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第三十二届中国控制会议

作者： LIU Xiwei CHENG Jiujun Department of Computer Science and Technology Tongji Universityand the Key Laboratory of Embedded System and Service ComputingMinistry of Education

ISBN: (纸本)9781479900305

In this paper,the cluster synchronization problem for nonlinearly coupled networks under periodically intermittent pinning control is *** first,a sufficient condition to guarantee cluster synchronization is ***,an adaptive intermittent control algorithm is designed to the control strength and its validity is rigorously ***,some numerical examples are presented to demonstrate the correctness of obtained theoretical results.

关键词： Cluster synchronization Adaptive Nonlinearly coupled Periodically Intermittent pinning.

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Feedback-based Coarse Time-Granularity POI Recommendation and Itinerary Planning

Feedback-based Coarse Time-Granularity POI Recommendation an...

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2021 China Automation Congress, CAC 2021

作者： Zong, Jie Ding, Zhijun The Key Laboratory of Embedded System and Service Computing Ministry of Education TONGJI University Shanghai China

ISBN: (纸本)9781665426473

For tourists, planning their own travel itinerary to a strange city is really challenging. Although there are many researches on Point of Interests (POIs) recommendation and itinerary planning, two problems occur in currently proposed recommendation algorithms. First, the time granularity of the algorithms is too fine. POI recommendation should be considered within a more coarse-grained time factor. For example, a visitor wouldn't like to visit a water park in winter, although he appears to love such places. However, seldom researches have taken this into consideration. Second, most of the algorithms build their models based on the tourists' trajectory, which means, they did not consider users' feedback. In fact, quite a number of tourists do not like their travel experience and give negative feedbacks after their visits. In this situation, we shouldn't recommend similar POIs to this user because he may not like such kind of scenic spots at all. To address these two problems, this paper proposes an algorithm called Feedback-based Coarse Time-Granularity POI Recommendation, which can extract information from comments and distinguish tourists' attitudes towards the POIs they have visited. At the same time, the coarse-grained time factor is considered while making recommendations. In addition, an itinerary planning algorithm is proposed. Experiments show that our algorithm can reach SOTA. © 2021 IEEE

关键词： Parks

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CONSISTENT REPRESENTATION LEARNING ACROSS MODALITIES FOR ZERO-SHOT IMAGE RECOGNITION

CONSISTENT REPRESENTATION LEARNING ACROSS MODALITIES FOR ZER...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Wang, Yu Zhao, Sev-Jue Key Laboratory of Embedded System and Service Computing Ministry of Education China HaiBa Technology China

ISBN: (纸本)9781665438643

Zero-shot learning (ZSL) recently has drawn widespread attention due to the demand for scalability of object recognition in real scenes. Existing approaches typically focus on directly learning various mapping functions from the visual space to the semantic space or vice versa. However, these methods fail to explicitly capture consistent representations reflecting the nature of different modalities of the same object, which results in the deterioration of the domain shift problem in the ZSL context during the testing stage. To this end, a consistent representation learning mechanism across visual modalities and the associated semantic modalities based on common subspace learning is proposed in this paper. We further impose an orthogonal constraint on the subspace for informative representations, as well as 21-norm regularization terms on projection matrices for automatic feature selection. Finally, an iterative process based on the ALM algorithm with an alternating direction strategy is displayed to resolve the proposed formulation. Extensive experimental results on four popular datasets demonstrate that our algorithm is promising. © 2021 IEEE

关键词： Zero-shot learning

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STMGT: A Long-Term Traffic Forecasting Method Based on Spatial-Temporal Multi-View Graph Transformer 24

STMGT: A Long-Term Traffic Forecasting Method Based on Spati...

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5th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Zheng, Fengkai The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9798400718007

Accurate long-term traffic forecasting is crucial for urban traffic management and planning. Existing methods focus mainly on short-term predictions and struggle with long-term spatial-temporal dependencies due to computational complexities. We propose a Spatial-Temporal Multi-View Graph Transformer (STMGT) based on spatial-temporal sequence decomposition. STMGT enhances long-term forecasting by decomposing traffic sequences into temporal position embeddings, periodicity-trend features, and residual components. This approach captures long-term correlations while managing computational cost by dividing data into multiple time blocks. A multi-head graph attention mechanism is used to explore dynamic spatial correlations. Experimental results on real-world datasets show that STMGT significantly outperforms baseline models, proving it effective for long-term traffic prediction. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： graph attention mechanism

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Observable liveness of Petri nets with controllable and observable transitions

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Science China(Information Sciences) 2017年第11期60卷 256-264页

作者： Guanjun LIU Changjun JIANG Department of Computer Science Tongji University Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University E-commerce Transactions and Information Services Collaborative Innovation Center of Shanghai University Knowledge Service Platform Tongji University

Observability and/or controllability are paid much attention to by researchers not only from the control science field[1–3]but also from the computer science field[4–9].Recently,Desel and Kilinc[4]proposed the notion of observable liveness for Petri nets with(un-)observable and(un-)controllable *** purpose of defining observable liveness is to represent that"a user can always enforce the occurrence of any observable transition only by stimulating the net by choosing appropriate enabled controllable transition"[4].An important assumption of

关键词： net cot PN Observable liveness of Petri nets with controllable and observable transitions

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Edge-weight-Based link prediction in heterogeneous graph 19

Edge-weight-Based link prediction in heterogeneous graph

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Zong, Jie Ding, Zhijun The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9781665472432

Link prediction is to predict whether there is a link between two nodes in the graph, it is a very important application and plays a great role in various industries. In recent years, with the development of graph neural network technology, many algorithms make effort to study the expression of each node from the original graph data and use them to infer new links. However, most of the existing algorithms have a common problem when facing heterogeneous graphs, which is, they do not consider the weight of edges in graphs. Instead, they put all their energies into computing node features. Although a few algorithms such as RGCN are trying to take the influence of different link types into account while extracting node features, these implicit feature extractions do not start from the global information, but just calculate independently for each node. In other words, in these algorithms, even the same type of links will be abstracted into different features on different nodes. This is obviously inconsistent with reality. On the same map, the feature of the same link should be relatively fixed and should not be changed just because of different positions. In addition, when the current graph neural network algorithm is applied to link prediction, the link type to be predicted must be specified in advance, which makes the algorithm extremely inflexible. In order to solve these problems, we propose an edge weight calculation algorithm that extracts the edge feature from the whole graph. We also propose the edge-weight-based link prediction algorithm. By introducing edge weight into the MLP, there is no need to specify the target link type at the beginning of model training. It improves both the performance and efficiency of the link prediction model. Experiments on two datasets show that this edge-weight-based link prediction algorithm performs better than current algorithms and reaches SOTA. © 2022 IEEE.

关键词： Graph neural networks

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Will the fibers of the future be considered fibers?

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National Science Review 2024年第10期11卷 15-17页

作者： Dewen Xu Ya Liu Hailiang Wang Wei Yan Changjun Jiang Meifang Zhu State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and Engineering Donghua University Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Department of Computer Science and Technology Tongji University

Fiber materials are key materials that have changed human history and promoted the progress of human civilization. In ancient times, humans used feathers and animal skins for clothing, and later they widely employed natural fibers such as cotton, hemp, silk and wool to make fabrics(Fig. 1a). Chinese ancestors had mastered the art of natural fiber weaving as early as the Neolithic *** thousand years ago, people were already familiar with and adept at techniques for spinning natural fibers [1].

关键词： future will considered fibers

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Adaptive Random Forest with Dynamic Detectors for Evolving Data Stream Classification 23

Adaptive Random Forest with Dynamic Detectors for Evolving D...

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9th International Conference on computing and Artificial Intelligence, ICCAI 2023

作者： Jin, Xuancheng Zhang, Yaying Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9781450399029

Evolving data streams containing concept drift breaks the assumption that data are independent and identically distributed (IID) in traditional machine learning models. A series of ensemble-based models have been adapted to data stream learning tasks due to the abilities of dynamic partially update and cooperate with drift detection methods. Adaptive Random Forests (ARF), one of the state-of-the-art in the stream setting, encounters high resource requirements in both efficiency and memory like all ensemble methods. We are committed to reducing running cost without compromising ARF’s performance. In this paper, we propose the Adaptive Random Forest with Dynamic Detectors (ARFDD) which utilizes diversity measurement to combine homogeneous detectors and split heterogeneous ones accordingly, leads to lower computing costs and thus speed up in applications. Experiments on all 8 synthetic and 4 real datasets show a great CPU-time thrift while avoiding performance degradation. © 2023 Copyright held by the owner/author(s).

关键词： Learning systems

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Research on Behavior Element Decision of Cloud Edge Collaboration Capability Based on 5g Automatic Driving Scene 5

Research on Behavior Element Decision of Cloud Edge Collabor...

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5th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Yan, Chungang Sheng, Shuo Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9781665415989

In order to meet the cloud side requirements of various traffic business scenarios, the construction of traffic cloud side collaboration platform realizes the collaboration and unification of various business resources and data on both sides of the cloud side, and the cloud side integration realizes the real-time and efficient "vehicle road cloud side"collaboration of traffic. In order to quickly meet the needs of various scenarios and provide customized cloud edge capability services, by drawing on the experience and technology R D capabilities of Companies in the industry, it helps to shorten the overall development period of the platform, reduce costs and technical risks, and quickly form product capabilities. Based on the cloud edge collaboration capability, the super edge node collects structured and unstructured data in real time, uses the edge computing power to label, filter and design thresholds, so as to detect and filter the scene behavior elements in 5g + traffic scenarios, further clarify the actor's data, gather it to the cloud for research and judgment, and improve the global decision-making ability of the cloud, Improve the real-time flow of dynamic traffic element resources. © 2021 IEEE.

关键词： 5G mobile communication systems

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Genetic Fuzzy + PI Path Tracking Control of a Nonholonomic Mobile Robot

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电子学报(英文版) 2011年第1期20卷 31-34页

作者： CAO Zhengcai ZHAO Yingtao WU Qidi College of Information Science and Technology Beijing University of Chemical Technology Beijing China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

A mobile robot is one of the well-known nonholonomic systems. The genetic fuzzy + PI path tracking control applied to a mobile robot is presented. The proposed controller composes of two fuzzy + PI controllers which are employed to control the angular velocity together, and one fuzzy + PI controller that is utilized to adjust the speed of mobile robot solely. Moreover, the fuzzy controller is designed to track reference path in approaching phase and PI controller is operated to diminish the tracking error in stable phase. In order to obtain better tracking performance, Genetic algorithm (GA) is used to optimize controller parameters. Finally, computer simulation results are given to illustrate the effectiveness of the proposed control scheme.

关键词：模糊PI控制器移动机器人机器人控制非完整系统路径跟踪遗传算法模糊控制器控制器参数

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