictionary and classifier parameters. Particularly, to ensure label consistency, the proposed algorithm introduces a correction weight to adaptively regulate the impact of each training data during the model update, enhancing robustness against variations in training data compared to previous dictionary learning methods. Simulation results on real datasets demonstrate that LC-GAWRLS achieves higher classification accuracy compared to existing state-of-the-art supervised dictionary learning methods, particularly in scenarios with limited training samples per class. © 2025 IEEE.

关键词： Self-supervised learning

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Safe Reinforcement Learning for Grid-forming Inverter Based Frequency Regulation with Stability Guarantee

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Journal of Modern Power Systems and Clean Energy 2025年第1期13卷 79-86页

作者： Hang Shuai Buxin She Jinning Wang Fangxing Li Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN37996USA

This study investigates a safe reinforcement learning algorithm for grid-forming(GFM)inverter based frequency *** guarantee the stability of the inverter-based resource(IBR)system under the learned control policy,a modelbased reinforcement learning(MBRL)algorithm is combined with Lyapunov approach,which determines the safe region of states and *** obtain near optimal control policy,the control performance is safely improved by approximate dynamic programming(ADP)using data sampled from the region of attraction(ROA).Moreover,to enhance the control robustness against parameter uncertainty in the inverter,a Gaussian process(GP)model is adopted by the proposed algorithm to effectively learn system dynamics from *** simulations validate the effectiveness of the proposed algorithm.

关键词： Inverter-based resource(IBR) virtual synchronous generator(VSG) safe reinforcement learning Lyapunov function frequency regulation grid-forming inverter

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Adversarial Attack on Autonomous Ships Navigation Using K-Means Clustering and CAM

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 1073-1095页

作者： Ingle, Ganesh Patil, Kailas Pawale, Sanjesh Department of Computer Engineering Vishwakarma University Pune India

As Maritime Autonomous Surface Ships (MASSs) increasingly become part of global maritime operations, the reliability and security of their object detection systems have become a major concern. These systems, which play a crucial role in identifying small yet critical maritime objects such as buoys, vessels, and kayaks, are particularly susceptible to adversarial attacks, especially clean-label poisoning attacks. These attacks introduce subtle manipulations into training data without altering their true labels, thereby inducing misclassification during model inference and threatening navigational safety. The objective of this study is to evaluate the vulnerability of maritime object detection models to such attacks and to propose an integrated adversarial framework to expose and analyze these weaknesses. A novel attack method is developed using K-means clustering to segment similar object regions and Class Activation Mapping (CAM) to identify high-importance zones in image data. Adversarial perturbations are then applied within these zones to craft poisoned inputs that target the YOLOv5 object detection model. Experimental validation is performed using the Singapore Marine Dataset (SMD and SMD-Plus), and performance is measured under different perturbation intensities. The results reveal a considerable decline in detection accuracy—especially for small and mid-sized vessels—demonstrating the effectiveness of the attack and its capacity to remain imperceptible to human observers. This research highlights a critical gap in the security posture of AI-based navigation systems and emphasizes the urgent need to develop maritime-specific adversarial defense strategies for ensuring robust and resilient MASS deployment. © (2025), (Science and Information Organization). All rights reserved.

关键词： Marine navigation

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Semi-Supervised Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning 6

Semi-Supervised Generalized Adaptive Weighted Recursive Leas...

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6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

ISBN: (纸本)9798331506520

This work presents a novel semi-supervised dictionary learning framework that updates the dictionary by online learning and is efficient in utilizing the training data. The method employs a two-stage process to train the dictionary: initial training with limited labeled data, followed by online refinement using abundant unlabeled data. We introduce an adaptive correction weight to control the influence of new unlabeled data on the dictionary update based on its consistency with the current model estimate. This approach enables efficient use of the training data set. Moreover, results in faster dictionary convergence and improves data representation accuracy, especially in scenarios with limited training data. Experimental results demonstrate significant enhancement in the classification accuracy of the proposed method compared to the state-of-the-art semi-supervised dictionary learning methods, particularly when dealing with a limited number of training samples. © 2025 IEEE.

关键词： Self-supervised learning

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A Novel Beam Tracking Scheme in RIS for Wideband THz Communication Systems

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IEEE Communications Letters 2025年第5期29卷 943-947页

作者： Dughri, Abdulrahman S. Elganimi, Taissir Y. Rabie, Khaled M. University of Tripoli Department of Electrical and Electronic Engineering Tripoli Libya Department of Computer Engineering Saudi Arabia

Terahertz (THz) massive multiple-input multiple-output (mMIMO) has recently gained significant attention because of its ability to deliver a high data rate and offer a high capacity. In this letter, a novel sub-surface of sub-arrays (SSoSA)-based beam tracking scheme is proposed to track mobile users in reconfigurable intelligent surface (RIS)-aided wideband THz mMIMO systems. By considering the sub-connected phase-delay-phase architecture, the proposed scheme can simultaneously track the physical directions of users, unlike traditional schemes that generate one frequency-independent beam in each time slot. Simulation results show that the proposed scheme offers near-optimal achievable rate performance with low training overhead. © 2025 IEEE.

关键词： Particle beam tracking

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Investigating the Impact of Hyper Parameters on Intrusion Detection System Using Deep Learning Based Data Augmentation

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 523-532页

作者： Iftikhar, Umar Ali, Syed Abbas Department of Computer & Information System Engineering NED University of Engineering & Technology Karachi Pakistan

The effects of changing learning rates, data augmentation percentage and numbers of epochs on the performance of Wasserstein Generative Adversarial Networks with Gradient Penalties (WGAN-GP) are evaluated in this study. The purpose of this research is to find out how they affect the data augmentation to enhance stability during training. In this research, the degree of system performance is measured using the Classification Model Utility approach. For this reason, this study aims to determine the interaction between learning rate, augmentation percentage and epoch value when using WGAN-GP to generate synthetic data for the recognition of the system performance. The results will provide the indications on how some of the hyper parameters can be adjusted up or down for having positive or negative consequences on the generation process for further research and use of WGAN-GP. It also provides insights into how the generative model is trained, and how that affects stability and quality of the result in various settings such as image synthesis or other generative tasks. © (2025), (Science and Information Organization). All rights reserved.

关键词： Deep learning

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Ergodic Capacity of Passive and Active IRS-aided MIMO Systems

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IEEE Sensors Journal 2025年第10期25卷 17283-17292页

作者： Gharibi, Hadi Rabiei, Amir Masoud Olfat, Ali University of Tehran College of Engineering Department of Electrical and Computer Engineering Tehran143995713 Iran

Intelligent reflecting surface (IRS) has emerged as a promising solution to address the ever-increasing demand for reliable and high-speed communication in next generation wireless communication systems. In this paper, the ergodic capacity (EC) of IRS-aided multiple-input multiple-output (MIMO) wireless systems is investigated. Analytical approximations for the ECs of active and passive IRS-aided MIMO systems are derived. As an application of the established theoretical approximations, an optimization problem is formulated to maximize the system’s energy efficiency by determining the optimal number of IRS elements and the optimal transmission power. It is shown that this problem can be readily solved using an alternative optimization technique applicable to both active and passive IRS configurations. Numerical results demonstrate the accuracy of the EC approximations. The results also clarify the impact of operational and environmental parameters on the system’s EC. © 2001-2012 IEEE.

关键词： Vectors Transmission line matrix methods Rician channels Optimization Energy efficiency Array signal processing Intelligent sensors Transmitting antennas Spectral efficiency Receiving antennas

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Automated Parkinson’s Disease Diagnosis Using Decomposition Techniques and Deep Learning for Accurate Gait Analysis

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IEEE Access 2025年 13卷 74078-74091页

作者： Jeba Priya, S. Anand Deva Durai, C. Subathra, M.S.P. Thomas George, S. Jeyabose, Andrew Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India King Khalid University College of Computer Science Department of Computer Science Abha Saudi Arabia Karunya Institute of Technology and Sciences Department of Biomedical Engineering Tamil Nadu Coimbatore India Karunya Institute of Technology and Sciences Department of Robotics Engineering Tamil Nadu Coimbatore India Manipal Academy of Higher Education Manipal Institute of Technology Department of Computer Science and Engineering Karnataka Manipal India

Parkinson's disease (PD) is a prevalent neurological disorder that significantly impacts posture and gait, leading to movement abnormalities due to malfunctions in the brain and nervous system. Gait signals are essential for identifying PD, and various techniques have been employed for classification, with a focus on spatiotemporal factors. Additionally, cognitive monitoring systems for PD symptoms have been developed. Recent advancements involve decomposing gait signals using techniques such as empirical mode decomposition (EMD), empirical wavelet transform (EWT), and variational mode decomposition (VMD) to streamline data for improved computational efficiency. Machine learning (ML) and deep learning (DL) algorithms are widely used to enhance classification accuracy. This study integrates decomposition techniques with ML algorithms such as support vector machines (SVMs), artificial neural networks (ANNs), decision trees (DTs), and k-nearest neighbors (k-NNs), as well as DL algorithms such as long short-term memory (LSTM), bidirectional long short-term memory (LSTM), and convolutional neural networks (CNNs), for PD classification. The combination of VMD with the 1D-CNN achieved the highest accuracy, sensitivity, and specificity, with values of 99.1 %, 100 %, and 100 %, respectively. This finding suggests a promising approach for further research in this field. The optimized VMD-1D-CNN combination demonstrated significant potential for accurately diagnosing PD based on gait dynamics. The successful application of these methods highlights the importance of advanced signal processing techniques in improving the detection and management of neurological disorders. © 2013 IEEE.

关键词： Wavelet decomposition

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Evaluating the adversarial robustness of Arabic spam classifiers

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Neural Computing and Applications 2025年第6期37卷 4323-4343页

作者： Alajmi, Anwar Ahmad, Imtiaz Mohammed, Ameer Department of Computer Engineering College of Engineering and Petroleum Kuwait University Kuwait City Kuwait

Several studies have exposed the vulnerability of Natural Language Processing (NLP) models to adversarial attacks, which are inputs crafted by attackers to deceive NLP models. Adversarial robustness measures the performance of these systems under such attacks. In the Arabic literature, very limited contributions exist in the field of NLP adversarial robustness. Hence, this study focuses on examining the adversarial robustness of an Arabic NLP model, especially in a classic black-box spam evasion scenario. This work introduces eight diverse adversarial attacks (character, word, sentence, and multi-level) against Arabic NLP models. Moreover, we employ local post-hoc explanations such as SHapely Additive exPlanations (SHAP) to optimize the attacks strategies. Despite the excellent unfortified model’s accuracy of 99.4%, three of the attacks reduced the accuracy by more than 90%. This work also employs local post-hoc explanations such as SHapely Additive exPlanations (SHAP) Nevertheless, the proposed adversarial attacks were effective in terms of maintaining high semantic similarity and low perturbation distance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Adversarial machine learning

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Comparison of Tribological Behavior of Hard Faced Fe-Based Metallic Composite Coatings Sprayed Using HVOF

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-18页

作者： Soni, Piyush Kumar Prasad, C. Durga Manjunath, H.R. Tiwari, Amit Raju, Vadali Pitchi Athithan, Senthil Deshmukh, Khemraj Kumar, Anjani Department of Mechanical Engineering New Horizon College of Engineering Karnataka Bengaluru India Department of Mechanical Engineering RV Institute of Technology and Management Karnataka Bengaluru India Department of Mechanical Engineering Ramaiah Institute of Technology Karnataka Bengaluru560054 India Department of Mechanical Engineering Suresh Gyan Vihar University Jaipur India Mechanical Engineering Department & amp Principal Indur Institute of Engineering and Technology Siddipet India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biomedical Engineering Parul University Gujarat Vadodara India Mechanical Engineering Department Government Engineering College Arwal India

Fe-based alloys have recently become well-known for their ability to resist wear and corrosion. Iron, the primary component of alloys, is a cheaper option than alloys made from Co and Ni. There is a need for affordable and easily obtainable coatings based on iron. Coatings in the Fe–Ni–Cr system have demonstrated superior wear resistance compared to other commonly used Fe-based metallic coatings. The Fe-12Ni17Cr2.5Mo2.3Si0.03C based (Metco 41C) metallic coatings were applied onto boiler steel substrates using the High Velocity Oxy-Fuel (HVOF) spraying method. The coatings were examined using X-ray diffraction (XRD), a scanning electron microscope (SEM), and a microhardness tester to determine their density, porosity, microstructure, hardness, and surface roughness. The sliding wear behavior of the substrate and coatings was studied using the pin-on-disk tribometer. Figuring out the wear loss and coefficient of friction. Comparison was made of wear results between the substrate, coatings, and composite coatings. By combining these carbide powders with 30% SiC and WC-12Co particles, the coating’s wear resistance is enhanced in comparison to the Metco 41C coating. In comparison to boiler steel, the Metco 41C + SiC and Metco 41C + WC–Co coatings exhibited a roughly 70% and 40% reduction in volume loss compared with boiler steel respectively. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： HVOF thermal spraying

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