Interactions between building integrated photovoltaics and microclimate in urban environments

BIPV (building integrated photovoltaics) has progressed in the past years and become an element to be considered in city planning. BIPV has significant influence on microclimate in urban environments and the performance of BIPV is also affected by urban climate. The thermal model and electrical performance model of ventilated BIPV are combined to predict PV temperature and PV power output in Tianjin, China. Then, by using dynamic building energy model, the building cooling load for installing BIPV is calculated. A multi-layer model AUSSSM of urban canopy layer is used to assess the effect of BIPV on the Urban Heat Island (UHI). The simulation results show that in comparison with the conventional roof, the total building cooling load with ventilation PV roof may be decreased by 10%. The UHI effect after using BIPV relies on the surface absorptivity of original building. In this case, the daily total PV electricity output in urban areas may be reduced by 13% compared with the suburban areas due to UHI and solar radiation attenuation because of urban air pollution. The calculation results reveal that it is necessary to pay attention to and further analyze interactions between BIPV and microdimate in urban environments to decrease urban pollution, improve BIPV performance and reduce cooling load. Copyright © 2006 by ASME.

Single camera pose estimation using Bayesian filtering and Kinect motion priors

Traditional approaches to upper body pose estimation using monocular vision rely on complex body models and a large variety of geometric constraints. We argue that this is not ideal and somewhat inelegant as it results in large processing burdens, and instead attempt to incorporate these constraints through priors obtained directly from training data. A prior distribution covering the probability of a human pose occurring is used to incorporate likely human poses. This distribution is obtained offline, by fitting a Gaussian mixture model to a large dataset of recorded human body poses, tracked using a Kinect sensor. We combine this prior information with a random walk transition model to obtain an upper body model, suitable for use within a recursive Bayesian filtering framework. Our model can be viewed as a mixture of discrete Ornstein-Uhlenbeck processes, in that states behave as random walks, but drift towards a set of typically observed poses. This model is combined with measurements of the human head and hand positions, using recursive Bayesian estimation to incorporate temporal information. Measurements are obtained using face detection and a simple skin colour hand detector, trained using the detected face. The suggested model is designed with analytical tractability in mind and we show that the pose tracking can be Rao-Blackwellised using the mixture Kalman filter, allowing for computational efficiency while still incorporating bio-mechanical properties of the upper body. In addition, the use of the proposed upper body model allows reliable three-dimensional pose estimates to be obtained indirectly for a number of joints that are often difficult to detect using traditional object recognition strategies. Comparisons with Kinect sensor results and the state of the art in 2D pose estimation highlight the efficacy of the proposed approach.