ESFuelCell2011-54937 THERMAL PROPERTIES OF GREEN ROOF MEDIA DURING PLANT ESTABLISHMENT AND GROWTH

ABSTRACT In order to ascertain the efficiency benefits of green roofs for buildings, a thermodynamic model must be created for the green roof. This work focuses on the thermal properties (conductivity and specific heat capacity) of several media and how they are affected by root growth within them. The results of this research will be used in creating more accurate thermodynamic green roof models. For this experiment, three repetitions of 16 different planting/media combinations were used to monitor the changing thermal properties of the media with environment changes; a focus being on root growth. This experiment shows that the conductance is probably affected by root propagation

Clustering Analysis Algorithms and Their Applications to Digital POSS-II Catalogs

. We report on the preliminary results of experiments using a Bayesian cluster method to cluster objects present in photographic images of the POSS-II. Our goal is to explore the power of unsupervised learning techniques to classify objects meaningfully, and perhaps to discover previously unrecognized object categories in digital sky surveys. Our primary finding is that the program we used, AutoClass, was able to form several sensible categories from a few simple attributes of the object images, separating the data into four recognizable and astronomically meaningful classes: stars, galaxies with bright central cores, galaxies without bright cores, and stars with a visible "fuzz" around them. Also, in an independent experiment we found out that the two types of galaxies have distinct color distributions (the more concentrated class being redder, as indeed expected if they are predominantly early Hubble types), although no color information was given to AutoClass. This illustrates the p..

Case Representation Issues for Case-Based Reasoning from Ensemble Research

Ensembles of classifiers will produce lower errors than the member classifiers if there is diversity in the ensemble. One means of producing this diversity in nearest neighbour classifiers is to base the member classifiers on different feature subsets. In this paper we show four examples where this is the case. This has implications for the practice of feature subset selection (an important issue in CBR and data-mining) because it shows that there is no best feature subset to represent a problem. We show that if diversity is emphasised in the development of the ensemble that the ensemble members appear to be local learners specializing in sub-domains of the problem space. The paper concludes with some proposals on how analysis of ensembles of local learners might provide insight on problem-space decomposition for hierarchical CBR