检索结果-内蒙古大学图书馆

IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD)

作者： Qiuyi Chen Panagiotis Tsilifis Jaime Renedo Anglada Department of Mechanical Engineering University of Maryland College Park MD USA Robotics AI & Computer Vision Group GE Vernova Advanced Research Niskayuna NY USA Electrical Systems GE Vernova Advanced Research Niskayuna NY USA

ISBN: (数字)9798331520748

ISBN: (纸本)9798331520755

The rapid growth of offshore wind energy needs robust condition monitoring to ensure the reliability and efficiency of direct-drive permanent magnet synchronous generators (PMSGs). Demagnetization, a critical fault in these generators, can severely impact performance and increase operational costs, especially in harsh marine environments. Traditional diagnostic techniques often struggle to detect subtle magnetic property changes under varying load conditions. This paper presents an innovative framework for demagnetization detection in PMSGs, leveraging the Electrical Multi-phase Imbalance Separation Technique (eMIST) combined with machine learning models. Using a dataset from offshore wind turbines, including simulated faulty conditions, the proposed methodology enhances anomaly detection by isolating imbalance signatures and correlating them with key operational parameters. Gaussian Process Regression (GPR) is employed to predict anomalies, offering high accuracy and confidence in fault classification. Results demonstrate the approach's ability to identify early-stage demagnetization, enabling predictive maintenance, reducing downtime, and improving the economic viability of offshore wind farms.

关键词： Costs Accuracy Wind energy Machine learning Gaussian processes Wind farms Generators Wind turbines Demagnetization Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Understanding Connection between PMP and HJB equations from the Perspective of Hamilton Dynamics

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年

作者： Zhang, Xiangteng Lyu, Yao Li, Shengbo Eben Duan, Jingliang Zhan, Guojian Liu, Chang Cheng, Bo Li, Keqiang Tsinghua University School of Vehicle and Mobility Beijing100084 China University of Science and Technology Beijing School of Mechanical Engineering China Peking University Department of Advanced Manufacturing and Robotics Beijing100871 China Peking University College of Engineering Beijing100871 China

Reinforcement learning (RL) is widely used to tackle optimal control problems, with optimality conditions as the principle of algorithm design. Two key optimality conditions are the Pontryagin Maximum Principle (PMP) and the Hamilton-Jacobi-Bellman (HJB) equation. The relationship between these conditions is vital for developing effective policy learning algorithms. Existing studies mainly focus on their relationship in the open-loop optimality, but this property cannot be directly extended to nonoptimal or closed-loop cases due to the absence of extreme condition and the existence of non-zero partial derivative term. This paper unifies the relationship between PMP and HJB equations in all cases by considering optimal control problems as nonholonomic Lagrange systems, and proves the intrinsic equivalence between value function and costate variable from the perspective of Hamilton dynamics. We redefine costate variable as Legendre transformation of state derivative in nonholonomic Lagrange systems, where the Weierstrass condition is selected as a constraint for optimal cases while fixed policy condition is chosen for nonoptimal cases. By utilizing the anti-symmetric property of canonical equations, we identify conservation properties in optimal control where symplectic form remains invariant across all cases. Additionally, we prove that the costate variable has the identical differential equation and boundary conditions as the partial derivative of value function with respect to state in both optimal and nonoptimal, open-loop and closed-loop cases. Numerical experiments are conducted to verify our theoretical results. This discovery can establish more readily available conservation conditions, thereby providing a high-level view angle on algorithm designs. © 2020 IEEE.

关键词： Lagrange multipliers

来源：评论

学校读者我要写书评

暂无评论

Neural Network Model of Continuum Soft Robots Based on WaveNets Equipped with Adaptive IIR

Neural Network Model of Continuum Soft Robots Based on WaveN...

引用

International Conference on Electrical engineering, Computing Science and Automatic Control (CCE)

作者： Oscar Federico Garcia-Castro Luis Enrique Ramos-Velasco Vicente Parra-Vega Rodolfo Garcia-Rodriguez Carlos Ernesto Vazquez-Garcia Robotics and Advanced Manufacturing CINVESTAV-IPN Ramos Arizpe Coahuila Mexico Posgraduate Program of Aeroespace Engineering Universidad Politecnica Metropolitana de Hidalgo Mexico Robotics and Advanced Manufacturing CINVESTAV-IPN Coahuila Mexico Facultad de Ciencias de la Administración Universidad Autónoma de Coahuila de Coahuila Mexico

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

Soft robots have attracted attention from many areas, such as robotics, materials, automatic control, and artificial intelligence. The particular class of continuum soft robot (cSR) is the most challenging given its highly nonlinear dynamics from the hyperviscoelasticity and varying stiffness, which arise from the modeling of an infinite conglomerate of particles enclosed in a deformable finite-dimensional body based on the lumped parameters assumption and the D’ Alambert principle. The cSR is a complex model that presents a significant challenge in the field. Its nonintuitive structure, variable natural frequency, and parametric uncertainties, including its input matrix and lack of reliable velocity measurements, make it difficult to approximate its dynamics using only input and partial output data. This paper proposes a wavelet-based neural network (WaveNet) with an adaptive infinite impulse response (IIR) output filter. The WaveNet sweeps over modal frequencies, while the IIR enforces feedback and forward loops to propagate current and past neural outputs. The input shapes a controllable canonical output form. Simulations for a 2-degree-of-freedom CSR show that a bounded approximate modeling error is guaranteed at a low computational cost.

关键词： Backpropagation Adaptation models Time-frequency analysis Adaptive systems Uncertainty Computational modeling Neural networks Soft robotics Velocity measurement Tuning

来源：评论

学校读者我要写书评

暂无评论

Visual Sensor-Based Trajectory Multimodality Prediction via Anchor-Free Query Attention Network

引用

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Wang, Ruiping Cheng, Jun Yu, Junzhi Nanyang Technological University School of Computer Science and Engineering Jurong West Singapore639798 Singapore Peking University College of Engineering Beijing100871 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China

As a core component of intelligent surveillance and autonomous driving systems, visual sensor-based trajectory multimodality prediction can significantly improve their perception and decision-making capabilities. Numerous existing trajectory prediction methods are dedicated to enhance prediction performance. However, they still fail to effectively model the complex spatiotemporal interactions among pedestrians and trajectory multimodality. To address the above challenges, this article presents one new trajectory multimodality prediction method via anchor-free query-based attention network, which can more effectively capture spatiotemporal interactions and model trajectory multimodality. First, the initial trajectory proposal module employs target-centered query-based attention to capture complex spatiotemporal interactions and generate multiple trajectory proposals corresponding to the multiple modes of the future trajectory, which allows the model to use different interaction information when decoding trajectory points at different time steps. Second, the novel trajectory refinement module utilizes the learnable trajectory proposals to construct proposal-level interaction mechanisms to refine future trajectories. Experimental results conducted on ETH and UCY datasets demonstrate that the proposed model can provide the state-of-the-art prediction performance. © 1963-2012 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Spatially Small-scale Approach-avoidance Behaviors Allow Learning-free Machine Inference of Object Preferences in Human Minds

引用

International Journal of Social robotics 2023年第6期15卷 999-1006页

作者： Huang, Tsung-Ren Chen, Tzu-Chun Lin, Ting-Yu Goh, Joshua O. S. Chang, Yu-Ling Yeh, Su-Ling Fu, Li-Chen Department of Psychology National Taiwan University No. 1 Sec. 4 Roosevelt Rd Taipei10617 Taiwan Graduate Institute of Brain and Mind Sciences National Taiwan University Taipei Taiwan Center for Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Estimating human preference is an essential capability of a social robot. Such a machine Theory of Mind for others’ preferences is studied predominantly under the framework of inverse reinforcement learning, which however requires millions of approach-avoidance trajectories in a simulated grid world as training samples to achieve state-of-the-art results. Here, the present study explores the utility of taking spatially small-scale approach-avoidance behaviors in real life as predictive cues for efficiently estimating human preferences. Using a toy-playing scenario as an example (ΣN = 58 young and older adults), we found that a person’s subjectively reported and objectively inferred levels of attention and happiness could well predict the person’s subjective levels of toy preference. Importantly, attention and happiness can be rapidly estimated from gaze directions and emotional expressions by computer vision techniques in a learning-free manner. Compared with preference learning from spatially larger-scale movement behaviors, robot estimation of human preferences from these smaller-scale behavioral cues can be more efficient and generalizable to unlearned situations in real life. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.

关键词： Human robot interaction

来源：评论

学校读者我要写书评

暂无评论

Data-driven design of high bulk modulus high entropy alloys using machine learning

Journal of Alloys and Metallurgical Systems

引用

Journal of Alloys and Metallurgical Systems 2024年 8卷

作者： Jain, Sandeep Jain, Reliance Kumar, Vinod Samal, Sumanta Department of Metallurgical Engineering and Materials Science Indian Institute of Technology Indore Indore453552 India School of Advanced Materials Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Automation & Robotics Engineering Prestige Institute of Engineering Management and Research Madhya Pradesh Indore452010 India

In the current research, machine learning (ML) models were used as a tool for predicting the bulk modulus of High Entropy Alloys (HEAs). ML was employed to optimize HEA compositions for superior bulk modulus values. The study assessed five regression models: Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost (XGB), Support Vector Regression (SVR), and Lasso regression. The XGB regression model delivered the best results, with an R-squared (R2) value of 95.2 % and an RMSE of 2.6 % on the validation dataset. The XGB model's performance was further validated by experimental work, showing an R2 value of 94.8 % and an RMSE of 3.6 %. The R-squared, RMSE, and MAE values during training, testing, and validation for the XGB model ranged from 93.2 % to 99.62 %, 0.97 to 3.64, and 0.12 to 1, respectively. Furthermore, we used the top three trained models to predict the bulk modulus of six new HEAs that were not part of the training, testing, or validation datasets. These predictions achieved R² values of 94.8 %, 93.4 %, and 92.4 %, RMSE values of 3.6 %, 4.1 %, and 4.4 %, along with MAE values of 3.4 %, 3.8 %, and 4.1 %, for the XGB, Lasso, and SVR models, respectively. This work advances the field by bridging the gap in HEA discovery and property evaluation, offering novel methods for designing HEAs with desirable bulk modulus values, and unlocking new possibilities for HEA applications. © 2024 The Authors

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Multi-layer Disturbance Observer-based Wheel Speed Control for Electric Vehicles

Multi-layer Disturbance Observer-based Wheel Speed Control f...

引用

Annual Conference of Industrial Electronics Society

作者： Yussuf Shakhin Ahmad Bala Alhassan Binh-Minh Nguyen Ton Duc Do Dept. of Robotics and Mechatronics School of Engineering and Digital Science (SEDS) Nazarbayev University Astana Kazakhstan Dept. of Advanced Energy The University of Tokyo Tokyo Japan

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

As the adoption of electric vehicles (EVs) increases globally, there is a need to provide effective control for the safety and stability of the vehicles. In this paper, a cascade observer-based wheel speed control of an EV is proposed. The scheme uses three-loop cascade observers to estimate the wheel speed, which is often difficult to measure and critical for the EV’s control. The study only uses the measurement of the motor speed and torque to estimate the wheel speed from the observation of the total disturbances acting on the EV. A PID control is then designed for the control of the wheel speed using the estimated speed. The effectiveness of the designed observers and the wheel speed control is investigated using Matlab simulation under the influence of parametric uncertainties and external disturbance.

关键词： Torque Uncertainty Simulation Velocity control Wheels Motors Electric vehicles Stability analysis Safety Torque measurement

来源：评论

学校读者我要写书评

暂无评论

A Novel Bio-Intelligent Real-Time Redundancy Resolution Method for Cable-Driven Parallel Manipulators

A Novel Bio-Intelligent Real-Time Redundancy Resolution Meth...

引用

RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： S. Ali A. Moosavian SeyedSina Seyedi HasanAbadi Arman Gholibeikian Department of Mechanical Engineering K. N. Toosi University of Technology Advanced Robotic and Automated Systems (ARAS) Center of Excellence in Robotics and Control Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Cable-driven parallel manipulators (CDPMs) are widely used where there is a need for high speeds and high accelerations, thanks to the low inertia and low cost of their links. However, cables can only exert tensional force. Due to this inherent property of the cables, CDPMs are required to be designed with at least one degree of redundancy in their joint space, to be able to generate an acceptable range of wrench within the workspace. Meanwhile, redundancy in joint space leads a problem called redundancy resolution, which is basically a force distribution from workspace to joint space. In this research, inspired by a simple mathematical model of the limbic system of the brain, a novel real-time redundancy resolution method is proposed. The presented method utilizes the kinematic information of the end-effector to find the solution to the redundancy resolution problem. The results show that this Brain Emotional Learning (BEL) method has a better performance in comparison with some common numerical optimization methods solved with a nonlinear programming solver like fmincon function in MATLAB, in terms of computational cost, continuity of the profile of the cables forces, and robustness.

关键词： Power cables Redundancy Force Optimization methods Kinematics Brain modeling Mathematical models Real-time systems Robustness Trajectory

来源：评论

学校读者我要写书评

暂无评论

Design, Optimization and Fabrication of a Sensorized-Insole using Taguchi Method

Design, Optimization and Fabrication of a Sensorized-Insole ...

引用

Iranian Conference of Biomedical engineering (ICBME)

作者： Amin Hoseini Amir Mohammad Mohammadzadeh Alireza Akbarzadeh Dept. of Mechanical Engineering Center of Advanced Rehabilitation and Robotics Research(FUM CARE) Ferdowsi University of Mashhad(FUM) Mashhad Iran

The human foot plays a vital role during locomotion, being the primary point of contact with the environment. Detecting foot-related issues early is crucial for injury prevention and well-being. Assessing foot irregularities requires measuring parameters like pressure, center of pressure, and force. The Ferdowsi University of Mashhad Center of advanced Rehabilitation and robotics (FUM-CARE) developed an insole with eight Force-Sensing Resistor (FSR) sensors to address these needs. The FUM insole optimized sensor placement to accurately detect the standard gait pattern. Experiments, following the Taguchi methodology, were conducted on the insole, considering three cost functions to determine two sensor arrangements. The cost functions attempted to follow the various aspects of the vertical ground reaction force, vGRF, of a gait cycle. The two sensor arrangements aimed to mimic different aspects of the vertical ground reaction force (vGRF) during a gait cycle. The first arrangement attempted to track magnitudes of valleys and peaks of a standard vGRF diagram, with the first, third, and seventh factors as key influencers. The second configuration, targeting precise vGRF replication, was influenced primarily by the first, third, and sixth factors. Remarkably, the first sensor placement had a 2.4% error at detection of peaks and valleys magnitude. The second arrangement exhibited a 97% cross-correlation with the standard vGRF diagram, confirming its alignment. Additionally, the FUM Insole’s construction employed a lamination method, which offered resistance to surface scratches and shear forces. This strategic approach significantly enhanced sensor durability for prolonged functionality.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Soft Inflatable Fingers: An Overview of Design, Prototyping and Sensorisation for Various Applications

Soft Inflatable Fingers: An Overview of Design, Prototyping ...

引用

IEEE International Conference on Soft robotics (RoboSoft)

作者： Faisal Aljaber Ahmed Hassan Taqi Abrar Ivan Vitanov Kaspar Althoefer Centre for Advanced Robotics @ Queen Mary (ARQ) Faculty of Science and Engineering Queen Mary University of London London U.K.

Fabric-based soft actuators, grippers and manipulators are gaining in popularity due to their ability to handle large payloads while being lightweight, extremely compliant, low-cost and fully collapsible. Achieving full-pose sensing of fabric fingers without compromising on these advantageous properties, however, remains a challenge. This paper overviews work on soft fabric-based inflatable finger design, actuation and sensorisation carried out at the Centre for advanced robotics at Queen Mary (ARQ), University of London. Further experimental analysis has been performed to examine features such as bending control and eversion (growing from the tip) in fabric fingers for grasping applications. In addition, two types of grasp force have been measured for a bi-fingered gripper: envelope grasping and pinch grasping. Beyond force measurement, this paper advances a new concept for the sensorisation of fabric grippers using soft optical waveguide sensors and proposes shape estimation using image processing.

关键词： Shape Fingers Grasping Bending Robot sensing systems Fabrics Optical sensors

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：