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Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
(
Institute of Electrical and Electronics Engineers Inc.
, 2016 , Article)
Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such ...
Handcrafted features with convolutional neural networks for detection of tumor cells in histology images
(
IEEE Computer Society
, 2016 , Conference Paper)
Detection of tumor nuclei in cancer histology images requires sophisticated techniques due to the irregular shape, size and chromatin texture of the tumor nuclei. Some very recently proposed methods employ deep convolutional ...
Short-term probabilistic building load forecasting based on feature integrated artificial intelligent approach
(
Elsevier Ltd
, 2022 , Article)
Due to various influential factors that lead to instability and volatility of the building load, short-term building load forecasting is a gruelling task. This paper proposes a hybrid short-term building load probability ...
Application of data-driven attack detection framework for secure operation in smart buildings
(
Elsevier Ltd
, 2021 , Article)
With the rapid advancement in the industrial control technologies and the increased deployment of the industrial Internet of Things (IoT) in the buildings sector, this work presents an analysis of the security of the ...
Hybrid attack detection framework for industrial control systems using 1D-convolutional neural network and isolation forest
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Conference Paper)
Industrial control systems (ICSs) are used in various infrastructures and industrial plants for realizing their control operation and ensuring their safety. Concerns about the cybersecurity of industrial control systems ...
Efficiency validation of one dimensional convolutional neural networks for structural damage detection using a SHM benchmark data
(
International Institute of Acoustics and Vibration, IIAV
, 2018 , Conference Paper)
In this paper, a novel one dimensional convolution neural network (1D-CNN) based structural damage assessment technique is validated with a benchmark study published by IASC-ASCE Structural Health Monitoring Task Group in ...
Structural damage detection in real time: Implementation of 1D convolutional neural networks for SHM applications
(
Springer
, 2017 , Conference Paper)
Most of the classical structural damage detection systems involve two processes, feature extraction and feature classification. Usually, the feature extraction process requires large computational effort which prevent the ...
Convolutional neural networks for real-time and wireless damage detection
(
Springer New York LLC
, 2020 , Conference Paper)
Structural damage detection methods available for structural health monitoring applications are based on data preprocessing, feature extraction, and feature classification. The feature classification task requires considerable ...
End-to-End Image Steganography Using Deep Convolutional Autoencoders
(
Institute of Electrical and Electronics Engineers Inc.
, 2021 , Article)
Image steganography is used to hide a secret image inside a cover image in plain sight. Traditionally, the secret data is converted into binary bits and the cover image is manipulated statistically to embed the secret ...