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A novel rotor dynamic balancing method based on blade tip clearance measurement without the once per revolution sensor

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Chinese Journal of Aeronautics 2025年第2期38卷 445-458页

作者： Weibo LI Weimin WANG Shuai ZHANG Jiale WANG Yulong LIN Tianqing LI Key Lab of Engine Health Monitoring-Control and Networking of Ministry of Education Beijing University of Chemical TechnologyBeijing 100029China State Key Laboratory of High-end Compressor and System Technology Beijing University of Chemical TechnologyBeijing 100029China

The existence of the aeroengine casing,limited monitoring points,and multi-fault characteristics make obtaining the rotor’s vibration transmission characteristics challenging,resulting in difficulties accurately identifying the rotor *** paper utilizes a high-frequency composite sensor to monitor the engine’s blade tip clearance(BTC)and extracts unbalanced information from BTC signals for rotor dynamic balancing,while avoiding the need for the once per revolution(OPR)***,the vibration characteristics of the rotor-blade system under multi-fault conditions are ***,based on BTC measurement,a none OPR method and an unbalance identification method are proposed,in which the radial vibration of the blade tip in the BTC signals at different speeds is extracted and operated in the time domain to obtain the rotor unbalanced vibration,the signal is reconstructed,and cross-correlation analysis is used to accurately identify the magnitude and phase of the unbalanced ***,a rotor test bench is utilized for experimental *** results reveal that the dynamic balancing method based on the BTC signal can more precisely identify the rotor unbalance than the traditional rotor dynamic balancing *** application of this technique will effectively improve engine health management and fault prediction.

关键词： Aeroengine Rotor unbalance Composite sensor None once per revolution Blade tip clearance Dynamic balancing

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IncEFL:a sharing incentive mechanism for edge-assisted federated learning in industrial IoT

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Digital Communications and Networks 2025年第1期11卷 106-115页

作者： Jiewei Chen Shaoyong Guo Tao Shen Yan Feng Jian Gao Xuesong Qiu State Key Laboratory of Networking and Switching Technology Beijing University of Post and TelecommunicationsBeijing 100087China Kunming University of Science and Technology Kunming 650031China Yunnan Provincial Academy of Science and Technology Kunming 650228China

As the information sensing and processing capabilities of IoT devices increase,a large amount of data is being generated at the edge of Industrial IoT(IIoT),which has become a strong foundation for distributed Artificial Intelligence(AI)***,most users are reluctant to disclose their data due to network bandwidth limitations,device energy consumption,and privacy *** address this issue,this paper introduces an Edge-assisted Federated Learning(EFL)framework,along with an incentive mechanism for lightweight industrial data *** order to reduce the information asymmetry between data owners and users,an EFL model-sharing incentive mechanism based on contract theory is *** addition,a weight dispersion evaluation scheme based on Wasserstein distance is *** study models an optimization problem of node selection and sharing incentives to maximize the EFL model consumers'profit and ensure the quality of training *** incentive-based EFL algorithm with individual rationality and incentive compatibility constraints is ***,the experimental results verify the effectiveness of the proposed scheme in terms of positive incentives for contract design and performance analysis of EFL systems.

关键词： Federated learning Data sharing Edge intelligence Incentives Contract theory

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An efficient quantum proactive incremental learning algorithm

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Science China(Physics,Mechanics & Astronomy) 2025年第1期68卷 45-53页

作者： Lingxiao Li Jing Li Yanqi Song Sujuan Qin Qiaoyan Wen Fei Gao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing 100876China

In scenarios where a large amount of data needs to be learned,incremental learning can make full use of old knowledge,signif-icantly reduce the computational cost of the overall learning process,and maintain high *** this paper,taking the MaxCut problem as our example,we introduce the idea of incremental learning into quantum computing,and propose a Quantum Proactive Incremental Learning algorithm(QPIL).Instead of a one-off training of quantum circuit,QPIL contains a multi-phase training on gradually-increased subgraphs of all vertices,proactively reducing large-scale problems to smaller ones to solve in steps,providing an efficient solution for ***,some vertices and corresponding edges are randomly selected for training to obtain optimized parameters of the quantum circuit at ***,in each incremental phase,the remaining vertices and corresponding edges are gradually added and the parameters obtained from the previous phase are reused in the parameter initialization of the current *** perform experiments on 120 different small-scale graphs,and it shows that QPIL performs superior to prevalent quantum and classical baselines in terms of approximation ratio(AR),time cost,anti-forgetting,and solv-ing *** particular,QPIL’s AR surpasses 20%of mainstream quantum baselines,and the time cost is less than 1/5 of *** idea of QPIL is expected to inspire efficient and high-quality solutions in large-scale MaxCut and other combinatorial optimization problems.

关键词： variational quantum algorithm incremental learning multi-phase training MaxCut quantum computing

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IEEE Internet of Things Journal 2025年第9期12卷 12034-12047页

作者： Fan, Senran Bao, Zhicheng Dong, Chen Liang, Haotai Xu, Xiaodong Zhang, Ping Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Peng Cheng Laboratory Department of Broadband Communication Shenzhen518066 China

With the rapid development of Internet of Things (IoT) technology, more sensors are required to operate in complex channel scenarios and under limited communication resources. Semantic communication, as an emerging paradigm, extracts, transmits, and reconstructs information at the semantic level, offering advantages, such as high compression rates and strong noise resistance. These features are expected to find widespread application across various IoT scenarios. However, widely used image similarity evaluation metrics like peak signal-to-noise ratio and multiscale structural similarity index primarily focus on pixel or structural features, making it challenging to accurately measure the loss of semantic-level information during transmission. This limitation poses challenges for the performance evaluation of visual semantic communication systems and restricts the emergence of more novel and efficient systems. To address this issue, we propose a new semantic evaluation metric-semantic similarity score (SeSS). This metric is based on Scene Graph Generation and graph matching techniques, transforming image similarity scores into graph matching scores. By manually annotating thousands of image pairs, we fine-tuned the hyperparameters within SeSS to align it more closely with human semantic perception. The performance of SeSS has been tested across various image datasets and specific IoT visual tasks. Experimental results demonstrate the effectiveness of SeSS in measuring differences in semantic-level information between images, making it a valuable tool for evaluating visual semantic communication systems. This development is expected to encourage the emergence of more robust systems suited for diverse IoT scenarios. © 2014 IEEE.

关键词： Latent semantic analysis

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Differential Privacy Federated Learning Based on Adaptive Adjustment

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Computers, Materials & Continua 2025年第3期82卷 4777-4795页

作者： Yanjin Cheng Wenmin Li Sujuan Qin Tengfei Tu The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China The School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing100876China

Federated learning effectively alleviates privacy and security issues raised by the development of artificial intelligence through a distributed training *** research has shown that attackers can compromise user privacy and security by stealing model ***,differential privacy is applied in federated learning to further address malicious ***,the addition of noise and the update clipping mechanism in differential privacy jointly limit the further development of federated learning in privacy protection and performance ***,we propose an adaptive adjusted differential privacy federated learning ***,a dynamic adaptive privacy budget allocation strategy is proposed,which flexibly adjusts the privacy budget within a given range based on the client’s data volume and training requirements,thereby alleviating the loss of privacy budget and the magnitude of model ***,a longitudinal clipping differential privacy strategy is proposed,which based on the differences in factors that affect parameter updates,uses sparse methods to trim local updates,thereby reducing the impact of privacy pruning steps on model *** two strategies work together to ensure user privacy while the effect of differential privacy on model accuracy is *** evaluate the effectiveness of our method,we conducted extensive experiments on benchmark datasets,and the results showed that our proposed method performed well in terms of performance and privacy protection.

关键词： Federated learning privacy protection differential privacy deep learning

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RecStream: Graph-aware Stream Management for Concurrent Recommendation Model Online Serving 31

RecStream: Graph-aware Stream Management for Concurrent Reco...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Guo, Shuxi Qi, Qi Sun, Haifeng Liao, Jianxin Wang, Jingyu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798891761971

Recommendation Models (RMs) are crucial for predicting user preferences and enhancing personalized experiences on large-scale platforms. As the application of recommendation models grows, optimizing their online serving performance has become a significant challenge. However, current serving systems perform poorly under highly concurrent scenarios. To address this, we introduce RecStream, a system designed to optimize stream configurations based on model characteristics for handling high concurrency requests. We employ a hybrid Graph Neural Network architecture to determine the best configurations for various RMs. Experimental results demonstrate that RecStream achieves significant performance improvements, reducing latency by up to 74%. ©2025 Association for Computational Linguistics.

关键词： Computational linguistics

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FEDNPAIT: Federated Learning with NADAM and PADAM for Instruction Tuning 24th

FEDNPAIT: Federated Learning with NADAM and PADAM for Ins...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Gao, Zhipeng Li, Yichen Yu, Xinlei The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Communications Beijing China

ISBN: (纸本)9789819615278

Large language models (LLMs) trained on massive publicly accessible data excel in general domain tasks. However, the performance of LLMs in specialized domains such as healthcare and mathematics frequently fails to meet established benchmarks. The primary obstacle to improving this performance is the scarcity of high-quality and domain-specific data due to stringent data privacy regulations. Federated learning, which facilitates the training of LLMs across distributed devices through a decentralized approach that precludes centralized data aggregation presents a potential solution. Nevertheless, federated learning for instruction tuning encounters challenges including data heterogeneity and substantial computational resource requirements. Consequently, this paper proposes FEDNPAIT, an innovative federated learning framework for instruction tuning that incorporates Nesterov-accelerated momentum and a new parameter for controlling the adaptive strength of gradient updates during optimization to improve the performance of LLMs in heterogeneous federated learning environments. Furthermore, FEDNPAIT effectively addresses computational resource limitations through the incorporation of Low-Rank Adaptation. Experimental results indicate that FEDNPAIT effectively mitigates the adverse effects of data heterogeneity in federated environments and outperforms existing federated learning algorithms. Specifically, the results from tuned Llama3-8B demonstrate enhanced generalization and robustness, illustrating how to incorporate complex optimization algorithms into federated training of LLMs, and opening new possibilities for federated instruction tuning in sensitive industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Feasibility study of cooperative sensing: radar cross section, synchronization, cooperative cluster,performance and prototype

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Science China(Information Sciences) 2025年第5期68卷 194-212页

作者： Guangyi LIU Rongyan XI Tao JIANG Xiaoqian WANG Lincong HAN Jing JIN Jing DONG Yajuan WANG Yuxiang ZHANG Qixing WANG Jiangzhou WANG Future Research Laboratory China Mobile Research Institute State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications School of Engineering University of Kent

This paper investigates the potential of integrated sensing and communication(ISAC) for the upcoming sixth generation(6G) mobile communications system, which is expected to go beyond communication and incorporate sensing capabilities. A major challenge with conventional ISAC systems is the self-interference caused by using a base station(BS)as both the sensing transmitter and receiver, which requires complex and costly hardware upgrades to enable full *** address this issue, we propose a cooperative sensing approach, where node A acts as the transmitter and node B as the receiver. In this study, we analyze the scattering characteristics of targets in a cooperative sensing scenario and find that the radar cross section(RCS) of unmanned aerial vehicle(UAV) follows a lognormal distribution. We then propose a time-frequency synchronization method, a cooperative sensing cluster construction method, and a multi-node signal-level fusion method to accurately determine the target's position. Simulation results, incorporating non-ideal factors, show that the proposed system can achieve precise target positioning. The accuracy is improved by more than five times compared to mono-static sensing and over 25% compared to result-level fusion methods. Additionally, prototype experimental results validate the feasibility of the proposed system.

关键词： 6G integrated sensing and communication synchronization bistatic RCS multi-node collaboration cooperative cluster prototype

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NeRFCom: Feature Transform Coding Meets Neural Radiance Field for Free-View 3D Scene Semantic Transmission

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IEEE Communications Letters 2025年第5期29卷 898-902页

作者： Yue, Weijie Si, Zhongwei Wu, Bolin Wang, Sixian Qin, Xiaoqi Niu, Kai Dai, Jincheng Zhang, Ping Beijing University of Posts and Telecommunications Key Laboratory of Universal Wireless Communications Ministry of Education Beijing100876 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

We introduce NeRFCom, a novel communication system designed for end-to-end 3D scene transmission. Compared to traditional systems relying on handcrafted NeRF semantic feature decomposition for compression and well-adaptive channel coding for transmission error correction, our NeRFCom employs a nonlinear transform and learned probabilistic models, enabling flexible variable-rate joint source-channel coding and efficient bandwidth allocation aligned with the NeRF semantic feature's different contribution to the 3D scene synthesis fidelity. Experimental results demonstrate that NeRFCom achieves free-view 3D scene efficient transmission while maintaining robustness under adverse channel conditions. © 1997-2012 IEEE.

关键词： Channel coding

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Diffusion Models Meet Network Management: Improving Traffic Matrix Analysis with Diffusion-Based Approach

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IEEE Transactions on Network and Service Management 2025年第2期22卷 1259-1275页

作者： Yuan, Xinyu Qiao, Yan Wei, Zhenchun Zhang, Zeyu Li, Minyue Zhao, Pei Hu, Rongyao Li, Wenjing Hefei University of Technology School of Computer Science and Information Engineering China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Macau University of Science and Technology Faculty of Information Technology China

Due to network operation and maintenance relying heavily on network traffic monitoring, traffic matrix analysis has been one of the most crucial issues for network management related tasks. However, it is challenging to reliably obtain the precise measurement in computer networks because of the high measurement cost, and the unavoidable transmission loss. Although some methods proposed in recent years allowed estimating network traffic from partial flow-level or link-level measurements, they often perform poorly for traffic matrix estimation nowadays. Despite strong assumptions like low-rank structure and the prior distribution, existing techniques are usually task-specific and tend to be significantly worse as modern network communication is extremely complicated and dynamic. To address the dilemma, this paper proposed a diffusion-based traffic matrix analysis framework named Diffusion-TM, which leverages problem-agnostic diffusion to notably elevate the estimation performance in both traffic distribution and accuracy. The novel framework not only takes advantage of the powerful generative ability of diffusion models to produce realistic network traffic, but also leverages the denoising process to unbiasedly estimate all end-to-end traffic in a plug-and-play manner under theoretical guarantee. Moreover, taking into account that compiling an intact traffic dataset is usually infeasible, we also propose a two-stage training scheme to make our framework be insensitive to missing values in the dataset. With extensive experiments with real-world datasets, we illustrate the effectiveness of Diffusion-TM on several tasks. Moreover, the results also demonstrate that our method can obtain promising results even with 5% known values left in the datasets. © 2004-2012 IEEE.

关键词： Level measurement

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