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Ocean Studies (ICOS), International Conference on

作者： Aleksey A. Kabanov Vadim A. Kramar Denis A. Tokarev Kirill V. Dementiev Department of Informatics and Control in Technical Systems Sevastopol State University Sevastopol Russian Federation Robotics and Intelligent Control System Laboratory Sevastopol State University Sevastopol Russian Federation

ISBN: (纸本)9781665473736

To create a platform for the Marine IoT for ensuring the interaction and digital navigation of heterogeneous marine robotic agents, it is necessary to combine into one network heterogeneous robotic agents, active and passive means of communication and illumination of the underwater and surface environment (marine communication buoys, underwater sonar stations, coastal connection posts). For their joint work, it is necessary to create a communication system unifying all elements of the platform, an integrated navigation and information management system for solving complex problems of navigation and motion control of robotic agents included in the platform to implement a joint mission. To do this, it is necessary to define the architecture of the platform to use a unified approach to data exchange.

关键词： Underwater communication Systems architecture Sonar Sea measurements Switches Network architecture User interfaces

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Active model-based nonlinear system identification of quad tilt-rotor UAV with flight experiments

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Science China(Information Sciences) 2022年第8期65卷 105-119页

作者： Zhong LIU Didier THEILLIOL Yuqing HE Feng GU Liying YANG Jianda HAN Xi'an Modern Control Technology Research Institute Faculte des Sciences et Techniques CRAN UMR 7039CNRSUniversity of Lorraine State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences College of Artificial Intelligence Nankai University

To propose an accurate and less complex model of a special unmanned aerial vehicle(UAV),namely tilt-rotor UAV(TRUAV), this study identifies the active model-based nonlinear system of a quadTRUAV. First, a nominal nonlinear model of the vehicle is formulated. Some unstructured nonlinearities are ignored to reduce the complexity of the model. Then, due to the unstable dynamics of the open-loop system,the format of this nominal model is considered to design an innovative smooth-switch attitude control via interconnection and damping assignment passivity-based control(IDA-PBC). Active model-based nonlinear system identification is studied for the vehicle with the designed control method and flight *** model error vector is defined and estimated by the unscented Kalman filter(UKF) to improve the accuracy of the model. The main contribution of this paper is to identify the nominal nonlinear model and to develop an active model method of the quad-TRUAV. The attitude control method with an innovative smooth-switch structure is another contribution to flight experiments. Numerical results are displayed to present the experimental results and effectiveness of the proposed nonlinear models.

关键词： tilt-rotor unmanned aerial vehicle flight control nonlinear system identification active model method unscented Kalman filter

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Dynamic Identification for a Manipulator Model based on Stribeck Friction using the Quasi-Newton Iterative Method

Dynamic Identification for a Manipulator Model based on Stri...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Xiao, Feng Zhang, Feilong Han, Bing Zhang, Hualiang Shenyang University of Technology School of Information Science and Engineering Shenyang110870 China University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China School of Mechanical Engineering Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310027 China

ISBN: (纸本)9798350325706

The performance of dynamic control is intimately tied to modeling accuracy. However, traditional estimation methods and friction models, such as the least squares method and the Coulomb plus viscous model, fail to reflect the actual characteristics accurately. Particularly, the linear nature of the Coulomb plus viscous model overlooks the nonlinear static features of joint friction at slower velocities. To improve the rationality of the model structure, we integrate the Stribeck friction model into the Coulomb plus viscous model. However, introducing such nonlinearities compromises the applicability of the least squares method. As a countermeasure, we propose a new strategy that combines the least square method and the Quasi-Newton iterative method to identify the parameters of the modified nonlinear model. Additionally, the design of the excitation trajectory is critical to achieve high identification accuracy. We utilized the inverse of the smallest singular value of the observation matrix as the objective function. By minimizing it with the interior point method, we generate the excitation trajectory well-suited to stimulate dynamic characteristics. Then we leverage the discrepancies between the measured and estimated torques to assess the precision of the dynamic parameters of the manipulator. Remarkably, our proposed algorithm reduces the mean absolute error of the estimated torque by over 20.40%. Finally, an experiment of the industrial manipulator by hand guiding grab and drag is performed and shows that the proposed approach can provide the manipulator with comprehensive torque compensation. © 2023 IEEE.

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WindowMixer: Intra-Window and Inter-Window Modeling for Time Series Forecasting

arXiv

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arXiv 2024年

作者： Liu, Quangao Li, Ruiqi Jiang, Maowei Yang, Wei Liang, Chen Pang, LongLong Zou, Zhuozhang Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shengyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Time series forecasting (TSF) is widely applied in various fields such as economic forecasting, weather prediction, traffic flow analysis, and public health surveillance. The core of TSF is to capture the underlying temporal patterns in the data. However, real-world time series data exhibit complex temporal patterns and may include additional noise, outliers, missing values, etc., making accurate forecasting highly challenging. Current mainstream methods typically naively treat each time point as an isolated entity, attempting to model point-to-point relationships and uncover temporal patterns within the data, which has limitations when dealing with complex time series data. Specifically, point-by-point modeling fails to take advantage of the abundant contextual information in time series data. This results in a reduced ability to capture temporal patterns and an increased susceptibility to noise. To address these issues, we examine temporal variations from a window-based perspective and present the WindowMixer model, which is built on an all-MLP framework. WindowMixer stems from a simple observation: Time series are recorded in a continuous manner, and the information at any given time point relies on the preceding and succeeding time points for a complete representation. WindowMixer decomposes time series into trend and seasonal components via a sequence decomposition and fusion architecture, handling them individually. For the trend component, we employ a straightforward fully connected (FC) layer to make predictions. For the seasonal component, we first introduce a time window at each time point and project each time window to produce window tokens. Then, we apply the Intra-Window-Mixer and Inter-Window-Mixer to further process these tokens. The Intra-Window-Mixer models relationships within each window token, while the Inter-Window-Mixer models relationships between window tokens. These dual modules allows us to capture more intricate patterns and long-range de

关键词： Time series

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Key Parameters Prediction for intelligent control and Energy Optimization Based on MIC-BWO-XGBoost 2

Key Parameters Prediction for Intelligent Control and Energy...

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2nd International Conference on Cyber-Energy Systems and intelligent Energy, ICCSIE 2024

作者： Lv, Changyao Wang, Yaqi Chen, Yudong Yu, Zhuo Hu, Guoliang Wu, Hongliang Li, Shuai Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China College of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China Shanghai Aircraft Manufacturing Co. Ltd. Shanghai201324 China

ISBN: (纸本)9798350373318

In the aviation industry, anodic oxidation serves as a core process for surface modification of aluminum alloy components, albeit accompanied by high energy consumption. Accurate prediction of key parameters can offer decision support for the intelligent control and energy optimization of anodic oxidation. However, the limited sample size resulting from the high cost of data collection and the complex nonlinear patterns caused by the synergy of multiple factors pose a challenge in accurately predicting key parameters. Therefore, we propose a key parameters prediction method based on the Maximum Information Coefficient (MIC), Beluga Whale Optimization algorithm (BWO), and XGBoost algorithm. Considering the adverse impact of low correlation parameters on prediction accuracy, we first use MIC to extract features from the original data. Then, XGBoost is used to integrate multiple decision trees to ensure the strong fitting ability for nonlinear relationships. Compared to deep learning models, XGBoost performs well even in a limited sample size. In addition, considering XGBoost is prone to overfitting training data as an ensemble learning algorithm, we employ the BWO to optimize four key parameters of the model that are difficult to choose, thereby enhancing model's generalization. The model's performance is evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with values of 0.04388, 0.09410, and 0.20730%, respectively. The experimental results demonstrate that the proposed method outperforms traditional approaches, enabling precise prediction of key parameters and holding significant value for the intelligent control and energy optimization of anode oxidation processes. © 2024 IEEE.

关键词： Anodic oxidation

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A Standing Assist Trajectory Generation Method of Transfer Robot

A Standing Assist Trajectory Generation Method of Transfer R...

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2022 IEEE International Conference on Real-Time Computing and robotics, RCAR 2022

作者： Yang, Jing Song, Zhen Wu, Ruikai Wang, Jingchuan Dalian Polytechnic University China Min. of Educ. of China Shanghai Eng. Res. Ctr. of Intelligent Contr. and Mgmt. Inst. of Med. Robotics Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

The increase in the number of the elderly and the disabled in China has brought a huge pressure to daily nursing work. Standing assistance for the elderly and the disabled is an important part of daily nursing work. Robots are used to assist nursing staff in completing this work, which reduces the work pressure of nursing staff. This article focuses on the research of a transfer robot system used to assist the elderly and the disabled to stand and transfer. Since the predefined standing assist trajectory need to collect the user's standing motion, this paper proposes a standing assist trajectory generation method based on the direct collocation method, which can generate the standing assist trajectory according to the user's height, weight and body state. © 2022 IEEE.

关键词： Trajectories

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A Novel Hybrid Method for Fault Diagnosis of Industrial Equipment Based on Vibration Signals 14

A Novel Hybrid Method for Fault Diagnosis of Industrial Equi...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Tao, Sen Wang, Kai Zeng, Peng Yu, Tianda Wu, Hao Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110169 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Nuclear Power Institute of China Chengdu610213 China State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing400044 China

ISBN: (纸本)9798350301359

Industrial equipment reliability is a critical issue that smart manufacturing systems must address. Implementation of online fault diagnosis for industrial equipment is a promising technology that can improve equipment reliability. Vibration analysis is a popular technique for equipment condition monitoring. For the non-linear and non-stationary features of industrial vibration signals, a hybrid model based on wavelet transform (WT), support vector machine (SVM), and grey wolf optimization algorithm (GWO) is proposed. The time-frequency domain features of fault signals are extracted by wavelet transform to solve the non-linearity of signals, and the obtained feature vectors are input into SVM for classification. Aiming at the problem of setting penalty coefficient and kernel function parameters, GWO is introduced to seek hyperparameters by using the hunting principle of wolves. In the experimental part, firstly, the comparison of the GWO-WT-SVM, GWO-SVM and SVM models was carried out. Secondly, Comparison with classical classification algorithms is made. Apparently, the GWO-WT-SVM model achieves higher accuracy than other models in bearing fault diagnosis. © 2023 IEEE.

关键词： component Fault diagnosis GWO PHM SVM

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Microsphere Superlenses-Based Optical Super-Resolution Imaging of Ovarian Tissue Sections

Microsphere Superlenses-Based Optical Super-Resolution Imagi...

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IEEE International Conference on Cyber Technology in Automation, control, and intelligent Systems

作者： Shuai Yuan Chaodi Jiang Hao Luo Shendi Li Xiaoduo Wang School of Electrical & Control Engineering Shenyang Jianzhu University Shenyang Liaoning Province China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China School of Shenyang Ligong University Shenyang China Institutes for Robotics and Intelligent Manufacturing Shenyang China

Tissue sections can reveal tumour-specific changes, and a tumour diagnosis can be made by analyzing the arrangement and distribution of cells on the surface. Although histopathological diagnosis based on tissue section observation has been the gold standard for tumour diagnosis, there is still much room for improvement in terms of scale due to the limited spatial resolution of conventional optical microscopy. Microsphere super-resolution microscopy offers a potential solution to these problems. Super-resolution imaging of the surface of tissue sections was achieved with the aid of microspheres fixed to the surface of the tissue section and a removable piezoelectric ceramic displacement stage. A single microsphere of barium titanate, measuring $50\mu\mathrm{m}$ in diameter, is capable of achieving super-resolution and is particularly suitable for observing the surface nanostructure of tissue sections. In our experiment, we successfully combined the microsphere super-resolution microscope with scanning stitching techniques to obtain tissue section structure images with a resolution lower than 200nm. Our research results will help improve the accuracy of tissue section observations and promote advancements in this field.

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The improved approach for calculating residual error rates in SIS communication 12

The improved approach for calculating residual error rates i...

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12th International Conference on CYBER Technology in Automation, control, and intelligent Systems, CYBER 2022

作者： Zhou, Xiufang Xu, Aidong Yan, Bingjun Sun, Yue Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

The residual error rate is an essential parameter for evaluating the functional safety of a communication system, based on the requirements of safety communication in the safety instrumented system (SIS). The traditional approach of quantifying residual error rates is incomplete in some cases. The proposed improvements focus on the different cases addressing full and partial redundancy models, time expectations and different verification approaches for connection identification. A hypergeometric distribution approach is used when the partial message redundancy mode is considered. © 2022 IEEE.

关键词： Redundancy

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Point Cloud based Motion State Estimation Method for Autonomous Driving

Point Cloud based Motion State Estimation Method for Autonom...

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2023 International Conference on Advanced Mechatronic Systems, ICAMechS 2023

作者： Jiang, Tao Zhu, Enzhi Wu, Sidong Zhang, Linshuai Gu, Shuoxin Xu, Lin Wang, Qian Tang, Xianggui Chengdu Univ. of Traditional Chinese Med. Unmanned Syst. Intelligent Percept. Contr. Technol. Eng. Laboratory of Sichuan Province Chengdu University of Information Technology School of Intelligent Medicine Chengdu China Chengdu University of Information Technology School of Automation Chengdu China Chengdu Univ. of Info. Technol. Unmanned Syst. Intelligent Perception Control Technology Engineering Laboratory of Sichuan Province Chengdu University of Information Technology School of Automation Chengdu China Chengdu University of Traditional Chinese Medicine School of Automation Chengdu Univ. of Info. Technol. Intl. Jt. Res. Ctr. of Robotics and Intell. Syst. of Sichuan Prov. School of Intelligent Medicine Chengdu China Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province School of Automation Chengdu China

ISBN: (纸本)9798350307474

Generally, motion vector information primarily stems from moving objects, while static objects contribute minimally to the estimation task. Traditional approaches for motion vector estimation typically rely on scene flow methods that depend on deep models to extract features from individual points at a high cost. These methods then acquire flow information through complex matching mechanisms or feature decoding. Such approaches are computationally expensive and exhibit substantial latency. Moreover, they neglect the importance of motion objects in motion vector estimation and the interference from static objects. Therefore, this paper introduces a novel method that first performs point cloud motion segmentation and subsequently estimates motion vectors. This approach focuses on leveraging point cloud information annotated with motion objects to estimate three-dimensional scene flow more effectively. By employing motion segmentation, we can obtain annotations for moving objects, enabling greater emphasis on the estimation of motion vectors for more challenging cases. In our experiments conducted on the KITTI dataset, the proposed method demonstrates superior performance compared to existing scene flow estimation methods. Specifically, without considering motion segmentation errors, the error in the motion direction is only 0.0363 m/s, showcasing better performance. Additionally, our method achieves an error of 0.076 m for three-dimensional endpoint error (EPE3D), showcasing distinct advantages over current scene flow networks. © 2023 IEEE.

关键词： Errors

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