Performance of modified wood in service - multi-sensor data fusion and its multi-way analysis

Recent developments in the field of electronic sensors and analytics provide new opportunity for accurate characterization of materials often based on portable and non-destructive methods. By using several complementary techniques, the material description is precise and complete. The data provided by multiple equipment, however, are often not directly comparable due to different resolution, sensitivity and/or data format. The complexity related to the data fusion step and its further interpretation often leads to not complete exploitation of the available data. This paper presents a multi-block approach used for merging experimental data collected by measurement of modified wood in service. Characterization of samples appearance (colour and gloss) is merged with spectral data that decodes information regarding chemical composition. Alternative approaches for data fusion on the low-, mid- and high-levels are introduced, discussed and confronted with the standard approach (single sensor data interpretation). Finally, the trial to analyse the data with multi-way method is presented and interpreted

Efficient Bayesian Model Selection in PARAFAC via Stochastic Thermodynamic Integration

International audienceParallel factor analysis (PARAFAC) is one of the most popular tensor factorization models. Even though it has proven successful in diverse application fields, the performance of PARAFAC usually hinges up on the rank of the factorization, which is typically specified manually by the practitioner. In this study, we develop a novel parallel and distributed Bayesian model selection technique for rank estimation in large-scale PARAFAC models. The proposed approach integrates ideas from the emerging field of stochastic gradient Markov Chain Monte Carlo, statistical physics, and distributed stochastic optimization. As opposed to the existing methods, which are based on some heuristics, our method has a clear mathematical interpretation, and has significantly lower computational requirements, thanks to data subsampling and parallelization. We provide formal theoretical analysis on the bias induced by the proposed approach. Our experiments on synthetic and large-scale real datasets show that our method is able to find the optimal model order while being significantly faster than the state-of-the-art

Application of Parallel Factor Analysis (PARAFAC) to Electrophysiological Data

The identification of important features in multi-electrode recordings requires the decomposition of data in order to disclose relevant features and to offer a clear graphical representation. This can be a demanding task. Parallel Factor Analysis (PARAFAC; Hitchcock, 1927; Carrol & Chang, 1970; Harshman, 1970) is a method to decompose multi-dimensional arrays in order to focus on the features of interest, and provides a distinct illustration of the results. We applied PARAFAC to analyse spatio-temporal patterns in the functional connectivity between neurons, as revealed in their spike trains recorded in cat primary visual cortex (area 18). During these recordings we reversibly deactivated feedback connections from higher visual areas in the pMS (posterior middle suprasylvian) cortex in order to study the impact of these top-down signals. Cross correlation was computed for every possible pair of the electrodes in the electrode array. PARAFAC was then used to reveal the effects of time, stimulus, and deactivation condition on the correlation patterns. Our results show that PARAFAC is able to reliably extract changes in correlation strength for different experimental conditions and display the relevant features. Thus, PARAFAC proves to be well-suited for the use in the context of electrophysiological (action potential) recordings

Application of Parallel Factor Analysis (PARAFAC) to electrophysiological data

The identification of important features in multi-electrode recordings requires the decomposition of data in order to disclose relevant features and to offer a clear graphical representation. This can be a demanding task. Parallel Factor Analysis (PARAFAC; Hitchcock, 1927; Carrol and Chang, 1970; Harshman, 1970) is a method to decompose multi-dimensional arrays in order to focus on the features of interest, and provides a distinct illustration of the results. We applied PARAFAC to analyse spatio-temporal patterns in the functional connectivity between neurons, as revealed in their spike trains recorded in cat primary visual cortex (area 18). During these recordings we reversibly deactivated feedback connections from higher visual areas in the pMS (posterior middle suprasylvian) cortex in order to study the impact of these top-down signals. Cross correlation was computed for every possible pair of the 16 electrodes in the electrode array. PARAFAC was then used to reveal the effects of time, stimulus, and deactivation condition on the correlation patterns. Our results show that PARAFAC is able to reliably extract changes in correlation strength for different experimental conditions and display the relevant features. Thus, PARAFAC proves to be well-suited for the use in the context of electrophysiological (action potential) recordings

Psychophysiologische Profile in nutzerzentrierten Mensch-Maschine-Systemen: Extraktion kardialer und elektrodermaler Profile zur Bewertung der mentalen Beanspruchung

Nutzerzentrierte Mensch-Maschine-Systemen sind darauf ausgerichtet, sich an die jeweiligen Anforderungen, Bedürfnisse und Gefühlszustände der nutzenden Person anzupassen. Dabei sollen adaptive Assistenzsysteme dem Menschen dann Unterstützung anbieten, sobald die kognitiven Ressourcen der Person an ihre Grenzen stoßen. Die kognitive Ressourcenauslastung wird durch das Konstrukt der mentalen Beanspruchung repräsentiert. Mit der physiologischen Messmethode kann die mentale Beanspruchung kontinuierlich und störungsfrei an der Schnittstelle zwischen Mensch und Maschine erfasst werden. Welche der zahlreichen physiologischen Parameter die mentale Beanspruchung zuverlässig abbilden, ist bis heute nicht eindeutig geklärt. Um valide von einer gemessenen physiologischen Aktivität auf die mentale Beanspruchung einer Person schließen zu können, fordern Cacioppo und Kollegen (Cacioppo & Tassinary, 1990; Cacioppo, Tassinary & Berntson, 2000, 2007, 2017) in ihrem theoretischen Rahmenmodell die Neustrukturierung physiologischer Einzelparameter zu physiologischen Profilen. Eine solche Neustrukturierung physiologischer Parameter wird in der vorliegenden Arbeit mit Hilfe von drei empirischen Studien umgesetzt. Hierfür werden mittels mehrdimensionaler Analyseverfahren kardiale und elektrodermale Profile aus den kardialen und elektrodermalen Einzelparametern abgeleitet. Diese sagen die mentale Beanspruchung nicht nur bedeutsam und spezifisch vorher, sondern sind auch über verschiedene mentale Belastungsfaktoren generalisierbar. Die physiologischen Profile werden in die Taxonomie von Cacioppo et al. (2000, 2007, 2017) eingeordnet und die Potentiale bei der Verwendung der Profile in einer nutzerzentrierten Mensch-Maschine-Schnittstelle dargelegt