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IEEE Internet of Things Journal 2025年

作者： Li, Yangning Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Wang, Jiacheng Zhao, Changyuan Niyato, Dusit Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

An expansion of Internet of Things (IoTs) has led to significant challenges in wireless data harvesting, dissemination, and energy management due to the massive volumes of data generated by IoT devices. These challenges are exacerbated by data redundancy arising from spatial and temporal correlations. To address these issues, this paper proposes a novel data-driven collaborative beamforming (CB)-based communication framework for IoT networks. Specifically, the framework integrates CB with an overlap-based multi-hop routing protocol (OMRP) to enhance data transmission efficiency while mitigating energy consumption and addressing hot spot issues in remotely deployed IoT networks. Based on the data aggregation to a specific node by OMRP, we formulate a node selection problem for the CB stage, with the objective of optimizing uplink transmission energy consumption. Given the complexity of the problem, we introduce a softmax-based proximal policy optimization with long-short-term memory (SoftPPO-LSTM) algorithm to intelligently select CB nodes for improving transmission efficiency. Simulation results show that the proposed OMRP improves network lifetime by 17% compared to benchmark routing protocols, while the SoftPPO-LSTM method for CB node selection achieves an 8.3% increase in throughput over benchmark algorithms. The results also reveal that the combined OMRP with the SoftPPO-LSTM method effectively mitigates hot spot problems and offers superior performance compared to traditional strategies. © 2014 IEEE.

关键词： Deep reinforcement learning

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Secrecy Performance Analysis of Artificial Noise Assisted Secure Transmission for MISO systems with Multiple Eavesdroppers

Secrecy Performance Analysis of Artificial Noise Assisted Se...

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2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

作者： Xu, Panpan Tan, Weijie Niu, Kun Li, Zhen Peng, Changgen School of Mathematics and Statistics Guizhou University Guiyang China Key Laboratory of Advanced Manufacturing Technology of Ministry of Education Guizhou China Guizhou Big Data Academy Guizhou University Guiyang China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China College of Big Data and Information Engineering Guizhou University Guiyang China

ISBN: (纸本)9798350316728

In the paper, we investigate the secure communication of multiple-input single-output (MISO) systems with multiple eavesdroppers. We jointly design the beamforming (BF) and the artificial noise (AN) in MISO systems with multiple eavesdroppers to enhance the secrecy rate. Furthermore, under the secrecy interrupt constraint, we obtain the calculation formulas of the secrecy interrupt probability and the optimal secrecy rate. The simulation results demonstrate that AN with a higher dimension can effectively obstruct eavesdroppers and enhance the system's optimal secrecy rate. In addition, it is proved that the 1/4 full-dimension AN's secrecy rate close to that of the full-dimension AN in the system with multiple eavesdroppers, allowing the trade-off between system secrecy performance and artificial noise design complexity to be realized. © 2023 IEEE.

关键词： Economic and social effects

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Multicriteria Fatigue Detection with Rapid Facial Tracking and Individualized Thresholds

Multicriteria Fatigue Detection with Rapid Facial Tracking a...

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2023 International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2023

作者： Wang, Yixuan Li, Qing Wu, Yutian Ma, Jinge School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing China School of Mathematics and Physics University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350318012

Fatigue driving is one of the main causes of traffic accidents. Detecting fatigue driving can identify the possible fatigue state of the driver and issue warnings to prevent potential dangerous situations. At present, there exist challenges for available detection methods, which contains lack of real-time, low accuracy and robustness. As is a solution to problems above, the method proposed in this paper improves comprehensive performance by raising 3 new algorithms: (1) rapid face tracking algorithm, which can locate and track the driver's facial region in real time under complex lighting and background conditions, improving the detection efficiency and accuracy;(2) multi-criteria fusion algorithm, which can comprehensively consider various physiological and behavioral features, as well as their interrelationships, and output a comprehensive fatigue score, improving the detection sensitivity and robustness;(3) individualized threshold algorithm, which can dynamically adjust the fatigue threshold according to the individual differences and habits of each driver, improving the detection adaptability and personalization. The experiment results with different subjects under various fatigue driving behaviors demonstrate that the proposed approach is more accurate, fast and robust than existing ones in terms of driver identification recognition and fatigue detection. © 2023 IEEE.

关键词： Face recognition

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An Optimization Method of Energy Demand Forecasting Method Based on Neural Network 5

An Optimization Method of Energy Demand Forecasting Method B...

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5th International Conference on Smart Power and Internet Energy Systems, SPIES 2023

作者： Zhao, Yufan Song, Ruizhuo Li, Chengfeng School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education Beijing100083 China Clean Energy Branch of Cnooc Energy Technology & Services Limited Tianjin Tianjin300452 China

ISBN: (纸本)9798350371741

User-side energy demand forecasting is one of the essential functions of smart grids in the future. By forecasting electricity consumption from consumers, energy companies can make appropriate adjustments to the amount of electricity generated, reducing the burden on the grid and reducing energy waste. In this paper, an optimization method for energy prediction method based on neural network is proposed. Firstly, a dual-CNN structure is used to extract the state of the electrical equipment at the user and the power required for the state and predict it. Then, the neural network-based genetic algorithm was used to adjust the weight of the neural network. Both methods are more mature methods, and the combination of the two methods may be able to build on the original methods and achieve accurate prediction of energy demand. © 2023 IEEE.

关键词： Genetic algorithms

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Integrated Energy Management Platform for Monitoring, Operation and Maintenance 16th

Integrated Energy Management Platform for Monitoring, Operat...

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16th International Conference on Modelling, Identification and Control, ICMIC 2024

作者： Zhang, Tao Wang, Hailun Ye, Jun Yang, Xu Zhang, Yixin Li, Dongze Shi, Zinan School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education Beijing100083 China Xi’an Jiaotong University Xi’an710000 China Beijing SyiTsing Energy Technology Co. LTD. Beijing100083 China

ISBN: (纸本)9789819617760

A seamless and comprehensive system for the clean utilization of energy remains elusive, with energy dispatch failing to harmonize the overall picture and the integration of clean energy sources. The judicious employment of holistic smart energy holds immense importance for societal advancement. To foster a more rational and efficient approach to the development and administration of smart energy, and to ensure the optimal utilization of new energy resources, the intelligent integrated energy management platform's development and design methodologies are being optimized in tandem with the Internet of Things technology. This paper outlines the construction of an integrated energy intelligent management system and platform, leveraging the ‘cloud management edge side’ approach. It bridges the gap between the various tiers of the integrated energy service system, achieving a comprehensive integration of energy flow, information flow, and value flow within the system. This platform stands poised to support comprehensive energy operators, data, and network services across the region, spurring regional economic growth, and offering a diverse array of financial products and services, grounded in high-quality user resources and industrial structure service capabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Discovering Reliable Information Extraction Patterns with Pre-Trained Model for Text with Writing Style

Discovering Reliable Information Extraction Patterns with Pr...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chenyang Bu Jiacheng Liu Jiaxuan Liu Shengwei Ji Hongbin Yang Ministry of Education Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China School of Artificial Intelligence and Big Data Hefei University China

Large-scale pre-trained models such as GPT and BERT have demonstrated remarkable performance in information extraction tasks. However, their black-box nature poses challenges for reliability and interpretability. In contrast, rule- based extraction methods have better interpretability, but typically require domain experts to manually establish rules, limiting their generalization ability. In industry, there is often a demand for reliable knowledge extraction to reduce the time spent on manual verification of each piece of knowledge. In this paper, we explore the idea of combining GPT and symbolic-based methods to automatically discover reliable extraction patterns in text with a particular writing style. This method leverages the characteristics of high information density and similar writing patterns in text with a specific writing style to generate verifiable and reliable patterns. We conduct experiments on two datasets with a specific writing style to demonstrate its effectiveness, validating the idea of combining large models for reliable information extraction pattern discovery in the tested datasets.

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Multiphysics Simulation Analysis of Excessive Circuit Resistance of Reactive Power Compensation Circuit Breaker 7

Multiphysics Simulation Analysis of Excessive Circuit Resist...

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7th International Conference on Electric Power Equipment - Switching Technology, ICEPE-ST 2024

作者： Jiang, Yuan Huang, Kai Zhang, Kaiyuan Cao, Rui Zhang, Qian Tong, Ziang School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing100083 China University of Liverpool Department of Electrical Engineering and Electronics LiverpoolL69 3GJ United Kingdom International Student Center University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9798350388947

In order to analyze the reasons for the excessive resistance of reactive power compensation SF6 circuit breaker in actual switching scenarios, firstly, the overvoltage and surge current situations during the switching process of shunt capacitor bank were simulated in this paper. Then, based on overvoltage and surge current data, a multi physics field simulation was conducted on the electromagnetic field, temperature field, and fluid field coupling of the arc chamber. The results show that the reignition overvoltage generated during the disconnection process of the shunt capacitor bank can reach up to 6 times the rated voltage of the system, and the surge current generated during the connection of the shunt capacitor bank can reach up to 16.8 times the rated current, which has a significant impact on the stable operation of the circuit breaker. The surge current generated during the connection process can cause temperature rise, with a maximum temperature close to 1000K. The reignition arc will burn the contact, and the maximum temperature of the contact can reach 7875 K. All of these situations will have an impact on the electrical contact performance of the contacts, which will cause the resistance of breaker circuit to exceed the standard. © 2024 IEEE.

关键词： Capacitor bank

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BERD+: A Generic Sequential Recommendation Framework by Eliminating Unreliable data with Item- and Attribute-level Signals

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ACM Transactions on Information Systems 2023年第2期42卷 1-33页

作者： Sun, Yatong Yang, Xiaochun Sun, Zhu Wang, Bin School of Computer Science and Engineering Northeastern University School of Computing Macquarie University China Australia Institute of High Performance Computing Centre for Frontier AI Research A*STAR Singapore School of Computer Science and Engineering Northeastern University China National Frontiers Science Center for Industrial Intelligence and Systems Optimization China Key Laboratory of Data Analytics and Optimization for Smart Industry [Northeastern University Ministry of Education China

A preliminary work of our study was published at IJCAI’21 [47], which is substantially extended in the following aspects: (1) In Section 1, we analyze the necessity of introducing item attributes for detecting unreliable instances, together with the problems and challenges that attributes may bring in. (2) In Section 2, we add discussions about the limitations of existing attribute-aware recommender systems (Section 2.2) and denoising methods (Section 2.3) in the context of detecting unreliable instances. (3) In Section 4.2, we further conduct an in-depth analysis at the attribute level to demonstrate the capability of attributes for rectifying instance loss and uncertainty, as well as the disturbance caused by attributes. (4) We generalize BERD to a generic framework BERD+ in Section 5.1, equipped with novel modules, i.e., HU-GCN (Section 5.2) and EPE (Section 5.4), which properly incorporate item attributes while reducing their disturbance for rectifying instance uncer tainty (Section 5.5) and loss (Section 5.6). The generic BERD+ can be flexibly plugged into existing SRSs for performance enhanced recommendation via eliminating unreliable data. (5) In Section 6.2, we apply our BERD+ framework to seven state-of-the-art SRSs on five real-world datasets to illustrate its superiority. (6) To avoid unfair comparison caused by item attributes, we build and compare with the baseline that combines the original BERD and an advanced attribute-aware recommender system, KSR [19]. (7) For more comprehensive comparison, in Section 6.2.2, we compare BRED+ with two state-of-the-art denoising approaches;in Section 6.2.3, to examine the efficacy of HU-GCN and EPE, we compare HU-GCN with various attribute embedding techniques, i.e., variants of graph neural networks, and compare EPE with different attribute fusing methods, i.e., adding, concatenation, and weighted sum. (8) In Section 6.2.4, a detailed ablation study is conducted to verify the effectiveness of each module of BERD+. (

关键词： Convolution

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Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model

arXiv

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arXiv 2023年

作者： Liu, Yixuan Zhao, Suyun Xiong, Li Liu, Yuhan Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China China Engineering Research Center Ministry of Education on Database and BI China Information School Renmin University of China China Department of Computer Science Emory University United States

Federated Learning, as a popular paradigm for collaborative training, is vulnerable against privacy attacks. Different privacy levels regarding users’ attitudes need to be satisfied locally, while a strict privacy guarantee for the global model is also required centrally. Personalized Local Differential Privacy (PLDP) is suitable for preserving users’ varying local privacy, yet only provides a central privacy guarantee equivalent to the worst-case local privacy level. Thus, achieving strong central privacy as well as personalized local privacy with a utility-promising model is a challenging problem. In this work, a general framework (APES) is built up to strengthen model privacy under personalized local privacy by leveraging the privacy amplification effect of the shuffle model. To tighten the privacy bound, we quantify the heterogeneous contributions to the central privacy user by user. The contributions are characterized by the ability of generating "echos" from the perturbation of each user, which is carefully measured by proposed methods Neighbor Divergence and Clip-Laplace Mechanism. Furthermore, we propose a refined framework (S-APES) with the post-sparsification technique to reduce privacy loss in high-dimension scenarios. To the best of our knowledge, the impact of shuffling on personalized local privacy is considered for the first time. We provide a strong privacy amplification effect, and the bound is tighter than the baseline result based on existing methods for uniform local privacy. Experiments demonstrate that our frameworks ensure comparable or higher accuracy for the global model. © 2023, CC BY.

关键词： Learning systems

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Enabling Generalized Zero-Shot Vulnerability Classification

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Hu, Jinghao Guo, Jinsong Luo, Chen Hu, Yang Lanzinger, Matthias Li, Zhanshan Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Software Changchun130012 China University College London Department of Computer Science United Kingdom Amazon United States United Kingdom University of Oxford Department of Computer Science United Kingdom Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Changchun130012 China

Regarding computer security, the growth of code vulnerability types presents a persistent challenge. These vulnerabilities, which may cause severe consequences, necessitate precise classification for effective mitigation. However, the rapid emergence of new vulnerability types complicates the classification process. Traditional methodologies, which often involve human expertise and the manual labeling or generation of example instances, are not only resource-intensive but also struggle to adapt to the dynamic nature of these vulnerabilities. This paper introduces VulnSense, an innovative method that harnesses the capabilities of Generalized Zero-Shot Learning (GZSL) to address the vulnerability classification problem. VulnSense learns about unseen vulnerability classes from the descriptions of these unseen classes, while not requiring to see any instances of these unseen classes. Specifically, VulnSense learns from three main resources: (1) seen classes with labeled code instances;(2) descriptions of these seen classes;and (3) descriptions of "unseen"classes which have no labeled instances. Our experiments underscore VulnSense's superiority over existing GZSL methods in classifying instances of unseen classes. Concurrently, it maintains a performance parity with traditional labeled-example based learning methods in classifying instances of seen vulnerabilities. VulnSense demonstrates the potential of using GZSL for vulnerability classification, while also highlighting challenges that inspire future work. © 2004-2012 IEEE.

关键词： Zero-shot learning

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