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9th International Conference on Fluid Power and Mechatronics, FPM 2023

作者： Zhao, Tian-Yu Wu, Shuai Li, Wen-Ding Li, Ren-Jie Wang, Yi Song, Bing Fu, Jun-Yong Jiao, Zong-Xia School of Automation Science and Electrical Engineering Beihang University Beijing China Research Institute for Frontier Science Beihang University Beijing China Ningbo Institute of Technology Beihang University Ningbo China Shanghai Aerospace Control Technology Institute Shanghai China Shanghai Servo-system Engineering Research Center Shanghai China Beihang University Key Laboratory of Advanced Airborne Systems Beijing China Beihang University Science and Technology on Aircraft Control Laboratory Beijing China Tianmushan Laboratory Hangzhou China

ISBN: (数字)9798350304947

ISBN: (纸本)9798350304947

In this paper, the dynamic stiffness characteristics of digital electro-hydraulic actuator are analyzed, and the influence of the introduction of digital valve on the electro-hydraulic actuator is studied. Based on the actual working conditions and requirements of digital electro-hydraulic actuator, the dynamic stiffness models of digital electro-hydraulic actuator and servo valve controlled electro-hydraulic actuator are established respectively. Two testing methods were used to complete closed-loop dynamic stiffness simulation, and the digital valve control system and the servo valve control system are compared and analyzed. By changing different system parameters, the sensitivity analysis of dynamic stiffness parameters of digital electro-hydraulic actuator is realized. Finally, the optimization of system control parameters is realized, and the optimization direction of the system parameters of the digital electro-hydraulic actuator is provided. © 2023 IEEE.

关键词： Stiffness

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Fixed-time neuroadaptive formation control for multiple QUAVs with external disturbance

Advanced Control for Applications: Engineering and Industria...

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advanced control for Applications: Engineering and Industrial systems 2024年第3期6卷

作者： Cheng, Shuai Xin, Bin Du, Zhaofeng Chen, Jie School of Automation Beijing Institute of Technology Beijing China The National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing China The Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China

This paper studies the formation control of multiple quadrotor unmanned aerial vehicle systems (MQUAVSs) with external disturbance. A new adaptive fixed-time cooperative control protocol is designed for MQUAVSs. A fixed-time command filtered compensation control technology is presented to overcome the "explosion of complexity" issue, and a new fixed-time error compensation signal is designed to compensate the filtering error, which improves the convergence speed of the system. Adaptive neural network technology is introduced to deal with unknown nonlinear functions in the system. A fixed-time stability theorem is presented for MQUAVSs to ensure that MQUAVSs can reach the predetermined formation and the formation tracking errors converge to the neighborhood of the origin in a fixed time. Finally, the effectiveness of the proposed method is verified by the formation simulation of MQUAVs. © 2024 John Wiley & Sons Ltd.

关键词： Error compensation

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Clustering single-cell RNA sequencing data by deep learning algorithm 9

Clustering single-cell RNA sequencing data by deep learning ...

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9th IEEE International Conference on Bioinformatics and Computational Biology, ICBCB 2021

作者： Bai, Litai Zhu, Yuan Yi, Ming Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Wuhan China

ISBN: (纸本)9780738132020

The development of single-cell RNA sequencing (scRNA-seq) technology provides a good opportunity to study cell heterogeneity and diversity. Especially, clustering is an important step in scRNA-seq analysis. With the advance of technology, many scRNA-seq data are available, which develop a lot of clustering methods. However, the existing methods usually employ the gene expression data, ignoring the related information between genes and the structure information in data. Therefore, we propose a new method (NDMgcn) to reconstruct the gene expression data based on the association of gene network, and cluster the data by Variational Autoencoder (V AE) and Graph Convolutional Network (GCN). The V AE learns low-dimensional information and the GCN learns structural information. The experimental results indicate that NDMgcn outperforms other popular algorithms in terms of NMI and ARI metrics. It provides a new insight for clustering scRNA-seq data from the network perspective. © 2021 IEEE.

关键词： RNA

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Finite-Time Synchronization of Flux-controlled Complex-Valued Memristive Neural Networks

Finite-Time Synchronization of Flux-Controlled Complex-Value...

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Neuromorphic Computing (ICNC), International Conference on

作者： Yonghuan Wang Leimin Wang Genping Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China

In this paper, by using the flux-controlled memristor model, the finite-time synchronization problem of delayed complex-valued memristive neural networks (MCNNs) is studied. Firstly, according to the proposed memristor model, we model the MCNNs as continuous systems on voltage-flux-time $(\wp, \varpi, t)$ domain. Then, we design a class of approaches to realize state synchronization between the drive and response systems, and the corresponding synchronization conditions are achieved. Finally, the effectiveness of results is illustrated with the simulations.

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Disturbance Observer-Based control of Weight on Bit in Drilling Process with Formation Variation

Disturbance Observer-Based Control of Weight on Bit in Drill...

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Asian control Conference

作者： Shipeng Chen Wangnian Li Chengda Lu Yang Zhou Weihua Cao Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China CCTEG Xi'an Research Institute (Group) Co. Ltd. Xi'an China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In deep geological drilling, encountering various formations is inevitable. Formation changes can lead to fluc-tuations in the weight on bit due to the bit-rock interaction. This paper proposes a disturbance observer-based weight on bit control system to mitigate the adverse effects of formation changes on system performance. First, an axis-torsion coupled dynamic model is established, and the relationship between the two dimensions is analyzed. The validity of the model is confirmed through comparison with field data collected from a geothermal well drilling operation. Then, a disturbance observer is designed to estimate and compensate for the disturbance at the control input, thereby reducing weight on bit fluctuations caused by formation variations. Simulation results considering formation variation during drilling verify the effectiveness of the proposed method.

关键词： Drilling Simulation System performance Geology Process control control systems Disturbance observers

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research on Urban Air Quality Prediction Method Based on the Fusion of Wide-area Spatiotemporal Distribution Data

Research on Urban Air Quality Prediction Method Based on the...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Zixing Tao Weihua Cao Chao Gan School of Future Technology China University of Geosciences (Wuhan) Wuhan China School of Future Technology School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences (Wuhan) Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Air quality data exhibit nonlinearity, sensitivity to environmental factors, and long-term dependencies. Numerous factors influence air quality, making accurate predictions based on a single-dimensional dataset impractical. This study proposes a method for urban air quality prediction that integrates wide-area spatiotemporal data, designed to address the unique characteristics of air quality datasets. First, relevant wide-area spatiotemporal data are selected, and their correlations with air quality are systematically analyzed. Second, a Long Short-Term Memory (LSTM) network-based Transformer model is utilized to capture the long-term dependencies in the air quality sequences. Finally, the model successfully generates hourly multi-step predictions for six air pollutants. The experimental results show that the proposed method outperforms the method that relies on air quality data alone for multi-step air quality prediction.

关键词： Deep learning Sensitivity Atmospheric modeling Time series analysis Predictive models Air quality Transformers Environmental factors Spatiotemporal phenomena Long short term memory

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Hand Gesture Recognition from an Open-Set Perspective

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

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Model predictive control of hemodynamics during intravenous general anesthesia

Model predictive control of hemodynamics during intravenous ...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Hamed Farbakhsh Erhan Yumuk Ghada Ben Othman Robin De Keyser Dana Copot Isabela Birs Clara M. Ionescu Department of Electromechanics Systems and Metal Engineering Research Group on Dynamical Systems and Control Tech Lane Science Park 125 Ghent University Zwijnaarde Belgium Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey Department of Automation Technical University of Cluj-Napoca Memorandumului no. 28 Cluj-Napoca Romania

In the operating rooms and the intensive care unit, it is crucial to manage the patient’s hemodynamic status, which includes factors like cardiac output and mean arterial pressure. Anesthesiologists confront a difficult task while monitoring high-risk patients. Cardiac output optimization has been found to enhance the result of high-risk patients in terms of hospital stay, mortality rate, post-operative problems, etc. The application of standard control approaches is restricted because the mean arterial pressure response of a patient using vasoactive medicines is modeled by a first-order dynamical system with time-varying parameters and a time-varying delay in the control input. In order to circumvent implementation challenges, this work develops an approximation technique that describes the system using a higher-order model. Predictive control is therefore used to comprehend the practical application of higher-order hemodynamic systems. The effectiveness of this strategy is demonstrated by the simulations and outcomes that are given.

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Scheduled Sampling Training Framework for ANN-Based PID control

Scheduled Sampling Training Framework for ANN-Based PID Cont...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Pau Comas Jose Lopez Vicario Antoni Morell Ramon Vilanova Wireless Information Networking (WIN) Research Group Escola d'Enginyeria Universitat Autònoma de Barcelona Bellaterra Spain Automation and Advanced Control Systems (ASAC) Research Group Escola d'Enginyeria Universitat Autònoma de Barcelona Bellaterra Spain

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

Proportional-Integral-Derivative (PID) controllers are extensively used in industrial control applications due to their simplicity and effectiveness in various control tasks. In recent years, there has been a growing emphasis on the integration of Artificial Neural Networks (ANNs) with control theory. This integration aims to harness the versatility of ANNs to facilitate controller design in dynamic environments. Additionally, it opens the possibility of transfer learning for these models, allowing knowledge gained from one control scenario to be applied to others' thereby enhancing adaptability and efficiency in control applications. In this work-in-progress paper, we propose a training framework that addresses the key challenges of modeling PIDs as ANNs, specifically the discrepancy between training and inference behaviors, known as exposure bias, commonly encountered in text summarization models. To tackle this, we integrate scheduled sampling, a technique devised for text sequence generation tasks, with an online control simulation environment.

关键词： Training Adaptation models PI control Transfer learning Text summarization Process control Artificial neural networks control theory PD control Manufacturing automation

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Multi-Modal Based Fuzzy Atmosfield in Human-Robot Interaction

Multi-Modal Based Fuzzy Atmosfield in Human-Robot Interactio...

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Chinese control Conference (CCC)

作者： Xin-Heng Li Zhen-Tao Liu School of Automation China University of Geosciences (Wuhan) Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

Recently, the emotional robot has basic functions of perceiving and expressing emotions, but it still hard to communicate naturally between humans and robots. One major reason is that communication atmosphere is seldom considered in Human-robot Interaction (HRI). We propose Multi-modal (i.e., music background, speech, and semantics) Based Fuzzy Atmosfield (FA), which can not only realize the dynamic adjustment of FA but also dynamically regulate human emotions. In the experiment, a Pepper robot is used and one hundred volunteers are invited for HRI, and soothing piano pieces are used as background music. Questionnaires were filled by the volunteers after the experiments, from which the results show that 90% of the volunteers felt the dynamic changes in the communication atmosphere and 77% of the volunteers felt significant emotional regulation, which demonstrates the effectiveness of our method.

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