Face set classification using maximally probable mutual modes

In this paper we consider face recognition from sets of face images and, in particular, recognition invariance to illumination. The main contribution is an algorithm based on the novel concept of maximally probable mutual modes (MMPM). Specifically: (i) we discuss and derive a local manifold illumination invariant and (ii) show how the invariant naturally leads to a formulation of "common modes" of two face appearance distributions. Recognition is then performed by finding the most probable mode, which is shown to be an eigenvalue problem. The effectiveness of the proposed method is demonstrated empirically on a challenging database containing the total of 700 video sequences of 100 individuals

Sociospatial distribution of access to facilities for moderate and vigorous intensity physical activity in Scotland by different modes of transport

Background People living in neighbourhoods of lower socioeconomic status have been shown to have higher rates of obesity and a lower likelihood of meeting physical activity recommendations than their more affluent counterparts. This study examines the sociospatial distribution of access to facilities for moderate or vigorous intensity physical activity in Scotland and whether such access differs by the mode of transport available and by Urban Rural Classification. Methods A database of all fixed physical activity facilities was obtained from the national agency for sport in Scotland. Facilities were categorised into light, moderate and vigorous intensity activity groupings before being mapped. Transport networks were created to assess the number of each type of facility accessible from the population weighted centroid of each small area in Scotland on foot, by bicycle, by car and by bus. Multilevel modelling was used to investigate the distribution of the number of accessible facilities by small area deprivation within urban, small town and rural areas separately, adjusting for population size and local authority. Results Prior to adjustment for Urban Rural Classification and local authority, the median number of accessible facilities for moderate or vigorous intensity activity increased with increasing deprivation from the most affluent or second most affluent quintile to the most deprived for all modes of transport. However, after adjustment, the modelling results suggest that those in more affluent areas have significantly higher access to moderate and vigorous intensity facilities by car than those living in more deprived areas. Conclusions The sociospatial distributions of access to facilities for both moderate intensity and vigorous intensity physical activity were similar. However, the results suggest that those living in the most affluent neighbourhoods have poorer access to facilities of either type that can be reached on foot, by bicycle or by bus than those living in less affluent areas. This poorer access from the most affluent areas appears to be reversed for those with access to a car.

The formation of complex microstructures after different deformation modes in advanced high-strength steels

The microstructure of transformation induced plasticity (TRIP) and dual phase (DP) multiphase steels after stamping of an industrial component at different strain levels was investigated using transmission electron microscopy. The TRIP steel microstructure showed a more complex dislocation substructure of ferrite at different strain levels than DP steel. The deformation microstructure of the stamped parts was compared to the deformation microstructure in these complex steels for different "equivalent" tensile strains. It was found that the microstructures are similar only at high levels of strain (>10 pct) for both steels. © 2014 The Minerals, Metals & Materials Society and ASM International.

Clustering and visualization of failure modes using an evolving tree

Despite the popularity of Failure Mode and Effect Analysis (FMEA) in a wide range of industries, two well-known shortcomings are the complexity of the FMEA worksheet and its intricacy of use. To the best of our knowledge, the use of computation techniques for solving the aforementioned shortcomings is limited. As such, the idea of clustering and visualization pertaining to the failure modes in FMEA is proposed in this paper. A neural network visualization model with an incremental learning feature, i.e., the evolving tree (ETree), is adopted to allow the failure modes in FMEA to be clustered and visualized as a tree structure. In addition, the ideas of risk interval and risk ordering for different groups of failure modes are proposed to allow the failure modes to be ordered, analyzed, and evaluated in groups. The main advantages of the proposed method lie in its ability to transform failure modes in a complex FMEA worksheet to a tree structure for better visualization, while maintaining the risk evaluation and ordering features. It can be applied to the conventional FMEA methodology without requiring additional information or data. A real world case study in the edible bird nest industry in Sarawak (Borneo Island) is used to evaluate the usefulness of the proposed method. The experiments show that the failure modes in FMEA can be effectively visualized through the tree structure. A discussion with FMEA users engaged in the case study indicates that such visualization is helpful in comprehending and analyzing the respective failure modes, as compared with those in an FMEA table. The resulting tree structure, together with risk interval and risk ordering, provides a quick and easily understandable framework to elucidate important information from complex FMEA forms; therefore facilitating the decision-making tasks by FMEA users. The significance of this study is twofold, viz., the use of a computational visualization approach to tackling two well-known shortcomings of FMEA; and the use of ETree as an effective neural network learning paradigm to facilitate FMEA implementations. These findings aim to spearhead the potential adoption of FMEA as a useful and usable risk evaluation and management tool by the wider community.