There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

An Introduction to MultiAgent Systems.

https://users.encs.concordia.ca/~ymzhang/publications/ARC32-2-98Zhang_pp.229-252.pdf

Bibliographical review on reconfigurable fault-tolerant control systems

One of the most important areas of the water utilities is the water quality management. This area is responsible of guaranteeing safety in the water supply to the citizens. The strategy to guarantee the safety is based on two principal elements: disinfection and monitoring. Disinfection techniques, such as chlorination, allow to prevent the growing of microorganisms present in the water. Moreover, in order to guarantee this safety in the whole water network and avoid any unexpected event, on-line sensors are required to monitor a set of quality parameters. The whole process is based on the assumption that the information retrieved from quality sensors is totally reliable. But due to the complexity of the calibration and maintenance of these chemical sensors, several factors affect the accuracy of the raw data collected. Consequently, any decision based on this raw data might be based on a non solid base. Therefore, this work presents a data analytics approach consisting in two modules: fault diagnosis and prognosis. The fault diagnosis module first discerns if a sensor is detecting a real change on water quality parameters or actually is providing inconsistent information due to some malfunction. The prognosis module aims to predict the fault instant due to a slow degradation, which is very common in chlorine sensors. This approach allows to apply a predictive maintenance strategy reducing corrective actions. The proposed methodology has been satisfactorily tested on the Barcelona Drinking Water Network.

/pdf/prognosis-of-quality-sensors-in-the-barcelona-drinking-water-4ywi5som92.pdf

Prognosis of quality sensors in the Barcelona drinking water network

The solar PV power sector in Spain has been developing at a spectacular rate in recent years. The energy cost and the dependence on fossil fuels can be reduced by improving the efficiency of photovoltaic energy production The performance of a solar radiation conversion system is affected by tilt angle with the horizontal plane. Thus, a photovoltaic array needs to be tilted at the correct angle to maximize its performance. In this paper, we investigate how to determine the optimum tilt angle. This procedure is illustrated for the city of Barcelona (Spain) located at latitude 41o 22' 56” North and longitude 2o 6'56” East. The optimal tilt angle varies according the seasons: In Winter (December, January, February) is 56.4, in Spring (March, April, May) is 29.11° in Summer (Jun, July, August) is 13.76° and in Autumn (September, October, November) is 48.14°. Finally, the annual optimum tilt angle for our latitude is 36.87°, with this optimal slope angel, maximum monthly and annual solar radiation is calculated. So, by means of the proposed approach, the energy generation is increased in 10.54% thanks to we are able to benefit from the maximum solar radiation.

/pdf/tilt-angle-optimization-of-photovoltaic-panels-hrko1fkfxk.pdf

Tilt Angle Optimization of Photovoltaic Panels

Trabajo presentado a la IEEE Conference on Control Applications (CCA) celebrada en Juan-les-Pins, Antibes (Francia) del 8 al 10 de octubre de 2014

/pdf/two-layer-scheduling-scheme-for-pump-stations-4xj252bq04.pdf

Two-layer scheduling scheme for pump stations

Conventional deterministic set-membership (SM) estimation is limited to unknown-but-bounded uncertainties. In order to exploit distributional information of probabilistic uncertainties, a probability-guaranteed SM state estimation approach is proposed for uncertain linear time-invariant systems. This approach takes into account polynomial dependence on probabilistic uncertain parameters as well as additive stochastic noises. The purpose is to compute, at each time instant, a bounded set that contains the actual state with a guaranteed probability. The proposed approach relies on the extended form of an observer representation over a sliding window. For the offline observer synthesis, a polynomial-chaos-based method is proposed to minimize the averaged H2 estimation performance with respect to probabilistic uncertain parameters. It explicitly accounts for the polynomial uncertainty structure, whilst most literature relies on conservative affine or polytopic overbounding. Online state estimation restructures the extended observer form, and constructs a Gaussian mixture model to approximate the state distribution. This enables computationally efficient ellipsoidal calculus to derive SM estimates with a predefined confidence level. The proposed approach preserves time invariance of the uncertain parameters and fully exploits the polynomial uncertainty structure, to achieve tighter SM bounds. This improvement is illustrated by a numerical example with a comparison to a deterministic zonotopic method.

/pdf/probability-guaranteed-set-membership-state-estimation-for-4am0cr4o8v.pdf

Vicenç Puig

Papers

Prognosis of quality sensors in the Barcelona drinking water network

Tilt Angle Optimization of Photovoltaic Panels

Two-layer scheduling scheme for pump stations

Probability-Guaranteed Set-Membership State Estimation for Polynomially Uncertain Linear Time-Invariant Systems

Robust fault detection using inverse images of interval functions