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

共 24666 条记录，以下是251-260 订阅

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Modelling of Shear Walls with Opening Using Machine Learning Techniques

Modelling of Shear Walls with Opening Using Machine Learning...

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international conference on Autonomous Robots and Agents, ICARA

作者： Yogesh Aggarwal Department of Civil Engineering NIT Kurukshetra Kurukshetra Haryana India

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

A Shear wall is the most appropriate structure shape in reinforced concrete buildings. Thus, examining the Shear Wall in structural systems is vital because it effectively resists lateral loads such as wind and seismic forces. The behaviour of such reinforced concrete building with Shear Wall differs from that of other reinforced concrete constructions. Thus, structural analysis of Shear Walls with openings for natural light, ventilation, entry, escape etc is required This factor has been analysed in terms of maximum storey displacement and base shear for model with Shear Walls and openings, using Equivalent static method to analyse the seismic performance of the structures. A comprehensive dataset is prepared using Staad Pro v8i software. The models are developed for the number of stories from 5 to 40, with different sizes of openings in the shear wall. A total of 240 models were analysed in III, IV and V zones of the earthquake, as defined by Indian code. The study involves the data set preparation and training, testing and validation using different machine learning techniques like deep neural networks, artificial neural networks and random forests. so that it will be helpful in predicting the seismic response of any similar multi-storey building in terms of Maximum storey displacement and Base shear.

关键词： Training Radio frequency Analytical models Buildings Artificial neural networks Predictive models Ventilation Concrete Random forests Testing

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Performance Analysis of ANFIS and DRL Based Speed Controllers for Sensorless Drives

Performance Analysis of ANFIS and DRL Based Speed Controller...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Kasim M. Al-Aubidy Abdullah F. Al-Saoudi Mechatronics Eng. Dept. Tishk International University Erbil Iraq

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Permanent magnet synchronous machines (PMSMs) are of great interest in automation and robotics applications due to their small size, high efficiency, and low maintenance requirements. However, these motors face challenges in controlling their speed and position, such as precise rotor detection. This situation is essential in many Industrial applications. Conventional controllers such as PID controllers are commonly used, but their performance is often affected by changes in system dynamics, necessitating updating of controller parameters based on the accurate mathematical model of the motor. This research aims to investigate several intelligent control strategies that do not rely on the mathematical model of the motor. The performance of the drive system will be evaluated using three intelligent controllers: a neural network controller, a neural fuzzy inference system (ANFIS) controller, and a deep reinforcement learning (DRL) controller. The performance of these smart controllers will be compared with that of the PID controller. The results presented in this paper demonstrate the ability of the proposed smart controllers to regulate the speed of PMSMs compared to a PID controller. Specifically, the ANFIS-based controller shows the best performance in terms of maximum overshoot, while the DRL controller demonstrates the best settling time.

关键词： automation Service robots System dynamics Velocity control Motors Robot sensing systems Control systems Mathematical models Maintenance Time factors

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Vision-Based Robotic Grasping: Integrating YOLOv8 and GRCNN for Dynamic Object Handling 2nd

Vision-Based Robotic Grasping: Integrating YOLOv8 and GRCNN ...

引用

2nd international conference on Intelligent Manufacturing and robotics, ICIMR 2024

作者： Lyu, Xingze Hajjaj, Sami Salama Hussen School of Robotics Xi’an Jiaotong-Liverpool University 111 Taicang Avenue Jiangsu Province Suzhou Taicang215400 China School of Engineering and Technology Sunway University No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia

ISBN: (纸本)9789819639489

This work presents an approach to vision-based robotic grasping, combining scene understanding with advanced neural network architectures. The primary goal is to develop a system that enables robots to grasp a wide variety of objects in unstructured environments using RGB-D data. The methodology integrates two key components: the YOLOv8 model for real-time object detection and the Generative Residual Convolutional Neural Network (GRCNN) for precise grasp pose inference. The experimental setup employs the PyBullet simulation environment, utilizing a Universal Robots UR5 model equipped with a Robotiq 2F-85 parallel-jaw gripper. The YOLOv8 model is trained using a custom dataset created in PyBullet and annotated via the Roboflow platform, ensuring accurate object detection and localization. The GRCNN model generates grasp quality maps, including quality, angle, and width maps, which are crucial for determining the optimal grasp points and orientations. The system is thoroughly tested in the PyBullet environment, where it demonstrates robust performance across various scenarios, accurately detecting objects and planning grasps in real-time. The simulation results are validated through real-world experiments using the UR5 robot, confirming the system’s effectiveness in practical applications. This work also addresses the limitations of traditional grasping methods, which often rely on pre-programmed instructions and detailed 3D models, making them unsuitable for dynamic environments. By leveraging deep learning and real-time processing, the proposed system overcomes these challenges, providing a scalable and efficient solution for robotic grasping in unstructured settings. Future work will involve optimizing neural networks, exploring adaptive grasping strategies, and enhancing language understanding capabilities to improve system responsiveness and versatility in complex tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Human robot interaction

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Deep Learning Meets Traditional Machine Learning: CNN-SVM Hybrid models for Parkinson's Diagnosis 3

Deep Learning Meets Traditional Machine Learning: CNN-SVM Hy...

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

作者： Mehta, Shiva Bhardwaj, Rishabh Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact & Outcome Punjab Rajpura140401 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798331542375

Parkinson's disease (PD) is a neurodegenerative disorder that needs early and accurate diagnosis for good management. Our approach in this study is to have a hybrid diagnostic framework which uses Convolutional Neural Networks (CNNs) for the feature extraction, and Support Vector Machines (SVMs) for the classification. By leveraging on the complementary strengths of the above-mentioned approaches, the model reports improved performance in detection of PD by using medical imaging data. We evaluated proposed CNN-SVM hybrid model on a dataset consist of 5,000 MRI images (2,500 with PD cases and 2,500 with healthy control) with achieved accuracy of 94.8%, sensitivity of 93.7%, and specificity of 95.5%. These results demonstrate a significant improvement over baseline methods, including standalone CNNs (accuracy: 91. The performance of the proposed methods is experimentally demonstrated to be better than SVMs with handcrafted features (accuracy: 84.5%, sensitivity: 83.2%, specificity: 85.0%) and even SVMs using Fisher vectors (accuracy: 81.4%, sensitivity: 86.1%, specificity: 81.1%) for certain classes (e.g. 2%, sensitivity: 89.5%, specificity: 92.3%). The class separability of the hybrid model was excellent, with an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.96. These findings were further confirmed under confusion matrix analysis with 2,343 true positives and 2,388 true negatives and with only 157 false negatives (sensitivity: 0.017%) and 112 false positives (specificity: 0.970%). The model demonstrated generalizability by the convergence of training and validation curves throughout 50 epochs with a final loss of 0.06 and validation error stabilizing at 0.04. The CNN-SVM hybrid model also can demonstrate the ability to become a reliable diagnostic tool for PD detection, with robust classification performance and few errors. Multi-modal data integration and scalability for use over a broader range of clinical applications is the subject of fut

关键词： Diseases

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An Embedding Algorithm Based on Neighborhood Aggregation for Attribute Graphs

An Embedding Algorithm Based on Neighborhood Aggregation for...

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international conference on Computer and automation Engineering, ICCAE

作者： Dongming Chen Shuyue Zhang Yumeng Zhao Mingzhao Xie Dongqi Wang Software College Northeastern University Shenyang China

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Attribute graph embedding algorithms based on graph neural networks aggregate information about the neighborhood of a target node to update the embedding of the target node, i.e. neighborhood aggregation. The shallow network architecture of existing models limits the generalization ability of the models on large datasets, and the number of nodes increases exponentially with the number of graph neural network layers, resulting in gradient explosion and the need for faster model training methods. In addition, the multiple iteration process of neighborhood aggregation changes the original attribute information of the nodes, especially when the attributes of the target node and the attribute information of the neighborhood nodes are extremely different, which makes the target node attributes deviate significantly from the original attributes. Excessive neighborhood aggregation mixes different clustered nodes, causing all nodes to converge to the same embedding, resulting in excessive smoothing. In order to solve the above problems while considering the supervised learning of the model and the utility of neighborhood aggregation, this paper proposes a subgraph sampling method based on graph sampling for attribute graph data pre-processing, and an attribute graph embedding algorithm for improving neighborhood aggregation using an adaptive learning strategy.

关键词： Training Adaptive learning Smoothing methods Supervised learning Network architecture Sampling methods Graph neural networks Explosions Data models Classification algorithms

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

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

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automation and Computation (AUTOCOM), international conference on

作者： Rajeev Kumar Bhaskar Balasubramaniam Kumaraswamy Department of CS & IT Kalinga University Raipur India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

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.

关键词： Accuracy Computational modeling Reinforcement learning Predictive models Data models Real-time systems Convolutional neural networks Long short term memory Predictive maintenance Industrial Internet of Things

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Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study between Efficient Deep Learning Approaches

Detection of Endangered Deer Species Using UAV Imagery: A Co...

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international conference on Unmanned Aircraft Systems (ICUAS)

作者： Agustin Roca Gastón Castro Gabriel Torre Leonardo J. Colombo Ignacio Mas Javier Pereira Juan I. Giribet Artificial Intelligence and Robotics Laboratory (LINAR) Universidad de San Andrés Victoria Provincia de Buenos Aires Argentina Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina Centre for Automation and Robotics (CSIC-UPM) Ctra. M300 Campo Real Km 0200 Arganda del Rey Madrid Spain Museo Argentino Bernardino Rivadavia Capital Federal Argentina

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This study compares the performance of state-of-the-art neural networks including variants of the YOLOv11 and RT-DETR models for detecting marsh deer in UAV imagery, in scenarios where specimens occupy a very small portion of the image and are occluded by vegetation. We extend previous analysis adding precise segmentation masks for our datasets enabling a fine-grained training of a YOLO model with a segmentation head included. Experimental results show the effectiveness of incorporating the segmentation head achieving superior detection performance. This work contributes valuable insights for improving UAV-based wildlife monitoring and conservation strategies through scalable and accurate AI-driven detection systems.

关键词： YOLO Deep learning Training Image segmentation Head Wildlife Neural networks Vegetation mapping Autonomous aerial vehicles Monitoring

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Discrete Fourier Transform-Based Point Cloud Compression for Efficient SLAM in Featureless Terrain

Discrete Fourier Transform-Based Point Cloud Compression for...

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international conference on Autonomous Robots and Agents, ICARA

作者： Riku Suzuki Ayumi Umemura Shreya Santra Kentaro Uno Kazuya Yoshida Department of Aerospace Engineering Space Robotics Lab. (SRL) Tohoku University Sendai Japan

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Simultaneous Localization and Mapping (SLAM) is an essential technology for the efficiency and reliability of unmanned robotic exploration missions. While the onboard computational capability and communication bandwidth are critically limited, the point cloud data handled by SLAM is large in size, attracting attention to data compression methods. To address such a problem, in this paper, we propose a new method for compressing point cloud maps by exploiting the Discrete Fourier Transform (DFT). The proposed technique converts the Digital Elevation Model (DEM) to the frequency-domain 2D image and omits its high-frequency components, focusing on the exploration of gradual terrains such as planets and deserts. Unlike terrains with detailed structures such as artificial environments, high-frequency components contribute little to the representation of gradual terrains. Thus, this method is effective in compressing data size without significant degradation of the point cloud. We evaluated the method in terms of compression rate and accuracy using camera sequences of two terrains with different elevation profiles.

关键词： Point cloud compression Simultaneous localization and mapping Image coding Discrete Fourier transforms Moon Focusing Real-time systems Deserts Reliability Mobile robots

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Experimental Validation of Intelligent Ad Hoc System for Sea Surface Velocity Approximation

Experimental Validation of Intelligent Ad Hoc System for Sea...

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international conference on Autonomous Robots and Agents, ICARA

作者： Karlo Jakac Luka Lanča Ante Sikirica Stefan Ivić Faculty of Engineering University of Rijeka Rijeka Croatia Center for Advanced Computing and Modelling University of Rijeka Rijeka Croatia

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

This paper presents the validation of a smart ad hoc system designed to approximate the sea surface velocity field using measurements obtained from multiple floating sensors. Recognizing that point data alone cannot fully capture the domain's circulation, we propose a method that uses a simplified two-dimensional flow model as a surrogate for submesoscale flow, reconstructing the entire velocity field from the scattered data. To ensure reliable flow dynamics, the approach addresses the problem as a model-fitting process, adjusting the boundary conditions of numerical simulations to align the flow with measured data. Experimental validation was performed with the collected measurements, which involved the deployment of Global Positioning System (GPS) drifters in the Kvarner Bay area. The results highlight the system's adaptability to various domains, measurement types, and conditions, demonstrating its effectiveness for real-world submesoscale applications where an approximate velocity field is acceptable.

关键词： Sea surface Surface reconstruction Sea measurements Data models Numerical models Velocity measurement Wind forecasting Optimization Intelligent sensors Global Positioning System

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A Survey on Fast Task Adaptation Techniques in Machine Learning

A Survey on Fast Task Adaptation Techniques in Machine Learn...

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international conference on Inventive Computation Technologies (ICICT)

作者： Ramesh G Kiran Raj K M Shantveer Kesti Charandeep B S Manvith Suvarna Akash Nayak Department of ISE NMAM Institute of Technology Nitte(Deemed to be University Udupi Karnataka India Department of Artificial Intelligence and Machine Learning Alva's Institute of Engineering and Technology Mangalore Karnataka India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Meta-reinforcement learning is a cutting-edge approach that facilitates swift and adaptable responses to new tasks in dynamic environments with significant resource limitations. Utilizing meta-learning principles offers substantial benefits, enabling Meta-RL frameworks to effectively learn from minimal data samples while improving their adaptability and resilience in various domains, including IoT, robotics, autonomous vehicles, and energy management. This paper examines different Meta-Rreinforcement learning approaches and frameworks, such as actor-critic models, Bayesian techniques, attention-driven networks, and adversarial learning methods. The adoption of these frameworks highlights their capabilities.

关键词： Metalearning Training Surveys Adaptation models Scalability Robustness Bayes methods Internet of Things Vehicle dynamics Robots

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