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arXiv 2024年

作者： Sha, Yichen Zhu, Siting Guo, Hekui Wang, Zhong Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China Dimensional Robot Shanghai China

We propose a globally consistent semantic SLAM system (GCSLAM) and a semantic-fusion localization subsystem (SF-Loc), which achieves accurate semantic mapping and robust localization in complex parking lots. Visual cameras (front-view and surround-view), IMU, and wheel encoder form the input sensor configuration of our system. The first part of our work is GCSLAM. GCSLAM introduces a novel factor graph for the optimization of poses and semantic map, which incorporates innovative error terms based on multi-sensor data and BEV (bird’s-eye view) semantic information. Additionally, GCSLAM integrates a Global Slot Management module that stores and manages parking slot observations. SF-Loc is the second part of our work, which leverages the semantic map built by GCSLAM to conduct map-based localization. SF-Loc integrates registration results and odometry poses with a novel factor graph. Our system demonstrates superior performance over existing SLAM on two real-world datasets, showing excellent capabilities in robust global localization and precise semantic mapping. Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping

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PS-Loc: Robust LiDAR Localization with Prior Structural Reference

PS-Loc: Robust LiDAR Localization with Prior Structural Refe...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Rui Li Wentao Zhao Tianchen Deng Yanbo Wang Jingchuan Wang Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Ministry of Education Key Laboratory of System Control and Information Processing Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Prior structural reference like floor plan is readily accessible in indoor scene, which exhibits the potential of improving localization quality without the requirements of a previously-built high-precision map. This paper introduces a novel optimal transport-based framework for prior structural reference-based localization, aiming to improve the robustness for the robot localization. Leveraging the spacial relations of structures, a matching method based on optimal transport theory is proposed and it improves the robustness of matching results in dynamic scene and rapid rotation conditions. Additionally, this paper handles metric inaccuracies in the known structural reference by implementing an prior guided plane adjustment-based updating strategy. This strategy combines prior and observational information to jointly optimize the structural information within a sliding window. The performance of the framework is validated through real-world experiments, demonstrating superior accuracy and robustness to disturbances from dynamic occlusion and rapid rotation compared to common state-of-the-art SLAM and localization methods.

关键词： Location awareness Measurement Laser radar Simultaneous localization and mapping Accuracy Robustness Robot localization Intelligent robots Floors

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Data-Driven Distributionally Robust Electricity-Hydrogen Trading Considering The Carbon Intensity of Hydrogen Production

Data-Driven Distributionally Robust Electricity-Hydrogen Tra...

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Asian control Conference

作者： Longyan Li Chao Ning Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper proposes a data-driven distributionally robust local electricity-hydrogen market framework, where Hydrogen-based Micro-Grids (HMGs) supply hydrogen to heterogeneous Hydrogen Users (HUs) including hydrogen refueling stations and industrial plants. In this framework, the coordination between HMGs and HUs is cast as a multi-leader multi-follower Stackelberg game. Specifically, HMGs determine an integrated hydrogen-carbon price and carry out electricity trading through a non-cooperative game. Meanwhile, HUs act as followers adjusting hydrogen purchase strategies. Furthermore, the self-dispatching of HMGs and HUs are modeled as distributionally robust optimization problems considering source-load and hydrogen demand uncertainties, respectively. To hedge against these uncertainties, a novel Bayesian nonparametric hybrid ambiguity set is constructed based on local Wasserstein balls and moment information. Finally, a distributed algorithm is developed to solve the market clearing problem. Comparative studies validate that the proposed framework outperforms existing methods, demonstrating a total income improvement of 12.3% and a carbon emission reduction of 11.6%.

关键词： Uncertainty Hydrogen Carbon dioxide Pricing Games Production Power markets

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AMG: A Depression Detection Model with Autoencoder and Multi-Head Graph Convolutional Network 42

AMG: A Depression Detection Model with Autoencoder and Multi...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Han-Guang Meng, Qing-Hao Jin, Li-Cheng Wang, Jian-Bang Hou, Hui-Rang Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China China Southern Power Grid Company Limited Guangzhou510623 China

ISBN: (纸本)9789887581543

Depression is a common chronic mental disorder characterized by high prevalence, recurrence, suicide rate, disability rate and heavy disease burden. Depression assessment based on questionnaires is subjective, therefore, a novel framework to is hereby designed to detect depression automatically and obj ectively. The framework first utilizes a two-way autoencoder to extract the hidden features from the original differential entropy features. The new features are used to generate the channel connectivity graph, using the multi-head attention mechanism. Next, the new features and graphs are input into the graph convolutional network (GCN) to aggregate spatial information. Finally, the output from GCN is passed to a fully connected layer and a soft layer to obtain the predicted label. Experiments are performed on Multimodal Open Dataset for Mental-disorder Analysis dataset. A final accuracy of 88.68% and a cross entropy loss of 2.4532 are obtained, when using ten-fold cross validation, outperforming other traditional machine learning methods including SVM, KNN, decision tree. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Decision trees

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A Simplified Economic Model Predictive control Algorithm for Soft Constrained System with Changing Economic Criteria

A Simplified Economic Model Predictive Control Algorithm for...

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第34届中国过程控制会议

作者： Xiaowen Qi Shaoyuan Li Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

To improve the economic performance of soft-constrained systems,this paper proposes a new soft-constrained Economic Model Predictive control(EMPC) approach to cope with changes in economic criteria caused by variations in raw material prices,product prices,market fluctuations,disturbances,*** the one hand,terminal states are permitted to be any admissible equilibrium *** the other hand,slack variables for soft constraints are considered in the cost stage to enhance dynamic operation *** ensure the asymptotic stability,the Lyapunov-like stability constraint is ***,the effectiveness of the proposed method is demonstrated by an isothermal stirred-tank reactor process.

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Stability Enhancement of Inverter-Current Feedback Grid-Following Inverters Using a Passivity-Based Voltage controller

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IEEE Transactions on Energy Conversion 2025年

作者： Huang, Yukai Xu, Jinbang Ye, Jie Huang, Songtao Ren, Ziao Shen, Anwen Hu, Yihua Huazhong University of Science and Technology National Key Laboratory of Science and Technology on Multispectral Information Processing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan430074 China King's College London Department of Engineering LondonWC2R 2LS United Kingdom

Grid-following inverters remain the primary interface for modern renewable energy systems due to their strong power output performance. As the global demand for clean and efficient energy conversion increases, optimizing the stability of inverter systems is crucial for integrating renewable sources into the power grid. In certain energy conversion scenarios, current sensors are placed on the inverter side due to equipment constraints and space limitations, which can impact the stability of the inverter. Although the stability of inverter-side current feedback (ICF) configurations has been studied, methods to further enhance their stability remain underexplored. In this paper, the stability of ICF grid-following inverters is analyzed using a passivity-based design approach, focusing on a point of common coupling voltage controller. The proposed controller is thoroughly analyzed, and a detailed design strategy is presented to ensure stability under varying grid impedance conditions. Experimental results validate the theoretical analysis, demonstrating the effectiveness of the proposed design in enhancing the stability of ICF grid-following inverters. These advancements contribute to improving energy conversion efficiency and reliability. © 2025 IEEE.

关键词： Energy conversion efficiency

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A third-party platoon coordination service: Pricing under government subsidies

A third-party platoon coordination service: Pricing under go...

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作者： Bai, Ting Johansson, Alexander Li, Shaoyuan Johansson, Karl Henrik Mårtensson, Jonas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden KTH Royal Institute of Technology Stockholm Sweden Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Digital Futures Stockholm Sweden

This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease. © 2023 The Authors. Asian Journal of control published by John Wiley & Sons Australia, Ltd on behalf of Chinese Automatic control Society.

关键词： Trucks

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Round-trip multi-band quantum access network

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Photonics Research 2023年第8期11卷 1449-1464页

作者： YUEHAN XU TAO WANG HUANXI ZHAO PENG HUANG GUIHUA ZENG State Key Laboratory of Advanced Optical Communication Systems and Networks Center of Quantum Sensing and Information ProcessingShanghai Jiao Tong UniversityShanghai 200240China Shanghai Research Center for Quantum Sciences Shanghai 201315China Hefei National Laboratory CAS Center for Excellence in Quantum Information and Quantum PhysicsHefei 230026China

The quantum network makes use of quantum states to transmit data,which will revolutionize classical communication and allow for some breakthrough *** key distribution(QKD)is one prominent application of quantum networks,and can protect data transmission through quantum *** this work,we propose an expandable and cost-effective quantum access network,in which the round-trip structure makes quantum states travel in a circle to carry information,and the multi-band technique is proposed to support multiuser *** on the round-trip multi-band quantum access network,we realize multi-user secure key sharing through the continuous-variable QKD(CV-QKD)*** to the encoding characteristics of CV-QKD,the quadrature components in different frequency bands can be used to transmit key information for different *** feasibility of this scheme is confirmed by comprehensive noise analysis,and is verified by a proof-of-principle *** results show that each user can achieve excess noise suppression and 600 bit/s level secure key generation under 30 km standard fiber *** networks have the ability of multi-user access theoretically and could be expanded by plugging in simple ***,it paves the way for near-term largescale quantum secure networks.

关键词： networks network quantum

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E-Nose-Based Domain-Generalization Recognition of Ignitable Liquids

E-Nose-Based Domain-Generalization Recognition of Ignitable ...

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第43届中国控制会议

作者： Tian-Shu Song Zhen-Peng Chen Hui-Rang Hou Hong-Yue Li Qing-Hao Meng Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Detecting ignitable liquids(ILs) at the scene of a fire is crucial for fire investigation. The electronic nose(e-nose) is crucial for detecting ILs due to its affordability and rapid response time. Process limitations and changes in environmental conditions can cause variations in the reactions of multiple e-noses with the same sensor array to the same input gas. This can significantly reduce the accuracy of identifying ILs when deploying a model trained on one e-nose to other e-noses. In order to tackle the issue of cross-platform identification of ILs using e-noses, we developed a feature mute gated network(FMG-Net).This network is capable of maintaining consistency in the shared components of different e-noses at the low-level feature level while effectively muting the distinct low-level features. FMG-Net is comprised of a main network and multiple branch networks that are specific to different domains. Initially, a subsidiary domain-specific convolutional neural network(SDS-CNN) is trained to learn the network parameters on various e-nose platforms with known labels. Additionally, the backbone network generates low-level features of the e-nose responses. Furthermore, the SDS-CNN incorporates random input of low-level features and generates a mask using the gradient. This mask is then used to explicitly remove the non-overlapping parts of the low-level features. At last, the low-level features are sent back to the backbone network for further training once the non-overlapping parts have been removed. Experiments were conducted to identify four types of ILs(diesel, gasoline, ethanol, and thinner) using five self-made e-nose platforms with identical sensor arrays. The results indicated that the proposed FMG-Net consistently outperforms other domain-generalization algorithms in terms of recognition accuracy and parameter sizes. FMG-Net has the ability to detect volatile gases in ILs using an e-nose, even without the need for a dataset.

关键词： Electronic Nose Fire Investigation Ignitable Liquids Convolutional Neural Network Cross Platform

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Non-Repetitive: A Promising LiDAR Scanning Pattern

Non-Repetitive: A Promising LiDAR Scanning Pattern

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Angchen Xie Yeqiang Qian Weihao Yan Chunxiang Wang Ming Yang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

LiDAR is an essential sensor for intelligent vehicles. Recently, LiDARs used in vehicles produced by different companies have significant differences in their scanning patterns. Some vehicles use mechanical and solid-state (repetitive) LiDARs, while others use prism-based (non-repetitive) LiDARs. The scanning pattern of a LiDAR has a profound impact on its scanning performance. To investigate the influence of LiDAR scanning patterns, we created the "Repetitive-or-not" dataset, which is collected simultaneously by LiDARs with both repetitive and non-repetitive scanning patterns in the CARLA simulation environment. Using this dataset, we conducted a comprehensive statistical analysis of the scanning ability of repetitive and non-repetitive LiDARs. Furthermore, we looked into the effects of these two LiDAR scanning patterns on the performance of various 3D object detection algorithms. Finally, we explored the domain gap in the point cloud data produced by repetitive and non-repetitive LiDARs. Through an in-depth investigation of the "Repetitive-or-not" dataset, we have discovered that non-repetitive LiDAR shows great promise. This conclusion is primarily supported by its superior object scanning capabilities.

关键词： Point cloud compression Laser radar Three-dimensional displays Statistical analysis Intelligent vehicles Object detection Companies Robot sensing systems Intelligent robots

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