scholarly journals Data-Driven Model-Free Control of Torque-Applying System for a Mechanically Closed-Loop Test Rig Using Neural Networks

2020 ◽  
Vol 66 (5) ◽  
pp. 337-347
Author(s):  
Aida Parvaresh ◽  
Mohsen Mardani
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
High Performance ◽  
Data Driven ◽  
Test Rig ◽  
Operational Conditions ◽  
Model Free ◽  
Actuation System ◽  
Data Driven Approach ◽  
Learning Factors

This paper presents a data-driven approach that utilizes the gathered experimental data to model and control a test rig constructed for the high-powered gearboxes. For simulating a wide variety of operational conditions, the test rig should be capable of providing different speeds and torques; this is possible using a torque-applying system. For this purpose, Electro-Hydraulic Actuators (EHAs) are used. Since applying accurate torque is a crucial demand as it affects the performance evaluation of the gearboxes, precise modelling of the actuation system along with a high-performance controller are required. In order to eliminate the need to solve complex nonlinear equations of EHA that originate from friction, varying properties of flow and similar, a data-driven system based on neural networks is used for modelling. In this manner, the model of the system, which captures the whole dynamic of the system, can be obtained without any simplifying assumptions. The model is validated with experimental data, and the learning factors are set to zero to reduce the high computational costs. After that, another network of neurons is used as a controller. The performance of the proposed controller under normal conditions and in the presence of disturbances are investigated. The results show a good tracking of this controller for various reference inputs in different conditions with acceptable characteristics. Additionally, the obtained results are compared with a conventional proportional-integral-derivative (PID) controller results, and the superior features of the proposed scheme is concluded.

scholarly journals Cardiac Activation Maps Reconstruction: A Comparative Study Between Data-Driven and Physics-Based Methods

2021 ◽  
Vol 12 ◽  
Author(s):  
Amel Karoui ◽  
Mostafa Bendahmane ◽  
Nejib Zemzemi
Keyword(s):  
Neural Networks ◽  
Comparative Study ◽  
Body Surface ◽  
Data Driven ◽  
Diagnostic Tools ◽  
L1 Norm ◽  
Surface Potentials ◽  
Body Surface Potential ◽  
Data Driven Approach ◽  
Classic Technique

One of the essential diagnostic tools of cardiac arrhythmia is activation mapping. Noninvasive current mapping procedures include electrocardiographic imaging. It allows reconstructing heart surface potentials from measured body surface potentials. Then, activation maps are generated using the heart surface potentials. Recently, a study suggests to deploy artificial neural networks to estimate activation maps directly from body surface potential measurements. Here we carry out a comparative study between the data-driven approach DirectMap and noninvasive classic technique based on reconstructed heart surface potentials using both Finite element method combined with L1-norm regularization (FEM-L1) and the spatial adaptation of Time-delay neural networks (SATDNN-AT). In this work, we assess the performance of the three approaches using a synthetic single paced-rhythm dataset generated on the atria surface. The results show that data-driven approach DirectMap quantitatively outperforms the two other methods. In fact, we observe an absolute activation time error and a correlation coefficient, respectively, equal to 7.20 ms, 93.2% using DirectMap, 14.60 ms, 76.2% using FEM-L1 and 13.58 ms, 79.6% using SATDNN-AT. In addition, results show that data-driven approaches (DirectMap and SATDNN-AT) are strongly robust against additive gaussian noise compared to FEM-L1.

scholarly journals Data-Driven Modeling of Mechanical Properties of Cast Iron Using Fuzzy Logic

2020 ◽  
Author(s):  
He Tan ◽  
Vladimir Tarasov ◽  
Vasileios Fourlakidis ◽  
Attila Dioszegi
Keyword(s):  
Experimental Data ◽  
Fuzzy Logic ◽  
Cast Iron ◽  
Materials Science ◽  
Fatigue Behavior ◽  
Relevant Literature ◽  
Data Driven ◽  
Fuzzy Logic Systems ◽  
Data Driven Approach ◽  
Data Driven Modeling

For many industries, an understanding of the fatigue behavior of cast iron is important but this topic is still under extensive research in materials science. This paper offers fuzzy logic as a data-driven approach to address the challenge of predicting casting performance. However, data scarcity is an issue when applying a data-driven approach in this field; the presented study tackled this problem. Four fuzzy logic systems were constructed and compared in the study, two based solely upon experimental data and the others combining the same experimental data with data drawn from relevant literature. The study showed that the latter demonstrated a higher accuracy for the prediction of the ultimate tensile strength for cast iron.

scholarly journals A data-driven approach to modeling cancer cell mechanics during microcirculatory transport

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Peter Balogh ◽  
John Gounley ◽  
Sayan Roychowdhury ◽  
Amanda Randles
Keyword(s):  
Experimental Data ◽  
Cancer Cells ◽  
Large Deformation ◽  
Cell Lines ◽  
Cancer Cell ◽  
Membrane Model ◽  
Data Driven ◽  
Specific Cell ◽  
Model Based ◽  
Data Driven Approach

AbstractIn order to understand the effect of cellular level features on the transport of circulating cancer cells in the microcirculation, there has been an increasing reliance on high-resolution in silico models. Accurate simulation of cancer cells flowing with blood cells requires resolving cellular-scale interactions in 3D, which is a significant computational undertaking warranting a cancer cell model that is both computationally efficient yet sufficiently complex to capture relevant behavior. Given that the characteristics of metastatic spread are known to depend on cancer type, it is crucial to account for mechanistic behavior representative of a specific cancer’s cells. To address this gap, in the present work we develop and validate a means by which an efficient and popular membrane model-based approach can be used to simulate deformable cancer cells and reproduce experimental data from specific cell lines. Here, cells are modeled using the immersed boundary method (IBM) within a lattice Boltzmann method (LBM) fluid solver, and the finite element method (FEM) is used to model cell membrane resistance to deformation. Through detailed comparisons with experiments, we (i) validate this model to represent cancer cells undergoing large deformation, (ii) outline a systematic approach to parameterize different cell lines to optimally fit experimental data over a range of deformations, and (iii) provide new insight into nucleated vs. non-nucleated cell models and their ability to match experiments. While many works have used the membrane-model based method employed here to model generic cancer cells, no quantitative comparisons with experiments exist in the literature for specific cell lines undergoing large deformation. Here, we describe a phenomenological, data-driven approach that can not only yield good agreement for large deformations, but explicitly detail how it can be used to represent different cancer cell lines. This model is readily incorporated into cell-resolved hemodynamic transport simulations, and thus offers significant potential to complement experiments towards providing new insights into various aspects of cancer progression.

scholarly journals Power Grid Dynamic Performance Enhancement via STATCOM Data-Driven Control

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2361
Author(s):  
David Rivera ◽  
Daniel Guillen ◽  
Jonathan C. Mayo-Maldonado ◽  
Jesus E. Valdez-Resendiz ◽  
Gerardo Escobar
Keyword(s):  
Reactive Power ◽  
Performance Enhancement ◽  
Dynamic Performance ◽  
Data Driven ◽  
Operating Point ◽  
Voltage Profile ◽  
Static Synchronous Compensator ◽  
Model Free ◽  
Free Data ◽  
Data Driven Approach

This work proposes a data-driven approach to controlling the alternating current (AC) voltage via a static synchronous compensator (STATCOM). This device offers a fast dynamic response injecting reactive power to compensate the voltage profile, not only during load variations but also depending on the operating point established by the grid. The proposed control scheme is designed to improve the dynamic grid performance according to the defined operating point into the grid. The mathematical fundamentals of the proposed control strategy are described according to a (model-free) data-driven-based controller. The robustness of the proposed scheme is proven with several tests carried out using Matlab/Simulink software. The analysis is performed with the well-known test power system of two areas, demonstrating that the proposed controller can enhance the dynamic performance under transient scenarios. As the main strength of the present work with respect to the current state-of-the-art, we highlight the fact that no prior knowledge of the system is required for the controller implementation, that is, a model or a system representation. The synthesis of the controller is obtained in a pure numerical way from data, while it can simultaneously ensure stability in a rigorous way, by satisfying Lyapunov conditions.

scholarly journals Clustering for Data-driven Unraveling Artificial Neural Networks

2020 ◽  
Author(s):  
Felipe Farias ◽  
Teresa Ludermir ◽  
Carmelo Bastos-Filho
Keyword(s):  
Neural Networks ◽  
Statistical Significance ◽  
Feature Space ◽  
Data Driven ◽  
P Value ◽  
Proposed Model ◽  
Data Driven Approach ◽  
Benchmark Datasets ◽  
Hidden Layer ◽  
Fold Cross Validation

This work presents an investigation on how to define Neural Networks (NN) architectures adopting a data-driven approach using clustering to create sub-labels to facilitate the learning process and to discover the number of neurons needed to compose the layers. We also increase the depth of the model aiming to represent the samples better, the more in-depth it flows into the model. We hypothesize that the clustering process identifies sub-regions in the feature space in which the samples belonging to the same cluster have strong similarities. We used seven benchmark datasets to validate our hypothesis using 10-fold cross validation 3 times. The proposed model increased the performance, while never decreased it, with statistical significance considering the p-value $< 0.05$ in comparison with a Multi-Layer Perceptron with a single hidden layer with approximately the same number of parameters of the architectures found by our approach.

A data-driven approach to weather forecast using convolutional neural networks

2020 ◽  
Author(s):  
Orlando Micolini ◽  
Luis Orlando Ventre ◽  
Agustin Martina ◽  
Ruben Esteban Ayme ◽  
Nestor J. Ortmann ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Weather Forecast ◽  
Data Driven ◽  
Data Driven Approach
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