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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

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Semantic Interpretation and Validation of Graph Attention-Based Explanations for GNN models

Semantic Interpretation and Validation of Graph Attention-Ba...

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international conference on Advanced robotics (ICAR)

作者： Efimia Panagiotaki Daniele De Martini Lars Kunze Department of Engineering Science Cognitive Robotics Group University of Oxford UK Department of Engineering Science Mobile Robotics Group Oxford Robotics Institute University of Oxford UK

In this work, we propose a methodology for investigating the use of semantic attention to enhance the explainability of Graph Neural Network (GNN)-based models. Graph Deep Learning (GDL) has emerged as a promising field for tasks like scene interpretation, leveraging flexible graph structures to concisely describe complex features and relationships. As traditional explainability methods used in eXplainable AI (XAI) cannot be directly applied to such structures, graph-specific approaches are introduced. Attention has been previously employed to estimate the importance of input features in GDL, however, the fidelity of this method in generating accurate and consistent explanations has been questioned. To evaluate the validity of using attention weights as feature importance indicators, we introduce semantically-informed perturbations and correlate predicted attention weights with the accuracy of the model. Our work extends existing attention-based graph explainability methods by analysing the divergence in the attention distributions in relation to semantically sorted feature sets and the behaviour of a GNN model, efficiently estimating feature importance. We apply our methodology on a lidar pointcloud estimation model successfully identifying key semantic classes that contribute to enhanced performance, effectively generating reliable post-hoc semantic explanations.

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BERT and Word Segmentation Algorithm Research on the Relationship Between Diabetes Symptoms and Traditional Chinese Medicine 6

BERT and Word Segmentation Algorithm Research on the Relatio...

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6th international conference on Intelligent Autonomous Systems, ICoIAS 2023

作者： Tang, Zhiming Lu, Yu'e Yang, Bo Li, Lei Hao, Jianghao Shanxi University Department of Automation Taiyuan030006 China Shanxi Acupuncture Hospital Taiyuan030006 China School of Mathematical Sciences Shanxi University Taiyuan030006 China

ISBN: (纸本)9798350371253

Diabetes is a common chronic Metabolic disorder, and its treatment has always been a hot research topic in the medical field. Traditional Chinese medicine has a significant therapeutic effect on it and has become one of the important ways for patients to choose treatment methods. In recent years, with the development and application of deep learning technology, more and more research has begun to attempt to use deep learning models to process traditional Chinese medicine case data, achieving automated information extraction and analysis. This study shows that BERT model and jieba segmentation algorithm have good effect in the study of the relationship between symptoms and Medication in diabetes medical record data, which is expected to provide scientific basis and reference for clinical treatment of traditional Chinese medicine. © 2023 IEEE.

关键词： Deep learning

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Predictive Value Chain Analysis to Improve Generalization Accuracy In Quality Assurance

Predictive Value Chain Analysis to Improve Generalization Ac...

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Control & automation, Electronics, robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE international conference on

作者： Harmayani Poltak Sihombing Mahyudin Marischa Elveny Postgraduate Program in Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Computer Science and Information Technology Postgraduate Doctorate in Computer Science Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

Adaptive predictive analysis model utilizing Spiking Neural Networks (SNNs) to enhance quality assurance processes in Smart Campus environments. Traditional predictive models often struggle with the dynamic and complex nature of educational data, leading to inaccurate predictions and inefficient decision-making. SNNs, inspired by biological neurons, are particularly suited for processing temporal data and adapting to evolving patterns. The proposed model integrates spike-timing-dependent plasticity (STDP) and dynamic weight adjustment to continuously learn and refine predictions in response to real-time data, ensuring improved accuracy and robustness. The study uses historical data from the Internal Quality Assurance System (SPMI) over a three-year period, focusing on key performance indicators such as student enrollment trends, faculty productivity, and resource utilization. Experimental results demonstrate that the SNN-based model outperforms traditional neural networks, achieving a 20% reduction in Mean Squared Error (MSE) and a higher R-squared value. These findings indicate that the model not only enhances predictive accuracy but also offers significant improvements in operational efficiency and resource management. By providing timely and precise insights, this model supports proactive decision-making in educational institutions, enabling administrators to optimize resource allocation, monitor academic quality, and anticipate future challenges. This research contributes to the growing field of artificial intelligence in education, offering a scalable, adaptive approach to managing complex data and improving institutional performance in real-time.

关键词： Adaptation models Quality assurance Accuracy Biological system modeling Decision making Spiking neural networks Predictive models Real-time systems Resource management Predictive analytics

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Comparison of Mechanistic and Learning-based Tip Force Estimation on Tendon-driven Soft Robotic Catheters 44

Comparison of Mechanistic and Learning-based Tip Force Estim...

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44th Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Yaftian, Pegah Bandari, Naghmeh Dargahi, Javad Hooshiar, Amir Concordia Univ Dept Mech Engn 1455 Blvd Maisonneuve Ouest Montreal PQ H3G M8 Canada McGill Univ Dept Surg 1650 Cedar Ave Montreal PQ H3G 1A4 Canada

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Researchers have adopted mechanistic and learning-based approaches for tip force estimation on soft robotic catheters. Typically the literature attributes the mechanistic methods with more accuracy while indicating the learning-based methods outpace in computational time. In this study, a previously validated mechanistic tip force estimation method was compared with four learning-based methods, i.e. support-vector-regression (SVR), random-forest (RF), AdaBoost (Ada), and deep neural network (DNN). The learning-based methods were trained on experimental data acquired from a robotic catheter, developed in-house. The accuracy of force estimation using the five methods were compared with the ground truth forces in a teleoperated catheter manipulation test. Moreover, the capability of the learning-based models in contact detection, i.e., detection of the onset of tip contact, were compared with the ground truth. The results showed that the mechanical model had a mean-absolute error (MAE) of 8.8 mN while the MAE of SVR, RF, Ada, and DNN were 5.6, 5.2, 5.3, and 5.1 mN, respectively. Moreover, the accuracy and precision of the mechanistic model for contact detection was 89.2% and 91.7%, respectively, while these were 97.0%, 97.7%, 97.6%,and 97% and 97.9%, 98.3%, 97.8%, and 98.8% for the SVR, RF, Ada, and DNN, respectively. The comparison showed that with hyper-parameter optimization the learning-based models surpassed the mechanistic model in accuracy and precision, while both method approaches revealed acceptable performance for the proposed application.

关键词： Radio frequency Learning systems Three-dimensional displays Force Estimation Phantoms Soft robotics

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Bridging the Gap between IT and OT with AAS Digital Twins and MDE Techniques: An Industrial Waste Management Case Study

Bridging the Gap between IT and OT with AAS Digital Twins an...

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international conference on Emerging Technologies and Factory automation (ETFA)

作者： Quang–Duy NGUYEN Saadia DHOUIB Eric LUCET Antoine Le MORTELLEC Fabien BALIGAND Université Paris-Saclay CEA List Palaiseau France PROSYST Valenciennes France

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

Industry 4.0, involving technological advances such as the Internet of Things, artificial intelligence, and autonomous robotics, promises to bring great benefits and outstanding innovations to traditional manufacturing. To make this vision a reality, one key challenge consists in harmonizing and adapting these new technologies to the industrial environment. However, there are two major obstacles. First, the technologies, divided into information technologies (IT) and operational technologies (OT), are not interoperable by default, making the IT and OT convergence a complex and uneasy problem. Second, deploying an Industry 4.0 system requires the participation of experts from different domains who have different technical vocabularies and knowledge. Therefore, communication and collaboration between these experts can be complicated, and every misunderstanding may cause the failure of a part or the whole system. Regarding the mentioned obstacles, this paper shares our successful experiences and approaches in an industrial waste management case study of the project OTPaaS. In detail, we have applied standardized modeling approaches, more specifically, the asset administration shell (AAS) digital twins models, for the interoperability between IT and OT involved in the system and used model-driven engineering (MDE) techniques to automate the translation of data models between the different stakeholders in the nroiect.

关键词： Vocabulary Technological innovation Solid modeling Industrial waste Three-dimensional displays Data models Fourth Industrial Revolution Digital twins Stakeholders Standards

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Learning Pre-Grasp Manipulation of Flat Objects in Cluttered Environments using Sliding Primitives

Learning Pre-Grasp Manipulation of Flat Objects in Cluttered...

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IEEE international conference on robotics and automation (ICRA)

作者： Jiaxi Wu Haoran Wu Shanlin Zhong Quqin Sun Yinlin Li State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China Department of Automation University of Science and Technology of China Heifei China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Beijing China Science and Technology on Thermal Energy and Power Laboratory Wuhan China

Flat objects with negligible thicknesses like books and disks are challenging to be grasped by the robot because of the width limit of the robot's gripper, especially when they are in cluttered environments. Pre-grasp manipulation is conducive to rearranging objects on the table and moving the flat objects to the table edge, making them graspable. In this paper, we formulate this task as Parameterized Action Markov Decision Process, and a novel method based on deep reinforcement learning is proposed to address this problem by introducing sliding primitives as actions. A weight-sharing policy network is utilized to predict the sliding primitive's parameters for each object, and a Q-network is adopted to select the acted object among all the candidates on the table. Meanwhile, via integrating a curriculum learning scheme, our method can be scaled to cluttered environments with more objects. In both simulation and real-world experiments, our method surpasses the existing methods and achieves pre-grasp manipulation with higher task success rates and fewer action steps. Without fine-tuning, it can be generalized to novel shapes and household objects with more than 85% success rates in the real world. Videos and supplementary materials are available at https://***/view/pre-grasp-sliding.

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Design of a multifunctional end-effector for robotic replacement of TBM disc cutters

Design of a multifunctional end-effector for robotic replace...

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IEEE international conference on Mechatronics and automation

作者： Jinshu Zhou Qiang Yang Liang Du Zhengtao Hu Sheng Bao Jianjun Yuan Jintao Xie Meng Li Weidong Wang Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai China Shield Ace Robotics (Shanghai) Co. Ltd. Shanghai China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

The robotic replacement of disc cutters in tunnel boring machine (TBM) is imperative for maintenance tasks' safety and efficiency. One major challenge is to managing the complex and cumbersome fastening components of disc cutters. This paper introduces a multifunctional end-effector that can grip a combined wrench to facilitate the alignment, fastening, and unfastening of cutter bolts, as well as grip the disc cutter. An integrated cutter house design eliminates the need for the robot to handle small bolts and nuts during remote operations. Additionally, a two-segment bolt design aids in the peg-in-hole procedure, ensuring a high success rate for robotic torque wrench alignment with the main fastening bolt. An alignment strategy for remote robot control is also proposed. We constructed a full-scale mock-up of the TBM disc cutter with the proposed cutter house structure to validate the proposed design and methods. Preliminary experimental results demonstrate the effectiveness of the system's mechanism and strategy in facilitating the swift assembly and disassembly of heavy disc cutters during replacement processes.

关键词： Torque Robot control Fasteners End effectors Safety Joining processes Task analysis

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Development of a Simple and Novel Digital Twin Framework for Industrial Robots in Intelligent robotics Manufacturing

Development of a Simple and Novel Digital Twin Framework for...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Tianyi Xiang Borui Li Xiaonan Pan Quan Zhang School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou Department of Electrical and Electronic Engineering University of Liverpool Liverpool UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

This paper has proposed an easily replicable and novel approach for developing a Digital Twin (DT) system for industrial robots in intelligent manufacturing applications. Our framework enables effective communication via Robot Web Service (RWS), while a real-time simulation is implemented in Unity 3D and Web-based Platform without any other 3rd party tools. The framework can do real-time visualization and control of the entire work process, as well as implement real-time path planning based on algorithms executed in MATLAB. Results verify the high communication efficiency with a refresh rate of only 17ms. Furthermore, our developed web-based platform and Graphical User Interface (GUI) enable easy accessibility and user-friendliness in real-time control.

关键词： Three-dimensional displays Service robots Industrial robots Real-time systems Hardware Mathematical models Manufacturing Digital twins MATLAB Graphical user interfaces

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Robust Model Predictive Control with Control Barrier Functions for Autonomous Surface Vessels

Robust Model Predictive Control with Control Barrier Functio...

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IEEE international conference on robotics and automation (ICRA)

作者： Wei Wang Wei Xiao Alejandro Gonzalez-Garcia Jan Swevers Carlo Ratti Daniela Rus Computer Science and Artificial Intelligence Lab (CSAIL) Massachusetts Institute of Technology Cambridge MA USA SENSEable City Laboratory Massachusetts Institute of Technology Cambridge MA USA Marine Robotics Lab Department of Mechanical Engineering College of Engineering University of Wisconsin-Madison Madison WI USA Department of Mechanical Engineering MECO Research Team KU Leuven Belgium Flanders Make@KU Leuven Belgium

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In autonomous robot navigation, the trajectories from path planners are considered to be safe regions, and deviations could endanger vessels. Model Predictive Control (MPC) stands as a popular choice for trajectory tracking problems as it naturally addresses operational constraints, such as dynamics and control constraints. Nevertheless, achieving robustness in changing environments like oceans and rivers, which are constantly subject to significant external disturbances, remains an ongoing challenge for MPC. It must consistently keep the system within a predefined safe region (such as a reference trajectory) even in the presence of model inaccuracies and perturbations. To address this challenge, we present a robust model predictive control strategy utilizing Control Barrier Functions (CBFs), which increases the disturbance-rejection abilities. We verify our method on an autonomous surface vessel in simulation and natural waters, both with external disturbances. Specifically, compared with the traditional MPC method, our proposed MPC-CBF strategy reduces tracking errors by 17.82% and 40.26% in simulations and field experiments, respectively. Although the control effort slightly increases by 7.78% and 4.20%, respectively, these results clearly demonstrate the enhanced resilience of MPC-CBF to disturbances.

关键词： Sea surface Trajectory tracking Navigation Perturbation methods Robustness Trajectory Rivers

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A Novel Approach to Grasping Control of Soft Robotic Grippers based on Digital Twin

A Novel Approach to Grasping Control of Soft Robotic Gripper...

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international conference on automation and Computing (ICAC)

作者： Tianyi Xiang Borui Li Quan Zhang March Leach Enggee Lim School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798350360882

ISBN: (纸本)9798350360899

This paper has proposed a Digital Twin (DT) framework for real-time motion and pose control of soft robotic grippers. The developed DT is based on an industrial robot workstation, integrated with our newly proposed approach for soft gripper control, primarily based on computer vision, for setting the driving pressure for desired gripper status in real-time. Knowing the gripper motion, the gripper parameters (e.g. curvatures and bending angles, etc.) are simulated by kinematics modelling in Unity 3D, which is based on four-piecewise constant curvature kinematics. The mapping in between the driving pressure and gripper parameters is achieved by implementing OpenCV based image processing algorithms and data fitting. Results show that our DT-based approach can achieve satisfactory performance in real-time control of soft gripper manipulation, which can satisfy a wide range of industrial applications.

关键词： Three-dimensional displays Computational modeling Fitting Kinematics Soft robotics Industrial robots Real-time systems Mathematical models Workstations Grippers

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