检索结果-南通市图书馆

A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

Computer Modeling in Engineering & Sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea school of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia school of automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Deep adaptive control with online identification for industrial robots

引用

Science China(Technological Sciences) 2022年第11期65卷 2593-2604页

作者： SHEN Tan QIAO XueChun DONG YunLong WANG YuRan ZHANG Wei YUAN Ye school of artificial inteligence and automation Huazhong University of Science and TechnologyWuhan 430074China Department of Mechanical and Energy Engineering Southerm University of Science and TechnologyShenzhen 518055China

Derivation of control equations from data is a critical problem in numerous scientific and engineering *** inverse dynamic control of robot manipulators in the field of industrial robot research is a key ***,researchers needed to obtain the robot dynamic model through physical modeling methods before developing ***,the robot dynamic model and suitable control methods are often elusive and difficult to tune,particularly when dealing with real dynamical *** this paper,we combine an enhanced online sparse Bayesian learning(OSBL)algorithm and a model reference adaptive control method to obtain a data-driven modeling and control strategy from data containing noise;this strategy can be applied to dynamical *** particular,we use a sparse Bayesian approach,relying only on some prior knowledge of its physics,to extract an accurate mechanistic model from the measured *** parameters are further identified from the modeling error through a deep neural network(DNN).By combining the identification model with a model reference adaptive control approach,a general deep adaptive control(DAC)method is obtained,which can tolerate unmodeled *** adaptive update law is derived from Lyapunov’s stability criterion,which guarantees the asymptotic stability of the ***,the Enhanced OSBL identification method and DAC scheme are applied on a six-degree-of-freedom industrial robot,and the effectiveness of the proposed method is verified.

关键词： industrial robots sparse Bayesian learning online identification adaptive control

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent Systems

引用

IEEE/CAA Journal of Automatica Sinica 2025年第1期 40-53页

作者： Qinglai Wei Shanshan Jiao Qi Dong Fei-Yue Wang IEEE the Institute of Systems Engineering Macau University of Science and Technology the State Key Laboratory of Multimodal artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences the school of artificial Intelligence University of Chinese Academy of Sciences China Academy of Electronics and Information Technology the State Key Laboratory of Multimodal artificial Intelligence Systems the school of artificial Intelligence University of Chinese Academy of Sciences

This paper highlights the utilization of parallel control and adaptive dynamic programming(ADP) for event-triggered robust parallel optimal consensus control(ETRPOC) of uncertain nonlinear continuous-time multiagent systems(MASs).First, the parallel control system, which consists of a virtual control variable and a specific auxiliary variable obtained from the coupled Hamiltonian, allows general systems to be transformed into affine systems. Of interest is the fact that the parallel control technique's introduction provides an unprecedented perspective on eliminating the negative effects of disturbance. Then, an eventtriggered mechanism is adopted to save communication resources while ensuring the system's stability. The coupled HamiltonJacobi(HJ) equation's solution is approximated using a critic neural network(NN), whose weights are updated in response to events. Furthermore, theoretical analysis reveals that the weight estimation error is uniformly ultimately bounded(UUB). Finally,numerical simulations demonstrate the effectiveness of the developed ETRPOC method.

关键词：

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

ControlService: a containerized solution for control-algorithm-as-a-service in cloud control systems

引用

Science China(Information Sciences) 2024年第8期67卷 172-189页

作者： Chenggang SHAN Runze GAO Zhen YANG Wei ZHANG Yuanqing XIA school of artificial Intelligence Zaozhuang University school of automation Beijing Institute of Technology

As an extension of networked control systems, cloud control systems(CCSs) have emerged as a new control paradigm to improve the service quality of emerging control missions, such as data-driven modeling and automated vehicles. Existing studies have used the workflow-based restructured method to optimize the computation-intensive algorithms in the CCSs. However, the challenges here are how to define and submit these algorithms' workflows as cloud services and execute these algorithms' workflows in a containerized manner. Based on these challenges, we propose a containerized solution for the control-algorithm-as-a-service(C3aS) in the CCSs, namely Control Service. It offers the control algorithm as a cloud workflow service and uses a customized workflow engine to realize the containerized execution. First, we employ a cloud workflow representation method to define a control algorithm into an abstract cloud workflow form. Afterward, we provide a cloud service representation of the abstract cloud workflow. Next, we design a workflow engine and submit the cloud service to this workflow engine to implement containerized execution of this cloud service in the CCSs. In the experiment, we discuss the cloud service form and containerized implementation of the subspace identification method. Experimental results show that the proposed Control Service has significant performance advantages in computational time, reduction percentage, and speedup ratio compared with the baseline method.

关键词： cloud control systems control-algorithm-as-a-service containerization workflow engine cloud workflow service subspace identification

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Spiking neural networks in intelligent control systems:a perspective

引用

Science China(Information Sciences) 2024年第7期67卷 324-326页

作者： Anguo ZHANG Yongduan SONG school of artificial Intelligence Anhui University school of automation Chongqing University

artificial intelligence (AI) has undergone substantial advancements in recent years, prompting an era of innovation in various domains. Among the groundbreaking contributions, neural networks stand at the forefront, powering applications ranging from computer vision and natural language processing to intelligent control systems [1, 2].

关键词：

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

SSVEP-based brain-computer interfaces are vulnerable to square wave attacks

引用

Science China(Information Sciences) 2022年第4期65卷 80-92页

作者： Rui BIAN Lubin MENG Dongrui WU school of artificial Intelligence and automation Huazhong University of Science and Technology Zhejiang Lab

Electroencephalogram(EEG) based brain-computer interfaces(BCIs) have attracted wide attention in recent years. Steady-state visual evoked potential(SSVEP) is one of the most popular BCI paradigms,which has high information transmission rate and short user calibration time. Recent research has shown that EEG-based BCIs are under the threat of adversarial examples. However, existing attack approaches are very difficult to implement in a real-world system. Considering SSVEP’s dependency on the frequency information, this paper proposes to use square wave signals as adversarial perturbations to attack SSVEP-based BCIs, which are easy to generate and apply in practice. EEG trials contaminated by the square wave perturbation can be classified into any target class specified by the attacker. Compared with previous approaches,our perturbation can be implemented much more easily. Experiments on two SSVEP datasets demonstrated the efficiency and robustness of the proposed approach in attacking two SSVEP classifiers based on canonical correlation analysis, exposing a critical security problem in SSVEP-based BCIs.

关键词： electroencephalogram brain-computer interface steady-state visual evoked potential adversarial attack

来源：

同方期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Unsupervised intrusion detection system for in-vehicle communication networks

引用

Journal of Safety Science and Resilience 2024年第2期5卷 119-129页

作者： Kabilan N Vinayakumar Ravi V Sowmya Center for Computational Engineering and Networking(CEN) Amrita School of Artificial InteligenceAmrita Vishwa VidyapeethamCoimbatoreIndia Center for artificial Intlligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia

In-vehicle communication has been optimized day to day to keep updated of the *** area network(CAN)is used as a standard communication method because of its efficient and reliable ***,CAN is prone to several network level attacks because of its lack in security *** methods have been introduced to incorporate this in *** proposed an unsupervised method of intrusion detection for in-vehicle communication networks by combining the optimal feature extracting ability of autoencoders and more precise clustering using fuzzy C-means(FCM).The proposed method is light weight and requires less computation *** performed an extensive experiment and achieved an accuracy of 75.51%with the ML35o in-vehicle intrusion *** experimental result,the proposed method also works better for other intrusion detection problems like wireless intrusion detection datasets such as WNS-DS with accuracy of 84.05%and network intrusion detection datasets such as KDDCup with accuracy 60.63%,UNSW_NB15 with accuracy 73.62%and Information Security Center of Excellence(Iscx)with accuracy 74.83%.Overall,the proposed method outperforms the existing methods and avoids labeled datasets when training an in-vehicle intrusion detection *** results of the experiment of our proposed method performed on various intru-sion detection datasets indicate that the proposed approach is generalized and robust in detecting intrusions and can be effectively deployed in real time to monitor CAN traffic in vehicles and proactively alert during attacks.

关键词： Controlarea network Intrusiondetection Unsupervised learning Autoencoders Fuzzy C-means

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Online identification of time-varying dynamical systems for industrial robots based on sparse Bayesian learning

引用

Science China(Technological Sciences) 2022年第2期65卷 386-395页

作者： SHEN Tan DONG YunLong HE DingXin YUAN Ye school of artificial Intelligence and automation Huazhong University of Science and TechnologyWuhan 430074China

Nowadays, industrial robots have been widely used in manufacturing, healthcare, packaging, and more. Choosing robots in these applications mainly attributes to their repeatability and precision. However, prolonged and loaded operations can deteriorate the accuracy and efficiency of industrial robots due to the unavoidable accumulated kinematical and dynamical errors. This paper resolves these aforementioned issues by proposing an online time-varying sparse Bayesian learning(SBL) method to identify dynamical systems of robots in real-time. The identification of dynamical systems for industrial robots is cast as a sparse linear regression problem. By constructing the dictionary matrix, the parameters of the robot dynamics are effectively estimated via a re-weighted1-minimization algorithm. Online recursive methods are integrated into SBL to achieve real-time system identification. By including sparsity and promoting online learning, the proposed method can handle time-varying dynamical systems and therefore improve operational stability and accuracy. Experimental results on both simulated and real selective compliance assembly robot arm(SCARA) robots have demonstrated the effectiveness of the proposed method for industrial robots.

关键词： industrial robots sparse Bayesian learning online identification

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Active poisoning: efficient backdoor attacks on transfer learning-based brain-computer interfaces

引用

Science China(Information Sciences) 2023年第8期66卷 211-232页

作者： Xue JIANG Lubin MENG Siyang LI Dongrui WU school of artificial Intel ligence and automation Huazhong University of Science and Technology Zhejiang Lab

Transfer learning(TL) has been widely used in electroencephalogram(EEG)-based braincomputer interfaces(BCIs) for reducing calibration efforts. However, backdoor attacks could be introduced through TL. In such attacks, an attacker embeds a backdoor with a specific pattern into the machine learning model. As a result, the model will misclassify a test sample with the backdoor trigger into a prespecified class while still maintaining good performance on benign samples. Accordingly, this study explores backdoor attacks in the TL of EEG-based BCIs, where source-domain data are poisoned by a backdoor trigger and then used in TL. We propose several active poisoning approaches to select source-domain samples, which are most effective in embedding the backdoor pattern, to improve the attack success rate and efficiency. Experiments on four EEG datasets and three deep learning models demonstrate the effectiveness of the approaches. To our knowledge, this is the first study about backdoor attacks on TL models in EEG-based BCIs. It exposes a serious security risk in BCIs, which should be immediately addressed.

关键词： brain-computer interface electroencephalogram transfer learning poisoning attack backdoor attack

来源：

同方期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

TransCrowd: weakly-supervised crowd counting with transformers

引用

Science China(Information Sciences) 2022年第6期65卷 48-61页

作者： Dingkang LIANG Xiwu CHEN Wei XU Yu ZHOU Xiang BAI school of artificial Intelligence and automation Huazhong University of Science and Technology school of Electronic Information and Communication Huazhong University of Science and Technology school of artificial Intelligence Beijing University of Posts and Telecommunications

The mainstream crowd counting methods usually utilize the convolution neural network(CNN)to regress a density map, requiring point-level annotations. However, annotating each person with a point is an expensive and laborious process. During the testing phase, the point-level annotations are not considered to evaluate the counting accuracy, which means the point-level annotations are redundant. Hence,it is desirable to develop weakly-supervised counting methods that just rely on count-level annotations, a more economical way of labeling. Current weakly-supervised counting methods adopt the CNN to regress a total count of the crowd by an image-to-count paradigm. However, having limited receptive fields for context modeling is an intrinsic limitation of these weakly-supervised CNN-based methods. These methods thus cannot achieve satisfactory performance, with limited applications in the real world. The transformer is a popular sequence-to-sequence prediction model in natural language processing(NLP), which contains a global receptive field. In this paper, we propose TransCrowd, which reformulates the weakly-supervised crowd counting problem from the perspective of sequence-to-count based on transformers. We observe that the proposed TransCrowd can effectively extract the semantic crowd information by using the self-attention mechanism of transformer. To the best of our knowledge, this is the first work to adopt a pure transformer for crowd counting research. Experiments on five benchmark datasets demonstrate that the proposed TransCrowd achieves superior performance compared with all the weakly-supervised CNN-based counting methods and gains highly competitive counting performance compared with some popular fully-supervised counting methods.

关键词： crowd counting visual transformer weakly supervised crowd analysis transformer

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

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：