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第33届中国过程控制会议

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

This paper is concerned with the output-feedback consensus problem of linear multi-agent systems on undirected *** focus is on reducing sensing and communication burdens between *** on relative output measurements,a novel dynamic output-feedback(DOF) protocol is proposed,which,by leveraging the closed-loop dynamic structure,integrates a hybrid time/event-triggered sampling mechanism such that only intermittent interaction between agents is *** conditions are established by the Lyapunov method and numerical simulations are provided for illustrating the effectiveness of the *** with the existing results,the proposed DOF protocol has the following merits:1) it relies on relative output measurements but does not need absolute information about agents;2) it only requires agents to intermittently interact with neighbors and thus can greatly reduce sensing and communication burdens between agents;and 3) the designed event-triggering conditions do not need to perform extra reconstruction operations that are computationally demanding.

关键词： Multi-agent system(MAS) Consensus Relative output Event-triggered control Intermittent interaction

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Multi-type and Multi-level Feature Fusion Network for RGBD Indoor Semantic Segmentation

Multi-type and Multi-level Feature Fusion Network for RGBD I...

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第34届中国控制与决策会议

作者： Yuwen Xia Chaochen Gu Kaijie Wu Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

RGBD images,which refer to the three-channel color images augmented with depth channel,have been greatly exploited to improve the performance of scene semantic *** semantic segmentation task is of great significance to help the development of robot navigation and ***,most existing methods addressing RGBD semantic segmentation lack effective feature fusion *** address the aforementioned problem,we propose a novel network with two novel modules for feature fusion,namely Channel-Attention based Complementary Feature Fusion Module(CAC-FFM) and Cross-Layer Feature Fusion Module(CL-FFM).Specifically,CAC-FFM,which is essentially based on the channel-attention mechanism,effectively utilizes the complementary information of RGB and depth to generate fusion features,and CL-FFM captures patch-wise features from low-level feature maps to assist the training of high-level features so as to further optimize the segmentation *** results on the publicly available NYUDv2 dataset validate the effectiveness and superiority of our proposed method.

关键词： RGBD semantic segmentation NYUDv2

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Securing Mobile Robotic Networks Against Replacement Attack

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IEEE Transactions on Automatic control 2025年

作者： Li, Yushan He, Jianping Chen, Cailian Guan, Xinping Cai, Lin Shanghai Jiao Tong University Dept. of Automation China Ministry of Education of China The Key Laboratory of System Control and Information Processing China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China University of Victoria Dept. of Electrical and Computer Engineering BC Canada

The security of mobile robotic networks (MRNs) has been an active research topic in recent years. This paper aims to secure the ubiquitous formation control of MRNs against the replacement attack, where an external robot can replace a formation robot by compromising the communication and physically interfering with the victim simultaneously. To counter this advanced attack, the novel idea of this work is to leverage the physical proximity of the formation shape and the interaction topology among robots for defense design. First, from the physical proximity perspective, we propose the convex neighbor polygon (CNP) to capture the geometric characteristic of the formation robots, and design a CNP-based security mechanism for the robots during the replacement attack. Then, from the interaction perspective, we introduce the indirect controllability to characterize the possibility that the attacker leverages the interaction between robots to deactivate the CNP mechanism, and establish the conditions regarding the topology structure and the attack input to counter the replacement. Finally, based on the obtained conditions, we demonstrate how to design the initiatory topology among the formation robots to enhance the defense performance. Comparative simulations verify the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Topology

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H∞ control of fractional-order singularly perturbed systems: The 1 < ν < 2 case

H∞ control of fractional-order singularly perturbed systems...

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Chinese control and Decision Conference, CCDC

作者： Long-Jun Zhu Jun-Guo Lu Department of Automation Shanghai Jiao Tong University School of Artificial Intelligence Shanghai Normal University Tianhua College Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

This paper addresses the problems of the H ∞ analysis and control of fractional-order singularly perturbed systems with the commensurate order 1 < ν < 2. Based on the bounded real lemma for fractional-order systems and singular fractional-order systems, the sufficient conditions for the H ∞ performance analysis of fractional-order singularly perturbed systems are firstly presented in terms of -independent linear matrix inequalities (LMIs). Then, the state feedback controllers are designed, and the sufficient -independent stabilization conditions are established via bilinear matrix inequalities(BMIs). Moreover, by the provided iterative algorithm, these BMI-based conditions can be solved based on LMIs. Finally, two numerical examples are exploited to verify the effectiveness of the proposed results.

关键词： Robust control State feedback Sufficient conditions Upper bound Perturbation methods Transfer functions Mathematical models

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Bidirectional LSTM with Multiple Input Multiple Fusion Strategy for Speech Emotion Recognition

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2021年第3期48卷 1-6页

作者： Fang, Yuanbo Fu, Hongliang Tao, Huawei Wang, Xia Zhao, Li Ministry of Education Zhengzhou450001 China Ministry of Education Zhengzhou450001 China Lecture of the Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Zhengzhou450001 China School of information science and engineering Southeast University Nanjing210096 China Lecture of the School of Information Science and Technology Nantong University Nantong226000 China Professor of the Laboratory of Underwater Acoustic Signal Processing Southeast University Nanjing210096 China

Speech emotion recognition is one of the important challenges in the field of artificial intelligence. In order to further improve the accuracy of speech emotion recognition, a double bidirectional long short-term memory network with multiple input multiple fusion strategy (DBL-MM) for speech emotion recognition is proposed. Firstly, log Mel spectrum feature and frame-level statistical feature are extracted from speech signals, and then the two types of frame-level features are input into two bidirectional LSTM networks for learning simultaneously. Secondly, attention pooling and average pooling are used to fuse the outputs of the two BiLSTM networks to obtain two high-level fusion features which are concatenated and batch normalized later. Finally, a softmax classifier is used to classify emotions. The proposed DBL-MM model simultaneously processes two different types of features and a multiple fusion strategy is used to better learn the subtle changes in emotion. The experimental results on two public datasets show the superiority of this method. © 2021. All Rights Reserved.

关键词： Long short-term memory

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Balanced Training for the End-to-End Autonomous Driving Model Based on Kernel Density Estimation

Balanced Training for the End-to-End Autonomous Driving Mode...

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IEEE Symposium on Intelligent Vehicle

作者： Tong Yao Wei Yuan Songan Zhang Chunxiang Wang Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

End-to-end autonomous driving models are widely used currently to avoid error transmission. The training of these models is affected by the imbalance of the training labels, such as imbalanced distributions of trajectory waypoint, steering, throttle, and brake labels. Therefore, the models cannot be trained enough on low-frequency labels. To mitigate this problem, a balanced training method is proposed in this paper. First, a general kernel density estimation method is used to estimate the probability function of labels. Then, to conduct balanced training, we design a cost-sensitive loss function that assigns different weights to each training data according to the probability of the labels. Comparative experiments on the Longest6 Benchmark show that the balanced training method proposed in this article improves the baseline significantly. We also conduct ablation studies to discuss the balanced training among different tasks in multi-task learning, as well as between two output forms: waypoint and action. Due to the generalization of kernel density estimation, the balanced training method proposed in this paper can be extended to any label distribution of two output forms in the end-to-end autonomous driving models.

关键词： Training Computational modeling Estimation Training data Benchmark testing Data models Trajectory

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Novel £2,1-Norm Based Least Square Regression Method for Classification

Novel £2,1-Norm Based Least Square Regression Method for Cl...

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International Conference on Informative and Cybernetics for Computational Social systems (ICCSS)

作者： Junwei Jin Qiuling Yan Chunhua Zhu Zhenghao Qin Lei Li Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Henan Key Laboratory of grain photoelectric detection and control College of Information Science and Engineering Henan University of Technology Unmanned System Research Institute Northwestern Polytechnical University

In the realm of linear regression, the concept of utilizing relaxed regression targets for classification has shown considerable success. However, it suffers from a lack of strong discriminative ability. In this study, we build upon the theory of relaxed regression targets and propose a more concise and discriminative model for multi-class classification. Our approach introduces the £ 2,1 -norm regularized term to enhance the efficacy and compactness of the learned projection matrix. This regularization term not only generates a sparse row structure but also facilitates feature selection during the training phase. Consequently, both the accuracy of classification and the convergence speed of the model are improved. Furthermore, when compared to various variants of the linear regression model, our designed method exhibits superior performance in image classification. Through extensive experiments conducted on image databases, we provide evidence that our proposed approach excels in recognition capability, surpassing the performance of state-of-the-art methods in terms of classification accuracy.

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3D bioprinting of in vitro porous hepatoma models:establishment,evaluation,and anticancer drug testing

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Bio-Design and Manufacturing 2024年第2期7卷 137-152页

作者： Xiaoyuan Wang Zixian Liu Qianqian Duan Boye Zhang Yanyan Cao Zhizhong Shen Meng Li Yanfeng Xi Jianming Wang Shengbo Sang Shanxi Key Laboratory of Micro Nano Sensors and Artificial Intelligence Perception College of Electronic Information and Optical EngineeringTaiyuan University of TechnologyTaiyuan 030024China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of TechnologyTaiyuan 030024China Shanxi Institute of 6D Artificial Intelligence Biomedical Science Taiyuan 030031China Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering Taiyuan 030024China Department of Pathology Shanxi Provincial Cancer HospitalTaiyuan 030024China General Hospital of Taiyuan Iron and Steel Company Taiyuan 030809China

Traditional tumor models do not tend to accurately simulate tumor growth in vitro or enable personalized treatment and are particularly unable to discover more beneficial targeted *** address this,this study describes the use of threedimensional(3D)bioprinting technology to construct a 3D model with human hepatocarcinoma SMMC-7721 cells(3DP-7721)by combining gelatin methacrylate(GelMA)and poly(ethylene oxide)(PEO)as two immiscible aqueous phases to form a bioink and innovatively applying fluorescent carbon quantum dots for long-term tracking of *** GelMA(10%,mass fraction)and PEO(1.6%,mass fraction)hydrogel with 3:1 volume ratio offered distinct pore-forming characteristics,satisfactorymechanical properties,and biocompatibility for the creation of the 3DP-7721 *** analysis and quantitative real-time fluorescence polymerase chain reaction(PCR)were used to evaluate the biological properties of the *** with the two-dimensional culture cell model(2D-7721)and the 3D mixed culture cell model(3DM-7721),3DP-7721 significantly improved the proliferation of cells and expression of tumor-related proteins and ***,we evaluated the differences between the three culture models and the effectiveness of antitumor drugs in the three models and discovered that the efficacy of antitumor drugs varied because of significant differences in resistance proteins and genes between the three *** addition,the comparison of tumor formation in the three models found that the cells cultured by the 3DP-7721 model had strong tumorigenicity in nude *** evaluation of the levels of biochemical indicators related to the formation of solid tumors showed that the 3DP-7721 model group exhibited pathological characteristics of malignant tumors,the generated solid tumors were similar to actual tumors,and the deterioration was *** research therefore acts as a foundation for the application of 3DP-7721 models in drug devel

关键词： 3D bioprinting Hepatoma tumor models Drug screening Antitumor drug development

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A GM-TCN based Hybrid Prediction Method for Ash Fouling Prediction in Coal-fired Power Plant Boiler

A GM-TCN based Hybrid Prediction Method for Ash Fouling Pred...

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2022 Chinese Automation Congress, CAC 2022

作者： Shi, Yuanhao Han, Tianxiang Cui, Fangshu Wen, Jie Qiao, Gangzhu North University of China School of Electrical and Control Engineering Taiyuan China Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China North University of China School of Data Science and Technology Taiyuan China

ISBN: (纸本)9781665465335

In response to the problem that ash deposition in boilers has a great impact on production efficiency as well as production safety, a grey level theory prediction model based on time convolution networks (GM-TCN) is proposed to predict the degree of ash in boiler heated areas by using a cleaning factor to assess the ash deposition condition on the heated surfaces. The accuracy of the prediction model is improved by utilizing the features of grey level theory that can predict the developmental changes in system behavior, combined with the high-precision approximation capability of the time-convolutional network. The modelling prediction of the ash level in the heated area of boilers during the production of coal-fired power stations is investigated. Experiments demonstrate that the model can predict the cleanliness factor with high accuracy, and the predicted values are closest to the true values compared to the other two models, verifying the accuracy and reliability of the model. © 2022 IEEE.

关键词： Convolution

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Iterative-Reweighting-Based Robust Iterative-Closest-Point Method

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Journal of Shanghai Jiaotong university(Science) 2021年第5期26卷 739-746页

作者： ZHANG Jianlin ZHOU Xuejun YANG Ming Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240China Noblelift Intelligent Equipment Co. Ltd.Huzhou 313100ZhejiangChina

In point cloud registration applications,noise and poor initial conditions lead to many false *** matches significantly degrade registration accuracy and speed.A penalty function is adopted in many robust point-to-point registration methods to suppress the influence of false ***,after applying a penalty function,problems cannot be solved in their analytical forms based on the introduction of ***,most existing methods adopt the descending *** this paper,a novel iterative-reweighting-based method is proposed to overcome the limitations of existing *** proposed method iteratively solves the eigenvectors of a four-dimensional matrix,whereas the calculation of the descending method relies on solving an eight-dimensional ***,the proposed method can achieve increased computational *** proposed method was validated on simulated noise corruption data,and the results reveal that it obtains higher efficiency and precision than existing methods,particularly under very noisy *** results for the KITTI dataset demonstrate that the proposed method can be used in real-time localization processes with high accuracy and good efficiency.

关键词： point cloud registration iterative reweighting iterative closest-point(ICP) robust localization

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