scholarly journals Background Load Denoising across Complex Load Based on Generative Adversarial Network to Enhance Load Identification

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5674
Author(s):  
Afifatul Mukaroh ◽  
Thi-Thu-Huong Le ◽  
Howon Kim
Keyword(s):  
Electricity Consumption ◽  
Load Identification ◽  
Generative Adversarial Network ◽  
Complex Load ◽  
Adversarial Network ◽  
Short Period ◽  
Event Information ◽  
Load Monitoring ◽  
Systems Identification ◽  
Background Load

Non-Intrusive Load Monitoring (NILM) allows load identification of appliances through a single sensor. By using NILM, users can monitor their electricity consumption, which is beneficial for energy efficiency or energy saving. In advance NILM systems, identification of appliances on/off events should be processed instantly. Thus, it is necessary to use an extremely short period signal of appliances to shorten the time delay for users to acquire event information. However, acquiring event information from a short period signal raises another problem. The problem is target load feature to be easily mixed with background load. The more complex the background load has, the noisier the target load occurs. This issue certainly reduces the appliance identification performance. Therefore, we provide a novel methodology that leverages Generative Adversarial Network (GAN) to generate noise distribution of background load then use it to generate a clear target load. We also built a Convolutional Neural Network (CNN) model to identify load based on single load data. Then we use that CNN model to evaluate the target load generated by GAN. The result shows that GAN is powerful to denoise background load across the complex load. It yields a high accuracy of load identification which could reach 92.04%.

scholarly journals AENN: A GENERATIVE ADVERSARIAL NEURAL NETWORK FOR WEATHER RADAR ECHO EXTRAPOLATION

2019 ◽  
Vol XLII-3/W9 ◽  
pp. 89-94
Author(s):  
J. R. Jing ◽  
Q. Li ◽  
X. Y. Ding ◽  
N. L. Sun ◽  
R. Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Weather Forecasting ◽  
Weather Radar ◽  
Weather Conditions ◽  
Short Term ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Radar Echo ◽  
Systems Identification

Abstract. Weather radar echo is one of the fundamental data for meteorological workers to weather systems identification and classification. Through the technique of weather radar echo extrapolation, the future short-term weather conditions can be predicted and severe convection storms can be warned. However, traditional extrapolation methods cannot offer accurate enough extrapolation results since their modeling capacity is limited, the recent deep learning based methods make some progress but still remains a problem of blurry prediction when making deeper extrapolation, which may due to they choose the mean square error as their loss function and that will lead to losing echo details. To address this problem and make a more realistic and accurate extrapolation, we propose a deep learning model called Adversarial Extrapolation Neural Network (AENN), which is a Generative Adversarial Network (GAN) structure and consist of a conditional generator and two discriminators, echo-frame discriminator and echo-sequence discriminator. The generator and discriminators are trained alternately in an adversarial way to make the final extrapolation results be realistic and accurate. To evaluate the model, we conduct experiments on extrapolating 0.5h, 1h, and 1.5h imminent future echoes, the results show that our proposed AENN can achieve the expected effect and outperforms other models significantly, which has a powerful potential application value for short-term weather forecasting.

scholarly journals Recognizing New Classes with Synthetic Data in the Loop: Application to Traffic Sign Recognition

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 583
Author(s):  
Gabriel Villalonga ◽  
Joost Van de Weijer ◽  
Antonio M. López
Keyword(s):  
Domain Adaptation ◽  
Synthetic Data ◽  
Recognition System ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Sign Recognition ◽  
Short Period ◽  
Synthetic Images

On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio ∼ 1 / 4 for new/known classes; even for more challenging ratios such as ∼ 4 / 1 , the results are also very positive.

scholarly journals Arrhythmia Detection Algorithm using GoogLeNet and Generative Adversarial Network with Lifelog Signals

2021 ◽  
Vol 15 ◽  
pp. 1-3
Author(s):  
Siho Shin ◽  
Jaehyo Jung ◽  
Mingu Kang ◽  
Youn Tae Kim
Keyword(s):  
Heart Attack ◽  
Detection Algorithm ◽  
Generative Adversarial Network ◽  
Arrhythmia Detection ◽  
Adversarial Network ◽  
Time Period ◽  
Short Period ◽  
Diagnosis Algorithm ◽  
Ecg Data

Arrhythmia is a cardiovascular disease with an irregular heartbeat, which can lead to a heart attack if it lasts for an excessive amount of time. Because the symptoms of arrhythmia occur irregularly, the heart needs to be monitored for a lengthy time period. This study suggests an arrhythmia diagnosis algorithm using GoogLeNet and a GAN. Because the algorithm proposed in this study can add to the number of data using a GAN, it can accurately diagnose an arrhythmic occurrence from measured lifelog over a short period of time. The classification of ECG data using GoogLeNet and a GAN showed an accuracy of approximately 99%.

scholarly journals Concatenate Convolutional Neural Networks for Non-Intrusive Load Monitoring across Complex Background

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1572 ◽  
Author(s):  
Qian Wu ◽  
Fei Wang
Keyword(s):  
Complex Background ◽  
Proposed Model ◽  
Short Period ◽  
Event Information ◽  
Load Monitoring ◽  
Save Energy ◽  
The Uk ◽  
Domestic Appliance ◽  
Operation Status ◽  
Short Time

Non-Intrusive Load Monitoring (NILM) provides a way to acquire detailed energy consumption and appliance operation status through a single sensor, which has been proven to save energy. Further, besides load disaggregation, advanced applications (e.g., demand response) need to recognize on/off events of appliances instantly. In order to shorten the time delay for users to acquire the event information, it is necessary to analyze extremely short period electrical signals. However, the features of those signals are easily submerged in complex background loads, especially in cross-user scenarios. Through experiments and observations, it can be found that the feature of background loads is almost stationary in a short time. On the basis of this result, this paper provides a novel model called the concatenate convolutional neural network to separate the feature of the target load from the load mixed with the background. For the cross-user test on the UK Domestic Appliance-Level Electricity dataset (UK-DALE), it turns out that the proposed model remarkably improves accuracy, robustness, and generalization of load recognition. In addition, it also provides significant improvements in energy disaggregation compared with the state-of-the-art.

scholarly journals A Generative Adversarial Network Structure for Learning with Small Numerical Data Sets

2021 ◽  
Vol 11 (22) ◽  
pp. 10823
Author(s):  
Der-Chiang Li ◽  
Szu-Chou Chen ◽  
Yao-San Lin ◽  
Kuan-Cheng Huang
Keyword(s):  
Machine Learning ◽  
Network Structure ◽  
Numerical Data ◽  
Small Sample ◽  
Model Verification ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Virtual Samples ◽  
Short Period

In recent years, generative adversarial networks (GANs) have been proposed to generate simulated images, and some works of literature have applied GAN to the analysis of numerical data in many fields, such as the prediction of building energy consumption and the prediction and identification of liver cancer stages. However, these studies are based on sufficient data volume. In the current era of globalization, the demand for rapid decision-making is increasing, but the data available in a short period of time is scarce. As a result, machine learning may not provide precise results. Obtaining more information from a small number of samples has become an important issue. Therefore, this study aimed to modify the generative adversarial network structure for learning with small numerical datasets, starting with the Wasserstein GAN (WGAN) as the GAN architecture, and using mega-trend-diffusion (MTD) to limit the bound of virtual samples that the GAN generates. The model verification of our proposed structure was conducted with two datasets in the UC Irvine Machine Learning Repository, and the performance was evaluated using three criteria: accuracy, standard deviation, and p-value. The experiment result shows that, using this improved GAN architecture (WGAN_MTD), small sample data can also be used to generate virtual samples that are similar to real samples through GAN.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

Role of General Adversarial Networks in Mammogram Analysis: A Review

2020 ◽  
Vol 16 (7) ◽  
pp. 863-877
Author(s):  
Annapoorani Gopal ◽  
Lathaselvi Gandhimaruthian ◽  
Javid Ali
Keyword(s):  
Breast Tumor ◽  
Deep Neural Networks ◽  
Training Data ◽  
Learning Technology ◽  
Breast Cancers ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Tumor Extraction

The Deep Neural Networks have gained prominence in the biomedical domain, becoming the most commonly used networks after machine learning technology. Mammograms can be used to detect breast cancers with high precision with the help of Convolutional Neural Network (CNN) which is deep learning technology. An exhaustive labeled data is required to train the CNN from scratch. This can be overcome by deploying Generative Adversarial Network (GAN) which comparatively needs lesser training data during a mammogram screening. In the proposed study, the application of GANs in estimating breast density, high-resolution mammogram synthesis for clustered microcalcification analysis, effective segmentation of breast tumor, analysis of the shape of breast tumor, extraction of features and augmentation of the image during mammogram classification have been extensively reviewed.

Sign in / Sign up

Export Citation Format

Share Document