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Photostriction effect and electric properties of La-doped PMN-PT transparent ferroelectric ceramics

International Journal of Smart and Nano Materials 2024年第2期15卷 296-311页

作者： Zhiqiang Song Zhuo Zhang Yirong Yu Dabin Lin Department of Automation and Robotics Engineering School of AutomationWuxi UniversityWuxiChina School of Optoelectronic Engineering Xian Technological UniversityXi'anChina

Ferroelectric ceramics display a remarkable photostriction effect,which could facilitate the prevention of electromagnetic interference and promote the integration and miniaturization of actuator systems,indicating potential applications in glim-mer *** this work,highly transparent La-doped 0.9Pb(Mg1/3Nb2/3)O3-0.1PbTiO3(0.9PMN-0.1PT)ceramicswere synthesized using a solid-state reaction method in an oxyge-nated *** and SEM were used to analyze the phase structure of the dense samples,which displayed a pure perovskite structure,with no trace of a pyrochlore *** La doping,the phase transitiontemperature shows a decreasing trend,and its dielectric constant could reach up to 20,*** P-E loop of the samples was gradually refined into a straight line with an increase in the doping *** La doping,the transparency of the 0.9PMN-0.1PT cera-mics was significantly enhanced and reached up to 61%at the near-infrared ***,the photogenerated voltage and deformation of the La-doped 0.9PMN-0.1PT ceramics were investigated under ultraviolet light of various *** 20 s of irradiation at an intensity of 800 mW/cm²,the max-imum voltage and deformation,with values of 40 V and 250 nm,respectively,were both found in the 1%La-doped 0.9PMN-0.1PT ceramics.

关键词： Photostriction ferroelectric Raman piezoelectric

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A trajectory-level robot skill learning method based on fuzzy systems

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Science China(Information Sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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Real-Time Prediction of Elbow Motion Through sEMG-Based Hybrid BP-LSTM Network

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Journal of Shanghai Jiaotong University (Science) 2024年 1-9页

作者： Ma, Yiyuan Chen, Huaiyuan Chen, Weidong Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

In the face of the large number of people with motor function disabilities, rehabilitation robots have attracted more and more attention. In order to promote the active participation of the user’s motion intention in the assisted rehabilitation process of the robots, it is crucial to establish the human motion prediction model. In this paper, a hybrid prediction model built on long short-term memory (LSTM) neural network using surface electromyography (sEMG) is applied to predict the elbow motion of the users in advance. This model includes two sub-models: a back-propagation neural network and an LSTM network. The former extracts a preliminary prediction of the elbow motion, and the latter corrects this prediction to increase accuracy. The proposed model takes time series data as input, which includes the sEMG signals measured by electrodes and the continuous angles from inertial measurement units. The offline and online tests were carried out to verify the established hybrid model. Finally, average root mean square errors of 3.52° and 4.18° were reached respectively for offline and online tests, and the correlation coefficients for both were above 0.98. © Shanghai Jiao Tong University 2024.

关键词： Long short-term memory

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Cy-CNN: cylinder convolution based rotation-invariant neural network for point cloud registration

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Science China(Information Sciences) 2023年第5期66卷 73-87页

作者： Hengwang ZHAO Zhidong LIANG Yuesheng HE Chunxiang WANG Ming YANG Department of Automation Shanghai Jiao Tong University Research Institute of Robotics Shanghai Jiao Tong University

Point cloud registration is a challenging problem in the condition of large initial misalignments and noises. A major problem encountered in the registration algorithms is the definition of correspondence between two point clouds. Point clouds contain rich geometric information and the same geometric structure implies the same feature even if they are in diferent poses, which motivates us to seek a rotation-invariant feature representation for calculating the correspondence. This work proposes a rotation-invariant neural network for point cloud registration. To acquire rotation-invariant features, we firstly propose a rotationinvariant point cloud representation(RIPR) at the input level. Instead of using the original coordinates,we propose to use point pair features(PPF) and the transformed coordinates in the local reference frame(LRF) to represent a point. Then, we design a new convolution operator named Cylinder-Conv which utilizes the symmetry of cylinder-shaped voxels and the hierarchical geometry information of the surface of3D shapes. By specifying the cylinder-shaped structures and directions, Cylinder-Conv can better capture the local neighborhood geometry of each point and maintain rotation-invariance. Finally, we combine RIPR and Cylinder-Conv to extract normalized rotation-invariant features to generate the correspondence and perform a diferentiable singular value decomposition(SVD) step to estimate the rigid transformation. The proposed network presents state-of-the-art performance on point cloud registration. Experiments show that our method is robust to initial misalignments and noises.

关键词： deep learning point cloud registration rotation invariant computer vision

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Neural Network Algorithm Based on LVQ for Myocardial Infarction Detection and Localization Using Multi-Lead ECG Data

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Computers, Materials & Continua 2025年第3期82卷 5257-5284页

作者： Kassymbek Ozhikenov Zhadyra Alimbayeva Chingiz Alimbayev Aiman Ozhikenova Yeldos Altay Department of Robotics and Engineering Tools of Automation Satbayev UniversityAlmaty050010Kazakhstan Joldasbekov Institute of Mechanics and Engineering Almaty050010Kazakhstan

Myocardial infarction(MI)is one of the leading causes of death globally among cardiovascular diseases,necessitating modern and accurate diagnostics for cardiac patient *** the available functional diagnostic methods,electrocardiography(ECG)is particularly well-known for its ability to detect ***,confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in *** study,therefore,proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of *** particular,the learning vector quantization(LVQ)algorithm was applied,considering the contribution of each ECG lead in the 12-channel system,which obtained an accuracy of 87%in localizing damaged *** developed model was tested on verified data from the PTB database,including 445 ECG recordings from both healthy individuals and MI-diagnosed *** results demonstrated that the 12-lead ECG system allows for a comprehensive understanding of cardiac activities in myocardial infarction patients,serving as an essential tool for the diagnosis of myocardial conditions and localizing their damage.A comprehensive comparison was performed,including CNN,SVM,and Logistic Regression,to evaluate the proposed LVQ *** results demonstrate that the LVQ model achieves competitive performance in diagnostic tasks while maintaining computational efficiency,making it suitable for resource-constrained *** study also applies a carefully designed data pre-processing flow,including class balancing and noise removal,which improves the reliability and reproducibility of the *** aspects highlight the potential application of the LVQ model in cardiac diagnostics,opening up prospects for its use along with more complex neural network architectures.

关键词： Electrocardiography 12-lead electrocardiogram myocardial infarction heart disease learning vector quantization algorithm machine learning

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Information-Theoretic Improvement of Model Predictive Control 9

Information-Theoretic Improvement of Model Predictive Contro...

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9th International Conference on Mechanical Engineering and robotics Research, ICMERR 2025

作者： Bania, Piotr AGH University of Krakow Department of Automation and Robotics Krakow Poland

ISBN: (纸本)9798331509262

An information-theoretic improvement of the stochastic Model Predictive Control has been proposed and examined. First, it was observed that optimal feedback must, if possible, actively generate information about the system state. Since standard MPC algorithm does not do this, it was hypothesized that adding a penalty for information deficit to the standard MPC cost function can generate a control strategy closer to the optimal one. An exact formula for the cost has been derived for a class of linear systems with control-dependent matrices. Then, it was shown on the basis of simple, two dimensional, example that the proposed modification generates significantly better strategy, for which the cost functional is more than five times smaller compared to the standard MPC algorithm. © 2025 IEEE.

关键词： Predictive control systems

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A Comprehensive Study on Weather Predicting automation Bot Using automation Software and Machine Learning

A Comprehensive Study on Weather Predicting Automation Bot U...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Joshi, Vedant Jadhav, Priya Symbiosis Institute of Technology Robotics and Automation Department Pune India

ISBN: (纸本)9798350361537

Weather forecasting is crucial in our daily lives, influencing everything from when to walk outside to what to dress. In this era of automation and data-driven decision-making, a new and reliable way of weather prediction is now possible thanks to the collaboration of automation software and machine learning. This study closely investigates the development and implementation of an automation bot for weather forecasting that employs machine learning and automation software techniques. This study investigates the complexity of this automation system, the underlying algorithms, its ease of use, and the accuracy of the forecasts it generates. The study shows how this unique concept not only increases weather forecast accuracy but also mentions the procedure, making it more accessible to users in daily life. This study shows the significant advancement in weather forecasting by combining the use of automation software with data-driven insights from machine learning. This will help in a more accurate and precise real-time understanding of climate change. © 2024 IEEE.

关键词： Machine learning algorithms Robotic Process automation Weather prediction

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Healthcare Monitoring Companion: The Social Robot 3

Healthcare Monitoring Companion: The Social Robot

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Goraksha, Rahul Bongale, Arunkumar Sayyad, Sameer Kumar, Satish Symbiosis Institute of Technology Department of Robotics and Automation Pune412115 India Symbiosis Centre for Applied Artificial Intelligence Department of Robotics and Automation Pune412115 India

ISBN: (纸本)9798350372120

There is a rising scarcity of healthcare professionals to meet the demand of taking care of aged people. Social robots are becoming more and more popular because of the benefits they provide;during the pandemic, in particular, several of their primary applications are advantageous. The purpose of this work is to build the prototype of social robots and carry out the integration of those robots into healthcare monitoring using Thing Speak and Raspberry Pi. This prototype of social robots with Thing Speak and Raspberry Pi hosts an array of sensors (for measuring a multitude of data from the body of humans) like heartbeat sensor, Dallas temperature sensor, Radio-Frequency Identification (RFID), ultrasonic sensor, and Infrared (IR) sensor along with other components like motor controlling module and DC motor. The data from these sensors are transmitted to the cloud platform Thing Speak, offering remote monitoring and data analysis capabilities and the Raspberry Pi acts as the central processing element in our design, which additionally helps in triggering corresponding alerts regarding the patient status. Finally, we experimentally validate our design using 2 cases and then we present the assessment of the patient alerting feature. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Resilient source seeking with robot swarms 63

Resilient source seeking with robot swarms

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Acuaviva, Antonio Bautista, Jesus Yao, Weijia Jimenez, Juan De Marina, Hector Garcia Universidad Complutense de Madrid Department of Computer Architecture and Automation Spain Department of Computer Engineering Automation and Robotics Granada Spain Hunan University School of Robotics China

ISBN: (纸本)9798350316339

We present a solution for locating the source, or maximum, of an unknown scalar field using a swarm of mobile robots. Unlike relying on the traditional gradient information, the swarm determines an ascending direction to approach the source with arbitrary precision. The ascending direction is calculated from field strength measurements at the robot locations and their relative positions concerning the swarm centroid. Rather than focusing on individual robots, we focus the analysis on the density of robots per unit area to guarantee a more resilient swarm, i.e., the functionality remains even if individuals go missing or are misplaced during the mission. We reinforce the algorithm's robustness by providing sufficient conditions for the swarm shape so that the ascending direction is almost parallel to the gradient. The swarm can respond to an unexpected environment by morphing its shape and exploiting the existence of multiple ascending directions. Finally, we validate our approach numerically with hundreds of robots. The fact that a large number of robots with a generic formation always calculate an ascending direction compensates for the potential loss of individuals. © 2024 IEEE.

关键词： Microrobots

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Characterization and Mechanical Behaviour of Nano-Al2O3 Reinforced Polymer Composites

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Journal of The Institution of Engineers (India): Series D 2024年 1-12页

作者： Mahesh, K.S. Satheesh, J. Department of Robotics & amp Automation JSS Academy of Technical Education Bengaluru India Department of Mechanical Engineering SJB Institute of Technology Bengaluru India

This study presents an experimental investigation into the mechanical behaviour and characterisation of Nano-Al2O3 reinforced polymer composites, aiming to enhance the understanding of their structural performance. The incorporation of nanoscale Al2O3 particles into polymer matrices has gained attention for its potential to improve the mechanical properties of the resulting composites. In this research, 1 wt% Nano-Al2O3 was dispersed within a polymer matrix through a systematic fabrication process. The mechanical behaviour of the developed composites was evaluated through a comprehensive set of experiments, including compression testing, flexural testing, and impact testing. The results provided insights into the effects of Nano-Al2O3 content on the strength, stiffness, and impact resistance of the composites. Microstructural characterisation using techniques such as scanning electron microscopy and X-ray diffraction were employed to analyse the dispersion of nanoparticles and the resulting changes in the composite structure. The findings from this experimental investigation contribute to the current knowledge base on Nano-Al2O3 reinforced polymer composites and offer valuable information for optimising the fabrication process to achieve desired mechanical properties. © The Institution of Engineers (India) 2024.

关键词： Compression testing

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