14,807 research outputs found
An Overview of Backtrack Search Satisfiability Algorithms
Propositional Satisfiability (SAT) is often used as the underlying model for a significan
Interactive analysis of high-dimensional association structures with graphical models
Graphical chain models are a capable tool for analyzing multivariate data. However, their practical use may still be cumbersome in some respect since fitting the model requires the application of an intensive selection strategy based on the calculation of an enormous number of different regressions. In this paper, we present a computer system especially designed for the calculation of graphical chain models which is not only planned to automatically carry out the model search but also to visualize the corresponding graph at each stage of the model fit on request by the user. It additionally allows to modify the graph and the model fit interactively
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
Parametric macromodeling of lossy and dispersive multiconductor transmission lines
We propose an innovative parametric macromodeling technique for lossy and dispersive multiconductor transmission lines (MTLs) that can be used for interconnect modeling. It is based on a recently developed method for the analysis of lossy and dispersive MTLs extended by using the multivariate orthonormal vector fitting (MOVF) technique to build parametric macromodels in a rational form. They take into account design parameters, such as geometrical layout or substrate features, in addition to frequency. The presented technique is suited to generate state-space models and synthesize equivalent circuits, which can be easily embedded into conventional SPICE-like solvers. Parametric macromodels allow to perform design space exploration, design optimization, and sensitivity analysis efficiently. Numerical examples validate the proposed approach in both frequency and time domain
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