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I know I don't know:an evidential deep learning framework for traffic classification

Frontiers of Computer science 2024年第5期18卷 219-221页

作者： Shangsen LI Lailong LUO Yun ZHOU Deke GUO Xiang XU National Key Laboratory of Information Systems Engineering National University of Defense TechnologyChangsha 410000China

Traffic classification is a crucial task for network *** of the most difficult challenges is to accurately identify the traffic of unknown applications as well as discriminate the known *** current learning-based classifiers can achieve high classification accuracy for the known traffic[1,2],but are infeasible toclassifyeither the unknown/unseen/unlabeled application or zero-day application traffic[3],which is known as identification of unknown applications(IUA).Although the clustering-based methods can identify the unknown traffic,they need lots of human intervention to thefeaturechoice,hyperparameter configuration and known/unknown traffic division[4].

关键词： network traffic classify

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Beyond Performance of Learning Control Subject to Uncertainties and Noise: A Frequency-Domain Approach Applied to Wafer Stages

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 198-214页

作者： Fazhi Song Ning Cui Shuaiqi Chen Kai Zhang Yang Liu Xinkai Chen Jiubin Tan IEEE the Center of Ultra-precision Optoelectronic Instrument Engineering Harbin Institute of Technology the Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technology the State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of Technology the Center of Ultraprecision Optoelectronic Instrument Engineering Harbin Institute of Technology the Key Laboratory of UltraPrecision Intelligent Instrumentation Ministry of Industry and Information Technology the Department of Electronic and Information Systems Shibaura Institute of Technology

The increasingly stringent performance requirement in integrated circuit manufacturing, characterized by smaller feature sizes and higher productivity, necessitates the wafer stage executing a extreme motion with the accuracy in terms of nanometers. This demanding requirement witnesses a widespread application of iterative learning control(ILC), given the repetitive nature of wafer scanning. ILC enables substantial performance improvement by using past measurement data in combination with the system model knowledge. However, challenges arise in cases where the data is contaminated by the stochastic noise, or when the system model exhibits significant uncertainties, constraining the achievable performance. In response to this issue, an extended state observer(ESO) based adaptive ILC approach is proposed in the frequency *** being model-based, it utilizes only a rough system model and then compensates for the resulting model uncertainties using an ESO, thereby achieving high robustness against uncertainties with minimal modeling effort. Additionally, an adaptive learning law is developed to mitigate the limited performance in the presence of stochastic noise, yielding high convergence accuracy yet without compromising convergence speed. Simulation and experimental comparisons with existing model-based and data-driven inversion-based ILC validate the effectiveness as well as the superiority of the proposed method.

关键词： Extended state observer learning control model uncertainties motion control stochastic noise

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Flexible CZTSSe solar cells with 11.21% efficiency enabled by O-doped CZTSSe/CdS heterojunction

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Journal of Energy Chemistry 2025年第6期105卷 806-813页

作者： Yuheng Zhang Quanzhen Sun Weihao Xie Yifan Li Zhenyi Su Wen Xu Weihuang Wang Hui Deng Shuying Cheng Institute of Micro-Nano Devices and Solar Cells College of Physics and Information Engineering Fuzhou University Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Jiangsu Collaborative Innovation Center of Photovoltaic Science and Engineering

High performances of Cu2ZnSn（S,Se）4（CZTSSe） solar cells are heavily influenced by the quality of ***,an oxygen（O） doping of CZTSSe/CdS heterojunction is performed to suppress the formation of the defects by an ultraviolet ozone（UV-O3） treatment for the efficient flexible CZTSSe solar *** introduction of O reduces the non-radiative recombination and increases the carrier concentration of the CdS ***,the defect density of the CdS film has been reduced from 8.24×1016to2.91×1016cm-3by the *** results indicate that the electron transport is effectively promoted due to the decreased conduction band offset（CBO） at the heterojunction *** a result,the champion flexible CZTSSe solar cell achieves a power conversion efficiency（PCE） of 11.21%,with a significantly improved short circuit current *** study for improving the CZTSSe/CdS heterojunction through O-doping treatment provides a new insight for enhancing the PCE of the flexible CZTSSe solar cells.

关键词： Flexible CZTSSe solar cells O-doping Sulfur vacancy defects passivation UV-O3 treatment

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STOD: toward semi-supervised tiny object detection

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Neural Computing and Applications 2024年第27期36卷 17107-17123页

作者： Guo, Yanan Feng, Yuxin Du, Kangning Cao, Lin Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100101 China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100101 China

Semi-supervised object detection aims to enhance object detectors by utilizing a large number of unlabeled images, which has gained increasing attention in natural scenes. However, when these methods are directly applied to scenes with tiny objects, they face the challenge of selecting pseudo-labels with high localization quality due to the minuscule and blurred characteristics of these objects. To address this issue, we propose a novel method called semi-supervised tiny object detection (STOD). Firstly, to enhance the localization accuracy of pseudo-labels, we design a dense IoU-aware head that evaluates the quality of bounding box localization by incorporating additional predicted overlap values. Secondly, to mine more potential pseudo-labels, we propose a GMM-based multi-threshold pseudo-labels mining module that dynamically generates multiple thresholds using classification scores and overlap values to classify bounding boxes into strong positive and weak positive pseudo-labels. Lastly, we design the localization-aware weighting loss to incorporate the localization quality of both positive and negative samples in order to enhance the accuracy of pseudo-label localization. The experimental results show that STOD achieves comparable performance when compared to both fully and semi-supervised methods. Notably, on the VisDrone-Partial benchmark, STOD achieves outstanding results by outperforming our baseline model with improvements of 3.1 mAP, 5.7 AP50, and 3.0 AP75. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Object detection

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Data-driven output regulation control for constrained linear systems

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science China(information sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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A 3D MCAM architecture based on flash memory enabling binary neural network computing for edge AI

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science China(information sciences) 2024年第12期67卷 302-310页

作者： Maoying BAI Shuhao WU Hai WANG Hua WANG Yang FENG Yueran QI Chengcheng WANG Zheng CHAI Tai MIN Jixuan WU Xuepeng ZHAN Jiezhi CHEN School of Information Science and Engineering Shandong University Center for Spintronic and Quantum Systems State Key Laboratory for Mechanical Behavior of MaterialsSchool of Materials Science and Engineering Xi'an Jiaotong University

The in-memory computing(IMC) architecture implemented by non-volatile memory units shows great possibilities to break the traditional von Neumann bottleneck. In this paper, a 3D IMC architecture is proposed whose unit is based on a multi-bit content-addressable memory(MCAM). The MCAM unit is comprised of two 65 nm flash memory and two transistors(2Flash2T), which is reconfigurable and multifunctional for both data write/search and XNOR logic operation. Moreover, the MCAM array can also support the population count(POPCOUNT) operation, which can be beneficial for the training and inference process in binary neural network(BNN) computing. Based on the well-known MNIST dataset, the proposed 3D MCAM architecture shows a 98.63% recognition accuracy and a 300% noise-tolerant performance without significant accuracy deterioration. Our findings can provide the potential for developing highly energy-efficient BNN computing for complex artificial intelligence(AI) tasks based on flash-based MCAM units.

关键词： reconfigurable multifunction multi-bit content-addressable memory (MCAM) bitwise operation binary neural network edge AI flash memory in-memory computing (IMC)

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Observation of memristive behavior in PDMS-glass nanofluidic chip

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Nano Research 2025年第2期18卷 725-732页

作者： Weiling Sun Yike Xiao Pingyuan Yan Fei Sun Xuan Zhang Chuanxiang Sheng Qi Wang Yefeng Yu School of Microelectronics Nanjing University of Science and TechnologyNanjing 210094China Key Laboratory of New Membrane Materials Ministry of Industry and Information TechnologySchool of Environmental and Biological EngineeringNanjing University of Science and TechnologyNanjing 210094China Department of Optical Science and Engineering School of Information Science and TechnologyFudan UniversityShanghai 200433China

Nanofluidic memristors,which use ions in electrolyte solutions as carriers,have been developed rapidly and brought new opportunities for the development of neuromorphic *** the transport and accumulation of ions in nanochannels to process information is an endeavor to realize the nanofluidic *** this study,we report a new nanofluidic memristor,which is a polydimethylsiloxane(PDMS)-glass chip with two platinum(Pt)electrodes and well-aligned multi-nanochannels within PDMS for ion enrichment and *** device not only exhibits typical bipolar memristive behavior and ion current rectification(ICR)but also demonstrates excellent endurance,maintaining stable performance after 100 sweep *** systematically investigate the key factors affecting ion transport behavior in this *** results show that the ICR ratio of the current-voltage(I-V)hysteresis curves decreases with increasing scan rate and solution *** potential measurements are introduced to reveal that the PDMS surface carries more negative charges in higher pH solutions,resulting in more pronounced memristive and ICR ***,our memristor can simulate short-term synaptic plasticity,such as paired-pulse facilitation(PPF)and paired-pulse depression(PPD),with a relatively low energy consumption of 12 pJ per spike per ***,the inherent accessibility and robustness of our nanofluidic memristors facilitate the optimization of device structure and *** important observations and investigations lay a foundation for advancing energy-saving and efficient neuromorphic computing.

关键词： polydimethylsiloxane(PDMS)-glass chip nanofluidic memristor nanochannel short-term synaptic plasticity(STP)

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Boosting for Distributed Online Convex Optimization

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Tsinghua science and technology 2023年第4期28卷 811-821页

作者： Yuhan Hu Yawei Zhao Lailong Luo Deke Guo Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410073China Medical Engineering Laboratory of Chinese PLA General Hospital and also with the School of Cyberspace SecurityDongguan University of TechnologyDongguan 523000China

Decentralized Online Learning(DOL)extends online learning to the domain of distributed ***,limitations of local data in decentralized settings lead to a decrease in the accuracy of decisions or models compared to centralized *** the increasing requirement to achieve a high-precision model or decision with distributed data resources in a network,applying ensemble methods is attempted to achieve a superior model or decision with only transferring gradients or models.A new boosting method,namely Boosting for Distributed Online Convex Optimization(BD-OCO),is designed to realize the application of boosting in distributed ***-OCO achieves the regret upper bound O(M+N/MNT)where M measures the size of the distributed network and N is the number of Weak Learners(WLs)in each *** core idea of BD-OCO is to apply the local model to train a strong global ***-OCO is evaluated on the basis of eight different real-world *** results show that BD-OCO achieves excellent performance in accuracy and convergence,and is robust to the size of the distributed network.

关键词： distributed Online Convex Optimization(OCO) online boosting Online Gradient Boosting(OGB)

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BayProNet: Advancing Few-Shot Learning with Bayesian Prototype Network 24

BayProNet: Advancing Few-Shot Learning with Bayesian Prototy...

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8th International Conference on Computer science and Artificial Intelligence, CSAI 2024

作者： Gao, Cheng Lang Miao, Jiang Hui, Chen Hong Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology HuNan ChangSha China

ISBN: (纸本)9798400718182

Text classification is a challenging task in the field of Natural Language Processing (NLP), and significant progress has been made using deep learning methods. Traditional deep-learning approaches for text classification require large amounts of data, thereby performing suboptimally in few-shot data scenarios. To alleviate this bottleneck, this study proposes a Bayesian Prototypical Network with Hybrid Attention (BayProNet) to improve the accuracy of few-shot text classification. Experimental results on the Clinc150 dataset show that the model significantly outperforms existing SOTA baseline models in terms of classification accuracy and also exhibits strong stability when handling noisy data. This research validates the effectiveness and superiority of Bayesian Prototypical Networks in few-shot text classification tasks. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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Time-Varying Formation Tracking Control of Heterogeneous Multi-Agent systems With Intermittent Communications and Directed Switching Networks

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 294-296页

作者： Yuhan Wang Zhuping Wang Hao Zhang Huaicheng Yan the Shanghai Research Institute for Intelligent Autonomous Systems Tongji University the National Key Laboratory of Autonomous Intelligent Unmanned Systems the Frontiers Science Center for Intelligent Autonomous System of Ministry of Education the College of Electronic and Information Engineering the Department of Control Science and Engineering Tongji University the Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education the School of Information Science and Engineering East China University of Science and Technology

Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so... 详细信息

关键词： Switches Topology Observers Formation control Event detection Switched systems Multi-agent systems Control theory Autonomous systems Stability

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