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共 1220 条记录，以下是421-430 订阅

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Fault Detection in Electric Drives Based on LSTM Autoencoder Model Machine Learning Approach

Fault Detection in Electric Drives Based on LSTM Autoencoder...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Galina Demidova Daniil Privalov Denis Semenov Dmitry Lukichev Zhitao Liu Alecksey Anuchin Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia Institute of Cyber-Systems & Control Zhejiang University Hangzhou China Department of Electric Drives Moscow Power Engineering Institute Moscow Russia

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

Fault detection in electric drives is crucial for ensuring operational reliability and minimizing downtime. this paper provides a brief overview of the methods based on machine learning used for fault detection in electric drives. Additional this research presents a machine learning approach that utilizes the Long Short-Term Memory Autoencoder model for fault detection deal with bearing faults. A flowchart illustrating the implementation steps of the Long Short-Term Memory Autoencoder algorithm is included. Additionally, a case study on bearing faults in an electric drive system is presented to demonstrate the advantage of the proposed approach. By training the Long Short-Term Memory Autoencoder on operational data, accurate prediction of bearing faults is achieved, enabling timely maintenance interventions. It is shown that in the 7th epoch of training, the model achieved a notably high accuracy more than $\mathbf{9 9 \%}$. the results highlight the potential of Long Short-Term Memory Autoencoder models as a valuable tool for fault detection in electric drives, thereby improving system reliability and performance.

关键词： Training Vibrations Fault diagnosis Machine learning algorithms Fault detection Machine learning Prediction algorithms

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ORBSLAM combined with electromagnetic tracking in duodenum 3D reconstruction 11

ORBSLAM combined with electromagnetic tracking in duodenum 3...

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11th IEEE Annual international conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2021

作者： Zou, Yan Yang, Yongming Wang, Chongyang Huang, Yongping Chang, Li Jiang, Zhenming Liu, Hao School of Information Science and Engineering Shenyang University of Technology Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China School of Information Science and Engineering Shenyang University of Technology Shenyang China

ISBN: (纸本)9781665425278

Accurate three-dimensional reconstruction of duodenum is very important in the navigation of ERCP surgery, which helps to improve the success rate of chathete-rization and reduce the difficulty of surgery. Due to the narrow anatomical space of the duodenum, the structure is tortuous, the surface is smooth with few texture features. the existing three-dimensional reconstruction and positioning methods still cannot achieve an effective performance. In this paper, the combination of electromagnetic sensing and ORBSLAM is used for the three-dimensional reconstruction of the duodenal biliary and pancreatic duct. First, we use the accurate tracking performance of electromagnetic sensors to improve the effectiveness of SLAM mapping. Second, we replaced the pose estimation of the camera in SLAM with electromagnetic sensing real-Time pose, which improved the real-Time performance of 3D reconstruction. the effectiveness and real-Time performance of the algorithm in the three-dimensional reconstruction of the duodenum, biliary and pancreatic regions are verified through high-simulation models and real clinical video sequences. © 2021 IEEE.

关键词： Surgery

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Motion Error Analysis of High-speed Parallel Robots

Motion Error Analysis of High-speed Parallel Robots

引用

robotics, Control and automation (ICRCA), international conference on

作者： Liangliang Zhu Sergey S. Gavriushin Jingzhong Zheng Guannan Chi Depart. Automated Production Computer Systems Bauman Moscow State Technical University Moscow Russian Depart. Instrumentation Technologies Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798350344721

ISBN: (纸本)9798350344738

To enhance the operational speed of robots and minimize energy expenditure, a majority of Delta parallel robots employed in the industry have a lightweight design. At high operating speeds, the lightweight construction of Delta robots often causes elastic deformation and vibration. Traditional analysis and control methods for rigid robots do not ensure positioning accuracy of the Delta robot. To address this problem, in this paper we have chosen the WSC-600DJ Delta robot as the subject of our research. Our focus is on analyzing the robot's rigid-flexible coupling dynamics. Firstly, the active and passive arms of the Delta robot are made flexible using Hypermesh software. And then, the rigid-flexible coupled dynamics model of the Delta robot is established in Adams program. Finally, the end effector trajectory is planned in MATLAB using the modified sinusoidal correcting keystone acceleration curve. the driving angle of each active arm is determined via inverse kinematics solutions, which then is imported into Adams for simulation. By comparing the results of modeling the rigid-flexible coupled dynamics of the Delta robot with the theoretical trajectories of the rigid model, we are able to estimate the positioning errors of the robot's actuators caused by the elastic deformation. the results of this study establish a foundation for designing a high-accuracy control system for this particular robot model.

关键词： Parallel robots Service robots Deformation Dynamics Mathematical models Software Trajectory

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Multi-Scale Vision Transformer with Local-Global Attention

Multi-Scale Vision Transformer with Local-Global Attention

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Ruomiao Zhang Shengdong Du Jie Hu Xiaole Zhao Yan Yang School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

the extraction of multi-scale features from input images is crucial for computer vision tasks. However, some backbone networks based on visual transformers lack the ability to effectively extract multi-scale features or use relatively simplistic methods to do so. this paper proposes a novel multi-scale visual transformer, called Multi-Scale Extraction Transformer (MSET), designed to proficiently tackle a range of downstream visual tasks. MSET incorporates innovative concepts of Multi-Scale feature extraction Embedding (MS Embedding) and Local-Global self-attention (LGSA). MS Embedding uses a unique architecture convolutional layer method that constructs hierarchical residual-like connections, allowing for the extraction of multi-scale features at finer granularity levels. Meanwhile, LGSA integrates local and global dependencies within the self-attention module, reducing computational complexity while facilitating connections between the global and local features of the image. the importance of position information in the embeddings and the impact of amplitude on model performance are also considered, allowing MSET to handle position bias information of variable size in the input image and to ensure stability during training. Numerous experiments have demonstrated the significant advantages of MSET over most existing state-of-the-art backbone networks across various visual tasks, including image classification, object detection, instance segmentation, and semantic segmentation tasks.

关键词： Training Visualization Computer vision Adaptation models Service robots Feature extraction Transformers Stability analysis thermal stability Image classification

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Novel Approach for Intelligent Fault Diagnosis in Electromechanical Drive Systems Using an Attention-Based Residual 1D Convolutional Neural Network

Novel Approach for Intelligent Fault Diagnosis in Electromec...

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Artificial Intelligence, robotics, and Communication (ICAIRC), IEEE international conference on

作者： Jonathan Okonda Danping Xu Bovic Kilundu School of Mechatronic Engineering and Automation Shanghai University Shanghai China Department of Mechanical Engineering Brussels Engineering School (ISIB) Haute Ecole de Bruxelles Brabant (HE2B) Brussels Belgium

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

Numerous existing deep learning-based fault diagnosis models, have been implemented for intelligent fault diagnosis in electromechanical drive systems (EMDS). However, these models often are designed to detect and diagnose single fault types at specific operating conditions. these models are frequently susceptible to overfitting, suffer from the vanishing gradient problem, and struggle with long-range feature dependencies. Such limitations significantly hinder their accuracy and reliability in diagnosing complex fault scenarios for EMDS under different operating conditions using noisy data. To address these limitations, we propose a novel deep learning-based fault diagnosis model named Attention-based Residual 1D Convolutional Neural Network (AR1D-CNN). this model is designed to effectively diagnose complex faults in EMDS across various load conditions using noisy and imbalanced vibration data. Experimental results demonstrate exceptional classification accuracies of 99.98% and 100.00% on the KAIST dataset for 0 and 2 Nm load conditions, respectively, and 99.31% on the AETWPSU broken rotor bar dataset. Notably, the AR1D-CNN outperforms existing state-of-the-art methods by approximately 4% on the AETWPSU dataset, highlighting its robustness and reliability. this research significantly enhances predictive maintenance strategies by enabling early and precise fault detection, ultimately improving the operational efficiency of EMDS.

关键词： Fault diagnosis Vibrations Accuracy Fault detection Rotors Data models Noise measurement Convolutional neural networks Load modeling Predictive maintenance

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Trustworthy Bayesian Perceptrons

Trustworthy Bayesian Perceptrons

引用

international conference on Information Fusion

作者： Markus Walker Hayk Amirkhanian Marco F. Huber Uwe D. Hanebeck Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany Department Cyber Cognitive Intelligence (CCI) Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany Institute of Industrial Manufacturing and Management IFF University of Stuttgart Germany

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Bayesian Neural Networks (BNNs) offer a sophisticated framework for extending classical neural network point estimates to encompass predictive distributions. Despite the high potential of BNNs, established BNN training methods such as Variational Inference (VI) and Markov Chain Monte Carlo (MCMC) grapple with issues such as scalability and hyperparameter dependence. In addressing these issues, our research focuses on the fundamental elements of BNNs, in particular perceptrons and their predictive capabilities. We introduce a new perspective on the closed-form solution for backward-pass computation for the Bayesian perceptron and prove that the state-of-the-art solution is equivalent to statistical linearization. To assess the efficacy of Bayesian perceptrons and provide insights into their performance in distinct input space regions, a novel methodology utilizing k-d trees as a space partitioning method is introduced to evaluate prediction quality within specific input space regions.

关键词： Training Systematics Monte Carlo methods Scalability Neural networks Estimation Predictive models Nonhomogeneous media Bayes methods Testing

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Modeling the Variable Pitch Screws by Using the Envelope theory Based on Cubic H-Bézier

Modeling the Variable Pitch Screws by Using the Envelope The...

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IEEE international conference on Mechatronics, robotics and automation (ICMRA)

作者： Yanwei Wu Gengbin Zhang Yu Ling Changqing Song Tie Zhang Guangzhou Power Supply Bureau of Guangdong Power Grid Co. Ltd Guangzhou China School of Mechanical and Automotive Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

this paper presents a novel approach to derive analytical expressions for variable pitch screw threads by employing three-segment composite H-Bézier curves as transitional curves at the beginning and end of the screw thread. Additionally, it introduces a methodology to represent the material surface as enveloping single-parameter surface families, enabling the calculation of the helical surface of variable pitch screw threads. the geometric model developed through this method finds applications in post-processing tasks such as material property analysis, numerical control machining, finite element analysis, and more. Finally, the proposed design and calculation methods for variable pitch screw threads are modeled and simulated within the MATLAB environment, demonstrating their practical value and feasibility.

关键词： Shape Design methodology Geometric modeling Machining Fasteners Harmonic analysis Polynomials Numerical models Trajectory Optimization

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Research on Haptic Feedback Control System of Bionic Pulse Diagnosis Equipment 12

Research on Haptic Feedback Control System of Bionic Pulse D...

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12th international conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2022

作者： Zhongzhi, Ji Yang, Wang Yulin, Huang Xing, Zhu Fanyu, Wang Jingjing, Luo Ji Hua Laboratory Foshan China Institute of Ai and Robotics Academy for Engineering and Technology Fudan University Shanghai China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Pulse diagnosis is one of the most characteristic diagnostic methods in Traditional Chinese Medicine(TCM), as practiced and providing medical service in disease prevention, diagnosis and treatmentterritory. Lack of standardization and subjective of pulse diagnosis, is the main concern in modern Chinese medicine. there are problems in present pulse diagnosis equipment, such as low precision of pre-pressure control for pulse wave acquisition, weak anti-interference ability in the acquisition process and poor ability to adapt to different groups of people. In this study, we proposed an interactive model between pulse acquisition dexterity finger and flexible skin was identified by using nonlinear autoregressive networkwith exogenous inputs (NARX)neural network model, which was further used to simulate and test the entire control strategy. Secondly, an adaptive control strategy based on Radical Basis Function (RBF) neural network was proposed, and the stability of the closed-loop system was proved by Lyapunov stability theory. Finally, the simulation and laboratory experiments were conducted. the simulation experimentsshow that the mean square error(MSE)of the system identification model, which is based on NARX neural network model accurately describes the mathematical relationship between 'Guan' wrist tissue and dexterous fingers, is 1.1878, and the percentage of relative mean square error is 0.36953%,and it could means that the model achieves the modeling accuracy needed for control purposes;the adaptive RBF neural network controller is at least 1.67 times faster than PID controller, 3.76 times smaller of maximum tracking negative error than PID controller, at the same time the laboratory experiments get the similar results. So it could be concluded that the adaptive RBF neural network controller has the characteristics of fast adjustment speed, small overshot and strong interference suppression ability, which achieves the precision and robustness of pre-pressure cont

关键词： Neural network models

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Towards the Application of Operational Design Domain Based Scene Generation for Artificial Intelligence Training in Railway automation the project “Rail automation with Artificial Intelligence for Detection of Exceptional Situations” (RailAIxs) received funding in the mFUND conveyor line by the German Federal Ministry for Digital and Transport under the funding code 19FS2031A-D.

Towards the Application of Operational Design Domain Based S...

引用

international conference on Intelligent Transportation

作者： Till Mersmann Friedrich Betz Julian Eichenbaum Fabian Hampel Simon Klamt Yannick Otten Ingrid Scholl Christian Schindler Faculty of Mechanical Engineering Institute for Rail Vehicle and Transport Systems (IFS) RWTH Aachen University Aachen Germany Faculty of Electrical Engineering and Information Technology Mobile Autonomous Systems and Cognitive Robotics Institute (MASCOR) FH Aachen University of Applied Sciences Aachen Germany

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

For automated, driverless rail transportation applications in open environments, Artificial Intelligence (AI)-based methods are gaining importance, especially in computer vision and perception tasks. the safe operation of complex automated systems requires validation processes. For this purpose, the concept of Operational Design Domains (ODDs), driven by recent developments in the automotive industry, is gaining momentum, allowing to describe different aspects of operating conditions as scenes and scenarios. With regard to safety and authorization using AI-based vision systems, data coverage is needed, which can be enhanced by employing virtual reality in different forms. the creation of virtual scenes and sensor models allows the generation of synthetic sensor data and metadata that can be used as a database for the training of the vision system.

关键词： Training Solid modeling automation Machine vision Virtual reality Rail transportation Safety Artificial intelligence Standards Synthetic data

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Enhancing Human Motion Imitation in Humanoid Robots: A Comparative Study of ANN, ANFIS, and GA-Optimized ANFIS for Inverse Kinematics of the Surena-V Humanoid's Arm

Enhancing Human Motion Imitation in Humanoid Robots: A Compa...

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RSI/ISM international conference on robotics and Mechatronics (ICROM)

作者： Amin Mozayyan Aghil Yousefi-Koma Alireza Naeini Sara Rahmati Kookandeh Center of Advanced Systems and Technologies (CAST) School of Mechanical Engineering University of Tehran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Humanoid robots are increasingly vital in automation, healthcare, and hazardous environments, where teleoperation enables them to replicate human movements for complex tasks. A key challenge in teleoperation is solving inverse kine-matics, which determines the joint angles required for a robotic arm to reach a specific end-effector position. this research addresses the challenge of solving the inverse kinematics of a redundant robotic arm of the Surena-V humanoid robot, to enhance the robot's capability to accurately imitate human arm movements. Using MediaPipe for human pose estimation, wrist coordinates were input into models to calculate joint angles. three methods were deployed: Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and ANFIS optimized using a Genetic Algorithm (GA). the GA-optimized ANFIS model achieved the lowest Root Mean Square Error (RMSE) in joint angle prediction, with values of 6.93, 7.23, 12.33, and 0.9, and superior end-effector accuracy with RMSEs of 0.0199 m, 0.0150 m, and 0.0203 m for the x, y, and z coordinates. these results underscore the effectiveness of GA-optimized ANFIS in handling complex nonlinearities, offering significant improvements in humanoid robot performance for teleoperation and human-robot interaction tasks.

关键词： Wrist Adaptation models Accuracy Robot kinematics Humanoid robots Kinematics Artificial neural networks Robot sensing systems End effectors Genetic algorithms

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