scholarly journals Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 130 ◽  
Author(s):  
Mohammad Navid Fekri ◽  
Ananda Mohon Ghosh ◽  
Katarina Grolinger
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Energy Consumption ◽  
Smart Grid ◽  
Time Series Data ◽  
Security And Privacy ◽  
Series Data ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Networks

The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial networks (GANs) have been mostly used for image tasks (e.g., image generation, super-resolution), but here they are used with time series data. Convolutional neural networks (CNNs) from image GANs are replaced with recurrent neural networks (RNNs) because of RNN’s ability to capture temporal dependencies. To improve training stability and increase quality of generated data, Wasserstein GANs (WGANs) and Metropolis-Hastings GAN (MH-GAN) approaches were applied. The accuracy is further improved by adding features created with ARIMA and Fourier transform. Experiments demonstrate that data generated by R-GAN can be used for training energy forecasting models.

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.

Multimodal Spatio-Temporal Prediction with Stochastic Adversarial Networks

2022 ◽  
Vol 13 (2) ◽  
pp. 1-23
Author(s):  
Divya Saxena ◽  
Jiannong Cao
Keyword(s):  
Latent Variables ◽  
Time Series Data ◽  
Series Data ◽  
Generative Adversarial Networks ◽  
Multiple Time ◽  
Model Learning ◽  
Temporal Prediction ◽  
Adversarial Networks ◽  
Spatio Temporal ◽  
Real World Datasets

Spatio-temporal (ST) data is a collection of multiple time series data with different spatial locations and is inherently stochastic and unpredictable. An accurate prediction over such data is an important building block for several urban applications, such as taxi demand prediction, traffic flow prediction, and so on. Existing deep learning based approaches assume that outcome is deterministic and there is only one plausible future; therefore, cannot capture the multimodal nature of future contents and dynamics. In addition, existing approaches learn spatial and temporal data separately as they assume weak correlation between them. To handle these issues, in this article, we propose a stochastic spatio-temporal generative model (named D-GAN) which adopts Generative Adversarial Networks (GANs)-based structure for more accurate ST prediction in multiple time steps. D-GAN consists of two components: (1) spatio-temporal correlation network which models spatio-temporal joint distribution of pixels and supports a stochastic sampling of latent variables for multiple plausible futures; (2) a stochastic adversarial network to jointly learn generation and variational inference of data through implicit distribution modeling. D-GAN also supports fusion of external factors through explicit objective to improve the model learning. Extensive experiments performed on two real-world datasets show that D-GAN achieves significant improvements and outperforms baseline models.

scholarly journals Prospects for Generative - Adversarial Networks in Network Traffic Classification Tasks

2021 ◽  
Vol 2096 (1) ◽  
pp. 012174
Author(s):  
G D Asyaev
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Training Sample ◽  
Generative Adversarial Networks ◽  
Traffic Classification ◽  
Learning Models ◽  
Generative Adversarial Network ◽  
Adversarial Networks ◽  
Network Traffic Classification ◽  
Machine Learning Models

Abstract The paper presents an approach that allows increasing the training sample and reducing class imbalance for traffic classification problems. The basic principles and architecture of generative adversarial networks are considered. The mathematical model of network traffic classification is described. The training sample taken to solve the problem has been analyzed. The data proprocessing is carried out and justified. An architecture of the generative-adversarial network is constructed and an algorithm for generating new features is developed. Machine learning models for traffic classification problem were considered and built: Logistic regression, k Nearest Neighbors, Decision tree, Random forest. A comparative analysis of the results of machine learning models without and with the generation of new features is conducted. The obtained results can be applied both in the tasks of network traffic classification, and in general cases of multiclass classification and exclusion of unbalanced features.

scholarly journals Color-Patterns to Architecture Conversion through Conditional Generative Adversarial Networks

Biomimetics ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 16
Author(s):  
Diego Navarro-Mateu ◽  
Oriol Carrasco ◽  
Pedro Cortes Cortes Nieves
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Color Pattern ◽  
Generative Adversarial Networks ◽  
Architecture Design ◽  
Learning Tools ◽  
Color Patterns ◽  
Adversarial Networks ◽  
Recent Developments ◽  
Evo Devo

Often an apparent complex reality can be extrapolated into certain patterns that in turn are evidenced in natural behaviors (whether biological, chemical or physical). The Architecture Design field has manifested these patterns as a conscious (inspired designs) or unconscious manner (emerging organizations). If such patterns exist and can be recognized, can we therefore use them as genotypic DNA? Can we be capable of generating a phenotypic architecture that is manifestly more complex than the original pattern? Recent developments in the field of Evo-Devo around gene regulators patterns or the explosive development of Machine Learning tools could be combined to set the basis for developing new, disruptive workflows for both design and analysis. This study will test the feasibility of using conditional Generative Adversarial Networks (cGANs) as a tool for coding architecture into color pattern-based images and translating them into 2D architectural representations. A series of scaled tests are performed to check the feasibility of the hypothesis. A second test assesses the flexibility of the trained neural networks against cases outside the database.

Offline Handwritten Signature Authentication with Conditional Deep Convolutional Generative Adversarial Networks

2021 ◽  
Author(s):  
David C. Yonekura ◽  
Elloá B. Guedes
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Real World ◽  
Generative Adversarial Networks ◽  
Equal Error Rate ◽  
Training Time ◽  
Adversarial Networks ◽  
Handwritten Signature ◽  
State Of Art

Handwritten signature authentication systems are important in many real world scenarios to avoid frauds. Thanks to Deep Learning, state-of-art solutions have been proposed to this problem by making use of Convolutional Neural Networks, but other models in this Machine Learning subarea are still to be further explored. In this perspective, the present article introduces a Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) approach whose experimental results in a realistic dataset with skilled forgeries have Equal Error Rate (EER) of 18.53% and balanced accuracy of 87.91%. These results validate a writerdependent cDCGAN-based solution to the signature authentication problem in a real world scenario where no forgeries are available nor required in training time.

scholarly journals Denoising EEG Signals for Real-World BCI Applications Using GANs

2022 ◽  
Vol 2 ◽  
Author(s):  
Eoin Brophy ◽  
Peter Redmond ◽  
Andrew Fleury ◽  
Maarten De Vos ◽  
Geraldine Boylan ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Real World ◽  
Time Series Data ◽  
Benchmark Dataset ◽  
Series Data ◽  
Generative Adversarial Networks ◽  
Eeg Signals ◽  
Adversarial Networks ◽  
Resource Setting

As a measure of the brain's electrical activity, electroencephalography (EEG) is the primary signal of interest for brain-computer-interfaces (BCI). BCIs offer a communication pathway between a brain and an external device, translating thought into action with suitable processing. EEG data is the most common signal source for such technologies. However, artefacts induced in BCIs in the real-world context can severely degrade their performance relative to their in-laboratory performance. In most cases, the recorded signals are so heavily corrupted by noise that they are unusable and restrict BCI's broader applicability. To realise the use of portable BCIs capable of high-quality performance in a real-world setting, we use Generative Adversarial Networks (GANs) that can adopt both supervised and unsupervised learning approaches. Although our approach is supervised, the same model can be used for unsupervised tasks such as data augmentation/imputation in the low resource setting. Exploiting recent advancements in Generative Adversarial Networks (GAN), we construct a pipeline capable of denoising artefacts from EEG time series data. In the case of denoising data, it maps noisy EEG signals to clean EEG signals, given the nature of the respective artefact. We demonstrate the capability of our network on a toy dataset and a benchmark EEG dataset developed explicitly for deep learning denoising techniques. Our datasets consist of an artificially added mains noise (50/60 Hz) artefact dataset and an open-source EEG benchmark dataset with two artificially added artefacts. Artificially inducing myogenic and ocular artefacts for the benchmark dataset allows us to present qualitative and quantitative evidence of the GANs denoising capabilities and rank it among the current gold standard deep learning EEG denoising techniques. We show the power spectral density (PSD), signal-to-noise ratio (SNR), and other classical time series similarity measures for quantitative metrics and compare our model to those previously used in the literature. To our knowledge, this framework is the first example of a GAN capable of EEG artefact removal and generalisable to more than one artefact type. Our model has provided a competitive performance in advancing the state-of-the-art deep learning EEG denoising techniques. Furthermore, given the integration of AI into wearable technology, our method would allow for portable EEG devices with less noisy and more stable brain signals.

scholarly journals Machine learning methods in finance

2021 ◽  
Vol 110 ◽  
pp. 05012
Author(s):  
Irina Karachun ◽  
Lyubov Vinnichek ◽  
Andrey Tuskov
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Predictive Modelling ◽  
Series Data ◽  
Barriers To Adoption ◽  
Finance Industry ◽  
Machine Learning Methods ◽  
Cast Machine

This article focuses on supervised learning and reinforcement learning. These areas overlap most with econometrics, predictive modelling, and optimal control in finance. We choose to focus on how to cast machine learning into various financial modelling and decision frameworks. This work introduces the industry context for machine learning in finance, discussing the critical events that have shaped the finance industry’s need for machine learning and the unique barriers to adoption. The finance industry has adopted machine learning to varying degrees of sophistication. Some key examples demonstrate the nature of machine learning and how it is used in practice. In particular, we begin to address many finance practitioner’s concerns that neural networks are a “black-box” by showing how they are related to existing well-established techniques such as linear regression, logistic regression, and autoregressive time series models. Neural networks can be shown to reduce to other well-known statistical techniques and are adaptable to time series data.

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