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Robust Transmission Design for Federated Learning Through Over-the-Air Computation

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China communications 2025年第03期22卷 65-75页

作者： Hamideh Zamanpour Abyaneh Saba Asaad Amir Masoud Rabiei school of Electrical and computer engineering College of Engineering University of Tehran Department of Electrical engineering and computer Science York University

Over-the-air computation(AirComp) enables federated learning(FL) to rapidly aggregate local models at the central server using waveform superposition property of wireless channel. In this paper, a robust transmission scheme for an AirCompbased FL system with imperfect channel state information(CSI) is proposed. To model CSI uncertainty, an expectation-based error model is *** main objective is to maximize the number of selected devices that meet mean-squared error(MSE)requirements for model broadcast and model aggregation. The problem is formulated as a combinatorial optimization problem and is solved in two ***, the priority order of devices is determined by a sparsity-inducing procedure. Then, a feasibility detection scheme is used to select the maximum number of devices to guarantee that the MSE requirements are met. An alternating optimization(AO) scheme is used to transform the resulting nonconvex problem into two convex subproblems. Numerical results illustrate the effectiveness and robustness of the proposed scheme.

关键词： federated learning imperfect CSI optimization over-the-air computing robust design

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Enhanced battery life prediction with reduced data demand via semi-supervised representation learning

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Journal of Energy Chemistry 2025年第2期101卷 524-534页

作者： Liang Ma Jinpeng Tian Tieling Zhang Qinghua Guo Chi Yung Chung school of Mechanical Materials Mechatronic and Biomedical Engineering University of Wollongong Department of Electrical and Electronic engineering and Research Centre for Grid Modernisation The Hong Kong Polytechnic University Photonics Research Institute The Hong Kong Polytechnic University school of Electrical Computer and Telecommunications Engineering University of Wollongong

Accurate prediction of the remaining useful life(RUL) is crucial for the design and management of lithium-ion *** various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for *** of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for ***,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL *** approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL *** approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge *** method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional *** also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder *** projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled *** approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.

关键词： Lithium-ion batteries Battery degradation Remaining useful life Semi-supervised learning

Electrochemical sensor for dopamine detection based on multiwalled carbon nanotube/molybdenum disulfide quantum dots with machine learning integration and anti-interference capability

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Tungsten 2025年第1期7卷 137-151页

作者： Geethukrishnan Onkar Apte Kiran Kumar Tadi Energy Electrodics and Electrocatalysis (EEE) Lab Department of Chemistry School of Advanced Sciences Vellore Institute of Technology school of computer Science and engineering Vellore Institute of Technology

The precise detection and measurement of dopamine（DA）,a crucial neurotransmitter in the human body,plays a significant role in diagnosing,preventing,and treating neurological diseases associated with its levels.A highly sensitive DA electrochemical sensor was constructed by combining molybdenum disulfide quantum dots（MSQDs） with multiwalled carbon nanotubes（MWCNTs）.The MSQDs were synthesized using the shear exfoliation *** sensors consist of MSQDs with Mo-S edge catalytic centers for the DA redox reaction,and MWCNTs amplify the sensor *** linearity of the sensor for the detection of DA was tested in the presence of ascorbic acid(AA,50 μmol·L-1) and uric acid(UA,200 μmol·L-1),and exhibited linearity from 2 to 966 μmol·L-1of DA with 0.097 μA(mol·L-1)-1sensitivity and a low limit of detection of0.6 μmol·L-1（the ratio between signal and noise,S/N=3）.Moreover,the sensitivity and selectivity of the sensor were also studied using *** is no increase in amperometric current after adding the most potentially interfering *** sensor was successfully applied to recover DA in human blood sera ***,machine learning algorithms were operated to aid in the near-precise detection of DA in the heterogeneous mixture containing AA and *** algorithms facilitate the identification and quantification of DA amidst coexisting interferents,including AA,that are commonly present in biological matrices.

关键词： Biosensor Complex mixture Dopamine Machine learning MoS2 Multiwalled carbon nanotubes

Dual circularly polarized monostatic STAR antenna with enhanced isolation

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Journal of Systems engineering and Electronics 2025年第01期 73-81页

作者： XIE Mingcong WEI Xizhang TANG Yanqun HU Dujuan school of Electronics and communication engineering Sun Yat-sen University school of Systems Science and engineering Sun Yat-sen University

Separated transmit and receive antennas are employed to improve transmit-receive isolation in conventional short-range radars, which greatly increases the antenna size and misaligns of the transmit/receive radiation patterns. In this paper,a dual circularly polarized(CP) monostatic simultaneous transmit and receive(MSTAR) antenna with enhanced isolation is proposed to alleviate the problem. The proposed antenna consists of one sequentially rotating array(SRA), two beamforming networks(BFN), and a combined decoupling structure. The SRA is shared by the transmit and receive to reduce the size of the antenna and to obtain a consistent transmit and receive *** BFN achieve right-hand CP for transmit and left-hand CP for receive. By exploring the combined decoupling structure of uniplanar compact electromagnetic band gap(UC-EBG) and ringshaped defected ground structure(RS-DGS), good transmitreceive isolation is achieved. The proposed antenna prototype is fabricated and experimentally characterized. The simulated and measured results show good agreement. The demonstrate transmit/receive isolation is height than 33 dB, voltage standing wave ratio is lower than 2, axial ratio is lower than 3 dB, and consistent radiation for both transmit and receive is within4.25-4.35 GHz.

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Regression analysis of squeezing-induced hybrid nanofluid flow in Darcy-Forchheimer porous medium

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Applied Mathematics and Mechanics(English Edition) 2025年第1期46卷 193-208页

作者： K.MUHAMMAD M.SARFRAZ Department of Humanities and Sciences School of Electrical Engineering and Computer Science National University of Sciences and Technology Department of Mathematics Quaid-i-Azam University

This article presents a mathematical model addressing a scenario involving a hybrid nanofluid flow between two infinite parallel plates. One plate remains stationary, while the other moves downward at a squeezing velocity. The space between these plates contains a Darcy-Forchheimer porous medium. A mixture of water-based fluid with gold(Au) and silicon dioxide(Si O2) nanoparticles is formulated. In contrast to the conventional Fourier's heat flux equation, this study employs the Cattaneo-Christov heat flux equation. A uniform magnetic field is applied perpendicular to the flow direction, invoking magnetohydrodynamic(MHD) effects. Further, the model accounts for Joule heating, which is the heat generated when an electric current passes through the fluid. The problem is solved via NDSolve in MATHEMATICA. Numerical and statistical analyses are conducted to provide insights into the behavior of the nanomaterials between the parallel plates with respect to the flow, energy transport, and skin *** findings of this study have potential applications in enhancing cooling systems and optimizing thermal management strategies. It is observed that the squeezing motion generates additional pressure gradients within the fluid, which enhances the flow rate but reduces the frictional drag. Consequently, the fluid is pushed more vigorously between the plates, increasing the flow velocity. As the fluid experiences higher flow rates due to the increased squeezing effect, it spends less time in the region between the plates. The thermal relaxation, however, abruptly changes the temperature, leading to a decrease in the temperature fluctuations.

关键词： convective boundary condition Darcy-Forchheimer medium hybrid nanofluid Joule heating magnetohydrodynamic (MHD) numerical solution O175.2 O361 regression analysis squeezing flow

A Generative Model-Based Network Framework for Ecological Data Reconstruction

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computers, Materials & Continua 2025年第1期82卷 929-948页

作者： Shuqiao Liu Zhao Zhang Hongyan Zhou Xuebo Chen school of Electronic and Information engineering University of Science and Technology LiaoningAnshan114051China school of computer Science and Software engineering University of Science and Technology LiaoningAnshan114051China

This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection *** Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data *** model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT *** model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample *** data is used to retain more semantic information to generate *** model was applied to species in Southern California,USA,citing SWOT analysis data to train the *** show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development *** model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data *** study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.

关键词： Convolutional Neural Network(CNN) VAE GAN TOPSIS data reconstruction

Feature selection strategy optimization for lithium-ion battery state of health estimation under impedance uncertainties

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Journal of Energy Chemistry 2025年第2期101卷 87-98页

作者： Xinghao Du Jinhao Meng Yassine Amirat Fei Gao Mohamed Benbouzid UMR CNRS 6027 IRDL University of Brest school of Electrical engineering Xi'an Jiaotong University LbISEN ISEN Yncréa Ouest school of Energy and computer Science University of Technology of Belfort-Montbeliard

Battery health evaluation and management are vital for the long-term reliability and optimal performance of lithium-ion batteries in electric *** impedance spectroscopy(EIS) offers valuable insights into battery degradation analysis and ***,previous studies have not adequately addressed the impedance uncertainties,particularly during battery operating conditions,which can substantially impact the robustness and accuracy of state of health(SOH) *** by this,this paper proposes a comprehensive feature optimization scheme that integrates impedance validity assessment with correlation *** utilizing metrics such as impedance residuals and correlation coefficients,the proposed method effectively filters out invalid and insignificant impedance data,thereby enhancing the reliability of the input ***,the extreme gradient boosting(XGBoost) modeling framework is constructed for estimating the battery degradation *** XGBoost model incorporates a diverse range of hyperparameters,optimized by a genetic algorithm to improve its adaptability and generalization *** validation confirms the effectiveness of the proposed feature optimization scheme,demonstrating the superior estimation performance of the proposed method in comparison with four baseline techniques.

关键词： Lithium-ion battery State of health Electrochemical impedance spectroscopy Extreme gradient boosting

A New Antenna Selection Scheme for MIMO–NOMA Systems with Multiple-Antenna Users

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China communications 2025年第2期22卷 160-172页

作者： Ehsan Alemzadeh Amir Masoud Rabiei school of Electrical and computer engineering College of EngineeringUniversity of TehranTehranIran

Non-orthogonal multiple access(NOMA)is a promising technology for the next generation wireless communication *** benefits of this technology can be further enhanced through deployment in conjunction with multiple-input multipleoutput(MIMO)*** selection plays a critical role in MIMO–NOMA systems as it has the potential to significantly reduce the cost and complexity associated with radio frequency *** paper considers antenna selection for downlink MIMO–NOMA networks with multiple-antenna basestation(BS)and multiple-antenna user equipments(UEs).An iterative antenna selection scheme is developed for a two-user system,and to determine the initial power required for this selection scheme,a power estimation method is also *** proposed algorithm is then extended to a general multiuser NOMA *** results demonstrate that the proposed antenna selection algorithm achieves near-optimal performance with much lower computational complexity in both two-user and multiuser scenarios.

关键词： antenna selection MIMO NOMA power allocation

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BEV-Locator:an end-to-end visual semantic localization network using multi-view images

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Science China(Information Sciences) 2025年第2期68卷 134-150页

作者： Zhihuang ZHANG Meng XU Wenqiang ZHOU Tao PENG Liang LI Stefan POSLAD school of Vehicle and Mobility Tsinghua University school of Information Technology & Management University of International Business and Economics Qcraft Inc. school of Electronic engineering and computer Science Queen Mary University of London

Accurate localization ability is fundamental in autonomous driving. Traditional visual localization frameworks approach the semantic map-matching problem with geometric models, which rely on complex parameter tuning and thus hinder large-scale deployment. In this paper, we propose BEV-Locator: an end-to-end visual semantic localization neural network using multi-view camera images. Specifically, a visual BEV(bird-eye-view) encoder extracts and flattens the multi-view images into BEV space. While the semantic map features are structurally embedded as map query sequences. Then a cross-model transformer associates the BEV features and semantic map queries. The localization information of ego-car is recursively queried out by cross-attention modules. Finally, the ego pose can be inferred by decoding the transformer outputs. This end-to-end model speaks to its broad applicability across different driving environments, including high-speed scenarios. We evaluate the proposed method in large-scale nuScenes and Qcraft datasets. The experimental results show that the BEV-Locator is capable of estimating the vehicle poses under versatile scenarios, which effectively associates the cross-model information from multi-view images and global semantic maps. The experiments report satisfactory accuracy with mean absolute errors of 0.052 m, 0.135 m and 0.251° in lateral, longitudinal translation and heading angle degree.

关键词： visual localization semantic map bird-eye-view transformer pose estimation