Machining vibration states monitoring based on image representation using convolutional neural networks

2017 ◽  
Vol 65 ◽  
pp. 240-251 ◽  
Author(s):  
Yang Fu ◽  
Yun Zhang ◽  
Yuan Gao ◽  
Huang Gao ◽  
Ting Mao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Representation

scholarly journals Spatio–Temporal Image Representation of 3D Skeletal Movements for View-Invariant Action Recognition with Deep Convolutional Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1932 ◽  
Author(s):  
Huy Hieu Pham ◽  
Houssam Salmane ◽  
Louahdi Khoudour ◽  
Alain Crouzil ◽  
Pablo Zegers ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Action Recognition ◽  
Large Scale ◽  
Image Representation ◽  
Human Action ◽  
Computational Time ◽  
Deep Convolutional Neural Networks ◽  
Classification Tasks ◽  
Spatio Temporal

Designing motion representations for 3D human action recognition from skeleton sequences is an important yet challenging task. An effective representation should be robust to noise, invariant to viewpoint changes and result in a good performance with low-computational demand. Two main challenges in this task include how to efficiently represent spatio–temporal patterns of skeletal movements and how to learn their discriminative features for classification tasks. This paper presents a novel skeleton-based representation and a deep learning framework for 3D action recognition using RGB-D sensors. We propose to build an action map called SPMF (Skeleton Posture-Motion Feature), which is a compact image representation built from skeleton poses and their motions. An Adaptive Histogram Equalization (AHE) algorithm is then applied on the SPMF to enhance their local patterns and form an enhanced action map, namely Enhanced-SPMF. For learning and classification tasks, we exploit Deep Convolutional Neural Networks based on the DenseNet architecture to learn directly an end-to-end mapping between input skeleton sequences and their action labels via the Enhanced-SPMFs. The proposed method is evaluated on four challenging benchmark datasets, including both individual actions, interactions, multiview and large-scale datasets. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches on all benchmark tasks, whilst requiring low computational time for training and inference.

scholarly journals Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3374 ◽  
Author(s):  
Anthony Faustine ◽  
Lucas Pereira
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Representation ◽  
Classification Performance ◽  
Feature Representation ◽  
Recognition Method ◽  
Signal Features ◽  
Load Monitoring ◽  
Power Measurements

Appliance recognition is one of the vital sub-tasks of NILM in which a machine learning classier is used to detect and recognize active appliances from power measurements. The performance of the appliance classifier highly depends on the signal features used to characterize the loads. Recently, different appliance features derived from the voltage–current (V–I) waveforms have been extensively used to describe appliances. However, the performance of V–I-based approaches is still unsatisfactory as it is still not distinctive enough to recognize devices that fall into the same category. Instead, we propose an appliance recognition method utilizing the recurrence graph (RG) technique and convolutional neural networks (CNNs). We introduce the weighted recurrent graph (WRG) generation that, given one-cycle current and voltage, produces an image-like representation with more values than the binary output created by RG. Experimental results on three different sub-metered datasets show that the proposed WRG-based image representation provides superior feature representation and, therefore, improves classification performance compared to V–I-based features.

scholarly journals Motion-Based Representations For Activity Recognition

2020 ◽  
Author(s):  
Carlos Caetano ◽  
Jefersson Alex Dos Santos ◽  
William Robson Schwartz
Keyword(s):  
Neural Networks ◽  
Optical Flow ◽  
Convolutional Neural Networks ◽  
Activity Recognition ◽  
Temporal Dynamics ◽  
State Of The Art ◽  
Image Representation ◽  
Motion Information ◽  
Linear Transformations ◽  
Skeleton Image

This work addresses the activity recognition problem. We propose two different representations based on motion information for activity recognition. The first representation is a novel temporal stream for two-stream Convolutional Neural Networks (CNNs) that receives as input images computed from the optical flow magnitude and orientation to learn the motion in a better and richer manner. The method applies simple non-linear transformations on the vertical and horizontal components of the optical flow to generate input images for the temporal stream. The second representation is a novel skeleton image representation to be used as input of CNNs. The approach encodes the temporal dynamics by explicitly computing the magnitude and orientation values of the skeleton joints. Experiments carried out on challenging well-known activity recognition datasets (UCF101, NTU RGB+D 60 and NTU RGB+D 120) demonstrate that the proposed representations achieve results in the state of the art, indicating the suitability of our approaches as video representations.

Learning Image Representation Based on Convolutional Neural Networks

2017 ◽  
pp. 642-652
Author(s):  
Zhanbo Yang ◽  
Fei Hu ◽  
Jingyuan Wang ◽  
Jinjing Zhang ◽  
Li Li
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Representation

scholarly journals Convolutional Neural Networks with Transfer Learning for Recognition of COVID-19: A Comparative Study of Different Approaches

AI ◽  
2020 ◽  
Vol 1 (4) ◽  
pp. 586-606
Author(s):  
Tanmay Garg ◽  
Mamta Garg ◽  
Om Prakash Mahela ◽  
Akhil Ranjan Garg
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Feature Selection ◽  
Convolutional Neural Networks ◽  
Image Representation ◽  
Principal Component ◽  
Classification Problem ◽  
The Other ◽  
X Ray ◽  
Image Representations

To judge the ability of convolutional neural networks (CNNs) to effectively and efficiently transfer image representations learned on the ImageNet dataset to the task of recognizing COVID-19 in this work, we propose and analyze four approaches. For this purpose, we use VGG16, ResNetV2, InceptionResNetV2, DenseNet121, and MobileNetV2 CNN models pre-trained on ImageNet dataset to extract features from X-ray images of COVID and Non-COVID patients. Simulations study performed by us reveal that these pre-trained models have a different level of ability to transfer image representation. We find that in the approaches that we have proposed, if we use either ResNetV2 or DenseNet121 to extract features, then the performance of these approaches to detect COVID-19 is better. One of the important findings of our study is that the use of principal component analysis for feature selection improves efficiency. The approach using the fusion of features outperforms all the other approaches, and with this approach, we could achieve an accuracy of 0.94 for a three-class classification problem. This work will not only be useful for COVID-19 detection but also for any domain with small datasets.

scholarly journals Spatio-Temporal Image Representation of 3D Skeletal Movements for View-Invariant Action Recognition with Deep Convolutional Neural Networks

2019 ◽  
Author(s):  
Huy Hieu Pham ◽  
Houssam Salmane ◽  
Louahdi Khoudour ◽  
Alain Crouzil ◽  
Pablo Zegers ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Action Recognition ◽  
Large Scale ◽  
Image Representation ◽  
Human Action ◽  
Computational Time ◽  
Deep Convolutional Neural Networks ◽  
Motion Feature ◽  
Spatio Temporal

Designing motion representations for the problem of 3D human action recognition from skeleton sequences is an important yet challenging task. An effective representation should be robust to noise, invariant to viewpoint changes and result in a good performance with low-computational demand. Two main challenges in this task include how to efficiently represent spatio-temporal patterns of skeletal movements and how to learn their discriminative features for classification task. This paper presents a novel skeleton-based representation and a deep learning framework for 3D action recognition using RGB-D sensors. We propose to build an action map called SPMF (Skeleton Posture-Motion Feature), which is a compact image representation built from skeleton poses and their motions. An Adaptive Histogram Equalization (AHE) algorithm is then applied on the SPMF to enhance their local patterns and form an enhanced action map, namely Enhanced-SPMF. For learning and classification tasks, we exploit Deep Convolutional Neural Networks based on the DenseNet architecture to learn directly an end-to-end mapping between input skeleton sequences and their action labels via the Enhanced-SPMFs. The proposed method is evaluated on four challenging benchmark datasets, including both individual actions, interactions, multiview and large-scale datasets. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches on all benchmark tasks, whilst requiring low computational time for training and inference.

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