25 research outputs found
Integrated Experimental design and parameter optimization under uncertain process circumstances
Increasing the artificial neural network based model building speed for supporting efficient technical diagnostics
Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations
We propose a data-driven approach to quantify the uncertainty of models
constructed by kernel methods. Our approach minimizes the needed distributional
assumptions, hence, instead of working with, for example, Gaussian processes or
exponential families, it only requires knowledge about some mild regularity of
the measurement noise, such as it is being symmetric or exchangeable. We show,
by building on recent results from finite-sample system identification, that by
perturbing the residuals in the gradient of the objective function, information
can be extracted about the amount of uncertainty our model has. Particularly,
we provide an algorithm to build exact, non-asymptotically guaranteed,
distribution-free confidence regions for ideal, noise-free representations of
the function we try to estimate. For the typical convex quadratic problems and
symmetric noises, the regions are star convex centered around a given nominal
estimate, and have efficient ellipsoidal outer approximations. Finally, we
illustrate the ideas on typical kernel methods, such as LS-SVC, KRR,
-SVR and kernelized LASSO.Comment: 18 figure
A simultaneous localization and mapping algorithm for sensors with low sampling rate and its application to autonomous mobile robots
Surface resistance of ESD-protected worksurfaces - measurement, modelling and estimation considerations
Enabling cross-sectorial, circular economy transition in SME via digital platform integrated operational services
”Big Data” Initiative as an IT Solution for Improved Operation and Maintenance of Wind Turbines
Production trend identification and forecast for shop-floor business intelligence
The paper introduces a methodology to define production trend classes and also the results to serve with trend prognosis in a given manufacturing situation. The prognosis is valid for one, selected production measure (e.g. a quality dimension of one product, like diameters, angles, surface roughness, pressure, basis position, etc.) but the applied model takes into account the past values of many other, related production data collected typically on the shop-floor, too. Consequently, it is useful in batch or (customized) mass production environments. The proposed solution is applicable to realize production control inside the tolerance limits to proactively avoid the production process going outside from the given upper and lower tolerance limits. The solution was developed and validated on real data collected on the shop-floor; the paper also summarizes the validated application results of the proposed methodology. © 2016, IMEKO-International Measurement Federation Secretariat. All rights reserved