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International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Ye Yuan Guang-Zhong Cao Aibin Zhu Xing Lyu Yulong Wang Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots Shenzhen University Shenzhen China Shaanxi Key Laboratory of Intelligent Robots Institute of Robotics & Intelligent Systems Xi'an Jiaotong University Xi'an China Shenzhen Second People's Hospital Rehabilitation Center The First Affiliated Hospital of Shenzhen University Shenzhen China

The following topics are dealt with: mobile robots; learning (artificial intelligence); robot vision; motion control; medical robotics; position control; control engineering computing; human-robot interaction; feature... 详细信息

ISBN: (数字)9781728157153

ISBN: (纸本)9781728157160

关键词： Robot sensing systems Cloud computing Protocols Exoskeletons Robot kinematics Logic gates

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An Accelerated Iterative Learning control Approach for X-Y precision plane motion stage

An Accelerated Iterative Learning Control Approach for X-Y p...

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作者： Tao Zhang Hongyu Yan Danping Jia Hualiang Zhang Bingjie Zhao Xu Lu Yang Liu 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 School of Electrical Engineering Dalian University of Technology School of Artificial Intelligence Shenyang University of Technology School of Information Science and Engineering Shenyang University of Technology

X-Y precision plane motion stage is widely used in precision motion control fields such as lithography machine workpiece stage,CNC machine tool,Laser measuring system,etc.,which requires high accuracy of the position *** X-Y precision plane motion stage uses two permanent magnet linear synchronous motors(PMLSM) as drivers to drive the motion table to perform XY two-degree-of-freedom *** open-closedloop PD-type iterative learning controller(ILC) approach based on accelerated convergence was proposed to improve trajectory tracking performance of *** interval was changed by real-time correction of the exponential gain matrix in the interval to accelerate the convergence *** to the λ norm,the monotone convergence of the proportion differentiation(PD) was developed for the *** controller consists of two terms:a PID controller which enhances robustness to disturbances and system parameter variability and an ILC which generally improves the performance in the repetitive tasks by using the information from previous *** experimental results show that tracking precision and convergence speed of PMLSM can be effectively improved by the scheme.

关键词： X-Y precision plane motion stage Iterative learning control tracking precision convergence rate

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Antenna Cross-correlation based Compressive Subspace Learning for Wideband Spectrum Sensing 19

Antenna Cross-correlation based Compressive Subspace Learnin...

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19th IEEE International Conference on Communication Technology, ICCT 2019

作者： Gong, Tierui Liu, Zhifeng Yang, Zhijia Wang, Gengshan Key Laboratory of Networked Control Systems State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781728105352

To address the performance degradation of wide-band spectrum sensing by sub-Nyquist sampling (SNS) in wireless fading channels, two compressive subspace learning (CSL) algorithms are proposed for signal subspace learning based on antenna cross-correlations for further improving the sensing performance. Both algorithms are developed based on different organizations of SNS samples, and both exploit space diversity and noise uncorrelations between antennas. We further establish the expressions for statistical covariance matrices (SCMs) obtained by SNS samples in the multi-antenna SNS cognitive radio system. Based on the derived SCM expressions, conditions to ensure SCMs without noise contamination are given. Simulations validate the derived conditions and show the improvement on sensing performance over related works. © 2019 IEEE.

关键词： Covariance matrix

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Fault Diagnosis Based on RseNet-LSTM for Industrial Process

Fault Diagnosis Based on RseNet-LSTM for Industrial Process

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IEEE Advanced Information Technology, Electronic and Automation control Conference (IAEAC)

作者： Peifu Yao Shaojie Yang Peng Li China Copper Co.LTD Kunming China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

Aiming at the problems that conventional data-driven diagnosis methods are difficult to adaptively extract effective features from industrial process data, and do not make full use of the time series characteristics of process data, in this paper, a fault diagnosis method based on residual convolutional neural networks and long short-term memory networks (ResNet-LSTM) is developed. Firstly, the local spatial features of process data are captured by the deep residual convolution network. Then, the time series characteristics of process data are extracted by LSTM. Finally, the output of the fault category is performed through the softmax classifier. This method can extract features adaptively, more fully extract features in time series fault data, and effectively reduce the difficulty of deep neural network training. The benchmark Tennessee-Eastman (TE) process is used to validate performance of the proposed method. The ResNet-LSTM model is compared with the CNN, LSTM, ResNets, CNN-LSTM models, and the experiment results show that the ResNet-LSTM method achieves better performance.

关键词： Fault diagnosis Training Time series analysis Neural networks Process control Feature extraction Data mining

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Fault Diagnosis Based on Multi-scale LSTM-FCNs for Industrial Process

Fault Diagnosis Based on Multi-scale LSTM-FCNs for Industria...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Shaojie Yang Peng Li Shuai Li Xiaofeng Zhou Shanghong Jiang Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Information Center of Ansteel Mining Limited Company Anshan China

ISBN: (纸本)9781665401166

Aiming at the characteristics of multi-scale, time series and nonlinear of the complex industrial process data, in order to extract process information more efficiently for improving the diagnosibility, a novel fault diagnosis method based on multi-scale long short term memory- fully convolutional networks (LSTM-FCNs) for industrial process is proposed. Firstly, variational mode decomposition (VMD) is used to obtain the multi-scale components of the process data. Then a parallel LSTM and FCNs optimized by batch normalization and random inactivation is built to extract features for each component. Finally, based on the fusion of multi-scale features, a fully connected layer is used for diagnosis. The LSTM-FCNs method is evaluated by the Tennessee Eastman (TE) benchmark industrial process, and compared with other methods, its effectiveness and superiority are verified.

关键词： Fault diagnosis Fault detection Time series analysis Benchmark testing Feature extraction Safety Data mining

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Printability and properties of PSNZT piezoelectric ceramics fabricated by DLP additive manufacturing

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Ceramics International 2025年

作者： Yu, Mengyuan Fan, Guifen Li, Yingkang Ling, Xile Gong, Feng Liu, Gang Bai, Jiaming Li, Mantian Zhou, Peng Zhang, Dongyang Sun, Lining Hubei Key Laboratory of Plasma Chemistry and Advanced Materials School of Materials Science and Engineering Wuhan Institute of Technology Wuhan430205 China Institute of Intelligent Manufacturing Technology Shenzhen Polytechnic University Shenzhen518055 China MOE Huazhong University of Science and Technology Wuhan430074 China Department of Mechanical and Electronic Engineering College of Mechatronics and Control Engineering Shenzhen University Shenzhen518055 China Shenzhen Blueswin Industrial Co. Ltd Shenzhen518117 China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China Institute of Critical Materials for Integrated Circuits Shenzhen Polytechnic University Shenzhen518055 China Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Suzhou215021 China

Achieving the synergy of low viscosity, high printing precision and enhanced curing ability for lead zirconate titanate (PZT) components is a critical issue in the field of digital light processing (DLP). Herein, the effect of calcination temperature on the morphology and grain size of PSNZT [(Pb0.92Sr0.08)(Zr0.533Ti0.443Nb0.024)O3]ceramic powder has been investigated, and a lowest viscosity, higher printing precision and enhanced curing ability of PSNZT ceramic slurry has been achieved, resulting in a high piezoelectric constant (d33 = 379pC/N) and a high dielectric constant (Εr = 1308@100 KHz) via DLP process combined with following sintering densification (1220 °C/3 h). © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Piezoelectric ceramics

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On the Robustness of Power IOT against Attack Based on General Framework

On the Robustness of Power IOT against Attack Based on Gener...

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Power and Energy Engineering (ICPEE), International Conference on

作者： Tianyu Wang Weiwei Miao Jianming Zhao Bowen Zhang Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China State Grid Jiangsu Electric Power Co. Ltd Information & Telecommunication Branch Nanjing China

ISBN: (纸本)9781665420471

Power Internet of Things (IOT) is the application of Internet of things in smart grid and the result of the development of information and communication technology to a certain stage. It will effectively integrate communication infrastructure resources and power system infrastructure resources, and improve the informationize level of power system. However, real-world complex systems are often vulnerable under disruptions. Power IOT is facing with different types of attack, which contains natural disasters and human intentional attacks. Motivated by this point, we propose a general framework of Power IOT robustness with different structures against diverse attacks. Evaluation on example IOT networks shows the effectiveness of our framework, and the characteristics of different structure against attacks. Our study contributes to a better understanding of Power IOT robustness, and reveals the designation of the Power IOT network improves the robustness of the network.

关键词： Robustness Power systems Smart grids Information and communication technology Internet of Things Complex systems

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Interpretable classification of epileptic EEG signals using ALIF decomposition and attention-augmented cascaded deep neural networks

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Applied Soft Computing 2025年 180卷

作者： Wei Zeng Minglin Zhang Liangmin Shan Yang Chen Zuoyong Li Shaoyi Du School of Physics and Mechanical and Electrical Engineering Longyan University Longyan 364012 PR China School of Mechanical Engineering and Automation Fuzhou University Fuzhou 350116 PR China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou 350121 PR China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an 710049 PR China

Epilepsy is a chronic neurological disorder characterized by recurrent seizures. Accurate diagnosis and effective monitoring require the precise classification of electroencephalogram (EEG) signals. In this study, we introduce a novel approach that combines Adaptive Local Iterative Filtering (ALIF) for signal decomposition with an attention-enhanced cascaded deep neural network (CDNN) architecture. The ALIF algorithm decomposes EEG signals into intrinsic mode functions (IMFs) that capture inherent oscillatory components. These IMFs are processed by the CDNN, which operates in two stages: a feature extraction module and a classification module. In the feature extraction stage, a SEblock channel attention mechanism dynamically prioritizes significant features within the IMFs. The classification stage employs a hybrid CNN-LSTM architecture that effectively captures both spatial and temporal dependencies. To enhance interpretability, the SHapley Additive exPlanations (SHAP) framework is incorporated to provide insights into the model’s decision-making process, while Gradient-weighted Class Activation Mapping (Grad-CAM) visualizes the most discriminative regions in the input data. Rigorously validated using 10-fold cross-validation on the Bonn and EEG Epilepsy databases, the proposed methodology achieved an exceptional classification accuracy of 100%, with sensitivity, specificity, and F1-scores exceeding 99% across various scenarios. The integration of SHAP and Grad-CAM not only elucidates the model’s decision processes but also contributes to a more interpretable and reliable system for epileptic EEG signal classification. This synergistic combination of advanced signal processing, deep learning, and interpretability techniques holds significant potential to enhance epilepsy diagnosis and strengthen trust in clinical decision support systems.

关键词： ALIF decomposition Cascaded deep neural networks Epileptic EEG signals SEBlock attention mechanism

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Fabrication of Translucent Graded Dental Crown Using Zirconia-Yttrium Multi-Slurry Tape Casting 3d Printer

SSRN

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SSRN 2024年

作者： Romario, Yulius Shan Bhat, Chinmai Ramezani, Maziar Pasang, Tim Chen, Zhangwei Jiang, Cho-Pei Graduate Institute of Manufacturing Technology National Taipei University of Technology 1 Sec. 3 Zhongxiao E. Rd. Taipei10608 Taiwan High-value Biomaterials Research and Commercialization Center National Taipei University of Technology 1 Sec. 3 Zhongxiao E. Rd. Taipei10608 Taiwan Department of Mechanical Engineering Auckland University of Technology Auckland1010 New Zealand Department of Engineering Design Manufacturing and Management Systems Western Michigan University KalamazooMI4900-5200 United States Additive Manufacturing Institute Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robotics College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China Department of Mechanical Engineering National Taipei University of Technology Taiwan 1 Sec. 3 Zhongxiao E. Rd. Taipei10608 Taiwan

This paper aims to fabricate functionally graded dental crown using a multi-slurry tape casting additive manufacturing technology. The different luminescence of the dental crown was obtained with different composition of zirconia and yttria. Zirconia with tunable mechanical properties and translucency are obtained by adding 3, 3.5, 4, 4.5, and 5 mol% of yttrium oxide to zirconia powder. After obtaining the printable slurry with maximum solid loading, the green bodies are prepared using the in-house built high speed multi-ceramic tape casting technology. They are later sintered with two-stage sintering method. After the successful fabrication, the mechanical properties and translucency of the specimens were evaluated with Vickers hardness, three-point bending and translucency parameter tests. Finally, an FGM tooth crown with five photocurable slurries is proposed to demonstrate the translucent gradient effect of sintered part. The solid loading of 80% zirconia and 20% resin delivered samples without any surface cracks. The shrinkage ratio analysis showed that the sintered sample dimension was reduced by 20%, 20%, and 23% along X, Y, and Z directions. The samples fabricated with 3% yttrium oxide to zirconia delivered excellent hardness (1687 HV) and flexural strength (650.6 MPa). However, the relative luminescence increased with increasing the yttrium oxide for 3 – 5 mol%. With the optimized process parameters, the proposed dental crown is fabricated and analyzed for their shrinkage ratio, mechanical, and translucency properties. The study proposes the potential of fabricating customized dental crown with gradient translucent appearance. © 2024, The Authors. All rights reserved.

关键词： Zirconia

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A Novel Text Personalized Analysis Model Based on BP Neural Network 6th

A Novel Text Personalized Analysis Model Based on BP Neural ...

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6th International Conference on Life System Modeling and Simulation, LSMS 2020, and 6th International Conference on intelligent Computing for Sustainable Energy and Environment, ICSEE 2020

作者： Wang, Zhongfeng Wang, Chu Li, Ligang Pan, Zhudong Zou, Yunfeng Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China DongFang Eleceronics CO. LTD Yantai264001 China State Grid Jiangsu Marketing Service Nanjing210019 China

ISBN: (纸本)9789813363779

In online interactive platform, text analysis has greatly changed people's communication, thinking, and promoted the explosive growth of user-generated information. A large number of texts generated by users have become one of the most representative data sources of big data in recent years. Mining and analyzing user-generated information has become essential part on research of social development. The sentiment analysis on social media text as an information processing technology for analyzing, processing, summarizing and reasoning subjective texts with emotions has received extensive attention in academia and industry in recent years, and has been used among many areas of social media and many applications. The traditional text sentiment analysis research work mainly focuses on analyzing emotions from texts, but ignores the individualized differences of users in emotional expression, thus affecting the quality of analysis results. To solve the problems, this paper is about solving the problem of personalized sentiment analysis of social media texts. Considering the wide application of BP neural network technology in social media was proposed to solve the possible challenges of social media text personalized sentiment analysis. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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