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Enhancing Federated Learning in Connected and Autonomous Vehicles Through Cost optimization and Advanced Model Selection

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第4期26卷 5276-5289页

作者： Cai, Xuelian Zhao, Pincan Liu, Sha Fu, Yuchuan Li, Changle Yu, Fei Richard Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Carleton Univ Sch Informat Technol Ottawa ON K1S 5B6 Canada Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

With the rapid evolution of vehicular network technology, the integration of Machine Learning (ML) with Connected and Autonomous Vehicles (CAVs) presents both remarkable opportunities and formidable challenges. This paper addresses the crucial need for efficient ML model training in the context of Federated Learning (FL) within vehicular networks. Recognizing the limitations imposed by the tradeoff between the high energy cost at the local level with the performance problem at the global level, we propose an innovative approach that harmonizes cost optimization with strategic model selection. Our strategy primarily focuses on optimizing energy consumption during model training and updating at the vehicle end, thereby resolving the prevalent issue of limited end-user participation in FL due to high energy demands. Additionally, we introduce an advanced model selection method, prioritizing local model uploads and adaptively allocating bandwidth to clients with more extensive training data. This method enhances the efficiency and reliability of model updates, ensuring robust global model performance. We validate our approach through extensive simulations, demonstrating not only improved learning performance but also a significant reduction in energy consumption among participating clients.

关键词： Costs Training Servers Adaptation models Data models optimization Computational modeling Federated learning Energy consumption Autonomous vehicles connected and autonomous vehicle cost analysis optimization algorithm model selection

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Smart cities optimization using computational intelligence in power-constrained IoT sensor networks

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Darabkh, Khalid A. Al-Akhras, Muna Univ Jordan Sch Engn Dept Comp Engn Amman 11942 Jordan

This paper introduces the Innovative Clustering Energy Efficient Equilibrium Optimizer-based Multi-Hop Routing Protocol (ICEE-EO-MHRP) for addressing the energy constraint in Internet of Things (IoT) network clustering utilizing the Equilibrium Optimizer (EO), a yet efficient computational intelligence method that is used for selecting Designated Cluster Head (DCH) and Backup DCH (BDCH). Additionally, ICEE-EO-MHRP deals with the IoT energy problem by incorporating a novel cost function that ends up of selecting Designated Relays (DRs) and backup DRs for the purpose of forwarding the traffic towards the sink node. Our protocol substantially reduces messages' exchanges between IoT Sensor Nodes (SNs) by making the replacement of DCH and BDCH dependent on their energy levels dropping below a threshold. To ensure a balanced communication load and efficient scheduling, an innovative deterministic distributed-time division multiple access system is employed. Not only to this extent, but we address data redundancy issue, raised among those quite adjacent SNs, and accordingly propose an efficient management that guarantees having a coherent protocol. In addition to that, device and link failures are professionally addressed by suggesting recovery mechanisms that optimize the proposed protocol. Dealing with these impairments puts our approach well ahead of the competition since it addresses the most practical issues and scenarios, particularly those with challenging environmental constraints. The simulation results demonstrate primarily that our protocol significantly improves the network lifetime by 157.83 % and 109.81 % in comparison to Particle Swarm optimization and Tabu Search (Tabu-PSO) and Energy-Efficient CH Selection by Improved Sparrow Search algorithm utilizing Differential Evolution (EECHS-ISSADE), respectively. Comparing ICEE-EO-MHRP to Tabu-PSO and EECHS-ISSADE reveals improvements in residual energy of 335.87 % and 230.05 %, respectively. Furthermore, in compar

关键词： IoT Clustering optimization algorithm EO Routing network lifetime Efficient energy Load balancing Average delay

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Concrete crack detection using ridgelet neural network optimized by advanced human evolutionary optimization

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SCIENTIFIC REPORTS 2025年第1期15卷 1-16页

作者： Lin, Yongqing Ahmadi, Mehdi Alnowibet, Khalid A. Bukhari, Fawzy A. Beijing Jiaotong Vocat Tech Coll Beijing Peoples R China King Saud Univ Coll Sci Stat & Operat Res Dept Riyadh 11451 Saudi Arabia Ankara Yildirim Beyazit Univ AYBU TR-06010 Ankara Turkiye

Concrete frameworks require strong structural integrity to ensure their durability and performance. However, they are disposed to develop cracks, which can compromise their overall quality. This research presents an innovative crack diagnosis algorithm for concrete structures that utilizes an optimized Deep Neural Network (DNN) called the Ridgelet Neural Network (RNN). The RNN model was then adjusted with a new advanced version of the Human Evolutionary optimization (AHEO) algorithm that is introduced in this study. The AHEO as a new method combines human intelligence and evolutionary principles to optimize the RNN model. To train the model, an image dataset has been used, consisting of labeled images categorized as either "cracks" or "no-cracks". The AHEO algorithm has been employed to refine the network's weights, adjust the output layer for binary classification, and enhance the dataset through stochastic rotational augmentation. The effectiveness of the RNN/AHEO model was evaluated using various metrics and compared to existing methods. The model's performance is evaluated by metrics such as accuracy, precision, recall, and F1-score, and is compared to existing methods including CNN, CrackUnet, R-CNN, DCNN, and U-Net, achieving an accuracy of 99.665% and an F1-score of 99.035%. The results demonstrated that the RNN/AHEO model outperformed other approaches in detecting concrete cracks. This innovative solution provides a robust method for maintaining the structural integrity of concrete frameworks.

关键词： Concrete Crack detection Ridgelet neural network Deep neural network Advanced human evolutionary optimization Structural integrity Image dataset Classification optimization algorithm

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Meta-optimization for improved efficiency: an integrated methodology

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International Journal of Information Technology (Singapore) 2025年第4期17卷 2375-2386页

作者： Singha, Anjani Kumar Yadav, Gaurav Hasanpuri, Vijay Patel, Rajiv Ranjan Department of Computer Applications SRM Institute of Science and Technology (‘Deemed-to-Be-University’) Delhi-NCR Campus Ghaziabad 201204 India Department of Computer Science Aligarh Muslim University Aligarh India UIET Kurukshetra University Kurukshetra India Haridwar University Roorkee India

The use ofa* optimization algorithms is essential to train neural networks effectively. The usage of a combination of two different optimizers is proposed in this method that, used together, can perform single optimizers in various problems. The objective of this study is to develop a comprehensive framework that combines meta-optimization techniques with traditional optimization methods to enhance performance metrics such as convergence speed, solution quality, and robustness against local minima. The researcher presents ATMO (Adaptive Meta Optimizers), recent deep neural network (DNN) models that are combined with a new optimizer that combines two different optimizers simultaneously. Meta-optimizer considers all of their contributions at once, rather than trying to enhance each one separately. Using a wide range of techniques, DNNs have been the subject of numerous investigations on text and image categorization. Results from these tests demonstrated that the suggested Adaptive Meta Optimizer outperforms individual optimizers. These experiments show that adequate training is achieved by considering a variety of factors such as Keras standard learning rate and convergence, and degradation on the CIFAR-100, CIFAR-10, and MNIST datasets, and the results are obtained using Python. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： AdapTiveMeta Optimizers-ATMO Deep learning Image classification optimization algorithm Optimizers Text classification

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High-precision machine learning for predicting latent heat in diverse multicomponent molten salts

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SOLAR ENERGY MATERIALS AND SOLAR CELLS 2025年 281卷

作者： Wang, Xue-meng Tao, Yi-dan Dong, Guan-chen Wang, Shuai-yu Miao, Qi Ding, Hong-liang Lv, Jing Wu, Qiong Jin, Yi Tan, Ling-hua Nanjing Univ Sci & Technol Sch Chem & Chem Engn Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Natl Special Superfine Powder Engn Res Ctr Nanjing 210014 Peoples R China Nanjing Jinhe Energy Mat Co Ltd Nanjing 210047 Jiangsu Peoples R China Nanjing Inst Technol Sch Mat Sci & Engn Jiangsu Key Lab Adv Struct Mat & Applicat Technol Nanjing 211167 Peoples R China

Molten salts in phase change materials offer significant advantages, including high thermal storage density, a wide operational temperature range, and low cost. However, the development of novel high-latent-heat molten salts remains largely empirical. Machine learning offers the potential to expedite theoretical advancements and enable precise, cost-efficient performance predictions. Nonetheless, the diversity of molten salt s complicates the accuracy and generalizability of machine learning models. This study proposes a novel latent heat prediction methodology that integrates data analysis and machine learning. A comprehensive dataset encompassing various inorganic salts was systematically analyzed to extract key features influencing latent heat. Subsequently, a predictive model was constructed by combining a backpropagation neural network (BPNN) with particle swarm optimization (PSO). The PSO-BPNN model demonstrated high predictive accuracy, achieving R2 values of 0.9389 and 0.9413 for binary and ternary molten salts, respectively, with experimental validation indicating prediction errors within 10 %. This approach establishes a high-precision, scalable framework for predicting the latent heat of multicomponent molten salts, thereby advancing the design of salts with tailored thermal properties and offering a valuable reference for predicting other thermophysical characteristics.

关键词： Phase change materials Molten salts Artificial neural network optimization algorithm Latent heat Wide-range prediction

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Deep Reinforcement Learning-Based Energy-Conscious Scheduling Under Time-of-Use Electricity Price

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 11490-11504页

作者： Wang, Caixia Fang, Wenxuan Dai, Xin He, Renchu Du, Wei Tang, Yang East China Univ Sci & Technol State Key Lab Ind Control Technol Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Huzhou Inst Ind Control Technol Huzhou 313099 Peoples R China Westlake Univ Trustworthy & Gen Artificial Intelligence Lab Hangzhou 310030 Zhejiang Peoples R China China Univ Petr Coll Artificial Intelligence Sch Dept Automat Beijing 102249 Peoples R China

In the context of carbon peaking and carbon neutrality, green production scheduling that considers energy has attracted increasing attention. Reinforcement learning (RL) has emerged as a topic for developing efficient algorithms for solving complex combinatorial optimization problems, such as real-world shop scheduling problems. In this paper, we investigate the energy-conscious scheduling problem (ECSP) under time-of-use (TOU) electricity price with the goal of minimizing both the waiting time and extra electricity cost. A mathematical model is formulated, and an efficient deep RL (DRL)-based optimization method is proposed to solve the problem effectively. We design a novel ECSP network (ECSPNet) tailored to handle various ECSP scales based on the characteristics of the problem. Moreover, the Tchebycheff decomposition method is used to solve the multi-objective optimization problems, complemented by the application of the policy gradient method from reinforcement learning to train the ECSPNet without size limitations. Experiments verify that the proposed ECSPNet outperforms state-of-the-art methods and is computationally efficient, even on instances of larger scales unseen in training. Real-world case studies reveal that the proposed method can reduce annual total electricity cost by approximately 30% while effectively maximizing production efficiency.

关键词： Electricity Costs Production Job shop scheduling Automation Energy consumption Training Conferences Power demand Decision making Deep reinforcement learning energy-conscious scheduling problem time of use electricity price optimization algorithm

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Efficient Automatic Design of IGBT Structural Parameters Using Differential Evolution and Machine Learning Model

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第3期44卷 1059-1069页

作者： Yao, Qing Chen, Jing Yang, Kemeng Yao, Jiafei Zhang, Jun Dai, Yuxuan Tang, Weihua Zhang, Bo Guo, Yufeng Nanjing Univ Posts & Telecommun Coll Integrated Circuit Sci & Engn Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Natl & Local Joint Engn Lab RF Integrat & Micropac Nanjing 210023 Peoples R China Univ Elect Sci & Technol China State Key Lab Elect Thin Films & Integrated Device Chengdu 610054 Peoples R China

Insulated gate bipolar transistors (IGBTs) are the key component in power electronics, and the intricate relationship between their performance and structural parameters poses a formidable challenge in the design process. This article proposes an automatic optimal design method for IGBT structural parameters to leverage the pretrained machine learning (ML) model to efficiently predict the initial IGBT device's performance, followed by utilizing the differential evolution (DE) algorithm to automatically adjust structural parameters based on the disparity between predicted and expected device performance until the expected performance is achieved. The method is validated in the design of punch-through IGBTs (PT-IGBTs) and trench gate field-stop IGBTs (FS-IGBTs), and the performance of technology computer-aided design (TCAD) simulation of the designed device is similar to the target performance. In particular, the simulation results of the designed FS-IGBT are highly fitted to the datasheet of the commercial device, which verifies the generalizability and effectiveness of the method. In addition, comparative analyses with various algorithms show DE provides the fastest optimization and extraordinary robustness under the exact specifications. Crucially, the proposed design scheme aligns with semiconductor physics. The method simplifies IGBT design without the need for manual tuning and TCAD tool simulation.

关键词： Insulated gate bipolar transistors Structural engineering Performance evaluation Predictive models optimization Training Prediction algorithms Artificial neural networks (ANNs) automatic optimization electrical performance insulated gate bipolar transistor (IGBT) optimization algorithm

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An Ultrasonic Synthetic Jet Piezo Pump for Air Delivery

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年第5期72卷 5177-5186页

作者： Wang, Meng Liu, Zenghao Wang, Conghui Liu, Guojun Jilin Univ Sch Mech & Aerosp Engn Changchun 130025 Peoples R China

A combination of piezoelectric drive and synthetic jet technology is proposed for an ultrasonic synthetic jet piezo pump. The piezo vibrator operates silently and has a resonant frequency exceeding 20 kHz. The inlet channel of the proposed piezo pump is equipped with a special Tesla structure to increase the flow resistance difference and reduce flow loss. The results demonstrate that the proposed piezo pump has a significant advantage over a typical pump in flow rate. The optimized piezo pump achieves a flow rate of 1037 mL/min when subjected to a typical square wave signal excitation. The optimized waveform enhances the flow rate to 1343.9 mL/min, resulting in a 29.6% improvement. Additionally, this article also investigates the ultrasonic synthetic jet piezo pump in the form of a dual cavity. The two cavities are positioned on either side of the piezo vibrator and alternate between suction and discharge. When the two cavities were connected in parallel, the flow rate was 1912.68 mL/min. Finally, the proposed piezo pump was explored for impact cooling applications. The impact cooling effect on the heated ceramic is good at an impact distance and angle of 30 mm and 60 degrees, respectively.

关键词： Acoustics Cooling Voltage Resonant frequency Vibrations Ceramics Surface acoustic waves Plasmas Heat pumps Genetic algorithms Impact cooling optimization algorithm piezo pump synthetic jet ultrasonic

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HEMLAB algorithm Applied to the High-Lift Japan Aerospace Exploration Agency Standard Model

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JOURNAL OF AIRCRAFT 2025年第1期62卷 207-218页

作者： Sukas, Hulya Sahin, Mehmet Istanbul Tech Univ Fac Aeronaut & Astronaut TR-34469 Istanbul Turkiye

The high-lift Japan Aerospace Exploration Agency (JAXA) standard model (HL-JSM) has been numerically analyzed in order to further validate the HEMLAB code for realistic aircraft configurations. The numerical algorithm is based on highly efficient edge-based data structure for a vertex-based finite volume algorithm on hybrid meshes. The data pattern is arranged to meet the requirements of a vertex-based finite volume algorithm by considering data access patterns and cache efficiency. A fully implicit version of the numerical algorithm has also been implemented based on the Portable, Extensible Toolkit for Scientific Computation (PETSc) library in order to improve the robustness of the algorithm. The resulting algebraic equations, including the one-equation Spalart-Allmaras, are solved in a monolithic manner using the restricted additive Schwarz preconditioner combined with the flexible generalized minimal residual method [FGMRES(m)] Krylov subspace algorithm. The numerical method is also combined with the metric-based anisotropic mesh refinement library Python Adaptive Mesh Geometry Suite-INRIA (pyAMG) from National Institute for Research in Digital Science and Technology in order to improve the numerical accuracy. The numerical algorithm is initially applied to the two-dimensional L1T2 (National High Lift Programme) high-lift system, and then the calculations around the HL-JSM are carried out at relatively high angles of attack. The numerical results with the anisotropic mesh refinement library pyAMG indicate significant improvements in numerical accuracy.

关键词： algorithms and Data Structures optimization algorithm Aircraft Components and Structure Aerodynamic Force Unsteady Reynolds Averaged Navier Stokes Adaptive Mesh Refinement Computational Fluid Dynamics High Performance Computing Fluid Flow Properties

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A CNN-Based Approach for Classical Music Recognition and Style Emotion Classification

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IEEE ACCESS 2025年 13卷 20647-20666页

作者： Shi, Yawen Henan Univ Sci & Technol Sch Art & Design Luoyang 471000 Henan Peoples R China

Music recognition refers to the process of automatically recognizing and classifying the musical content in audio signals using computer technology and algorithms. Music recognition technology can help people recognize information such as the music title, artist, musical style, rhythm, and the emotions conveyed by the music in the audio, thus enabling applications like automated music information retrieval and recommendation systems. Classical music, due to its vast quantity, diverse types, and time span covering several centuries, presents challenges that existing music recognition software and traditional music recognition algorithms cannot effectively address. In this study, The model based on convolutional neural networks (CNNs) is proposed, allowing people to recognize the classical music title, style, and emotions contained in a piece of music. The proposed model is particularly beneficial for individuals who are interested in classical music but lack extensive knowledge about it, as it provides essential information about the pieces. By extracting multidimensional features from classical music, the model can recognize the title, style, and emotions expressed. To improve the model's recognition accuracy, various noises are introduced to the dataset. Meanwhile, in this study, a novel loss function has been devised to more effectively assess the model's performance. For searching for optimal performance of the model, a novela* optimization algorithm also be proposed to find optimal hyperparameters of loss function. The experiment results show average title recognition accuracy is 0.98, average style recognition accuracy is 0.89 and average emotion recognition accuracy is 0.93. These results adequately demonstrate that the proposal model significantly enhances the model's ability to accurately recognize the titles, styles, and emotions of classical music, achieving high recognition rates even in noisy environments.

关键词： Emotion recognition Accuracy Feature extraction Multiple signal classification Classification algorithms Noise Deep learning Recommender systems Proposals Heuristic algorithms CNN deep learning music recognition music retrieval optimization algorithm

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