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9th IEEE International Conference on Data science in Cyberspace, DSC 2024

作者： Zhai, Xueying Peng, Yunfeng Guo, Xiuping Zhang, Liang Zhang, Wei University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Beijing University of Posts and Telecommunications School of Economics and Management Beijing China Guizhou Institute of Technology Guizhou Key Laboratory of Electric Power Big Data Guizhou China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. Data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization. © 2024 IEEE.

关键词： Low emission

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Thermal camera based on frequency upconversion and its noise-equivalent temperature difference characterization

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Advanced Photonics Nexus 2023年第4期2卷 44-49页

作者： Zheng Ge Zhi-Yuan Zhou Jing-Xin Cheng Li Chen Yin-Hai Li Yan Li Su-Jian Niu Bao-Sen Shi University of Science and Technology of China CAS Key Laboratory of Quantum InformationHefeiChina University of Science and Technology of China CAS Center for Excellence in Quantum Information and Quantum PhysicsHefeiChina Science and Technology on Electro-Optical Information Security Control Laboratory TianjinChina

We present a scheme for estimating the noise-equivalent temperature difference(NETD)of frequency upconversion detectors(UCDs)that detect mid-infrared(MIR)*** particular,we investigate the frequency upconversion of a periodically poled crystal based on lithium niobate,where an MIR conversion bandwidth of 220 nm can be achieved in a single-poled period by a special ***,for an MIR radiating target at a temperature of 95℃,the NETD of the device was estimated to be 56 mK with an exposure time of 1 ***,a direct measurement of the NETD was performed utilizing conventional methods,which resulted in 48 *** also compared the NETD of our UCD with commercially available direct MIR *** show that the limiting factor for further NETD reduction of our device is not primarily from the upconversion process and camera noise but from the limitations of the heat source and laser *** detectors have good temperature measurement performance and can be used for a variety of applications involving temperature object identification and material structure detection.

关键词： mid-infrared detection frequency upconversion noise equivalent temperature difference

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Predicting Student Performance in Online Learning Using a Highly Efficient Gradient Boosting Decision Tree 12th

Predicting Student Performance in Online Learning Using a Hi...

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12th IFIP TC 12 International Conference on Intelligent information Processing, IIP 2022

作者： Wang, Chang Chang, Liang Liu, Tieyuan Guangxi Key Laboratory of Trusted Software Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China School of Information and Communication Guilin University of Electronic Technology Guilin514000 China

ISBN: (纸本)9783031039478

Online learning has become a popular way of learning due to the rapid development of informatization of education, the rise of online learning platforms has provided great convenience to students’ learning, breaking the limitations of time and space, enabling students to study various courses anytime, anywhere. However, due to the lack of an effective supervision mechanism, online learning undermined by low quality tutoring and a substantial dropout rate of students. In this study, we used an efficient ensemble learning algorithm, LightGBM, to develop a student performance prediction model. Basing on the interaction behavior data of students’ online learning, the prediction model can predict whether a student will be able to pass the course. As a result of identifying at-risk students, teachers can provide targeted interventions to improve the learning performance of these students. During the experiments, we compared with ten classical machine learning algorithms on a public dataset. It reflected that LightGBM outperformed in predicting student performance. The study also analyzed the impact of different interaction behaviors on students’ online learning performance, which will help teachers and educational researchers to better understand the relationship between students’ online learning behavior and students’ learning performance. This will help teachers guide students in online learning and optimize the design of online learning courses. © 2022, IFIP International Federation for information Processing.

关键词： Students

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Synergistic Task-Offloading in 6g Edge Networks Based on Propagation Dynamics

SSRN

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SSRN 2025年

作者： Zhu, Chao Zhang, Yuexia Wang, Xinyi Zhu, Xuzhen The Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China The Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

In future 6G edge networks, Device-to-Device (D2D)-assisted Mobile Edge Computing (MEC) can fully utilize the idle resources of smart terminals and alleviate the burden on backhaul links. However, the limited idle resources of smart terminals and the over-reliance on D2D-assisted computation offloading may result in a large number of terminals experiencing task overload, which could lead to the risk of edge network paralysis. To address these issues, this paper establishes a Service-Auxiliary-Request-Healing (SARH) task-offloading propagation model based on propagation dynamics theory. This model describes the dynamic transmission process of offloaded tasks in 6G edge networks and constructs two linear threshold functions to characterize the differences in task processing capabilities between user terminals and edge servers. Furthermore, the proposed task-offloading propagation model is theoretically analyzed using edge compartment theory, and the propagation dynamics equations are established to derive the saddle point and critical conditions leading to task overload in a large number of user terminals, providing theoretical guidance for preventing network paralysis. Finally, simulation results show that the SARH model effectively describes the task-offloading propagation process in edge networks, and by controlling key factors such as the proportion of user terminals selecting D2D-assisted MEC synergistic task-offloading, network connectivity density, and network degree distribution heterogeneity, network paralysis can be avoided. © 2025, The Authors. All rights reserved.

关键词： Computation offloading

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Optimal controller Design for Non-Affine Nonlinear Power Systems with Static Var Compensators for Hybrid UAVs

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Tsinghua science and technology 2022年第1期27卷 196-206页

作者： Yanchu Li Qingqing Ding Shufang Li Stanimir Valtchev Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networkand School of Information&Communication EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Control and Simulation of Power System and Generation Equipments Tsinghua UniversityBeijing 100084China the Nova School of Science and Technology(FCT/UNL) Caparica 2829-516LisbonPortugal Technical University of Sofia(TUS) Sofia 2125Bulgaria

A generalized non-affine nonlinear power system model is presented for a single machine bus power system with a Static Var Compensator(SVC)or State Var System(SVS)for hybrid Unmanned Aerial Vehicles(UAVs).The model is constructed by differential algebraic equations on the MATLAB-Simulink platform with the programming technique of its *** the inverse system method and the Linear Quadratic Regulation(LQR),an optimized SVC controller is *** simulations under three fault conditions show that the proposed controller can effectively improve the power system transient performance.

关键词： Static Var Compensator(SVC) Unmanned Aerial Vehicles(UAV) power system non-affine nonlinear control inverse system method Linear Quadratic Regulation(LQR)

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Integrated communication and Learned Recognizer with Customized RIS Phases and Sensing Durations

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IEEE Transactions on communications 2025年

作者： Huang, Yixuan Yang, Jie Wen, Chao-Kai Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education The Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan Southeast University Frontiers Science Center for Mobile Information Communication and Security National Mobile Communications Research Laboratory Nanjing210096 China

Future wireless communication networks are expected to be smarter and more aware of their surroundings, enabling a wide range of context-aware applications. Reconfigurable intelligent surfaces (RISs) are set to play a critical role in supporting various sensing tasks, such as target recognition. However, current methods typically use RIS configurations optimized once and applied over fixed sensing durations, limiting their ability to adapt to different targets and reducing sensing accuracy. To overcome these limitations, this study proposes an advanced wireless communication system that multiplexes downlink signals for environmental sensing and introduces an intelligent recognizer powered by deep learning techniques. Specifically, we design a novel neural network based on the long short-term memory architecture and the physical channel model. This network iteratively captures and fuses information from previous measurements, adaptively customizing RIS phases to gather the most relevant information for the recognition task at subsequent moments. These configurations are dynamically adjusted according to scene, task, target, and quantization priors. Furthermore, the recognizer includes a decision-making module that dynamically allocates different sensing durations, determining whether to continue or terminate the sensing process based on the collected measurements. This approach maximizes resource utilization efficiency. Simulation results demonstrate that the proposed method significantly outperforms state-of-the-art techniques while minimizing the impact on communication performance, even when sensing and communication occur simultaneously. Part of the source code for this paper can be accessed at https://***/kiwi1944/CRISense. © 1972-2012 IEEE.

关键词： Long short-term memory

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An Asymmetric Channel Attack Monitoring Method for Round-Trip Fiber Time Synchronization System

An Asymmetric Channel Attack Monitoring Method for Round-Tri...

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European Frequency and Time Forum (EFTF)

作者： Xuesong Xu Yichen Zhang Hailong Xu Yiming Bian Yang Li Bingjie Xu Song Yu State Key Laboratory of Information Photonics and Optical Communications School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Science and Technology on Communication Security Laboratory Institute of Southwestern Communication Chengdu China

ISBN: (数字)9798350354270

ISBN: (纸本)9798350354287

We present a method for monitoring potential asymmetric channel attacks within a round-trip fiber time synchronization system. This is achieved by observing and tracking the time difference at the remote end. Experimental results show that the attacking operation causes unavoidably obvious fluctuations in time difference, with the time difference measured by our monitoring module changing by nanoseconds when an attack occurs, which can be used for attack recognition. Our work provides a reference for further improving the security of fiber time synchronization systems and lays the foundation for the development of more advanced time synchronization attack identification techniques.

关键词： Time-frequency analysis Fluctuations Europe Time measurement Synchronization security Frequency synchronization Monitoring

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Angle-Based SLAM on 5G mmWave Systems: Design, Implementation, and Measurement

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IEEE Internet of Things Journal 2023年第20期10卷 17755-17771页

作者： Yang, Jie Wen, Chao-Kai Xu, Jing Que, Hang Wei, Haikun Jin, Shi Southeast University Sch. of Automat. and the Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China National Sun Yat-sen University Institute of Communications Engineering Kaohsiung804 Taiwan Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Nanjing210096 China

Simultaneous localization and mapping (SLAM) is a key technology that provides user equipment (UE) tracking and environment mapping services, enabling the deep integration of sensing and communication. The millimeter-wave (mmWave) communication, with its larger bandwidths and antenna arrays, inherently facilitates more accurate delay and angle measurements than sub-6 GHz communication, thereby providing opportunities for SLAM. However, none of the existing works have realized the SLAM function under the 5G new radio (NR) standard due to specification and hardware constraints. In this study, we investigate how 5G mmWave communication systems can achieve situational awareness without changing the transceiver architecture and 5G NR standard. We implement 28-GHz mmWave transceivers that deploy OFDM-based 5G NR waveform with 160-MHz channel bandwidth, and we realize beam management following the 5G NR. Furthermore, we develop an efficient successive cancellation-based angle extraction approach to obtain angles of arrival and departure from the reference signal received power measurements. On the basis of angle measurements, we propose an angle-only SLAM algorithm to track UE and map features in the radio environment. Thorough experiments and ray tracing-based computer simulations verify that the proposed angle-based SLAM can achieve submeter-level localization and mapping accuracy with a single base station and without the requirement of strict time synchronization. Our experiments also reveal many propagation properties critical to the success of SLAM in 5G mmWave communication systems. © 2014 IEEE.

关键词： 5G mobile communication systems

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External Attention Mechanism-Based Modulation Classification

External Attention Mechanism-Based Modulation Classification

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2022 International Conference on Scientific Computation and Applied Statistics, ICSCAS 2022

作者： Tao, Xueying Shao, Huaizong Li, Qiang Pan, Ye Fu, Zhongqi School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China Peng Cheng Laboratory Shenzhen China

This paper considers the modulation classification of radio frequency (RF) signals. An external attention mechanism-based convolution neural network (EACNN) is proposed. Thanks to the external attention layers, the EACNN network can capture the potential correlations of different modulation data, which helps reduce computational consumption and memory costs efficiently during training. Moreover, to account for the variation of the signals induced by channel fading, we further propose a customized batch normalization (BN) layer in EACNN to improve the classification accuracy with less training time. Numerical experiments on RML2016.a dataset shows that the proposed method outperforms the baseline method CNN2 by 7% in terms of classification accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Fading channels

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Modulation Recognition Based on Lightweight Residual Network via Hybrid Pruning 7

Modulation Recognition Based on Lightweight Residual Network...

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7th International Conference on Computer and communications, ICCC 2021

作者： Ji, Heng Xu, Wangyang Gan, Lu Xu, Zhengwu Science and Technology On Communication Information Security Control Laboratory Jiaxing314000 China University of Electronic Science and Technology of China Chengdu611731 China Yibin Institute of UESTC Yibin643000 China

ISBN: (纸本)9781665409506

Signal modulation recognition is an important and complicated problem in spectrum monitoring. In recent years, many advanced deep learning methods have been proposed for this problem. However, the high computational cost of deep neural networks largely hinders practical deployment. In this paper, we consider the signal modulation recognition problem via the deep residual network and propose a hybrid Pruning scheme to reduce the model size and memory cost. This is achieved by simultaneously enforcing channel-level and layer-level sparsity in residual network in a simple way. We take wide and large networks as input models, but during training, insignificant channels and layers are automatically identified and pruned afterwards, yielding compact and shallower networks with comparable accuracy. The results show that with 0 dB snr, the trainable parameters and multiply-accumulate operations are both reduced by 2/3. In summary, the proposed method can effectively reduce the parameters and complexity of recognition model. © 2021 IEEE.

关键词： Deep neural networks

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