ENBIS-18 in Nancy2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018
The following abstracts have been accepted for this event:
Simultaneous Sparse Approximation for Variable Selection and Classification. Application to Near Infrared Spectra and Hyperspectral Images of Wood Wastes
Authors: El-Hadi Djermoune (Université de Lorraine, CNRS), Thomas Aiguier (Université de Lorraine, CNRS), Cédric Carteret (Université de Lorraine, CNRS), David Brie (Université de Lorraine, CNRS)
Primary area of focus / application: Other: Machine learning for automatic diagnosis and decision-making
Keywords: Simulatenous sparse approximation, Variable selection, Classification, NIR spectroscopy, Hyperspectral Image
Submitted at 23-May-2018 22:46 by David Brie
Authors: Joseph Voelkel (Rochester Institute of Technology)
Primary area of focus / application: Design and analysis of experiments
Keywords: Experimental design, Orthogonal array, D-efficiency, Minimum chi-square
Submitted at 28-May-2018 14:45 by Joseph Voelkel
Accepted (view paper)
Little investigation has been done for the design of such order-of-addition (OofA) experiments until our recent work. Based on an idea from Van Nostrand (1995), we define and explain a reference standard of OofA experiments by extending the idea of orthogonal arrays. (We try to show this and other ideas as simply as possible.) For strength 2 designs, we find that OofA orthogonal arrays require that the number of runs N is divisible by 12 when the number m of components exceeds 3. (For m = 3, the full design requires only 6 runs.) We consider a chi-square criterion to measure the balance of an OofA array, and show that for strength 2 designs, OofA OA’s appear to be equivalent to D-optimal designs. In many situations, a number of non-isomorphic (distinct) designs exist; in such cases we use secondary measures to determine an optimal design. We have found OofA orthogonal arrays for m = 4 and 5 when N = 12, for m = 5, 6, and (near-optimal) 7 when N = 24.
We also extend these optimal OofA designs to incorporate standard process variables so that, for example, temperature or mixing speeds may be included. Our methods can also take into account natural restrictions that the experimenter may have, such as requiring that one component is always added before another. Finally, we show and analyze examples of some recent experiments in m = 4, 5, and 6 components.
Spatio-Temporal PCA for Image Monitoring in Additive Manufacturing
Authors: Bianca Maria Colosimo (Politecnico di Milano), Marco Grasso (Politecnico di Milano)
Primary area of focus / application: Process
Secondary area of focus / application: Quality
Keywords: SPC, Additive manufacturing, PCA, Spatio-temporal, Image monitoring
Submitted at 28-May-2018 17:16 by Bianca Maria Colosimo
Mixing Natural Language Processing and Image Segmentation for Pre-Diagnosis in Medicine. Application to Breast Cancer
Authors: Stéphane Jankowski (Quantmetry), Pablo Valverde (Quantmetry), Antoine Simoulin (Quantmetry), Nicolas Bousquet (Quantmetry), Sébastien Molière (Hôpitaux Universitaires de Strasbourg), Carole Mathelin (Hôpitaux Universitaires de Strasbourg)
Primary area of focus / application: Modelling
Secondary area of focus / application: Modelling
Keywords: Deep learning, Medical imaging, Natural language processing, Breast cancer
Submitted at 28-May-2018 22:40 by Nicolas Bousquet
Statistical Engineering: A Glimpse into the Future
Authors: Geoff Vining (Virginia Tech Statistics Department)
Primary area of focus / application: Other:
Keywords: Six Sigma, Scientific method, Solving complex problems, Future of statistics
Submitted at 29-May-2018 15:43 by Geoff Vining
Accepted (view paper)
The proper model for this new discipline is chemical engineering and its systems approach for creating chemical processes based on the concept of “unit operations,” such as distillation, chemical reactor design, and heat transfer. Chemical engineering uses a systems approach to put the proper unit operations together in novel ways to build new chemical process, as well as improve existing processes, efficiently and effectively. Obviously, chemical engineering does not replace chemistry, but is rather complementary to it, figuring out how to best utilize the science of chemistry to develop large-scale chemical processes.
The statistical engineering analogs of unit operations are: data acquisition, data exploration, analysis/modeling, inference (back to the original problem), deployment of a tentative solution, and solution confirmation. The engineering challenge is how to put these tools together based on previous experience, and the unique nature of the problem at hand. Statistical engineering must combine the tools taught in university statistics curricula with the practical subject-matter knowledge based, and experience on previous successful solutions. The scientific method, properly understood, is an important key to success.
The International Statistical Engineering Association (ISEA) is a new global professional society dedicated to advancing the theory and practice of statistical engineering. The ISEA membership model is based on ENBIS. We are always looking for other people who share our vision and passion for this new discipline.
Statistical Engineering from a Corporate Perspective
Authors: William Brenneman (Procter & Gamble Company)
Primary area of focus / application: Other: Statistical Engineering
Keywords: Statistical engineering, Applied statistics, Industrial statistics, Process
Submitted at 29-May-2018 15:58 by William Brenneman