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2021 International Conference on Electrotechnical Complexes and Systems, ICOECS 2021

作者： Fedorov, Evgenii Mingazov, Artur Ferenets, Andrey University Named after A.N. Tupolev - KAI Institute of Automation and Electronic Engineering Kazan National Research Technical Department of Electrical Equipment Kazan Russia

ISBN: (纸本)9781665407274

The features and limitations of the development of a DC power generation system with a voltage of 12 V for a twin-engine light aircraft equipped with two generators of different types and power installed on each engine are considered. The limitations are due to the layout scheme of the aircraft, the requirement to minimize the mass and the best use of the installed power of power sources during partial and emergency operation of the system. © 2021 IEEE.

关键词： Aircraft

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Human-in-the-loop Optimization for Adaptive Assist-as-Needed Rehabilitation 12

Human-in-the-loop Optimization for Adaptive Assist-as-Needed...

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12th International Conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2022

作者： Li, Ning Yang, Yang Yang, Tie Chen, Wenyuan Wang, Yihan Yu, Peng Wang, Wenxue Xi, Ning Liu, Lianqing Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of the Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang10016 China School of Electrical and Electronics Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Emerging Technologies Institute Department of Industrial and Manufacturing Systems Engineering University of Hong Kong Pokfulam999077 Hong Kong

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Active voluntary participation in robot-assisted rehabilitation promote the recovery of stroke hemiplegia. However, different patients have personalized recovery states, which needs the adaptive control strategy to improve the patient's participation. Therefore, in this study, a Mirror Adaptive Assist-As-Needed (MAAN) scheme is proposed to encourage subjects to participate actively and avoid the slack of the subjects including two modules: Functional Capability Estimation (FCE) and Mirror Adaptation (MAD). Specifically, the first MSFE module can obtain joint functional capability based on fused biological and motional signals. The second MAD module acquires the needed assistance via the estimated torques of FCE based on healthy limb. Through the iteration and collaboration of these two modules, a human-in-the-loop adaptive training is achieved to promote the patient participation. The results demonstrate that the MAAN strategy can generate suitable assistance for the patients to correct abnormal muscle activation patterns and promote active voluntary participation. Compared with other methods, the proposed strategy improves motion stability and consistency with the natural physiological state of healthy limbs. This research can be expected to greatly enhance the rehabilitation and power assistance outcome of exoskeleton robot in more practical applications. © 2022 IEEE.

关键词： Mirrors

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research on Path Planning Algorithm Based on D∗ Search

Research on Path Planning Algorithm Based on D∗ Search

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2023 International Conference on Ocean Studies, ICOS 2023

作者： Leng, Jiajun Fu, Li Wang, Lingling Tang, Ning Laboratory of Science and Technology on Marine Navigation and Control China State Shipbuilding Corporation Beijing100036 China Tianjin Navigation Instruments Research Institute Tianjin300131 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Bauman Moscow State Technical University Moscow Department of Automatic Control Systems 105005 Russia

ISBN: (纸本)9798350382457

Path planning algorithms are current research hotspots. Heuristic algorithms that can solve dynamic environment problems are gradually becoming the mainstream research direction. The D∗ algorithm, as one of the new heuristic algorithms, has the advantages of being more computationally efficient than the A ∗ algorithm and being able to solve dynamic problems, and has played an important role in two-dimensional path planning projects such as the NASA Mars rover. But its theoretical research and practical application in 3D environment are very rare. In this context, this paper carries out an in-depth study of the path planning problem based on the D∗ algorithm, and completes the theoretical derivation and simulation process in 2D and 3D environments. In this paper, a complex random 2D map is established by random generation, and the simulation of D∗ algorithm is realized on the map with the help of Python language, which realizes the finding of the relative optimal path. Then, in order to study the feasibility of D∗ solving in 3D environment, the simulation of D∗ algorithm under 3D map is realized with the help of Python and Unity3d. The research results show that the simulation results are good, which is valuable for the theoretical research and practical application of heuristic algorithms, and plays a role in the application of robot path planning algorithms. © 2023 IEEE.

关键词： Python

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research on Pretreatment Technology of Thermal Power Unit Operating Parameters Based on Big Data Platform 6

Research on Pretreatment Technology of Thermal Power Unit Op...

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6th International Conference on Power and Renewable Energy, ICPRE 2021

作者： Song, Hongqi Qian, Yuliang Yu, Wei Lai, Xinyi Shanghai University of Electric Power School of Automation Engineering Shanghai China HuaDian Electric Research Institute Electrical and Thermal Control Technology Department Hangzhou China

ISBN: (纸本)9781665449618

With the gradual deepening of the degree of digital transformation of power plants, the scale of data generated in the production link of power plants is increasing day by day. The traditional data processing mode has been difficult to meet the processing requirements of massive data. How to efficiently process the huge scale of data is one of the important problems faced by power enterprises. This paper first introduces the application of big data in the power industry. Then, according to the characteristics of big data, a large-scale data preprocessing method based on Spark data processing engine is proposed. Finally, the validity of data processing and the speed advantage of distributed computing platform in data processing are verified through experiments. © 2021 IEEE.

关键词： Big data

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Efficient RRT*-based Safety-Constrained Motion Planning for Continuum Robots in Dynamic Environments

arXiv

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arXiv 2023年

作者： Luo, Peiyu Yao, Shilong Yue, Yiyao Wang, Jiankun Yan, Hong Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Jiaxing Research Institute Southern University of Science and Technology Jiaxing China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Continuum robots, characterized by their high flexibility and infinite degrees of freedom (DoFs), have gained prominence in applications such as minimally invasive surgery and hazardous environment exploration. However, the intrinsic complexity of continuum robots requires a significant amount of time for their motion planning, posing a hurdle to their practical implementation. To tackle these challenges, efficient motion planning methods such as Rapidly Exploring Random Trees (RRT) and its variant, RRT*, have been employed. This paper introduces a unique RRT*-based motion control method tailored for continuum robots. Our approach embeds safety constraints derived from the robots’ posture states, facilitating autonomous navigation and obstacle avoidance in rapidly changing environments. Simulation results show efficient trajectory planning amidst multiple dynamic obstacles and provide a robust performance evaluation based on the generated postures. Finally, preliminary tests were conducted on a two-segment cable-driven continuum robot prototype, confirming the effectiveness of the proposed planning approach. This method is versatile and can be adapted and deployed for various types of continuum robots through parameter adjustments. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Style transfer-enabled Sim2Real framework for efficient learning of robotic transesophageal echocardiography using simulation

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Procedia Computer Science 2023年 226卷 99-105页

作者： Keyu Li Xinyu Mao Chengwei Ye Ang Li Yangxin Xu Max Q.-H. Meng Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong China Shenzhen Key Laboratory of Robotics Perception and Intelligence and the Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen 518055 China Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen 518057 China

Recently, various machine learning techniques have been proposed to realize automatic ultrasound (US) image analysis for robotic US acquisition tasks. However, obtaining large amounts of real US images for training is usually expensive or even infeasible in some clinical applications, such as transesophageal echocardiography (TEE). An alternative is to build a simulator to generate synthetic US data for training, but the differences between simulated and real US images may result in poor model performance. This work presents a novel Sim2Real framework to learn robotic US image analysis based on simulated data to address the challenges of limited real US data for robotic TEE. A style transfer model is proposed based on unsupervised contrastive learning to convert real US images into the simulation style. Moreover, a task-relevant model is designed to combine CNNs with vision transformers and trained only on the simulated data to generate task-dependent predictions. We demonstrate the effectiveness of our method in an image regression task to predict the probe position based on US images in robotic TEE. Our results show that using only simulated US data and a small amount of unlabelled real data for training, our method can achieve comparable performance to semi-supervised and fully supervised learning methods. Moreover, the effectiveness of our previously proposed CT-based US image simulation method is also indirectly confirmed.

关键词： Medical robots and systems Robotic ultrasound Transesophageal echocardiography

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DEAL: Decoupled Classifier with Adaptive Linear Modulation for Group Robust Early Diagnosis of MCI to AD Conversion

arXiv

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arXiv 2024年

作者： Lee, Donggyu Park, Juhyeon Moon, Taesup The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of The Department of Electrical and Computer Engineering Automation and Systems Research Institute Institute of New Media and Communications Interdisciplinary Program in Artificial Intelligence Artificial Intelligent Institute Seoul National University Seoul08826 Korea Republic of

While deep learning-based Alzheimer's disease (AD) diagnosis has recently made significant advancements, particularly in predicting the conversion of mild cognitive impairment (MCI) to AD based on MRI images, there remains a critical gap in research regarding the group robustness of the diagnosis. Although numerous studies pointed out that deep learning-based classifiers may exhibit poor performance in certain groups by relying on unimportant attributes, this issue has been largely overlooked in the early diagnosis of MCI to AD conversion. In this paper, we present the first comprehensive investigation of the group robustness in the early diagnosis of MCI to AD conversion using MRI images, focusing on disparities in accuracy between groups, specifically sMCI and pMCI individuals divided by age. Our experiments reveal that standard classifiers consistently underperform for certain groups across different architectures, highlighting the need for more tailored approaches. To address this, we propose a novel method, dubbed DEAL (DEcoupled classifier with Adaptive Linear modulation), comprising two key components: (1) a linear modulation of features from the penultimate layer, incorporating easily obtainable age and cognitive indicative tabular features, and (2) a decoupled classifier that provides more tailored decision boundaries for each group, further improving performance. Through extensive experiments and evaluations across different architectures, we demonstrate the efficacy of DEAL in improving the group robustness of the MCI to AD conversion prediction. © 2024, CC BY-NC-SA.

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Magnet temperature estimation of permanent magnet synchronous motor using search coils

Magnet temperature estimation of permanent magnet synchronou...

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International Conference on electrical Machines and Systems (ICEMS)

作者： Yuan Cheng Jinfeng Chen Wan Huang Bochao Du Shumei Cui Chongqing Research Institute of Harbin Institute of Technology Chongqing China Department of Electrical Engineering and Automation Harbin Institute of Technology (HIT) Harbin China

ISBN: (数字)9781665493024

ISBN: (纸本)9781665493031

Magnet temperature estimation in permanent magnet synchronous machines (PMSMs) is of great importance for high performance control and to avoid PM demagnetization. However, PM temperature sensing is difficult because of its installation in the rotor and is not normally implemented in standard machines. Alternatively, PM temperature could be estimated by some means. Among the others, using search coils (SCs) is a way to easily measure the motor back electromotive force (BEMF) and furthermore to get the estimated magnet temperature according to the flux and temperature linear relationship. In this paper, two methods of PM temperature estimation are proposed. The simulation results show that these two methods have very high accuracy, thus PM temperature can be estimated accurately.

关键词： Temperature measurement Temperature sensors Coils Neural networks Fitting Estimation Permanent magnet motors

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FEIF: Feature Excitation and Interactive Fusion for 6D Object Pose Estimation

FEIF: Feature Excitation and Interactive Fusion for 6D Objec...

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IEEE International Conference on robotics and Biomimetics

作者： Qiwei Meng Shiqiang Zhu Jianjun Gu Zheyuan Lin Ling Zhong Wei Song Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

In this paper, we present FEIF, a novel framework for 6D object pose estimation from a single RGBD image. Compared with previous approaches, this model fully leverages the complementary RGB and depth information through lay-erwise feature excitation and interactive fusion, thus improving the effectiveness and robustness of extracted features. The representative RGBD features can be subsequently applied for object keypoints prediction and pose computation. In addition, our FEIF includes a self-evaluation module to make robots aware of the confidence of pose estimation, so they are flexible to take the corresponding strategy for following tasks. Experiment results prove the SOTA performance of our model on LineMOD, Occlusion-LineMOD and YCB-Video datasets, and we also demonstrate its applicability and robustness in real-world robotic manipulation.

关键词： Technological innovation Computational modeling Biomimetics Pose estimation Feature extraction Robustness Data mining

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Distribution Steering for Discrete-Time Uncertain Ensemble Systems

arXiv

引用

arXiv 2024年

作者： Wu, Guangyu Tsiotras, Panagiotis Lindquist, Anders The Department of Automation Shanghai Jiao Tong University Shanghai China The School of Aerospace Engineering The Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta United States School of Artificial Intelligence Anhui University Hefei China The Smart Sensor Fusion Research Laboratory Shanghai China

Ensemble systems appear frequently in many engineering applications and, as a result, they have become an important research topic in control theory. These systems are best characterized by the evolution of their underlying state distribution. Despite the work to date, few results exist dealing with the problem of directly modifying (i.e., "steering") the distribution of an ensemble system. In addition, in most of the existing results, the distribution of the states of an ensemble of discrete-time systems is assumed to be Gaussian. However, in case the system parameters are uncertain, it is not always realistic to assume that the distribution of the system follows a Gaussian distribution, thus complicating the solution of the overall problem. In this paper, we address the general distribution steering problem for first-order discrete-time ensemble systems, where the distributions of the system parameters and the states are arbitrary with finite first few moments. Both linear and nonlinear system dynamics are considered using the method of power moments to transform the original infinite-dimensional problem into a finite-dimensional one. We also propose a control law for the ensuing moment system, which allows us to obtain the power moments of the desired control inputs. Finally, we solve the inverse problem to obtain the feasible control inputs from their corresponding power moments. We provide numerical results to validate our theoretical developments. These include cases where the parameter distribution is uniform, Gaussian, non-Gaussian, and multi-modal, respectively. © 2024, CC BY-NC-ND.

关键词： Method of moments

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