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Chinese automation Congress (CAC)

作者： Liang Xu Hao Wang Ying Liu Wei Xue Ningning Li Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China National Key Laboratory for Complex Systems Simulation Beijing China Beijing Aerospace Propulsion Institute Beijing China

ISBN: (纸本)9781665426480

To address the problem of aeroengine PID parameter optimization and tuning, this work employs the excellent performance of the sparrow search algorithm (SSA) with high convergence accuracy and fast convergence speed to propose a self-tuning PID control method (SSA-PID) for aeroengine parameters. Considering that the oil supply output by the actuator may have a substantial overshoot, the proposed technique adds a penalty mechanism for the overshoot at the output, designs and improves the fitness function weighted by performance indicators, and effectively avoids the possible excessive overshoot and sharp oil supply phenomenon of the engine oil supply. The SSA-PID simulates and verifies the mathematical models of the turbofan engine’ s steady-state operating points on the ground and at high altitudes. The results show that the SSA-PID control method has better parameter tuning and optimization effects, a faster speed step response, and generates a fuel supply that does not have a targe overshoot, thereby proving its effectiveness and feasibility.

关键词： PI control Oils Search problems Mathematical models Steady-state PD control Fuels

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Simulation and Analysis of Dynamic Stiffness of Electro-hydrostatic Actuators

Simulation and Analysis of Dynamic Stiffness of Electro-hydr...

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International Conference on Fluid Power and Mechatronics (FPM)

作者： Chaofan Jiang Yaoxing Shang Tian Yu Mingyang Li Shuai Wu Zhicheng Liu School of Automation Science and Electrical Engineering Beihang University Beijing China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China Ningbo Institute of Technology Beihang University Ningbo China Tianmushan Laboratory Xixi Octagon City Yuhang District Hangzhou China Science and Technology on Aircraft Control Laboratory Beihang University Beijing China Research Institute for Frontier Science Beihang University Beijing China

ISBN: (数字)9798350304947

ISBN: (纸本)9798350304954

With the development of multi-electrochemical aircraft, the application of electro-hydrostatic actuators(EHA) in aircraft actuator system is increasing. The dynamic stiffness of the actuator has a non-negligible effect on the fluttering characteristics of the rudder surface. So it is necessary to study the dynamic stiffness of EHA. In this research, a method for calculating the dynamic stiffness of EHA is proposed. The mathematical model of EHA is established. The bulk modulus of hydraulic oil affected by gas content and pressure is added to the model. Build the simulation model in Simulink. The dynamic stiffness of EHA is simulated in frequency domain. Analyze the simulation results. The EHA dynamic stiffness decreases with the increase of hydraulic oil gas content in the whole frequency range. The trough frequency of dynamic stiffness amplitude decreases with the increase of hydraulic oil gas content. The EHA dynamic stiffness decreases with the increase of pump leakage coefficient in low frequency band. Therefore, in order to improve the dynamic stiffness of EHA, the hydraulic oil gas content and the piston pump leakage coefficient should be reduced.

关键词： Pistons Actuators Mechatronics Software packages Simulation Frequency-domain analysis Pumps

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Cross-Membrane Penetration to Nucleus of Adherent Cells Using Micropipettes Made by Borosilicate Glass and Quartz 17

Cross-Membrane Penetration to Nucleus of Adherent Cells Usin...

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17th IEEE International Conference on Mechatronics and automation, ICMA 2020

作者： Xu, Xinyue Liu, Xiaoming Lin, Yuqing Li, Pengyun Liu, Fengyu Tang, Xiaoqing Huang, Qiang Arai, Tatsuo Beijing Institute of Technology Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Advanced Innovation Center for Intelligent Robots and Systems School of Mechatronical Engineering Beijing100081 China

ISBN: (纸本)9781728164151

In the biomicromanipulation field, single-cell manipulation is receiving attention due to its potential applications in many aspects of biomedicine. Cell injection, as a typical cell micromanipulation technique, is devoted to introducing foreign substances into living cells by penetrating a fine micropipette into cell membranes. Direct access to the intracellular environment, especially the nucleus, provides an ideal way to gain insight into the dynamic processes of living cells. This paper presents the comparison of controlled penetrations of two types of adherent cells and their nuclei by two types of micropipettes, made of borosilicate glass and quartz respectively. Due to the differences in the cell properties (e.g., height, adhesion strength), two corresponding penetration strategies optimizing the puncture angle and puncture height, are proposed to improve the success rate of the puncture process and the survival rate of cells after the puncture. Real nucleus puncture experiments adopting the optimal puncture strategy for each type of cells and carried out by a semi-automatic system, indicated a better puncture effect by quartz pipettes. Through this method, functional materials such as peptides and nanoparticles can be potentially introduced to perturb the cell activities and then realize its full automation. © 2020 IEEE.

关键词： Quartz

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An Obstacles Avoidance Algorithm Based on Improved Artificial Potential Field 17

An Obstacles Avoidance Algorithm Based on Improved Artificia...

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17th IEEE International Conference on Mechatronics and automation, ICMA 2020

作者： Lu, SiXi Li, En Guo, Rui Yuquan Road Beijing100049 China Institute of Automation Chinese Academy of Science State Key Laboratory of Management and Control for Complex System 95 East ZhongGuanCun Road Beijing China State Grid Shandong Electric Power Company Jinan250001 China

ISBN: (纸本)9781728164151

Obstacle avoidance is one of the most important issue in the motion planning and control of robot. There are many algorithms to avoid obstacles, but obstacle avoidance algorithm for manipulator is needed consider not only end effector, but also all links of manipulator. In terms of real-time and speed of all system, the algorithm also should be easy as possible. In this paper, an algorithm based on the improved artificial potential field is proposed to solve the problem of the local optimum. The idea of improvement is to find a path by fitting curve with target point and critical oscillating points. There is also a comparison in this paper with planning a path by tangent line. The conclusion proves that the path is much smoother by the algorithm based on improved artificial potential field in this paper. Furthermore, the problem of judder on the trajectory of traditional artificial potential field is also solved in this paper by an improved repulsive potential field function. Finally, the algorithm is proved by planning a smooth path for obstacle avoidance on manipulator: KinovaGen3 in MATLAB. © 2020 IEEE.

关键词： Manipulators

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Digital Twin and Its Application in Transportation Infrastructure

Digital Twin and Its Application in Transportation Infrastru...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Yan Gao Shuyue Qian Zihan Li Ping Wang Feiyue Wang Qing He Key Laboratory of High-Speed Railway Engineering Ministry of Education Southwest Jiaotong University School of Civil Engineering Southwest Jiaotong University Chengdu China The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China

A prevailing challenge lies in how to properly design, construct and maintain transportation infrastructure engineering. Due to the development of sensor technology and computing technology, the amount of equipment monitoring data has multiplied and it causes difficulties in real-time state assessment and prediction under the traditional modeling condition. The introduction of Digital Twin technology, which aims to reflect the performance of the real-world product by simulating a virtual space, can solve these problems effectively. This paper reviews recent applications of four types of transportation infrastructure: railways, highways, bridges, and tunnels. Also, the existing research gaps are identified.

关键词： Road transportation Digital twin Transportation Predictive models Real-time systems Rail transportation Data models

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An optimization Strategy for Deep Neural Networks Training

An optimization Strategy for Deep Neural Networks Training

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Tingting Wu Peng Zeng Chunhe Song State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems 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

ISBN: (纸本)9781665464697

Learning rate is one of the essential hyperparameters influencing the training process and the accuracy of deep neural networks. However, until now, it is challenging to determine an optimal learning rate. A large learning rate can accelerate the training process but may bring instability in the training process and miss the global optimum. In contrast, a small learning rate would be in a stable training process, but the training speed would be slow, and the training process is easy to fall into local optimum. In this paper, first, the impact of the learning rate is analyzed. It is found that a learning rate schedule should consist of two stages to take into account the speed and accuracy of the training process simultaneously. Based on this consideration, this paper proposes an improvement strategy of learning rate schedules: a two-stage integration strategy of a large fixed learning rate and a rapid decay learning rate. Second, the proposed strategy is applied to optimize a series of widely used learning rate settings. Extensive experiments on CIFAR-10 and CIFAR-100 datasets with VGG19, ResNets, ResNext, DenseNets, SeNet, and some other models demonstrate that the proposed strategy optimizes the learning rate settings, the performance of trained models is enhanced.

关键词： Training Deep learning Measurement Schedules Computational modeling Neural networks Predictive models

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Learning Greenhouse Climate control Policy from Monitored Data

Learning Greenhouse Climate Control Policy from Monitored Da...

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Chinese automation Congress (CAC)

作者： Xiaoxuan Zhao Haoyu Wang Xiujuan Wang Udom Lewlomphaisarl Dong Li Jing Hua Mengzhen Kang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Institute of Automation Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technologies Beijing China National Electronics and Computer Technology Center Pathum Thani Thailand Institute of Digital Agriculture Zhejiang Academy of Agricultural Sciences Hangzhou China Qingdao AgriTech Co. Ltd. Qingdao China

The knowledge of solar greenhouse growers on environment control plays an important role in greenhouse production and management. We proposed a way to extract the control strategies from the monitored data of greenhouses by building a long short-term memory (LSTM) model. The result is verified according to the real monitored data of a solar greenhouse, which shows that the model can learn the control strategy of a ventilator in the solar greenhouse. Through monitored data and models, the knowledge of greenhouse ventilation control can be learned, and automatic control can be achieved in a greenhouse with a similar configuration.

关键词： Ventilators Green buildings Buildings Production Ventilation Data models Data mining

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A Comparison of Wearable Sensor Configuration Methods for Human Activity Recognition Using CNN

A Comparison of Wearable Sensor Configuration Methods for Hu...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Lina Tong Qianzhi Lin Chuanlei Qin Liang Peng School of Mechanical Electronic & Information Engineering China University of Mining and Technology-Beijng Beijing China State Key Laboratory of Management and Controlfor Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426565

The number and location configuration methods of wearable sensors for human activity recognition (HAR) are analytically discussed. Based on the publicly available Daily and Sports Activities data set, a convolutional neural network (CNN) was built to recognize 19 kinds of daily and sports activities, and then the model was optimized for better performance. The results of numerous comparative experiments show that deep learning-based HAR is better than machine learning-based HAR in terms of accuracy, and its improvement in accuracy is not directly related to the increase of sensor quantity. Due to its strong capability of feature extraction, deep learning extracts not only activity-related features but also individual differences, therefore, the location with less individual randomness should be selected according to practical engineering. Moreover, the results are also influenced by the limb symmetry in the data set. Finally, the feasibility of achieving higher accuracy with fewer sensors is proved.

关键词： Deep learning Interference Activity recognition Feature extraction Data models Convolutional neural networks Informatics

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Nonlinear Cooperative Output Regulation with Input Delay Compensation

arXiv

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arXiv 2024年

作者： Zheng, Shiqi Ahn, Choon Ki Jiang, Xiaowei Yan, Huaicheng Shi, Peng The School of Automation China University of Geosciences Wuhan430079 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China The Shaoyang Institute of Advanced Manufacturing Technology Shaoyang422000 China The School of Electrical Engineering Korea University Seoul136-701 Korea Republic of The Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education East China University of Science and Technology Shanghai200237 China The School of Electrical and Mechanical Engineering The University of Adelaide AdelaideSA5005 Australia

This paper investigates the cooperative output regulation (COR) of nonlinear multi-agent systems (MASs) with long input delay based on periodic event-triggered mechanism. Compared with other mechanisms, periodic event-triggered control can automatically guarantee a Zeno-free behavior and avoid the continuous monitoring of triggered conditions. First, a new periodic event-triggered distributed observer, which is based on the fully asynchronous communication data, is proposed to estimate the leader information. Second, a new distributed predictor feedback control method is proposed for the considered nonlinear MASs with input delay. By coordinate transformation, the MASs are mapped into new coupled ODE-PDE target systems with some disturbance-like terms. Then, we show that the COR problem is solvable. At last, to further save the communication resource, a periodic event-triggered mechanism is considered in the sensor-to-controller transmission in every agent. A new periodic event-triggered filter is proposed to deal with the periodic event-triggered feedback data. The MASs with input delay are mapped into coupled ODE-PDE target systems with sampled data information. Then, Lyapunov-Krasovskii functions are constructed to demonstrate the exponential stability of the MASs. Simulations verify the validity of the proposed results. © 2024, CC BY-NC-ND.

关键词： Feedback control

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

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

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Prognostics and System Health management Conference (PHM-Qingdao)

作者： Sen Tao Kai Wang Peng Zeng Tianda Yu Hao Wu Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China 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 Shenyang China Nuclear Power Institute of China Chengdu China State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing China

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.

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