23 research outputs found
Convergence of Learning Dynamics in Information Retrieval Games
We consider a game-theoretic model of information retrieval with strategic
authors. We examine two different utility schemes: authors who aim at
maximizing exposure and authors who want to maximize active selection of their
content (i.e. the number of clicks). We introduce the study of author learning
dynamics in such contexts. We prove that under the probability ranking
principle (PRP), which forms the basis of the current state of the art ranking
methods, any better-response learning dynamics converges to a pure Nash
equilibrium. We also show that other ranking methods induce a strategic
environment under which such a convergence may not occur
Statistical hypothesis testing versus machine-learning binary classification: distinctions and guidelines
Making binary decisions is a common data analytical task in scientific
research and industrial applications. In data sciences, there are two related
but distinct strategies: hypothesis testing and binary classification. In
practice, how to choose between these two strategies can be unclear and rather
confusing. Here we summarize key distinctions between these two strategies in
three aspects and list five practical guidelines for data analysts to choose
the appropriate strategy for specific analysis needs. We demonstrate the use of
those guidelines in a cancer driver gene prediction example
A new hybrid prognostic methodology
Methodologies for prognostics usually centre on physics-based or data-driven approaches. Both have advantages and disadvantages, but accurate prediction relies on extensive data being available. For industrial applications this is very rarely the case, and hence the chosen method’s performance can deteriorate quite markedly from optimal. For this reason a hybrid methodology, merging physics-based and data-driven approaches, has been developed and is reported here. Most, if not all, hybrid methods apply physics-based and data-driven approaches in different steps of the prognostics process (i.e. state estimation and state forecasting). The presented technique combines both methods in forecasting, and integrates the short-term prediction of a physics-based model with the longer term projection of a similarity-based data-driven model, to obtain remaining useful life estimation. The proposed hybrid prognostic methodology has been tested on two engineering datasets, one for crack growth and the other for filter clogging. The performance of the presented methodology has been evaluated by comparing remaining useful life estimations obtained from both hybrid and individual prognostic models. The results show that the presented methodology improves accuracy, robustness and applicability, especially in the case of minimal data being available
Demonstration of a Response Time Based Remaining Useful Life (RUL) Prediction for Software Systems
Prognostic and Health Management (PHM) has been widely applied to hardware
systems in the electronics and non-electronics domains but has not been
explored for software. While software does not decay over time, it can degrade
over release cycles. Software health management is confined to diagnostic
assessments that identify problems, whereas prognostic assessment potentially
indicates when in the future a problem will become detrimental. Relevant
research areas such as software defect prediction, software reliability
prediction, predictive maintenance of software, software degradation, and
software performance prediction, exist, but all of these represent diagnostic
models built upon historical data, none of which can predict an RUL for
software. This paper addresses the application of PHM concepts to software
systems for fault predictions and RUL estimation. Specifically, this paper
addresses how PHM can be used to make decisions for software systems such as
version update and upgrade, module changes, system reengineering, rejuvenation,
maintenance scheduling, budgeting, and total abandonment. This paper presents a
method to prognostically and continuously predict the RUL of a software system
based on usage parameters (e.g., the numbers and categories of releases) and
performance parameters (e.g., response time). The model developed has been
validated by comparing actual data, with the results that were generated by
predictive models. Statistical validation (regression validation, and k-fold
cross validation) has also been carried out. A case study, based on publicly
available data for the Bugzilla application is presented. This case study
demonstrates that PHM concepts can be applied to software systems and RUL can
be calculated to make system management decisions.Comment: This research methodology has opened up new and practical
applications in the software domain. In the coming decades, we can expect a
significant amount of attention and practical implementation in this area
worldwid
Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation.
In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations