Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Complexity in forecasting and predictive models

Te challenge of this special issue has been to know the state of the problem related to forecasting modeling and the creation of a model to forecast the future behavior that supports decision making by supporting real-world applications. Tis issue has been highlighted by the quality of its research work on the critical importance of advanced analytical methods, such as neural networks, sof computing, evolutionary algorithms, chaotic models, cellular automata, agent-based models, and fnite mixture minimum squares (FIMIX-PLS).info:eu-repo/semantics/publishedVersio

Interpolation of nonstationary high frequency spatial-temporal temperature data

The Atmospheric Radiation Measurement program is a U.S. Department of Energy project that collects meteorological observations at several locations around the world in order to study how weather processes affect global climate change. As one of its initiatives, it operates a set of fixed but irregularly-spaced monitoring facilities in the Southern Great Plains region of the U.S. We describe methods for interpolating temperature records from these fixed facilities to locations at which no observations were made, which can be useful when values are required on a spatial grid. We interpolate by conditionally simulating from a fitted nonstationary Gaussian process model that accounts for the time-varying statistical characteristics of the temperatures, as well as the dependence on solar radiation. The model is fit by maximizing an approximate likelihood, and the conditional simulations result in well-calibrated confidence intervals for the predicted temperatures. We also describe methods for handling spatial-temporal jumps in the data to interpolate a slow-moving cold front.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS633 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Marker effects and examination reliability: a comparative exploration from the perspectives of generalizability theory, Rasch modelling and multilevel modelling

This study looked at how three different analysis methods could help us to understand rater effects on exam reliability. The techniques we looked at were: generalizability theory (G-theory) item response theory (IRT): in particular the Many-Facets Partial Credit Rasch Model (MFRM) multilevel modelling (MLM) We used data from AS component papers in geography and psychology for 2009, 2010 and 2011 from Edexcel.</p

Umbrella species as a conservation planning tool

In northern Europe, a long history of anthropogenic land use has led to profound changes within forest ecosystems. One of the proposed approaches for conservation and restoration of forest biodiversity is the use of umbrella species, whose conservation would confer protection to large numbers of naturally co-occurring species. This thesis aims to evaluate some of the prerequisites to the umbrella species concept, focusing on resident birds in hemiboreal and boreal forests. The study was performed in four areas belonging to the southern Baltic Sea region: central and southern Sweden, south-central Lithuania and northeastern Poland. A review of empirical evaluations of the umbrella species concept performed in various systems suggested that multispecies approaches addressing the requirements of both the umbrellas and the beneficiary species have better potential than approaches based coarsely on the area needs of single species. An analysis of co-occurrence patterns among resident forest birds in landscape units of 100 ha showed that some species reliably indicated high species richness through their presence. For birds of deciduous forests, there was high cross-regional consistency in the identity of the best indicators. Specialised woodpeckers (Picidae) were prominent among the species that performed well as indicators. Their presence in the landscape units was generally linked positively to the degree of naturalness of the forest and to the amounts of resources that have become scarce in intensively managed forests, such as dead wood and large trees. In Sweden, occurrence of the white-backed woodpecker (Dendrocopos leucotos) in bird atlas squares was positively related to species richness among forest birds of conservation concern, as well as to the area of deciduous and mixed forests of high value for conservation. Moreover, the number of red-listed cryptogam species linked to deciduous trees and dead wood was higher where the woodpecker bred. Those results for birds of northern forests suggest that the umbrella species concept may constitute a useful component of conservation planning, especially in the work towards the derivation of quantitative targets. However, umbrella species are not a panacea and should therefore be seen as part of a complementary suite of approaches

Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods

Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as current sensory devices are developed with ever higher resolution, and problems involving multimodal data sources become more common. A plethora of feature extraction methods are available in the literature collectively grouped under the field of Multivariate Analysis (MVA). This paper provides a uniform treatment of several methods: Principal Component Analysis (PCA), Partial Least Squares (PLS), Canonical Correlation Analysis (CCA) and Orthonormalized PLS (OPLS), as well as their non-linear extensions derived by means of the theory of reproducing kernel Hilbert spaces. We also review their connections to other methods for classification and statistical dependence estimation, and introduce some recent developments to deal with the extreme cases of large-scale and low-sized problems. To illustrate the wide applicability of these methods in both classification and regression problems, we analyze their performance in a benchmark of publicly available data sets, and pay special attention to specific real applications involving audio processing for music genre prediction and hyperspectral satellite images for Earth and climate monitoring