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Trusted Encrypted Traffic Intrusion Detection Method Based on Federated Learning and Autoencoder

China Communications 2024年第8期21卷 211-235页

作者： Wang Zixuan Miao Cheng xu yuhua Li Zeyi Sun Zhixin Wang Pan School of Modern Posts Nanjing University of Posts&TelecommunicationsNanjing 210003China School of Computer Science Nanjing University of Posts&TelecommunicationsNanjing 210003China

With the rapid development of the Internet,network security and data privacy are increasingly *** classical Network Intrusion Detection System(NIDS)based on Deep Learning(DL)models can provide good detection accuracy,but collecting samples for centralized training brings the huge risk of data privacy ***,the training of supervised deep learning models requires a large number of labeled samples,which is usually ***“black-box”problem also makes the DL models of NIDS *** this paper,we propose a trusted Federated Learning(FL)Traffic IDS method called FL-TIDS to address the above-mentioned *** FL-TIDS,we design an unsupervised intrusion detection model based on autoencoders that alleviates the reliance on marked *** the same time,we use FL for model training to protect data *** addition,we design an improved SHAP interpretable method based on chi-square test to perform interpretable analysis of the trained *** conducted several experiments to evaluate the proposed *** first determine experimentally the structure and the number of neurons of the unsupervised AE ***,we evaluated the proposed method using the UNSW-NB15 and CICIDS2017 *** exper-imental results show that the unsupervised AE model has better performance than the other 7 intrusion detection models in terms of precision,recall and ***,federated learning is used to train the intrusion detection *** experimental results indicate that the model is more accurate than the local learning ***,we use an improved SHAP explainability method based on Chi-square test to analyze the *** analysis results show that the identification characteristics of the model are consistent with the attack characteristics,and the model is reliable.

关键词： autoencoder federated learning intrusion detection model interpretation unsupervised learning

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生态工业园区建设的环境管理策略研究

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黑龙江环境通报 2024年第7期37卷 120-122页

作者：徐育华深圳市巧匠环保科技有限公司广东深圳518000

生态工业园区建设的核心目标是实现工业与生态的协调发展。通过科学规划和有效管理,可以最大限度地减少环境污染和生态破坏。引入清洁生产技术、循环经济模式和环境治理措施,可有效防止和减少污染物的排放,保护自然资源和生态系统。本... 详细信息

生态工业园区建设的核心目标是实现工业与生态的协调发展。通过科学规划和有效管理,可以最大限度地减少环境污染和生态破坏。引入清洁生产技术、循环经济模式和环境治理措施,可有效防止和减少污染物的排放,保护自然资源和生态系统。本文主要介绍了生态工业园区建设的环境管理策略,希望为相关研究提供参考。

关键词：生态工业园区环境管理策略

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基于复杂网络的中国碳排放权价格风险传染研究

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工业技术经济 2024年第4期43卷 25-35页

作者：徐玉华刘梦娜王浚丞南京审计大学统计与数据科学学院南京211815 南京审计大学金融学院南京211815

明晰碳价影响因素网络变化,有助于发现中国碳市场现阶段的主要风险源及风险传染特征,对维持碳价稳定与推动经济社会绿色低碳发展有着重大的理论及实践意义。本文基于格兰杰因果检验,构建了含有20个指标的碳价影响因素风险网络。首先通... 详细信息

明晰碳价影响因素网络变化,有助于发现中国碳市场现阶段的主要风险源及风险传染特征,对维持碳价稳定与推动经济社会绿色低碳发展有着重大的理论及实践意义。本文基于格兰杰因果检验,构建了含有20个指标的碳价影响因素风险网络。首先通过网络拓扑性质,识别关键性指标,分析网络结构,然后利用k-core算法对网络进行分层分析及传播动力学分析,最后运用最小树形图筛选网络连边,分析指标间的风险传染路径。结果表明:全国碳市场运行后,国际碳价与国外经济形势处于风险网络的核心位置,有着较强的风险传染能力;网络整体风险传染能力减弱,风险传染范围变窄,传染速度变慢;网络的层次变浅,风险更易感知;利率与汇率中介作用显著减弱,而中国大庆原油现货价格有着较强的中介作用。

关键词：复杂网络碳排放权价格风险传染格兰杰因果检验网络结构统计特征

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不同粒级铜尾矿对CaO-MgO-Al_(2)O_(3)-SiO_(2)微晶玻璃析晶性能的影响

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材料科学与工程学报 2024年第3期42卷 387-393页

作者：施麟芸徐玉华匡敬忠魏琦江西理工大学资源与环境工程学院江西赣州341000 江西省建筑材料工业科学研究设计院江西南昌330001 南昌大学材料科学与工程学院江西南昌330001

采用不同粒级铜尾矿通过熔融法制备CaO-MgO-Al_(2)O_(3)-SiO_(2)(CMAS)微晶玻璃,通过差示扫描量热仪(DSC)、X射线衍射仪(XRD)和扫描电子显微镜-能谱仪(SEM-EDS)等测试方法分析了不同细度的铜尾矿对微晶玻璃析晶行为的影响,并借助JMA方... 详细信息

采用不同粒级铜尾矿通过熔融法制备CaO-MgO-Al_(2)O_(3)-SiO_(2)(CMAS)微晶玻璃,通过差示扫描量热仪(DSC)、X射线衍射仪(XRD)和扫描电子显微镜-能谱仪(SEM-EDS)等测试方法分析了不同细度的铜尾矿对微晶玻璃析晶行为的影响,并借助JMA方程拟合了不同粒级铜尾矿微晶玻璃的析晶动力学行为。研究表明,不同粒级组成的铜尾矿中含铁矿物的种类和价态影响微晶玻璃的析晶行为。铜尾矿中的铁氧化物来源于原生矿物,如菱铁矿、硫铁矿、磁铁矿等含铁矿物多为Fe^(2+),不具备成核诱导作用且促进了玻璃体和中间体的形成,而外加氧化铁为Fe^(3+),具有较好的成核诱导作用并促进了高铁辉石晶相的析出,从而提高了微晶玻璃的各项性能。

关键词：铜尾矿粒级微晶玻璃晶核剂含铁矿物析晶性能析晶动力学

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InStereo2K: a large real dataset for stereo matching in indoor scenes

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Science China(Information Sciences) 2020年第11期63卷 136-146页

作者： Wei BAO Wei WANG yuhua xu Yulan GUO Siyu HONG Xiaohu ZHANG School of Electrical Engineering and Automation Hefei University of Technology School of Electronics and Communication Engineering Sun Yat-sen University College of Electronic Science and Technology National University of Defense Technology School of Aeronautics and Astronautics Sun Yat-sen University

Deep neural networks have shown great success in stereo matching in recent years. On the KITTI datasets, most top performing methods are based on neural networks. However, on the Middlebury datasets,these methods usually do not perform well. The KITTI datasets are collected in outdoor scenes while the Middlebury datasets are collected in indoor scenes. It is commonly believed that the community still lacks a large labelled dataset for stereo matching in indoor scenes. In this paper, we introduce a new stereo dataset called InS tereo2K. It contains 2050 pairs of stereo images with highly accurate groundtruth disparity maps, including 2000 pairs for training and 50 pairs for test. Experimental results show that our dataset can significantly improve the performance of several latest networks(including StereoNet and PSMNet) on the Middlebury 2014 dataset. The large scale, high accuracy and rich diversity of the proposed InS tereo2K dataset provide new opportunities to researchers in the area of stereo matching and beyond. It also takes end-to-end stereo matching methods a step towards practical applications.

关键词： stereo matching depth estimation convolutional neural network dataset

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Leveraging partially overlapping channels for intra-and inter-coalition communication in cooperative UAV swarms

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Science China(Information Sciences) 2021年第4期64卷 68-83页

作者： Kailing YAO yuhua xu Hong LI Jin CHEN Shihua ZHANG Xingyue YU College of Communications Engineering PLA Army Engineering University Nanjing Military Representative Bureau of PLA Rocket Force

Cooperative UAV swarms typically adopt coalition-based network structures for executing tasks more efficiently. Coalition heads in such networks need to do both intra-and inter-coalition communication and may operate on different channels. While being equipped with multiple transceivers or switching among channels are alternative methods, this option would result in larger payload or incur delays. Fortunately,partially overlapping channels(POCs) can be used to forward messages on different channels since communication can be made on adjacent overlapped channels. This can help realize both intra-and inter-coalition communication with heads being equipped with only one transceiver and no switching. Therefore, this paper proposes a POC-based communication method where each coalition selects one of the POCs and UAVs in the same coalition operating on the same channel. While POCs enable information exchange among coalitions,they also incur inter-coalition interference and therefore the POC access problem is investigated. Owing to the coupled relationships among the strategies of coalitions, the problem is a combinatorial optimization one and an online learning algorithm is proposed. The algorithm is distributed and reduces the computation complexity to a great extent. Based on the knowledge of the potential game theory, the algorithm is proved to converge to the optimal solution of each stage asymptotically. Under three representative settings,simulations are made to verify the effectiveness of the proposed method.

关键词： UAV swarm coalition partially overlapping channel learning algorithm game theory

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Social welfare maximization for SRSNs using bio-inspired communitycooperation mechanism

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Chinese Science Bulletin 2012年第1期57卷 125-131页

作者： xu yuhua WU QiHui WANG JinLong YAO YuDong Institute of Communications Engineering PLA University of Science and TechnologyNanjing 210007China Department of Electrical and Computer Engineering Stevens Institute of TechnologyNew Jersey 07030USA

This paper considers social welfare maximization for spatial resource sharing networks(SRSNs),in which multiple autonomous users are spatially located and mutual influence only occurs between nearby *** cope with a lack of central control and the restriction that only local information is available,a spatial resource sharing game is ***,individual selfishness in traditional game models generally leads to inefficiency and *** by local cooperative behavior in biological sys- tems,a community cooperation mechanism(CCM)is proposed to improve the efficiency of the ***,when a user makes a decision,it maximizes the aggregate payoffs for its local community rather than selfishly consider *** is analytically shown that with the bio-inspired CCM,the social optimum of SRSNs is achieved with an exchange of local *** proposed bio-inspired CCM is very general and can be applied to various communication networks.

关键词：社会福利合作机制最大化仿生游戏模式空间资源生物系统信息交换

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A Game-Theoretic Perspective on Resource Management for Large-Scale UAV Communication Networks

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China Communications 2021年第1期18卷 70-87页

作者： Jiaxin Chen Ping Chen Qihui Wu yuhua xu Nan Qi Tao Fang Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information TechnologyCollege of Electronic and Information EngineeringNanjing University of Aeronautics and AstronauticsNanjing 211106China State Key Laboratory of Air Traffic Management System and Technology Nanjing 210014China th Research Institute of China Electronics Technology Group Corporation Nanjing 210007China College of Communications Engineering Army Engineering University of PLANanjing 210007China

As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated *** it poses numerous advantages,resource management among various domains in large-scale UAV communication networks is the key challenge to be solved ***,due to the inherent requirements and future development trend,distributed resource management is *** this article,we investigate the resource management problem for large-scale UAV communication networks from game-theoretic perspective which are exactly coincident with the distributed and autonomous *** exploring the inherent features,the distinctive challenges are ***,we explore several gametheoretic models that not only combat the challenges but also have broad application *** provide the basics of each game-theoretic model and discuss the potential applications for resource management in large-scale UAV communication ***,mean-field game,graphical game,Stackelberg game,coalition game and potential game are *** that,we propose two innovative case studies to highlight the feasibility of such novel game-theoretic ***,we give some future research directions to shed light on future opportunities and applications.

关键词： large-scale UAV communication networks resource management game-theoretic model

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Hierarchical Cognition Cycle for Cognitive Radio Networks

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China Communications 2015年第1期12卷 108-121页

作者： WANG Jinlong FENG Shuo WU Qihui ZHENG xueqiang xu yuhua College of Communications Engineering PLA University of Science and Technology

Cognitive radio(CR) can bring about remarkable improvement in spectrum *** cognition cycles have been proposed in recent ***,most of the existing works only emphasize functional or operational aspects of cognition cycle,regardless of other indispensable aspects and the connection between *** deal with the emerging situation of "data rich,information vague,knowledge poor" in cognitive radio networks(CRNs),we propose the hierarchical cognition cycle(HCC) as a new transdisciplinary research field in this *** investigates a fundamental problem,which is how to manage available resources in the complex environment to meet various demands in CRN.A comprehensive theoretical framework of HCC is established in terms of the core,the essence loop,the function loop,the operation loop,and the external loop of *** reduction of uncertainty in CRN is studied and several new metrics in HCC are ***,a few research challenges ahead are presented as well.

关键词： cognitive radio network hierarchical cognition cycle theoretical framework uncertainty research challenge

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A Heterogeneous Information Fusion Deep Reinforcement Learning for Intelligent Frequency Selection of HF Communication

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China Communications 2018年第9期15卷 73-84页

作者： Xin Liu yuhua xu Yunpeng Cheng Yangyang Li Lei Zhao Xiaobo Zhang Guangxi key Laboratory Fund of Embedded Technology and Intelligent System Guilin University of Technology College of Communication Engineering Army Engineering University of PLA

The high-frequency(HF) communication is one of essential communication methods for military and emergency application. However, the selection of communication frequency channel is always a difficult problem as the crowded spectrum, the time-varying channels, and the malicious intelligent jamming. The existing frequency hopping, automatic link establishment and some new anti-jamming technologies can not completely solve the above problems. In this article, we adopt deep reinforcement learning to solve this intractable challenge. First, the combination of the spectrum state and the channel gain state is defined as the complex environmental state, and the Markov characteristic of defined state is analyzed and proved. Then, considering that the spectrum state and channel gain state are heterogeneous information, a new deep Q network(DQN) framework is designed, which contains multiple sub-networks to process different kinds of information. Finally, aiming to improve the learning speed and efficiency, the optimization targets of corresponding sub-networks are reasonably designed, and a heterogeneous information fusion deep reinforcement learning(HIF-DRL) algorithm is designed for the specific frequency selection. Simulation results show that the proposed algorithm performs well in channel prediction, jamming avoidance and frequency channel selection.

关键词： HF communication anti-jamming intelligent frequency selection markov decision process deep reinforcement learning

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