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Corrections to “data-driven modeling of Mach-Zehnder Interferometer-Based Optical Matrix Multipliers”

Journal of Lightwave Technology 2024年第7期42卷 2632-2632页

作者： Ali Cem Siqi Yan Yunhong Ding Darko Zibar Francesco Da Ros Department of Electrical and Photonics Engineering Technical University of Denmark Lyngby Denmark School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan China

Presents corrections to the paper, (data-driven modeling of Mach-Zehnder Interferometer-Based Optical Matrix Multipliers).

关键词： data-driven modeling Optical interferometry Adaptive optics

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Reduced-order model based dynamic tracking for soft manipulators: data-driven LPV modeling, control design and experimental results

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CONTROL ENGINEERING PRACTICE 2023年第1期138卷

作者： Li, Shijie Nguyen, Anh-Tu Guerra, Thierry-Marie Kruszewski, Alexandre Univ Polytech Hauts De France LAMIH Lab UMR CNRS 8201 Valenciennes France INSA Hauts De France Valenciennes France Univ Lille CRIStAL Lab DEFROST team INRIACent LilleUMR CNRS 9189 Villeneuve Dascq France

We propose a generic nonlinear reduced-order tracking control method for elastic soft robots. To this end, a new linear parameter varying (LPV) control framework is developed using the data collected from the soft robots. Specifically, for LPV modeling we first derive a nonlinear robot model, which is large-scale by nature, using finite element methods (FEM). Then, a proper orthogonal decomposition (POD) algorithm is used to generate a set of linearized reduced-order models, representing the local behaviors of the soft robot at different operating points within the workspace. Via a unified POD projector, not only the order of these linearized models can be significantly reduced but also their mechanical structure and stability properties can be preserved for LPV modeling and control design. Next, using radial basis function (RBF) networks, we propose an iterative training method to build the LPV robot model by interpolating a subset of selected linearized models with a specified interpolation error. For LPV control design, the equivalent-input-disturbance (EID) concept is exploited to develop a dynamic tracking control scheme, which is composed of three core components: feedforward control, disturbance-estimator control and feedback control. The feedforward control is designed to account for the effects of the trajectory reference and the time-varying affine term, issued from the FEM-based model linearization. The disturbance-estimator control is obtained from a generalized proportional integral LPV observer, which also provides the estimated reduced-order states for feedback control. The observer-based feedback control design is reformulated as a convex optimization problem under linear matrix inequality (LMI) constraints. The globally uniformly ��& INFIN;stability of the closed-loop LPV robot model is guaranteed by means of Lyapunov stability theory. Experimental tests are conducted with a soft Trunk robot, inspired by the elephant trunk, under several scenari

关键词： Soft robots LPV control Motion control data-driven modeling Model order reduction RBF networks

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A data-driven modeling Method for Power Quality Disturbance sources in Distribution Networks

A Data-driven Modeling Method for Power Quality Disturbance ...

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International Conference on Power System Technology (POWERCON)

作者： Dandan Feng Tongxun Wang Shengjun Zhou Yi Li Guo-Qiang Su State Key Laboratory of Advanced Power Transmission Technology Global Energy Interconnection Research Institute Co. Ltd Beijing China State Grid Shandong Electric Power Research Institute State Grid Shandong Electric Power company jinan China

ISBN: (纸本)9781665407380

With the large-scale access of distributed energy, the power quality of the distribution network has attracted more and more attention. The power quality disturbance source modeling of the distribution network is the prerequisite for analyzing the influence of the disturbance source on the power grid, identifying and locating the disturbance source, and improving the disturbance source management. In recent years, power companies have extensively carried out online power quality monitoring and accumulated massive amounts of power quality disturbance source operating data, which lays a good foundation for disturbance source modeling. This paper proposes a data-driven power quality disturbance source modeling method for distribution networks, including data-driven modeling procedures, data preprocessing methods, timing and statistical characteristic parameters acquisition methods. Compared with the traditional model method based on mechanism simulation, the data-driven model has the advantages of continuous improvement and complete characteristics. Finally, an example of a photovoltaic scenario verifies that the method has good convergence and engineering practicability.

关键词： Photovoltaic systems data-driven modeling Analytical models Power quality data preprocessing Distribution networks Power grids

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data-driven spatiotemporal modeling for structural dynamics on irregular domains by stochastic dependency neural estimation

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2023年 404卷

作者： Wen, Ziming Li, Yu Wang, Hu Peng, Yong Hunan Univ Coll Mech & Vehicle Engn State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China Beijing Inst Technol Shenzhen Automot Res Inst Shenzhen 518000 Peoples R China Cent South Univ Sch Traff & Transportat Engn Key Lab Traff Safety Track Minist Educ Changsha 410000 Peoples R China Cent South Univ Joint Int Res Lab Key Technol Rail Traff Safety Changsha 410000 Peoples R China Hunan Univ Coll Mech & Vehicle Engn State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China

Numerical simulations for spatiotemporal processes involving material, geometrical and contact nonlinearities might be computationally prohibitive for many-evaluation applications. Considering the strong nonlinearity of dynamic processes, the prediction of high-dimensional spatiotemporal responses remains challenging for most surrogate-based models. To address such issues, a Stochastic Dependency Neural Estimator (SDNE) is proposed to construct the mapping from the input parameter domain to the high-dimensional spatiotemporal response domain, revealing the underlying relationship between the high-dimensional regression estimation and manifold distribution hypothesis. Moreover, Graph Neural Networks (GNN) and Temporal Convolutional Neural Networks (TCN) are integrated to capture nonlinear features from irregular geometries and time series simultaneously. Two high-speed impact cases involving large deformation and highly nonlinear transients are investigated to validate the proposed method. The proposed SDNE achieves highly accurate prediction compared with Finite Element (FE) evaluations. To further validate the practical application of the proposed method, multi-objective crashworthiness optimizations are performed with the proposed SDNE. The results show that the proposed SDNE provides reliable predictions for spatiotemporal dynamics (c) 2022 Elsevier B.V. All rights reserved.

关键词： data-driven modeling Spatiotemporal dynamics Deep learning Graph neural networks

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Predictive Simulations of the Impedance-Matched Multi-Axis Test Method Using data-driven modeling

Predictive Simulations of the Impedance-Matched Multi-Axis T...

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作者： Moreno, Kevin Joel Virginia Tech | University

Environmental testing is essential to certify systems to withstand the harsh dynamic loads they may experience in their service environment or during transport. For example, satel- lites are subjected to large vibration and acoustic loads when transported into orbit and need to be certified with tests that are representative of the anticipated loads. However, tra- ditional certification testing specifications can consist of sequential uniaxial vibration tests, which have been found to severely over- and under-test systems needing certification. The recently developed Impedance-Matched Multi-Axis Test (IMMAT) has been shown in the literature to improve upon traditional environmental testing practices through the use of multi-input multi-output testing and impedance matching. Additionally, with the use of numerical models, predictive simulations can be performed to determine optimal testing pa- rameters. Developing an accurate numerical model, however, requires precise knowledge of the system’s dynamic characteristics, such as boundary conditions or material properties. These characteristics are not always available and would also require additional testing for verification. Furthermore, some systems may be extremely difficult to model using numerical methods because they contain millions of finite elements requiring impractical times scales to simulate or because they were fabricated before mainstream use of computer aided drafting and finite element analysis but are still in service. An alternative to numerical modeling is data-driven modeling, which does not require knowledge of a system’s dynamic characteris- tics. The Continuous Residue Interpolation (CRI) method has been recently developed as a novel approach for building data-driven models of dynamical systems. CRI builds data- driven models by fitting smooth, continuous basis functions to a subset of frequency response function (FRF) measurements from a dynamical system. The resulting fitted basis functions ca

关键词： Environmental Testing data-driven modeling MIMO Testing Thesis

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DEM data-driven modeling of repose angle of granular materials

DEM data-driven modeling of repose angle of granular materia...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Zhou Hu Xiaoyan Liu Cancan Chu College of Electrical and Information Engineering Hunan University Changsha China

ISBN: (数字)9781728182162

ISBN: (纸本)9781728182179

Repose angle is an important property of granular materials and is usually simulated using Discrete Element Method (DEM). However, DEM simulation is computationally intensive and is thus unsuitable for online applications where parameters are frequently changed. To solve this problem, we propose a DEM data-driven modeling method for fast prediction of repose angle. Firstly, variables affecting the repose angle are analyzed; by Latin hypercube sampling of parameter spaces, 100 sets of DEM simulations are performed to generate data of repose angle. Based on these data, a support vector machine (SVM) model is then established and trained for fast prediction of repose angle under various conditions. Tests and comparison show that the reposed angle predicted by the SVM model is close to the DEM simulation result while the required computing time is greatly decreased (from 43.8 hours to 0.17 seconds), and it outperforms BP neural network and Kriging interpolation method in terms of prediction accuracy. The SVM model for repose angle is also verified by physical experiments, with prediction error less than ± 1 °. The established model can replace DEM, and is suitable for applications where fast prediction of repose angle is required.

关键词： Support vector machines Predictive models Computational modeling Friction data-driven modeling Optimization Kernel

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Computational applications using data driven modeling in process Systems: A review

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DIGITAL CHEMICAL ENGINEERING 2023年 8卷

作者： Bishnu, Sumit K. Alnouri, Sabla Y. Al-Mohannadi, Dhabia M. IIT Madras Res Pk Detect Technol Chennai 600113 India Qatar Univ Coll Engn Gas Proc Ctr POB 2713 Doha Qatar Texas A&M Univ Qatar Dept Chem Engn PO 23874 Doha Qatar

modeling and optimization of various processes enable more efficient operations and better planning activities for new process developments. With recent advances in computing power, data driven models, such as Machine Learning (ML), are being extensively applied in many areas of chemical engineering topics. Compared to mechanistic models that often do not reflect the realities of field conditions and the high costs associated with them, these techniques are relatively easier to implement. data-driven models generated via ML techniques can be regularly updated, thereby giving an accurate picture of the system. Due to these inherent benefits, such tools are increasingly gaining a lot of traction in process systems. Even though data-driven models have the potential to be used as a replacement for traditional optimization tools that can be implemented in various process industries, it was found that applications of such models in process systems were quite limited to reactor modeling, molecular design, as well as safety, and relatability. The challenge still exists for data-driven modeling due to the lack of specialized tools tailored for macro systems and scale up. Most datasets were found to be derived from experimental studies which are limited in nature and only fit into microsystems. Hence, this paper provides a state of the art review on recent applications for data driven modeling research in process systems, and discusses the prominent challenges and future outlooks that were observed.

关键词： modeling Optimization Machine learning data-driven modeling

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data-driven Online modeling and Parameter Estimation of Highway Traffic Flow

Data-driven Online Modeling and Parameter Estimation of High...

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Chinese Automation Congress (CAC)

作者： Qing Liu Heng Deng Liguo Zhang Faculty of Information Technology Beijing University of Technology Beijing China

Traffic flow models are an important tool to describe highway traffic conditions. With the advent of the big data era, it is necessary to develop a data-driven model to identify the spatial-temporal characteristics of highway traffic flow from partially observed and noisy traffic data. For such a purpose, we propose a data-driven modeling method of highway traffic flow using Dynamic Mode Decomposition (DMD) algorithms. To accurately describe highway traffic flow, a recursive method for updating model parameters is proposed. A small weight term for the historical data and a penalty term for the cost function are considered, which can be more accurate and suitable. Finally, extensive numerical simulations are conducted to validate the effectiveness of the recursive data-driven modeling method. The results show that the proposed recursive algorithm has high accuracy in reconstructing traffic states compared with classical DMD. It may also have the potential to predict the highway traffic flow in real time.

关键词： Road transportation data-driven modeling Parameter estimation Heuristic algorithms Prediction algorithms Numerical simulation Real-time systems

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data-driven modeling and Pattern Recognition of Dynamical Systems

Data-Driven Modeling and Pattern Recognition of Dynamical Sy...

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作者： Chattopadhyay, Pritthi PennState University Libraries

学位级别：Doctor of Philosophy

Human-engineered complex systems need to be monitored consistently to ensuretheir safety and efficiency, which might be affected due to degradation over timeor unanticipated disturbances. For systems that change at a fast time scale, insteadof active health monitoring, preventative system design is more feasible andeffective. Both active health monitoring and preventative system design can bedone using physics-based or data-driven models. In comparison to physics-basedmodels, data-driven models do not require knowledge of the underlying systemdynamics; they determine the relation between the relevant input and output variablesfrom a training data set. This is useful when there is lack of understandingof the system dynamics or the developed models are inadequate. One such scenariois combustion, where the difficulties include nonlinear dynamics involvingseveral input parameters; existence of bifurcations in the dynamic behavior andextremely high sensitivity of the combustor behavior to even small changes insome of the design parameters. Similarly, for batteries, sufficient knowledge of theelectrochemical characteristics is necessary to develop models for parameter identification at different operating points of the nonlinear battery dynamics. Thisdissertation develops dynamic data-driven models for combustor design and batteryhealth monitoring, using concepts of machine learning and statistics, whichdo not require much knowledge of the underlying system *** the performance of a data-driven algorithm depends on many factors namely:1. Availability of training data which covers all events of interest. For applicationsinvolving time series data, each individual time series must also besufficiently long, to encompass the dynamics of the underlying system foreach event.2. The quality of extracted features, i.e. whether they capture all the informationabout the system.3. The relation between the relevant input and output variables remaining constantduring the time t

关键词： data-driven modeling time series analysis

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data driven modeling Social Media Influence using Differential Equations

Data Driven Modeling Social Media Influence using Differenti...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Bailu Jin Weisi Guo Cranfield University Cranfield Bedford UK

Individuals modify their opinions towards a topic based on their social interactions. Opinion evolution models conceptualize the change of opinion as a uni -dimensional continuum, and the effect of influence is built by the group size, the network structures, or the relations among opinions within the group. However, how to model the personal opinion evolution process under the effect of the online social influence as a function remains unclear. Here, we show that the uni -dimensional continuous user opinions can be represented by compressed high-dimensional word embeddings, and its evolution can be accurately modelled by an ordinary differential equation (ODE) that reflects the social network influencer interactions. We perform our analysis on 87 active users with corresponding influencers on the COVID-19 topic from 2020 to 2022. The regression results demonstrate that 99% of the variation in the quantified opinions can be explained by the way we model the connected opinions from their influencers. Our research on the COVID-19 topic and for the account analysed shows that social media users primarily shift their opinion based on influencers they follow (e.g., model explains for 99% variation) and self-evolution of opinion over a long time scale is limited.

关键词： COVID-19 data-driven modeling Analytical models Social networking (online) Ordinary differential equations Mathematical models data mining

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