ENBIS: European Network for Business and Industrial Statistics
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ENBIS14 in Linz
21 – 25 September 2014; Johannes Kepler University, Linz, Austria Abstract submission: 23 January – 22 June 2014The following abstracts have been accepted for this event:

Planning Efficient Paths for Spatial Field Observation by an Autonomous Agent
Authors: Carolina Sotto (CNRS  UNSA), MariaJoao Rendas (CNRS  UNSA)
Primary area of focus / application: Design and analysis of experiments
Keywords: Experimental designs, Spatial observation, IMSE
Observation of natural fields traditionally resorts to networks of fixed instrumented buoys or dedicated campaigns, the corresponding designs establishing an heuristic compromise between knowledge about the observed fields, budget, and other operational limitations. Use of autonomous instrumented platforms improves maneuverability and allows alltime operation.
We concentrate on the problem of interpolating the acquired measures over a region of interest A, and the problem is addressed in the framework of Kriging techniques, the efficiency of an observation path being measured by the predicted IMSE. Choice of the (unconstrained) IMSEbest design of size N is an NPhard problem. For stationary GP models and under weak measures correlation, space filling designs can been shown to nearoptimal. A number of authors have addressed determination of optimal designs for Kriging with stationary covariance models, for which greedy approaches identify closetooptimal solutions, but less attention has been devoted to find optimal observation paths. An interesting approach has been presented in [1], considering optimization of the mutual information between sampled points and the nonsampled (grid) positions, relying on a spatial decomposition of the region of interest based on the assumption of an isotropic decrease of correlation with distance.
In this presentation we present a stochastic search algorithm that finds nearoptimal (IMSE) observation paths for regions of arbitrary regions, for generic nonstationary correlation models, identified from mathematical models or from historical data. The algorithm can be tuned to trade optimality of the resulting design and numerical complexity, while use of a spectral approach [2] to compute the IMSE significantly decreases the execution time compared to usual methods.
Results are illustrated by application to realistic datasets produced by the biogeochemical model MIRO&CO for the Southern Bight of the North Sea [3]. The efficiency of the resulting constrained designs is compared to use of simple greedy approaches. Our numerical experiments also reveal the impact of modelisation of the field’s nonstationarity.
[1] A. Singh, A. Krauser, C. Guestrin, W. Kaiser, Efficient Informative Sensing Using Multiple Robots, J. Artificial Intelligence, Research, 34 (2009), 707755.
[2] B. Gauthier, L. Pronzato, J. Rendas, An alternative for the computation of IMSEoptimal designs of experiments, Book of Abstracts of the Seventh International Workshop on Simulation, with L. Pronzato and J. Rendas (2013).
[3] Lacroix, G. and Ruddick, K. and Park, Y. and Gypens, N. and Lancelot, C. (2007). Validation of the 3D biogeochemical model MIRO&CO with field nutrient and phytoplankton data and MERISderived surface chlorophyll $_{\alpha}$ images. Journal of Marine Systems 64, 6688. 
Describing Multiple Normal Operating States in Continuous Chemical Processes
Authors: Gustavo Matheus de Almeida (Federal Univ. of Sao Joao delRei), Cássia Regina Santos Nunes Almeida (Federal Univ. of Sao Joao delRei), Song Won Park (University of Sao Paulo)
Primary area of focus / application: Process
Keywords: Multiple normal operating states, Hidden Markov model, Process monitoring, False alarm rate, Fault detection, Continuous chemical process, Heat exchanger
Submitted at 23Jun2014 13:07 by Gustavo Almeida
Accepted
Reference:
Almeida, G.M., Park, S.W. Fault detection in continuous industrial chemical processes: A new approach using the hidden Markov modeling. Case study: A boiler from a Brazilian cellulose pulp mill. Yin, H. et al. (eds.) 13th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), LNCS, 7435, 743752, Springer, 2012. 
Using DoE and Tolerance Intervals to Verify Specifications
Authors: Pat Whitcomb (StatEase, Inc.)
Primary area of focus / application: Design and analysis of experiments
Keywords: DoE, Tolerance intervals, Software, Specifications

Comparison of Some Factorial Designs when the Variation around Nominal is Noise in Robust Design
Authors: Magnus Arnér (Tetra Pak)
Primary area of focus / application: Design and analysis of experiments
Keywords: Robust design, Variation around nominal, Combined arrays, Central composite designs
Submitted at 26Jun2014 21:45 by Magnus Arnér
Accepted
A common type of noise factors is aberration from a nominal value. This may be the case for, say, the shore value of rubber bushings, the thickness of a washer, or in any other case of mass production and parttopart variation. There are then several possible factorial designs that can be used for robust design activities. One is to treat the noise and control as separate factors even though they represent the same physical attribute. In the experimental design, the control factors would then form a design cube, and the noise factors smaller cubes around each corner of the design cube of the control factors, so that the sensitivity to a small disturbance in the control factor cube is investigated. The key in the analysis is the controlbynoise interactions. However, since the aim is to get df(x,z)/dz small, which in this case alternatively could be expressed as df(x+z)/dz another possible experimental design is a central composite design. Then, look for second order effects rather than interaction effects. In this presentation, we look at the drawbacks and advantages of these two approaches. 
Practical Issues Related to SLPBased Load Allocation in Liberalized Electricity and Gas Markets
Authors: Christian Ritter (Université Catholique de Louvain), Anne De Frenne (MathX)
Primary area of focus / application: Modelling
Keywords: Electricity load, SLP estimation, Load allocation, Belgian electricity market
Submitted at 2Jul2014 17:39 by Christian Ritter
Accepted

Optimal Monitoring Design for Energy Transmission Systems
Authors: Dirk Surmann (Technische Universität Dortmund), Uwe Ligges (Technische Universität Dortmund), Claus Weihs (Technische Universität Dortmund)
Primary area of focus / application: Design and analysis of experiments
Keywords: Energy transmission systems, Design of Experiments, Doptimal designs, Low frequency oscillation
Submitted at 2Jul2014 17:43 by Dirk Surmann
Accepted
In order to achieve our aim we derive a parameter based on the model of Low Frequency
Oscillation which characterises every single node. Via analysing the behaviour of each node with respect to its neighbours, we construct a feasible linear metamodel over the whole transmission system. We apply convex design of experiment theory, especially the Dcriterion, to the metamodel. This results in a subset of nodes which contain the most information about the European electrical transmission system. The talk will describe the quality of the Doptimal design by comparing it with differently selected subsets of nodes and a uniform design over all nodes. In comparison to the alternatives the Doptimal design comprises a subset with the minimum number of nodes guaranteeing sufficient amount of data.