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

Discriminating qualitative model generation from classified data

Modeling is quite critical and remains a bottleneck for modelbased diagnosis in many application domains. Quantitative models that are developed during the design stage are not applicable as so to model-based diagnosis engines. This paper proposes to take advantage of discretization algorithms used by the machine learning community to discretize the domain value of continuous variables and generate a behavioral qualitative model from the data clusters corresponding to classified data. The results of this approach are illustrated and discussed with the two tanks benchmark example

A decentralized fault detection and isolation scheme for spacecraft: bridging the gap between model-based fault detection and isolation research and practice

This paper introduces a decentralized fault diagnosis and isolation (FDI) architecture for spacecraft and applies it to the attitude determination and control system (ADCS) of a satellite. A system is decomposed into functional subsystems. The architecture is composed of local diagnosers for subsystems which work with local models. Fault ambiguities due to interactions between subsystems are resolved at a higher level by a supervisor, which combines the partial view of the local diagnosers and performs isolation on request. The architecture is hierarchically scalable. The structure of the ADCS is modeled as constraints and variables and used to demonstrate the decentralized architecture

Model Based Diagnostic Module for a FCC Pilot Plant

International audienceThis paper presents a diagnostic module developed by IFP and tested off-line on a FCC (Fluid Catalytic Cracking) pilot plant. The method uses four successive complementary techniques. They enable to go step by step from the observations to a sentence in natural language describing the faults. First, a quantitative causal model is elaborated from a quantitative behavioural model. Causality is obtained from the structure of each equation. Then, global and local alarms are generated using residuals (differences between measures and outputs of the model) and fuzzy logic reasoning. Then, a hitting set algorithm is applied to determine sets of components or equipment which are suspected to have an abnormal behaviour. Finally, expert human operator knowledge about those components is used to identify the fault(s) and produce messages for the operators. This software is currently tested off-line on the FCC pilot plant at IFP. The performance of the diagnostic module is illustrated on four practical scenarios of abnormal behaviour. This work is conducted as part of the CHEM EC funding project