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Geometric Algebra Multi-order Graph Neural Network for Traffic Prediction

IEEE Transactions on Big Data 2024年 1-15页

作者： Zang, Di Cui, Zhe Wang, Zengqiang Lei, Juntao Ding, Yongjie Wei, Chenguang Zhang, Junqi Department of Computer Science and Technology and the Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Accurate traffic prediction is crucial for urban traffic management. Spatial-temporal graph neural networks, which combine graph neural networks with time series processing, have been extensively employed in traffic prediction. However, traditional graph neural networks only capture pairwise spatial relationships between road network nodes, neglecting high-order interactions among multiple nodes. Meanwhile, most work for extracting temporal dependencies suffers from implicit modeling and overlooks the internal and external dependencies of time series. To address these challenges, we propose a Geometric Algebraic Multi-order Graph Neural Network (GA-MGNN). Specifically, in the temporal dimension, we design a convolution kernel based on the rotation matrix of geometric algebra, which not only learns internal dependencies between different time steps in time series but also external dependencies between time series and convolution kernels. In the spatial dimension, we construct a tokenized hypergraph and integrate dynamic graph convolution with attention hypergraph convolution to comprehensively capture multi-order spatial dependencies. Additionally, we design a segmented loss function based on traffic periodic information to further improve prediction accuracy. Extensive experiments on seven real-world datasets demonstrate that GAMGNN outperforms state-of-the-art baselines IEEE

关键词： Graph neural networks

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Locally differentially private high-dimensional data synthesis

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Science China(Information Sciences) 2023年第1期66卷 25-42页

作者： Xue CHEN Cheng WANG Qing YANG Teng HU Changjun JIANG Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University

In local differential privacy(LDP), a challenging problem is the ability to generate highdimensional data while efficiently capturing the correlation between attributes in a dataset. Existing solutions for low-dimensional data synthesis, which partition the privacy budget among all attributes, cease to be effective in high-dimensional scenarios due to the large-scale noise and communication cost caused by the high dimension. In fact, the high-dimensional characteristics not only bring challenges but also make it possible to apply some technologies to break this bottleneck. This paper presents Sam Priv Syn for high-dimensional data synthesis under LDP, which is composed of a marginal sampling module and a data generation *** marginal sampling module is used to sample from the original data to obtain two-way marginals. The sampling process is based on mutual information, which is updated iteratively to retain, as much as possible,the correlation between attributes. The data generation module is used to reconstruct the synthetic dataset from the sampled two-way marginals. Furthermore, this study conducted comparison experiments on the real-world datasets to demonstrate the effectiveness and efficiency of the proposed method, with results proving that Sam PrivSyn can not only protect privacy but also retain the correlation information between the attributes.

关键词： local differential privacy high-dimensional data synthesis sampling data privacy privacy-preserving protocols

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A QUANTUM MULTI-AGENT BASED NEURAL NETWORK MODEL FOR FAILURE PREDICTION

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Journal of systems Science and systems Engineering 2016年第2期25卷 210-228页

作者： Wei Wu Min Liu Qing Liu Weiming Shen School of Electronic and Information Engineering Tongji University Shanghai 201804 China The Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University Shanghai 200092 China

An effective prognostic program is crucial to the predictive maintenance of complex equipment since it can improve productivity, prolong equipment life, and enhance system safety. This paper proposes a novel technique for accurate failure prognosis based on back propagation neural network and quantum multi-agent algorithm. Inspired by the extensive research of quantum computing theory and multi-agent systems, the technique employs a quantum multi-agent strategy, with the main characteristics of quantum agent representation and several operations including fitness evaluation, cooperation, crossover and mutation, for parameters optimization of neural network to avoid the deficiencies such as slow convergence and liability of getting stuck to local minima. To validate the feasibility of the proposed approach, several numerical approximation experiments were firstly designed, after which real vibrational data of bearings from the laboratory of Cincinnati University were analyzed and used to assess the health condition for a given future point. The results were rather encouraging and indicated that the presented forecasting method has the potential to be utilized as an estimation tool for failure prediction in industrial machinery.

关键词： Failure prediction complex equipment quantum-inspired multi-agent algorithm back propagation neural network

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A Novel Neighborhood-Weighted Sampling Method for Imbalanced Datasets

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Chinese Journal of Electronics 2022年第5期31卷 969-979页

作者： GUANG Mingjian YAN Chungang LIU Guanjun WANG Junli JIANG Changjun Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University

The weighted sampling methods based on k-nearest neighbors have been demonstrated to be effective in solving the class imbalance problem. However,they usually ignore the positional relationship between a sample and the heterogeneous samples in its neighborhood when calculating sample weight. This paper proposes a novel neighborhood-weighted based sampling method named NWBBagging to improve the Bagging algorithm's performance on imbalanced datasets. It considers the positional relationship between the center sample and the heterogeneous samples in its neighborhood when identifying critical samples. And a parameter reduction method is proposed and combined into the ensemble learning framework, which reduces the parameters and increases the classifier's diversity. We compare NWBBagging with some state-of-the-art ensemble learning algorithms on 34 imbalanced datasets, and the result shows that NWBBagging achieves better performance.

关键词： parameter reduction method neighborhood-weighted sampling method pattern classification class imbalance problem NWBBagging k-nearest neighbors sampling methods imbalanced datasets nearest neighbour methods ensemble learning algorithm Bagging algorithm

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The influence of limited visual sensing on the Reynolds flocking algorithm 3

The influence of limited visual sensing on the Reynolds floc...

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3rd IEEE International Conference on Robotic computing (IRC)

作者： Soria, Enrica Schiano, Fabrizio Floreano, Dario Department of Computer Science and Technology the Key Laboratory of Embedded System Service Computing Shanghai China

ISBN: (纸本)9781538692455

The interest in multi-drone systems flourished in the last decade and their application is promising in many fields. We believe that in order to make drone swarms flying smoothly and reliably in real-world scenarios we need a first intermediate step which consists in the analysis of the effects of limited sensing on the behavior of the swarm. In nature, the central sensor modality often used for achieving flocking is vision. In this work, we study how the reduction in the field of view and the orientation of the visual sensors affect the performance of the Reynolds flocking algorithm used to control the swarm. To quantify the impact of limited visual sensing, we introduce different metrics such as (i) order, (ii) safety, (iii) union and (iv) connectivity. As Nature suggests, our results confirm that lateral vision is essential for coordinating the movements of the individuals. Moreover, the analysis we provide will simplify the tuning of the Reynolds flocking algorithm which is crucial for real-world deployment and, especially for aerial swarms, it depends on the envisioned application. We achieve the results presented in this paper through extensive Monte-Carlo simulations and integrate them with the use of genetic algorithm optimization.

关键词： Robot sensing systems Visualization Animals Nickel Azimuth

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A Reduced Reachability Tree for a Class of Unbounded Petri Nets

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IEEE/CAA Journal of Automatica Sinica 2015年第4期2卷 345-352页

作者： Shouguang Wang Mengdi Gan Mengchu Zhou Dan You School of Information and Electronic Engineering Zhejiang Gongshang University State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Ministry of Education(MoE)Key Laboratory of Embedded System and Service Computing Tongji University MoE Key Laboratory of Embedded System and Service Computing Tongji University Department of Electrical and Computer Engineering New Jersey Institute of Technology

As a powerful analysis tool of Petri nets, reachability trees are fundamental for systematically investigating many characteristics such as boundedness, liveness and reversibility. This work proposes a method to generate a reachability tree, called ωRT for short, for a class of unbounded generalized nets called ω-independent nets based on new modified reachability trees (NMRTs). ωRT can effectively decrease the number of nodes by removing duplicate and ω-duplicate nodes in the tree, and verify properties such as reachability, liveness and deadlocks. Two examples are provided to show its superiority over NMRTs in terms of tree size. © 2014 Chinese Association of Automation.

关键词： Petri nets

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Counting pedeatrian in crowded subway scene

Counting pedeatrian in crowded subway scene

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2009 2nd International Congress on Image and Signal Processing, CISP'09

作者： Keju, Zu Fuqiang, Liu Zhipeng, Li Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9781424441310

When the high occlusion occurs in crowded scene, face detection is a better substitute for detecting pedestrian. In this paper, we present a novel crowd analysis method based on discriminative descriptor of faces and support vector machine SVM ensemble. Through manipulating the input features in the same sample set, the different input features of faces are extracted to train two SVM classifiers. The classification scores of two generated classifiers are combined adaptively to make a collective decision. The first SVM, as the principal classifier gives out most of face hypotheses, while the second SVM serves as secondary one to rejecting the false positive. We present experiment to test the proposed method in crowded subway video, and the result shows that the SVM ensemble outperforms the single SVM in counting the pedestrian. ©2009 IEEE.

关键词： Face recognition

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Real-time stereovision approach of object detection for driving assistance

Real-time stereovision approach of object detection for driv...

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2009 2nd International Congress on Image and Signal Processing, CISP'09

作者： Chao, Xu Fuqiang, Liu Zhipeng, Li Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9781424441310

This paper will present an obstacle detection approach that relies on the three-dimensional information from stereo vision. Concerning real-time response of the system and high accuracy of the reconstructed points, instead of the traditional stereo matching, a new way that combines three-dimensional features with wavelet-based hierarchical technique is proposed to decrease the computational cost and maintain its credibility. The matched points are selected for grouping into objects, based on neighborhood criteria, depth distance and the point density. Our system is able to reliably detect the far distance moving objects and function in real time. ©2009 IEEE.

关键词： Stereo vision

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A Behavioral Authentication Method for Mobile Based on Browsing Behaviors

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IEEE/CAA Journal of Automatica Sinica 2020年第6期7卷 1528-1541页

作者： Dongxiang Chen Zhijun Ding Chungang Yan Mimi Wang Department of Computer Science and Technology Tongji UniversityShanghai 201804China Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji UniversityShanghai 201804China IEEE

The passwords for unlocking the mobile devices are relatively simple,easier to be stolen,which causes serious potential security *** important research direction of identity authentication is to establish user behavior models to authenticate *** this paper,a mobile terminal APP browsing behavioral authentication system architecture which synthesizes multiple factors is *** architecture is suitable for users using the mobile terminal APP in the daily *** architecture includes data acquisition,data processing,feature extraction,and sub model *** can use this architecture for continuous authentication when the user uses APP at the mobile terminal.

关键词： Behavioral authentication browsing behaviors mobile device multiple factors

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An improved foreground object detection method based on Gaussian mixture models

An improved foreground object detection method based on Gaus...

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International Conference on Multimedia Communications

作者： Zhang, Xiayi Liu, Fuqiang Li, Zhipeng Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9780769541365

Statistical background subtraction has proved to be a robust and effective approach for segmenting and extracting objects without any prior information of the foreground objects. This paper presents two contributions on this topic. The first contribution of this paper proposes a novel approach which introduces the motion mask into the Gaussian Mixture Models to reduce the errors of classical GMMs, which always classifies the moving objects as background incorrectly, and affects the accuracy of the steps followed by, when the objects are still in long periods. The second contribution regards the connected component labeling based on the contour tracking algorithm. Experimental results validate the effectiveness of the proposed approach. © 2010 IEEE.

关键词： Object recognition

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