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Climate Change Impact on Hydrological Extremes: Preliminary Results from the Polish-Norwegian Project

Romanowicz R. J., Bogdanowicz E., Debele S. E., Doroszkiewicz J., Hisdal H., Lawrence D., Meresa H. K., Napiórkowski J. J., Osuch M., Strupczewski W. G., Wilson D., Wong W. K.

Acta Geophysica

2016

Vol. 64, no. 2

477--509

This paper presents the background, objectives, and preliminary outcomes from the first year of activities of the Polish–Norwegian project CHIHE (Climate Change Impact on Hydrological Extremes). The project aims to estimate the influence of climate changes on extreme river flows (low and high) and to evaluate the impact on the frequency of occurrence of hydrological extremes. Eight “twinned” catchments in Poland and Norway serve as case studies. We present the procedures of the catchment selection applied in Norway and Poland and a database consisting of near-natural ten Polish and eight Norwegian catchments constructed for the purpose of climate impact assessment. Climate projections for selected catchments are described and compared with observations of temperature and precipitation available for the reference period. Future changes based on those projections are analysed and assessed for two periods, the near future (2021-2050) and the far-future (2071-2100). The results indicate increases in precipitation and temperature in the periods and regions studied both in Poland and Norway.

Bayesian model for multimodal sensory information fusion in humanoid

Wong W. K., Loo L. C., Neoh T. M., Liew Y. W., Lee E. K.

Journal of Automation Mobile Robotics and Intelligent Systems

2011

Vol. 5, No. 1

16-22

In this paper, the Bayesian model for bimodal sensory information fusion is presented. It is a simple and biological plausible model used to model the sensory fusion in human’s brain. It is adopted into humanoid robot to fuse the spatial information gained from analyzing auditory and visual input, aiming to increase the accuracy of object localization. Bayesian fusion model requires prior knowledge on weights for sensory systems. These weights can be determined based on standard deviation (SD) of unimodal localization error obtained in experiments. The performance of auditory and visual localization was tested under two conditions: fixation and saccade. The experiment result shows that Bayesian model did improve the accuracy of object localization. However, the fused position of the object is not accurate when both of the sensory systems were bias towards the same direction.