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42nd Chinese control Conference, CCC 2023

作者： Wang, De-Xiu Meng, Qing-Hao Han, Rui-Xue Li, Hong-Yue 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 Tianjin Navigation Instruments Research Institute Tianjin300131 China

ISBN: (纸本)9789887581543

Identification of the type of combustion-supporting agents (CSAs) by an electronic nose (e-nose) is severely limited due to the absence of untested gas concentration in the e-nose training set. In order to solve this problem, this study designs an e-nose for classifying four CSAs (Diesel, Gasoline, Ethanol and Thinner), and builds cross-concentrations data set. This study uses Gramian Angular Field method that converts one-dimensional time series data to two-dimensional data, and proposes an algorithm named the Multi-Branch Feature Fusion network (M-BFFNet). This algorithm passes the one-dimensional and two-dimensional data through two different networks and obtains two types of features. First, the algorithm can strength the one-dimensional raw data, integrate the advantages of different networks, and fuse the characteristics of different networks. Second, it can learn the characteristics of volatile gas with specific concentrations to identify the unknown concentrations. Last, a feature fusion module is designed to improve the algorithm ability of learning odor fingerprint. In order to verify the superiority of the algorithm, this study tests the recognition performance of the e-nose based systems and compares the proposed M-BFFNet with traditional algorithms. The results show that the proposed M-BFFNet improves the identification accuracy compared to other algorithms. According to the analysis of this work, the M-BFFNet can be used to identify CSAs with unknown concentrations. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Electronic nose

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Tacformer: A Self-attention Spiking Neural network for Tactile Object Recognition 16th

Tacformer: A Self-attention Spiking Neural Network for Tact...

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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Hu, Jiarui Wang, Zhipeng Lu, Ping Yuan, Philip F. Zhou, Yanmin Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai201210 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China College of Architecture and Urban Planning Tongji University Shanghai201804 China

ISBN: (纸本)9789819964949

Tactile sensor plays an important role in the human-robot interaction by providing environmental information to robots. How to effectively use the haptic information of array sensors to encode and extract haptic features for object recognition is the focus of current research. Previous studies have provided methods to construct array sensors in order to acquire tactile information efficiently. New advances in event-driven array sensors could capture spatial spiking tactile data, which include rich spatial information. In this paper, we propose a Residualized Graph Self-attention Spiking Neural network (Tacformer) for tactile object recognition. The proposed Tacformer pays attention to both the spatial properties and the temporal sensing values of each sensor point in event-driven array sensors. Experimental results on two tactile datasets show that the accuracy, precision, recall and F1-value of the method in this paper are improved. On the EvTouch-Objects dataset, Tacformer has improved the accuracy by 8.34% compared with the GNN-SNN based a baseline approach. On the EvTouch-Containers dataset, Tacformer also has increased the accuracy by 16.17%. The Tacformer could be applied to facilitate robot tasks like grasping and sorting. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Object recognition

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Research on Vibration Displacement Measurement of Sleeper Based on Machine Vision 1

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering network, TEPEN 2023

作者： Chi, Yuliang Gao, Yunpeng Liu, Guiyi Zhang, Chao School of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou014000 China Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems in Inner Mongolia Autonomous Region Baotou014000 China Baotou Railway Vocational and Technical College Baotou014000 China

ISBN: (数字)9783031494215

ISBN: (纸本)9783031494208

Machine vision technology has the characteristics of high precision, fast speed, non-contact and high degree of automation in displacement detection. It is feasible to apply machine vision technology to the detection of vibration displacement of railway sleeper. In this paper, the vibration displacement measurement technology of railway sleeper is studied, and a machine vision method based on monocular vision measurement is proposed. According to the geometric relationship between the feature points of the target and the corresponding feature points of the space target in the shot image, the pose information of the space target relative to the camera coordinate system is calculated. By monitoring the change of the attitude of the target rigidly connected to the sleeper, the vibration displacement results of the sleeper when the train passes are obtained. The experimental results show that the proposed method meets the requirements of sleeper vibration measurement in terms of measurement accuracy, sampling comment and stability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Displacement measurement

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CT-NeRF: Incremental Optimization of Neural Radiance Field and Camera Poses with Complex Trajectory

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Ran, Yunlong Li, Yanxu Ye, Qi Huo, Yuchi Cui, Zhaopeng Bai, Zechun Sun, Jiahao Chen, Jiming Zhejiang University College of Control Science and Engineering Hangzhou China Zhejiang University College of Control Science and Engineering State Key Laboratory of Industrial Control Technology China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China Zhejiang University State Key Lab of CAD and CG China

Neural radiance field (NeRF) has achieved impressive results in high-quality 3D scene reconstruction. However, NeRF heavily relies on precise camera poses. While recent works like BARF have introduced camera pose optimization within NeRF, their applicability is limited to simple trajectory scenes. Existing methods struggle while tackling complex trajectories involving large rotations. To address this limitation, we propose CT-NeRF, an incremental reconstruction and optimization pipeline using only RGB images without pose and depth input. In this pipeline, we first propose a local-global bundle adjustment under a pose graph connecting neighboring frames to enforce the consistency between poses to escape the local minima caused by only pose consistency with the scene structure. Further, we instantiate the consistency between poses as a reprojection error constraint resulting from pixel-level correspondences between input image pairs. Through the incremental reconstruction, CT-NeRF enables the recovery of both camera poses and scene structure and is capable of handling scenes with complex trajectories. We evaluate the performance of CT-NeRF on two real-world datasets, NeRF-Buster and Free-Dataset, which feature complex trajectories. Results show CT-NeRF outperforms existing methods in novel view synthesis and pose estimation accuracy. © 1991-2012 IEEE.

关键词： 3D Reconstruction bundle adjustment NeRF novel view synthesis structure from motion

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LI-GS: Gaussian Splatting with LiDAR Incorporated for Accurate Large-Scale Reconstruction

arXiv

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arXiv 2024年

作者： Jiang, Changjian Gao, Ruilan Shao, Kele Wang, Yue Xiong, Rong Zhang, Yu State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Collaborative sensing and autonomous unmanned systems of Zhejiang Province Hangzhou China

Large-scale 3D reconstruction is critical in the field of robotics, and the potential of 3D Gaussian Splatting (3DGS) for achieving accurate object-level reconstruction has been demonstrated. However, ensuring geometric accuracy in outdoor and unbounded scenes remains a significant challenge. This study introduces LI-GS, a reconstruction system that incorporates LiDAR and Gaussian Splatting to enhance geometric accuracy in large-scale scenes. 2D Gaussain surfels are employed as the map representation to enhance surface alignment. Additionally, a novel modeling method is proposed to convert LiDAR point clouds to plane-constrained multimodal Gaussian Mixture Models (GMMs). The GMMs are utilized during both initialization and optimization stages to ensure sufficient and continuous supervision over the entire scene while mitigating the risk of over-fitting. Furthermore, GMMs are employed in mesh extraction to eliminate artifacts and improve the overall geometric quality. Experiments demonstrate that our method outperforms state-of-the-art methods in large-scale 3D reconstruction, achieving higher accuracy compared to both LiDAR-based methods and Gaussian-based methods with improvements of 52.6% and 68.7%, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical radar

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Distributed Fault-Tolerant Consensus control for Linear Multi-Agent System with K-Step Hierarchical Protocol

Distributed Fault-Tolerant Consensus Control for Linear Mult...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Wang, Yuanwen Dong, Jiuxiang College of Information Science and Engineering Northeastern University Shenyang110819 China Liaoning Province Key Laboratory of Safe Operation Technique for Autonomous Unmanned Systems Northeastern University Shenyang110819 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education Northeastern University Shenyang110819 China

ISBN: (纸本)9789811966125

The distributed fault-tolerant consensus control using k-step hierarchical protocol for linear multi-agent system (MAS) is studied in this note. Unlike most existing fault-tolerant methods for compensating actuator fault, a hierarchical protocol consisting of a virtual layer and a actual layer that considers both fault compensation and introducing fault information into the trajectory planning process which means that the motion of the MAS does not exceed the fault-tolerant compensation range of the faulty agent is proposed. Trajectory planning is responsible for the virtual layer consisting of virtual agent system and a co-controller that introduces fault information. The actual layer, consisting of the actual agent system, actuator fault observer, and tracking trajectory controller, traces the virtual layer for consensus. It is shown that the integrity of the MAS is guaranteed. Finally, the validity of the developed protocols is illustrated by simulation examples. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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ERASOR++: Height Coding Plus Egocentric Ratio Based Dynamic Object Removal for Static Point Cloud Mapping

arXiv

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arXiv 2024年

作者： Zhang, Jiabao Zhang, Yu State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

Mapping plays a crucial role in location and navigation within automatic systems. However, the presence of dynamic objects in 3D point cloud maps generated from scan sensors can introduce map distortion and long traces, thereby posing challenges for accurate mapping and navigation. To address this issue, we propose ERASOR++, an enhanced approach based on the Egocentric Ratio of Pseudo Occupancy for effective dynamic object removal. To begin, we introduce the Height Coding Descriptor, which combines height difference and height layer information to encode the point cloud. Subsequently, we propose the Height Stack Test, Ground Layer Test, and Surrounding Point Test methods to precisely and efficiently identify the dynamic bins within point cloud bins, thus overcoming the limitations of prior approaches. Through extensive evaluation on open-source datasets, our approach demonstrates superior performance in terms of precision and efficiency compared to existing methods. Furthermore, the techniques described in our work hold promise for addressing various challenging tasks or aspects through subsequent migration. Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping

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A Stackelberg Game Framework for Energy Internet System By Operator Approach

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IEEE Transactions on network Science and Engineering 2025年

作者： Chen, Yue Yi, Peng Lei, Jinlong Li, Xiangjun Tongji University Department of Control Science and Engineering Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China China Electric Power Research Institute National Key Laboratory of Renewable Energy Grid-Integration Energy Storage and Electrical Engineering Department Beijing100192 China

This paper proposes a Stackelberg game framework for managing an Energy Internet System that integrates renewable energy generation, energy conversion, and energy trading. The system comprises hubs controlled by competing energy companies, incorporating both external energy trading and peer-to-peer energy exchanges. It involves multiple stakeholders whose decisions are interdependent, creating challenges in decision-making. To tackle these challenges, we propose a networked Stackelberg game framework with two main objectives. First, from the energy companies' perspective, the companies make decisions by considering strategic interactions with other companies while accounting for the best responses of passive consumers within peer-to-peer constraints. Second, from the consumer side, consumers adjust their decisions based on their observations of the companies' strategies. The framework adopts a hierarchical decision-making structure in which energy companies act as leaders, formulating strategies that anticipate consumer demand responses, while competing within a Nash game. Meanwhile, consumer clusters, managed by demand management centers, modify their energy usage based on an incentive price mechanism. To search for the equilibrium, we develop an operator-theoretic approach that combines implicit gradient methods with proximal operator splitting techniques, and prove its convergence. Numerical studies and simulations on a combined IEEE 37-bus and gas source model with multiple energy hubs validate the model's effectiveness and the efficiency of the proposed algorithms. The results demonstrate the operational efficiency and strategic stability of the system, emphasizing the advantages of active market participation for both energy companies and consumers. © 2013 IEEE.

关键词： Demand response

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ERASOR++: Height Coding Plus Egocentric Ratio Based Dynamic Object Removal for Static Point Cloud Mapping

ERASOR++: Height Coding Plus Egocentric Ratio Based Dynamic ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jiabao Zhang Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

关键词： Point cloud compression Technological innovation Accuracy Three-dimensional displays Image analysis Navigation Encoding

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A Multi-Gas Detection System Based on Convolutional Neural network

A Multi-Gas Detection System Based on Convolutional Neural N...

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第43届中国控制会议

作者： Si-Wen Chen Hong-Yue Li Hui-Rang Hou 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

The emission of carbon oxides（COX）, nitrogen oxides（NOX）, sulfur compounds, and volatile organic compounds（VOCs） from vehicles has significantly impacted the air quality, thus posing threats to the environmental resources and human health. This work presents the development of a non-dispersive infrared（NDIR） multi-gas detection system featuring three broadband light sources, a sealed gas chamber, and three multi-channel pyroelectric detectors with a response time shorter than 32 ms. The proposed system is used for constructing an odor dataset comprising five target gases, including carbon dioxide（CO）,carbon monoxide（CO）, nitric oxide（NO）, sulfur dioxide（SO）, and propane（CH）. We perform odor recognition experiments using the traditional machine learning methods, as well as one-dimensional convolutional neural network（1D-CNN） and depthwise separable convolutional neural network（DS-CNN）. The results show that the proposed DS-CNN model achieves an accuracy of 94.75% in recognizing different odors, thus outperforming other classification algorithms. This work demonstrates that the proposed detection system exhibits rapid response and precise recognition, thus establishing it as an effective approach for analyzing the primary components of automobile exhaust.

关键词： Non-Dispersive Infrared Multi-gas Detection System Pattern Recognition Convolutional Neural network

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