A Model-Based, Open Architecture for Mobile, Spatially Aware Applications

OOIS 2001 ◽  
2001 ◽  
pp. 392-401 ◽  
Author(s):  
Daniela Nieklas ◽  
Bernhard Mitschang
Author(s):  
Daniela Nicklas ◽  
Matthias Großmann ◽  
Thomas Schwarz ◽  
Steffen Volz ◽  
Bernhard Mitschang

1998 ◽  
Vol 120 (1) ◽  
pp. 57-67 ◽  
Author(s):  
A. M. Shawky ◽  
M. A. Elbestawi

In this paper a real-time model-based geometric control system is proposed for workpiece accuracy in bar turning. An on-line ultrasonic measurement system that operates in the presence of cutting fluid is employed for workpiece diameter measurement. A state space mechanistic model is used to overcome the delay in the feedback loop. A Kalman Filter is used in a predictor-corrector fashion to update model predictions using on-line measurements. A full state feedback controller with an integral control action is designed and used to manipulate the tool position in real-time for machining error compensation. The performance of the proposed control strategy is evaluated through cutting experiments performed on a CNC turret lathe retrofitted with an open architecture controller.


Author(s):  
Parikshit Mehta ◽  
Laine Mears

Model based control of machining processes is aimed at improving the performance of CNC systems by using the knowledge of machining process to reduce cost, improving machining accuracy and improving overall productivity. In this paper, real time control of the machining process to maintain dimensional quality when turning a slender bar is addressed. The goal is to actively control the machining feed rate to maintain constant and predicable deflection through a combined force-stiffness model integrated to the process controller. A brief review is presented on manufacturing process models, process monitoring, and model based control strategies such as Model Predictive Control (MPC). The main objective of this paper is to outline a method for deploying such models to process control. To demonstrate this, model of the deflection of the workpiece under tool cutting forces is developed. Unknown process parameters have been calculated using series of FEA simulations and verified with basic experimental data. A simple but effective control strategy has been formulated and simulated. In the initial results, the diameter of bar is maintained within 1.04% error with controller as opposed to up to 4% error without controller. Ultimately, the goal is to deploy such control strategies in the industrial control system. With the continual development in physical understanding of machining processes and affordable computing technology (both software and hardware) coupled with Open Architecture Control (OAC) applied to CNC machine tools, such approaches are now computationally feasible. This will be an enabling factor to deploy model based control in an industrial environment. The last section discusses the proposed hardware architecture to achieve this. The paper concludes with a brief plan for the future work and a summary.


2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.


2001 ◽  
Vol 7 (S2) ◽  
pp. 578-579
Author(s):  
David W. Knowles ◽  
Sophie A. Lelièvre ◽  
Carlos Ortiz de Solόrzano ◽  
Stephen J. Lockett ◽  
Mina J. Bissell ◽  
...  

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.


Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery

Author(s):  
Jonathan Jacky ◽  
Margus Veanes ◽  
Colin Campbell ◽  
Wolfram Schulte
Keyword(s):  

2008 ◽  
Author(s):  
Ryan K. Jessup ◽  
Jerome R. Busemeyer ◽  
Joshua W. Brown

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