检索结果-内蒙古大学图书馆

2023 IEEE MTT-S International Wireless Symposium, IWS 2023

作者： Dong, Jiancheng Zhu, Xu Yang, Tao Nie, Yi Wang, Yong Research Center of Advanced Rf Chips and Systems Nanhu Laboratory Jiaxing314001 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Technology Beijing100191 China University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350336535

In this paper, an on-chip wideband bandpass filter (BPF) with high frequency selectivity and low insertion loss is proposed using gallium arsenide (GaAs) integrated passive device (IPD) technology. Multiple transmission zeros can be provided by the capacitors loaded on the series and parallel LC resonators for the proposed design. Comparing with the state-of-the-art on-chip wideband BPFs, the proposed design has the advantages of high frequency selectivity and low loss. The final filter centered at 1.93 GHz with the 3-dB fractional bandwidth of 64.2% is demonstrated, which exhibits the excellent shape factor (30-dB BW/3-dB BW) of 1.28 and the minimum insertion loss of 1.17 dB. © 2023 IEEE.

关键词： Gallium arsenide

来源：评论

学校读者我要写书评

暂无评论

Optimal Tracking Performance of Networked Control systems Under DoS and Its Applications

Optimal Tracking Performance of Networked Control Systems Un...

引用

2024 China Automation Congress, CAC 2024

作者： Ren, Xinyu Li, Bo Zhao, Feng Chen, Xiangyong Zhang, Xianhe Jiang, Xiaowei School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Automation and Electrical Engineering Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

ISBN: (纸本)9798350368604

This paper investigates the optimal tracking performance (OTP) of multiple-input multiple-output (MIMO) discrete networked control systems (NCSs) under the influence of Denial of Service (DoS) attack, channel noise, and bandwidth constraints. The channel noise is assumed to be additive Gaussian white noise (AWGN). Due to the randomness of DoS attack, a Bernoulli distribution is used for DoS modeling. Utilizing frequency domain analysis methods, coprime factorization, all-pass decomposition, and Youla parameterization, an explicit expression for the optimal tracking performance (OTP) of the system under the two-degree-of-freedom (TDOF) controller is derived. The analysis indicates that non-minimum phase (NMP) zeros, unstable poles (UPs), DoS attack, and network constraints affect the OTP of NCSs. Finally, the theoretical results are validated through simulations of MIMO NCS and vehicle control system. © 2024 IEEE.

关键词： White noise

来源：评论

学校读者我要写书评

暂无评论

Automated Sewer Pipe Defect Classification Using Inspection Robot Imagery and Deep Neural Network 12

Automated Sewer Pipe Defect Classification Using Inspection ...

引用

12th RSI International Conference on Robotics and Mechatronics, ICRoM 2024

作者： Goharinezhad, Saleh Hadi, Alireza Mirzaei, Sayeh School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology University of Tehran Advanced Service Robots Laboratory Department of Mechatronics Tehran Iran School of Engineering Science College of Engineering University of Tehran Tehran Iran

ISBN: (纸本)9798331529734

Sewer systems play a critical role in transporting wastewater. Efficient maintenance requires accurate identification of sewer pipe defects, traditionally performed through manual interpretation of Closed-Circuit Television (CCTV) images from robot inspections. This manual process is time-consuming, labor-intensive, and susceptible to human error. While conventional computer vision methods (machine learning methods) have been explored, they often involve extensive image pre-processing and intricate feature extraction. In this study, we propose an automated approach for classifying sewer pipe defects in Iran using deep learning, specifically leveraging the ResNet50 architecture for defect classification. We applied this deep convolutional neural network (CNN) model to automatically detect three common defect types as root intrusions, deposits, and open joints within a dataset 4630 images extracted from CCTV inspection videos of sewer pipes. Our model achieved an average testing accuracy of 96%, highlighting its potential for automating the interpretation of sewer CCTV footage. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

State of Charge Estimation of Lithium-Ion Batteries Using Convolutional Neural Networks in Electric Vehicle Applications

State of Charge Estimation of Lithium-Ion Batteries Using Co...

引用

Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Marwa Gaich Sabeur Jemmali Bilal Manai LATIS- Laboratory of Advanced Technology and Intelligent Systems National School of Engineers of Sousse University of Sousse Sousse Tunisia Department of Electrical Engineering CEGEP de l'Outaouais Gatineau Québec Québec Canada

ISBN: (数字)9798350389838

ISBN: (纸本)9798350389845

The emergence of Electric Vehicles (EVs) has introduced new issues and complexities, particularly in accurately gauging the remaining battery capacity or the distance an EV can travel before needing a recharge, known as the State of Charge (SoC) estimation problem. This study aims to develop an innovative solution to this problem while considering the usual computational complexity requirements. It emphasizes maintaining low memory and computational demands for practical implementation within a Battery Management System (BMS). By leveraging contemporary tools from artificial intelligence, especially Machine Learning (ML) models, the findings indicate that accurate SoC prediction can be achieved with simple ML models, given a well-curated dataset. Specifically, a Convolutional Neural Network (CNN) with two hidden layers and a limited number of hidden neurons shows promising results in estimating the SoC for a lithium-ion battery cell while maintaining low computational complexity. At 25°C, the CNN model reports a mean absolute error (MAE) of 0.97 and a root mean squared error (RMSE) of 1.71. This research offers significant potential for future advancements in SoC estimation, providing a solid foundation for addressing the issue and suggesting an optimal ML architecture for future BMSs to improve the SoC prediction accuracy.

关键词： Lithium-ion batteries Accuracy Computational modeling Voltage Predictive models Electric vehicles Convolutional neural networks State of charge Computational complexity Biological neural networks

来源：评论

学校读者我要写书评

暂无评论

Research on Robust Measurement Method of Heart Rate Using Remote Photoplethysmography Based on Adversarial Learning Network with High and Low Frequency Features

引用

IEEE Transactions on Circuits and systems for Video technology 2025年第6期35卷 5208-5222页

作者： Zhai, Dezhao Chen, Wei Ding, Yinghao Yu, Ming Li, Qinwei Wu, Hang Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China People's Liberation Army Systems Engineering Institute Academy of Military Sciences Tianjin300161 China Civil Aviation University of China Tianjin Key Laboratory for Advanced Signal Processing Tianjin300300 China Nankai University School of Artificial Intelligence Tianjin300381 China

Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, breaking the constraints of contact measurements and offering broad application prospects. However, in real monitoring scenarios, the distance of subjects and facial illumination often vary. This leads to degradation of rPPG signals due to changes in facial resolution and light intensity. This paper presents the High and Low Frequency Feature Adversarial Learning Network (HLFF-AL), which utilizes feature space interpolation and composite feature capture to perform multi-band global and local signal extraction. By adopting an adversarial learning strategy, it enhances the capability to capture semantic information of rPPG under varying resolutions and lighting, bridging the differences in rPPG signal generation due to resolution changes. While maintaining accuracy in HR measuring, the robustness of the network is improved to achieve robust measurement of HR via rPPG. Even under high-resolution conditions, the Mean Absolute Error (MAE) for the three public datasets - unchanging lighting (COHFACE, UBFC-rPPG) and varying lighting (BUAA-MIHR) - reached 1.10 bpm, 1.64 bpm, and 4.41 bpm, respectively, with a mere 0.29 bpm difference in MAE between high and low resolutions on the BUAA-MIHR dataset. This demonstrates that HLFF-AL can predict more robust rPPG signals in various resolutions and lighting scenarios, achieving competitive results compared to state-of-the-art methods. © 1991-2012 IEEE.

关键词： Heart

来源：评论

学校读者我要写书评

暂无评论

Prediction Method of Blast Furnace Fuel Ratio Based on Spectral Clustering and Extreme Learning Machine Model 41

Prediction Method of Blast Furnace Fuel Ratio Based on Spect...

引用

第41届中国控制会议

作者： Jingjie Liang Yan Yuan Shuqiao Wang Zhuofu Zhang 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The blast furnace fuel ratio,as the main index reflecting the blast furnace fuel consumption,plays an important role in guiding blast furnace *** order to solve the problem of insufficient stability of the existing fuel ratio prediction model under the condition of process parameters fluctuation,a prediction method of blast furnace fuel ratio based on spectral clustering and extreme learning machine model is ***,on the basis of analyzing the influencing factors of fuel ratio,the classification results after clustering are visualized with t-SNE dimension reduction downscaling and spectral clustering method;Secondly,for the classification results after clustering,the extreme learning machine is used to establish the sub-models,and the PSO algorithm was used to optimize the input weights of hidden layer and the bias of hidden layer *** results show that the proposed method has high prediction accuracy.

关键词： Fuel Ratio Spectral Clustering Extreme Learning Machine(ELM) t-SNE Particle Swarm Optimization(PSO)

来源：评论

学校读者我要写书评

暂无评论

Event-Triggered Asynchronous Fault Detection for Stochastically Switched systems With Sojourn Probabilities 41

Event-Triggered Asynchronous Fault Detection for Stochastica...

引用

第41届中国控制会议

作者： Xingyu Tang Xiongbo Wan 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper investigates the event-triggered asynchronous fault detection（FD） problem for discrete-time switched systems with sojourn *** propose a novel asynchronous FD filter（FDF）,of which the sojourn probability is related to the mode of the switched system.A hybrid adjusting variables-dependent event-triggered mechanism（ETM） is devised to reduce the waste of network *** construct a new Lyapunov function that contains the mode of the switched system and a flexible variable in the ETM.A sufficient condition in terms of matrix inequalities is obtained under which the error dynamics is stochastically stable with a prescribed H *** parameters of the FDF are explicitly given based on the feasible solutions to these matrix inequalities.A numerical example is provided to demonstrate the effectiveness of the proposed event-triggered asynchronous FD method.

关键词： Asynchronous fault detection event-triggered switched systems sojourn probabilities

来源：评论

学校读者我要写书评

暂无评论

PI-funnel and inverse hysteresis compensation cascade control of smart soft dielectric elastomer actuator

引用

Control Theory and technology 2024年第4期22卷 552-567页

作者： Peng Huang Yawu Wang Yue Zhang Jundong Wu Chun-Yi Su School of Automation China University of GeosciencesWuhan430074HubeiChina Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074HubeiChina Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan430074HubeiChina Gina Cody School of Engineering and Computer Science Concordia UniversityMontrealQCH3G 1M8Canada

Smart soft dielectric elastomer actuators(SSDEAs)possess wide applications in soft robotics due to their properties similar to natural muscles,including large deformation ratio,high energy density,and fast response ***,the complicated asymmetric and rate-dependent hysteresis property,creep property and quadratic input property of the SSDEA pose enormous challenges to its dynamic modeling and motion *** this paper,first,we construct the dynamic model of the SSDEA by connecting a square module,a one-sided Prandtl–Ishlinskii(OSPI)model and a linear system in series to describe the above *** key and innovative aspect of the dynamic modeling lies in cascading the square module in series with the OSPI model to construct the asymmetric hysteresis ***,a PI-funnel and inverse hysteresis compensation(PIFIHC)cascade control method of the SSDEA is proposed to actualize its tracking control *** performing the inversion operation on the asymmetric hysteresis model,the inverse hysteresis compensation controller(IHCC)is designed to compensate the asymmetric hysteresis property and quadratic input property of the *** addition,a PI-funnel controller is designed to cascade with the IHCC to construct the PIFIHC cascade controller to obtain a good tracking ***,the stability analysis of the PIFIHC cascade control system of the SSDEA is performed to theoretically prove that the tracking error can be controlled within the performance funnel and the steady-state error converges to ***,several practical tracking control experiments of the SSDEA are conducted,and RRMSEs are less than 2.30%for all *** experimental results indicate the effectiveness and feasibility of the proposed PIFIHC cascade control method of the SSDEA.

关键词： Smart soft dielectric elastomer actuator Dynamic modeling Inverse hysteresis compensation controller PI-funnel controller Cascade controller

来源：评论

学校读者我要写书评

暂无评论

Design and application of intelligent monitoring system for geological hazards 41

Design and application of intelligent monitoring system for ...

引用

第41届中国控制会议

作者： Yutong Pan Chengda Lu Wanke Yu Min Wu 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development and utilization of natural resources,geological hazards have posed a great threat to people's life and property *** is of great significance to realize the design of distributed geological disaster monitoring experimental system aimed at a series of problems,such as ecological environmental damage and major natural ***,this paper conducts the system requirements analysis based on the Three Gorges area as an ***,the system architecture and function is designed,and the system hardware configuration and application software design are carried ***,geological hazards intelligent monitoring system is developed and has been verified in Zigui-Badong region of the Three Gorges,which can effectively improve the information utilization rate and monitoring efficiency in the process of geological hazards *** results show that the system works well and can effectively monitor the occurrence of geolo gical hazards.

关键词： Geological hazards system design hardware configuration application software the Three Gorges

来源：评论

学校读者我要写书评

暂无评论

Design of intelligent Monitoring System for Blast Furnace Hearth Erosion 41

Design of Intelligent Monitoring System for Blast Furnace He...

引用

第41届中国控制会议

作者： Shuqiao Wang Yan Yuan Jingjie Liang Zhuofu Zhang 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper takes the iron-making blast furnace in operation of a steel plant as the specific object,analyzes the erosion status and characteristics of different parts of the furnace hearth and bottom combined with iron-making process and erosion mechanism of the blast furnace,designs an intelligent monitoring system for erosion of the furnace hearth lining,visualizes the temperature measurement *** erosion calculation model is established by using the heat transfer mechanism to quickly solve the erosion boundary for the area with low erosion degree and rate,and performs a fast solution of the erosion *** areas with high erosion degree and rate,the particle swarm optimization algorithm is selected by using its adaptive *** solves the erosion boundary accurately from the rough solution of the mechanism model,and the residual thickness of furnace lining is output and displayed,which helps ironmakers optimize the blast furnace production operation,maintenance strategy,improve production efficiency and reduce energy consumption.

关键词： Blast furnace lining erosion Particle swarm optimization algorithm intelligent monitoring system

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：