检索结果-内蒙古大学图书馆

How Regulatory Orders and Public Fear Affect Vehicle Mobility Under Covid-19: A Global Perspective from Urban Overall Vehicles Using Multisource Data

SSRN

引用

SSRN 2024年

作者： Tang, Li Tang, Chuanli Luo, Hao He, Zhengbing Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province Xihua University Chengdu China Engineering Research Center of Ministry of Education for Intelligent Air-Ground Fusion Vehicles and Control Xihua University Chengdu China School of Automobile and Transportation Xihua University Chengdu China Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA United States

As one of the largest pandemics in human history, COVID-19 has dealt a heavy blow to the global economy and social life. Although the pandemic situations in most countries have been stable, lessons are highly needed to learn to get more prepared for the future. In the field of transportation, the impact of COVID-19 on public transit and shared travel has been extensively studied with a relatively consistent conclusion. However, how and why the overall vehicle travel demand (including public and private) changes have not been fully addressed. This paper investigates the impact of COVID-19 on urban overall vehicle mobility as to the magnitude, duration, and driving factors. To this end, the Prophet time series model was introduced to predict vehicle mobility without COVID-19. By comparing the predicted with the real value, the mobility loss was obtained. The causes of the loss were deeply explored by applying multiple linear regression, especially focusing on the interaction effects of the strictness of regulatory orders and public fear. A large-scale dataset with more than 4 billion raw data was built for the empirical analysis from different sources, including a three-year city-level Automatic Number Plate Recognition data and data crawled from websites relating to explanatory variables. Results show that urban vehicle mobility loss almost all occurs during the national outbreak period, which is 29.66%. The factor that represents nationwide confirmed cases has a positive lagging effect on travel mobility loss, and the lag time is around seven days. Loss of urban motorized travel demand is largely caused by the interaction of perceived risks and control policy. The marginal effects of the factors work differently, indicating strong evidence of the existence of caution fatigue and shedding light on the impact mechanism. © 2024, The Authors. All rights reserved.

关键词： Multiple linear regression

来源：评论

学校读者我要写书评

暂无评论

Smoke vehicle detection based on multi-feature fusion and hidden Markov model

Smoke vehicle detection based on multi-feature fusion and hi...

引用

作者： Tao, Huanjie Lu, Xiaobo School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

Existing smoke vehicle detection methods and vision-based smoke detection methods are vulnerable to false alarms. This paper presents an automatic smoke vehicle detection method based on multi-feature fusion and hidden Markov model (HMM). In this method, we first detect moving objects using an improved visual background extractor (ViBe) algorithm and obtain smoke-colored blocks using color histogram features in the HSI (hue, saturation, and intensity) color space. The adaptive scale local binary pattern (AS-LBP) and the discriminative edge orientation histogram (disEOH) are proposed and combined to characterize the smoke-colored blocks. More specifically, the proposed AS-LBP, a texture feature descriptor, is based on the quadratic fitting of our labelled data to obtain the best scale. The proposed disEOH, a gradient-based feature descriptor, is robust to noise by extracting discriminative edge information using Gaussian filters and principal component analysis (PCA). The discrete cosine transform (DCT) is employed to extract frequency domain information from the region fused by smoke blocks. To utilize the dynamic features, the HMMs are employed to analyze and classify the smoke-colored block sequences and region sequences in continuous frames. The experimental results show that the proposed method achieves better performances than existing smoke detection methods, especially achieves lower false alarms. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Smoke

来源：评论

学校读者我要写书评

暂无评论

Nash Equilibrium Estimation of Non-Cooperative Games with Time-Varying Delay

Nash Equilibrium Estimation of Non-Cooperative Games with Ti...

引用

Chinese Automation Congress (CAC)

作者： Ziqi Liu Chao Zhai China University of Geosciences Wuhan Campus School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

Time delay has great impacts on the stability and the reliability and real-time of the communication of multi-agent systems. In multi-agent communication network, due to network congestion, transmission distance and other factors, there are various communication delays. In this paper, we study the deviation of convergence value after adding time-varying delays under gradient descent method, and the upper bound related to delay time is estimated. This upper bound can be used to analyze the magnitude of deviation under different time delays and minimize the loss caused by delays, and provide more explicit information for system optimization and resource allocation. Numerical simulation is conducted to verify the proposed approach.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Data-driven control of consensus tracking for discrete-time multi-agent systems

引用

Journal of the Franklin Institute 2023年第7期360卷 4661-4674页

作者： Xiufeng Zhang Gang Wang Jian Sun Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming 650504 China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming 650504 China School of Automation Beijing Institute of Technology Beijing 100081 China Key Lab of Intelligent Control and Decision of Complex Systems Beijing 100081 China

This paper investigates the consensus tracking problem of leader-follower multi-agent systems. Different from most existing works, dynamics of all the agents are assumed completely unknown, whereas some input-output data about the agents are available. It is well known from the Willems et al. Fundamental Lemma that when inputs of a linear time-invariant (LTI) system are persistently exciting, all possible trajectories of the system can be represented in terms of a finite set of measured input-output data. Building on this idea, the present paper proposes a purely data-driven distributed consensus control policy which allows all the follower agents to track the leader agent’s trajectory. It is shown that for a linear discrete-time multi-agent system, the corresponding controller can be designed to ensure the global synchronization with local data. Even if the data are corrupted by noises, the proposed approach is still applicable under certain conditions. Numerical examples corroborate the practical merits of the theoretical results.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Adaptive control of Discrete-time Nonlinear systems Using ITF-ORVFL

引用

IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 556-563页

作者： Xiaofei Zhang Hongbin Ma Wenchao Zuo Man Luo School of Automation Beijing Institute of TechnologyBeijing 100081 School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China School of Automation Beijing Institute of Technologyand also with the State Key Laboratory of Intelligent Control and Decision of Complex Systems(Beijing Institute of Technology)Beijing 100081China School of Automation Beijing Institute of TechnologyBeijing 100081and he is with Beijing Institute of Electronic System EngineeringBeijing 100854China School of Automation Beijing Institute of TechnologyBeijing 100081and she is with Ant GroupBeijing 310013China

Random vector functional ink(RVFL)networks belong to a class of single hidden layer neural networks in which some parameters are randomly *** network structure in which contains the direct links between inputs and outputs is unique,and stability analysis and real-time performance are two difficulties of the control systems based on neural *** this paper,combining the advantages of RVFL and the ideas of online sequential extreme learning machine(OS-ELM)and initial-training-free online extreme learning machine(ITFOELM),a novel online learning algorithm which is named as initial-training-free online random vector functional link algo rithm(ITF-ORVFL)is investigated for training *** link vector of RVFL network can be analytically determined based on sequentially arriving data by ITF-ORVFL with a high learning speed,and the stability for nonlinear systems based on this learning algorithm is *** experiment results indicate that the proposed ITF-ORVFL is effective in coping with nonparametric uncertainty.

关键词： Adaptive control initial-training-free online learning algorithm random vector functional link networks

来源：评论

学校读者我要写书评

暂无评论

A Collaborative Robot Torque Prediction Method Based on CNN-TCN Model

A Collaborative Robot Torque Prediction Method Based on CNN-...

引用

IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Lina Tong Decheng Cui Chen Wang Liang Peng School of Mechanical Electronic and Information Engineering China University of Mining and Technology Beijing Beijing China State Key Laboratory of Management and Control for Complex Systems (SKLMCCS) Institute of Automation Chinese Academy of Science Beijing China

The traditional dynamical models show lower accuracy when predicting joint movement, and should be compensated. This paper proposed a model combined with the convolutional network(CNN) and temporal convolutional network(TCN) to compensate for the joint torque prediction values that are calculated from the sensing information. The experiments on the Cooperative Universal Robotic Assistant 6 DoF(CURA6) open dataset, including multi-load and multi-velocity, showed the prediction error can be reduced by 20% compared to other network models. Since there are many kinds of joint movement information, the input data form of the deep learning model should be improved. Thus, the kinetic linearization model is proposed to modify the input of sensing data. According to the different motion types of the CURA6 dataset, comparative experiments were taken, and the mean absolute error was less than 6.8%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hand Gesture Recognition from an Open-Set Perspective

引用

IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

来源：评论

学校读者我要写书评

暂无评论

Stealthy False Data Injection Attacks against Extended Kalman Filter Detection in Power Grids

Stealthy False Data Injection Attacks against Extended Kalma...

引用

2021 International Conference on Information, Cybernetics, and Computational Social systems, ICCSS 2021

作者： Liu, Yifa Xue, Wenchao He, Shuping Cheng, Long Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100190 China Chinese Academy of Sciences Key Laboratory of System and Control Academy of Mathematics and System Science Beijing100190 China Anhui University School of Electrical Engineering and Automation Hefei230601 China

ISBN: (纸本)9781665402453

The power grid is a kind of national critical infrastructure directly affiliated to human daily life. Because of the vital functions and potentially significant losses, the power grid becomes an excellent target for many malicious attacks. Due to the special nonlinear measurements, many detection methods do not match the grid very well. The extended Kalman filter based detection is one of the few methods suitable for nonlinear system detection, and therefore can be used in power system to spot malicious attacks. However, the reliability and effectiveness of the extended Kalman filter based detection have not been fully studied and adequately guaranteed. By proposing a two-step false data injection attack strategy, this paper gives a stealthy way to inject false data of increasing magnitude into the power grid, which can eventually cause a certain degree of deviation of the grid state without being detected. In the simulation, the method proposed in this paper caused a voltage deviation of more than 25% before being discovered in the power system. © 2021 IEEE.

关键词： Extended Kalman filters

来源：评论

学校读者我要写书评

暂无评论

An Aeromagnetic Compensation Algorithm Based on Broad Learning System

An Aeromagnetic Compensation Algorithm Based on Broad Learni...

引用

Robotics, Artificial Intelligence and Intelligent control (RAIIC), International Conference on

作者： Qilong Wang Junlei Song Chao Zuo Kaifeng Dong Fang Jin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo- Exploration Ministry of Education Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China

ISBN: (数字)9798350352627

ISBN: (纸本)9798350352634

Aeromagnetic surveys, renowned for their operational flexibility and high efficiency, serve as a crucial technique for measuring the geomagnetic field. However, aeromagnetic surveys are easily affected by magnetic interference from navigation platforms, making the compensation of aeromagnetic interference a crucial step in the measurement process. To address the inadequate consideration of nonlinear magnetic field interference in traditional compensation algorithms, this paper introduces an aeromagnetic compensation approach based on broad learning system. The broad learning system employs an incremental learning mechanism aimed at enhancing the precision of the network alongside the increase in nodes. With each expansion of the network node, computation is streamlined to calculating the pseudo-inverse of the expansion node, eliminating the necessity for retraining the entire network structure. Leveraging the nonlinear fitting characteristics of the broad learning system, this paper improves the accuracy of aeromagnetic interference compensation. Through UAV flight experiments, the broad learning system is compared with methodologies using particle swarm optimization (PSO) and BP neural network. Compared with PSO, training time was reduced by $21.3 \%$ and magnetic interference by $33.6 \%$. Compared with BP neural networks, training time was reduced by $34.9 \%$ and magnetic interference by $28.6 \%$. This paper provides references and ideas for the selection of aeromagnetic interference compensation algorithms.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Nonlinear Model Predictive Clamping Force control for Electromechanical Brake System

Nonlinear Model Predictive Clamping Force Control for Electr...

引用

International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Wenpeng Wei Tianyi He Yifan Men Jinxiang Wang Lu Xiong Guodong Yin Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Mechanical Engineering Southeast University Nanjing China Department of Mechanical and Aerospace Engineering Utah State University Logan UT USA ZDrive.ai 7th Floor Block T2 Ali Center 1398 Shenchang Road Minhang District Shanghai China School of Mechanical Engineering Southeast University Nanjing China School of Automotive Studies Tongji University Shanghai China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

In this paper, the problem of clamping force control for the Electromechanical Brake (EMB) system in autonomous vehicles is addressed. A nonlinear model predictive control framework is proposed to solve for the optimal maneuver at each optimization step to realize accurate clamping force tracking. First, the EMB system is modeled to a state-space representation through the first-principle modeling; Second, a control framework that includes three blocks is proposed to achieve clamping force tracking. The first block designs a clamping force separation strategy and an unknown input observer based on Kalman filter is applied to estimate the clamping force to replace the force sensors; the second block develops an adaptive estimation of polynomial coefficients to deal with the time-varying coefficients to improve model adaptiveness; the last block conceives the nonlinear model predictive control to track the reference force, and the resulted nonlinear programming is solved by the multiple shooting method. Finally, the proposed framework is validated to achieve faster tracking speed and more accurate tracking in multiple simulation scenarios.

关键词： Adaptation models Force Predictive models Observers Prediction algorithms Polynomials Clamps Kalman filters Force control Predictive control

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：