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Robust Event-Triggered Control of Nonlinear systems Under a Global Sector-Bound Condition

Guidance, Navigation and Control 2022年第2期2卷 59-79页

作者： Pengpeng Zhang Tengfei Liu Zhong-Ping Jiang Department of Control Science&Engineering Frontiers Science Center for Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819P.R.China Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

This paper studies an event-tniggered control problem for nonlinear systems subject to both external disturbancoes and dy namic *** is assumed that the system satisfies a global sector bound *** avold infnitely fast samplng,a novel eventriggred sampling mechanism is propoeed,which use8 not only the measuned system state but also an estimation of the inluence of the *** the propoeed design,the intersampling intervals an be lower bounded by a poeitive constant,and it is independent of botb external disturbances and dynamie ***,the doeedl loop event-tniggered system i proved to be input-torstate stable with repect to the extemal *** smalgain techmigues are;used for the stability analysis of the dloeeil-bop system.

关键词： Event-triggered control external disturbance dynamic uncertainty input-to-state stability

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Fact-checking based fake news detection: a review

arXiv

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arXiv 2024年

作者： Yang, Yuzhou Zhou, Yangming Ying, Qichao Qian, Zhenxing Zeng, Dan Liu, Liang School of Computer Science Fudan University Shanghai200433 China Key Laboratory of Culture & Tourism Intelligent Computing of Ministry of Culture & Tourism Fudan University Shanghai200433 China School of Communication & Information Engineering Shanghai University Shanghai200444 China Technical Application Department of Audiovisual New Media Center China Media Group Beijing100026 China

There are many groundbreaking methods in the field of fake news detection based on deep learning that can automatically detect fake news through feature extraction and detection. A common methodology framework consists of extracting features from news content by pre-trained models and developing algorithms for detection. Major approaches within the scope identify fake news by learning common feature patterns in them, such as writing style, word usage, etc.. The performance of such models highly relies on large well-annotated data sets, but obtaining and annotating fake news data is laborious. Moreover, newly forged fake news often avoids utilizing the writing style of previous fake news, resulting in poor generalization ability in terms of timeliness. In recent research, fake news detection based on fact verification provides new ideas to address the above problems. Approaches within the scope verify the authenticity of the news event, matching between description and factual information, and so on, to provide more reliable and explicable detection, greatly addressing the bias of previous methods that rely on semantic and writing style features. In this paper, we sort out the research findings of fact-based fake news detection from the perspectives of tasks and problems, algorithms, datasets, and so on. First, this paper illustrates the task definition and core problems of fact-based fake news detection. Next, existing approaches are summarized and organized in terms of algorithms. Subsequently, classic and newly published datasets are analyzed, and extended by summarizing experimental results evaluation. Finally, this paper elaborates on the pros and cons of existing approaches from an overall perspective, pointing out some substantial challenges and expectations of this field. It is expected to provide references for future works in the field of fake news detection. © 2024, CC BY.

关键词： Semantics

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Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

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Security and Safety 2022年第1期1卷 121-141页

作者： Zhong Li Xianke Wu Changjun Jiang College of Information Science and Technology Donghua University Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science Tongji University Shanghai Network Financial Security Collaborative Innovation Center Tongji University

Nowadays, large numbers of smart sensors(e.g., road-side cameras) which communicate with nearby base stations could launch distributed denial of services(DDoS) attack storms in intelligent transportation systems. DDoS attacks disable the services provided by base stations. Thus in this paper, considering the uneven communication traffic flows and privacy preserving, we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the future. However, in the federated learning,we need to consider the problem of poisoning attacks due to malicious participants. The poisoning attacks will lead to the intelligent transportation systems paralysis without security protection. Traditional poisoning attacks mainly apply to the classification model with labeled data. In this paper, we propose a reinforcement learning-based poisoning method specifically for poisoning the prediction model with unlabeled data. Besides, previous related defense strategies rely on validation datasets with labeled data in the server. However, it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving, and our datasets are also unlabeled. Furthermore, we give a validation dataset-free defense strategy based on Dempster–Shafer(D–S) evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS prediction. In our experiments, we simulate 3000 points in combination with DARPA2000 dataset to carry out evaluations. The results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short time. Meanwhile, we compare our proposed defense algorithm with three popularly used defense algorithms. The results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability.

关键词： Poisoning attacks Defenses Multi-step DDoS prediction Unlabeled data Intelligent transportation systems

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Predicting Ejection Fraction from Electrocardiogram Signals using a Multi-task Learning Model

Predicting Ejection Fraction from Electrocardiogram Signals ...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Gaoyan Zhong Yueyi Wang Sen Liu Xintao Deng Aiguo Wang Cuiwei Yang Center for Biomedical Enginering School of Information Science and Technology Fudan Shanghai China Department of Cardiology Xinghua City People’s Hospital Jiangsu China Center for Biomedical Enginering School of Information Science and Technology Fudan The Key Laboratory of Medical Imaging Computing and Computer Assisted Intercention of Shanghai Shanghai China

The aim of this study was to investigate the feasibility and effectiveness of using electrocardiogram (ECG) signals to predict ejection fraction (EF) in an extremely imbalanced dataset. We collected ECG signals from 9365 patients and conducted a correlation analysis with EF. After collecting and preprocessing the ECG signal, we developed a deep learning-based multi-task learning model designed to extract features from ECG signals and perform predictions. Our study employed a model based on Transformer, multi-scale convolutional neural networks (CNN), channel attention, and pre-trained ResNet to predict EF (EF > 50 or EF ≤ 50) and EF value. The experimental results demonstrate that the proposed model exhibits excellent predictive performance, with an AUC of 0.825 and MAE of 4.855 for predicting EF. It outperforms other models and shows better results in comparison. Our study validates the feasibility and effectiveness of using ECG signals to predict EF and provides strong support for early diagnosis and treatment of cardiovascular diseases.

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements

A Multi-Player Potential Game Approach for Sensor Network Lo...

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IEEE Conference on Decision and Control

作者： Gehui Xu Guanpu Chen Baris Fidan Yiguang Hong Hongsheng Qi Thomas Parisini Karl H. Johansson Department of Electrical and Electronic Engineering Imperial College London London UK Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Control Science and Engineering Tongji University shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies.

关键词： Location awareness Noise Games Position measurement Nash equilibrium Distance measurement Noise measurement

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TiCoSS: Tightening the Coupling between Semantic Segmentation and Stereo Matching within A Joint Learning Framework

arXiv

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arXiv 2024年

作者： Tang, Guanfeng Wu, Zhiyuan Li, Jiahang Zhong, Ping Chen, Xieyuanli Lu, Huimin Fan, Rui The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Shanghai Institute of Intelligent Science and Technology Tongji University The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The Department of Engineering King's College London LondonWC2R 2LS United Kingdom The Department of Computer Science and Technology Central South University China The College of Intelligence Science and Technology National University of Defense Technology China

Semantic segmentation and stereo matching, respectively analogous to the ventral and dorsal streams in our human brain, are two key components of autonomous driving perception systems. Addressing these two tasks with separate networks is no longer the mainstream direction in developing computer vision algorithms, particularly with the recent advances in large vision models and embodied artificial intelligence. The trend is shifting towards combining them within a joint learning framework, especially emphasizing feature sharing between the two tasks. The major contributions of this study lie in comprehensively tightening the coupling between semantic segmentation and stereo matching. Specifically, this study introduces three key novelties: (1) a tightly coupled, gated feature fusion strategy, (2) a hierarchical deep supervision strategy, and (3) a coupling tightening loss function. All these technical contributions help tightly couple semantic segmentation and stereo matching tasks, collectively resulting in TiCoSS, a state-of-the-art joint learning framework. Through extensive experiments on the KITTI and vKITTI2 datasets, along with both qualitative and quantitative analyses, we validate the effectiveness of our developed strategies and loss function. Our approach demonstrates superior performance compared to prior arts, with a notable increse in mIoU by over 9%. Our source code will be publicly available at ***/TiCoSS upon publication. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Towards an intelligent photonic system

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science China(Information sciences) 2020年第6期63卷 80-96页

作者： Weiwen ZOU Bowen MA Shaofu XU Xiuting ZOU Xingjun WANG State Key Laboratory of Advanced Optical Communication Systems and Networks Intelligent Microwave Lightwave Integration Innovation Center (iMLic) Department of Electronic EngineeringShanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University

The emerging intelligence technologies represented by deep learning have broadened their applications to various fields. Beyond the conventional electronics-based processing systems, the convergence of photonics and artificial intelligence(AI) technology enhances the performance and learning ability of AI. In this review, we propose the concept of an intelligent photonic system(IPS), illustrating it as a developing architecture with three different versions. For each version of IPS, we review several representative *** we discuss the challenges towards an IPS and provide some prospects for the future development.

关键词： intelligent photonic system deep learning artificial intelligence optical neural network neuromorphic photonics

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Hybrid Data-Free Knowledge Distillation 39

Hybrid Data-Free Knowledge Distillation

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Tang, Jialiang Chen, Shuo Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Center for Advanced Intelligence Project RIKEN Japan Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)157735897X

Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic examples, respectively. However, they inevitably become weak in practical scenarios due to the difficulties in gathering or emulating sufficient real-world data. To solve this problem, we propose a novel method called Hybrid Data-Free Distillation (HiDFD), which leverages only a small amount of collected data as well as generates sufficient examples for training student networks. Our HiDFD comprises two primary modules, i.e., the teacher-guided generation and student distillation. The teacher-guided generation module guides a Generative Adversarial Network (GAN) by the teacher network to produce high-quality synthetic examples from very few real-world collected examples. Specifically, we design a feature integration mechanism to prevent the GAN from overfitting and facilitate the reliable representation learning from the teacher network. Meanwhile, we drive a category frequency smoothing technique via the teacher network to balance the generative training of each category. In the student distillation module, we explore a data inflation strategy to properly utilize a blend of real and synthetic data to train the student network via a classifier-sharing-based feature alignment technique. Intensive experiments across multiple benchmarks demonstrate that our HiDFD can achieve state-of-the-art performance using 120 times less collected data than existing methods. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： Personnel training

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Gnn-Mgrpool: Enhanced Graph Neural Networks with Multi-Granularity Pooling for Graph Classification

SSRN

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SSRN 2023年

作者： Sun, Haichao Wang, Guoyin Liu, Qun Guo, Yike Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology 999077 Hong Kong School of Computer Engineering and Science Shanghai University Shanghai200444 China

Graph neural networks (GNNs) have gained significant attention and have been applied in various domain tasks. Currently, numerous pooling approaches have been proposed to aggregate node features and obtain node embeddings. However, current GNNs are almost black-box models that typically use a flat or single pooling step to aggregate nodes by only considering the similarity between nodes within the cluster. These approaches ignore the influence of relationships within and between clusters. To address this issue, we propose a novel multi-granular pooling method to aggregate nodes by simultaneously considering density and relationships among nodes and clusters. This method allows to obtain multi-granular node embedding clusters. The clusters in the current layer are built upon the previous layer, and these clusters change from fine to coarse as the number of clusters decreases, which is achieved by using multi-granular pooling (MgrPool for short). Additionally, the node representation of each layer is established through the ratio of node distance within clusters to that of between clusters. Finally, we conduct several experiments on node and graph classification tasks by this pooling approach. The results demonstrate that our GNN-MgrPool model outperforms the state-of-the-art similar algorithms and largely improves the interpretability of the learning process. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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A design method for high fabrication tolerance integrated optical mode multiplexer

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science China(Information sciences) 2020年第6期63卷 184-194页

作者： Bitao SHEN Haowen SHU Linjie ZHOU Xingjun WANG State Key Laboratory on Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer Science Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Nano-optoelectronics Frontier Center of Ministry of Education Peking University

The tapered asymmetric directional coupler has the potential to realize high fabrication tolerance and high transmission efficiency on-chip mode multiplexer. However, the geometry parameter selection of tapered structure remains empirical. In this paper, we propose a design method for the tapered structure based on genetic algorithm. Combined with the adjusted coupling equations and interpolation method, lowtime-cost optimization can be realized. Three mode multiplexers(TE0&TE1, TE0&TE2 and TE0&TE5)are designed by our method. According to simulation results, the insertion loss of the designed devices is lower than 1.8 dB and the crosstalk is lower than-15 dB when the fabrication error is within required range(±10 nm for TE0&TE2 and TE0&TE5, and ±20 nm for TE0&TE1) in the bandwidth of 1.5–1.6 μm. In addition, the entire optimization process takes only 2 h for each device, which is around the time cost of a single 3 D simulation.

关键词： mode multiplexer directional coupler taper structure coupled-mode theory genetic algorithm

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