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

This paper presents a computational procedure based on viability theory to evaluate the fault tolerance admissibility of a given fault configuration of a nonlinear system controlled by means of a predictive control law. The admissible solution set for the control problem, including the effect of faults, is determined using viability kernel and capture basin. Finally, water heater part of pasteurization process is provided as benchmark in order to show the usefulness of viability theory for fault tolerance evaluation.

/pdf/fault-tolerance-evaluation-of-nonlinear-systems-using-2hgl8lky7p.pdf

Fault tolerance evaluation of nonlinear systems using viability theory

In this paper, a robust economic model predictive controller is proposed for a water transport network. Considering that forecast of demand is required in the drinking water network (DWN) for future predictions of states in the MPC formulation, robustification of the proposed controller is undertaken considering demand uncertainties as unknown but bounded in a zonotopic set. Based on this uncertainty description, a robust MPC controller is designed that ensures that robust constraint satisfaction, stability and performance under uncertainties is guaranteed. The proposed approach is satisfactorily tested in a part of the Barcelona water transport network.

Robust Economic Model Predictive Control of Water Transport Networks

Dealing with prognostics uncertainties: Combination of direct and recursive remaining useful life estimations

Critical infrastructure systems (CIS) are complex large-scale systems which in turn require highly sophisticated supervisory control systems to ensure that high performance can be achieved and maintained under adverse conditions. The global CIS Real-Time Control (RTC) need of operating in adverse conditions involves, with a high probability, sensor and actuator malfunctions (faults). This problem calls for the use of an on-line Fault Detection and Isolation (FDI) system able to detect such faults. This paper proposes a FDI mechanism that extends the classical Boolean fault signature matrix concept taking into account several fault signal properties to isolate faults in CIS. To exemplify the proposed FDI scheme in CIS, the Barcelona drinking water network is used as a case study.

/pdf/fault-detection-and-isolation-in-critical-infrastructure-54fp8vlmvg.pdf

Fault Detection and Isolation in Critical Infrastructure Systems

This paper proposes a methodology for leak detection and isolation in Water Distribution Systems (WDS). Our work is based on the construction of regression models with optimal parameters which describe the behavior of the network in a normal scenario (no leaks) and comparing these models against predicted output for actual data obtained from pressure measurements along a time horizon. The proposed detection method takes into account possible presence of noise in the demand (assumed known) and/or in the measurements obtained from sensors located in the nodes of the network. A typical water network testbed is employed to validate the proposed methodology. Epanet® software was used to perform the simulations of leak scenarios in order to validate the effectiveness of the proposed approach. Encouraging results are obtained in scenarios with noise and different demand patterns.

Vicenç Puig

Papers

Fault tolerance evaluation of nonlinear systems using viability theory

Robust Economic Model Predictive Control of Water Transport Networks

Dealing with prognostics uncertainties: Combination of direct and recursive remaining useful life estimations

Fault Detection and Isolation in Critical Infrastructure Systems

Leak detection in water distribution networks with optimal linear regression models