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检索条件"机构=Key Laboratory of Machine Vision and Intelligence Control Technology"

共 188 条记录，以下是111-120 订阅

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About Step-Length of ZeaD（Zhang et al Discretization） Formula 4IgSY for Future Minimization via Fan Equations

About Step-Length of ZeaD（Zhang et al Discretization） Form...

引用

第30届中国控制与决策会议

作者： Yunong Zhang Zhiyuan Qi Huihui Gong Jian Li Binbin Qiu School of Information Science and Technology Sun Yat-sen University(SYSU) Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

Future minimization,i.e.,discrete time-varying minimization,is a difficult and meaningful *** has been successfully solved by Zhang et al using zeroing dynamics(ZD) and discretization *** this paper,a type of discrete-time ZD(DT-ZD) model,which is obtained via utilizing Zea D(Zhang et al Discretization) formula 4 I2)S Y,is analyzed and investigated to ensure its ***,via theoretical guarantees,we propose the step-length domain,or say,the effective domain of the step-length,which makes the discrete-time model ***,we make further efforts to obtain the step-length optimum which provides the optimal stability of the DT-ZD ***,numerical experiments are performed to validate the step-length domain and the step-length optimum of the DT-ZD model for future minimization.

关键词： Future minimization Zeroing dynamics(ZD) Zhang et al discretization(ZeaD) formula Step-length domain Optimum

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Jerk-Level Cyclic Motion Planning and control for Constrained Redundant Robot Manipulators Using Zhang Dynamics:Theoretics

Jerk-Level Cyclic Motion Planning and Control for Constraine...

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第30届中国控制与决策会议

作者： Min Yang Yunong Zhang Huanchang Huang Dechao Chen Jian Li School of Information Science and Technology Sun Yat-sen University(SYSU) Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

In this paper,based on Zhang dynamics method（ZDM）,a cyclic motion planning（CMP） scheme is developed to remedy the joint drift phenomenon of redundant robot manipulators constrained by joint physical *** existing works resolved at velocity level or acceleration level,the scheme in this paper is resolved at jerk *** achieve superior performance,the jerk-level CMP scheme is improved by the feedback control,which further leads to a jerk-level cyclic motion planning and control（CMPC） *** cyclic motion criterion and the avoidance of joint physical limits（e.g.,joint-angle,joint-velocity,joint-acceleration and joint-jerk limits） are both considered into the jerklevel CMP and CMPC *** importantly,the correctness of the presented jerk-level schemes is guaranteed by the corresponding engineering-type theoretical analyses and ***,the jerk-level CMPC scheme is reformulated as a dynamical quadratic program（DQP） which can be solved by a piecewise-linear projection equation neural network（PLPENN）,and also can be solved by a suitable numerical algorithm.

关键词： Constrained robot manipulators Joint drift phenomenon Jerk level Feedback control Theoretics

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Output Tracking of Time-Varying Linear and Nonlinear Systems Using ZN and ZG controllers with Pseudo Division-by-Zero Phenomena Shown

Output Tracking of Time-Varying Linear and Nonlinear Systems...

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第30届中国控制与决策会议

作者： Jinjin Guo Xuyun Yang Yunong Zhang Jian Li Min Yang School of Information Science and Technology Sun Yat-sen University SYSU Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

This paper introduces Zhang neurodynamics(ZN) method and Zhang-gradient(ZG) method used to design ZN and ZG controllers for the output tracking of systems, especially time-varying nonlinear system. Thereinto, ZG method is composed of the ZN method and gradient neurodynamics(GN) method. By applying the ZN method and the ZG method, respectively, to the output tracking control problems of time-varying linear(TVL) system and time-varying nonlinear(TVN) system, specific ZN controllers and ZG controllers are obtained. Particularly, TVL system and TVN system discussed in this paper may both have pseudo division-by-zero phenomena. From the numerical results, though ZN controllers and ZG controllers perform the output tracking of TVL system and TVN system well, the infinite value of the former and the finite value of the latter at singular instants verify that the ZG method is more effective in dealing with pseudo division-by-zero problem.

关键词： Time-varying linear system Time-varying nonlinear system Output tracking control problem ZN controller ZG controller Pseudo division-by-zero phenomena

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Different-Level Dynamic Quadratic Minimization Using Square Root Matrix Based Zhang Equivalency

Different-Level Dynamic Quadratic Minimization Using Square ...

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第30届中国控制与决策会议

作者： Min Yang Huanchang Huang Jian Li Haifeng Hu Yunong Zhang School of Information Science and Technology Sun Yat-sen University (SYSU) Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

For quadratic minimization(QM), it is usually assumed that the solution objective is time-invariant and at the same level. However, the QM is always time-varying(i.e., naturally and implicitly dynamic) and may be at different levels(i.e., explicitly dynamic) in practice. In this paper, different from the general QM, a novel different-level dynamic QM(DLDQM) is proposed and investigated. First, the DLDQM solving is split up into two general time-varying QM(TVQM) solving. Then, a solution model is thus proposed and investigated for the DLDQM, by applying square root matrix based Zhang equivalency and Moore-Penrose(MP) generalized inverse. In addition, it is worth pointing out that solving the DLDQM is a new and important attempt of this research. Finally, illustrative numerical experiments are conducted with numerical results presented and analyzed.

关键词： Different-level dynamic quadratic minimization(DLDQM) Square root matrix Zhang equivalency MoorePenrose(MP) generalized inverse

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Computer Simulations and Comparisons of Jerk-Level Cyclic Motion Planning and control for CRRM

Computer Simulations and Comparisons of Jerk-Level Cyclic Mo...

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第37届中国控制会议

作者： Dechao Chen Min Yang Huanchang Huang Jian Li Yunong Zhang School of Information Science and Technology Sun Yat-sen University (SYSU) Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

In this paper, a jerk-level cyclic motion planning and control（CMPC） scheme is presented to remedy the joint drift phenomenon of the constrained redundant robot manipulators（CRRM）. Furthermore, the path-tracking simulations are presented to substantiate the effectiveness and accuracy of the jerk-level CMPC scheme. Comprehensive and detailed comparisons between the presented jerk-level CMPC scheme and other existing schemes [i.e., the Jacobian pseudo-inverse based（JPIB） scheme, the minimum jerk norm（MJN） scheme and the acceleration-level cyclic motion planning（ALCMP） scheme] illustrate that the presented CMPC scheme resolved at jerk level is superior and more applicable.

关键词： Constrained redundant robot manipulators （CRRM） jerk level cyclic motion planning and control （CMPC） scheme computer simulations comparisons

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Knowledge Base Question Answering via Structured Query Generation using Question domain

Knowledge Base Question Answering via Structured Query Gener...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Jiecheng Li Zizhen Peng Xiaoying Zhu Keda Lu Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China

The core problem of Knowledge Base Question Answering(KBQA) is to find queries from user questions to knowledge bases. Specifically, natural language questions need to be transformed into structured queries before associating with knowledge bases, and the answers can be found from the knowledge graph using the structured queries. We found structured queries in similar question domains tend to have repetitive reasoning steps. Also, humans often use cases identical to the question and information from these cases to assist in answering new questions. Hence, we propose a new KBQA framework based on similar question domains. We separately design the inference information retriever module to extract cases with a similar structure to the question and the relation information retriever module to narrow the scope of reasoning relation extraction. Finally, we used the retrieved inference cases and relation candidate sets as auxiliary information and generated an executable Knowledge-oriented Programming Language(KoPL) through the program generation module. Experiments have shown that the model can handle complex question answering and has a strong reasoning ability. Our methodology has resulted in new state-of-the-art performance on WebQSP and CWQ datasets.

关键词： Splicing Computational modeling Knowledge based systems Natural languages Knowledge graphs Programming Ubiquitous computing

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The Performance Evaluation of SSVEP-BCI Actuated Wheelchair with Parameter Setting of Time-Window Length and Stimulation Layout

The Performance Evaluation of SSVEP-BCI Actuated Wheelchair ...

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Workshop on Metrology for Industry 4.0 and IoT

作者： Jun Xie Xiaojun Wu Peng Fang Guanglin Li Guozhi Cao Tao Xue Science and Technology on Electrooptic Control Laboratory Luoyang China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology & Shenzhen Engineering Laboratory of Neural Rehabilitation Shenzhen China School of Mechanical Engineering Xi'an Jiaotong University Xi'an China

ISBN: (数字)9781728148922

ISBN: (纸本)9781728148939

Due to the advantages of prominent brain responses, less training duration, and robustness to artifacts, steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has been recently applied to intelligent wheelchair navigation. Recent researches mainly focused on the implementation of a specific recognition method to analyze the wheelchair navigation performance under fixed stimulation target number. The navigation performance evaluation under different recognition methods and different BCI target numbers is rarely compared. In this paper, the navigation performance comparison between different numbers of stimulation targets and different recognition methods was carried out. The multichannel integrated GT 2 circ and canonical correlation analysis (CCA) recognition methods were used to compare the wheelchair navigation performance under different time-window (TW) lengths. And the 3-target and 4-target arrangement layouts were adopted to compare the navigation performance between the “No feedback” and the “Feedback” conditions. Experimental results showed that the GT 2 circ recognition method with TW length of 2 s would promote the navigation efficiency and different stimulation target number could be tailored for different wheelchair application purposes, which would bring tutorial instructions to the applicable navigation of the BCI-actuated wheelchair.

关键词： brain-computer interface (BCI) steady-state visual evoked potential (SSVEP) wheelchair navigation time-window (TW) length stimulation layout Performance Evaluation Wheelchair parameter setting navigation Navigation efficiency Brain-Computer Interfaces Recognition (Psychology) Stimuli layouts

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Visual Adapt for RGBD Tracking

Visual Adapt for RGBD Tracking

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guangtong Zhang Qihua Liang Zhiyi Mo Ning Li Bineng Zhong Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China

Recent RGBD trackers have employed cueing techniques by overlaying Depth modality images as cues onto RGB modality images, which are then fed into the RGB-based model for tracking. However, the direct overlaying interaction method between modalities not only introduces more noise into the feature space but also exhibits the inadaptability of the RGB-based model to mixed-modality inputs. To address these issues, we introduce Visual Adapt for RGBD Tracking (VADT). Specifically, we maintain the input of the RGB-based model as the RGB modality. Additionally, we have devised a fusion module to enable modality interaction between depth and RGB features. Subsequently, a Depth Adapt module has been formulated to facilitate image interaction with the fused features. This module involves cross-attending to the obtained depth-assisted features and the RGB search frame features produced by the RGB-based model’s output. Experimental results indicate that our proposed tracker achieves state-of-the-art results on various RGBD benchmark tests.

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Traffic Flow Prediction via Weighted Combination of ARIMA and WASDNN Models

Traffic Flow Prediction via Weighted Combination of ARIMA an...

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第33届中国控制与决策会议

作者： Xiao Liu Yunong Zhang Min Yang Zhongxian Xue Chengxu Ye School of Electronics and Information Technology Sun Yat-sen University Guangdong Key Laboratory of Modern Control Technology Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education School of Data and Computer Science Sun Yat-sen University School of Computer Science Qinghai Normal University

The modeling and prediction of traffic flow are important in transportation study and traffic network *** from individual models,a hybrid model which is a combination of autoregressive integrated moving average(ARIMA) and weights and structure determination neural network(WASDNN) models,is proposed for modeling and prediction of traffic *** with individual models like ARIMA and WASDNN,the proposed ARIMAWASDNN model has better performance in modeling and prediction of traffic flow of Shenzhen city as an *** addition,prediction results of different activation functions of hidden layer neurons are *** is more,the proposed ARIMA-WASDNN model also performs better than the popular long short-term memory(LSTM) model in predicting traffic flow of Shenzhen *** effectiveness and superiority of ARIMA-WASDNN model are substantiated by results of numerical experiments.

关键词： Time series traffic flow prediction autoregressive integrated moving average(ARIMA) model weights and structure determination(WASD) algorithm

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Diagnosis of Autism Spectrum Disorder Based on Contrastive Functional Connectivity Graph Learning Network

Diagnosis of Autism Spectrum Disorder Based on Contrastive F...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shuaiqi Liu Siqi Wang Beibei Liang Bing Li Jianpeng Xu College of Electronic and Information Engineering Hebei University Machine Vision Technology Innovation Center of Hebei Province Baoding China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

To reduce the dependence on tagged data, we proposed a Contrastive Functional Connectivity Graph Learning Network (CFCG-Net) for the diagnosis of autism spectrum disorder. CFCG-Net is mainly composed of three parts: construction of contrastive Functional Connection (FC) graphs, learning of contrastive FC graphs, and dynamic graph classification based on population graph. Firstly, we constructed contrastive FC graphs for each subject based on the original brain functional connectivity. Secondly, we constructed a graph convolutional network to learn the contrastive FC graphs to obtain the contrastive embeddings of each subject, and further defined the population graph through the contrastive embeddings. Finally, we trained a dynamic graph classifier to predict the class probability of each subject. CFCG-Net is tested on the ABIDE I dataset, which verified the effectiveness of CFCG-Net.

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