Major challenges in prognostics: study on benchmarking prognostic datasets

Even though prognostics has been defined to be one of the most difficult tasks in Condition Based Maintenance (CBM), many studies have reported promising results in recent years. The nature of the prognostics problem is different from diagnostics with its own challenges. There exist two major approaches to prognostics: data-driven and physics-based models. This paper aims to present the major challenges in both of these approaches by examining a number of published datasets for their suitability for analysis. Data-driven methods require sufficient samples that were run until failure whereas physics-based methods need physics of failure progression

Feature-based decision rules for control charts pattern recognition: A comparison between CART and QUEST algorithm

Control chart pattern (CCP) recognition can act as a problem identification tool in any manufacturing organization. Feature-based rules in the form of decision trees have become quite popular in recent years for CCP recognition. This is because the practitioners can clearly understand how a particular pattern has been identified by the use of relevant shape features. Moreover, since the extracted features represent the main characteristics of the original data in a condensed form, it can also facilitate efficient pattern recognition. The reported feature-based decision trees can recognize eight types of CCPs using extracted values of seven shape features. In this paper, a different set of seven most useful features is presented that can recognize nine main CCPs, including mixture pattern. Based on these features, decision trees are developed using CART (classification and regression tree) and QUEST (quick unbiased efficient statistical tree) algorithms. The relative performance of the CART and QUEST-based decision trees are extensively studied using simulated pattern data. The results show that the CART-based decision trees result in better recognition performance but lesser consistency, whereas, the QUEST-based decision trees give better consistency but lesser recognition performance

Systematic evaluation of perceived spatial quality

The evaluation of perceived spatial quality calls for a method that is sensitive to changes in the constituent dimensions of that quality. In order to devise a method accounting for these changes, several processes have to be performed. This paper shows the development of scales by elicitation and structuring of verbal data, followed by validation of the resulting attribute scales

Acoustic Scene Classification

This work was supported by the Centre for Digital Music Platform (grant EP/K009559/1) and a Leadership Fellowship (EP/G007144/1) both from the United Kingdom Engineering and Physical Sciences Research Council

A Comparison of Machine-Learning Methods to Select Socioeconomic Indicators in Cultural Landscapes

Cultural landscapes are regarded to be complex socioecological systems that originated as a result of the interaction between humanity and nature across time. Cultural landscapes present complex-system properties, including nonlinear dynamics among their components. There is a close relationship between socioeconomy and landscape in cultural landscapes, so that changes in the socioeconomic dynamic have an effect on the structure and functionality of the landscape. Several numerical analyses have been carried out to study this relationship, with linear regression models being widely used. However, cultural landscapes comprise a considerable amount of elements and processes, whose interactions might not be properly captured by a linear model. In recent years, machine-learning techniques have increasingly been applied to the field of ecology to solve regression tasks. These techniques provide sound methods and algorithms for dealing with complex systems under uncertainty. The term ‘machine learning’ includes a wide variety of methods to learn models from data. In this paper, we study the relationship between socioeconomy and cultural landscape (in Andalusia, Spain) at two different spatial scales aiming at comparing different regression models from a predictive-accuracy point of view, including model trees and neural or Bayesian networks

Clefts: Quite the contrary!

Much of the previous literature on English it-clefts – sentences of the form ‘It is X that Z’ – concentrates on the nature and status of the exhaustivity inference (‘nobody/nothing other than X Z’). This paper concerns the way in which it-clefts signal contrast. We argue that it-clefts signal a type of contrast that does not merely involve a salient antecedent, as on more traditional characterizations of contrast such as those of e.g. Kiss (1998) and Rooth (1992), but also involves a conflict between the speaker’s and the hearer’s beliefs, as under the characterization of contrast given by Zimmermann (2008, 2011), which we term contrariness. Results of a felicity judgment experiment suggest that clefts do have a preference for contrariness, and one which has a gradient effect on felicity judgments: the more strongly interlocutors appear committed to an apparently false notion, the better it is to repudiate them with a cleft.https://4f669968-a-62cb3a1a-s-sites.googlegroups.com/site/sinnundbedeutung21/proceedings-preprints/Destruel-Beaver-Coppock-SuB2016-FINAL.pdf?attachauth=ANoY7cpfzckWBy6psH6QCmbOCeXWS2nlL4bGgHHud2GpjKB1YQolksB00UtYzuvPRANOzWvWgfHdLZ7BP8zDYcT5wYIwr-1dBjw2g0-TC0Bic1ByVfjgj68pPdE9novwXm427ehkZI1E59JmiIvJnBKGxzYpI_AxMcKc-gEQuzu6DHXwJoLtzwm1FzFaHEX1LBq_yFSDgBzZajW2AHEFSiqmz1OVPTICm4zLB30AaHUxrtTBhWI1r0pmmX42IwVk9DtYfp0m6uvrsJLxJuvDhBPe-l3sJmHPcH2qhAtt6wqVMT7b-H6wX08=&attredirects=0Published versio