Computational intelligence for industrial and environmental applications

Computational Intelligence (CI) techniques are being increasingly used for automatic monitoring and control systems, especially regarding industrial and environmental applications, due to their performance, their capabilities in fusing noisy or incomplete data obtained from heterogeneous sensors, and the ability in adapting to variations in the operational conditions. Moreover, the increase in the computational power and the decrease of the size of the computing architectures allowed a more pervasive use of CI techniques in a great variety of situations. In this paper, we propose a brief review of the most important CI techniques applied in each step of the design of a monitoring and control system for industrial and environmental applications, and describe how these techniques are integrated in the development of efficient industrial and environmental applications

GARCH-based robust clustering of time series

3nopartially_openIn this paper we propose different robust fuzzy clustering models for classifying heteroskedastic (volatility) time series, following the so-called model-based approach to time series clustering and using a partitioning around medoids procedure. The proposed models are based on a GARCH parametric modelingof the time series, i.e. the unconditional volatility and the time-varying volatility GARCH representation of the time series. We first suggest a timid robustification of the fuzzy clustering. Then, we propose three robust fuzzy clustering models belonging to the so-called metric, noise and trimmed approaches, respectively. Each model neutralizes the negative effects of the outliers in the clustering process in a different manner. In particular, the first robust model, based on the metric approach, achieves its robustness with respect to outliers by taking into account a “robust” distance measure; the second, based on the noise approach, achieves its robustness by introducing a noise cluster represented by a noise prototype; the third, based on the trimmed approach, achieves its robustness by trimming away a certain fraction of outlying time series. The usefulness and effectiveness of the proposed clustering models is illustrated by means of a simulation study and two applications in finance and economics.embargoed_20180131De Giovanni, Livia; D'Urso, Pierpaolo; Massari, RiccardoDE GIOVANNI, Livia; D'Urso, Pierpaolo; Massari, Riccard

Autoregressive model-based fuzzy clustering and its application for detecting information redundancy in air pollution monitoring networks

Fuzzy clustering enables the simultaneous membership of objects in two or more clusters. This is particularly pertinent where time series are concerned, because very often patterns of time series change over time. Thus, a time series might belong to different clusters over different periods of time, in which case, crisp clustering is unable to capture this multi-cluster membership. In this paper, we adopt a Fuzzy C-Medoids approach to clustering time series based on autoregressive estimates of models fitted to the time series. We illustrate very good performance of this approach in a range of simulation studies. By means of two applications, we also show the usefulness of this clustering approach in the air pollution monitoring, by considering air pollution time series, i.e., CO time series, CO2 time series and NO time series monitored on world and urban scales. In particular, we show that, by considering in the clustering process, the autoregressive representation of these air pollution time series, we are able to detect possible information redundancy in the monitoring networks and then, decreasing the number of monitoring stations, to reduce the monitoring costs and then to increase the monitoring efficiency of the networks

An analysis of changing dietary trends and the implications for global health.

Worldwide, obesity has reached epidemic proportions and has almost tripled between 1975 and 2016. Acknowledging that weight gain is a complex and multifactorial condition involving changes in both dietary and physical activity patterns, this research focuses on the dietary origin of obesity. Given the dearth of empirical literature on food consumption at the global level, the aim of this research is to explore dietary trends and dietary types around the world linked with the indications that they imply for obesity and global health. To this aim, annual food availability data collated by the Food and Agriculture Organisation of the United Nations (FAO) covering the period from 1961 to 2013 for 118 countries are scrutinised by econometric convergence tests, clustering techniques and spatial analysis. Results indicate that countries with lower levels of initial calories tend to exhibit higher growth rates of calorie consumption. However, this process is not homogeneous across countries. Low-income countries have converged at the fastest pace and the convergence rate reduces as income rises. In addition, the dietary convergence is conditioned by a range of structural indicators including agroecological, demographic and socio-economic variables. Evidence suggests that economic factors have become a more important determinant of the dietary convergence since the Millennium. Applying innovative fuzzy clustering algorithms which allow multiple diets to coexist within a single country, several dietary trends and dietary types are detected. While the identified clusters are all associated with relentlessly increasing calories and deteriorating dietary healthiness over the past half a century, the most calorific cluster has shown signs of stabilising calorie consumption. A notable contribution of this research is the examination of spatial patterns of global food consumption using both traditional and non-traditional measures of spatial proximity. Differing from the earlier literature emphasising the role of geographical closeness, this research utilises an economic indicator for proximity and finds that countries with similar income levels tend to have similar diets. Spatial convergence analysis reveals a convergence process that is about three times as fast as the non-spatial model; thus, ignoring the spatial relationship leads to biased results. Incorporating the spatial dimension in cluster analysis also affects the clustering results dramatically. Cluster profiling shows that only the segment of more educated and health-aware populations exhibits the behavioural changes towards better diets, hence underlining the importance of improved education and access to knowledge. The finding that dietary evolutions are ‘spatially’ dependent provides a basis for the development of group-specific interventions that target populations at risk of worsening diets. While these policy measures are often place-based, this research lays foundations for the implementation of coherent food policies beyond geographical boundaries. Overall, this research highlights that healthier diets are possible, but we need to act now. As we are living longer but not necessarily healthier, current attempts to improve diets are obviously inadequate and existing efforts need to be redoubled. This is an urgent message for policymakers considering the sobering fact that no country has been able to significantly reverse the rise in obesity