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Dynamic Characteristics of Lubricating Oil Film of Slipper Pair in Radial Piston Pump under Impact Load

International Journal of Fluid Machinery and systems 2024年第3期17卷 164-177页

作者： Li, Shaonian Yun, Zhengang Wang, Kaifeng Fu, Liting College of Energy and Power Engineering Lanzhou University of Technology No.287 Langongping Road Lanzhou730050 China Key Laboratory of Advanced Pumps Valves and Fluid Control System Ministry of Education Lanzhou University of Technology No.287 Langongping Road Lanzhou730050 China College of Energy Engineering Xinjiang Institute of Engineering No.1350 Aidinghu Road Urumqi830023 China

To reduce the failure, deformation, and damage of slipper pair in radial piston pumps, Theoretical research and numerical modeling were conducted on the structure, kinematics principle, and stroke ring-slipper components force analysis of the radial piston pump, Through the theoretical analysis of the hydrostatic bearing oil film of the slipper pair, The obtained the static characteristic curve of the slipper pair, the oil film backthrust, the oil film stiffness, the power loss, and the optimal oil film thickness, reflecting the relationship between the oil film thickness, operating pressure, and the former. Subsequently, the dynamic characteristics equation of the oil film for the slipper pair under impact load was established, and the conclusion obtained by using the fourth-order Runge-Kutta method is as follows: during the oil film squeezing process, the fluctuation of the oil film increases monotonically with time t, during the establishment of the oil film, oil film fluctuation generates damping oscillations, eventually tending to balance the oil film thickness. Conversely, without considering the squeezing effect, the dynamic process of the oil film diverges. Finally, the influence of the slipper structural parameters on the dynamic characteristics of the slipper pair oil film under impact load was studied, The results show that when the sealing strip area decreases or Δp and h0 increases, the fluctuation caused by oil film squeezing becomes larger. However, as the oil film establishes, the fluctuation amplitude of the oil film varies directly with the sealing strip area 、Δp and h0, When the adjustment time ts＜t is reached, the ratio of the fluctuation of the oil film to the equilibrium oil film thickness is less than 3%. © 2024, Turbomachinery Society of Japan. All rights reserved.

关键词： Runge Kutta methods

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Distributed Neighbor Selection on Matrix-weighted Networks 38

Distributed Neighbor Selection on Matrix-weighted Networks

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Wei, Haoyu Pan, Lulu Shao, Haibin Li, Dewei Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

ISBN: (纸本)9798350303636

This paper explores distributed neighbor selection in matrix-weighted networks, where a matrix-valued weight captures the interdependencies among agents' states. We demonstrate the feasibility of preserving and, in certain cases, improving the convergence rate of matrix-weighted networks by having agents exclusively engage with a subset of their neighbors. Leveraging the inherent monotonicity of Laplacian matrix eigenvectors, we introduce a novel neighbor selection criterion that reduces network connections while enhancing convergence rates. Existing neighbor selection strategies often require acquiring information from all nodes before making decisions. In order to propose a distributed neighbor selection strategy for matrix-weighted networks, we establish a numerical relationship between Laplacian eigenvectors and a locally measurable metric. In essence, this paper extends prior findings from scalar-weighted networks to matrix-weighted networks. © 2023 IEEE.

关键词： convergence rate Distributed neighbor selection Laplacian matrix matrix-weighted networks

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Multi-Channel Based on Attention Network for Infrared Small Target Detection

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Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 414-427页

作者：张彦军王碧云蔡云泽 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Institute of Aerospace Control Technology Shanghai 201109China Infrared Detection Technology R&D Center of China Aerospace Science and Technology Corporation Shanghai 201109China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China Institute of Marine Equipment Shanghai Jiao Tong UniversityShanghai 200240China

Infrared detection technology has the advantages of all-weather detection and good concealment,which is widely used in long-distance target detection and tracking ***,the complex background,the strong noise,and the characteristics of small scale and weak intensity of targets bring great difficulties to the detection of infrared small targets.A multi-channel based on attention network is proposed in this paper,aimed at the problem of high missed detection rate and false alarm rate of traditional algorithms and the problem of large model,high complexity and poor detection performance of deep learning ***,given the difficulty in extracting the features of infrared multiscale and small dim targets,the multiple channels are designed based on dilated convolution to capture multiscale target ***,the coordinate attention block is incorporated in each channel to suppress background clutters adaptively and enhance target *** addition,the fusion of shallow detail features and deep abstract semantic features is realized by synthesizing the contextual attention fusion ***,it is verified that,compared with other state-of-the-art methods based on the datasets SIRST and MDFA,the proposed algorithm further improves the detection effect,and the model size and computational complexity are smaller.

关键词： infrared image small target detection deep learning attention mechanism feature fusion

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Optimized Dispatching of Integrated Energy system Based on Cyber-Physical-Social system Under the Influence of Electricity Price 3

Optimized Dispatching of Integrated Energy System Based on C...

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3rd International Signal Processing, Communications and engineering Management Conference, ISPCEM 2023

作者： Li, Jiayin Yu, Yuan Zhang, Zhen Wang, Donglai Zhao, Yan Shenyang Institute of Engineering Key Laboratory of Regional Multi- Energy System Integration and Control of Liaoning Province Shenyang China College of International Education Shenyang Institute of Engineering Shenyang China

ISBN: (纸本)9798350307023

The development and integration of information technology will make the data of the integrated energy system more real-time and transparent, which will help improve the demand-side management of Integrated Energy systems (IES). For the demand side of IES, the Cyber-Physical-Social system (CPSS) architecture is constructed and analyzed. An interactive energy management method of CPSS is presented to cover supply-demand interaction and real-virtual interaction. Aiming at the social attributes of power users, the demand-side guidance control strategy mediated by network guidance information is proposed. The impact of the difference in electricity price on the consumption psychology of the users is considered. Simulations of the model are conducted. The results show that the CPSS architecture proposed in this paper can effectively improve the economic benefits of the integrated energy system and make power users actively participate in the CPSS integrated energy system. © 2023 IEEE.

关键词： Real time systems

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Deep Learning Based Fault Diagnosis for Chemical Process with Statistical Feature Fusion 42

Deep Learning Based Fault Diagnosis for Chemical Process wit...

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42nd Chinese control Conference, CCC 2023

作者： Zhang, Lei Xia, Hao Cha, Bao School of Control Science and Control Engineering Dalian University of Technology Dalian116024 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610091 China

ISBN: (纸本)9789887581543

The existing fault diagnosis methods based on deep neural networks mostly rely on process data for fault classification and diagnosis, which makes it difficult to distinguish minor faults with similar data features. Therefore, this paper proposes a deep learning-based fault diagnosis model that combines statistical indicators and neural networks. By analyzing and calculating the relevant information entropy to screen the statistical signal features of time domain and frequency domain, we can extract more hidden information from the raw data, and use the convolutional autoencoder to compress the statistical indicators for feature extraction, and use CNN network to extract features from raw data. Finally, the two sets of features are fused and input into the LSTM network to obtain temporal information for fault diagnosis. Tests conducted on the Tennessee Eastman (TE) process show that the proposed model outperforms typical classifiers and improves the diagnostic accuracy of minor faults. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Failure analysis

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Unilateral payoff control is infeasible in network attack-defense interactions

Unilateral payoff control is infeasible in network attack-de...

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2024 China Automation Congress, CAC 2024

作者： Zhao, Kaiwen Xia, Yu Su, Qi Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9798350368604

Network resilience generally refers to the ability of a network to maintain normal operation in the face of various failures and disturbances, which has many applications in social, financial, communication, and transportation systems. There is a large amount of research on designing strategies to disrupt the network from the perspective of attack and to protect the network from the defense standpoint. However, attacks and defenses in the real world often occur simultaneously and repeatedly. The study of network resilience should not be limited to the structural characteristics but should consider the functional characteristics of the network. We develop a general game-theoretic framework to study attack-defense repeated interactions on complex networks and examine the possibility of achieving precise control over the payoffs of attackers and defenders. Our findings reveal that neither the attacker nor the defender can unilaterally control their own payoff, the opponent’s payoff, or any linear combination thereof. This work highlights the limitations of unilateral payoff control in real-world interaction scenarios, a consideration often overlooked in numerous theoretical studies. © 2024 IEEE.

关键词： Economic and social effects

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On convergence of covariance matrix of empirical Bayes hyper-parameter estimator

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control Theory and Technology 2024年第2期22卷 149-162页

作者： Yue Ju Biqiang Mu Tianshi Chen School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong KongShenzhenShenzhen518172GuangdongChina Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technology10044StockholmSweden Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and System ScienceChinese Academy of SciencesBeijing100190China

Regularized system identification has become the research frontier of system identification in the past *** related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to *** this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been ***,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is *** general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance ***,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter *** this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting ***,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.

关键词： Regularized system identification Hyper-parameter estimator Empirical Bayes Convergence of covariance matrix

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SAR Image Registration Based on Feature Points Using Improved CSP-DenseNet 6

SAR Image Registration Based on Feature Points Using Improve...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Wang, Ziwei Yang, Yanjiao Qu, Guangzhou Chi, Qingxi Beijing Electro-Mechanical Engineering Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9798350325485

Synthetic aperture radar (SAR) image registration is widely used in integrated navigation system with development of guidance system in sensors and other applications. However, there exists some difficulty because of the quality and imaginary principle of SAR images. In this article, a brand-new method based on feature points using improved CSP-DenseNet is proposed to solve the problems of SAR image registration with weak and noisy texture. Deep features of interest points in SAR images are extracted using the proposed network from the crops of search image and template image respectively. The method has an advantage of preserving abundant feature information in SAR images under the influence of insufficient image information and noise of SAR images. The CSP-DenseNet architecture has a cross-stage construction optimizing fused by CSP-Net and DenseNet to extract matching features. The network is partially connected in specific convolutional layers for feature reusing and computation resources saving. Then, Brute Force Matcher and RANSAC voting are successively applied for a larger number of matching pairs and high-precise matching vertexes. Experimental results on various SAR image matching methods show that the proposed method provides better performance than other approaches compared. © 2023 IEEE.

关键词： Synthetic aperture radar

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Bearing fault signal detection based on whale algorithm to optimize stochastic resonance parameters 34

Bearing fault signal detection based on whale algorithm to o...

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第34届中国控制与决策会议

作者： Huang Weichao Zhang Ganggang Faculty of automation and Information Engineering Xi'an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781665478977

Aiming at the problem that the traditional stochastic resonance system cannot adaptively adjust the structural parameters in the bearing fault signal detection,this paper proposes a parameter adaptive stochastic resonance bearing fault signal detection method based on the whale optimization *** method is based on the bistable stochastic resonance system model,and the structural parameters of the bistable stochastic resonance are optimized through the adaptive whale algorithm,so that the fault signal is enhanced,and the bearing fault signal detection is *** method was verified by actual bearing fault signals,and a series of comparative experiments were carried *** results show that the method in this paper has a simple model,few algorithm parameters,fast convergence speed,and a large output signal-to-noise ratio of the stochastic resonance system,which can accurately and efficiently detect bearing fault signals.

关键词： Stochastic Resonance Whale Optimization Algorithm Bearing Failure Signal

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ST-Petri: A Visual Executable Semantic Model for PLC Structured Text Language 22

ST-Petri: A Visual Executable Semantic Model for PLC Structu...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Hu, Xiaoyu Liang, Yonghui Zhu, Shibo Li, Hui Zhu, Shanying Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China

ISBN: (纸本)9798331527471

As very important controllers in automated factories, the correctness and safety of Programmable Logic controller (PLC) and their programs determine the stability of the production process. Structured Text (ST) is one of the most widely used PLC programming languages. However, there are not enough formal semantics and different venders may have their own implementations. In this work, a visual executable semantic model ST-Petri for ST has been proposed. The model uses Petri nets to formally represent ST semantics while using graphs to visualise program control flow. To validate the correctness of the semantic model, we use programs from Github and mutations of existing source programs as a test set to execute and compare the results with the open source OpenPLC platform. Experimental results show that ST-Petri has significant advantages in terms of compilation pass rate and error indication as a semantic model and a ST program compiler. © 2024 IEEE.

关键词： Petri nets

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