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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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A Novel Adaptive Deep Belief Network for Bearing Fault Diagnosis in Variable Working Conditions Based on Salp Swarm Algorithm Under Small Sample

A Novel Adaptive Deep Belief Network for Bearing Fault Diagn...

引用

Advanced Information, Mechanical Engineering, robotics and automation (AIMERA), IEEE international conference on

作者： Qingbin Tong Xin Qi Xuedong Jiang Feiyu Lu Ziwei Feng Jianjun Xu School of Electrical Engineering Beijing Jiaotong University Beijing People's Republic of China

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

The actual operating conditions of equipment are complicated and changeable, and it is not easy to obtain enough training sample data quickly. Meanwhile, setting neural network structure parameters by experience or repeated trials will also affect the diagnosis results. To solve this issue, a Domain Adaptive Deep Belief Network based on SSA and Second-Order Statistics Transfer (SSA-SOST-DADBN) of features is proposed. Firstly, the structure parameters of deep belief network (DBN) were optimized by salp swarm algorithm. By solving the envelope spectrum of bearing signal, the hidden information of fault characteristic frequency is obtained, and the training dataset of DBN is constructed. The covariance matrix of distribution of features across the different domains is considered to realize transfer training under different operating situations. The dropout method is employed for effective small-sample rolling bearing fault diagnosis so as to mitigate the risk of overfitting in the network. Based on the public dataset of CWRU, the experiment proves that the network has high accuracy. The method can achieve an average classification accuracy as 95.08% under different working conditions, which has higher accuracy and generalization compared with the other 5 methods.

关键词： Training Fault diagnosis Employee welfare Accuracy Adaptive systems Neural networks Rolling bearings robotics and automation Mechanical engineering Covariance matrices

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Survey and Performance Analysis on Point Cloud Classification models

Survey and Performance Analysis on Point Cloud Classificatio...

引用

international conference on Control, automation and Systems ( ICCAS)

作者： Chaitali Bhattacharyya Sungho Kim Department of Electronics Engineering Yeungnam University Gyeongsan Korea

Point clouds are a popular representation for 3D data, widely used in applications such as autonomous driving, robotics, and computer graphics. Classifying objects within point clouds poses unique challenges due to the unordered nature of the data and the lack of spatial structure. In this paper, we propose a survey and performance analysis of SOTA models on point cloud data classification. PointNet leverages a novel neural network architecture that directly consumes raw point clouds, eliminating the need for pre-processing steps like voxelization or rendering. Our work is to better analyse the mnodel's performance in with different dataset such as ModelNet and compare them. PointNet was the first ground-breaking approach to handle the classification problem which is still giving competetive performance qulality.

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Deep Reinforcement Learning-Based Swing Suppression Control for Unmanned Multirotor Transportation System

Deep Reinforcement Learning-Based Swing Suppression Control ...

引用

IEEE international conference on Mechatronics and automation

作者： Teng Wang Shunxuan Wang Yi Chai Xiao Liang Institute of Robotics and Automatic Information System College of Artificial Intelligence Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin P. R. China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Unmanned multirotors have demonstrated excellent flexibility in complex real-world scenarios, making them promising for aerial payload transportation. However, it is challenging to achieve stable control of the unmanned multirotor transportation system due to its double under-actuated property. Traditional control methods are limited by model complexity and parameter tuning difficulty, resulting in deficient control capabilities. In response to these shortcomings, an end-to-end controller based on deep reinforcement learning is proposed in this article, ensuring that the payload arrives at the target while suppressing the process swing. Based on the Soft Actor Critic (SAC) algorithm and the design of state-action space, the efficiency of the controller is enhanced. By comparing with PID and Fuzzy PID control methods, the proposed approach presents superior payload swing suppression capacity with comparable position control performance.

关键词： PI control Simulation Transportation Process control Position control Aerospace electronics Deep reinforcement learning

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Scalable Learning of Safety Guarantees for Autonomous Systems using Hamilton-Jacobi Reachability

Scalable Learning of Safety Guarantees for Autonomous System...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Herbert, Sylvia Choi, Jason J. Sanjeev, Suvansh Gibson, Marsalis Sreenath, Koushil Tomlin, Claire J.

ISBN: (纸本)9781728190778

Autonomous systems like aircraft and assistive robots often operate in scenarios where guaranteeing safety is critical. methods like Hamilton-Jacobi reachability can provide guaranteed safe sets and controllers for such systems. However, often these same scenarios have unknown or uncertain environments, system dynamics, or predictions of other agents. As the system is operating, it may learn new knowledge about these uncertainties and should therefore update its safety analysis accordingly. However, work to learn and update safety analysis is limited to small systems of about two dimensions due to the computational complexity of the analysis. In this paper we synthesize several techniques to speed up computation: decomposition, warm-starting, and adaptive grids. Using this new framework we can update safe sets by one or more orders of magnitude faster than prior work, making this technique practical for many realistic systems. We demonstrate our results on simulated 2D and 10D near-hover quadcopters operating in a windy environment.

关键词： Uncertainty automation Autonomous systems System dynamics conferences Control systems Safety

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Chicken Disease Diagnosis Model Using YOLOv8 Object Detection Algorithm with SE-Attention Mechanism

Chicken Disease Diagnosis Model Using YOLOv8 Object Detectio...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： Jinghui Quan Hang Shi Zhihua Zhang Zhiyuan Zhao Hongzhen Cai Langwen Zhang Bohui Wang School of Automation Science and Technology South China University of Technology Guangzhou China Qingnong Group Co. Ltd. School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Traditional chicken disease diagnosis methods rely on manual observation and experiential judgment, which are inefficient and susceptible to subjective factors. This paper aims to construct a chicken disease diagnostic model with deep learning algorithm for enhancing the accuracy and efficiency. Firstly, this paper proposes a chicken disease diagnostic model based on YOLOv8 algorithm. An SE-attention mechanism is designed for YOLOv8 structure to improve the detection accuracy. The SE-Attention based YOLOv8 detection model can identify and classify diseases by analyzing the feces images of chickens. Experiments on chicken disease diagnosis are performed to validate the proposed model's feasibility and effectiveness. Ablation study is constructed to validate the advantages of the SE-attention mechanism.

关键词： Deep learning Accuracy Service robots Prevention and mitigation Object detection Manuals Classification algorithms Medical diagnosis Diseases Farming

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HPF-SLAM: An Efficient Visual SLAM System Leveraging Hybrid Point Features

HPF-SLAM: An Efficient Visual SLAM System Leveraging Hybrid ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Xin Su Sebastian Eger Adam Misik Dong Yang Rastin Pries Eckehard Steinbach Department of Computer Engineering School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) Chair of Media Technology Technical University of Munich Germany Siemens Technology Munich Germany Nokia Munich Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Visual SLAM is an essential tool in diverse applications such as robot perception and extended reality, where feature-based methods are prevalent due to their accuracy and robustness. However, existing methods employ either hand-crafted or solely learnable point features and are thus limited by the feature attributes. In this paper, we propose incorporating hybrid point features efficiently into a single system. By integrating hand-crafted and learnable features, we seek to capitalize on their complementary attributes in both key-point identification and descriptor expressiveness. To this purpose, we design a pre-processing module, which includes extraction, inter-class processing, and post-processing of hybrid point features. We present an efficient matching approach to exclusively perform the data association within the same class of features. Moreover, we design a Hybrid Bag-of-Words (H-BoW) model to deal with hybrid point features in matching and loop-closure-detection. By integrating the proposed framework into a modern feature-based system, we introduce HPF-SLAM. We evaluate the system on EuRoC-MAV and TUM-RGBD benchmarks. The experimental results show that our method consistently surpasses the baseline at comparable speed.

关键词： Visualization Simultaneous localization and mapping Accuracy Extended reality Semantics Benchmark testing Feature extraction

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Camera calibration for robotic lab setup

Camera calibration for robotic lab setup

引用

Automatics and Informatics (ICAI), international conference

作者： Zhivko Zhekov Elena Vasileva Department of Automation Technical University of Varna Varna Bulgaria

ISBN: (数字)9798350353907

ISBN: (纸本)9798350353914

The article deals with one main problem in setting up the work of any industrial robot using camera - calibration of the coordinate systems of the camera and the robot. Machine vision in industrial robotics implies the use of image recognition algorithms, whose accuracy and satisfactory result depend on the correct synchronization of the operation of the camera-robot system. Of interest to the present research is the use of neural networks in setting up the camera-robot coordinate systems.

关键词： Accuracy Service robots Robot kinematics Robot vision systems Neurons Artificial neural networks Cameras Mathematical models Calibration Biological neural networks

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ARC-LfD: Using Augmented Reality for Interactive Long-Term Robot Skill Maintenance via Constrained Learning from Demonstration

ARC-LfD: Using Augmented Reality for Interactive Long-Term R...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Luebbers, Matthew B. Brooks, Connor Mueller, Carl L. Szafir, Daniel Hayes, Bradley Univ Colorado Coll Engn & Appl Sci Comp Sci Dept Boulder CO 80309 USA Univ Colorado ATLAS Inst Boulder CO 80309 USA

ISBN: (纸本)9781728190778

Learning from Demonstration (LfD) enables novice users to teach robots new skills. However, many LfD methods do not facilitate skill maintenance and adaptation. Changes in task requirements or in the environment often reveal the lack of resiliency and adaptability in the skill model. To overcome these limitations, we introduce ARC-LfD: an Augmented Reality (AR) interface for constrained Learning from Demonstration that allows users to maintain, update, and adapt learned skills. This is accomplished through in-situ visualizations of learned skills and constraint-based editing of existing skills without requiring further demonstration. We describe the existing algorithmic basis for this system as well as our Augmented Reality interface and the novel capabilities it provides. Finally, we provide three case studies that demonstrate how ARC-LfD enables users to adapt to changes in the environment or task which require a skill to be altered after initial teaching has taken place.

关键词： Training Visualization automation conferences Maintenance engineering Trajectory Task analysis

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Incremental Nonlinear Dynamic Inversion for a Dual-Ducted-Fan UAV Attitude Control under In-Ground-Effect

Incremental Nonlinear Dynamic Inversion for a Dual-Ducted-Fa...

引用

Intelligent robotics and Automatic Control (IRAC), international conference on

作者： Zixiao Cheng Hailong Pei Kai Fang College of Automation Science and Engineering South China University of Technology Guangzhou China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangzhou China Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle Systems Guangzhou China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

This paper is aimed at modeling and attitude control of a dual-ducted-fan UAV under in-ground-effect(IGE). As the UAV flies and hovers over ground or above obstacles, the IGE can cause changes in thrust/torque that drastically affects flight behavior, especially during takeoff/landing. To address this problem, the paper derives a six degree-of-freedom model of the aircraft dynamics firstly. Then, key parameters and in-ground-effect features are obtained through measurements. In order to achieve stable attitude tracking control with in-ground-effect adapting, an incremental nonlinear dynamic inversion(INDI) based cascade controller is designed. Finally, the performance of the designed INDI controller is verified by stability proof, simulations and real flight experiments.

关键词： Adaptation models Fans Attitude control Atmospheric modeling Autonomous aerial vehicles Stability analysis Data models Nonlinear dynamical systems Motion measurement Aircraft

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Understanding Internet Usage Patterns and Behaviors Among College Students: A Machine Learning Approach

Understanding Internet Usage Patterns and Behaviors Among Co...

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

作者： Alvin T. Remolado Darrel A. Cardaña College of Science and Management Bohol Island State University Bohol Philippines College of Technology and Allied Sciences Bohol Island State University Bohol Philippines

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

This study explores internet usage patterns and behaviors among students enrolled in Information Technology Education (ITE) programs in the province of Bohol, Philippines. Despite the widespread availability of Internet resources, there is limited understanding of how ITE students utilize the Internet for academic and personal purposes. This research aims to identify key patterns and factors influencing internet usage frequency at school, home, and other locations. Utilizing machine learning techniques and exploratory data analysis (EDA), the study analyzed data from a structured questionnaire administered to ITE students through Google Forms. Classification models, including Random Forest Classifier, Decision Trees, and Support Vector Classifier, were employed to predict internet usage levels based on demographic and behavioral data. Results indicate that ITE students exhibit high levels of internet usage, with significant correlations between the frequency of internet usage at school, home, and for various purposes with overall internet usage levels. Among the models, SVC achieved the best performance, with an accuracy of 99.53%. In comparison, Random Forest Classifier attained 92.63% accuracy, while Decision Trees performed at 84.02% accuracy. These insights can inform educational institutions and policymakers in developing strategies to enhance internet accessibility and digital literacy among students, ultimately improving educational outcomes and digital inclusion.

关键词： Accuracy Correlation Education Static VAr compensators Predictive models Vectors Decision trees Digital intelligence Information technology Random forests

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