AIMC Topic: Wind

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Evaluating the predictability of PM grades in Seoul, Korea using a neural network model based on synoptic patterns.

Environmental pollution (Barking, Essex : 1987)
As of November 2014, the Korean Ministry of Environment (KME) has been forecasting the concentration of particulate matter with diameters ≤ 10 μm (PM) classified into four grades: low (PM ≤ 30 μg m), moderate (30 < PM ≤ 80 μg m), high (80 < PM ≤ 150 ...

Modeling the reflection of Photosynthetically active radiation in a monodominant floodable forest in the Pantanal of Mato Grosso State using multivariate statistics and neural networks.

Anais da Academia Brasileira de Ciencias
The study of radiation entrance and exit dynamics and energy consumption in a system is important for understanding the environmental processes that rule the biosphere-atmosphere interactions of all ecosystems. This study provides an analysis of the ...

FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine.

ISA transactions
As per modern electrical grid rules, Wind Turbine needs to operate continually even in presence severe grid faults as Low Voltage Ride Through (LVRT). Hence, a new LVRT Fault Detection and Identification (FDI) procedure has been developed to take the...

Forecasting PM10 in Algiers: efficacy of multilayer perceptron networks.

Environmental science and pollution research international
Air quality forecasting system has acquired high importance in atmospheric pollution due to its negative impacts on the environment and human health. The artificial neural network is one of the most common soft computing methods that can be pragmatic...

An Interval-Valued Neural Network Approach for Uncertainty Quantification in Short-Term Wind Speed Prediction.

IEEE transactions on neural networks and learning systems
We consider the task of performing prediction with neural networks (NNs) on the basis of uncertain input data expressed in the form of intervals. We aim at quantifying the uncertainty in the prediction arising from both the input data and the predict...

Adaptive Neural Control of a Class of Output-Constrained Nonaffine Systems.

IEEE transactions on cybernetics
In this paper, we present a novel tracking controller for a class of uncertain nonaffine systems with time-varying asymmetric output constraints. Firstly, the original nonaffine constrained (in the sense of the output signal) control system is transf...

Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

IEEE transactions on neural networks and learning systems
Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-base...

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...

Use of a Deep Recurrent Neural Network to Reduce Wind Noise: Effects on Judged Speech Intelligibility and Sound Quality.

Trends in hearing
Despite great advances in hearing-aid technology, users still experience problems with noise in windy environments. The potential benefits of using a deep recurrent neural network (RNN) for reducing wind noise were assessed. The RNN was trained using...