Data Mining - Concepts and Techniques.

The notion of fuzziness as defined in this paper relates to situations in which the source of imprecision is not a random variable or a stochastic process, but rather a class or classes which do not possess sharply defined boundaries, e.g., the “class of bald men,” or the “class of numbers which are much greater than 10,” or the “class of adaptive systems,” etc. A basic concept which makes it possible to treat fuzziness in a quantitative manner is that of a fuzzy set, that is, a class in which there may be grades of membership intermediate between full membership and non-membership. Thus, a fuzzy set is characterized by a membership function which assigns to each object its grade of membership (a number lying between 0 and 1) in the fuzzy set. After a review of some of the relevant properties of fuzzy sets, the notions of a fuzzy system and a fuzzy class of systems are introduced and briefly analyzed. The paper closes with a section dealing with optimization under fuzzy constraints in which an approach to...

Fuzzy sets and systems

Smoking bans have been implemented in a variety of settings, as well as being part of policy in many jurisdictions to protect the public and employees from the harmful effects of secondhand smoke (SHS). They also offer the potential to influence social norms and the smoking behaviour of those populations they affect. Since the first version of this review in 2010, more countries have introduced national smoking legislation banning indoor smoking.

Legislative smoking bans for reducing harms from secondhand smoke exposure, smoking prevalence and tobacco consumption

A systematic literature review of machine learning methods applied to predictive maintenance

Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

In this paper, an approach to detect stator winding short-circuit faults in squirrel-cage induction motors based on Random Forest and Park's Vector is proposed. This is accomplished by scoring the unbalance in the current and voltage waveforms as well as in Park's Vector, both for current and voltage. To score the unbalance in the d-q space, a Principal Component Analysis is applied to Park's Vector and with the first two principal components the eccentricity is calculated, while the first principal component is used to determine the phase in short-circuit. The proposed strategy has been experimentally tested on a special 400-V, 50-Hz, 4-pole, 2.2-kW induction motor with reconfigurable stator windings in which it was possible to emulate different types of inter-turn short-circuits. The results are quite promising, even only using 1-kHz sampling frequency to acquire the current and voltage waveforms in the three phases, and the use of the Fast Fourier Transform is avoided. The developed solution may be used for tele-monitoring of the motor condition and to implement advanced predictive maintenance strategies.

Stator winding short-circuit fault diagnosis in induction motors using random forest

Spatio-temporal clustering is a sub field of data mining that is increasingly gaining more scientific attention due to the advances of location-based or environmental devices that register position, time and, in some cases, other semantic attributes. This process pretends to group objects based in their spatial and temporal similarity helping to discover interesting patterns and correlations in large data sets. One of the main challenges of this area is the ability to integrate several dimensions in a general-purpose approach. In this paper, such general approach is proposed, based on an extension of the SNN (Shared Nearest Neighbor) algorithm. The 4D+SNN algorithm allows the integration of space, time and one or more semantic attributes in the clustering process. This algorithm is able to deal with different data sets and different discovery purposes as the user has the ability to weight the importance of each dimension in the discovery process. The results obtained are very promising as show interesting findings on data and open the possibility of integration of several dimensions of analysis in the clustering process.

4D+SNN: A Spatio-Temporal Density-Based Clustering Approach with 4D Similarity

While statistical information on socio-economic activities is widely available, the data are often collected or released only at a relatively aggregated level. In these aggregated forms, the data are useful for broad-scale assessments, although we often need to disaggregate the source data in order to provide more localized estimates, and in order to analyze correlations against geophysical variables. Spatial disaggregation techniques can be used in this context, to transform data from a set of source zones into a set of target zones, with different geometry and with a higher general level of spatial resolution. Still, few previous studies in the area have attempted to leverage state-of-the-art spatial disaggregation procedures in the context of socio-economic variables, instead focusing on applications related to population modeling. In this article, we report on experiments with a hybrid spatial disaggregation technique that combines state-of-the-art regression analysis procedures with the classic methods of dasymetric mapping and pycnophylactic interpolation. The hybrid procedure was used together with population density, land coverage, nighttime satellite imagery, and OpenStreetMap road density, as ancillary data to disaggregate different types of socio-economic indicators to a high-resolution grid. Our test specifically leveraged data relative to the Portuguese territory, resulting in the production of raster datasets with a resolution of 30 arc-seconds per cell. The article discusses the spatial disaggregation methodology and the quality of the obtained results under different experimental conditions.

A hybrid approach for the spatial disaggregation of socio-economic indicators

High-resolution population grids built from historical census data can ease the analyses of geographical population changes, at the same time also facilitating the combination of population data with other GIS layers to perform analyses on a wide range of topics. This article reports on experiments with a hybrid spatial disaggregation technique that combines the ideas of dasymetric mapping and pycnophylactic interpolation, using modern machine learning methods to combine different types of ancillary variables, in order to disaggregate historical census data into a 200 m resolution grid. We specifically report on experiments related to the disaggregation of historical population counts from three different national censuses which took place around 1900, respectively in Great Britain, Belgium, and the Netherlands. The obtained results indicate that the proposed method is indeed highly accurate, outperforming simpler disaggregation schemes based on mass-preserving areal weighting or pycnophylactic interpolation. The best results were obtained using modern regression methods (i.e., gradient tree boosting or convolutional neural networks, depending on the case study), which previously have only seldom been used for spatial disaggregation.

Spatial Disaggregation of Historical Census Data Leveraging Multiple Sources of Ancillary Information

Nowadays, road accidents are a major public health problem, which increase is forecasted if road safety is not treated properly, dying about 1.2 million people every year around the globe. In 2012, Portugal recorded 573 fatalities in road accidents, on site, revealing the largest decreasing of the European Union for 2011, along with Denmark. Beyond the impact caused by fatalities, it was calculated that the economic and social costs of road accidents weighted about 1.17% of the Portuguese gross domestic product in 2010.Visual Analytics allows the combination of data analysis techniques with interactive visualizations, which facilitates the process of knowledge discovery in sets of large and complex data, while the Geovisual Analytics facilitates the exploration of space-time data through maps with different variables and parameters that are under analysis. In Portugal, the identification of road accident accumulation zones, in this work named black spots, has been restricted to annual fixed windows. In this work, it is presented a dynamic approach based on Visual Analytics techniques that is able to identify the displacement of black spots on sliding windows of 12 months. Moreover, with the use of different parameterizations in the formula usually used to detect black spots, it is possible to identify zones that are almost becoming black spots. Through the proposed visualizations, the study and identification of countermeasures to this social and economic problem can gain new grounds and thus the decision-making process is supported and improved.

João Moura Pires

Papers

Stator winding short-circuit fault diagnosis in induction motors using random forest

4D+SNN: A Spatio-Temporal Density-Based Clustering Approach with 4D Similarity

A hybrid approach for the spatial disaggregation of socio-economic indicators

Spatial Disaggregation of Historical Census Data Leveraging Multiple Sources of Ancillary Information

Detection of road accident accumulation zones with a visual analytics approach