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Multi-Agent Distributed DQN and Transfer Learning for Energy-Efficient Power Management in Solar Energy-Harvested Small-Cell Networks

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IEEE Internet of Things Journal 2025年

作者： Cho, Hyebin Kim, Hyungsub Na, JeeHyeon Lim, Seung-Chan Lee, Howon Hankyong National University School of Electrical Electronic and Control Engineering Anseong17579 Korea Republic of Electronics and Telecommunications Research Institute Daejeon34141 Korea Republic of Hankyong National University School of ICT Robotics and Mechanical Engineering and IITC Anseong17579 Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

The integration of solar energy harvesting into small-cell networks is a promising solution for achieving energy-efficient and sustainable wireless communications. However, the inherent variability and intermittency of solar energy, coupled with precise inter-cell interference management, significantly hinder efficient network operation. To resolve these challenges, we propose a multi-agent distributed deep Q-network (MA-DDQN) framework, where the distributed base stations learn the optimal transmit power control policies. To further enhance adaptability under varying solar conditions, we present a daily model transfer with a fine-tuning approach, enabling efficient deployment without extensive training overhead. Simulation results demonstrate that the proposed methods remarkably improve energy efficiency while maintaining robust adaptability under dynamic solar conditions, revealing their potential for sustainable small-cell network deployments. © 2014 IEEE.

关键词： Solar cell efficiency

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A Feature Fusion Attention-based Deep Learning Algorithm for Mammographic Architectural Distortion Classification

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Rehman, Khalil Ur Jianqiang, Li Yasin, Anaa Bilal, Anas Basheer, Shakila Ullah, Inam Jabbar, Muhammad Kashif Tian, Yibin Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Beijing University of Technology College of Software Engineering Beijing100124 China Hainan Normal University College of Information Science and Technology Hainan Haikou571158 China Princess Nourah Bint Abdulrahman University Department of Information Systems College of Computer and Information Science Riyadh11671 Saudi Arabia Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan

Architectural Distortion (AD) is a common abnormality in digital mammograms, alongside masses and microcalcifications. Detecting AD in dense breast tissue is particularly challenging due to its heterogeneous asymmetries and subtle presentation. Factors such as location, size, shape, texture, and variability in patterns contribute to reduced sensitivity. To address these challenges, we propose a novel feature fusion-based Vision Transformer (ViT) attention network, combined with VGG-16, to improve accuracy and efficiency in AD detection. Our approach mitigates issues related to texture fixation, background boundaries, and deep neural network limitations, enhancing the robustness of AD classification in mammograms. Experimental results demonstrate that the proposed model achieves state-of-the-art performance, outperforming eight existing deep learning models. On the PINUM dataset, it attains 0.97 sensitivity, 0.92 F1-score, 0.93 precision, 0.94 specificity, and 0.96 accuracy. On the DDSM dataset, it records 0.93 sensitivity, 0.91 F1-score, 0.94 precision, 0.92 specificity, and 0.95 accuracy. These results highlight the potential of our method for computer-aided breast cancer diagnosis, particularly in low-resource settings where access to high-end imaging technology is limited. By enabling more accurate and timely AD detection, our approach could significantly improve breast cancer screening and early intervention worldwide. © 2013 IEEE.

关键词： Deep neural networks

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Data-Driven Nonconvex Reachability Analysis using Exact Multiplication

arXiv

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arXiv 2025年

作者： Zhang, Zhen Niazi, M. Umar B Chong, Michelle S. Johansson, Karl H. Alanwar, Amr School of Computation Information and Technology Technical University of Munich Germany Division of Decision and Control Systems Digital Futures School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical Engineering Eindhoven University of Technology Netherlands

This paper addresses a fundamental challenge in data-driven reachability analysis: accurately representing and propagating non-convex reachable sets. We propose a novel approach using constrained polynomial zonotopes to describe reachable sets for unknown LTI systems. Unlike constrained zonotopes commonly used in existing literature, constrained polynomial zonotopes are closed under multiplication with constrained matrix zonotopes. We leverage this property to develop an exact multiplication method that preserves the non-convex geometry of reachable sets without resorting to approximations. We demonstrate that our approach provides tighter over-approximations of reachable sets for LTI systems compared to conventional methods. © 2025, CC BY.

关键词： Geometry

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Frequency stabilization of a sophisticated multi-area interconnected hybrid power system considering non-inertia sources

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International Journal of Electrical Power and Energy Systems 2025年 169卷

作者： Gawad, Diaa M. Magdy, Gaber Ebrahim, M.A. Petlenkov, Eduard Electrical Engineering Department Faculty of Engineering at Shoubra Benha University Cairo Egypt National Energy Control Center Ministry of Electricity and Renewable Energy Cairo Egypt Department of Electrical Engineering Faculty of Energy Engineering Aswan University Aswan81528 Egypt Faculty of Engineering King Salman International University El-Tor46511 Egypt Centre for Intelligent Systems Department of Computer Systems Tallinn University of Technology Tallinn19086 Estonia

This study introduces a novel interconnected power system model, which combines four real areas, integrating conventional power plants with renewable energy sources (RESs). This comprehensive system model aims to address the load frequency control (LFC) challenges that such systems face. Where the integration of RESs into power systems poses significant challenges, particularly in maintaining frequency stability. The LFC approach utilizes a proportional-integral-derivative (PID) controller, meticulously optimized through the Runge-Kutta (RUN) optimizer based on its mathematical principles. Dealing with the uncertainties of RESs and system nonlinearities poses a significant challenge for this controller. The efficacy of the proposed PID controller based on the RUN algorithm is examined by analyzing the frequency stability of the Egyptian power system (EPS), which stays interconnected with neighboring grids in Jordan, Libya, and Sudan. This study accounts for various loading scenarios and system nonlinearities to validate the algorithm's effectiveness. Furthermore, the superior performance of the RUN optimizer is verified by contrasting its results with other widely recognized optimization techniques, such as the Ant Lion optimizer (ALO) and Chernobyl disaster optimizer (CDO). Additionally, the PID controller based on the RUN optimizer is also evaluated against literature that employed a moth swarm algorithm (MSA) to design the PID controller-based LFC in the EPS. Simulation results in the MATLAB environment demonstrate the effectiveness and robustness of the proposed PID controller based on the RUN algorithm. The RUN-based PID controller outperforms other PID controllers optimized using algorithms from the literature (e.g., ALO, CDO, and MSA), achieving superior performance across various contingencies, including load variations, RES uncertainties, and system nonlinearities. The results highlight the effectiveness of the proposed controller compared to those in the l

关键词： Electric power system interconnection

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Topological Dictionary Learning

arXiv

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arXiv 2025年

作者： Grimaldi, Enrico Battiloro, Claudio Di Lorenzo, Paolo Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy Biostatistics Department of Harvard University 655 Huntington Ave BostonMA02115 United States Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Via Eudossiana 18 Rome00184 Italy

The aim of this paper is to introduce a novel dictionary learning algorithm for sparse representation of signals defined over combinatorial topological spaces, specifically, regular cell complexes. Leveraging Hodge theory, we embed topology into the dictionary structure via concatenated sub-dictionaries, each as a polynomial of Hodge Laplacians, yielding localized spectral topological filter frames. The learning problem is cast to jointly infer the underlying cell complex and optimize the dictionary coefficients and the sparse signal representation. We efficiently solve the problem via iterative alternating algorithms. Numerical results on both synthetic and real data show the effectiveness of the proposed procedure in jointly learning the sparse representations and the underlying relational structure of topological signals. © 2025, CC BY.

关键词： Topology

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ESARSA-MRFO-FS: Optimizing Manta-ray Foraging Optimizer using Expected-SARSA reinforcement learning for features selection

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Knowledge-Based Systems 2025年 321卷

作者： AbdulAzeem, Yousry Magdy Balaha, Hossam Bamaqa, Amna Badawy, Mahmoud Elhosseini, Mostafa A. Zewail City of Science and Technology October Gardens 6th of October City Giza12578 Egypt Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Computers and Control Systems Engineering Department Mansoura University Faculty of Engineering Mansoura35516 Egypt Department of Computer Science and Information Applied College Taibah University Madinah42353 Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

Disease prediction with the help of computers has achieved significant progress in this area;however, it still requires a more accurate identification of each data feature. In the past few years, ML-based medical diagnoses have become increasingly dependent on the datasets rather than the algorithms. Consequently, the more features in a dataset, the more difficult the model training process will be. A feature selection (FS) strategy can be used to eliminate unwanted features before the training in order to improve the model performance. Due to the great increase in the number of features, FS causes a complex combinatorial problem, which is NP-hard. The primary goal of this research is to create a novel framework for improving the Manta-ray Foraging Optimizer (MRFO) algorithm's performance. By the method of exploration–exploitation toggle adjustments, this process becomes the most efficient one by using the Expected State–action-reward-state–action (ESARSA) reinforcement learning strategy. The study employed 12 datasets as well as two classifiers (K-Nearest-Neighbor and Decision-Tree), both with and without hyperparameter adjustment. The study compared the performances of three feature selection methods: no feature selection, the original MRFO algorithm, and the proposed ESARSAMRFO-FS algorithm. The results revealed that ESARSA-MRFO-FS is the most effective of the three algorithms with classifiers and optimization scenarios. Besides, experiments comparing MRFO and ESARSA-MRFO performance on 44 benchmarks are presented in the article. In five functions, ESARSA-MRFO outperforms MRFO. These results confirm that the proposed method is efficient for feature selection in medical diagnosis because of its accuracy and low processing costs. © 2025 Elsevier B.V.

关键词： Nearest neighbor search

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Enhancing HBIM-to-VR workflows: Semi-automatic generation of virtual heritage experiences using enriched IFC files

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Digital Applications in Archaeology and Cultural Heritage 2025年 36卷

作者： Fiorenza, Jacopo Rimella, Nicola Calandra, Davide Osello, Anna Lamberti, Fabrizio Department of Control and Computer Engineering Politecnico di Torino Corso Duca degli Abruzzi 24 Turin 10129 Italy Department of Structural Geotechnical and Building Engineering Politecnico di Torino Corso Duca degli Abruzzi 24 Turin 10129 Italy

In recent years, the digitization of Cultural Heritage (CH) has gained momentum, with Historical Building Information Modeling (HBIM) playing a vital role in developing services that enhance the understanding of built heritage. However, current HBIM-to-VR workflows, crucial for Virtual Heritage Experiences (VHEs), often rely on proprietary tools that may limit interoperability. To address this issue, some studies have used the Industry Foundation Classes (IFC) file format which, unfortunately, lacks functionalities for VHE creation, complicating the process for non-IT users. This paper introduces a semi-automated HBIM-to-VR workflow using an enriched IFC file. The process includes a manual phase for preparing the IFC file with metadata and a subsequent automated phase where a VR platform generates a virtual visit. A use case involving the “Hall of Seasons” in Palazzo Carignano, Turin, demonstrates the workflow's potential to improve accessibility and scalability in creating VHEs. © 2024 The Authors

关键词： Cultural heritage Historical building information modeling Industry foundation classes Virtual reality Virtual visits

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A novel digital twin-enabled three-stage feature imputation framework for non-contact intelligent fault diagnosis

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Advanced engineering Informatics 2025年 66卷

作者： Yu, Yue Karimi, Hamid Reza Gelman, Len Liu, Xin Department of Mechanical Engineering Politecnico di Milano via La Masa 1 Milan20156 Italy School of Computing and Engineering University of Huddersfield Queensgate HuddersfieldHD1 3DH United Kingdom Department of Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao06600 China

Vibration-based fault diagnosis methods are widely used in industrial applications due to their high accuracy and reliability. However, their implementation is often hindered by challenges such as confined spaces, harsh environmental conditions, and cost limitations. Moreover, traditional deep learning-based approaches frequently overlook the critical relationship between virtual and physical signals, which can limit their diagnostic performance. To overcome these challenges, this study introduces a novel digital twin-driven feature imputation framework (NIFD-Net) for non-contact intelligent fault diagnosis. The NIFD-Net framework consists of three key stages: (1) codebook generation, where features from simulated vibration signals (SVS) and non-contact signals (NCS) are compressed and regularized using diagnosis-oriented codebooks;(2) mapping construction, where a Transformer is employed to establish the relationship between SVS and NCS in a compressed feature space;and (3) diagnostic enhancement, where imputed SVS features are integrated with original NCS features to improve diagnostic performance. First, digital twin (DT)-enabled models, developed using structural parameters and fault characteristics, dynamically simulate the operating conditions of rolling bearings to generate high-quality SVS samples. The relationship between the two feature spaces of SVS and NCS is then learned by a transformer to impute the SVS features. Finally, the imputed SVS features are integrated with the original NCS features to enhance fault diagnostic performance. Extensive experiments on three case studies demonstrate that NIFD-Net outperforms state-of-the-art methods, achieving an average accuracy of 98.34% across diverse tasks. Notably, the framework exhibits strong noise robustness, maintaining high diagnostic performance even under adverse conditions (e.g., SNR = -10 dB). The results highlight the effectiveness of integrating simulated and real signals for non-contact fault diag

关键词： Vibrations (mechanical)

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IOT based wearable sensor system architecture for classifying human activity

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Measurement: Sensors 2025年 39卷

作者： Mahalakshmi, V. Kumar, Pramod Bhende, Manisha Keshta, Ismail Rathod, Swatiben Yashvantbhai Ramesh, Janjhyam Venkata Naga Department of Computer Science College of Engineering and computer science Jazan University Jazan45142 Saudi Arabia Computer science and Technology Ganga Institute of Technology and Management Kablana Haryana Jhajjar124104 India Dr. D. Y. Patil Vidyapeeth Pune India Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Instrumentation And Control Department Vishwakrma Government Engineer College Chandkheda Ahmedabad382424 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist. Andhra Pradesh 522302 India

Human Activity Recognition (HAR) has applications in diverse fields, including sports management and behavior classification. Existing HAR methods can be categorized into three main approaches: camera-based, wearable sensor-based, and Wi-Fi sensing-based. Camera-based methods suffer from privacy concerns, while wearable sensor-based methods face limitations in battery longevity and continuous monitoring. Wi-Fi sensing methods mitigate privacy and battery issues but rely on costly Intel 5300 network cards or software-defined radio (SDR) platforms, limiting scalability. This paper presents a cost-effective IoT-based human activity recognition system using ESP32, leveraging its Wi-Fi sensing capabilities. The proposed system follows a structured workflow: (i) channel state information (CSI) is extracted from ESP32 modules, (ii) data preprocessing is performed using Hampel and Gaussian filters for noise and outlier removal, (iii) dimensionality reduction is achieved through Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT), and (iv) activity classification is conducted using Dynamic Time Warping (DTW) and the K-Nearest Neighbors (KNN) algorithm. Experimental evaluations demonstrate that the proposed system achieves an average recognition accuracy of 98.6 % across six human activities, comparable to high-end Intel 5300-based HAR systems, while significantly reducing hardware costs and improving ease of deployment. © 2025

关键词： Discrete wavelet transforms

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H∞ Delayed Filtering of Markov Jump Fuzzy Systems in Consumer Electronics: Input–Output Analysis

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IEEE Transactions on Consumer Electronics 2025年

作者： Aslam, Muhammad Shamrooz Bilal, Hazrat Chang, Wen-Jer Kumar, Neeraj Khan, Izaz Ahmad Vasilakos, Athanasios V. China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou221110 China College of Mechatronics and Control Engineering College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Marine Engineering Keelung202 Taiwan Thapar University Patiala India Bacha Khan University Charsadda Department of Computer Science Pakistan Grimstad Norway

This article investigates the design of robust H∞ filters for Takagi–Sugeno (T–S) fuzzy systems with time-varying delays, with a critical challenge in many consumer electronics applications. We extend existing research by incorporating Markovian jump parameters to model system uncertainties and considering internal–like time–varying delays, which are prevalent in real-world scenarios such as wireless communication and networked control systems in consumer devices. To address the complexities introduced by time-varying delays, we employ a three-term approximation model that more accurately captures the system dynamics compared to traditional approaches. Furthermore, we incorporate the time derivative of the membership functions (MFs) into the filter design, and its impact on system stability and performance. To ensure filter stability, we derive novel stability conditions based on the mode–dependent Lyapunov-Krasovskii functional approach, incorporating constraints on the higher bounds of the MF time derivatives. Leveraging the small–gain theorem with scaling techniques, we propose the design of both full-order and reduced-order H∞ filters, expressed in terms of linear matrix inequalities (LMIs). These LMI–based solutions provide a systematic and computationally efficient approach for filter synthesis. Finally, we validate the effectiveness of our proposed filtering strategies through illustrative examples drawn from relevant consumer electronics applications, such as noise cancellation in audio devices, image stabilization in cameras, and vibration suppression in wearable devices. The results demonstrate the enhanced robustness and performance of the proposed filters in the presence of time-varying delays and uncertainties. © 1975-2011 IEEE.

关键词： Membership functions

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