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Self-Attention-Augmented Generative Adversarial Networks for data-driven modeling of Nanoscale Coating Manufacturing

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MICROMACHINES 2022年第6期13卷 847-847页

作者： Ji, Shanling Zhu, Jianxiong Yang, Yuan Zhang, Hui Zhang, Zhihao Xia, Zhijie Zhang, Zhisheng Southeast Univ Sch Mech Engn Nanjing 211189 Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Chinese Acad Sci State Key Lab Transducer Technol Shanghai 200050 Peoples R China

Nanoscale coating manufacturing (NCM) process modeling is an important way to monitor and modulate coating quality. The multivariable prediction of coated film and the data augmentation of the NCM process are two common issues in smart factories. However, there has not been an artificial intelligence model to solve these two problems simultaneously. Focusing on the two problems, a novel auxiliary regression using a self-attention-augmented generative adversarial network (AR-SAGAN) is proposed in this paper. This model deals with the problem of NCM process modeling with three steps. First, the AR-SAGAN structure was established and composed of a generator, feature extractor, discriminator, and regressor. Second, the nanoscale coating quality was estimated by putting online control parameters into the feature extractor and regressor. Third, the control parameters in the recipes were generated using preset parameters and target quality. Finally, the proposed method was verified by the experiments of a solar cell antireflection coating dataset, the results of which showed that our method performs excellently for both multivariable quality prediction and data augmentation. The mean squared error of the predicted thickness was about 1.6 similar to 2.1 nm, which is lower than other traditional methods.

关键词： data-driven modeling generative adversarial network nanoscale coating manufacturing self-attention

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Beyond Persistent Excitation: Online Experiment Design for data-driven modeling and Control

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IEEE CONTROL SYSTEMS LETTERS 2022年 6卷 319-324页

作者： van Waarde, Henk J. Univ Cambridge Dept Engn Control Grp Cambridge CB2 1PZ England

This letter presents a new experiment design method for data-driven modeling and control. The idea is to select inputs online (using past input/output data), leading to desirable rank properties of data Hankel matrices. In comparison to the classical persistency of excitation condition, this online approach requires less data samples and is even shown to be completely sample efficient.

关键词： Trajectory Design methodology Linear systems data-driven modeling Noise measurement Adaptive systems Tuning Identification linear systems

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data-driven modeling and Control of Semicontinuous Distillation Process

Data-Driven Modeling and Control of Semicontinuous Distillat...

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American Control Conference (ACC)

作者： Sakthi Prasanth Aenugula Aswin Chandrasekar Prashant Mhaskar Thomas A. Adams Department of Chemical Engineering McMaster University Hamilton Ontario Canada Department of Energy and Process Engineering Norwegian University of Science and Technology (NTNU) Trondheim Norway

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

A semicontinuous distillation process is effectively used in the separation of a multi-component mixture with low to medium production rates. This work focuses on building a data-driven model predictive control (MPC) framework to optimize the performance of a semicontinuous process by reducing total annualized cost (TAC) per tonne of feed processed while meeting the specified product quality. A data-driven modeling technique is considered in this work because of the unavailability of a highly complex and accurate first-principle model. An Aspen Plus Dynamics simulation is used as a test bed to collect the data from the process. A multi-model framework developed by modifying the traditional subspace algorithm is adapted in the shrinking horizon MPC (SHMPC) scheme to minimize TAC per tonne of feed processed. Visual Basic for Application (VBA) is used as a third tool to communicate the inputs from MPC developed in MATLAB to the process in Aspen Plus Dynamics. The simulation results illustrate that the MPC reduced the TAC/tonne of feed by 11.4% compared to the existing PI control configuration.

关键词： data-driven modeling Visual BASIC Simulation Production Product design Mathematical models Quality assessment

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data-driven modeling of Comprehensive Academic Level and Physical/Moral Education for Students in Control Discipline

Data-driven Modeling of Comprehensive Academic Level and Phy...

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Chinese Control and Decision Conference, CCDC

作者： Ziyan Zhao Shuangru Li Shipeng Li College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Physical education and moral education, as two important components, have a profound impact on the overall development of students. This work deeply studies the relationship between physical/moral education and students’ comprehensive academic level through data-driven modeling. Based on students’ grades in physical/moral-related courses, three machine learning models, logistic regression, support vector machine and random forest, are constructed to realize the high-accuracy classification of the students having different comprehensive academic level. Experimental results show that the constructed data-driven models can achieve significant accuracy and stability in predicting students’ comprehensive academic level. It proves that physical education and moral education have obvious positive correlation with students’ comprehensive academic level, and thus highlights the importance of physical/moral education in students’ all-round development.

关键词： data-driven modeling Support vector machines Ethics Logistic regression Accuracy Education Predictive models

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data-driven modeling for Grid Edge IBRs: A Digital Twin Perspective of User-Defined Models

Data-driven Modeling for Grid Edge IBRs: A Digital Twin Pers...

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Annual Conference of Industrial Electronics Society

作者： Kaveri Mahapatra Bhaskar Mitra Soumya Kundu Pacific Northwest National Laboratory United States

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Recent Odessa disturbance events [1], [2] have brought attention to the challenges associated with the interaction between Inverter-Based Resources (IBRs) and the transmission and distribution system. The NERC event diagnosis report has highlighted several issues, emphasizing the need for continuous performance monitoring of these IBRs by system operators. Key areas of concern include the mismatch of control and protection performance of IBRs between the original equipment manufacturer (OEM)-provided models and field measurements. The inability to replicate the realistic response can result in incorrect reliability and resilience studies. In this paper, we developed an approach on how to emulate the behavior of an IBR using measurement data obtained for system operators to utilize in real-time and long-term planning. Two experiments are conducted in the phasor domain and electromagnetic transients (EMT) domain to emulate the behavior for grid forming and grid following inverters under various operating conditions and the effectiveness of the proposed model is demonstrated in terms of accuracy and ease of utilizing user-defined models (UDMs).

关键词： data-driven modeling Accuracy Predictive models Real-time systems Digital twins Reliability Load modeling Grid forming Testing Resilience

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data-driven modeling of Inverter-Fed Induction Motor Drives using DMDc for Faulty Conditions

Data-Driven Modeling of Inverter-Fed Induction Motor Drives ...

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IEEE International Electric Machines and Drives Conference (IEMDC)

作者： Gultekin, Muhammed Ali Zhang, Zhe Bazzi, Ali Univ Connecticut Storrs CT 06269 USA

ISBN: (纸本)9781665405102

modeling faulty behavior of systems has benefits in diagnosis and control. In this paper a data-driven method, dynamic mode decomposition with control (DMDc), is employed for modeling an inverter-fed induction machine. Results are shown and compared for two scenarios: A step input change and an inverter fault. For both cases, the algorithm can correctly predict behavior of the system. The advantage of this model is its independence from the system parameters. The results show promise for data-drivenfault diagnostics and system modeling.

关键词： data-driven modeling induction machine modeling dynamic mode decomposition

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data-driven modeling Method of Multi-Operation Impedance Identification for DFIG Based on Transfer Learning Theory

Data-Driven Modeling Method of Multi-Operation Impedance Ide...

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Energy Storage and Conversion (ISESC), CPSS & IEEE International Symposium on

作者： Yu Wang Han Li Fuqiang Zhang Zhen He Junxing Zhang Heng Nian Xiangshan Off-shore Wind Power Co. Ltd. of Guodian Power Co. Ltd Ningbo China College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350380514

ISBN: (纸本)9798350380521

The multi-operation impedance identification of the doubly fed induction generator (DFIG) is essential to analyze the DFIG-grid interaction stability considering various operating conditions. However, the existing identification methods require a substantial amount of measurement data and lack effective transferability to other DFIGs with different parameters or control structures. To address this issue, this paper proposes a data-driven modeling method of multi-operation impedance identification for DFIG based on transfer learning theory. The multi-operation impedance model is established based on the common features of the DFIGs derived from theoretical formulas. Then, transfer learning theory is adopted to enhance flexibility of the model, allowing for appropriate architectural adjustments to adapt for different DFIGs. Finally, a serial update method for measured datasets and the identification model is developed. The proposed method can significantly reduce the required data amount and improve transferability of the identification model. The experiments based on control-hardware-in-loop (CHIL) are conducted to verify the effectiveness of the proposed method.

关键词： data-driven modeling Adaptation models Analytical models Transfer learning Doubly fed induction generators Stability analysis Impedance Energy storage

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data-driven modeling of Nonlinear Delay Differential Equations with Gap Effects using SINDy

Data-Driven Modeling of Nonlinear Delay Differential Equatio...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Jiamin Xu Nazli Demirer Vy Pho Kaixiao Tian He Zhang Ketan Bhaidasna Robert Darbe Dongmei Chen University of Texas at Austin Austin TX USA Halliburton Houston TX USA Halliburton Singapore SG Singapore

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

An accurate modeling approach to predict the trajectory of a drillstring plays a critical role in drilling operation. Nonlinear Delay Differential Equations (DDEs) have been considered as an effective tool to serve the purpose. This paper introduces a novel data-driven approach to model the borehole propagation dynamics by incorporating nonlinear DDEs with Linear Complementarity Problem (LCP) using the Sparse Identification of Nonlinear Dynamics (SINDy) method. The developed model can predict borehole propagation without relying on physics-based information while retaining the same dynamics as those predicted by physics-based nonlinear DDEs. To assess the resilience of the proposed approach, we introduce noise into the dataset, demonstrating the robustness of the SINDy method. Additionally, a stability analysis of the data-driven DDEs offering insights into its reliability and potential applications.

关键词： Drilling data-driven modeling Noise Differential equations Predictive models Mathematical models Stability analysis

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Probing Signal-Based data-driven modeling of Power Electronics Smart Converter Dynamics Using Power Hardware-in-the-Loop

Probing Signal-Based Data-Driven Modeling of Power Electroni...

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作者： Guruwacharya, Nischal South Dakota State University

学位级别：Ph.D., Doctor of Philosophy

The main objective of this dissertation is to develop a generalized simulation and modeling framework for extracting dynamics of power electronic converters (PECs) with grid support functions (GSFs) and validate model accuracy through experimental comparison with physical measurements. The dynamic models obtained from this modeling framework aim to facilitate accurate dynamic analysis of a highly integrated power system comprising inverter-based resources (IBRs), specifically for stability assessment. These dynamic models helped in reducing simulation time and computational complexity, thereby enhancing efficiency. Moreover, it provides valuable insights for utilities and grid operators involved in effective system planning, operation, and *** dynamics of the current power grid are poised to undergo substantial changes due to the replacement of traditional generators and the integration of distributed energy resources (DERs) based on PECs equipped with advanced GSFs. The utilization of these smart PECs is expected to increase in the future, primarily because they conform to the voltage and frequency support requirements outlined in the Institute of Electrical and Electronics Engineers (IEEE) 1547-2018 standard. However, the dynamic behavior of PECs, particularly when providing various ancillary services, is attributed to the adoption of modern control algorithms. Consequently, the system exhibits more stochastic and nonlinear dynamics, posing significant challenges to power system stability and control. Accurate modeling of these underlying nonlinear dynamics is required to ensure the stability and reliability of converter-dominated power system (CDPS). However, the proprietary nature and unknown parameters of the PECs control systems, coupled with the increasing system size, using a traditional modeling approach to obtain full dynamics becomes increasingly challenging and computationally expensive. Therefore, new modeling techniques are needed to accuratel

关键词： data-driven modeling Power system dynamics System identification Grid support functions Inverter-based resources

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data-driven modeling of Linear Dynamical Systems with Quadratic Output in the AAA Framework

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JOURNAL OF SCIENTIFIC COMPUTING 2022年第1期91卷 16-16页

作者： Gosea, Ion Victor Gugercin, Serkan Max Planck Inst Dynam Complex Tech Syst Data Driven Syst Reduct & Identificat DRI Grp Sandtorstr 1 D-39106 Magdeburg Germany Virginia Tech Dept Math Acad Data Sci Blacksburg VA 24061 USA Virginia Tech Computat Modeling & Data Analyt Div Acad Data Sci Blacksburg VA 24061 USA

We extend the Adaptive Antoulas-Anderson (AAA) algorithm to develop a data-driven modeling framework for linear systems with quadratic output (LQO). Such systems are characterized by two transfer functions: one corresponding to the linear part of the output and another one to the quadratic part. We first establish the joint barycentric representations and the interpolation theory for the two transfer functions of LQO systems. This analysis leads to the proposed AAA-LQO algorithm. We show that by interpolating the transfer function values on a subset of samples together with imposing a least-squares minimization on the rest, we construct reliable data-driven LQO models. Two numerical test cases illustrate the efficiency of the proposed method.

关键词： data-driven modeling Model reduction Nonlinear dynamics Interpolation Least-squares fit Barycentric form

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