检索结果-内蒙古大学图书馆

43rd Chinese control Conference, CCC 2024

作者： Zhang, Zhongyi Xu, Chenyang Jin, Licheng Hou, Huirang Meng, Qinghao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China

ISBN: (纸本)9789887581581

Electroencephalography (EEG) contains a wealth of information, including neuron activity, allowing for a partial understanding of the brain's state. As a reliable tool, EEG, combined with deep neural networks, has been increasingly utilized to assist in the detection of depression and has achieved satisfactory results. However, there are still some limitations in the current models. Firstly, it is worth noting that many existing models are binary classification models. These models can determine whether a subject is depressed or not, but they lack the capability to differentiate the severity of depression. Besides, the variations in EEG signals among various individuals have not been thoroughly taken into account, leading to subpar performance when applying the trained model to new subjects. To address the aforementioned issues, we suggest implementing a multi-classification model that utilizes a graph convolutional neural network (GCN) to identify levels of depression. Additionally, to mitigate the problem of imbalanced samples in multi-classification tasks, we introduce a penalty coefficient for the smaller categories. At the same time, the issue of signal discrepancies among subjects was taken into account, leading to the introduction of the domain generalization module. Finally, the depression level identification task on the MODMA and PRED+CT dataset achieved an accuracy of 75.47% and 77.97% respectively, surpassing the performance of the state-of-the-art (SOTA) model. In addition, numerous ablation experiments were conducted to validate the efficacy of each module. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

A Container Cloud Elastic Scaling Method Based on GRU Attention Mechanism

A Container Cloud Elastic Scaling Method Based on GRU Attent...

引用

Computer Engineering and Intelligent control (ICCEIC) International Conference on

作者： Yue Zhang Yong Sun Chunhe Song Pengpei Gao State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

In modern container cloud computing environments, achieving efficient elastic scaling is crucial to optimizing resource utilization and improving system performance. This paper proposes a new prediction model based on gated recurrent unit (GRU) and attention mechanism for load prediction and elastic scaling strategy optimization. The model uses the time series processing capability of GRU combined with the optimization characteristics of the attention mechanism to improve the accuracy and real-time performance of load prediction. Experimental results show that compared with traditional methods, the model can more accurately capture the load change trend and achieve more agile and efficient resource scheduling and management, thereby effectively reducing resource waste and improving service quality. This study provides a novel and effective solution for elastic scaling in container cloud environments.

关键词： Cloud computing Attention mechanisms Computational modeling System performance Time series analysis Containers Predictive models Resource management Optimization Load modeling

来源：评论

学校读者我要写书评

暂无评论

A new SPICE modeling approach of annular MOSFET in 350 nm commercial CMOS process 3

A new SPICE modeling approach of annular MOSFET in 350 nm co...

引用

3rd International Conference on Electronic Information Engineering and Computer, EIECT 2023

作者： Cheng, He Yang, Zhijia Zhang, Chao Zhang, Zhipeng Key Laboratory of Networked Control Systems Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350357707

Annular gate MOSFETs have emerged as potential solutions for radiation-hardened electronics, demonstrating exceptional total ionizing dose tolerance. However, accurate modeling of annular MOSFETs is crucial when designing analog integrated circuits for use in radiation environments, especially when implementing commercial CMOS processes. To enable circuit simulation using tools like SPECTRE/SPICE, foundry-provided PDKs must incorporate a rigorously verified compact model of annular MOSFET behavior. TCAD simulation can then be used to numerically evaluate the current characteristics of the annular MOSFET against the refined compact model constructed from the foundry-provided PDK. In this paper, by aligning the current characteristics of a standard NMOS between TCAD and SPICE simulations, we can verify the material process conditions. Then, based on the material process parameters, the model of the annular gate MOSFET is established using the TCAD simulator. Finally, reasonable agreements are obtained between the TCAD and SPICE simulation results for the IDS-VGS and IDS-VDS characteristics. © 2023 IEEE.

关键词： Foundries

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation of Industrial Real-Time Ethernet Redundant Network Based on Parallel Redundancy Protocol 3

Design and Implementation of Industrial Real-Time Ethernet R...

引用

3rd International Conference on Computer Science, Electronic Information Engineering and Intelligent control Technology, CEI 2023

作者： Gang, Mingxu Yan, Bingjun Hu, Bo Chinese Academy of Sciences Key Laboratory of Networked Control Systems China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350372052

Industrial real-time Ethernet technology is widely used in modern industrial control systems due to its high communication rate and low application cost. However, in complex network environments, industrial real-time Ethernet may encounter data collisions, data loss, and other issues, which reduce the overall reliability of industrial control systems. To address these issues, a method for designing and implementing industrial real-time Ethernet redundancy networks based on parallel redundancy protocols is proposed. The method first analyzes the redundant data frame structure, dual-connected node (DANP) structure, and redundancy implementation principle of parallel redundancy protocols (PRP). Then, aiming at the standard redundancy discard algorithm principle and its shortcomings, an improved sliding window redundancy discard algorithm is proposed to adapt to complex network environments. Based on the discard algorithm, dual-connected redundancy nodes are designed and implemented. Finally, the dual-connected redundancy nodes are used as the core to construct industrial real-time Ethernet redundancy networks. The test results show that in a PRP-based industrial real-time Ethernet redundant network, when a single redundant network fails, the system can switch without delay, greatly improving the reliability of the industrial control system communication network © 2023 IEEE.

关键词： Redundancy

来源：评论

学校读者我要写书评

暂无评论

Model Predictive control for Nonlinear Bilateral Teleoperation systems With Time Delay 41

Model Predictive Control for Nonlinear Bilateral Teleoperati...

引用

第41届中国控制会议

作者： Yu Liu Chuanzhi Zang Yuqi Liu Peng Zeng School of Artificial Intelligence Shenyang University of Technology Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Bilateral teleoperation systems have received extensive attention as a substitute for the human to perform tasks in remote and hazardous *** paper proposes a model predictive control strategy for nonlinear bilateral teleoperation systems based on Long-Short Term Memory(LSTM) ***,model predictive control is used to achieve good tracking performance in the presence of input constraints in the *** the same time,considering the influence of different proficiency operators operating the master manipulator on the system performance,the error between the actual master manipulator trajectory and the reference trajectory is introduced into a linear feedback term to compensate for the decrease in system control accuracy caused by the error of ***,the LSTM network is used to predict the trajectory of the master manipulator in the future time equivalent to the network transmission *** predictions are used to control the remote slave manipulator,thereby eliminating the impact of communication delay on the ***,in slave sides,the trajectory creators are applied to generate the desired trajectories to achieve good force feedback *** experiments are carried out to verify the proposed control strategy,which can guarantee good performance with both position tracking and force feedback under time delay.

关键词： Model predictive control bilateral teleoperation long-short term memory network communication delay

来源：评论

学校读者我要写书评

暂无评论

A policy gradient algorithm integrating long and short-term rewards for soft continuum arm control

引用

Science China(Technological Sciences) 2022年第10期65卷 2409-2419页

作者： DONG Xiang ZHANG Jing CHENG Long XU WenJun SU Hang MEI Tao School of Electrical Engineering and Automation Anhui UniversityHefei 230601China State Key Laboratory for Control and Management of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Robotics Research Center Peng Cheng LaboratoryShenzhen 518055China

The soft continuum arm has extensive application in industrial production and human life due to its superior safety and flexibility. Reinforcement learning is a powerful technique for solving soft arm continuous control problems, which can learn an effective control policy with an unknown system model. However, it is often affected by the high sample complexity and requires huge amounts of data to train, which limits its effectiveness in soft arm control. An improved policy gradient method, policy gradient integrating long and short-term rewards denoted as PGLS, is proposed in this paper to overcome this issue. The shortterm rewards provide more dynamic-aware exploration directions for policy learning and improve the exploration efficiency of the algorithm. PGLS can be integrated into current policy gradient algorithms, such as deep deterministic policy gradient(DDPG). The overall control framework is realized and demonstrated in a dynamics simulation environment. Simulation results show that this approach can effectively control the soft arm to reach and track the targets. Compared with DDPG and other model-free reinforcement learning algorithms, the proposed PGLS algorithm has a great improvement in convergence speed and performance. In addition, a fluid-driven soft manipulator is designed and fabricated in this paper, which can verify the proposed PGLS algorithm in real experiments in the future.

关键词： soft arm control Cosserat rod deep reinforcement learning policy gradient algorithm high sample complexity

来源：评论

学校读者我要写书评

暂无评论

A Bioinspired Soft Robot Combining the Growth Adaptability of Vine Plants with a Coordinated control System

引用

Research 2021年第1期2021卷 1266-1273页

作者： Pengchun Li Yongchang Zhang Guangyu Zhang Dekai Zhou Longqiu Li State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbinHeilongjiang 150001China

Tip-extending soft robots,taking flexible film or rubber as body material and fluid pressure as input power,exhibit excellent advantages in constrained and cluttered environments for detection and ***,existing soft continuum robots are of great challenges in achieving multiple,mutually independent,and on-demand active steering over a long distance without precise steering *** this paper,we introduce a vine-like soft robot made up of a pressurized thin-walled vessel integrated with the high controllability of a control system with multiple degrees of freedom in three ***,steering and kinematic models to relate the steering angle and robot length to the location of the robot tip are provided,and a dynamic finite element model for analyzing the motion of the spatial consecutive steering is *** demonstrate the abilities of disinfection of the robot moving in a long and tortuous pipeline and detection in a multi-obstacle constrained *** is established that the robot exhibits great advantages in active consecutive steering over a long distance,high controllability in completing more complex path planning,and significant ability of carrying operational tools for ventilation pipeline disinfection and multi-obstacle *** bionic soft robot shows great promise for use in environment sensing,target detecting,and equipment servicing.

关键词： robot Robot obstacle

来源：评论

学校读者我要写书评

暂无评论

A Monocular Biological Simultaneous Localization and Mapping Method Based on a Bionic Vision Model

A Monocular Biological Simultaneous Localization and Mapping...

引用

2023 International Conference on Frontiers of robotics and Software Engineering, FRSE 2023

作者： Guo, Sitong Zhang, Yu Zhejiang University Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou China

ISBN: (纸本)9798350301113

Animals can navigate robustly through large unknown environments, utilizing inaccurate sensor and egomotion cues. Inspired by the mechanism of the human visual system, this paper presents a biological simultaneous localization and mapping (SLAM) system based on a monocular vision sensor to perform navigation tasks in complex environments. Specifically, our contributions are two folds: an improved monocular visual odometry with a scale recovery algorithm to calibrate the estimated robot pose, and a bionic vision model based on image enhancement algorithm and feature extraction approach to detect closed-loop. Experiments on public datasets demonstrate that our proposed method performs competitively in illumination-varying scenarios indoors and outdoors, which achieve a trade-off to maintain performances in terms of accuracy, execution time, and robustness. © 2023 IEEE.

关键词： Vision

来源：评论

学校读者我要写书评

暂无评论

A Multi-Model Fusion Approach for Transformer Fault Detection 4

A Multi-Model Fusion Approach for Transformer Fault Detectio...

引用

4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Zhou, Ke Zhang, Wei Li, Ligang Pan, Shaoming Huang, Chengwei Electric Power Research Institute of Guangxi Power Grid CO. LTD. Guangxi Power Grid Equipment Monitoring and Diagnosis Technology Innovation Center Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Nanning530023 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control Systems China Power Supply Bureau of Guangxi Hezhou Power Grid Co. Ltd. China

ISBN: (纸本)9798350363586

Addressing the weak generalization capability and suboptimal prediction performance of single mechanistic and data-driven models, this paper proposes a transformer fault detection method based on the fusion of mechanism and data models. Firstly, vibration and current data are collected on-site to train the SVM vibration model and the Adaboost current model. Secondly, current simulation data is obtained using laboratory simulation software. This simulation data, combined with the current data collected under the same conditions on-site, is used as input to train the GBDT error compensation model. Finally, these three models are used as inputs to the GRNN, completing the overall model design. This fusion of mechanistic and data-driven models not only addresses the parameter mismatch issue of the mechanistic model under complex working conditions but also deeply mines time-series data. Experimental results show that The mean squared error of the fusion model is 0.002 lower than that of the data model, and the coefficient of determination is improved by 5.07% compared to the mechanistic model, enhancing the overall predictive accuracy of the model. © 2024 IEEE.

关键词： Data accuracy

来源：评论

学校读者我要写书评

暂无评论

Neural Dynamics for Cooperative Motion control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

引用

IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1605-1620页

作者： Yufeng Lian Xingtian Xiao Jiliang Zhang Long Jin Junzhi Yu Zhongbo Sun the Department of Control Engineering Changchun University of TechnologyChangchun 130012China IEEE the College of Information Science and Engineering Northeastern UniversityShenyang 110819 the National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China the School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China the State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

关键词： Cooperative motion control noise-tolerant zeroing neural network(NTZNN) omnidirectional mobile manipulator(OMM) repetitive motion planning

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：