Application of Radiant Floor Heating in Large Space Buildings with Significant Cold Air Infiltration through Door Openings

Radiant Floor Heating System (RFHS) has been commonly used in railway stations in cold regions of China for its advantages in thermal comfort and energy efficiency. However, the uneven distribution and extremely cold area of the heating floor, caused by cold air infiltration through door openings, are commonly found in our filed measurements. This impact is not considered in the standardized design methods, resulting in an underestimation of the design heat flux. In this paper, CFD simulations are used to quantify the impacts of natural infiltration on surface heat transfer process. Model validation was made against field measurements. 13 simulations were performed for different speeds. As a result, the mean radiant heat flux at floor surface decreased by 36.8% as the infiltration air speed increased from 0.05 m/s to 1.2 m/s, and the noneffective area increased more than 16 times. This result highlights a significant influence of natural infiltration. Regression models were finally developed as a simple method for rough estimation of this impact on radiation, which can make up the limitations of current methods and inform designers to improve their initial design of RFHS when natural infiltration is present

A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network

In this paper, we employ Probabilistic Neural Network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition algorithm. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90%. Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation.Comment: 6 pages, 3 figures, 2 table