Review on deep learning applications in frequency analysis and control of modern power system
The penetration of renewable energy resources (RES) generation and the interconnection of
regional power grids in wide area and large scale have led the modern power system to …
regional power grids in wide area and large scale have led the modern power system to …
Randomness in neural networks: an overview
S Scardapane, D Wang - Wiley Interdisciplinary Reviews: Data …, 2017 - Wiley Online Library
Neural networks, as powerful tools for data mining and knowledge engineering, can learn
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …
An overview and comparative analysis of recurrent neural networks for short term load forecasting
The key component in forecasting demand and consumption of resources in a supply
network is an accurate prediction of real-valued time series. Indeed, both service …
network is an accurate prediction of real-valued time series. Indeed, both service …
Reservoir computing approaches for representation and classification of multivariate time series
Classification of multivariate time series (MTS) has been tackled with a large variety of
methodologies and applied to a wide range of scenarios. Reservoir computing (RC) …
methodologies and applied to a wide range of scenarios. Reservoir computing (RC) …
Nonvolatile CMOS memristor, reconfigurable array, and its application in power load forecasting
The high cost, low yield, and low stability of nanomaterials significantly hinder the
application and development of memristors. To promote the application of memristors …
application and development of memristors. To promote the application of memristors …
A deep learning approach to automatic road surface monitoring and pothole detection
B Varona, A Monteserin, A Teyseyre - Personal and Ubiquitous Computing, 2020 - Springer
Anomalies in road surface not only impact road quality but also affect driver safety, mechanic
structure of the vehicles, and fuel consumption. Several approaches have been proposed to …
structure of the vehicles, and fuel consumption. Several approaches have been proposed to …
Echo state property of deep reservoir computing networks
C Gallicchio, A Micheli - Cognitive Computation, 2017 - Springer
In the last years, the Reservoir Computing (RC) framework has emerged as a state of-the-art
approach for efficient learning in temporal domains. Recently, within the RC context, deep …
approach for efficient learning in temporal domains. Recently, within the RC context, deep …
ResInNet: A novel deep neural network with feature reuse for Internet of Things
Deep neural networks (DNNs) have widely used in various Internet-of-Things (IoT)
applications. Pursuing superior performance is always a hot spot in the field of DNN …
applications. Pursuing superior performance is always a hot spot in the field of DNN …
Deep randomized neural networks
C Gallicchio, S Scardapane - Recent Trends in Learning From Data …, 2020 - Springer
Abstract Randomized Neural Networks explore the behavior of neural systems where the
majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical …
majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical …
Intelligent passive detection of aerial target in space-air-ground integrated networks
Passive detection of moving target is an important part of intelligent surveillance. Satellite
has the potential to play a key role in many applications of space-air-ground integrated …
has the potential to play a key role in many applications of space-air-ground integrated …