Human-centric light sensing and estimation from RGBD images: the invisible light switch

Lighting design in indoor environments is of primary importance for at least two reasons: 1) people should perceive an adequate light; 2) an effective lighting design means consistent energy saving. We present the Invisible Light Switch (ILS) to address both aspects. ILS dynamically adjusts the room illumination level to save energy while maintaining constant the light level perception of the users. So the energy saving is invisible to them. Our proposed ILS leverages a radiosity model to estimate the light level which is perceived by a person within an indoor environment, taking into account the person position and her/his viewing frustum (head pose). ILS may therefore dim those luminaires, which are not seen by the user, resulting in an effective energy saving, especially in large open offices (where light may otherwise be ON everywhere for a single person). To quantify the system performance, we have collected a new dataset where people wear luxmeter devices while working in office rooms. The luxmeters measure the amount of light (in Lux) reaching the people gaze, which we consider a proxy to their illumination level perception. Our initial results are promising: in a room with 8 LED luminaires, the energy consumption in a day may be reduced from 18585 to 6206 watts with ILS (currently needing 1560 watts for operations). While doing so, the drop in perceived lighting decreases by just 200 lux, a value considered negligible when the original illumination level is above 1200 lux, as is normally the case in offices

Information theory measures for the engineering validation of ground-motion simulations

This short communication introduces a quantitative approach for the engineering validation of ground-motion simulations based on information theory concepts and statistical hypothesis testing. Specifically, we use the Kullback-Leibler divergence to measure the similarity of the probability distributions of recorded and simulated ground-motion intensity measures (IMs). We demonstrate the application of the proposed validation approach to ground-motion simulations computed by using a variety of methods, including Graves and Pitarka hybrid broadband, the deterministic composite source model, and a stochastic white noise finite-fault model. Ground-motion IMs, acting as proxies for the (nonlinear) seismic response of more complex engineered systems, are considered herein to validate the considered ground-motion simulation methods. The list of considered IMs includes both spectral-shape and duration-related proxies, shown to be the optimal IMs in several probabilistic seismic demand models of different structural types, within the framework of performance-based earthquake engineering. The proposed validation exercise (1) can highlight the similarities and differences between simulated and recorded ground motions for a given simulation method and/or (2) allow the ranking of the performance of alternative simulation methods. The similarities between records and simulations should provide confidence in using the simulation method for engineering applications, while the discrepancies should help in improving the tested method for the generation of synthetic records

Coatings for graphite fibers

Graphite fibers released from composites during burning or an explosion caused shorting of electrical and electronic equipment. Silicon carbide, silica, silicon nitride and boron nitride were coated on graphite fibers to increase their electrical resistances. Resistances as high as three orders of magnitude higher than uncoated fiber were attained without any significant degradation of the substrate fiber. An organo-silicone approach to produce coated fibers with high electrical resistance was also used. Celion 6000 graphite fibers were coated with an organo-silicone compound, followed by hydrolysis and pyrolysis of the coating to a silica-like material. The shear and flexural strengths of composites made from high electrically resistant fibers were considerably lower than the shear and flexural strengths of composites made from the lower electrically resistant fibers. The lower shear strengths of the composites indicated that the coatings on these fibers were weaker than the coating on the fibers which were pyrolyzed at higher temperature

Study of high resistance inorganic coatings on graphite fibers

Coatings made of boron, silicon carbide, silica, and silica-like materials were studied to determine their ability to increase resistance of graphite fibers. The most promising results were attained by chemical vapor depositing silicon carbide on graphite fiber followed by oxidation, and drawing graphite fiber through ethyl silicate followed by appropriate heat treatments. In the silicon carbide coating studies, no degradation of the graphite fibers was observed and resistance values as high as three orders of magnitude higher than that of the uncoated fiber was attained. The strength of a composite fabricated from the coated fiber had a strength which compared favorably with those of composites prepared from uncoated fiber. For the silica-like coated fiber prepared by drawing the graphite fiber through an ethyl silicate solution followed by heating, coated fiber resistances about an order of magnitude greater than that of the uncoated fiber were attained. Composites prepared using these fibers had flexural strengths comparable with those prepared using uncoated fibers, but the shear strengths were lower

Multicriteria decision making for selecting an optimal survey approach for large building portfolios

Technological advances and innovations have led to various pre- and post-disaster data collection alternatives to traditional sidewalk surveys. Hence, selecting a suitable survey approach may be challenging for different decision-makers. This paper proposes a multicriteria decision-making (MCDM) method to choose the optimal survey approach to gather exposure information needed for reliable multi-hazard risk assessment of large building and infrastructure portfolios. Both deterministic and stochastic implementations of MCDM are investigated, considering primary sources of aleatory and epistemic uncertainties. The applicability of the proposed framework is demonstrated for a portfolio of 13,200 buildings in a hypothetical multi-hazard prone region. The results show that informed decisions on identifying an optimal survey technique could be efficiently derived using MCDM and a number of relevant criteria. The proposed methodology can support various decision-makers in pre- and post-disaster risk modeling and management/reduction

Geodesic Distance Histogram Feature for Video Segmentation

This paper proposes a geodesic-distance-based feature that encodes global information for improved video segmentation algorithms. The feature is a joint histogram of intensity and geodesic distances, where the geodesic distances are computed as the shortest paths between superpixels via their boundaries. We also incorporate adaptive voting weights and spatial pyramid configurations to include spatial information into the geodesic histogram feature and show that this further improves results. The feature is generic and can be used as part of various algorithms. In experiments, we test the geodesic histogram feature by incorporating it into two existing video segmentation frameworks. This leads to significantly better performance in 3D video segmentation benchmarks on two datasets

Modification of stochastic ground motion models for matching target intensity measures

Stochastic ground motion models produce synthetic time‐histories by modulating a white noise sequence through functions that address spectral and temporal properties of the excitation. The resultant ground motions can be then used in simulation‐based seismic risk assessment applications. This is established by relating the parameters of the aforementioned functions to earthquake and site characteristics through predictive relationships. An important concern related to the use of these models is the fact that through current approaches in selecting these predictive relationships, compatibility to the seismic hazard is not guaranteed. This work offers a computationally efficient framework for the modification of stochastic ground motion models to match target intensity measures (IMs) for a specific site and structure of interest. This is set as an optimization problem with a dual objective. The first objective minimizes the discrepancy between the target IMs and the predictions established through the stochastic ground motion model for a chosen earthquake scenario. The second objective constraints the deviation from the model characteristics suggested by existing predictive relationships, guaranteeing that the resultant ground motions not only match the target IMs but are also compatible with regional trends. A framework leveraging kriging surrogate modeling is formulated for performing the resultant multi‐objective optimization, and different computational aspects related to this optimization are discussed in detail. The illustrative implementation shows that the proposed framework can provide ground motions with high compatibility to target IMs with small only deviation from existing predictive relationships and discusses approaches for selecting a final compromise between these two competing objectives

Hazard-compatible modification of stochastic ground motion models

A computationally efficient framework is presented for modification of stochastic ground motion models to establish compatibility with the seismic hazard for specific seismicity scenarios and a given structure/site. The modification pertains to the probabilistic predictive models that relate the parameters of the ground motion model to seismicity/site characteristics. These predictive models are defined through a mean prediction and an associated variance, and both these properties are modified in the proposed framework. For a given seismicity scenario, defined for example by the moment magnitude and source-to-site distance, the conditional hazard is described through the mean and the dispersion of some structure-specific intensity measure(s). Therefore, for both the predictive models and the seismic hazard, a probabilistic description is considered, extending previous work of the authors that had examined description only through mean value characteristics. The proposed modification is defined as a bi-objective optimization. The first objective corresponds to comparison for a chosen seismicity scenario between the target hazard and the predictions established through the stochastic ground motion model. The second objective corresponds to comparison of the modified predictive relationships to the pre-existing ones that were developed considering regional data, and guarantees that the resultant ground motions will have features compatible with observed trends. The relative entropy is adopted to quantify both objectives, and a computational framework relying on kriging surrogate modeling is established for an efficient optimization. Computational discussions focus on the estimation of the various statistics of the stochastic ground motion model output needed for the entropy calculation

Validation of hazard-compatible stochastic ground motion model modification techniques

An important consideration for the adoption of stochastic ground motion models in performance-based earthquake engineering applications is that the probability distribution of target intensity measures from the developed suites of time-histories is compatible with the prescribed hazard at the site and structure of interest. The authors have recently developed a computationally efficient framework to modify existing stochastic ground motion models to facilitate such a compatibility. For a given seismicity scenario, the framework identifies the modified stochastic ground motion model that can sufficiently match the prescribed hazard while maintaining similarity to regional physical ground motion model characteristics. This paper extends this effort through a validation study. Suites of recorded and stochastic ground motions, whose spectral acceleration statistics match the mean and variance of target spectra within a period range of interest, are utilized as input to perform response history analysis of inelastic single-degree-of-freedom case-study systems. The resultant engineering demand parameters distributions are then compared to assess the effect of the proposed modification

Optimising the inherent fire capacity of structures

This paper introduces a structural design optimisation methodology aimed at minimising the consequences of a fire. The methodology considers the trade-off between implementing passive fire protection measures and enhancing a structure's “inherent fire capacity”, defined as its ability to retain integrity/functionality without additional fire safety measures. The feasibility of the methodology is demonstrated through the fire safety design of a single-span, steel girder bridge. The optimisation process generates multiple Pareto-optimal solutions for minimising the maximum bridge deflection after a given fire exposure time. Passive protection ensures the bridge's functionality when facing a heavy goods vehicle fire. In the case of exposure to a car fire, solutions requiring fire protection in specific limited girder regions are identified. The decision-making process is further supported by investigating the robustness of the solutions to uncertainties in material properties and the heat flux model