Population Growth of the Floating Weed Salvinia molesta: Field Observation and a Global Model Based on Temperature and Nitrogen

1986 ◽  
Vol 23 (3) ◽  
pp. 1013 ◽  
Author(s):  
P. M. Room ◽  
P. A. Thomas
2002 ◽  
Vol 11 (03) ◽  
pp. 327-346 ◽  
Author(s):  
Y. PENCOLÉ ◽  
M.-O. CORDIER ◽  
L. ROZÉ

We address the problem of diagnosing complex discrete-event systems such as telecommunication networks. Given a flow of observations from the system, the goal is to explain those observations by identifying and localizing possible faults. Several model-based diagnosis approaches deal with this problem but they need the computation of a global model which is not feasible for complex systems like telecommunication networks. Our contribution is the proposal of a decentralized approach which permits to carry out an on-line diagnosis without computing the global model. This paper describes the implementation of a tool based on this approach. Given a decentralized model of the system and a flow of observations, the program analyzes the flow and computes the diagnosis in a decentralized way. The impact of the merging strategy on the global efficiency is demonstrated and illustrated by experimental results on a real application.


2001 ◽  
Vol 34 (7) ◽  
pp. 389-394 ◽  
Author(s):  
Karl-Petter Lindegaard ◽  
Thor I. Fossen

2010 ◽  
Vol 11 (1) ◽  
pp. 139-155 ◽  
Author(s):  
C. Lu ◽  
H. Yuan ◽  
E. I. Tollerud ◽  
N. Wang

Abstract Global precipitation forecasts from numerical weather prediction (NWP) models can be verified using the near-global coverage of satellite precipitation retrievals. However, inaccuracies in satellite precipitation analyses complicate the interpretation of forecast errors that result from verification of an NWP model against satellite observations. In this study, assessments of both a global quantitative precipitation estimate (QPE) from a satellite precipitation product and corresponding global quantitative precipitation forecast (QPF) from a global NWP model are conducted using available global land-based gauge data. A scale decomposition technique is devised, coupled with seasonal and spatial classifications, to evaluate these inaccuracies. The results are then analyzed in context with various physical precipitation systems, including heavy monsoonal rains, light Mediterranean winter rains, and North American convective-related and midlatitude cyclone–related precipitation. In general, global model results tend to consistently overforecast rainfall, whereas satellite measurements present a mixed pattern, underestimating many large-scale precipitation systems while overestimating many convective-scale precipitation systems. Both global model QPF and satellite-retrieved QPE showed better correlation scores in large-scale precipitation systems when verified with gauge measurements. In this case, model-based QPF tends to outperform satellite-retrieved QPE. At convective scales, there are significant drops in both model QPF and satellite QPE correlation scores, but satellite QPE performs slightly better than model QPF. These general results also showed regional and seasonal variation. For example, in tropical monsoon systems, satellite QPE tended to outperform model-based QPF at both scales. Overall, the results suggest potential improvements for both satellite estimates and weather forecast systems, in particular as applied to global precipitation forecasts.


2011 ◽  
Vol 135-136 ◽  
pp. 3-9
Author(s):  
Yun Ge ◽  
Yan Feng Sun ◽  
Bao Cai Yin ◽  
Heng Liang Tang

3D face sample is an important data platform for model training, algorithm design. Subject to the constraint of data acquisition equipment the size of current 3D face databases are relatively small and insufficient. To solve this problem, this paper presents a modeling way for generating 3D novel samples based on surface stitching. First we use morphable model to build a global model. Then, we replace each patch of the global model based on surface stitching. We demonstrate that with appropriate choice of local models it is possible to reliably generate new realistic face samples.


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