检索结果-内蒙古大学图书馆

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zijian Zhu Yichi Zhang Hai Chen Yinpeng Dong Shu Zhao Wenbo Ding Jiachen Zhong Shibao Zheng Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Dept. of Comp. Sci. and Tech. THBI Lab Institute for AI Tsinghua University BNRist Center Zhongguancun Laboratory Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Computer Science and Technology Anhui University SAIC Motor AI Lab

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations; 2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features; 3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on intelligent IoT

引用

IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

来源：评论

学校读者我要写书评

暂无评论

Manipulability and Robustness Optimization of the Cable-Driven Redundant Soft Manipulator

Manipulability and Robustness Optimization of the Cable-Driv...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Yi Shen Yang Hong Wei Zhou Ruochen Tai Ye Yuan Han Ding School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan People’s Republic of China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan People’s Republic of China State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan People’s Republic of China

ISBN: (纸本)9781665405362

Redundancy makes soft manipulators manage to complete a variety of tasks in complex environments. However, a pseudo-inverse kinematics controller for redundant soft manipulators simply maximizes the manipulability while ignoring some material constraints, e.g., actuation saturation and control noise. As manipulability is the indicator for the dexterity of motion in all directions of the redundant soft manipulators, this work proposes an optimization based controller, where the manipulability and robustness are optimized for a cable-driven redundant soft manipulator to adapt actuation saturation and control noise. Experiments are conducted to demonstrate the effectiveness of the proposed controller.

关键词： Conferences Biomimetics Redundancy Kinematics Manipulators Robustness Trajectory

来源：评论

学校读者我要写书评

暂无评论

An iterative Q-learning based global consensus of discrete-time saturated multi-agent systems

引用

CHAOS 2019年第10期29卷 103127-103127页

作者： Long, Mingkang Su, Housheng Wang, Xiaoling Jiang, Guo-Ping Wang, Xiaofan Huazhong Univ Sci & Technol Educ Minist China Image Proc & Intelligent Control Key Lab Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Nanjing Univ Posts & Telecommun Sch Automat Nanjing 210023 Jiangsu Peoples R China Jiangsu Engn Lab IOT Intelligent Robots Nanjing 210023 Jiangsu Peoples R China Shanghai Univ Dept Automat Shanghai 200072 Peoples R China

This paper addresses the consensus problem of discrete-time multiagent systems (DTMASs), which are subject to input saturation and lack of the information of agent dynamics. In the previous works, the DTMASs with input saturation can achieve semiglobal consensus by utilizing the low gain feedback (LGF) method, but computing the LGF matrices by solving the modified algebraic Riccati equation requires the knowledge of agent dynamics. In this paper, motivated by the reinforcement learning method, we propose a model-free Q-learning algorithm to obtain the LGF matrices for the DTMASs achieving global consensus. Firstly, we define a Q-learning function and deduce a Q-learning Bellman equation, whose solution can work out the LGF matrix. Then, we develop an iterative Q-learning algorithm to obtain the LGF matrix without the requirement of the knowledge about agent dynamics. Moreover, the DTMASs can achieve global consensus. Lastly, some simulation results are proposed to validate the effectiveness of the Q-learning algorithm and show the effect on the rate of convergence from the initial states of agents and the input saturation limit. Published under license by AIP Publishing.

关键词： discrete time systems iterative methods matrix algebra multi-agent systems nonlinear dynamical systems Riccati equations

来源：评论

学校读者我要写书评

暂无评论

Sample Weighted Gradient Regularization for Source-Free Domain Adaptation

SSRN

引用

SSRN 2024年

作者： Long, Yunchong Xu, Bingrong Zhang, Qingyong Yin, Jianhua Zeng, Zhigang School of Automation Wuhan University of Technology Wuhan430070 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430063 China The Engineering Research Center of Transportation Information and Safety Ministry of Education Wuhan430063 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Image Processing and Intelligent Control Education Ministry of China Wuhan430074 China

Conventional Domain Adaptation(DA) algorithms typically rely on access to the source data,which may not always be practical due to privacy concerns or limited storage *** address these challenges,Source-Free Domain Adaptation(SFDA) has emerged as an alternative that relies solely on a pre-trained source model rather than the actual source *** SFDA,pseudo-label supervised learning is commonly employed and initially plays a significant role in enhancing model performance during the early stages of target domain ***,as adaptation progresses,the performance can stagnate or even deteriorate due to the noise inherent in pseudo-labels,a phenomenon known as Confirmation *** noise present in target domain pseudo-labels introduces additional uncertainty and overfitting risks to the *** this study,a novel framework named Sample Weighted Gradient Regularization (SWGR) was introduced to tackle this *** first propose the label Uncertainty Estimation Metric (LUEM),a reweighting strategy that assesses the reliability of *** metric evaluates label uncertainty from both the source domain pre-trained model and spatial distribution of target domain samples,allowing the model to achieve improved cross-domain transfer performance with high-confidence samples in the target domain. Additionally,we impose a constraint on the gradient descent process of the total loss function in the form of an inequality to prevent entropy from *** Gradient Regularization eliminates overfitting while also guiding the model in lowering prediction uncertainty *** approach reduces the uncertainty in the target domain and enhances the model's generalization *** results demonstrate that our method surpasses competing SFDA approaches and even outperforms many traditional DA methods which require extensive amounts of source domain data. © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

-Gain analysis of event-triggered networked control systems: a discontinuous Lyapunov functional approach

引用

International Journal of Robust and Nonlinear control 2012年第11期23卷

作者： Songlin Hu Dong Yue Department of Control Science and Engineering and the Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

In this paper, the problems of exponential stability and -gain analysis of event-triggered networked control systems (NCSs) with network-induced delays are studied. We first propose event-triggering conditions in the sensor side and controller side, respectively. Because the implementation of our event-triggering scheme only needs periodic supervision of the system state at the constant sampling instants, instead of being monitored continuously, it is expected that the scheme will improve the resource utilization. Taking the network-induced delays into account and using delay system approach, we constructed a unified model of NCSs with hybrid event-triggering schemes. On the basis of this model, sufficient conditions for the exponential stability and -gain analysis are developed in the form of LMIs by using a discontinuous Lyapunov–Krasovskii functional approach. Moreover, the corresponding results can be further extended to more general cases, where the system matrices of the considered plant contain parameter uncertainties, represented in either polytopic or norm-bounded frameworks. In addition, as a special case, we also present the exponential stability, -gain analysis, and the control feedback gain design of event-triggered NCSs without considering the effects of network-induced delays and event-triggering condition in the controller side. Finally, a simulation example is provided to illustrate the usefulness and effectiveness of the proposed hybrid event-triggering *** © 2012 John Wiley & Sons, Ltd.

关键词： event-triggering exponential stability -gain Lyapunov–Krasovskii functional NCSs

来源：评论

学校读者我要写书评

暂无评论

Optimal Fault Detection Observer Design using Excluding Degree

Optimal Fault Detection Observer Design using Excluding Degr...

引用

IEEE Conference on Decision and control

作者： Feng Xu Yiming Wan Ye Wang Division of Information Science and Technology Shenzhen International Graduate School Tsinghua University Shenzhen P.R. China School of Artificial Intelligence and Automation Huazhong University of Science and Technology and Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan P.R. China Department of Electrical and Electronic Engineering The University of Melbourne Parkville VIC Australia

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper proposes a novel optimal gain design method for robust fault detection using set-valued observers. First, a new fault detection performance specification is proposed for set-valued observers based on a new notion named the excluding degree of the origin from a zonotope. Second, a fractional programming problem is formulated to describe fault detection optimality of set-valued observers. Third, optimal fault detection gains are obtained by solving the fractional programming problem. Finally, numerical examples are used to illustrate the effectiveness of the proposed method.

关键词： Fault detection Design methodology Observers Programming Computational complexity Optimization Numerical stability

来源：评论

学校读者我要写书评

暂无评论

Hierarchical P systems with randomized right-hand sides of rules 18th

Hierarchical P systems with randomized right-hand sides of r...

引用

18th International Conference on Membrane Computing, CMC 1018

作者： Alhazov, Artiom Freund, Rudolf Ivanov, Sergiu Institute of Mathematics and Computer Science Academy of Sciences of Moldova Academiei 5 Chişinău2028 Moldova Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Wuhan430074 China Faculty of Informatics TU Wien Favoritenstraße 9–11 Vienna1040 Austria LACL Université Paris Est – Créteil Val de Marne 61 av. Général de Gaulle Créteil94010 France TIMC-IMAG/DyCTiM Faculty of Medicine of Grenoble 5 avenue du Grand Sablon La Tronche38700 France

ISBN: (纸本)9783319733586

P systems are a model of hierarchically compartmentalized multiset rewriting. We introduce a novel kind of P systems in which rules are dynamically constructed in each step by non-deterministic pairing of left-hand and right-hand sides. We define three variants of right-hand side randomization and compare each of them with the power of conventional P systems. It turns out that all three variants enable non-cooperative P systems to generate exponential (and thus non-semi-linear) number languages. We also give a binary normal form for one of the variants of P systems with randomized rule right-hand sides. © 2018, Springer International Publishing AG.

关键词： Hierarchical systems

来源：评论

学校读者我要写书评

暂无评论

Edge-Aware Graph Attention Network for Ratio of Edge-User Estimation in Mobile Networks

Edge-Aware Graph Attention Network for Ratio of Edge-User Es...

引用

International Conference on Pattern Recognition

作者： Jiehui Deng Sheng Wan Xiang Wang Enmei Tu Xiaolin Huang Jie Yang Chen Gong PCA Lab the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Hong Kong Polytechnic University Hong Kong SAR China

Estimating the Ratio of Edge-Users (REU) is an important issue in mobile networks, as it helps the subsequent adjustment of loads in different cells. However, existing approaches usually determine the REU manually, which are experience-dependent and labor-intensive, and thus the estimated REU might be imprecise. Considering the inherited graph structure of mobile networks, in this paper, we utilize a graph-based deep learning method for automatic REU estimation, where the practical cells are deemed as nodes and the load switchings among them constitute edges. Concretely, Graph Attention Network (GAT) is employed as the backbone of our method due to its impressive generalizability in dealing with networked data. Nevertheless, conventional GAT cannot make full use of the information in mobile networks, since it only incorporates node features to infer the pairwise importance and conduct graph convolutions, while the edge features that are actually critical in our problem are disregarded. To accommodate this issue, we propose an Edge-Aware Graph Attention Network (EAGAT), which is able to fuse the node features and edge features for REU estimation. Extensive experimental results on two real-world mobile network datasets demonstrate the superiority of our EAGAT approach to several state-of-the-art methods.

关键词： Deep learning Fuses Estimation Switches Pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Languages generated by numerical P systems with thresholds

引用

THEORETICAL COMPUTER SCIENCE 2024年 988卷

作者： Zhang, Luping Xu, Fei East China Univ Technol Jiangxi Engn Technol Res Ctr Nucl Geosci Data Sci Sch Informat Engn Jiangxi Engn Lab Radioact Geosci & Big Data Techno Nanchang 330013 Jiangxi Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Hubei Peoples R China

Numerical P systems with thresholds (NPT systems) are a type of distributive and parallel computing model in the framework of membrane computing, inspired by the mechanism of information processing in cells. In membrane computing, the study of language generating/accepting power of a computing model is essential for understanding its capabilities and limitations, while the language generating/accepting power of NPT systems remains open. In this work, a language hierarchy for NPT systems is established by considering polynomials of degree one as production functions. Specifically, if each production function is a variable with a positive integer coefficient, the NPT systems characterize regular languages. If each production function is a variable with an integer coefficient, the languages generated by the NPT systems are no more than context -sensitive. Further, the NPT systems generate all recursively enumerable languages if each production function is a linear combination of no more than two variables and each term of a production function is a variable with an integer coefficient. The results show that the language generating power of NPT systems is closely related to the number of variables and the range of coefficients in production functions.

关键词： Bio-inspired computing Membrane computing Numerical P system Language

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：