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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Li, Jixiu Huang, Yisen Ng, Wing Yin Cheng, Truman Wu, Xixin Dou, Qi Meng, Helen Heng, Pheng Ann Liu, Yunhui Chan, Shannon Melissa Navarro-Alarcon, David Hang Ng, Calvin Sze Yan Chiu, Philip Wai Li, Zheng The Department of Surgery The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The T Stone Robotics Institute The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Mechanical Engineering The Hong Kong Polytechnic University Hong Kong Hong Kong The Department of Surgery and the Chow Yuk Ho Technology Centre for Innovative Medicine The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Surgery ChowYuk Ho Technology Centre of Innovative Medicine Li Ka Shing Institute of Health Science Multiscale Medical Robotics Center Ltd. The Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781665481090

In minimally invasive surgery (MIS), controlling the endoscope view is crucial for the operation. Many robotic endoscope holders were developed aiming to address this prob-lem,. These systems rely on joystick, foot pedal, simple voice command, etc. to control the robot. These methods requires surgeons extra effort and are not intuitive enough. In this paper, we propose a speech-vision based multi-modal AI approach, which integrates deep learning based instrument detection, automatic speech recognition and robot visual servo control. Surgeons could communicate with the endoscope by speech to indicate their view preference, such as the instrument to be tracked. The instrument is detected by the deep learning neural network. Then the endoscope takes the detected instrument as the target and follows it with the visual servo controller. This method is applied to a magnetic anchored and guided endoscope and evaluated experimentally. Preliminary results demonstrated this approach is effective and requires little efforts for the surgeon to control the endoscope view intuitively. © 2022 IEEE.

关键词： Endoscopy

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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Computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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Evaluating the Effect of Speed and Acceleration on Human Factors During an Assembly Task in Human-Robot Collaboration

SSRN

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SSRN 2023年

作者： Apraiz Iriarte, Ainhoa Lasa, Ganix Mazmela, Maitane Arana-Arexolaleiba, Nestor Serrano, Antonio Elguea, Íñigo Etxabe, Amaia Design Innovation Centre Faculty of Engineering Mechanical and Industrial Production Mondragon Unibertsitatea Arrasate20500 Spain Robotics and Automation Electronics and Computer Science Department Mondragon Unibertsitatea Arrasate20500 Spain Research & Development Department Electrotécnica Alavesa S.L. Vitoria-Gasteiz1010 Spain

In Industry 5.0, the workers' well-being is the central pillar. Therefore, research on methods and technics to improve the workers' user experience in a human-robot collaborative environment is necessary. While the effects of dynamic variables, such as speed and acceleration, on human safety have been extensively studied, their impact on human perception has not been fully explored. This study investigates the effects of the robot's speed and acceleration on humans. An experimental research approach was used, where 20 participants (10 women and 10 men) performed an assembly task collaborating with a robot. Two experiments were defined, and the participants were evenly distributed: in the first experiment, the participants started by performing the task at a slow robot speed and then performed the same task at a faster speed. In the second experiment, the other half followed the opposite procedure. Key Performance Indicators (KPIs), physiological values (via EEG and GSR), and perceptual values (using the standardised UEQ-S questionnaire) were collected. The results showed that the robot's speed and acceleration impact the task's completion time and participants' emotional responses. Our results lead to a new concept, "HRI speed bell", which indicates that it is necessary to investigate the optimal speed and acceleration to ensure good trust and perceived safety. Furthermore, the task sequence also influences participants' expectations and performance. Finally, gender-differentiated results were also observed. © 2023, The Authors. All rights reserved.

关键词： Robots

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Teleoperation of Multi-functional Neurosurgical Tool*

Teleoperation of Multi-functional Neurosurgical Tool*

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WRC Symposium on Advanced robotics and automation (WRC SARA)

作者： Xudong Ma Lechen Yao Anqi Chen Junchen Wang Changsheng Li Wenyong Liu Yida David Hu Bai-Quan Su Medical Robotics Laboratory School of Automation Beijing University of Posts and Tele-communications Beijing China Affiliated High School of Peking University Beijing China School of Mechanical Engineering and Automation Beihang University Beijing China Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China Brigham and Women’s Hospital Harvard Medical School Boston MA USA

Fluid removal and electrotome coagulation are routine procedures in neurosurgery operations, and the doctor’s remote control can reduce physical fatigue. In this paper, an integrated multi-functional surgical tool which can realize electrocoagulation and liquid aspiration is designed. At the same time, the multi-functional integrated surgical device is combined with the remote operation method to realize the remote operation of the instrument. This multifunctional instrument includes CCD-imaging, suction and electrical coagulation. The system can complete the remote operation function of the special design tool, and can isolate the doctor from the operation. The teleoperation of this instrument is integrated with the teleoperation of other functional instruments to realize the teleoperation of the complete surgical process.

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A machine learning approach for vibration signal based fault classification on hydraulic braking system through C4.5 decision tree classifier and logistic model tree classifier

A machine learning approach for vibration signal based fault...

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2020 SAE International Conference on Advances in Design, Materials, Manufacturing and Surface engineering for Mobility, ADMMS 2020

作者： Joshuva, A. Anaimuthu, Shridhar Selvaraju, Nandakumar Jenoris Muthiya, S. Centre for Automation and Robotics [ANRO Department of Mechanical Engineering Hindustan Institute of Technology and Science Rajiv Gandhi Salai Padur Chennai Tamil Nadu603103 India

Car hydraulic brakes are important safety components for passengers and are thus the good condition of brakes are essential for braking. By using the vibrational signatures, the state of the brake components can be determined. In this proposed study, electronic condition monitoring is suggested as a possible solution to such issues by using a machine learning method with a piezo-electric transducer and a dynamic data acquisition system. Ford EcoSport setup was used to acquire the vibration signals for both good and bad braking conditions. The mathematical Descriptive statistical features from the vibration signals were obtained and the feature selection has been done with the C4.5 decision tree classifier. The appropriate number of features needed to classify a particular problem is not determined by a specific method. A thorough study is, therefore, necessary to find the right number of features. The fault analysis of the Ford EcoSport hydraulic braking system has been established through the use of the C4.5 decision tree classifier and logistic model tree (LMT) classifier. © 2020 SAE International and SAE India. All rights reserved.

关键词： Condition monitoring

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Compact Multiple Functional Integrated Medical Robotic End-Effector for Neurosurgery*

Compact Multiple Functional Integrated Medical Robotic End-E...

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WRC Symposium on Advanced robotics and automation (WRC SARA)

作者： Yinjia Zhang Weihan Li Haotian Wang Xudong Ma Qian Yang Jie Tang Yuanzheng Hou Yida David Hu Changsheng Li Wenyong Liu Junchen Wang Bai-Quan Su Medical Robotics Laboratory School of Intellgent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China Graduate School of Engineering The University of Tokyo Tokyo Japan Department of Neurosurgery Xuanwu Hospital Capital Medical University Beijing China Harvard Medical School Brigham and Women’s Hospital Boston MA USA School of Mechatronical Engineering Beijing Institute of Technology Beijing China Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China School of Mechanical Engineering and Automation Beihang University Beijing China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

In neurosurgery, surgeons use single-function surgical tools such as aspirator and electric-coagulation cutters to deal with blood vessel bleeding. Compared with multi-functional surgical tools, the surgical efficiency of using single-function tools is lower. In this paper, a multiple functional integrated medical robotic end-effector which integrates CCD imaging, electric-coagulation and suction is designed. The end-effector can be installed at the end of the serial robotic arm. The CAD model of the end-effector was constructed, and the motion analysis is carried out. The end-effector is proved to be effective by three experiments. The operator can use the design to suction blood, spotting of bleeding spots, and electric-coagulation lesion. In operations, there is no need to change the surgical tools, improve the efficiency of the operation.

关键词： Charge coupled devices Solid modeling Medical robotics Design automation Instruments Prototypes Three-dimensional printing

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Model-Based Shape Estimation and Closed-Loop Control of Magnetic Continuum Robot Based on Monocular Vision

Model-Based Shape Estimation and Closed-Loop Control of Magn...

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IEEE International Conference on robotics and Biomimetics

作者： Junfeng Zhu Yuanrui Huang Jian Chen Hongbin Liu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences (CASIA) Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Centre of AI and Robotics Hong Kong Institute of Science Innovation Chinese Academy of Sciences School of Biomedical Engineering and Imaging Sciences King's College London UK

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Magnetic continuum robots (MCRs) show promising potential for applications in narrow lumens, such as vascular interventions. Accurate shape sensing of these robots is crucial for the success of interventional surgeries. However, the submillimeter dimensions of MCRs poses significant challenges for integrating additional sensors. An alternative approach is shape estimation from images, yet the lack of precise MCR kinematics can make this process time-consuming. In this work, we propose a novel MCR design featuring a rectangular tip capable of specific planar bending, along with a corresponding kinematic model and a mechanical model. Additionally, we introduce a model-based shape estimation method that enables the estimation of the MCR's shape from a single-view image without the need for external markers. Experimental results demonstrate that our method can estimate an MCR's shape with a mean absolute error (MAE) of $\mathbf{0.58}\pm \mathbf{0.41}\mathbf{mm}$ within 85ms, facilitating real-time application. Furthermore, closed-loop control based on this method has achieved an end position error below 11% of the tip's total length, highlighting the accuracy and practicality of our approach for surgical interventions.

关键词： Solid modeling Shape Computational modeling Magnetic resonance imaging Estimation Kinematics Robot sensing systems Real-time systems Continuum robots Magnetosphere

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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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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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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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