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Wind

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Multi-task learning for the prediction of wind power ramp events with deep neural networks.

Neural networks : the official journal of the International Neural Network Society
In Machine Learning, the most common way to address a given problem is to optimize an error measure by training a single model to solve the desired task. However, sometimes it is possible to exploit latent information from other related tasks to impr...

A novel compound wind speed forecasting model based on the back propagation neural network optimized by bat algorithm.

Environmental science and pollution research international
Wind power, a clean and renewable resource, is regarded as one of the most promising and economical resources during the transformation from fossil fuels to new energy resources. Thus, the accuracy of wind speed forecasting work is very important to ...

Artificial intelligence based approaches to evaluate actual evapotranspiration in wetlands.

The Science of the total environment
Wetlands are extraordinary ecosystems and important climate regulators that also contribute to reduce natural disaster risk. Unfortunately, wetlands are declining much faster than forests. The safeguarding of the wetlands also needs knowledge of the ...

Soft biohybrid morphing wings with feathers underactuated by wrist and finger motion.

Science robotics
Since the Wright Flyer, engineers have strived to develop flying machines with morphing wings that can control flight as deftly as birds. Birds morph their wing planform parameters simultaneously-including sweep, span, and area-in a way that has prov...

Short-Time Wind Speed Forecast Using Artificial Learning-Based Algorithms.

Computational intelligence and neuroscience
The need for an efficient power source for operating the modern industry has been rapidly increasing in the past years. Therefore, the latest renewable power sources are difficult to be predicted. The generated power is highly dependent on fluctuated...

Dynamic performances of a bird-like flapping wing robot under randomly uncertain disturbances.

PloS one
The nonlinear dynamics of a bird-like flapping wing robot under randomly uncertain disturbances was studied in this study. The bird-like flapping wing robot was first simplified into a two-rod model with a spring connection. Then, the dynamic model o...

Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance.

Neural networks : the official journal of the International Neural Network Society
In this study, we use a deep convolutional neural network (CNN) to develop a model that predicts ozone concentrations 24 h in advance. We have evaluated the model for 21 continuous ambient monitoring stations (CAMS) across Texas. The inputs for the C...

Prediction of environmental effects in received signal strength in FM/TV station based on meteorological parameters using artificial neural network and data mining.

Journal of environmental management
In this paper, meteorological parameters, electric field strength and transmitters' output power measured during six months in a TV/FM station. There are 13 frequencies in FM and UHF frequency bands in pilot broadcast station. The analysis of results...

Recovering Wind-Induced Plant Motion in Dense Field Environments via Deep Learning and Multiple Object Tracking.

Plant physiology
Understanding the relationships between local environmental conditions and plant structure and function is critical for both fundamental science and for improving the performance of crops in field settings. Wind-induced plant motion is important in m...

Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison:A case study in hangzhou, China.

Environmental pollution (Barking, Essex : 1987)
Tropospheric ozone in the surface air has become the primary atmospheric pollutant in Hangzhou, China, in recent years. Previous analysis is not enough to decode it for better regulation. Therefore, we use the traditional atmospheric model, Weather R...