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.

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.

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.

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.

Machine Learning Data Center Workloads Using Generative Adversarial Networks

2020 ◽  
Vol 48 (2) ◽  
pp. 21-23
Author(s):  
Boudewijn R. Haverkort ◽  
Felix Finkbeiner ◽  
Pieter-Tjerk de Boer
Keyword(s):  
Machine Learning ◽  
Data Center ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Learning Data

scholarly journals Generalization of Deep Neural Networks for Imbalanced Fault Classification of Machinery Using Generative Adversarial Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 111168-111180 ◽  
Author(s):  
Jinrui Wang ◽  
Shunming Li ◽  
Baokun Han ◽  
Zenghui An ◽  
Huaiqian Bao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Fault Classification ◽  
Generative Adversarial Networks ◽  
Adversarial Networks

scholarly journals Implementing Convolutional Neural Networks for Simple Image Classification

2019 ◽  
Vol 9 (2) ◽  
pp. 3616-3619
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
High Volume ◽  
Classification Systems ◽  
Learning Tools ◽  
Huge Amount ◽  
Recognition Systems

In recent years, huge amounts of data in form of images has been efficiently created and accumulated at extraordinary rates. This huge amount of data that has high volume and velocity has presented us with the problem of coming up with practical and effective ways to classify it for analysis. Existing classification systems can never fulfil the demand and the difficulties of accurately classifying such data. In this paper, we built a Convolutional Neural Network (CNN) which is one of the most powerful and popular machine learning tools used in image recognition systems for classifying images from one of the widely used image datasets CIFAR-10. This paper also gives a thorough overview of the working of our CNN architecture with its parameters and difficulties.

scholarly journals Handling Imbalanced Data in Intrusion Detection Systems using Generative Adversarial Networks

2020 ◽  
Vol 2020 (1) ◽  
pp. 1-13
Author(s):  
Ly Vu ◽  
Quang Uy Nguyen
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Imbalanced Data ◽  
Original Data ◽  
Machine Learning Techniques ◽  
Generative Adversarial Networks ◽  
Detection Systems ◽  
Adversarial Networks ◽  
Attack Data

Machine learning-based intrusion detection hasbecome more popular in the research community thanks to itscapability in discovering unknown attacks. To develop a gooddetection model for an intrusion detection system (IDS) usingmachine learning, a great number of attack and normal datasamples are required in the learning process. While normaldata can be relatively easy to collect, attack data is muchrarer and harder to gather. Subsequently, IDS datasets areoften dominated by normal data and machine learning modelstrained on those imbalanced datasets are ineffective in detect-ing attacks. In this paper, we propose a novel solution to thisproblem by using generative adversarial networks to generatesynthesized attack data for IDS. The synthesized attacks aremerged with the original data to form the augmented dataset.Three popular machine learning techniques are trained on theaugmented dataset. The experiments conducted on the threecommon IDS datasets and one our own dataset show thatmachine learning algorithms achieve better performance whentrained on the augmented dataset of the generative adversarialnetworks compared to those trained on the original datasetand other sampling techniques. The visualization techniquewas also used to analyze the properties of the synthesizeddata of the generative adversarial networks and the others.

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