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2024 International Conference on Generative Artificial Intelligence and information Security, GAIIS 2024

作者： Wang, Shen Cai, Fei Zhang, Mengxi National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha410073 China

ISBN: (纸本)9798400709562

Multilingual Event Causality Identification (Multilingual ECI) is the task of detecting causal relations between events mentioned in the multilingual text. The biggest challenge of this task is the small size of the minor language corpus, making it very difficult to train a model with good performance. To address this issue, we propose a meta-learning method that integrates multilingual knowledge, which mainly includes two modules: the multilingual knowledge obtain module, which alleviates data scarcity by introducing multilingual knowledge from outside;The meta-learning module utilizes ProtoMAML combined with l2 standardization to extract language independent causal features from text and learn better event representations in different languages. The experimental results on the latest publicly available MECI dataset show that our model outperforms the current best baseline, with an average F1 improvement of 3.9% in different languages. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Learning systems

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A Unified Framework for Analysis and Optimization of RIS-Assisted MIMO Multiple Access Networks

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IEEE Internet of Things Journal 2025年第13期12卷 25224-25239页

作者： Wang, Siqiang Zheng, Zhong Guo, Jing Fei, Zesong Sun, Zhi Zhang, Qin Beijing Institute of Technology School of Information and Electronics Beijing China Beijing Institute of Technology School of Information and Electronics National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China Tsinghua University Department of Electronic Engineering Beijing China

Recently, reconfigurable intelligent surfaces (RISs) are gaining increasing attention in communication systems due to their ability to adapt themself according to the wireless environment. Therefore, the communication systems with massive RIS deployments are able to extend high-quality coverage ubiquitously. This is especially beneficial for Internet of Things (IoT) systems as the IoT devices can be located in hard-to-reach area. On the other hand, current methods to analyze and optimize the performance of communication systems with multiple RISs deployment are ad hoc, which depends on the specific system configurations. There lacks a unified theoretical framework that provides general analytical treatment for both passive and active RIS-assisting systems, possibly having multiple concatenated reflections via RISs, which is typical in IoT communication scenarios. Thus, we investigate general uplink RIS-assisted multi-user multiple-input multiple-output (MU-MIMO) communication systems under general Rician fading channels, where the number of cascaded RIS panels are arbitrary and the RIS elements can be active or passive. By utilizing the linearization trick and the operator-valued free probability theory, a unified analytic expression of the ergodic sum rate for the considered MU-MIMO systems is derived. Then, we further propose a low-complexity optimization approach to design the RISs’ phase shifts, thus enhancing the ergodic sum rate. Numerical results verify the accuracy of the analytical results for RIS-assisted systems. In addition, the convergence and effectiveness of the proposed optimization algorithm are demonstrated. © 2014 IEEE.

关键词： Fading channels

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Introducing ionic mediators to construct peak-splitting manganese oxide structures for efficient oxygen reduction reactions

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Journal of Materials science & technology 2024年第34期201卷 139-148页

作者： Haoliang Ma Xiong Li Mengmeng Tian Lanlan Li Xiaofei Yu Xinghua Zhang Zunming Lu Xiaojing Yang School of Materials Science and Engineering Tianjin Key Laboratory of Materials Laminating Fabrication and Interface Control TechnologyHebei University of TechnologyTianjin 300130China School of Artificial Intelligence and Information Technology Nanjing University of Chinese MedicineNanjing 210023China

Manganese oxides have received extensive attention in Oxygen Reduction Reaction(ORR)***-ever,the poor electrical conductivity and oxygen adsorption capacity of manganese oxides limit their development,so it is necessary to prepare catalysts with highly active *** propose a method to introduce an ionically conductive medium and induce structural distortion via thermodynamics,re-sulting in novel peak-splitting highly active structures for enhanced oxygen reduction *** the aid of refined structural analysis,High-angle annular dark-field(HAADF-STEM)imaging,and theoretical calculations,it is elucidated that this peak-splitting structure results from thermodynamically induced structural distortions and atomic *** introduction of the ionically conductive medium promotes the formation of Ag-O-Mn conductive bond bridges,which regulate the energy level matching of manganese oxides and oxygen *** thermodynamically induced structural distortion ex-poses new atomic planes in the material,and the creation of this highly active surface is accompanied by a redistribution of surface charges,which modulates the adsorption of manganese active sites with oxy-gen intermediates and greatly enhances the oxygen reduction ***,this Ag-OMS-2 with a novel split structure exhibits excellent activity at 0.836 V,superior to Pt/C kinetics and good *** preparation of such highly active structures is instructive for the development of manganese oxides.

关键词： Fuel cells Electrochemistry Manganese Structural distortion Ionically conductive medium

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Single-atom Ni-N_(4) for enhanced electrochemical sensing

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Nano Research 2024年第8期17卷 7658-7664页

作者： Zhuhui Qin Bo Tang Guiru Zhang Chongqing Zhu Kun Jiang Bowei Zhang Fu-Zhen Xuan Shanghai Key Laboratory of Intelligent Sensing and Detection Technology East China University of Science and TechnologyShanghai 200237China Key Laboratory of Pressure Systems and Safety of Ministry of Education East China University of Science and TechnologyShanghai 200237China School of Mechanical and Power Engineering East China University of Science and TechnologyShanghai 200237China Key Laboratory of Theoretical and Computational Photochemistry Ministry of EducationCollege of ChemistryBeijing Normal UniversityBeijing 100875China Interdisciplinary Research Center School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

Single-atom catalysts(SACs)attract widespread attention in heterogeneous catalysis due to their maximum atomic utilization efficiency and unique physical and chemical ***,their applications in chemical sensing keep huge potential but remain ***,a Ni-N_(4)-C SAC was synthesized for the trace detection of dopamine(DA)and uric acid(UA).The Ni-N_(4)-C SAC exhibited superior sensing performance compared to the Ni *** detection range for DA and UA were 0.05–75μM and 5–90μM with detection limits of 0.027 and 0.82μM,*** functional theory(DFT)computations indicate that Ni-N_(4)-C has a lower reaction barrier during electrochemical process,indicating that the atomic Ni sites possess higher intrinsic activity than Ni ***,DA and UA show strong potential dependency on the Ni-N_(4)-C catalyst,indicating its applicability for their concurrent *** work extends the application of SACs in chemical sensing.

关键词： single-atom catalysts chemical sensing dopamine uric acid

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AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

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Computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Versatile Bipedal Locomotion and Walking-Running Transition: Coordinating Supervised Learning and Nonlinear Optimization

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IEEE Transactions on Automation science and engineering 2025年 22卷 16224-16236页

作者： Chen, Huanzhong Huang, Gao Chen, Xuechao Yu, Zhangguo Dong, Chencheng Li, Qingqing Huang, Qiang Beijing Institute of Technology School of Mechantronical Engineering Beijing100081 China Beijing University of Technology Faculty of Information Technology Beijing100124 China Ministry of Education Key Laboratory of Biomimetic Robots and Systems Beijing100081 China

Online gait planning plays a crucial role for the locomotion of humanoid robots. While simplified models often fail to capture critical dynamic features of the robot’s motion, making online gait modifications with constrained nonlinear optimization in complex models is highly challenging with current computational power. This paper introduces a gait planning method that leverages supervised learning to expedite the gait planning and optimization process. Building upon our previous work, this paper extends a three-body model to the three-dimensional (3D) case. This model incorporates the angular momentum and height variation of body as well as the influence of leg motions, thus facilitating the generation of omnidirectional walking and running patterns. Due to the complexity of the model, an online gait planning modification is impractical. Therefore, supervisedlearning is employed to train a policy derived from the modelbased gait planning approach. This policy is then implemented online to produce versatile locomotion. Furthermore, the gradient of the trained neural network is utilized for nonlinear optimization of gait parameters, significantly improving the robot’s balance against external perturbations. The effectiveness of the proposed method is validated through a series of experiments conducted in simulation and on the real robot BHR-T, confirming its capability to generate adaptive walking and running motions in response to varying demands and disturbances. © 2004-2012 IEEE.

关键词： Supervised learning

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Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

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Digital Communications and Networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis Intelligent prediction Improved CNN

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Optimized online filter stack spectrometer for ultrashort X-ray pulses

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Nuclear science and Techniques 2024年第5期35卷 85-98页

作者： Jia-Xing Wen Ge Ma Ming-Hai Yu Yu-Chi Wu Yong-Hong Yan Shao-Yi Wang Huai-Zhong Gao Lu-Shan Wang Yu-Gang Zhou Qiang Li Yue Yang Fang Tan Xiao-Hui Zhang Jie Zhang Wen-Bo Mo Jing-Qin Su Wei-Min Zhou Yu-Qiu Gu Ming Zeng Zong-Qing Zhao Science and Technology on Plasma Physics Laboratory Laser Fusion Research CenterCAEPMianyang 621900China Department of Engineering Physics Tsinghua UniversityBeijing 100084China Key Laboratory of Particle and Radiation Imaging(Tsinghua University) Ministry of EducationBeijing 100084China School of Information Engineering Southwest University of Science and TechnologyMianyang 621010China

Currently,with the advent of high-repetition-rate laser-plasma experiments,the demand for online diagnosis for the X-ray spectrum is increasing because the laser-plasma-generated X-ray spectrum is very important for characterizing electron dynamics and *** this study,scintillators and silicon PIN(P-type–intrinsic-N-type semiconductor)diodes were used to construct a wideband online filter stack *** X-ray sensor and filter arrangement was optimized using a genetic algorithm to minimize the condition number of the response ***,the unfolding error was significantly reduced based on numerical *** detector responses were quantitatively calibrated by irradiating the scintillator and PIN diode with various nuclides and comparing the measuredγ-ray peaks.A prototype 15-channel spectrometer was developed by integrating an X-ray detector with front-and back-end *** prototype spectrometer could record X-ray pulse signals at a repetition rate of 1 ***,an optimized spectrometer was employed to record the real-time spectra of laser-driven bremsstrahlung *** optimized spectrometer offers a compact solution for spectrum diagnostics of ultrashort X-ray pulses,exhibiting improved accuracy in terms of spectrum measurements and repetition rates,and could be widely used in next-generation high-repetition-rate high-power laser facilities.

关键词： Filter stack spectrometer Laser plasma diagnostics X-ray diagnostics Scintillator PIN diode

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Effect of radical scavenger on electrical tree in cross-linked polyethylene with large harmonic superimposed DC voltage

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High Voltage 2023年第4期8卷 739-748页

作者： Lewei Zhu Zhonglei Li Kai Hou Maritime College Tianjin Key Laboratory for Control Theory and Applications in Complicated Industry SystemsTianjin University of TechnologyTianjinChina Key Laboratory of Smart Grid of Ministry of Education School of Electrical and Information EngineeringTianjin UniversityTianjinChina

In this study,three kinds of radical scavenger Chimassorb 944,Tinuvin 622,and Tinuvin 770 are used to suppress the growth of electrical trees in cross-linked polyethylene(XLPE)with transient superimposed voltage under the temperature *** tree morphology,tree length,accumulated damage,and time to breakdown are used to investigate the effect of radical scavenger on the electrical treeing *** is found that under the temperature gradient caused by the temperature rise on the high voltage side,only Tinuvin 622 can always suppress the electrical tree as the temperature gradient *** the temperature gradient caused by the temperature rise on the ground side,the three radical scavengers can all suppress the electrical *** breakdown of electrical tree exhibits the strong DC polarity ***,energy levels of these three radical scavengers are calculated through quantum chemistry,and the results indicate that radical scavengers have greater electron affinity,smaller ionisation energy,and smaller energy gap than *** to the surface potential decay test results at 60℃,it is found that all three radical scavengers can introduce deep *** different performances of radical scavengers under different temperature gradients and voltages are determined by the trap distribution characteristics,the molecular structure and chemical reaction of the additives *** is concluded that Tinuvin 622 has potential for use in high voltage direct current XLPE cable application.

关键词： radical electrical harmonic

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Representation learning via an integrated autoencoder for unsupervised domain adaptation

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Frontiers of Computer science 2023年第5期17卷 75-87页

作者： Yi ZHU Xindong WU Jipeng QIANG Yunhao YUAN Yun LI School of Information Engineering Yangzhou UniversityYangzhou 225127China Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education of China) Hefei University of TechnologyHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China

The purpose of unsupervised domain adaptation is to use the knowledge of the source domain whose data distribution is different from that of the target domain for promoting the learning task in the target *** key bottleneck in unsupervised domain adaptation is how to obtain higher-level and more abstract feature representations between source and target domains which can bridge the chasm of domain ***,deep learning methods based on autoencoder have achieved sound performance in representation learning,and many dual or serial autoencoderbased methods take different characteristics of data into consideration for improving the effectiveness of unsupervised domain ***,most existing methods of autoencoders just serially connect the features generated by different autoencoders,which pose challenges for the discriminative representation learning and fail to find the real cross-domain *** address this problem,we propose a novel representation learning method based on an integrated autoencoders for unsupervised domain adaptation,called *** capture the inter-and inner-domain features of the raw data,two different autoencoders,which are the marginalized autoencoder with maximum mean discrepancy(mAE)and convolutional autoencoder(CAE)respectively,are proposed to learn different feature *** higher-level features are obtained by these two different autoencoders,a sparse autoencoder is introduced to compact these inter-and inner-domain *** addition,a whitening layer is embedded for features processed before the mAE to reduce redundant features inside a local *** results demonstrate the effectiveness of our proposed method compared with several state-of-the-art baseline methods.

关键词： unsupervised domain adaptation representation learning marginalized autoencoder convolutional autoen-coder sparse autoencoder

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