Distributed multivariate regression with unknown noise covariance in the presence of outliers: an MDL approach

We consider the problem of estimating the coefficients in a multivariable linear model by means of a wireless sensor network which may be affected by anomalous measurements. The noise covariance matrices at the different sensors are assumed unknown. Treating outlying samples, and their support, as additional nuisance parameters, the Maximum Likelihood estimate is investigated, with the number of outliers being estimated according to the Minimum Description Length principle. A distributed implementation based on iterative consensus techniques is then proposed, and it is shown effective for managing outliers in the data.Peer ReviewedPostprint (author's final draft

Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets dagger

In This Paper, A Monitoring System Of Agricultural Production Is Modeled As A Data Fusion System (Data From Local Fairs And Meteorological Data). The Proposal Considers The Particular Information Of Sales In Agricultural Markets For Knowledge Extraction About The Associations Among Them. This Association Knowledge Is Employed To Improve Predictions Of Sales Using A Spatial Prediction Technique, As Shown With Data Collected From Local Markets Of The Andean Region Of Ecuador. The Commercial Activity In These Markets Uses Alternative Marketing Circuits (Cialco). This Market Platform Establishes A Direct Relationship Between Producer And Consumer Prices And Promotes Direct Commercial Interaction Among Family Groups. The Problem Is Presented First As A General Fusion Problem With A Network Of Spatially Distributed Heterogeneous Data Sources, And Is Then Applied To The Prediction Of Products Sales Based On Association Rules Mined In Available Sales Data. First, Transactional Data Is Used As The Base To Extract The Best Association Rules Between Products Sold In Different Local Markets, Knowledge That Allows The System To Gain A Significant Improvement In Prediction Accuracy In The Spatial Region Considered.This work was supported in part by Project MINECO TEC2017-88048-C2–2-R, Salesian Polytechnic University of Quito-Ecuador and by Commercial Coordination Network, Ministry of Agriculture and Livestock, Ecuado

Online Tool Condition Monitoring Based on Parsimonious Ensemble+

Accurate diagnosis of tool wear in metal turning process remains an open challenge for both scientists and industrial practitioners because of inhomogeneities in workpiece material, nonstationary machining settings to suit production requirements, and nonlinear relations between measured variables and tool wear. Common methodologies for tool condition monitoring still rely on batch approaches which cannot cope with a fast sampling rate of metal cutting process. Furthermore they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online tool condition monitoring approach based on Parsimonious Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly flexible principle where both ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. The paper presents advancement of a newly developed ensemble learning algorithm, pENsemble+, where online active learning scenario is incorporated to reduce operator labelling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilising real-world manufacturing data streams and comparisons with well known algorithms were carried out. Furthermore, the efficacy of pENsemble was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic

Modeling, Identification and Control at Telemark University College

Master studies in process automation started in 1989 at what soon became Telemark University College, and the 20 year anniversary marks the start of our own PhD degree in Process, Energy and Automation Engineering. The paper gives an overview of research activities related to control engineering at Department of Electrical Engineering, Information Technology and Cybernetics

Modeling, Identification and Control at Telemark University College

Master studies in process automation started in 1989 at what soon became Telemark University College, and the 20 year anniversary marks the start of our own PhD degree in Process, Energy and Automation Engineering. The paper gives an overview of research activities related to control engineering at Department of Electrical Engineering, Information Technology and Cybernetics

Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach

Over the past few years, there has been a noticeable advancement in environmental models and information fusion systems taking advantage of the recent developments in sensor and mobile technologies. However, little attention has been paid so far to quantifying the relationship between environment changes and their impact on our bodies in real-life settings. In this paper, we identify a data driven approach based on direct and continuous sensor data to assess the impact of the surrounding environment and physiological changes and emotion. We aim at investigating the potential of fusing on-body physiological signals, environmental sensory data and on-line self-report emotion measures in order to achieve the following objectives: (1) model the short term impact of the ambient environment on human body, (2) predict emotions based on-body sensors and environmental data. To achieve this, we have conducted a real-world study ‘in the wild’ with on-body and mobile sensors. Data was collected from participants walking around Nottingham city centre, in order to develop analytical and predictive models. Multiple regression, after allowing for possible confounders, showed a noticeable correlation between noise exposure and heart rate. Similarly, UV and environmental noise have been shown to have a noticeable effect on changes in ElectroDermal Activity (EDA). Air pressure demonstrated the greatest contribution towards the detected changes in body temperature and motion. Also, significant correlation was found between air pressure and heart rate. Finally, decision fusion of the classification results from different modalities is performed. To the best of our knowledge this work presents the first attempt at fusing and modelling data from environmental and physiological sources collected from sensors in a real-world setting