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arXiv 2024年

作者： Liu, Shuhong Deng, Tianchen Zhou, Heng Li, Liuzhuozheng Wang, Hongyu Wang, Danwei Li, Mingrui Department of Information Science and Technology Department of Complexity Science and Engineering The University of Tokyo Tokyo113-8654 Japan Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Department of Mechanical Engineering Columbia University New York10027 United States School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science Dalian University of Technology Dalian116024 China

Gaussian Splatting SLAMs have made significant advancements in improving the efficiency and fidelity of real-time reconstructions. However, these systems often encounter incomplete reconstructions in complex indoor environments, characterized by substantial holes due to unobserved geometry caused by obstacles or limited view angles. To address this challenge, we present Manhattan Gaussian SLAM, an RGB-D system that leverages the Manhattan World hypothesis to enhance geometric accuracy and completeness. By seamlessly integrating fused line segments derived from structured scenes, our method ensures robust tracking in textureless indoor areas. Moreover, The extracted lines and planar surface assumption allow strategic interpolation of new Gaussians in regions of missing geometry, enabling efficient scene completion. Extensive experiments conducted on both synthetic and real-world scenes demonstrate that these advancements enable our method to achieve state-of-the-art performance, marking a substantial improvement in the capabilities of Gaussian SLAM systems. © 2024, CC BY.

关键词： Gaussian distribution

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GCN-Based Multiview User Representation Learning for Recommendation

GCN-Based Multiview User Representation Learning for Recomme...

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第32届中国过程控制会议(CPCC2021)

作者： Mingjie Cheng Jing Wu Shaoyuan Li Dan Huang Wenda Lu Lanxin Qiu Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University the Communication Division of State Grid Zhejiang Electric Power Co. Ltd

To alleviate the problems of sparsity and cold start,knowledge graph is introduced as auxiliary information to the recommendation ***,the existing methods based on graph convolutional network do not sufficiently mine the information in the knowledge graph,ignoring the relationship between users and the attributes of the items in knowledge *** this paper,we propose a MultiView User Representation learning(MVUR) method and a GCNbased recommendation model to directly mine the relationship between users and ***,we construct multiple user-attribute views to explore the user's preference for different attributes,thereby obtaining high-quality user representation and improving the recommendation *** evaluate our MVUR model on three real-world datasets and have better performance in Recall and NDCG indices,which prove that our MVUR model have a better effectiveness.

关键词： Recommendation Graph Convolutional Network Knowledge Graph Multiview

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Robust Stability and Stabilization of Commensurate Fractional Multi-Order systems with Norm-bounded Uncertainties

Robust Stability and Stabilization of Commensurate Fractiona...

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第33届中国控制与决策会议

作者： Xin-Yu Sha Jun-Guo Lu Institute of Paris-tech Shanghai Jiao Tong University Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

This paper considers robust stability and stabilization problems of commensurate fractional multi-order systems with norm-bounded *** the lowest fractional order 0 <α<1,robust sufficient stability conditions are *** depending on the accessibility of the systems' state vector and its fractional derivatives,a robust state feedback controller and a robust static output feedback controller are designed for *** results are presented in the form of linear matrix inequality to take advantages of the LMI mathematical ***,numerical examples are implemented to demonstrate the effectiveness of the proposed results.

关键词： Commensurate fractional multi-order systems linear matrix inequality norm-bounded uncertainties stability stabilization

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Structural Robustness of Complex Networks: A Survey of A Posteriori Measures

arXiv

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arXiv 2023年

作者： Lou, Yang Wang, Lin Chen, Guanrong The Graduate School of Information Science and Technology Osaka University Osaka Suita565-0871 Japan The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various industrial and social networks against malicious attacks, which has various meanings in different research contexts and here it refers to the ability of a network to sustain its functionality when a fraction of the network fail to work due to attacks. The rapid development of complex networks research indicates special interest and great concern about the network robustness, which is essential for further analyzing and optimizing network structures towards engineering applications. This comprehensive survey distills the important findings and developments of network robustness research, focusing on the a posteriori structural robustness measures for single-layer static networks. Specifically, the a posteriori robustness measures are reviewed from four perspectives: 1) network functionality, including connectivity, controllability and communication ability, as well as their extensions;2) malicious attacks, including conventional and computation-based attack strategies;3) robustness estimation methods using either analytical approximation or machine learning-based prediction;4) network robustness optimization. Based on the existing measures, a practical threshold of network destruction is introduced, with the suggestion that network robustness should be measured only before reaching the threshold of destruction. Then, a posteriori and a priori measures are compared experimentally, revealing the advantages of the a posteriori measures. Finally, prospective research directions with respect to a posteriori robustness measures are recommended. © 2023, CC BY.

关键词： Complex networks

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Topology Inference on Partially Observable Mobile Robotic Networks under Formation control

Topology Inference on Partially Observable Mobile Robotic Ne...

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European control Conference (ECC)

作者： Yushan Li Jianping He Lin Cai The Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China University of Victoria BC Canada

Mobile robotic networks (MRNs) have received considerable attention in recent years. Among their wide applications, the interaction topology plays a critical role in achieving efficient cooperation. We demonstrate that the observable interaction process of MRNs under formation control will reveal the interaction topology, presenting increasingly viable threats to the security of MRNs. To practice, we focus on a general situation where the external observer has only partial observation over the global MRN and aims to infer the interaction topology. This is a very challenging problem because the observable state evolution is a coupled consequence of the underlying interaction topology, the unknown leader dynamics, and the unobservable part. To address this issue, we novelly identify the steady pattern of the MRN and filter the impacts of the observable parts by shrinking the inference range. By constructing the informative dataset reflecting the interaction process, the local interaction topology is approximated with high accuracy. Simulations illustrate the feasibility and effectiveness of the proposed method.

关键词： Sufficient conditions Network topology Shape Process control Europe Estimation Observers

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PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators

arXiv

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arXiv 2023年

作者： Cong, Runmin Yang, Wenyu Zhang, Wei Li, Chongyi Guo, Chun-Le Huang, Qingming Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China College of Computer Science Nankai University Tianjin300350 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we propose a physical model-guided GAN model for UIE in this paper, referred to as PUGAN. The entire network is under the GAN architecture. On the one hand, we design a Parameters Estimation subnetwork (Par-subnet) to learn the parameters for physical model inversion, and use the generated color enhancement image as auxiliary information for the Two-Stream Interaction Enhancement subnetwork (TSIE-subnet). Meanwhile, we design a Degradation Quantization (DQ) module in TSIE-subnet to quantize scene degradation, thereby achieving reinforcing enhancement of key regions. On the other hand, we design the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results. Extensive experiments on three benchmark datasets demonstrate that our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics. The code and results can be found from the link of https://***/proj_***. © 2023, CC BY-NC-SA.

关键词： Discriminators

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High-Resolution representation object pose estimation from monocular images

High-Resolution representation object pose estimation from m...

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Chinese Automation Congress (CAC)

作者： Qi Guan Wenfa Li Shibei Xue Dewei Li Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665426480

This paper addresses the 6D challenge of monocular 6D pose estimation on small targets and weak texture objects. We propose a monocular object pose estimation algorithm, High Resolution Object Pose Estimation (HRPE) based on the high-resolution feature representation, which detects the 2D keypoints and uses the Perspective-n-Point (PnP) algorithm to compute the 6D pose. HRPE can maintain high-resolution feature representation through parallel convolution of multi-resolution feature branches and repeated cross-scale fusion, generating more precise pose estimation. Experimental results show that HRPE surpasses the existing state-of-the-arts, and achieves a significant accuracy improvement for small objects, which demonstrates the effectiveness of high-resolution feature representation on pose estimation.

关键词： Measurement Automation Convolution Pose estimation Feature extraction

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Abstraction-Based Verification of Approximate Pre-Opacity for control systems

arXiv

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arXiv 2022年

作者： Hou, Junyao Liu, Siyuan Yin, Xiang Zamani, Majid Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University China Department of Electrical and Computer Engineering Technical University of Munich Germany Department of Computer Science LMU Munich Germany Department of Computer Science University of Colorado Boulder United States

In this paper, we consider the problem of verifying pre-opacity for discrete-time control systems. Preopacity is an important information-flow security property that secures the intention of a system to execute some secret behaviors in the future. Existing works on pre-opacity only consider non-metric discrete systems, where it is assumed that intruders can distinguish different output behaviors precisely. However, for continuous-space control systems whose output sets are equipped with metrics (which is the case for most real-world applications), it is too restrictive to assume precise measurements from outside observers. In this paper, we first introduce a concept of approximate pre-opacity by capturing the security level of control systems with respect to the measurement precision of the intruder. Based on this new notion of pre-opacity, we propose a verification approach for continuous-space control systems by leveraging abstraction-based techniques. In particular, a new concept of approximate pre-opacity preserving simulation relation is introduced to characterize the distance between two systems in terms of preserving pre-opacity. This new system relation allows us to verify pre-opacity of complex continuous-space control systems using their finite abstractions. We also present a method to construct pre-opacity preserving finite abstractions for a class of discrete-time control systems under certain stability assumptions. Copyright © 2022, The Authors. All rights reserved.

关键词： Opacity

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Dynamic Multi-objective Optimization via Sliding Time Window and Parallel Computing 12th

Dynamic Multi-objective Optimization via Sliding Time Window...

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12th International Conference on Advances in Swarm Intelligence, ICSI 2021

作者： Fan, Qinqin Wang, Yihao Ersoy, Okan K. Li, Ning Chu, Zhenzhong Logistics Research Center Shanghai Maritime University Shanghai201306 China School of Electronic and Computer Engineering Purdue University West LafayetteIN47906 United States Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China Logistics Engineering College Shanghai Maritime University Shanghai201306 China

ISBN: (纸本)9783030788100

Tracking changing Pareto front (PF) in the objective space and Pareto set (PS) in the decision space is an important task in dynamic multi-objective optimization (DMO). Similarly, maintaining population diversity and reusing previous evolutionary information are useful to explore promising regions and to find high-quality solutions quickly in time-varying environments. To this end, a sliding time window based on parallel computing (STW-PC) is introduced in the present study. In the STW-PC, obtained time-sequence solution sets aim to preserve the diversity and facilitate a fast convergence since problems in successive time/environments are usually related. The parallel computing method is also employed to reduce the computational time. The STW-PC is incorporated into a multi-objective evolutionary algorithm and is compared with two competitors on 12 dynamic multi-objective optimization problems. The results show that the STW-PC can both improve the tracking performance of the selected algorithm in different degrees of changes, and significantly reduce the calculation time compared with transfer learning. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Tooth segmentation of dental mesh based on improved region growing

Tooth segmentation of dental mesh based on improved region g...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Gu, Chenlan Zhao, Ning Wang, Hesheng Shanghai Jiao Tong University Department of Automation Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Orthodontics Shanghai200240 China Shanghai Jiao Tong University Institute of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Department of Automation Shanghai200240 China

ISBN: (纸本)9781665436786

Computer-aided design (CAD) systems have gained more and more concern in orthodontics over the last decades. Aimed at tooth segmentation task of three dimension (3D) dental models in orthodontic CAD systems, we propose an efficient and robust framework based on geometric features. To make region growing algorithm flexible and apply it to 3D tooth segmentation, we extend the region similarity description from single-parameter to multi-parameter. A parameter adaptive method is designed to get rid of manual parameter adjustment and raise efficiency. Moreover, we provide a multi-level label optimization algorithm for segmentation refinement. Some experiments are conducted with various dental models from real cases to validate various algorithmic components. The results demonstrate that our pipeline is superior on efficiency and robustness. © 2021 IEEE.

关键词： Efficiency

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