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An object-based model for convective cold pool dynamics

100%

Böing S. J.

Mathematics of Climate and Weather Forecasting

2016

tom 2

nr 1

A simple model of the organization of atmospheric moist convection by cold outflows is presented. The model consists of two layers: a lower layer where instability gradually builds up, and an upper layer where instability is rapidly released. Its formulation is inspired by Abelian sandpile models: instability and convection are both represented in terms of particles that are coupled to a lattice grid. An excess of particles in the lower layer triggers a particle release into the upper (cloud) layer. Particles in the upper layer also induce particle movement in the lower layer: this reverse coupling represents the effect of precipitation and the associated cold outflows. The model shows two behavioral regimes. Activity is scattered when the reverse coupling is weak, but when it is strong, convection forms cellular patterns. Though this model does not contain a detailed representation of physical processes in convection, it captures some key dynamical features of precipitating convection seen in satellite observations and LES studies. These include the formation of open cells, temporal oscillations in convective intensity, hysteresis, and the effect of precipitation on the scale of convection. We argue that an object-based representation of convection may be able to capture properties of convective organization that are missing in traditional parameterizations.

Neural network based identification of hysteresis in human meridian systems

100%

Tan Y., Dong R., Chen H., He H.

International Journal of Applied Mathematics and Computer Science

2012

tom 22

nr 3

685-694

Developing a model based digital human meridian system is one of the interesting ways of understanding and improving acupuncture treatment, safety analysis for acupuncture operation, doctor training, or treatment scheme evaluation. In accomplishing this task, how to construct a proper model to describe the behavior of human meridian systems is one of the very important issues. From experiments, it has been found that the hysteresis phenomenon occurs in the relations between stimulation input and the corresponding response of meridian systems. Therefore, the modeling of hysteresis in a human meridian system is an unavoidable task for the construction of model based digital human meridian systems. As hysteresis is a nonsmooth, nonlinear and dynamic system with a multi-valued mapping, the conventional identification method is difficult to be employed to model its behavior directly. In this paper, a neural network based identification method of hysteresis occurring in human meridian systems is presented. In this modeling scheme, an expanded input space is constructed to transform the multi-valued mapping of hysteresis into a one-to-one mapping. For this purpose, a modified hysteretic operator is proposed to handle the extremum-missing problem. Then, based on the constructed expanded input space with the modified hysteretic operator, the so-called Extreme Learning Machine (ELM) neural network is utilized to model hysteresis inherent in human meridian systems. As hysteresis in meridian system is a dynamic system, a dynamic ELM neural network is developed. In the proposed dynamic ELM neural network, the output state of each hidden neuron is fed back to its own input to describe the dynamic behavior of hysteresis. The training of the recurrent ELM neural network is based on the least-squares algorithm with QR decomposition.

Ograniczanie wyników

1 International Journal of Applied Mathematics and Computer Science

1 Mathematics of Climate and Weather Forecasting

1 Böing S. J.

1 Chen H.

1 Dong R.

1 He H.

1 Tan Y.

1 2016

1 2012

Wyniki wyszukiwania

An object-based model for convective cold pool dynamics

Neural network based identification of hysteresis in human meridian systems