Hybrid Automaton Incremental Construction for Online Diagnosis

This paper proposes a method to track the system mode and diagnose a hybrid system without building an entire diagnoser off-line. The method is supported by a hybrid automaton model that represents the hybrid system continuous and discrete behavioral dynamics. Diagnosis is performed by interpreting the events and measurements issued by the physical system directly on the hybrid automaton model. This interpretation leads to building the useful parts of the diagnoser incrementally, developing only the branches that are required to explain the occurrence of the incoming events. The resulting diagnoser adapts to the system operational life and is much less demanding in terms of memory storage. The proposed framework subsumes previous works in that it copes with both structural and non-structural faults. The method is validated on an application case study based on the sewer network of the Barcelona city.Peer ReviewedPostprint (author’s final draft

An extended chronicle discovery approach to find temporal patterns between sequences

Sequences of events describing the behavior and actions of users or systems can be collected in sev eral domains. An episode is a collection of events that occurs relatively close to each other in a given partial order. Also, chronicles are a special type of temporal patterns, where temporal orders of events are quantified with numerical bounds and reflect the temporal evolution of the system over the time. In this paper, the problem of finding rules for de scribing or predicting the behavior of the sequences with the intention of characterizing some interest ing tasks is considered. Obtaining these patterns is the main objective of this work, where an automatic method to learn relevant and discriminating chron icles is proposed. The method extends existing al gorithms that have been proposed to find frequent episodes/chronicles in a single event sequence to the case of multiple sequences.Ministerio de Economía y Competitividad TIN2009-14378-C02-01 (ARTEMISA)Junta de Andalucía TIC-8052 (Simon

Use of measurement theory for operationalization and quantification of psychological constructs in systems dynamics modelling

The analytical tools available to social scientists have traditionally been adapted from tools originally designed for analysis of natural science phenomena. This article discusses the applicability of systems dynamics - a qualitative based modelling approach, as a possible analysis and simulation tool that bridges the gap between social and natural sciences. After a brief overview of the systems dynamics modelling methodology, the advantages as well as limiting factors of systems dynamics to the potential applications in the field of social sciences and human interactions are discussed. The issues arise with regards to operationalization and quantification of latent constructs at the simulation building stage of the systems dynamics methodology and measurement theory is proposed as a ready and waiting solution to the problem of dynamic model calibration, with a view of improving simulation model reliability and validity and encouraging the development of standardised, modular system dynamics models that can be used in social science research