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research on bidirectional isolated on-board charger based on CLLC resonant converter

International Journal of Power Electronics 2023年第3期18卷 235-260页

作者： Zhou, Kai Liu, Zan Sun, Dongyang Jin, Ningzhi Engineering Research Center of Automotive Electronics Drive Control and System Integration Ministry of Education Harbin University of Science and Technology No. 52 Xuefu Road Heilongjiang Harbin150080 China

This paper presents a bidirectional isolated on-board charging system for electric vehicles. The charging system is composed of a front-end circuit and a back-end circuit. The front-end circuit is a bidirectional totem-pole power factor correction converter, and the back-end circuit is a bidirectional CLLC resonant converter. According to the design requirements of the charging power supply system, the key parameters of the CLLC resonant converter are selected and calculated. Through sampling, the output voltage and input current of the front-end circuit are obtained, and compared with the reference value and compensated, and the on-off time of the switch in the front-end circuit is finally controlled. The latter circuit uses the loop control mode to control the switch frequency of the switch to achieve constant voltage and current charging. A 3.3-kW vehicle charging system prototype was constructed. The simulation and experimental results verify the correctness of the design. © 2023 Inderscience Enterprises Ltd.

关键词： Zero voltage switching

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Design of a novel double closed loop system of the Vienna rectifier in the pre-stage of UPS

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International Journal of Power Electronics 2023年第1期18卷 18-39页

作者： Jin, Ningzhi Zhang, Long Chen, Hongchao Zhou, Kai Wu, Xiaogang Engineering Research Center of Automotive Electronics Drive Control and System Integration Ministry of Education Harbin University of Science and Technology No. 52 Xuefu Road Heilongjiang Harbin150080 China

Aiming at the problems of complex parameter design and slow system dynamic response in the traditional double closed-loop PI control system of the Vienna rectifier, this paper proposes a novel double closed-loop control strategy of variable exponential approach law sliding mode control (SMC) and finite control set model predictive control (MPC). To begin with, a mathematical model of Vienna rectifier circuit is established. Then, the control algorithms of SMC voltage loop and MPC current loop are derived and designed. Finally, the applied control method is verified with simulation in MATLAB. The results show that the proposed system can improve the input current quality, and leads to a better steady-state response and dynamic performance of the DC output voltage, current and neutral point potential. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Model predictive control

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Electric Field Coupled Wireless Power Transmission Based on Three-Phase system 7

Electric Field Coupled Wireless Power Transmission Based on ...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Geng, Xin Li, YouCheng Engineering Research Center Ministry of Education of Automotive Electronics Drive Control and System Integration Harbin University of Science and Technology Harbin China College of Electrical and Electronic Engineering Harbin University of Science and Technology Harbin China

ISBN: (纸本)9798350359558

For the common capacitive coupling mechanism of single-phase quadrupole plate, an equivalent method is often used to simplify the six-capacitor into four-capacitor, which has become the most widely used capacitance equivalent model. Compared with the single-phase electric field coupled wireless power transmission system, in order to improve the output power, this paper refers to this method to design a three-phase input three-pair plate wireless power transmission system. By making every two pairs of plates take an equivalent method, the expression of the equivalent coupling capacitance value of the three pairs of plates is obtained. Then the equivalent three-phase system is simplified to a single-phase network, and the characteristics of the system with constant voltage and constant current output are derived. And the output power expression is obtained. It is proposed to control the system by controlling the system frequency to make the reactive power 0. Through the finite element simulation software Maxwell, the equivalent coupling capacitance values of the three pairs of plates are simulated and calculated. The three capacitance values are approximately equal, which verifies the feasibility of the equivalent method. After that, the proposed system is modeled and simulated. The two results verify each other, which proves the constant voltage output characteristics of the system and the characteristics of the resonant output at constant voltage, and also verifies the correctness of the formula derivation. © 2024 IEEE.

关键词： Electric fields

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An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations

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Numerical Mathematics(Theory,Methods and Applications) 2023年第2期16卷 298-322页

作者： Shuyan Shi Ding Liu Ruirui Ji Yuchao Han School of Automation and Information Engineering Xi‘an University of TechnologyXi‘an 710048P.R.China National&Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi‘an 710048P.R.China Xi’an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information ProcessingXi‘an 710048P.R.China

Physics-Informed Neural Network(PINN)represents a new approach to solve Partial Differential Equations(PDEs).PINNs aim to solve PDEs by integrating governing equations and the initial/boundary conditions(I/BCs)into a loss ***,the imbalance of the loss function caused by parameter settings usually makes it difficult for PINNs to converge,*** they fall into local *** other words,the presence of balanced PDE loss,initial loss and boundary loss may be critical for the *** addition,existing PINNs are not able to reveal the hidden errors caused by non-convergent boundaries and conduction errors caused by the PDE near the ***,these problems have made PINN-based methods of limited use on practical *** this paper,we propose a novel physics-informed neural network,*** adaptive physics-informed neural network with a two-stage training *** algorithm adds spatio-temporal coefficient and PDE balance parameter to the loss function,and solve PDEs using a two-stage training process:pre-training and formal *** pre-training step ensures the convergence of boundary loss,whereas the formal training process completes the solution of PDE by balancing various loss *** order to verify the performance of our method,we consider the imbalanced heat conduction and Helmholtz equations often appearing in practical *** Klein-Gordon equation,which is widely used to compare performance,reveals that our method is able to reduce the hidden *** results confirm that our algorithm can effectively and accurately solve models with unbalanced loss function,hidden errors and conduction *** codes developed in this manuscript are publicy available at https://***/callmedrcom/ATPINN.

关键词： Physics informed neural networks partial differential equations two-stage learning scientific computing

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Simulation study on control characteristics of semiconductor grade monocrystalline silicon growth process

Simulation study on control characteristics of semiconductor...

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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Yuyu Liu, Ding Song, Zezhong Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi`an University of Technology Xi'an710048 China

ISBN: (纸本)9781665465335

The growth of Czochralski(CZ) monocrystalline silicon is a nonlinear dynamic time-varying system with complex physical changes and multi-field and multi-phase coupling. In order to meet the needs of information development, it is urgent to develop large-scale, high-quality monocrystalline silicon. However, it is extremely difficult to establish an accurate control system model. Therefore, this paper considers the complex physical changes of multi-field coupling and non-linear processes in the growth of CZ monocrystalline silicon, a lumped parameter model for the growth of monocrystalline silicon is established based on energy balance model, fluid mechanics model, and geometry model, according to the internal mechanism of the monocrystalline silicon growth and the transfer mechanism of internal material flow. According to the model, a CZ monocrystalline silicon growth simulation control system is established under the Matlab/Simulink environment, and the real-time simulation data of each state variable of crystal growth is obtained. So that the control characteristics of each state variable in the crystal growth process can be analyzed. Finally, the validity of the established model is checked by the records evaluation of the semiconductor industry production process. Finally, the validity of the established model is verified based on the data analysis of the semiconductor industry production process. The results show that the model has high accuracy and can better describe the dynamic behavior of the actual monocrystalline silicon growth system. © 2022 IEEE.

关键词： MATLAB

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Model predictive control of event triggering in silicon single crystal growth processes

Model predictive control of event triggering in silicon sing...

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2022 Chinese Automation Congress, CAC 2022

作者： Li, Yankai Ren, Biao Liu, Ding Xi'An University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an710048 China

ISBN: (纸本)9781665465335

In this paper, the control issue of the semiconductor silicon monocrystalline growth process is studied through an event triggered model prediction approach. Firstly, a state-space model of silicon monocrystalline growth process is developed to describe the energy conversion and the dynamics and the geometry of the isodiametric stage silicon monocrystalline via the Czochralski method. Secondly, a small perturbation linearization is used to obtain a linear continuous model, which is translated into a linear discrete model under zero-order retainers. Finally, an event triggered model prediction controller is established to decrease the calculation of model prediction in the rolling optimization process. Based on experimental results, the event-triggered model prediction control algorithm is confirmed to be an effectively control scheme for the silicon monocrystalline growth process. © 2022 IEEE.

关键词： Model predictive control

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research on workflow recognition for liver rupture repair surgery

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Mathematical Biosciences and Engineering 2024年第2期21卷 1844-1856页

作者： Men, Yutao Zhao, Zixian Chen, Wei Wu, Hang Zhang, Guang Luo, Feng Yu, Ming Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Medical Support Technology Research Department Systems Engineering Institute Academy of Military Sciences People’s Liberation Army Tianjin300161 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China

Liver rupture repair surgery serves as one tool to treat liver rupture, especially beneficial for cases of mild liver rupture hemorrhage. Liver rupture can catalyze critical conditions such as hemorrhage and shock. Surgical workflow recognition in liver rupture repair surgery videos presents a significant task aimed at reducing surgical mistakes and enhancing the quality of surgeries conducted by surgeons. A liver rupture repair simulation surgical dataset is proposed in this paper which consists of 45 videos collaboratively completed by nine surgeons. Furthermore, an end-to-end SA-RLNet, a self attention-based recurrent convolutional neural network, is introduced in this paper. The self-attention mechanism is used to automatically identify the importance of input features in various instances and associate the relationships between input features. The accuracy of the surgical phase classification of the SA-RLNet approach is 90.6%. The present study demonstrates that the SA-RLNet approach shows strong generalization capabilities on the dataset. SA-RLNet has proved to be advantageous in capturing subtle variations between surgical phases. The application of surgical workflow recognition has promising feasibility in liver rupture repair surgery. ©2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons.

关键词： Image classification

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Optimization of Extreme Learning Machine Based on Improved Beetle Antennae Search for Slot Die Coating Prediction

Optimization of Extreme Learning Machine Based on Improved B...

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第35届中国控制与决策会议

作者： Haonan Yang Ding Liu Jun-Chao Ren Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

In the actual production of slot die coating,the minimum coating thickness and the maximum substrate moving speed could only be judged by production experience,and there was no accurate prediction model due to the nonlinear characteristics of fluid ***,building a reasonable and efficient prediction model for slot die coating is now an urgent and challenging *** this paper,an optimized extreme learning machine(ELM) based on improved beetle antennae search(IBAS) algorithm is proposed for slot die coating *** optimized ELM model can well learn the nonlinear characteristics of the system and make accurate predictions,thus solving the traditional inaccurate empirical *** the prediction accuracy of ELM depends on the selection of weights and biases,the IBAS optimization algorithm is used to quickly search for the optimal value of weights and biases in the ELM *** algorithm improves the generation mechanism of antennae on the basis of the original algorithm,so that the algorithm can converge *** the same time,the search strategy of the algorithm is improved to avoid falling into the local optimal *** predicting the production data of slit coating,the feasibility and effectiveness of IBAS-ELM model are proved.

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Reform of experimental teaching by integrating virtual simulation and remote control 23

Reform of experimental teaching by integrating virtual simul...

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8th International Conference on Distance Education and Learning, ICDEL 2023

作者： Hu, Wenhua He, Yuncheng Li, Jing Chen, Wei Wu, Ruiqin Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China Research Center for Wind Engineering and Engineering Vibration Guangzhou University China Smart City College Beijing Union University China

ISBN: (纸本)9798400700422

Remote online experimental system is the inevitable trend of experimental teaching in the future. Compared with the traditional offline experimental mode, the online experimental system has better operability and more intuitive teaching significance. It can carry out experiments anytime and anywhere without many actual environmental restrictions, saving the time of teachers and students. The number of students in an online class is far more than the number of offline experiments, which can improve teachers' teaching efficiency and teaching quality. This paper will start from an experimental example, design the simulation system based on Automation Studio (AS) and build the actual system based on WS290 hydraulic experimental rig, and then design the image recognition remote control program, and let users achieve the purpose of experimental teaching and control through the program. © 2023 ACM.

关键词： Remote control

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Optimization of Extreme Learning Machine Based on Improved Beetle Antennae Search for Slot Die Coating Prediction

Optimization of Extreme Learning Machine Based on Improved B...

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Chinese control and Decision Conference, CCDC

In the actual production of slot die coating, the minimum coating thickness and the maximum substrate moving speed could only be judged by production experience, and there was no accurate prediction model due to the nonlinear characteristics of fluid motion. Therefore, building a reasonable and efficient prediction model for slot die coating is now an urgent and challenging task. In this paper, an optimized extreme learning machine (ELM) based on improved beetle antennae search (IBAS) algorithm is proposed for slot die coating prediction. The optimized ELM model can well learn the nonlinear characteristics of the system and make accurate predictions, thus solving the traditional inaccurate empirical judgment. As the prediction accuracy of ELM depends on the selection of weights and biases, the IBAS optimization algorithm is used to quickly search for the optimal value of weights and biases in the ELM network. IBAS algorithm improves the generation mechanism of antennae on the basis of the original algorithm, so that the algorithm can converge quickly. At the same time, the search strategy of the algorithm is improved to avoid falling into the local optimal solution. By predicting the production data of slit coating, the feasibility and effectiveness of IBAS-ELM model are proved.

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