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

共 24757 条记录，以下是4861-4870 订阅

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

Robust Model Predictive Control with Control Barrier Functio...

引用

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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Enhancing Short-term Electric Load Forecasting with EMD-GA Optimized LSTM 6

Enhancing Short-term Electric Load Forecasting with EMD-GA O...

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6th international conference on Power and Energy Applications, ICPEA 2023

作者： Yihan, Liu Liang, Huang Ruiqi, Mao Wuhan University of Technology School of Automation Wuhan China

ISBN: (纸本)9798350359053

To enhance the accuracy of load forecasting, this paper considers both the inherent patterns within historical load data and external influencing factors. A short-term load forecasting approach is proposed that combines Empirical Mode Decomposition (EMD), Genetic Algorithm (GA), and Long Short-Term Memory (LSTM). Inputs include weather conditions, temperature, hourly weights, and date weights, while load data is the output. Historical data from a building in an industrial park in the Wuhan region is utilized as the dataset. Various prediction models are compared, including Back Propagation Neural Network (BP), Support Vector Regression (SVR), Random Forest (RF), and Long Short-Term Memory (LSTM). The evaluation metrics (MAE, RMSE, R2) indicate that LSTM outperforms the others. To further enhance the LSTM model, Empirical Mode Decomposition (EMD) is introduced for data preprocessing, and Genetic Algorithm (GA) is employed to optimize LSTM's hyperparameters. The results show that the EMD-GA-LSTM model achieves the best performance in load forecasting with MAE, RMSE, and R2 values of 6.18, 7.31, and 0.994, respectively, which are better than the other compared methods. © 2023 IEEE.

关键词： Empirical mode decomposition

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Harnessing CNN and LSTM models for Enhanced Predictive Maintenance in Industrial IoT Systems 3

Harnessing CNN and LSTM Models for Enhanced Predictive Maint...

引用

3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Bhaskar, Rajeev Kumar Kumaraswamy, Balasubramaniam Kalinga University Department of Cs & It Raipur India

ISBN: (纸本)9798331542375

In this research, the problem of predictive maintenance using the Industrial Internet of Things (IIoT) systems is investigated with the use of hybrid Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) models. Depending on the temporal dependency in the sensor data we want to bring in the LSTM to capture the temporal relations in the data as well as to extract the spatial feature using the CNN. Using publicly available IIoT datasets, including NASA Prognostics Data Repository to train and evaluate the models. It results in accuracy of 94.5% vs traditional machine learning algorithms. Finally, edge computing was tested for real-time deployment, making these predictions within a second and maintenance interventions prompt. It is shown that the hybrid CNN-LSTM model drastically improves PdM accuracy over other models, thereby reducing downtime and maintenance costs. It is a promising solution for promoting predictive maintenance in industrial cases, based on the scalability and robustness of this approach. Finally, future work will understand data quality challenges and more advanced methods, such as reinforcement learning and digital twins, to further improve model performance. © 2025 IEEE.

关键词： Predictive maintenance

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Extracting Physical and Technical Structured Information from Natural Language Documents

Extracting Physical and Technical Structured Information fro...

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international conference on Control Systems, Mathematical Modelling, automation and Energy Efficiency (SUMMA)

作者： Dmitry Korobkin Nikita Orlov Sergey Fomenkov Artyom Bobunov CAD department Volgograd State Technical University Volgograd Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

The paper presents a practical implementation of the developed method for extracting physical and technical structured information from natural language documents. The novelty of the method lies in the cooperation of deep learning technologies based on a tagged corpus of texts containing descriptions of technical and physical effects and methods of semantic-ontological text analysis. •An unlabeled corpus of natural language texts extracted from a unified knowledge base on physical topics was prepared. A tagged corpus of texts containing physical and technical structured information in the form of descriptions of physical effects and their technical implementations was formed for deep learning. Neural network models keyT5, T5 and Bert were trained to extract physical and technical information.

关键词： Training Deep learning Adaptation models Analytical models Text analysis Natural languages Writing Transformers Data models Mathematical models

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A new approach of modeling the ultra-short channel ballistic GAA MOSFETs 2

A new approach of modeling the ultra-short channel ballistic...

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2nd international conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2022

作者： Cheng, He Zhang, Chao Xie, Chuang Liu, Tiefneg Yang, Zhijia Zhang, Zhipeng Key Laboratory of Networked Control Systems Chinese Academy of Sciences Liaoning Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning Shenyang110169 China

ISBN: (数字)9781665476164

ISBN: (纸本)9781665476164

Compact models are useful to avoid spending a numerous amount of time in numerical simulations which can accurately evaluate the ultra-short channel device properties. One numerical compact model proposed here is describing the drain current in ballistic mode by using one unique smoothing function representing the transmission coefficients for all operating regions. The model is addressing the compact model for the subthreshold region basing on our previous study, in which the DIBL and the SDT effects are taken into a consideration. In this paper, a method of establishing the smoothing function is introduced for expressing the important parameters in the model overall operating regions. Compared with the TCAD NEGF simulation, this proposed numerical compact model demonstrates a good agreement. © 2022 IEEE.

关键词： Ballistics

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Predictive Control of Vehicle Dynamics Equipped with Intelligent Tire Sensors via Gaussian Process Regression of Lateral Tire Force

Predictive Control of Vehicle Dynamics Equipped with Intelli...

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international conference on Control and automation (ICCA)

作者： Kunhee Ryu Jinsung Kim Minkyu Han Juhoon Back Vehicle Control Technology Development Team Division of Advanced Vehicle Platform Hyundai Motor Company Republic of Korea School of Robotics Kwangwoon University Seoul Republic of Korea

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This paper investigates the estimation of lateral tire forces and predictive control for vehicles equipped with intelligent tires. Motivated by the capability of intelligent tires to estimate lateral tire forces, we propose a control scheme that includes a predictor for lateral tire forces, utilizing the Gaussian Process Regression technique. In addition, a metric for online data management is proposed, which has the characteristic of retaining more data in regions where the change of the function value is relatively large. The proposed metric can be interpreted as an extension of the existing method, allowing for the control of dataset quality within its limited size. We apply the proposed control scheme to the model predictive contouring control problem. Numerical simulations demonstrate the robustness of the proposed control scheme to tire parameter uncertainty, in comparison to a baseline controller.

关键词： Measurement Uncertain systems Gaussian processes Predictive models Numerical simulation Tires Robustness

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Structure-Aware Policy to Improve Generalization among Various Robots and Environments

Structure-Aware Policy to Improve Generalization among Vario...

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2022 IEEE international conference on robotics and Biomimetics, ROBIO 2022

作者： Xu, Wei Gao, Yue Nie, Buqing Shanghai Jiao Tong University Department of Automation Shanghai200240 China Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence and AI Institute Shanghai200240 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

ISBN: (纸本)9781665481090

Recently, Deep Reinforcement Learning (DRL) has been used to solve complex robot control tasks with outstanding success. However, previous DRL methods still exist some shortcomings, such as poor generalization performance, which makes policy performance quite sensitive to small vari-ations of the task settings. Besides, it is quite time-consuming and computationally expensive to retrain a new policy from scratch for new tasks, hence restricts the applications of DRL-based methods in the real world. In this work, we propose a novel DRL generalization method called GNN-embedding, which incorporates the robot hardware and the environment simultaneously with GNN-based policy network and learnable embedding vectors of tasks. Thus, it can learn a unified policy for different robots under different environment conditions, which improves the generalization performance of existing DRL robot policies. Multiple experiments on the Hopper-v2 robot are conducted. The experimental results demonstrate the effectiveness and efficiency of GNN-embedding on generalization, including multi-task learning and transfer learning problems. © 2022 IEEE.

关键词： Reinforcement learning

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Enhanced Blood Group Prediction with Fingerprint Images using Deep Learning

Enhanced Blood Group Prediction with Fingerprint Images usin...

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automation, Computing and Renewable Systems (ICACRS), international conference on

作者： C. Sivamurugan B. Perumal Yelchuri Siddarthha Vavilala Krishna Murthi Yaramati Nanda Sankar Yerra Varun Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Blood group prediction is a crucial process in medical diagnostics and emergency care, traditionally carried out through serological tests that require blood samples and specialized laboratory equipment. These conventional methods, while accurate, are invasive, time-consuming, and may be impractical in situations where resources are limited or unavailable in urgent situations. This research explores a novel approach of using fingerprint images for blood group detection, applying deep learning, specifically Convolutional Neural Networks (CNN) with good architectures. By analyzing the unique patterns in fingerprints, these models may offer a non-invasive, faster, and more efficient alternative to traditional methods. This study examines the feasibility of this approach, providing insights into how deep learning could potentially be utilized for blood group prediction.

关键词： Deep learning Renewable energy sources Analytical models automation Image matching Computer architecture Fingerprint recognition Convolutional neural networks Medical diagnosis Blood

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The Effectiveness of CNN-Based models in Macroscopic Motion Direction Detection

The Effectiveness of CNN-Based Models in Macroscopic Motion ...

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robotics and Computer Vision (ICRCV), international conference on

作者： Zhiyu Qiu Tianqi Chen Yuxiao Hua Yuki Todo Division of Electrical Engineering and Computer Science Graduate School of Natural Science & Technology Kanazawa University Kanazawa Japan Faculty of Electrical Information and Communication Engineering Kanazawa University Kanazawa Japan

ISBN: (数字)9798331527426

ISBN: (纸本)9798331527433

Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision, offering significant advancements in image and video recognition, classification, and segmentation tasks. Leveraging hierarchical pattern recognition capabilities, CNNs have demonstrated superior performance over traditional machine learning methods. One particularly critical application of CNNs is in motion direction detection, which is essential for enhancing security in surveillance systems, ensuring safe navigation in autonomous vehicles, and providing valuable insights in sports analytics. In this study, our primary goal was to investigate how different types of input data impact the accuracy of motion direction detection. We applied five distinct preprocessing methods to test the effect of input data on accuracy. Furthermore, we conducted experiments in various object and background scenarios to evaluate adaptability of simple 4-layer CNN, EfficientNetBO and ResNet50 in detecting motion direction. Finally, we tested the noise resistance of these three models. The results indicate that CNNs are capable of extracting the macroscopic motion direction of objects in images. Our study highlights the potential of CNN-based models for efficient and effective motion direction detection.

关键词： Resistance Training Computer vision Adaptation models Accuracy Noise Dynamics Data models Convolutional neural networks Residual neural networks

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Impact of Robotic Process automation on Enterprise Resource Planning Systems

Impact of Robotic Process Automation on Enterprise Resource ...

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Confluence of Advancements in robotics, Vision and Interdisciplinary Technology Management (IC-RVITM), international conference on th

作者： Dileep Kumar Yendluri Jainath Ponnala Ramya Thatikonda M. Kempanna Reshmi Tatikonda A. Bhuvanesh Masters in Computer Science University of the Cumberlands USA Bachelors in ECE Jawaharlal Technological University Hyderbad India PhD in Information Technology University of the Cumberlands Williamsburg USA Department of Artificial Intelligence and Machine Learning Bangalore Institute of Technology Bengaluru Karnataka India Masters in Computer Information Systems University of the Central Missouri Warrensburg USA Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India

ERP systems are merging their powers to generate a dynamic collaboration. By examining the integration between RPA (Robotic Process automation) and ERP (Enterprise Resource Planning) systems, we can identify their shared potential for enhancing process flow. Combining automation and ERP integration makes RPA a powerful tool for process management across multiple industries. The paper examines the basics of RPA and its ERP integration, highlighting the advantages of enhanced productivity and fewer errors. Addressing security, compliance, and change management issues simultaneously. In addition to top-down and bottom-up methods, process analysis and selecting suitable processes are covered. Practical examples offer insightful observations on this integration’s success. The union of RPA and ERP creates a powerful combination that can elevate operational effectiveness, facilitate strategic insights, and reshape the workplace.

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