ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

My abstracts

 

The following abstracts have been accepted for this event:

  • Which Runs to Skip in Two Level Factorial Designs when not all Can Be Performed

    Authors: Xavier Tort-Martorell (UPC Universitat Politécnica de Catalunya, Barcelona Tech), Rafel Xampeny (UPC Universitat Politecnica de Catalunya), Pere Grima (UPC Universitat Politécnica de Catalunya)
    Primary area of focus / application: Design and analysis of experiments
    Keywords: Factorial designs, Missing values, Negligible interactions, Saving runs, Effects’ variance, Effects’ correlation
    Submitted at 11-Apr-2018 13:16 by Xavier Tort-Martorell
    Accepted
    4-Sep-2018 16:20 Which Runs to Skip in Two Level Factorial Designs when not all Can Be Performed
    When a two level factorial design allows estimating contrasts that can be considered negligible from scratch, as high order interactions, it is possible to omit some runs and later estimate their values by equating to zero the expressions of some or all of that contrasts (Draper and Stoneman, 1964). This article presents the combinations of runs to be omitted in 8 and 16 run two level factorial designs so that the responses can be estimated in such a way as to produce the least possible impact on the desired properties of the estimated contrasts: low and equal variance and the smallest possible correlation among them.
  • Change-Point Detection Methods in the Online Context

    Authors: Nassim Sahki (Université de Lorraine, IECL, Inria BIGS), Sophie Mézières (Université de Lorraine, IECL, Inria BIGS), Anne Gégout-Petit (Université de Lorraine, IECL, Inria BIGS)
    Primary area of focus / application: Other: Applied statistics.
    Secondary area of focus / application: Modelling
    Keywords: Online change-point detection, CUSUM and Shiryaev-Roberts, Simulation study, Dynamic threshold detection, Non-parametric approache, Test statistic
    Submitted at 13-Apr-2018 11:32 by nassim sahki
    Accepted (view paper)
    3-Sep-2018 15:30 Change-Point Detection Methods in the Online Context
    We propose a work based on the two classical methods, CUSUM and Shiryaev-Roberts, for the online change-point detection. These methods are based on sequential tests of likelihood ratio using recursive detection statistics.
    Our work is mainly built on the non-parametric versions of these approaches, suggested in the article [Tartakovsky, A. G. and all (2013)], by replacing the likelihood.
    We have proposed an extension of the detection procedures by using dynamic threshold detection which depends on time, and other rule-stops upon which detection procedures are based in the purpose of controlling especially the False Alarm Rate (FAR) and Average Delay of Detection (ADD).
    In order to assess the methods, we have performed an extensive simulation study in the purpose to exhibit the effect of the different parameters of the statistics and that of the stopping rules on the FAR and the ADD.
  • A Multiresolution Framework for Building Industrial Soft Sensors

    Authors: Tiago Rato (University of Coimbra), Marco Reis (University of Coimbra)
    Primary area of focus / application: Modelling
    Keywords: Multiresolution data, Partial least squares, Dynamic partial least squares, Stepwise regression
    Submitted at 18-Apr-2018 16:42 by Tiago Rato
    Accepted
    3-Sep-2018 14:00 A Multiresolution Framework for Building Industrial Soft Sensors
    The quality features of industrial processes are typically obtained offline with a considerable delay and by resort to expensive equipment. Therefore, in order to make the quality assessment faster and decrease the experimental burden in the routine quality laboratories, a variety of data-driven soft sensors have been developed. These models are expected to capture the dominant relationships between the different process variables (predictors) and the relevant quality variables (responses), while accounting for their high-dimensional, dynamic and multiresolution structure. The first two characteristics are often addressed by standard latent variables models or variable selection algorithms. However, as current methodologies tacitly assume that all variables carry delocalized information about the process on exactly the same time period (i.e., they assume that the variables have the same resolution), the multiresolution aspect is usually disregarded. Furthermore, multiresolution is often confused with a multirate problem: multiresolution occurs when variables have different levels of granularity due to, for instance, automatic averaging operations over certain time windows; while a multirate structure is caused by the existence of different sampling rates, without changing the granularity of the variables.

    The inconsistent use of the variables’ resolution limits the application of current soft sensor approaches to multiresolution data structures, namely their interpretational and predictive capabilities. Furthermore, even if the data is available at a single-resolution, it is not guaranteed that the native resolution of the predictors is the most appropriate for process modelling. Therefore, soft sensor methodologies must address not only the selection of the best subset of predictors to be included in the model, but also the optimum resolution to adopt for each predictor. For this purpose, novel feature selection algorithms are proposed for continuous and batch processes. The performance of the developed multiresolution soft sensors is comparatively assessed against their single-resolution counterparts. The results demonstrate that the optimized multiresolution soft sensors are bounded to be at least as good as current single-resolution methodologies and that they are almost always substantially better.
  • Predicting CO2 Emissions from Maritime Transport with Feature-Oriented Methods

    Authors: Dario Bocchetti (Grimaldi Group), Christian Capezza (University of Naples Federico II), Antonio Lepore (University of Naples Federico II), Biagio Palumbo (University of Naples Federico II), Ricardo Rendall (University of Coimbra), Marco Seabra dos Reis (University of Coimbra)
    Primary area of focus / application: Process
    Keywords: Statistical pattern analysis, Profile driven techniques, Feature-oriented methods, CO2 emission monitoring
    Submitted at 20-Apr-2018 15:13 by Christian Capezza
    Accepted
    4-Sep-2018 09:20 Predicting CO2 Emissions from Maritime Transport with Feature-Oriented Methods
    Prediction of CO2 emissions from maritime transportation is both a strategic and mandatory activity for shipping companies, which, from January 2018, are forced by the European regulations to set up a system for daily Monitoring, Reporting and Verification (MRV) of emissions from their fleet. Today’s multi-sensor systems are able to acquire measurements for a massive number of variables, often collinear and with a non-stationary behaviour, that are stored every five minutes, taking the form of profiles.

    In this setting, multivariate statistical process monitoring methods commonly used for monitoring batch processes, can be applied. However, the use of those methods is sometimes not straightforward and requires considerable additional data pre-processing, such as, for example, unfolding the data to handle the three-way structure (which leads to a very large number of pseudovariables), aligning and synchronizing the data (because each voyage has a different length and therefore different number of observations need to be warped into the same domain as well as the main events of the profiles should be aligned). The results can be, indeed, influenced by pre-processing techniques as well as the order in which they are applied.

    For those reasons, feature-oriented methods were proposed, that reduce the modelling complexity and lead to more parsimonious modelling. Examples include statistical pattern analysis and profile-driven techniques. In particular, statistical pattern analysis make use of four statistical features computed for the measured data, corresponding to the first four moments. On the other hand, profile-driven features claim better performance by identifying a dictionary of features that are characteristic of each variable.

    In this work, with the goal of predicting CO2 emissions, four classes of regression methods, namely variable selection, latent variable, penalized regression, and tree-based ensembles, are compared by means of the root mean squared error resulting from 50 iterations of double cross-validation. A real case study on shipping data acquired on board of a roll-on/roll-off passenger cruise ship owned by the shipping company Grimaldi Group is presented to illustrate both the satisfactory predictive performances of the proposed methods and the ease of use, which do not require neither pre-processing nor data alignment.
  • An EWMA Control Chart for Categorical Processes with Applications to Social Network Monitoring

    Authors: Marcus Perry (University of Alabama)
    Primary area of focus / application: Process
    Secondary area of focus / application: Quality
    Keywords: Directed networks, EWMA control chart, Hierarchical graph, Multinomial process, Reciprocity, Transitivity
    Submitted at 21-Apr-2018 23:52 by Marcus Perry
    Accepted (view paper)
    5-Sep-2018 09:00 An EWMA Control Chart for Categorical Processes with Applications to Social Network Monitoring
    Statistical process control (SPC) charts have traditionally been applied to manufacturing processes; however, more recent developments highlight their application to social networks. We propose a network monitoring strategy using the exponentially-weighted moving average (EWMA) control chart, with the goal of detecting shifts in the hierarchical tendency of directed graphs over time. Such a strategy might prove useful to an organization's stakeholders when interest lies in monitoring for shifts in the general health of the organization. Although development of the proposed control chart was motivated by a network monitoring problem, our method is generally applicable to multinomial categorical processes. We study the detection performance of our proposed control chart, relative to that of the multinomial cumulative sum (CUSUM) alternative. Results suggest that if the out-of-control shift in the multinomial probabilities cannot be specified a priori, the proposed EWMA control chart should be considered as an alternative to the CUSUM strategy. Application of the proposed method is demonstrated on a real data set; namely, the open source Enron email corpus.
  • Is It Time for the Fishbone Diagram to Retire?

    Authors: Jonathan Smyth-Renshaw (Jonathan Smyth-Renshaw & Associates Ltd)
    Primary area of focus / application: Business
    Keywords: Retirement, Fishbone diagram, Conceptual model, Problem Solving
    Submitted at 22-Apr-2018 12:53 by Jonathan Smyth-Renshaw
    Accepted
    4-Sep-2018 09:20 Is It Time for the Fishbone Diagram to Retire?
    One potential outcome from any production process is failure. Failure can be analysed by using a fishbone or cause and effect diagram.The fishbone diagram was first proposed by Ishikawa in the 1950’s as a method to structure the possible root cause of failure. This approach is now, based on UK pension age, near to retirement age, so the question discussed in this paper, is it time to retire the Fishbone diagram? This paper details the development of approaches to solve failure in quality particularly, those which use the fishbone diagram. This includes an assessment of a conceptual model proposed by the author in 2012. This paper provides further development of that conceptual model to address weaknesses and shortcomings in the original detailed process.