ENBIS-14 in Linz21 – 25 September 2014; Johannes Kepler University, Linz, Austria Abstract submission: 23 January – 22 June 2014
The following abstracts have been accepted for this event:
Modeling of Non-Continuous Time-Pressure Curve Classes based on Pre-Volumes
Authors: Astrid Ruck (Autoliv B.V.&Co. KG)
Primary area of focus / application: Modelling
Secondary area of focus / application: Design and analysis of experiments
Keywords: Modeling, Non-continuous curve classes, Robustness, Effects
Submitted at 20-Jun-2014 15:23 by Astrid Ruck
For the purpose of modeling the burst pressure and its corresponding time for a given pre-volume we transform the curves in a fixed environment of the bursting time. This approach can be applied to numerous curves affected by several other factors.
A Two-Step Method by Clustering and Forecasting a State Variable for Real Data Application
Authors: Thomas Lehmann (Daimler AG), Joerg Keller (Daimler AG)
Primary area of focus / application: Reliability
Secondary area of focus / application: Quality
Keywords: Forecasting, Clustering, Time series, Resampling, Automotive application
Submitted at 20-Jun-2014 17:46 by Thomas Lehmann
Accepted (view paper)
Evidence Accumulation and model-based clustering in terms of Growth Mixture Models are used as methods of clustering. The validation is done with Delete-1 Jackknife Resampling. Forecasting of detected groups is done through AR-/MA and ARIMA time series models, through Rollin Origin Cross Validation.
For the experiment a real world data set from automotive sector is chosen. We explain the benefit of group specific over object specific forecasting for transient data.
Two groups are derived, for which the decreasing state variable is predicted up to a given level on a given time frame.
When the Heart is Racing
Authors: Bernard Francq (University of Glasgow), Suzanne Lloyd (University of Glasgow), Elaine Clark (University of Glasgow), Peter Macfarlane (University of Glasgow)
Primary area of focus / application: Consulting
Secondary area of focus / application: Modelling
Keywords: Linear regression, Quantile regression, Predictive interval, ECG normalization
Submitted at 22-Jun-2014 18:30 by Bernard Francq
Two statistical approaches were undertaken to assess the effect of race, age and gender on the ECG results and to define 96% normalized intervals.
First, the well-known linear regression where the means are modelled assuming the normality of the data (with, if needed, a mathematical transformation undertaken prior to the analysis). The classical hyperbolic predictive intervals are not suitable in this context but straight predictive intervals can easily be computed from the predicted values of the linear regression (the predicted means).
Second, the quantile regression where the desired quantile is directly modelled. This approach is obviously more flexible and does not assume the normality of the data.
This presentation will compare both statistical approaches with simulations and the real data. The flexibility of the quantile regression will be discussed as well as the BLUP property of the linear regression. The ‘normalized intervals’ computed from both approaches will be compared to the observed quantiles (computed per age, race and gender) or moving quantiles with real data.
How to Regress and Predict in a Bland and Altman Plot?
Authors: Bernard Francq (Université Catholique de Louvain)
Primary area of focus / application: Metrology & measurement systems analysis
Keywords: Measurement methods comparison, (Correlated)-errors-in-variables-regressions, Bland and Altman plot, Predictive interval, Tolerance interval
Submitted at 22-Jun-2014 18:35 by Bernard Francq
To assess equivalence in method comparison studies, two main methodologies are presented separately in the literature. First, the approach based on errors-in-variables regressions that focuses on confidence intervals. Two devices are considered equivalent when they provide similar measures notwithstanding the random measurement errors.
Second, the well-known Bland and Altman approach with its agreement intervals. Two devices are considered interchangeable when the differences in their measurements are not meaningful in practice.
This presentation will explicit and compare both approaches. Tolerance intervals will be presented as better alternatives than agreement intervals and two new consistent regressions are proposed to compute predictive intervals in a Bland and Altman plot. The two methodologies will be reconciled and their similarities discussed with simulations and real data.
It will be then concluded that errors-in-variables can (unfortunately) not be avoided in method comparison studies, although the Bland and Altman approach was, initially, proposed and applied to avert the complexity of this statistical method.
Multilevel Functional Principal Component Analysis of Façade Sound Insulation Data
Authors: Raffaele Argiento (CNR-IMATI), Pier Giovanni Bissiri (CNR-IMATI), Antonio Pievatolo (CNR-IMATI), Chiara Scrosati (CNR-ITC)
Primary area of focus / application: Modelling
Keywords: Functional data analysis, Mixed effects models, Sound insulation data, Principal component analysis
Submitted at 22-Jun-2014 20:06 by Raffaele Argiento
In these studies, it is important to assess the within and between group variability in the measurements of façade sound insulation. Moreover, in the engineering literature it is known that the indices of sound insulation are more variable at low frequencies, compared to higher frequencies. Therefore, we employ a multilevel
functional principal component analysis (FPCA, Di et al~2009) to decompose the
functional variance both at the data and at the group level.
Our method allows ranking the performance of the operators on the basis of their measurements' variability and their different performances at either low frequency (relative high variability) and high frequency (relative low variability) spectra.
Fuzzy Logic in the Assessment of Alternative Measurement Systems
Authors: Magdalena Diering (Poznań University of Technology), Krzysztof Dyczkowski (Adam Mickiewicz University of Poznań), Agnieszka Kujawińska (Poznań University of Technology)
Primary area of focus / application: Six Sigma
Secondary area of focus / application: Process
Keywords: Quality control, Alternative measurement system analysis, Cross tab method, Fuzzy logic
Submitted at 22-Jun-2014 23:12 by Magdalena Diering
Accepted (view paper)
Alternative control is a special case of quality control. It can be performed by measuring or checking and classifying the object (product) into one of a number of states (in the specific case – into one of two, for example: good/bad or OK/No OK).
To assure that quality control of manufacturing is a reliable process and its outcomes are on accepted level, measurement system must be evaluated (variation of the measurement system should be known and accepted). There are many procedures to assess the capability and reliability of measurement system.
The paper presents the directions of attribute measurement systems analysis (MSA) development. The study pointed out the possibility of using the fuzzy logic elements in this type of measurement systems. The work presents the basic methods and procedures used in the MSA studies, and also pointed out aspects of their integration with fuzzy logic tools. Case study is presented.