914 resultados para units-invariant benchmarking


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The majority of previous research on social capital and health is limited to social capital in residential neighborhoods and communities. Using data from the Finnish 10-Town study we examined social capital at work as a predictor of health in a cohort of 9524 initially healthy local government employees in 1522 work units, who did not change their work unit between 2000 and 2004 and responded to surveys measuring social capital at work and health at both time-points. We used a validated tool to measure social capital with perceptions at the individual level and with co-workers' responses at the work unit level. According to multilevel modeling, a contextual effect of work unit social capital on self-rated health was not accounted for by the individual's socio-demographic characteristics or lifestyle. The odds for health impairment were 1.27 times higher for employees who constantly worked in units with low social capital than for those with constantly high work unit social capital. Corresponding odds ratios for low and declining individual-level social capital varied between 1.56 and 1.78. Increasing levels of individual social capital were associated with sustained good health. In conclusion, this longitudinal multilevel study provides support for the hypothesis that exposure to low social capital at work may be detrimental to the health of employees. (c) 2007 Elsevier Ltd. All rights reserved.

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In this paper we demonstrate a simple and novel illumination model that can be used for illumination invariant facial recognition. This model requires no prior knowledge of the illumination conditions and can be used when there is only a single training image per-person. The proposed illumination model separates the effects of illumination over a small area of the face into two components; an additive component modelling the mean illumination and a multiplicative component, modelling the variance within the facial area. Illumination invariant facial recognition is performed in a piecewise manner, by splitting the face image into blocks, then normalizing the illumination within each block based on the new lighting model. The assumptions underlying this novel lighting model have been verified on the YaleB face database. We show that magnitude 2D Fourier features can be used as robust facial descriptors within the new lighting model. Using only a single training image per-person, our new method achieves high (in most cases 100%) identification accuracy on the YaleB, extended YaleB and CMU-PIE face databases.

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This paper presents an Invariant Information Local Sub-map Filter (IILSF) as a technique for consistent Simultaneous Localisation and Mapping (SLAM) in a large environment. It harnesses the benefits of sub-map technique to improve the consistency and efficiency of Extended Kalman Filter (EKF) based SLAM. The IILSF makes use of invariant information obtained from estimated locations of features in independent sub-maps, instead of incorporating every observation directly into the global map. Then the global map is updated at regular intervals. Applying this technique to the EKF based SLAM algorithm: (a) reduces the computational complexity of maintaining the global map estimates and (b) simplifies transformation complexities and data association ambiguities usually experienced in fusing sub-maps together. Simulation results show that the method was able to accurately fuse local map observations to generate an efficient and consistent global map, in addition to significantly reducing computational cost and data association ambiguities.

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This paper examines power quality benchmarks in the electricity supply industry (ESI) and impact of standards for the reduction of voltage dip incidents. The paper considers adherence to particular standards and is supported by several case studies from incidents where voltage dips have been detected and assessed by the power systems division of Scottish Power and where improvements have been implemented to help militate against subsequent incidents.