1. |
- Földváry Ličina, Veronika, et al.
(author)
-
Development of the ASHRAE Global Thermal Comfort Database II
- 2018
-
In: Building and Environment. - : Elsevier BV. - 0360-1323. ; 142, s. 502-512
-
Journal article (peer-reviewed)abstract
- Recognizing the value of open-source research databases in advancing the art and science of HVAC, in 2014 the ASHRAE Global Thermal Comfort Database II project was launched under the leadership of University of California at Berkeley's Center for the Built Environment and The University of Sydney's Indoor Environmental Quality (IEQ) Laboratory. The exercise began with a systematic collection and harmonization of raw data from the last two decades of thermal comfort field studies around the world. The ASHRAE Global Thermal Comfort Database II (Comfort Database), now an online, open-source database, includes approximately 81,846 complete sets of objective indoor climatic observations with accompanying “right-here-right-now” subjective evaluations by the building occupants who were exposed to them. The database is intended to support diverse inquiries about thermal comfort in field settings. A simple web-based interface to the database enables filtering on multiple criteria, including building typology, occupancy type, subjects' demographic variables, subjective thermal comfort states, indoor thermal environmental criteria, calculated comfort indices, environmental control criteria and outdoor meteorological information. Furthermore, a web-based interactive thermal comfort visualization tool has been developed that allows end-users to quickly and interactively explore the data.
|
|
2. |
- Zhang, Yongchao, et al.
(author)
-
Wideband Sparse Reconstruction for Scanning Radar
- 2018
-
In: IEEE Transactions on Geoscience and Remote Sensing. - 0196-2892. ; 56:10, s. 6055-6068
-
Journal article (peer-reviewed)abstract
- Recently, the generalized sparse iterative covariance-based estimation algorithm was extended to allow for varying norm constraints in scanning radar applications. In this paper, further to this development, we introduce a wideband dictionary framework which can provide a computationally efficient estimation of sparse signals. The technique is formed by initially introducing a coarse grid dictionary constructed from integrating elements, spanning bands of the considered parameter space. After forming estimates of the initially activated bands, these are retained and refined, whereas nonactivated bands are discarded from the further optimization, resulting in a smaller and zoomed dictionary with a finer grid. Implementing this scheme allows for reliable sparse signal reconstruction, at a much lower computational cost as compared to directly forming a larger dictionary spanning the whole parameter space. Simulation and real data processing results demonstrate that the proposed wideband estimator offers significant computational savings, without noticeable loss of performance.
|
|