A Data-Driven Approach for Executing the CG Method on Reconfigurable High-Performance Systems

2013 ◽  
pp. 171-182 ◽  
Author(s):  
Fabian Nowak ◽  
Ingo Besenfelder ◽  
Wolfgang Karl ◽  
Mareike Schmidtobreick ◽  
Vincent Heuveline
Keyword(s):  
High Performance ◽  
Data Driven ◽  
Data Driven Approach ◽  
High Performance Systems

scholarly journals On-sky results for adaptive optics control with data-driven models on low-order modes

2020 ◽  
Vol 498 (3) ◽  
pp. 3228-3240
Author(s):  
Baptiste Sinquin ◽  
Léonard Prengère ◽  
Caroline Kulcsár ◽  
Henri-François Raynaud ◽  
Eric Gendron ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
High Performance ◽  
Temporal Dynamics ◽  
Data Driven ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Integral Action ◽  
High Performance Systems ◽  
Performance Results ◽  
Low Order

ABSTRACT Dedicated tip–tilt loops are commonly implemented on adaptive optics (AO) systems. In addition, a number of recent high-performance systems feature tip–tilt controllers that are more efficient than the integral action controller. In this context, linear–quadratic–Gaussian (LQG) tip–tilt regulators based on stochastic models identified from AO telemetry have demonstrated their capacity to effectively compensate for the cumulated effects of atmospheric disturbance, windshake and vibrations. These tip–tilt LQG regulators can also be periodically retuned during AO operations, thus allowing to track changes in the disturbances’ temporal dynamics. This paper investigates the potential benefit of extending the number of low-order modes to be controlled using models identified from AO telemetry. The global stochastic dynamical model of a chosen number of turbulent low-order modes is identified through data-driven modelling from wavefront sensor measurements. The remaining higher modes are modelled using priors with autoregressive models of order 2. The loop is then globally controlled using the optimal LQG regulator build from all these models. Our control strategy allows for combining a dedicated tip–tilt loop with a deformable mirror that corrects for the remaining low-order modes and for the higher orders altogether, without resorting to mode decoupling. Performance results are obtained through evaluation of the Strehl ratio computed on H-band images from the scientific camera, or in replay mode using on-sky AO telemetry recorded in 2019 July on the CANARY instrument.

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 Shearlets as feature extractor for semantic edge detection: the model-based and data-driven realm

2020 ◽  
Vol 476 (2243) ◽  
pp. 20190841
Author(s):  
Héctor Andrade-Loarca ◽  
Gitta Kutyniok ◽  
Ozan Öktem
Keyword(s):  
Edge Detection ◽  
High Performance ◽  
Hybrid Methods ◽  
Tomographic Reconstruction ◽  
Data Driven ◽  
Model Based ◽  
Edge Classification ◽  
Wide Range ◽  
Data Driven Approach ◽  
Sparse Approximations

Semantic edge detection has recently gained a lot of attention as an image-processing task, mainly because of its wide range of real-world applications. This is based on the fact that edges in images contain most of the semantic information. Semantic edge detection involves two tasks, namely pure edge detection and edge classification. Those are in fact fundamentally distinct in terms of the level of abstraction that each task requires. This fact is known as the distracted supervision paradox and limits the possible performance of a supervised model in semantic edge detection. In this work, we will present a novel hybrid method that is based on a combination of the model-based concept of shearlets, which provides probably optimally sparse approximations of a model class of images, and the data-driven method of a suitably designed convolutional neural network. We show that it avoids the distracted supervision paradox and achieves high performance in semantic edge detection. In addition, our approach requires significantly fewer parameters than a pure data-driven approach. Finally, we present several applications such as tomographic reconstruction and show that our approach significantly outperforms former methods, thereby also indicating the value of such hybrid methods for biomedical imaging.

A Data-Driven Approach to Predict Hand Positions for Two-Hand Grasps of Industrial Objects

2016 ◽  
Author(s):  
Erhan Batuhan Arisoy ◽  
Guannan Ren ◽  
Erva Ulu ◽  
Nurcan Gecer Ulu ◽  
Suraj Musuvathy
Keyword(s):  
High Performance ◽  
Digital Simulation ◽  
User Involvement ◽  
Industrial Applications ◽  
Data Driven ◽  
User Input ◽  
Twin Models ◽  
Data Driven Approach ◽  
Cad Models ◽  
Rapid Generation

The wide spread use of 3D acquisition devices with high-performance processing tools has facilitated rapid generation of digital twin models for large production plants and factories for optimizing work cell layouts and improving human operator effectiveness, safety and ergonomics. Although recent advances in digital simulation tools have enabled users to analyze the workspace using virtual human and environment models, these tools are still highly dependent on user input to configure the simulation environment such as how humans are picking and moving different objects during manufacturing. As a step towards, alleviating user involvement in such analysis, we introduce a data-driven approach for estimating natural grasp point locations on objects that human interact with in industrial applications. Proposed system takes a CAD model as input and outputs a list of candidate natural grasping point locations. We start with generation of a crowdsourced grasping database that consists of CAD models and corresponding grasping point locations that are labeled as natural or not. Next, we employ a Bayesian network classifier to learn a mapping between object geometry and natural grasping locations using a set of geometrical features. Then, for a novel object, we create a list of candidate grasping positions and select a subset of these possible locations as natural grasping contacts using our machine learning model. We evaluate the advantages and limitations of our method by investigating the ergonomics of resulting grasp postures.

scholarly journals Designed high-performance lithium-ion battery electrodes using a novel hybrid model-data driven approach

2021 ◽  
Vol 36 ◽  
pp. 435-458
Author(s):  
Xinlei Gao ◽  
Xinhua Liu ◽  
Rong He ◽  
Mingyue Wang ◽  
Wenlong Xie ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Hybrid Model ◽  
High Performance ◽  
Lithium Ion ◽  
Data Driven ◽  
Model Data ◽  
Data Driven Approach

Data Driven Approach to Forecast Building Occupant Complaints

2020 ◽  
Author(s):  
Sena Assaf ◽  
Mohamad Awada ◽  
Issam Srour
Keyword(s):  
Data Driven ◽  
Data Driven Approach

Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

2012 ◽  
Author(s):  
Michael Ghil ◽  
Mickael D. Chekroun ◽  
Dmitri Kondrashov ◽  
Michael K. Tippett ◽  
Andrew Robertson ◽  
...  
Keyword(s):  
Dynamic Models ◽  
Data Driven ◽  
Stochastic Dynamic ◽  
Extended Range ◽  
Data Driven Approach ◽  
Low Dimensional
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