ENBIS-13 in Ankara

15 – 19 September 2013 Abstract submission: 5 February – 5 June 2013

My abstracts


The following abstracts have been accepted for this event:

  • European Funding

    Authors: Alessandro Di Bucchianico (Eindhoven University of Technology), Rainer Goeb (University of Würzburg)
    Primary area of focus / application: Other: Funding
    Keywords: European Union , Horizon 2020, industry-university collaboration
    Submitted at 2-Aug-2013 16:06 by Alessandro Di Bucchianico
    Accepted (view paper)
    16-Sep-2013 12:15 European Funding
    In this session we will introduce the audience to the world of European Union funding and discuss the opportunities that EU funding offers to ENBIS and its members.
    The session starts off with a discussion of the importance of obtaining EU funding from various points of view (academia, industry, European Union). Then we will present an overview of the various funding programmes. We will explain the different goals of these funding programmes, show how to obtain the relevant information and discuss.
    The second part of the session will review previous participation of ENBIS in EU funded projects and then proceed with ideas for acquiring new projects. We conclude the session with an open discussion with the audience about ideas and wishes for EU funded projects with ENBIS members.

    It will be important to embed statistical approaches into topics of wider interest from the perspective of science, society, technology, economy, and business management. The following topics seem particularly appropriate: risk management; business continuity management; data integration, big data; business intelligence and analytics, in particular supply chain analytics, customer relationship analytics; energy, in particular energy demand, energy security, energy infrastructure, electricity nets, renewable energy; counter-terrorism; cyber defence; environmental security, in particular health risks, climate change, water scarcity, disaster and catastrophe prevention, water and food security, protection of natural resources, sustainability; metrology; demography, in particular ageing population; healthcare; transportation, mobility, traffic.
  • Statistical Quality Control in a Service Environment

    Authors: Fugee Tsung (Hong Kong University of Science and Technology)
    Primary area of focus / application: Quality
    Keywords: statistical quality control, service engineering, optimization, productivity
    Submitted at 16-Aug-2013 11:53 by Fugee Tsung
    16-Sep-2013 09:30 Keynote: Statistical Quality Control in a Service Environment
    Driven by a new business environment including globalized economy, business automation, and business and technology innovations, the service sector keeps growing and now accounts for more than 50 percent of the labor force in the developed economies. It reaches as high as 80 percent in the United States and the United Kingdom. With the shift in economic focus from manufacturing to service, industrial and academic research facilities may need to apply more scientific rigor to the practices of service, such as discovering better methods to use statistics and mathematical optimization to increase quality, productivity, and efficiency to meet the challenges. This talk will focus on the development of statistical quality techniques, and discuss several technical challenges and recent extensions to the service engineering research area.
  • Probability Management: Unambiguous Uncertainty

    Authors: Sam Savage (Stanford University)
    Primary area of focus / application: Finance
    Keywords: Risk modeling, Portfolios in energy and pharmaceutics
    Submitted at 22-Aug-2013 18:01 by Sam Savage
    18-Sep-2013 11:45 Keynote: Probability Management: Unambiguous Uncertainty
    A new trend in risk modeling involves networking analytical systems together through data bases of simulated scenarios. The SIPmath open standard from the non-profit ProbabilityManagement.org is a cross platform approach to managing such scenarios.
    · It is actionable, in that uncertainties may be used in interactive calculations by decision makers themselves, taking the meeting room beyond Power Point.
    · It is additive, in that the results of legacy analytical systems may be aggregated across the enterprise to create consolidated risk statements.
    · It is auditable, in that uncertainties are represented as unambiguous data including provenance with respect to accuracy and security.

    Interactive simulation examples demonstrated in Excel, without macros or add-ins will include portfolios of energy projects, econometric forecasting models, and simple universal calculators for functions of random variables.
  • Robust Design and Design for Robust Processes

    Authors: David Steinberg (Tel Aviv University)
    Primary area of focus / application: Design and analysis of experiments
    Keywords: robust parameter design, robust processes, experimental design, process improvement
    Submitted at 7-Sep-2013 11:38 by David Steinberg
    17-Sep-2013 12:30 Robust Design and Design for Robust Processes
    George Box introduced the term "robust" to the statistical lexicon to refer to methods and procedures that are not overly sensitive to assumptions. He applied this concept to experimental design, as well, highlighting the need to consider varied, and sometimes conflicting, aims and objectives when planning an experiment. In the 1980's, robust parameter design became a major topic in industrial statistics. There are many commonalities between the desire for robust experiments and for experiments that promote robust processes. Much of my own research has focused on various aspects of robustness in both DOE and process improvement. In this talk I will review some of that work, beginning with Box's ideas and then extending to some of my contributions. Among the topics that will be covered are: model-robust experimental design, follow-up designs, robust parameter design, design for GLM's and design of computer experiments.
  • Exploiting Expert Knowledge in Chemometrics

    Authors: Wouter Saeys (KU Leuven)
    Primary area of focus / application: Modelling
    Keywords: chemometrics, expert knowledge, multivariate calibration, correlations
    Submitted at 7-Sep-2013 13:02 by Wouter Saeys
    16-Sep-2013 17:00 Exploiting Expert Knowledge in Chemometrics
    Chemometrics is the science of relating measurements made on a chemical system or process to the state of the system via application of statistical methods. As these measurements are typically done with spectroscopic sensors, there are typically many, highly correlated predictor variables leading to multicollinearity problems in the model estimation. Thanks to its ability to efficiently cope with this multicollinearity by capturing the covariance between predictor and predicted variables in a small number of latent variables, Partial Least Squares (PLS) regression has become by far the most popular regression method in chemometrics. However, the success of the conventional PLS approach highly depends on the availability of a ‘representative calibration data set’, which covers all possible variations and covariations which can be expected at the prediction stage. When the concentration of the known interferents and their correlation with the analyte of interest change in a fashion which is not covered in the calibration set, the predictive performance of the multivariate calibration models may can deteriorate. However, in most cases some expert knowledge is available about the system at hand, which could be exploited in the model building to reduce the sensitivity to unspecific correlations. Different types of expert knowledge and methods for efficiently incorporating these in the multivariate calibration models are presented and their potential to robustify the models is discussed.
  • Integrated Powertrain Optimization with Statistical Techniques

    Authors: Edwige Castagna (General Motors Powertrain Europe)
    Primary area of focus / application: Process
    Secondary area of focus / application: Design and analysis of experiments
    Keywords: Model Based Calibration, Synergistic Optimization, design of experiments, engine performance
    Submitted at 7-Sep-2013 13:06 by Edwige Castagna
    17-Sep-2013 14:45 Integrated Powertrain Optimization with Statistical Techniques
    Synergistic Optimization of complex systems (characterized by several electronic variables) has been becoming more widely used in combination with DoE techniques in order to calibrate a performing
    combined system with a high number of parameters. This technology is famous as MBC Model Based Calibration. The ability to identify an effective engineer strategy together with DoE approach, provide data that will be modeled and optimized to calibrate all engine performances. During my past years I carried out a methodological task to refine the engine testing introducing the concepts
    of measurement uncertainty, statistical data analysis, modeling and optimization. This new calibration process required to evolve from a traditional one factor- at-time experimental approach to the modern DoE techniques. The speech will present this MBC process.