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Recent Patents for control Devices of Fluid Pressure

Recent Patents on Engineering 2022年第3期16卷 43-53页

作者： Xie, Baocheng Wang, Yi Li, Chaoxiang Zhang, Hanyang Zhang, Yongxing Su, Haobin Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education Harbin University of Science and Technology Harbin150080 China Beijing Institute of Control Engineering Beijing200085 China

Fluid pressure, an important parameter of fluid flow, affects the control accuracy and stability of fluid transmission. The control devices of fluid pressure are widely applied to the mechanical transmission control, which is the main means to change the fluid pressure and improve control accuracy and stability in the mechanical transmission system. To control the accuracy and stability of the fluid transmission, the pressure control device is divided into pressure reduction devices, pressurization devices, and stabilizing pressure devices. The characteristics of pressure control devices with different functions are analyzed from the structure of the device. Thus, more and more attention has been paid to the control devices of fluid pressure. To meet the increasing requirement of control accuracy and stability of fluid transmission, the control devices of fluid pressure, such as pressure reduction devices, pressurization devices, and stabilizing pressure devices, are being improved continuously. This paper reviews various current representative patents related to pressure reduction devices, pressurization devices, and stabilizing pressure devices of fluid pressure. By investigating various patents on the control devices of fluid pressure, the main current existing problems of the pressure control devices, such as low control accuracy and poor stability, are summarized and analyzed. Furthermore, the development trend of the control devices of fluid pressure has also been discussed. The optimization of the structure and function of the control devices of fluid pressure is conducive to improve the control accuracy and stability of transmission of the fluid. More relevant patents will be invented in the future. © 2022 Bentham science Publishers.

关键词： Stability

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An Approach for Upper Limb Movement Intention Recognition Using EEG and sEMG Fusion based on the MCPSA-CIIM 5

An Approach for Upper Limb Movement Intention Recognition Us...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhang, Weiming Zhang, Xiaodong Xu, Cheng Zhou, Guchuan Zhang, Teng Wang, Yu Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robot School of Mechanical Engineering Shaanxi Xi'an China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China

ISBN: (纸本)9798350307320

The upper limb motion intention recognition method based on electroencephalography (EEG) and surface electromyography (sEMG) fusion has achieved significant results in fields such as prosthetic control. However, most existing fusion methods use manual means to select features, which cannot capture temporal and spatial features at different scales, and ignore the correlation features between the two types of signals. To address these issues, this article proposes a fusion recognition method for upper limb motion intention EEG and sEMG based on Multi-scale Convolution, Polarized Self-Attention, and Cross Intelligence Integration Module. This article extracts multidimensional temporal and spatial features of EEG and sEMG through multi-scale convolution, and introduce polarized self-attention mechanism to filter the extracted multi-scale features. Simultaneously using cross enhancement strategy to extract correlation features between EEG and sEMG. Finally, the features are input into the classification network for recognition. This method was validated on the Jeong database, and the results showed that compared with CNN-LSTM and EEGNet, the recognition accuracy of this method increased by 2.63% and 3.15%, respectively. © 2023 IEEE.

关键词： Electroencephalography

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Gesture Recognition of sEMG Based on Res-LSTM 17th

Gesture Recognition of sEMG Based on Res-LSTM

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17th International Conference on intelligent Robotics and Applications, ICIRA 2024

作者： Zhao, Yujia Zou, Chunlong Yun, Juntong Jiang, Du Huang, Li Liu, Ying Jiang, Guozhang Xie, Yuanmin Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442000 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China School of Mechanical Engineering Hubei Engineering University Xiaogan432000 China

ISBN: (纸本)9789819607761

sEMG (surface electromyography) signal control of bionic prostheses has been widely studied over the past few years. In particular, sparse sEMG signals are rapidly developing in the field of gesture recognition for their convenience, noninvasiveness, and ease of access. However, compared with high-density EMG signals, sparse EMG signals lack rich feature information, which in turn affects gesture recognition accuracy. In order to reduce the loss of feature information of sparse EMG signals in the spatio-temporal dimension, this paper proposes a hybrid neural network Res-LSTM combining residual network and long short-term memory network. The ordinary convolutional blocks in the CNN network are replaced with residual blocks, and the final fully connected layer is removed and a constant mapping layer is added to adequate extraction of spatial feature information of the data. The Res layer’s output is utilized as the input for the long and short-term memory (LSTM) network, which further extracts the features of the data in the temporal dimension, and finally completes the categorization output through a fully connected layer. The average accuracy of 91.11% for gesture-motion classification was verified by training on a homebrew dataset;and tested on a public dataset, NinaPro DB1, with an average accuracy of 91.03%. The experimental findings indicate that the proposed Res-LSTM network framework contributes to solving the problem of lack of feature information of sparse sEMG signals and improves the pattern recognition accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gesture recognition

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Spatiotemporal evolution laws of sector-shaped dielectric-barrier-discharge plasma actuator

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Plasma science and technology 2024年第10期26卷 108-115页

作者： Borui ZHENG Jianbo ZHANG Shaojie QI Jianghua XU Yiche LI Yuanzhong JIN Dongliang BIAN School of Automation and Information Engineering Xi’an University of TechnologyXi’an 710048People’s Republic of China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an 710048People’s Republic of China School of Mechanical and Precision Instrument Engineering Xi’an University of TechnologyXi’an 710048People’s Republic of China Science and Technology on Plasma Dynamics Laboratory Air Force Engineering UniversityXi’an 710038People’s Republic of China

Dielectric barrier discharge(DBD)plasma actuators are widely used in active flow control due to their simple design and rapid ***,they need more effectiveness and discharge *** overcome these limitations,a sector-shaped dielectric barrier discharge(SS-DBD)plasma actuator with an adjustable jet angle was developed to enhance flow control *** flow field dynamics induced by the SS-DBD plasma actuator were quantitatively analyzed using particle image velocimetry(PIV).Experimental investigations showed that precise adjustments to the actuation voltage can modulate the maximum velocity of the induced ***,a quasi-linear relationship between the sector-shaped angles of the SS-DBD and the deflected jet angles was established,indicating that changes in the sector-shaped angles directly influence the direction of the deflected *** correlation enables precise control over jet angles,significantly enhancing flow control by adjusting the SS-DBD-PA's sector-shaped angle.

关键词： plasma flow control plasma actuator particle image velocimetry

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Demand-Side Frequency control in Power Network Based on Multi-Agent Deep Reinforcement Learning 5

Demand-Side Frequency Control in Power Network Based on Mult...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Liu, Xianggang Liu, Zhi-Wei Chi, Ming Zhang, Yifan Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education College of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9798350325294

The integration of renewable energy sources into the power grid has led to new challenges in maintaining the stability of the system frequency. This paper proposes a novel approach to address the Optimal Demand Side Frequency control (ODFC) problem using Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method. The proposed method models the ODFC problem as a Markov game, with centralized training based on multi-agent cooperative self-learning and associative storage service. In the decentralized execution stage, each agent independently outputs control actions to the controlled plant using local observations. Numerical simulations show that the proposed method effectively addresses the ODFC problem with superior performance compared to traditional methods. © 2023 IEEE.

关键词： Numerical methods

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Characterization and sources of childhood PAEs exposure from residential airborne dust in China cities

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Journal of Environmental sciences 2025年第6期152卷 1-13页

作者： Chao Wang Shuhan Gong Yun Cao Mengmeng Liu Wenying Zhang Xiaotong Zhang Lin Fan Li Li Hang Du Mats Tysklind Xu Yang Xianliang Wang China CDC Key Laboratory of Environment and Population Health National Institute of Environmental HealthChinese Center for Disease Control and PreventionBeijing 100021China National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases National Institute of Environmental HealthChinese Center for Disease Control and PreventionBeijing 100021China School of Public Health Anhui Medical UniversityHefei 230032China Department of Chemistry Umea UniversityUmea SE-90187Sweden College of Life Science Huazhong Normal UniversityWuhan 430079China Xianning Medical College Hubei University of Science and TechnologyXianning 437100China

To explore the multicenter characteristics of endocrine-like phthalate esters(PAEs)in household dust and propose effective control strategies for global indoor public *** on-site observational investigation was conducted in nine Chinese cities from 2018 to 2019.A total of 246 household dust sampleswere collected and analyzed for ten PAE congeners using Gas Chromatography-Mass Spectrometry(GC-MS).Questionnaires were used to gather information on building conditions,indoor behaviors,and ventilation *** residential dust from the nine cities,the total concentrations of the ten PAE congeners(PAEs)ranged from 0.921 to 29097.297μg/*** phthalate(DCHP)and di(2-ethylhexyl)phthalate(DEHP)were the dominant congeners in*** exposure to PAEs through dust ingestion was four orders of magnitude higher than through inhalation,with a carcinogenic risk of 5.47×10^(−6) for DEHP exposure in household ***PAEs concentrations were associated with higher temperature,double glazing,wall paint usage,television and computer use,and indoor plant *** multicenter on-site investigation confirmed PAE pollution characteristics and uncovered the inacceptable risk of daily DEHP exposure in household dust under real living *** mitigation measures based on household-related information,residential characteristics,decoration materials,and lifestyle should be taken to build a healthy household environment.

关键词： Phthalate esters Household dust Source analysis Health risk Influencing factors

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Discrete Adaptive control of Hypersonic Vehicles Based on Characteristic Models with Fuzzy Neural Network Observer

Discrete Adaptive Control of Hypersonic Vehicles Based on Ch...

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Chinese control and Decision Conference, CCDC

作者： Yafei Chang Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

A novel discrete adaptive control scheme is developed based on the characteristic modeling theory, for the attitude control of a hypersonic vehicle with strong nonlinearities and uncertainties. A new structure of the characteristic model is proposed firstly, in which an extended state is added to describe the unmolded dynamics and disturbance. Then a fuzzy neural network observer is constructed to approximate the extended state, and the updated law of the characteristic parameters and the net parameters are designed simultaneously based on the structure of the characteristic model. Further, to reject the undesired influence of the time-variation of characteristic parameters and the approximation error of the neural network, a nonlinear term containing a saturation function is added to the parameter adaption law. Then an error-surface based control law is proposed on the basis of the characteristic model. Using the Lyapunov stability theory, the closed-loop system is proved stable and convergence properties of the system are assured. Finally, the effectiveness of the proposed adaptive control scheme is illustrated by several representative simulations.

关键词： Adaptation models Fuzzy control Uncertainty Neural networks Observers Fuzzy neural networks Robustness

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Continuous Self-Learning control Under System Identification Based on Hopfield Neural Network

Continuous Self-Learning Control Under System Identification...

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Chinese Automation Congress (CAC)

作者： Tan Tian-Le Zhu He Hou Yue-Yang Zhang Wen-Jing Shanghai Key Laboratory of Space Shanghai Aerospace Control Technology Institute Intelligent Control Technology Shanghai China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Considering the dynamic modeling of an unknown and time-varying complex dynamic system in the model-based control, this paper develops a data-driven system identification method based on the Hopfield neural network (abbreviated as HNN). The normalization and denormalization transformation are designed to preprocess the training samples, and by the sliding window to refresh the sampling continuously, an online iteration method is proposed to identify the dynamic parameters. Furthermore, a continuous self-learning control based on HNN is developed in coordination with the model-based prediction and inversion control. Simulation results on the manipulator show that the HNN could efficiently identify the dynamic model in real time. The identified model has an approximately isomorphic equivalent form to the dynamic mechanism model, which could be used for the qualitative and quantitative analysis of the control quality. Results also verify the dynamic adaptability of the HNN continuous self-learning control for the nonlinear unknown and time-varying system.

关键词： Adaptation models Analytical models Neural networks Hopfield neural networks Predictive models Real-time systems System identification Nonlinear dynamical systems Time-varying systems Manipulator dynamics

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Frequency Information Matters for Image Matting 1

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Luo, Rongsheng Wei, Rukai Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory For Optoelectronics Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9783031476341

ISBN: (纸本)9783031476334

Image matting aims to estimate the opacity of foreground objects in order to accurately extract them from the background. Existing methods are only concerned with RGB features to obtain alpha mattes, limiting the perception of local tiny details. To address this issue, we introduce frequency information as an auxiliary clue to accurately distinguish foreground boundaries and propose the Frequency Matting Network (FMN). Specifically, we deploy a Frequency Boosting Module (FBM) in addition to the Discrete Cosine Transform (DCT) to extract frequency information from input images. The proposed FBM is a learnable component that empowers the model to adapt to complex scenarios. Furthermore, we design a Domain Aggregation Module (DAM) to effectively fuse frequency features with RGB features. With the assistance of frequency clues, our proposed FMN achieves significant improvements in matting accuracy and visual quality compared with state-of-the-art methods. Extensive experiments on Composition-1k and Distinctions-646 datasets demonstrate the superiority of introducing frequency information for image matting. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Discrete cosine transforms

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GraphLoc: Enhancing Fingerprint-Based Localization With Graph Representation Learning

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IEEE Internet of Things Journal 2025年第12期12卷 21593-21603页

作者： Zhang, Lingyan Qiu, Yuanfeng Wu, Shaohua Zhang, Tingting Zhang, Qinyu Heilongjiang University School of Electronics Engineering Harbin150080 China Hong Kong University of Science and Technology Thrust of Artificial Intelligence Guangzhou511466 China Harbin Institute of Technology School of Electronics and Information Engineering Shenzhen518055 China Guangdong Provincial Key Laboratory of Space-Aerial Networking and Intelligent Sensing Shenzhen China

For 6G inherent intelligent capability, deep-learning-based wireless localization will become a promising technology for offering commercial location-based services (LBSs). Classical deep neural networks (DNNs) have been designed to learn feature representation for localization tasks. However, due to the uncertainty of radio measurements in complicated wireless propagation, the existing solution has achieved unsatisfactory performance with environmental dynamics. To address this issue, we propose GraphLoc, a novel approach to enhancing fingerprint-based localization with graph representation learning which can encode the structural information underlying radio fingerprints for robust localization. We first adopt graph signal processing of CSI fingerprints to create an unweighted graph. GraphLoc transforms the tasks of location estimations into node classification in a constructed graph. Then, we develop multilayer graph attention networks (GATs) with the residual structure (Res-GAT) to learn graph representation by collecting the neighboring node features and aggregating their neighboring embeddings. Furthermore, in order to guarantee and speed up our Res-GAT convergence, we propose a training strategy to overcome training difficulty and overfitting for improving the quality of graph representation. Finally, extensive experimental results in many typical indoor scenarios demonstrate that the GraphLoc system can achieve better accuracy than other comparative schemes, even with the robustness of environmental dynamics, effectively facilitating fingerprint-based localization for fully practical LBS. © 2014 IEEE.

关键词： Graph neural networks

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