Validation and reconstruction of flow meter data in the Barcelona water distribution network

This paper presents a signal analysis methodology to validate (detect) and reconstruct the missing and false data of a large set of flow meters in the telecontrol system of a water distribution network. The proposed methodology is based on two time-scale forecasting models: a daily model based on a ARIMA time series, while the 10-min model is based on distributing the daily flow using a 10-min demand pattern. The demand patterns have been determined using two methods: correlation analysis and an unsupervised fuzzy logic classification, named LAMDA algorithm. Finally, the proposed methodology has been applied to the Barcelona water distribution network, providing very good results.     

MIMO Smith predictor: Global and structured robust performance analysis

The purpose of this work is to extend the analysis of the Smith predictor structure to multiple input multiple output (MIMO) systems with uncertain multiple delays. This is applied to the set of models that can be factorized into a rational MIMO model in series with left/right diagonal (multiple) delay matrices. Necessary and sufficient conditions on the plant’s model to achieve this factorization are proved. This factorized structure is instrumental for the structured robustness analysis and applies to multiple pool open flow canals. Nominal and robust performance and stability are analyzed for the case of plants with multiple uncertain delays for two different uncertainty structures: global dynamic and structured parametric. The first uncertainty structure could also accommodate the dynamic uncertainty of the plant’s rational part as well. This analysis is applied to a controller designed for a two-pool canal system.     

Observer gain effect in linear interval observer-based fault detection

An interval observer has been shown to be a suitable passive robust strategy to generate an adaptive threshold to be used in residual evaluation when model uncertainty is located in parameters (interval model). In such an approach, the observer gain plays an important role since it determines the minimum detectable fault for a given type of fault and allows enhancing the observer fault detection properties. The aim of this paper is to analyze the influence of the observer gain on the time evolution of the residual sensitivity to a fault. Thereby, as a result of this sensitivity study, the minimum detectable fault time evolution for a given type of fault and the interval observer fault detection performance could be determined. In particular, three types of faults according to their detectability time evolution are introduced: permanently (strongly) detected, non-permanently (weakly) detected or just non-detected. An example based on a mineral grinding-classification process is used to illustrate the results derived.     

On the measure and the estimation of evenness and diversity

Modelling word or species frequency count data through zero truncated Poisson mixture models allows one to interpret the model mixing distribution as the distribution of the word or species frequencies of the vocabulary or population. As a consequence, estimates of their mixing density can be used as a fingerprint of the style of the author in his texts or of the ecosystem in its samples. Definitions of measure of the evenness and of measure of the diversity within a vocabulary or population are given, and the novelty of these definitions is explained. It is then proposed that the measures of the evenness and of the diversity of a vocabulary or population be approximated through the expectation of these measures under the word or species frequency distribution. That leads to the assessment of the lack of diversity through measures of the variability of the mixing frequency distribution estimates described above.     

Reliability measure for shape-from-focus

Shape-from-focus (SFF) is a passive technique widely used in image processing for obtaining depth-maps. This technique is attractive since it only requires a single monocular camera with focus control, thus avoiding correspondence problems typically found in stereo, as well as more expensive capturing devices. However, one of its main drawbacks is its poor performance when the change in the focus level is difficult to detect. Most research in SFF has focused on improving the accuracy of the depth estimation. Less attention has been paid to the problem of providing quality measures in order to predict the performance of SFF without prior knowledge of the recovered scene. This paper proposes a reliability measure aimed at assessing the quality of the depth-map obtained using SFF. The proposed reliability measure (the R-measure) analyzes the shape of the focus measure function and estimates the likelihood of obtaining an accurate depth estimation without any previous knowledge of the recovered scene. The proposed R-measure is then applied for determining the image regions where SFF will not perform correctly in order to discard them. Experiments with both synthetic and real scenes are presented.     

Robust fault detection of non-linear systems using set-membership state estimation based on constraint satisfaction

In this paper, the robust fault detection problem for non-linear systems considering both bounded parametric modelling errors and measurement noises is addressed. The non-linear system is monitored by using a state estimator with bounded modelling uncertainty and bounded process and measurement noises. Additionally, time-variant and time-invariant system models are taken into account. Fault detection is formulated as a set-membership state estimation problem, which is implemented by means of constraint satisfaction techniques. Two solutions are presented: the first one solves the general case while the second solves the time-variant case, being this latter a relaxed solution of the first one. The performance of the time-variant approach is tested in two applications: the well-known quadruple-tank benchmark and the dynamic model of a representative portion of the Barcelona's sewer network. In both applications, different scenarios are presented: a faultless situation and some faulty situations. All considered scenarios are intended to show the effectiveness of the presented approach.     

Worst-Case Simulation of Discrete Linear Time-Invariant Interval Dynamic Systems

In this paper, a new approach to worst-case simulation of discrete linear time- invariant interval dynamic systems is proposed. For stable systems, the new approach solves the problem of worst-case simulation by determining the interval hull enclosing the system states region at every iteration through optimisation. The originality of this approach is that it maintains time-invariant parametric uncertainties during the simulation process. Several previous algorithms have considered the case of parametric time-varying uncertainties (El Ghauoi, L., Calafiore, G.: Worst-Case Simulation of Uncertain Systems, in: Garulli, A., Tesi, A., and Vicino, A. (eds), Robustness in Identification and Control, Springer, London, 1999). However, Cuguer' o (Avoiding Possible Instability in Robust Simulation of Stable Parametric Uncertain Time-Invariant Systems, in: Proceedings of 40th Conference on Decision and Control, Florida, 2001) has presented possible instability problems when simulating a time-invariant uncertain system as if it were time-varying, this result being the motivation for the approach proposed in this paper. The optimisation problem associated with the approach proposed must be solved globally in order to guarantee that the minimum volume box enclosing the region of system states has been derived. In this paper, a global optimisation algorithm based on an interval branch and bound strategy is proposed. Finally, two real application examples are used to test the performance of the approach proposed.     

Liquid-liquid phase separation in blends of a linear low-density polyethylene with a low-density polyethylene

A linear low-density butene copolymer, of overall branch content 3 mol %, has been blended with a low-density polyethylene. The low-density polyethylene has an overall branch content of 5 mol %, including both long and short branches. The two materials were blended in a wide range of compositions and the phase behavior investigated using indirect experimental methods, the examination of quenched blends by differential scanning calorimetry, and transmission electron microscopy. After quenching from temperatures up to 170°C, blends, of almost all compositions, show two crystal populations, separated on a micron scale. It is argued that this implies that the blends were phase separated in the melt before quenching. This behavior shows good agreement with predictions based on previous extensive studies of binary and ternary blends of linear with lightly branched polyethylenes. © 1997 John Wiley & Sons, Inc. J Appl Polym Sci 65: 1921–1931, 1997