Episodic Reasoning for Vision-Based Human Action Recognition

Smart Spaces, Ambient Intelligence, and Ambient Assisted Living are environmental paradigms that strongly depend on their capability to recognize human actions. While most solutions rest on sensor value interpretations and video analysis applications, few have realized the importance of incorporating common-sense capabilities to support the recognition process. Unfortunately, human action recognition cannot be successfully accomplished by only analyzing body postures. On the contrary, this task should be supported by profound knowledge of human agency nature and its tight connection to the reasons and motivations that explain it. The combination of this knowledge and the knowledge about how the world works is essential for recognizing and understanding human actions without committing common-senseless mistakes. This work demonstrates the impact that episodic reasoning has in improving the accuracy of a computer vision system for human action recognition. This work also presents formalization, implementation, and evaluation details of the knowledge model that supports the episodic reasoning

Non-linear classifiers applied to EEG analysis for epilepsy seizure detection

This work presents a novel approach for automatic epilepsy seizure detection based on EEG analysis that exploits the underlying non-linear nature of EEG data. In this paper, two main contributions are presented and validated: the use of non-linear classifiers through the so-called kernel trick and the proposal of a Bag-of-Words model for extracting a non-linear feature representation of the input data in an unsupervised manner. The performance of the resulting system is validated with public datasets, previously processed to remove artifacts or external disturbances, but also with private datasets recorded under realistic and non-ideal operating conditions. The use of public datasets caters for comparison purposes whereas the private one shows the performance of the system under realistic circumstances of noise, artifacts, and signals of different amplitudes. Moreover, the proposed solution has been compared to state-of-the-art works not only for pre-processed and public datasets but also with the private datasets. The mean F1-measure shows a 10% improvement over the second-best ranked method including cross-dataset experiments. The obtained results prove the robustness of the proposed solution to more realistic and variable conditions. (C) 2017 Elsevier Ltd. All rights reserved

Hierarchical Task Network Planning with Common-Sense Reasoning for Multiple-People Behaviour Analysis

Safety on public transport is a major concern for the relevant authorities. We address this issue by proposing an automated surveillance platform which combines data from video, infrared and pressure sensors. Data homogenisation and integration is achieved by a distributed architecture based on communication middleware that resolves interconnection issues, thereby enabling data modelling. A common-sense knowledge base models and encodes knowledge about public-transport platforms and the actions and activities of passengers. Trajectory data from passengers is modelled as a time-series of human activities. Common-sense knowledge and rules are then applied to detect inconsistencies or errors in the data interpretation. Lastly, the rationality that characterises human behaviour is also captured here through a bottom-up Hierarchical Task Network planner that, along with common-sense, corrects misinterpretations to explain passenger behaviour. The system is validated using a simulated bus saloon scenario as a case-study. Eighteen video sequences were recorded with up to six passengers. Four metrics were used to evaluate performance. The system, with an accuracy greater than 90% for each of the four metrics, was found to outperform a rule-base system and a system containing planning alone

Autonomous CPSoS for cognitive large manufacturing industries.

The general aim of a cognitive Cyber Physical System of Systems (CPSoS) is to provide managed access to data in a smart fashion such that sensing and actuation capabilities are connected. Whilst there is significant funding and research devoted to this area, focus remains purely on creating bespoke systems. This paper presents a novel approach, based on a set of components to leverage Situational Awareness and Smart Actuation in large manufacturing industries with the focus on enabling predictive maintenance for asset and abnormal situation management. This paper presents a novel generic platform, named AtiCoS, that combines case-based and common-sense reasoning, as the enabling methodologies for enhancing CPSoS with cognitive capabilities

Molecular dynamics simulations of the mechanisms controlling the propagation of bcc/fcc semi-coherent interfaces in iron

Molecular dynamics simulations have been used to study the effects of different orientation relationships between fcc and bcc phases on the bcc/fcc interfacial propagation in pure iron systems at 300 K. Three semi-coherent bcc/fcc interfaces have been investigated. In all the cases, results show that growth of the bcc phase starts in the areas of low potential energy and progresses into the areas of high potential energy at the original bcc/fcc interfaces. The phase transformation in areas of low potential energy is of a martensitic nature while that in the high potential energy areas involves occasional diffusional jumps of atoms.(OLD) MSE-

Effect of the anisotropy of martensitic transformation on ferrite deformation in Dual-Phase steels

The volume increase and shape change during austenite to martensite transformation in dual-phase (DP) steels are largely accommodated in the microstructure by the deformation of the surrounding ferrite matrix. Accurate estimation of transformation-induced deformation of ferrite via experiments and modeling is essential for predicting the subsequent mechanical behavior of DP steels. This study aims to illustrate the disadvantages of simplifying the anisotropic transformation deformation of martensite to isotropic dilatation for modeling the transformation-induced deformation of ferrite. A novel methodology is developed comprising sequential experimental and numerical research on DP steels to quantify transformation-induced strains in ferrite. This methodology combines the results of prior austenite grain reconstruction, phenomenological theory of martensite crystallography and electron backscatter diffraction (EBSD) orientation data to estimate variant-specific transformation deformation. Subsequently, by comparison of full-field micromechanical calculation results on a virtual DP steel microstructure with experimental EBSD kernel average misorientation and geometrically necessary dislocation measurement results it is shown that neglecting the shear deformation associated with the martensitic transformation leads to significant underestimation in the prediction of transformation-induced strains in ferrite.</p

Coalescence of martensite under uniaxial tension of iron crystallites by atomistic simulations

Molecular dynamics simulations are used to study the effects of tensile loading on nucleation and subsequent growth of bcc phase in pure fcc iron. The results show that orientation variant selection occurs during the stress-induced fcc-to-bcc transformation, which leads to the coalescence of neighbouring bcc platelets with identical orientation. The bcc phase nucleates mainly following Nishiyama–Wassermann and Kurdjumov–Sachs orientation relationships with the parent fcc phase. The present simulations contribute to a better understanding of mechanisms controlling mechanically induced martensitic transformation as well as coalescence of bcc platelets in steels.Materials Science and Engineering(OLD) MSE-