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Exploring Age-of-information Weighting in Federated Learning under Data Heterogeneity

IEEE Transactions on Wireless Communications 2025年第6期24卷 5023-5036页

作者： Wang, Kaidi Ding, Zhiguo So, Daniel K. C. Ding, Zhi The University of Manchester Department of Electrical and Electronic Engineering ManchesterM13 9PL United Kingdom University of Manchester Department of Electronic and Electrical Engineering United Kingdom Khalifa University Department of Computer and Information Engineering United Arab Emirates University of California at Davis Department of Electrical and Computer Engineering DavisCA95616 United States

This paper investigates wireless federated learning in data heterogeneous scenarios, where device selection usually leads to a degradation in learning performance. This paper is motivated by the fact that while training deep learning networks using federated stochastic gradient descent (FedSGD) on non-independent and identically distributed (non-IID) datasets, device selection can generate gradient errors that accumulate, leading to potential weight divergence, which is further exacerbated with low device participation. To mitigate weight divergence, an age-weighted FedSGD algorithm is designed in this paper to scale local gradients according to the previous device selection results. Furthermore, by revealing the relationship between device participation and latency, an energy consumption minimization problem is formulated accordingly, which consists of resource allocation and sub-channel assignment. By transforming the resource allocation problem into convex and utilizing KKT conditions, we derive the optimal resource allocation solution. Moreover, this paper develops a matching based algorithm to generate the enhanced sub-channel assignment. Simulation results indicate that i) age-weighted FedSGD is able to outperform conventional FedSGD in terms of convergence rate and achievable accuracy, and ii) the proposed resource allocation and sub-channel assignment strategies can significantly reduce energy consumption and improve learning performance by increasing device participation. © 2025 IEEE. All rights reserved.

关键词： Federated learning

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Short-Term Power Load Forecasting Method Based on GRU-Transformer Combined Neural Network Model

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Journal of Computing and information Technology 2024年第1期32卷 1-14页

作者： Mao, Weiwei Yu, Suping Chen, Wenqing College of Computer and Information Engineering Luoyang Institute of Science and Technology Luoyang China School of Electrical Engineering and Automation Luoyang Institute of Technology Luoyang China

Load Forecast (LF) is an important task in the planning, control and application of public power systems. Accurate Short Term Load Forecast (STLF) is the premise of safe and economical operation of a power system. In the research of short-term power load forecasting, machine learning and deep learning are the most popular methods at present, but there still exists a problem that the single and simple structure of power load forecasting model leads to low accuracy of load forecasting. In order to improve the accuracy of STLF, a Gated Cycle Unit (GRU)-Transformer combined neural network model is proposed. Transformer encoder structure is used as feature extractor to mine the complex mapping relationships between the input features and load. The advantage of self-attention mechanism is used to solve the problem of information loss of long sequences in short-term power load forecasting. At the same time, the multivariate time series model of GRU is used for model training. The experimental results on the power load data set of a certain region in southwest China and Panama City show that the proposed combined model prediction method has higher accuracy than those proposed in other literatures, which further proves its feasibility and superiority. © 2024, University of Zagreb Faculty of electrical engineering and Computing. All rights reserved.

关键词： Neural network models

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Hybrid Recommender System Using Systolic Tree for Pattern Mining

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computer Systems science & engineering 2023年第2期44卷 1251-1262页

作者： S.Rajalakshmi K.R.Santha Department of Computer Science Engineering Sri Venkateswara College of EngineeringSriperumbudurIndia Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudurIndia

A recommender system is an approach performed by e-commerce for increasing smooth users’*** pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of *** work will present the implementation of sequence pattern mining for recommender systems within the domain of *** work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender *** feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target *** will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy *** work pre-sents a new hybrid recommender system based on optimized feature selection and systolic *** features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)*** systolic tree is used for pattern mining,and based on this,the recommendations are *** proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes conﬁrmed the efﬁciency of the *** was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativeﬁltering,the precision of 0.89 was achieved.

关键词： Recommender systems hybrid recommender systems frequent pattern mining collaborativeﬁltering systolic tree river formation dynamics particle swarm optimization

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Provision of Ramp-rate Limitation as Ancillary Service from Distribution to Transmission System:Definitions and Methodologies for Control and Sizing of Central Battery Energy Storage System

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Journal of Modern Power Systems and Clean Energy 2023年第5期11卷 1507-1518页

作者： Spyros I.Gkavanoudis Kyriaki-Nefeli D.Malamaki Eleftherios O.Kontis Aditya Shekhar Umer Mushtaq Sagar Bandi Venu Charis S.Demoulias Department of Electrical&Computer Engineering Aristotle University of ThessalonikiThessalonikiGreece Electrical Engineering Mathematics&Computer Science DepartmentDelft University of TechnologyDelftNetherlands FENECON Gmbh DeggendorfGermany

The variability of the output power of distributed renewable energy sources(DRESs)that originate from the fastchanging climatic conditions can negatively affect the grid ***,grid operators have incorporated ramp-rate limitations(RRLs)for the injected DRES power in the grid *** the DRES penetration levels increase,the mitigation of high-power ramps is no longer considered as a system support function but rather an ancillary service(AS).Energy storage systems(ESSs)coordinated by RR control algorithms are often applied to mitigate these power ***,no unified definition of active power ramps,which is essential to treat the RRL as AS,currently *** paper assesses the various definitions for ramp-rate RR and proposes RRL method control for a central battery ESS(BESS)in distribution systems(DSs).The ultimate objective is to restrain high-power ramps at the distribution transformer level so that RRL can be traded as AS to the upstream transmission system(TS).The proposed control is based on the direct control of theΔP/Δt,which means that the control parameters are directly correlated with the RR requirements included in the grid *** addition,a novel method for restoring the state of charge(So C)within a specific range following a high ramp-up/down event is ***,a parametric method for estimating the sizing of central BESSs(BESS sizing for short)is *** BESS sizing is determined by considering the RR requirements,the DRES units,and the load mix of the examined *** BESS sizing is directly related to the constant RR achieved using the proposed ***,the proposed methodologies are validated through simulations in MATLAB/Simulink and laboratory tests in a commercially available BESS.

关键词： Battery energy storage system distributed renewable energy resource sizing distribution system transmission system ramp-rate limitation state of charge

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Blockchain-Enhanced Security for LEO Satellite Firmware Updates in Beyond-5G NTN Networks 29

Blockchain-Enhanced Security for LEO Satellite Firmware Upda...

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29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Worku, Yonatan M. Tshakwanda, Petro M. Tsegaye, Henok B. Christodoulou, Christos Devetsikiotis, Michael Sacchi, Claudio University of New Mexico Department of Electrical and Computer Engineering United States University of Trento Department of Information Engineering and Computer Science Italy

ISBN: (纸本)9798350377644

The integration of terrestrial and Non-Terrestrial Networks (NTNs) represents a significant advancement in communication technology in the framework of the ongoing beyond-5G standardization process. This integration offers broader coverage, especially in remote and underserved areas, and improved reliability through diversified network paths. Additionally, it paves the way for new services and applications that benefit from ubiquitous connectivity. To achieve enhanced Quality of Service (QoS) and Service Level Agreements (SLAs), the end-to-end network should be open and visible to service providers and users. However, such a clear-mode approach introduces various security threats, making the network susceptible to unauthorized access and service disruption. To address these challenges, a blockchain-based satellite firmware update strategy across terrestrial 5G core and access networks is proposed. This study explores the tradeoff between throughput and QoS in the 5G NTN network when blockchain is implemented. Our experimental setup involved a network of Low Earth Orbit (LEO) satellites integrated with a 5G core, deploying a blockchain-based authentication mechanisms to secure satellite communications. The results show a tangible improvement in authentication efficiency, despite a throughput degradation of around 10 % and a latency increase of 17 % from the baseline values obtained without blockchain security. This tradeoff highlights the cost of enhanced security counter-measures in terms of network performance. Nonetheless, the security benefits, measured through successful authentication attempts, fully justify blockchain integration in scenarios where security is paramount. The proposed approach promises to secure communications in '5G and beyond' NTN hybrid environments by ensuring integrity and confidentiality to the traffic flows. © 2024 IEEE.

关键词： 5G mobile communication systems

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Correction to: Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images (Physical and engineering sciences in Medicine, (2024), 10.1007/s13246-024-01420-1)

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Physical and engineering sciences in Medicine 2025年第1期48卷 19-20页

作者： Mostafavi, Mohammadreza Ko, Seok-Bum Shokouhi, Shahriar Baradaran Ayatollahi, Ahmad Department of Electrical Engineering Electronic Engineering Iran University of Science and Technology Tehran Iran Division of Biomedical Engineering Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada

In this article the legend of Fig. 6 was presented without a reference. The legend of Fig. 6 has been changed from "The general framework for knowledge distillation involving a teacher-student relationship" to "The generic teacher-student framework for knowledge distillation [33]." In Sharma et al.’s research, two results obtained from the "KNN with ten-fold cross-validation" and "KNN with leave-one-subject-out cross-validation (LOSOCV)" methods are reported as the final results. Unfortunately, we mistakenly attributed the accuracy of one method. While we correctly listed the name of the method used, which is KNN with ten-fold cross-validation, we inadvertently entered the precision of the KNN with leave-one-subject-out cross-validation (LOSOCV) method. Therefore, we updated our results table (Table 7). The correct Table 7 is shown below. (Table presented.) Comparison of the performance of the proposed method with existing methods Study Method Dataset Accuracy Sensitivity Specificity Shim et al. [38] SVM 34 SZ-34 healthy 88.24% – – Siuly et al. [7] EBT – EMD (Empirical Mode Decomposition) 49 SZ-32 healthy 89.59% 0.8976 0.8932 Phang et al. [39] Multi-domain connectome CNN 45 SZ-39 healthy 91.69% 0.9111 0.925 Jahmunah et al. [40] Non-linear features, decision-tree, neural network, SVM 14 SZ and 14 healthy 92.90% 0.9345 0.9224 Akbari et al. [41] Phase space dynamic & graphical features 14 SZ and 14 healthy 94.8% 0.943 0.952 Shalbaf et al. [20] ResNet-18-SVM 14 SZ and 14 healthy 94.84% 0.980 0.9239 Bagherzadeh et al. [42] DenseNet121 model 14 SZ and 14 healthy 96.26% 0.9548 0.9702 Sharma et al. [43] KNN with ten-fold 14 SZ and 14 healthy 99.21% 0.9942 0.9905 LOSOCV 97.20% 0.9649 0.9806 Proposed method CWT—Self distillation- VGG16 14 SZ and 14 healthy 97.81 0.970 0.986 The original article has been corrected. © Australasian College of Physical Scientists and Engineers in Medicine 2024.

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LSTM-Based Resource Prediction for Disaggregated RAN in 5G Non-Terrestrial Networks 2

LSTM-Based Resource Prediction for Disaggregated RAN in 5G N...

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2nd IEEE Virtual Conference on Communications, VCC 2024

作者： Tsegaye, Henok B. Tshakwanda, Petro M. Worku, Yonatan M. Sacchi, Claudio Christodoulou, Christos Devetsikiotis, Michael University of New Mexico Department of Electrical and Computer Engineering United States University of Trento Department of Information Engineering and Computer Science Italy

ISBN: (纸本)9798331530099

The growing demand for advanced beyond 5G connectivity solutions explores the deployment of end-to-end 5G Non- Terrestrial Networks (NTNs) in cloud-native environments. With the increasing reliance on mobile communications, leveraging next-generation radio access network (NG-RAN) architectures with functional splits has become essential. In the 5G NTN network, some split NG-RAN components can be moved to the satellite node to improve connectivity and resilience. This paper investigates advanced beyond 5G connectivity by deploying end-to-end 5G Non-Terrestrial Networks (NTNs) in cloud-native environments, focusing on Low Earth Orbit (LEO) satellites operating in regenerative mode. Specifically, it explores the implementation of F1 and E1 interface splits within such networks. The first architecture extends the F1 interface over the satellite radio interface (F1 over SRI), linking terrestrial central units (gNB-CU) with satellite-based distributed units (gNB-DU). The second architecture incorporates both F1 and E1 splits, facilitating connections between terrestrial control plane units (gNB-CUCP) and user plane units (gNB-CUUP) on the satellite via the E1 interface over SRI (F1-E1 over SRI). The study's primary goal is to predict the resource utilization-specifically CPU, memory, and bandwidth-of gNB-DU and gNB-CUUP functioning as satellite payloads. Employing Long-Short-Term Memory (LSTM) neural networks, this research aims to enhance network resilience by enabling proactive monitoring and resource allocation decisions, addressing the significant computational and bandwidth demands of payloading gNB-CUUP compared to gNB-DU. © 2024 IEEE.

关键词： 5G mobile communication systems

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Insider Attack Prevention:LAPUP—Lightweight Authentication Protocol Using PUF

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Journal of Communications and information Networks 2024年第2期9卷 192-206页

作者： Siranjeevi Rajamanickam Satyanarayana Vollala N.Ramasubramanian Department of Computer Engineering Government Polytechnic CollegeTiruchirappalli 620022India Department of Computer Science and Engineering Dr.Shyama Prasad Mukherjee International Institute of Information Technology-Naya RaipurChhattisgarh 493661India Department of Computer Science and Engineering National Institute of TechnologyTiruchirappalli 620015India

The incredible progress in technologies has drastically increased the usage of Web *** share their credentials like userid and password or use their smart cards to get authenticated by the application *** cards are handy to use,but they are susceptible to stolen smart card attacks and few other notable security *** prefer to use Web applications that guarantee for security against several security attacks,especially insider attacks,which is *** of several existing schemes prove the security pitfalls of the protocols from preventing security attacks,specifically insider *** paper introduces LAPUP:a novel lightweight authentication protocol using physically unclonable function(PUF)to prevent security attacks,principally insider *** PUFs are used to generate the security keys,challenge-response pair(CRP)and hardware signature for designing the *** transmitted messages are shared as hash values and encrypted by the keys generated by *** messages are devoid of all possible attacks executed by any attacker,including insider *** is also free from stolen verifier attacks,as the databases are secured by using the hardware signature generated by *** analysis of the protocol exhibits the strength of LAPUP in preventing insider attacks and its resistance against several other security *** evaluation results of the communication and computation costs of LAPUP clearly shows that it achieves better performance than existing protocols,despite providing enhanced security.

关键词： physically unclonable function(PUF) insiderattacks authenticationkeymanagementserver(AKMS) security

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Vision-based Robotic Arm in Defect Detection and Object Classification Applications

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Sensors and Materials 2024年第2期36卷 655-670页

作者： Lin, Cheng-Jian Jhang, Jyun-Yu Gao, Yi-Jyun Huang, Hsiu-Mei Department of Computer Science & Information Engineering National Chin-Yi University of Technology Taichung411 Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404 Taiwan Department of Information Management National Taichung University of Science and Technology Taichung404 Taiwan

Robotic arms have been widely used in industrial fields. However, researchers have seldom considered the factors affecting the actual factory environment. For example, when objects are conveyed in a factory, conveyor belts are often used to dynamically plan the overall production line. In addition, each object requires multiple checkpoints for repeated audits and inspections to ensure its quality. In this study, a vision-based robotic arm system equipped with multiple functionalities was developed. The development process consisted of three steps: detecting multiple dynamic objects, determining the size of each object, and identifying object defects. In the first step, You Only Look Once was used to detect multiple dynamic objects on a conveyor belt in real time. In the second step, the original image of the object was converted into a grayscale image, and the edge contour of the object was drawn using a Canny edge detection algorithm. Objects in the image were then rotated for vertical and horizontal projections, and then an artificial neural network (ANN) was used to calculate the size of each object. In the third step, a convolutional fuzzy neural network (CFNN) was used to identify object defects. This network was divided into an input layer, a convolution pooling layer, a feature fusion layer, a fuzzy layer, a regular layer, and a defuzzification layer. According to the experimental results, the standard error of the mean between the object size obtained by the ANN and the actual size was 0.009. In addition, the accuracy, recall, precision, and F1-score obtained by the CFNN in object defect detection were 0.9580, 0.9535, 0.9535, and 0.9535, respectively. Compared with other deep neural network models, such as AlexNet and LeNet, the proposed CFNN has fewer parameters and higher performance. © MYU K.K.

关键词： Fuzzy neural networks

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Simulation of Sensorless BLDC Motors and a Two-axis Gimbal Using MATLAB/Simulink

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Journal of Institute of Control, Robotics and Systems 2024年第7期30卷 750-754页

作者： Lee, In-Woo Yi, Soo-Yeong Department of Electrical and Information Engineering Seoul National University of Science and Technology Korea Republic of

Brushless direct current (BLDC) motors are widely used in many industrial applications because they have several advantages compared to conventional DC motors, including high speed, high torque, and low maintenance. General BLDC motors have built-in hall sensors to detect the rotor position. However, because the output signal from the hall sensors is vulnerable to environmental noise, sensorless BLDC motors are adopted in critical environments. To control the sensorless BLDC motor, it is necessary to estimate the rotor position based on back-electro magnetic force (EMF) signal measurements. Sensorless BLDC motors have the benefits of low cost and compact size and are often used in gimbal systems, which is a type of motion stabilizer that has two or more axes of rotation and requires nonlinear dynamic control. In this paper, a nonlinear dynamic control simulation is implemented using MATLAB/Simulink for a two-axis gimbal with a sensorless BLDC motor. © 2024, Institute of Control, Robotics and Systems. All rights reserved.

关键词： Brushless DC motors

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