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8th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2019

作者： Ma, Liyan Jia, Li Zhou, Liuming College of Mechatronics Engineering and Automation Shanghai University Department of Automation Shanghai200444 China Shanghai Key Laboratory of Power Station Automation Technology College of Mechatronics Engineering and Automation Shanghai University Department of Automation Shanghai200444 China

ISBN: (纸本)9781728114545

An improved iterative dynamic programming (IDP) control strategy is proposed in this paper. It uses a Neuro-fuzzy model (NFM) to predict output values of batch processes, which avoids the disadvantage that traditional IDP relies on mathematical models. Moreover, to match the real plant better, a model parameter adaptive adjustment method is introduced. Next, a model predictive control (MPC) strategy with decreasing prediction horizon is introduced to time-axis to adjust the control law obtained from IDP during one batch. Lastly, the method is applied to a typical batch process. The simulation results show the effectiveness of the proposed method. © 2019 IEEE.

关键词： Model predictive control

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A constitutive model incorporating grain refinement strengthening on metallic alloys

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Journal of Materials Science & Technology 2021年第29期88卷 233-239页

作者： Shan Cecilia Cao Xiaochun Zhang Yuan Yuan Pengyau Wang Lei Zhang Na Liu Yi Liu Jian Lu Material Genome Institute Shanghai UniversityShanghai 200444China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China Department of Mechanical Engineering City University of Hong KongHong KongChina Department of Computer Science University of CaliforniaBerkeleyCA 94720USA College of Material and Textile Engineering Jiaxing UniversityZhejiang 314001.China School of Mechatronics Engineering and Automation Shanghai UniversityShanghai 200444China CityU-Shenzhen Futian Research Institute Shenzhen 518045China Centre for Advanced Structural Materials City University of Hong Kong Shenzhen Research InstituteGreater Bay Joint DivisionShenyang National Laboratory for Materials ScienceShenzhen 518057China

Surface nano-crystallization techniques have been recently developed as one of the most effient ways to optimize materials'structure,and therefore develop the local and global mechanical behavior as to increase strength without compromising *** this work,we present a constitutive model incorporating grain refinement hardening to simulate the nano-crystallization technique,specifically,surface mechanical attrition *** computation is implemented using user-defined VUMAT *** an example of its application,a geometry model with full coverage of random impacts are *** results show that the model has rather precise predictability of grain size evolution during plastic *** readily embedded with a computational code of material dynamics enables this novel model to be a promising tool to study the dynamic evolution of microstructures under plastic deformation.

关键词： Surface grain refinement Constitutive model Full coverage random impact Surface mechanical attrition treatment(SMAT)

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Internet of Things (IoT) based system development for robotic waste segregation management

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Materials Today: Proceedings 2021年

作者： Nakandhrakumar, R.S. Rameshkumar, P. Parthasarathy, V. Thirupathy Rao, B. Centre for Automation and Robotics Department of Mechanical Engineering Hindustan Institute of Technology and Science Chennai India Department of Mechatronics Engineering Sri Manakula Vinayagar Engineering College Puducherry India Department of Physics Hindustan Institute of Technology and Science Chennai India

Segregation of wastes and its management are difficult task for sustainable green environment. A case assumed here is on Chennai, high density population in Tamilnadu, India. In chennai, the household wastes are collected in terms of tonnes per month. And the separation of this waste for recycling are challenging task. This project focuses on separation of wastes such as paper, plastic and metals through the developed system is with the capable of identifying materials using various sensors such as proximity, inductive and capacitive sensors. Integration of these sensors into PLC, Arduino can be used to operate Robotic arm in systematic procedure. This integrated mechanisms is also providing information through an IoT establishment with sensors. The system is enabled to read, collect and transmit huge volume of data over the internet and also segregation through robot. The observed experimental results showed the system developed through this procedure is helpful to achieve about 85% accuracy for the categorization of selective different kinds of waste. Results of this project work can be used to routine segregation work and reduces human involvement during repetitive task. Data received through sensors have been helpful to operate the Robotic arm in time. Usage of this robotic arm has been presented in this project work. © 2021

关键词： Waste management

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Optimization of Flexible Bronchoscopy Shape Sensing Using Fiber Optic Sensors

Optimization of Flexible Bronchoscopy Shape Sensing Using Fi...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Xinran Liu Hao Chen Hongbin Liu School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Professor at Chinese Academy of Sciences Institute of Automation (CASIA) Executive Director for the Centre of AI and Robotics (CAIR) Hong Kong Institute of Science & Innovation Chinese Academy of Sciences An Adjunct Reader and Director of the Haptic Mechatronics and Medical Robotics (HaMMeR) lab at the School of Biomedical Engineering and Imaging Sciences King’s College London (KCL) UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This work presents a novel shape evaluation and optimization approach for shape sensing, specifically targeting the constrained, irregular, and intricate spatial shapes of flexible bronchoscopes (FB) in human bronchial tree. The proposed evaluation criteria and optimization methods combine clinical significance related to bronchial anatomical structures and address issues related to singular points and discontinuities in traditional shape reconstruction models. Three-dimensional experiments were conducted within eight spatial complex configurations printed from a proportional bronchial model. The 3D experiment results demonstrate an average reduction of approximately 34.1% in shape reconstruction errors across all eight airway models compared to the traditional model, validating the effectiveness and feasibility.

关键词： Bronchoscopy Solid modeling Optical fiber sensors Three-dimensional displays Shape Atmospheric modeling Optimization methods

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Developing Algorithms for Personalized Recommendations Based on User Behavior

Developing Algorithms for Personalized Recommendations Based...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： M. Sangeetha B Srinivasa Kumar K Chokkanathan Ashok Kumar S. Krishna Prabha Sivakumar Sattanathan J K Periasamy Department of Computer Science Dr.N.G.P. Arts and Science College Coimbatore Tamil Nadu India Department of Engineering Mathematics Koneru Lakshmaiah Education Foundation Guntur(D.T) Andhra Pradesh India Department of AI Madanapalle Institute of Technology and Science Madanapalle Andhra Pradesh India Department of Computer Science BanasthaliVidyapith Rajasthan India Department of Mathematics PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

To enhance the performance of user personalized recommendation algorithms, this study offers a user personalized recommendation calculation based on deep learning network. Efficient and accurate personalized recommendation algorithms can effectively increase user experience fulfillment. Considering user behavior, this research suggests a useful advice. We offer a preference network that might gather user preferences based on item qualities since customers intuitively convey their considerations based on some specific attributes of items. Moreover, weighted affiliation rules are used to identify these patterns to enhance the nature of recommendations because there are some sequential patterns in item purchases. We offer a preference network that might gather user preferences based on item qualities since customers intuitively convey their contemplations based on some specific attributes of items. Moreover, weighted affiliation rules are used to identify these patterns to enhance the nature of recommendations because there are some sequential patterns in item purchases. The methodology solves the sparsity problem and outperforms existing algorithms. Implementing a user behavior-based recommendation method, which gauges users' interests based on certain evaluations of item qualities, is the primary commitment. Furthermore, this strategy makes use of a sequential purchase pattern to raise the caliber of recommendations.

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Robotics in space – A review

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AIP Conference Proceedings 2024年第1期3035卷

作者： S. Sanjay Adhinath J. Kanchana B. Vinod Department of Mechatronics Engineering PSG Institute of Advanced Studies Coimbatore India. Department of Robotics and Automation Engg. PSG College of Technology Coimbatore India.

Robots are essential for carrying out experiments and activities in Space environment, with or without human presence. This reduces the associated safety issues for humans. This paper attempts to provide an overview o...

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Modeling and Compensation of Self-Twisting Angle for Shape Sensing in Flexible Bronchoscopy

Modeling and Compensation of Self-Twisting Angle for Shape S...

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IEEE International Conference on Robotics and Biomimetics

作者： Xinran Liu Hao Chen Yuanrui Huang Jian Chen Hongbin Liu School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Institute of Automation (CASIA) Executive Director for the Centre of AI and Robotics (CAIR) Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Adjunct Reader and Director of the Haptic Mechatronics and Medical Robotics (HaMMeR) lab at the School of Biomedical Engineering and Imaging Sciences King’s College London (KCL) UK

This work presents a novel scheme for shape sensing, targeting the inherent characteristics and challenges of flexible bronchoscopes (FB) with a large slenderness ratio, low stiffness, asymmetrical multi-channel structures, and significant bending deformations. The approach analyzes the coupling issue between bending angles and self-twist angles in shape reconstruction and proposes a viable compensation model to enhance shape-sensing accuracy. Furthermore, experimental results conducted on a scaled human bronchial model demonstrate an average absolute error (MAE) of 2.28mm and a root mean square error (RMSE) of 1.23mm for the proposed scheme, validating the effectiveness and feasibility.

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Stochastic Resonance Array for Designing Noise-Boosted Filter Banks to Enhance Weak Multi-Harmonic Fault Characteristics of Machinery

SSRN

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SSRN 2023年

作者： Qiao, Zijian Zhang, Chenglong Zhang, Cailiang Ma, Xin Zhu, Ronghua Lai, Zhihui Zhou, Shengtong Zhejiang Provincial Key Laboratory of Part Rolling Technology School of Mechanical Engineering and Mechanics Ningbo University Zhejiang Ningbo315211 China Yangjiang Offshore Wind Power Laboratory Guangdong Yangjiang529500 China Ocean College Zhejiang University Zhejiang Zhoushan316021 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong China Guangdong Provincial Key Laboratory of Micro/Nano Optomechatronics Engineering College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure East China Jiaotong University Jiangxi Nanchang330013 China

Noise in stochastic resonance (SR) is beneficial to enhance weak useful information and has been widely stuided and applied to real-world signal detection. In tristable SR, however, the depth of middle potential well is not equal to those of both left and right potential wells and adjusting the system parameters would change the depth and width of three potential wells simultaneously. Therefore, the role of potential-well width and potential-barrier height or potential-well depth in a triple-well potential on SR has not been investigated separately yet and the effect of a both uniform depth and different depth between left/right potential well and middle well in a triple-well potential on SR has not been explored yet. Meanwhile, SR is used to weak signal detection and early fault diagnosis, but it ofter processes a complex signal into a sine shaped one, resulting in the loss of multi-harmonic signature embedded in the raw signal. To advoid this, this paper investigates underdamped SR induced by a symmetric triple-well potential with a uniform depth. Then, a SR array based noise-boosted filter bank method is proposed to enhance weak multi-harmonic fault characteristics for early fault diagnosis of machinery. Theoretical results using spectral amplification factor to quantify SR indicate that tristable SR induced by a symmetric triple-well potential with a uniform depth is supperior to that induced by a symmetric triple-well potential with a different depth, increasing the potential-well width would maximize the spectral amplification factor of the tristable SR but keep the optimal noise intensity unchange basically, and increasing the potential-well depth would decrease the spectral amplification factor of the tristable SR but enlarge the optimal noise intensity obviously. Experimental results demonstrate that the proposed method is able to enhance weak multi-harmonic fault characteristics for early fault diangosis of roller bearings. Comparing with accugram and mini

关键词： Failure analysis

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Production Scheduling Application Software for Small Medium Enterprises

Production Scheduling Application Software for Small Medium ...

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International Conference on System Science and engineering (ICSSE)

作者： Xuan-Thuyen Le The-Anh-Tu Nguyen Ngoc-Hieu Pham Ngoc-Huy Tran Innovation Fablab Ho Chi Minh City University of Technology (HCMUT) VNU-HCM Ho Chi Minh City Vietnam Department of Control & Automation Faculty of Electrical & Electronics Engineering Ho Chi Minh City University of Technology (HCMUT) VNU-HCM Ho Chi Minh City Vietnam Department of Mechatronics Cao Thang Technical College Ho Chi Minh City Vietnam

Derived from the current implementation of the production plan as well as the production schedule, Enterprise Resource Planning software with a built-in production scheduling algorithm helps optimize the uses of resources, reduce production time, increase productivity, and increase customer service levels. In this research, the available management platform (Odoo) with Python as the main programming language is proposed to build a system to support suitable schedule production plans helps shorten processing time and reduce order delay. In addition, the results are shown in detail and can be flexibly modified manually on the Gantt chart. The feasibility of the production scheduling application is shown by the Gantt chart before and after the scheduling. Moreover, the order delay rate has been reduced from 15% to 3.33%.

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A New Hybrid Forecasting Architecture of Wind Power Based on A Newly Developed Temporal Convolutional Networks

A New Hybrid Forecasting Architecture of Wind Power Based on...

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Data Driven Control and Learning Systems (DDCLS)

作者： Yang Zhao Li Jia College of Mechatronics Engineering and Automation Shanghai University Shanghai P. R. China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Wind energy planning, dispatching and control in the generation and conversion of wind power generation industry are becoming more and more important for today's electrical power system, which needs accurate and stable wind speed prediction A new nonlinear hybrid model, SSA-TCN is proposed to improve the accuracy of wind power prediction. Singular Spectral Analysis (SSA) is introduced into the proposed method to decompose the original wind power sequence into four parts: trend, primary detail components, secondary detail components and noise. Moreover, a new Temporal Convolutional Networks (TCN) network is adopted in this paper instead of the current most advanced LSTM network. The output of TCN model has a longer actual memory range and is better able to cope with the gradient disappearance. In order to verify the advancement of SSA-TCN, the experimental part shows that the performance comparison between SSA-TCN, TCN and LSTM. The result shows that SSA-TCN has the best performance of all.

关键词： Convolution Wind power generation Market research Hybrid power systems Trajectory Predictive models Kernel

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