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2022 Power system and Green Energy Conference, PSGEC 2022

作者： Han, Bin Hu, Chenxu Yuan, Xue Yu, Mingkai Wang, Jianxiao Zhang, Lu Chang, Naichao Mu, Yongzheng Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology China Electric Power Research Institute Beijing China School of Electrical and Electronic Engineering North China Electric Power University Beijing China College of Information and Electrical Engineering China Agricultural University Beijing China National Power Dispatch Control Center Beijing China

ISBN: (数字)9781665499934

ISBN: (纸本)9781665499934

The paper presents an in-depth analysis of the clearing process of the interregional renewable energy spot market currently in operation and the trading path construction method, and explains the issues that need to be considered for the future development of this market. On this basis, a centralized and efficient interregional spot market clearing model is proposed, which makes full use of the current trading path construction method, considering the same optimization objective of price difference matching method and security constraints of interregional and regional power grids. With high scalability, this model can meet the requirements of power grid safe operation and further improve the enforceability of market clearing results. It can provide theoretic support for the development of interregional spot market and the effective coordination between cross regional and regional spot market in the future. A case study of a 5-region system is adopted to verify the correctness and effectiveness of the proposed model. © 2022 IEEE.

关键词： Electric power transmission networks

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A Jamming Decision-Making Method Based on Next State Prediction SAC

A Jamming Decision-Making Method Based on Next State Predict...

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IEEE International Conference on Electronic information and communication technology (ICEICT)

作者： Geng Chen Jingpeng Gao Tianran Zhang Zhiye Jiang Chen Shen College of Information and Communication Engineering Harbin Engineering University Haibin China Beijing Institute of Electronic System Engineering Beijing China National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics Beijing Institute of Space Long March Vehicle Beijing China

In light of the growing complexity of the electromagnetic environment, traditional jamming decision-making methods have become less effective due to poor generalization and inefficient jamming performance. This paper proposes a jamming decision-making method based on next state prediction SAC (Soft Actor-Critic) to address this issue. Firstly, we design the state, action, and reward elements under electronic warfare scenarios. Secondly, a next state prediction critic network is proposed to accurately estimate the value function by modeling the environment dynamics and extracting richer representational information. Finally, a negative activation ReLU is introduced to improve the utilization ratio of network parameters. Simulation results show that the proposed method achieves a 98.4% success rate at a distance of 90 km from the radar and a 70.0% success rate at 30 km. Therefore, this method exhibits good adaptability and jamming performance in the field of jamming decision-making.

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Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples

arXiv

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arXiv 2024年

作者： Zhou, Ziqi Li, Minghui Liu, Wei Hu, Shengshan Zhang, Yechao Wan, Wei Xue, Lulu Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab. China Cluster and Grid Computing Lab. China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Nevertheless, recent works have exposed a vulnerability in pre-trained encoders, highlighting their susceptibility to downstream-agnostic adversarial examples (DAEs) meticulously crafted by attackers. The lingering question pertains to the feasibility of fortifying the robustness of downstream models against DAEs, particularly in scenarios where the pre-trained encoders are publicly accessible to the attackers. In this paper, we initially delve into existing defensive mechanisms against adversarial examples within the pre-training paradigm. Our findings reveal that the failure of current defenses stems from the domain shift between pre-training data and downstream tasks, as well as the sensitivity of encoder parameters. In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models. Gen-AF employs a genetic-directed dual-track adversarial fine-tuning strategy in its first stage to effectively inherit the pre-trained encoder. This involves optimizing the pre-trained encoder and classifier separately while incorporating genetic regularization to preserve the model’s topology. In the second stage, Gen-AF assesses the robust sensitivity of each layer and creates a dictionary, based on which the top-k robust redundant layers are selected with the remaining layers held fixed. Upon this foundation, we conduct evolutionary adaptability fine-tuning to further enhance the model’s generalizability. Our extensive experiments, conducted across ten self-supervised training methods and six

关键词： Topology

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A Binary Sparrow-Hybridized Beluga Whale Optimization Algorithm for Jamming Task Assignment

A Binary Sparrow-Hybridized Beluga Whale Optimization Algori...

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IEEE International Conference on Electronic information and communication technology (ICEICT)

作者： Chen Shen Jingpeng Gao Zhiye Jiang Tianran Zhang Geng Chen College of Information and Communication Engineering Harbin Engineering University Harbin China National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics Beijing Institute of Space Long March Vehicle Beijing China Beijing Institute of Electronic System Engineering Beijing China

In the context of non-cooperative electronic countermeasures, the problem of formulating effective jamming task assignments arises due to insufficient collaborative jamming resources. This paper proposes a binary sparrow- hybridized beluga whale optimization algorithm based on jamming task assignments. The algorithm combines the binary sparrow search algorithm(BSSA) and the binary beluga whale optimization(BBWO), which is set with multi-dimensional constraints in the time, frequency and spatial domains. To achieve the optimal strategy for collaborative interference task assignment, a fitness function is constructed using both radar coverage and the signal-to-jamming ratio received by each radar. When the number of UAVs is 7 and the number of radars is 10, the simulation results show that the optimization speed and accuracy of the proposed algorithm are significantly higher than the BSSA and BBWO. Therefore, the algorithm has good coverage and performance in the field of jamming task assignments.

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Bayesian Framework for Multi-User Cooperative Radio SLAM

Bayesian Framework for Multi-User Cooperative Radio SLAM

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IEEE International Symposium on Personal, Indoor and Mobile Radio communications (PIMRC)

作者： Hang Que Jie Yang Tao Du Shuqiang Xia Chao-Kai Wen Shi Jin National Mobile Communications Research Laboratory Southeast University Nanjing China Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China ZTE Corporation Shenzhen China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

The advancement of millimeter-wave communication technology heralds new sensing capabilities. By leveraging channel multipath parameter estimates, we can harness simultaneous localization and mapping (SLAM) for precise user equipment (UE) localization and radio map construction in 6 G communication systems. Particularly in multi-UE scenarios, SLAM empowers base stations to amalgamate the local radio maps of various UEs efficiently. This study introduces a novel Bayesian framework specifically designed for multi-UE SLAM, complemented by a tailored factor graph. We also unveil a two-stage multi-UE SLAM algorithm. Our simulation results reveal that this algorithm substantially enhances radio map construction accuracy by $\mathbf{4 8. 5 \%}$ and UE localization accuracy by $13.5 \%$, outperforming single-UE cases. Moreover, the algorithm demonstrates remarkable adaptability to environmental changes, showcasing its potential for long-term evolution in dynamic settings.

关键词： Location awareness Simultaneous localization and mapping Accuracy communication systems Heuristic algorithms Simulation Bayes methods Sensors Millimeter wave communication Land mobile radio

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Fiber-delay-line-referenced optical frequency combs:three stabilization schemes

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Chinese Optics Letters 2022年第2期20卷 55-59页

作者： Kai Wang Haochen Tian Fei Meng Baike Lin Shiying Cao Yihan Pi Yan Han Zhanjun Fang Youjian Song Minglie Hu Ultrafast Laser Laboratory Key Laboratory of Opto-Electronic Information Technical Science of Ministry of EducationSchool of Precision Instrument and Opto-Electronics EngineeringTianjin UniversityTianjin 300072China Division of Time and Frequency Metrology National Institute of MetrologyBeijing 100029China Key Laboratory of Advanced Optical Communication System and Networks School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China College of Physics and Optoelectronics Taiyuan University of TechnologyTaiyuan 030024China

We demonstrate the stabilization of an optical frequency comb(OFC) using a segment of fiber delay line as a reference. A mode-locked Er-doped fiber laser is phase locked to a kilometer-long fiber delay line using three different schemes. The short-term stability of the comb modes in the OFC stabilized by these schemes is obviously enhanced, down to the 10;level at millisecond average time. Among these three schemes, phase locking two bunches of comb modes in the OFC to the same fiber delay line exhibits the lowest residual phase noise. Fiber-delay-line-referenced OFCs can provide reliable laser sources in precise metrology owing to the advances of low cost, compactness, and high integration.

关键词： optical frequency comb delay line frequency noise

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Molecular mechanisms of iron nanominerals formation in fungal extracellular polymeric substances (EPS) layers during fungus-mineral interactions

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Chemosphere 2024年 367卷 143660页

作者： Xiao, Jian Chi, ZhiLai Huang, XiaoDan Yu, GuangHui School of Ecology and Applied Meteorology Nanjing University of Information Science & Technology Nanjing210044 China College of Environmental and Biological Engineering Fujian Provincial Key Laboratory of Ecology-toxicological Effects & Control for Emerging Contaminants Putian University Fujian Putian351100 China Institute of Surface-Earth System Science School of Earth System Science Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim Bohai Coastal Critical Zone National Observation and Research Station Tianjin University Tianjin300072 China

Extracellular polymeric substances (EPS), which envelop on fungal hyphae surface, interact strongly with minerals and play a crucial role in the formation of nanoscale minerals during biomineralization in nature environments. However, it remains poorly understood about the molecular mechanisms of nanominerals (i.e., iron nanominerals) formation in fungal EPS halos during fungus-mineral interactions. This process is vital because fungi typically grow attached to various mineral surfaces in nature. According to the changes of thickness of the fungal cell and EPS layers during the Trichoderma guizhouense NJAU 4742 and hematite cultivation experiments, we found that fungal biomineralization could trigger the formation of EPS layers. Fe-dominated nanominerals, aromatic C (283-286.1 eV), alkyl C (287.6-288.3 eV), and carboxylic C (288.4-289.1 eV) were the dominant chemical groups on the EPS layers, as determined by nanoscale secondary ion mass spectrometry (NanoSIMS), high-resolution transmission electron microscope (HRTEM), and carbon 1s near-edge X-ray absorption fine structure (NEXAFS) spectroscopy. Further, evidence from Fe K-edge X-ray absorption near-edge structure (XANES) and X-ray photoelectron spectroscopy (XPS) spectra indicated that oxygen vacancy (OV) was formed on the Fe-dominated nanomineral surface during fungus-mineral interactions, which played an important role in catalyzing H2O2 decomposition and HO∗ production. Taken together, the intrinsic peroxidase-like activity by reactive oxygen species (ROS) could modulate the Fe-dominated nanominerals formation in EPS layers to newly form a physical barrier between the cell and the external environments around hyphae, providing novel insights into the effects of ROS-mediated fungal-mineral interactions on fungal nutrient recycling, attenuation of contaminants, and biological control in nature environments. © 2024

关键词： Hematite

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High-Speed Trajectory Tracking control for Quadrotors via Deep Reinforcement Learning

High-Speed Trajectory Tracking Control for Quadrotors via De...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Hean Hua Hang Zhong Hui Zhang Yongchun Fang Yaonan Wang College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Technology Changsha China School of Robotics Hunan University Changsha China Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This paper presents a learning-based high-speed trajectory tracking control strategy for quadrotors, which achieves efficient learning and strong reliability by the collaboration of deep reinforcement learning (RL) and self-tuning mechanism. Different from existing methods, the proposed strategy is designed to explore optimal control performance by taking advantage of model-based self-tuning mechanism and deep reinforcement learning. Specifically, the self-tuning guided deep RL scheme is put forward for quadrotors, with superior learning efficiency and strong adaptability. Firstly, a novel self-tuning mechanism is constructed and some auxiliary variables are introduced to enhance the tracking performance. Then, based on the model-driven self-tuning design, the deep RL is proposed to achieve model-guided learning, where the tuning actions are adopted in the evaluation process during training, aiming at removing the bad explorations by the carefully designed parallel evaluation. Finally, the convergence is analyzed based on the proposed learning framework, which indicates the efficient cooperation of exploration and self-tuning mechanism. To verify the effectiveness of the proposed controller, the guided training and hardware experiments are implemented to show efficient cooperation and satisfactory high-speed trajectory tracking control of the proposed method.

关键词： Training Adaptation models Trajectory tracking Heuristic algorithms Optimal control Deep reinforcement learning Hardware

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PEPT: Expert Finding Meets Personalized Pre-training

arXiv

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arXiv 2023年

作者： Peng, Qiyao Xu, Hongyan Wang, Yinghui Liu, Hongtao Huo, Cuiying Wang, Wenjun School of New Media and Communication Tianjin University Tianjin China College of Intelligence and Computing Tianjin University Tianjin China Key Laboratory of Information System and Technology Beijing Institute of Control and Electronic Technology Beijing China Du Xiaoman Technology Beijing China Yazhou Bay Innovation Institute Hainan Tropical Ocean University Hainan China

Finding experts is essential in Community Question Answering (CQA) platforms as it enables the effective routing of questions to potential users who can provide relevant answers. The key is to personalized learning expert representations based on their historical answered questions, and accurately matching them with target questions. Recently, the application of Pre-trained Language Models (PLMs) have gained significant attraction due to their impressive capability to comprehend textual data, and are widespread used across various domains. There have been some preliminary works exploring the usability of PLMs in expert finding, such as pre-training expert or question representations. However, these models usually learn pure text representations of experts from histories, disregarding personalized and fine-grained expert modeling. For alleviating this, we present a personalized pre-training and fine-tuning paradigm, which could effectively learn expert interest and expertise simultaneously. Specifically, in our pre-training framework, we integrate historical answered questions of one expert with one target question, and regard it as a candidate aware expert-level input unit. Then, we fuse expert IDs into the pre-training for guiding the model to model personalized expert representations, which can help capture the unique characteristics and expertise of each individual expert. Additionally, in our pre-training task, we design: 1) a question-level masked language model task to learn the relatedness between histories, enabling the modeling of question-level expert interest;2) a vote-oriented task to capture question-level expert expertise by predicting the vote score the expert would receive. Through our pre-training framework and tasks, our approach could holistically learn expert representations including interests and expertise. Our method has been extensively evaluated on six real-world CQA datasets, and the experimental results consistently demonstrate the superio

关键词： Question answering

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Coalition Formation for Heterogeneous Federated Learning Enabled Channel Estimation in RIS-assisted Cell-free MIMO

arXiv

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arXiv 2025年

作者： Qi, Nan Liu, Haoxuan Tsiftsis, Theodoros A. Boulogeorgos, Alexandros-Apostolos A. Zhou, Fuhui Jin, Shi Wu, Qihui Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Informatics and Telecommunications University of Thessaly Lamia35100 Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani50100 Greece National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China

Downlink channel estimation remains a significant bottleneck in reconfigurable intelligent surface-assisted cell-free multiple-input multiple-output communication systems. Conventional approaches primarily rely on centralized deep learning methods to estimate the high-dimensional and complex cascaded channels. These methods require data aggregation from all users for centralized model training, leading to excessive communication overhead and significant data privacy concerns. Additionally, the large size of local learning models imposes heavy computational demands on end users, necessitating strong computational capabilities that most commercial devices lack. To address the aforementioned challenges, a coalition-formation-guided heterogeneous federated learning (FL) framework is proposed. This framework leverages coalition formation to guide the formation of heterogeneous FL user groups for efficient channel estimation. Specifically, by utilizing a distributed deep reinforcement learning (DRL) approach, each FL user intelligently and independently decides whether to join or leave a coalition, aiming at improving channel estimation accuracy, while reducing local model size and computational costs for end users. Moreover, to accelerate the DRL-FL convergence process and reduce computational burdens on end users, a transfer learning method is introduced. This method incorporates both received reference signal power and distance similarity metrics, by considering that nodes with similar distances to the base station and comparable received signal power have a strong likelihood of experiencing similar channel fading. Massive experiments performed that reveal that, compared with the benchmarks, the proposed framework significantly reduces the computational overhead of end users by 16%, improves data privacy, and improves channel estimation accuracy by 20%. © 2025, CC BY.

关键词： Federated learning

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