scholarly journals Deep Transfer Learning-Based Feature Extraction: An Approach to Improve Nonintrusive Load Monitoring

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Diego L. Cavalca ◽  
Ricardo A. S. Fernandes
Keyword(s):  
Feature Extraction ◽  
Transfer Learning ◽  
Load Monitoring

scholarly journals An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning

2020 ◽  
Vol 10 (19) ◽  
pp. 6885
Author(s):  
Sahar Ujan ◽  
Neda Navidi ◽  
Rene Jr Landry
Keyword(s):  
Feature Extraction ◽  
Radio Frequency ◽  
Transfer Learning ◽  
Communication Networks ◽  
Continuous Wave ◽  
Critical Role ◽  
Video Stream ◽  
Radio Frequency Interference ◽  
Wave Interference ◽  
Continuous Wave Interference

Radio Frequency Interference (RFI) detection and characterization play a critical role in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable complicated feature extraction step in ML, we propose an efficient Deep Learning (DL)-based methodology using transfer learning to determine both the type of received signals and their modulation type. To this end, the scalogram of the received signals is used as the input of the pretrained convolutional neural networks (CNN), followed by a fully-connected classifier. This study considers a digital video stream as the signal of interest (SoI), transmitted in a real-time satellite-to-ground communication using DVB-S2 standards. To create the RFI dataset, the SoI is combined with three well-known jammers namely, continuous-wave interference (CWI), multi- continuous-wave interference (MCWI), and chirp interference (CI). This study investigated four well-known pretrained CNN architectures, namely, AlexNet, VGG-16, GoogleNet, and ResNet-18, for the feature extraction to recognize the visual RFI patterns directly from pixel images with minimal preprocessing. Moreover, the robustness of the proposed classifiers is evaluated by the data generated at different signal to noise ratios (SNR).

scholarly journals Plant Recognition Using Morphological Feature Extraction and Transfer Learning over SVM and AdaBoost

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 356
Author(s):  
Shubham Mahajan ◽  
Akshay Raina ◽  
Xiao-Zhi Gao ◽  
Amit Kant Pandit
Keyword(s):  
Feature Extraction ◽  
Transfer Learning ◽  
Plant Species ◽  
Species Recognition ◽  
Morphological Features ◽  
Support Vector ◽  
Additional Advantage ◽  
Adaptive Boosting ◽  
Vast Number ◽  
Axis Length

Plant species recognition from visual data has always been a challenging task for Artificial Intelligence (AI) researchers, due to a number of complications in the task, such as the enormous data to be processed due to vast number of floral species. There are many sources from a plant that can be used as feature aspects for an AI-based model, but features related to parts like leaves are considered as more significant for the task, primarily due to easy accessibility, than other parts like flowers, stems, etc. With this notion, we propose a plant species recognition model based on morphological features extracted from corresponding leaves’ images using the support vector machine (SVM) with adaptive boosting technique. This proposed framework includes the pre-processing, extraction of features and classification into one of the species. Various morphological features like centroid, major axis length, minor axis length, solidity, perimeter, and orientation are extracted from the digital images of various categories of leaves. In addition to this, transfer learning, as suggested by some previous studies, has also been used in the feature extraction process. Various classifiers like the kNN, decision trees, and multilayer perceptron (with and without AdaBoost) are employed on the opensource dataset, FLAVIA, to certify our study in its robustness, in contrast to other classifier frameworks. With this, our study also signifies the additional advantage of 10-fold cross validation over other dataset partitioning strategies, thereby achieving a precision rate of 95.85%.

Smart paddy field monitoring system using deep learning and IoT

2021 ◽  
pp. 1063293X2198894
Author(s):  
Prabira Kumar Sethy ◽  
Santi Kumari Behera ◽  
Nithiyakanthan Kannan ◽  
Sridevi Narayanan ◽  
Chanki Pandey
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Transfer Learning ◽  
Paddy Field ◽  
New Technology ◽  
Support Vector ◽  
Nitrogen Status ◽  
Deep Feature ◽  
Per Capita ◽  
Deep Feature Extraction

Paddy is an essential nutrient worldwide. Rice gives 21% of worldwide human per capita energy and 15% of per capita protein. Asia represented 60% of the worldwide populace, about 92% of the world’s rice creation, and 90% of worldwide rice utilization. With the increase in population, the demand for rice is increased. So, the productivity of farming is needed to be enhanced by introducing new technology. Deep learning and IoT are hot topics for research in various fields. This paper suggested a setup comprising deep learning and IoT for monitoring of paddy field remotely. The vgg16 pre-trained network is considered for the identification of paddy leaf diseases and nitrogen status estimation. Here, two strategies are carried out to identify images: transfer learning and deep feature extraction. The deep feature extraction approach is combined with a support vector machine (SVM) to classify images. The transfer learning approach of vgg16 for identifying four types of leaf diseases and prediction of nitrogen status results in 79.86% and 84.88% accuracy. Again, the deep features of Vgg16 and SVM results for identifying four types of leaf diseases and prediction of nitrogen status have achieved an accuracy of 97.31% and 99.02%, respectively. Besides, a framework is suggested for monitoring of paddy field remotely based on IoT and deep learning. The suggested prototype’s superiority is that it controls temperature and humidity like the state-of-the-art and can monitor the additional two aspects, such as detecting nitrogen status and diseases.

scholarly journals Unmanned Aerial Vehicle Operating Mode Classification Using Deep Residual Learning Feature Extraction

Aerospace ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 79
Author(s):  
Carolyn J. Swinney ◽  
John C. Woods
Keyword(s):  
Feature Extraction ◽  
Transfer Learning ◽  
Operating Mode ◽  
Classification Problem ◽  
Large Signal ◽  
Signal Representations ◽  
Novel Approach ◽  
Power Spectral ◽  
Aerial Vehicle ◽  
Deep Convolution Neural Network

Unmanned Aerial Vehicles (UAVs) undoubtedly pose many security challenges. We need only look to the December 2018 Gatwick Airport incident for an example of the disruption UAVs can cause. In total, 1000 flights were grounded for 36 h over the Christmas period which was estimated to cost over 50 million pounds. In this paper, we introduce a novel approach which considers UAV detection as an imagery classification problem. We consider signal representations Power Spectral Density (PSD); Spectrogram, Histogram and raw IQ constellation as graphical images presented to a deep Convolution Neural Network (CNN) ResNet50 for feature extraction. Pre-trained on ImageNet, transfer learning is utilised to mitigate the requirement for a large signal dataset. We evaluate performance through machine learning classifier Logistic Regression. Three popular UAVs are classified in different modes; switched on; hovering; flying; flying with video; and no UAV present, creating a total of 10 classes. Our results, validated with 5-fold cross validation and an independent dataset, show PSD representation to produce over 91% accuracy for 10 classifications. Our paper treats UAV detection as an imagery classification problem by presenting signal representations as images to a ResNet50, utilising the benefits of transfer learning and outperforming previous work in the field.

Feature Extraction for Non-intrusive Load Monitoring System

2021 ◽  
Author(s):  
Qiuzhan Zhou ◽  
Jiahui Wei ◽  
Mingyu Sun ◽  
Cong Wang ◽  
Jing Rong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Monitoring System ◽  
Load Monitoring

scholarly journals DNN Transfer Learning Based Non-Linear Feature Extraction for Acoustic Event Classification

2017 ◽  
Vol E100.D (9) ◽  
pp. 2249-2252 ◽  
Author(s):  
Seongkyu MUN ◽  
Minkyu SHIN ◽  
Suwon SHON ◽  
Wooil KIM ◽  
David K. HAN ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Transfer Learning ◽  
Event Classification ◽  
Acoustic Event ◽  
Linear Feature ◽  
Linear Feature Extraction ◽  
Non Linear

scholarly journals Dense-Res Net for Endoscopic Image Classification

2021 ◽  
Author(s):  
Quoc-Huy Trinh ◽  
Minh-Van Nguyen
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Transfer Learning ◽  
Fine Tuning ◽  
Anatomical Landmarks ◽  
Pathological Findings ◽  
Gi Tract ◽  
Target Class ◽  
Endoscopic Procedures ◽  
Endoscopic Image

We propose a method that configures Fine-tuning to a combination of backbone DenseNet and ResNet to classify eight classes showing anatomical landmarks, pathological findings, to endoscopic procedures in the GI tract. Our Technique depends on Transfer Learning which combines two backbones, DenseNet 121 and ResNet 101, to improve the performance of Feature Extraction for classifying the target class. After experiment and evaluating our work, we get accuracy with an F1 score of approximately 0.93 while training 80000 and test 4000 images.

scholarly journals A Multi-Agent NILM Architecture for Event Detection and Load Classification

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4396
Author(s):  
André Eugenio Lazzaretti ◽  
Douglas Paulo Bertrand Renaux ◽  
Carlos Raimundo Erig Lima ◽  
Bruna Machado Mulinari ◽  
Hellen Cristina Ancelmo ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Event Detection ◽  
Classification Accuracy ◽  
Agent Architecture ◽  
Performance Improvements ◽  
Load Monitoring ◽  
Multi Agent ◽  
Improved Performance ◽  
New Algorithms

A multi-agent architecture for a Non-Intrusive Load Monitoring (NILM) solution is presented and evaluated. The underlying rationale for such an architecture is that each agent (load event detection, feature extraction, and classification) outperforms others of the same type in particular scenarios; hence, by combining the expertise of these agents, the system presents an improved performance. Known NILM algorithms, as well as new algorithms, proposed by the authors, were individually evaluated and compared. The proposed architecture considers a NILM system composed of Load Monitoring Modules (LMM) that report to a Center of Operations, required in larger facilities. For the purposed of evaluating and comparing performance, five load event detect agents, five feature extraction agents, and five classification agents were studied so that the best combinations of agents could be implemented in LMMs. To evaluate the proposed system, the COOLL and the LIT-Dataset were used. Performance improvements were detected in all scenarios, with power-ON and power-OFF detection improving up to 13%, while classification accuracy improved up to 9.4%.

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