scholarly journals Stochastic Detection of Interior Design Styles Using a Deep-Learning Model for Reference Images

2020 ◽  
Vol 10 (20) ◽  
pp. 7299 ◽  
Author(s):  
Jinsung Kim ◽  
Jin-Kook Lee
Keyword(s):  
Deep Learning ◽  
Interior Design ◽  
Learning Model ◽  
Data Driven ◽  
Data Preparation ◽  
Reference Images ◽  
Data Driven Approach ◽  
Image Set ◽  
Design Style ◽  
Deep Learning Model

This paper describes an approach for identifying and appending interior design style information stochastically with reference images and a deep-learning model. In the field of interior design, design style is a useful concept and has played an important role in helping people understand and communicate interior design. Previous studies have focused on how the interior design style categories can be defined. On the other hand, this paper focuses on how stochastically recognizing the design style of given interior design reference images using a deep learning-based data-driven approach. The proposed method can be summarized as follows: (1) data preparation based on a general design style definition, (2) implementing an interior design style recognition model using a pre-trained VGG16 model, (3) training and evaluating the trained model, and (4) demonstration of stochastic detection of interior design styles for reference images. The result shows that the trained model automatically recognizes the design styles of given interior images with probability values. The recognition results, model, and trained image set contribute to facilitating the management and utilization of an interior design references database.

scholarly journals Derivation and Uncertainty Quantification of a Data-Driven Subcooled Boiling Model

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5987
Author(s):  
Jerol Soibam ◽  
Achref Rabhi ◽  
Ioanna Aslanidou ◽  
Konstantinos Kyprianidis ◽  
Rebei Bel Fdhila
Keyword(s):  
Heat Transfer ◽  
Deep Learning ◽  
Void Fraction ◽  
Wall Temperature ◽  
Flow Boiling ◽  
Learning Model ◽  
Data Driven ◽  
Subcooled Boiling ◽  
Deep Learning Model ◽  
Quantities Of Interest

Subcooled flow boiling occurs in many industrial applications where enormous heat transfer is needed. Boiling is a complex physical process that involves phase change, two-phase flow, and interactions between heated surfaces and fluids. In general, boiling heat transfer is usually predicted by empirical or semiempirical models, which are horizontal to uncertainty. In this work, a data-driven method based on artificial neural networks has been implemented to study the heat transfer behavior of a subcooled boiling model. The proposed method considers the near local flow behavior to predict wall temperature and void fraction of a subcooled minichannel. The input of the network consists of pressure gradients, momentum convection, energy convection, turbulent viscosity, liquid and gas velocities, and surface information. The outputs of the models are based on the quantities of interest in a boiling system wall temperature and void fraction. To train the network, high-fidelity simulations based on the Eulerian two-fluid approach are carried out for varying heat flux and inlet velocity in the minichannel. Two classes of the deep learning model have been investigated for this work. The first one focuses on predicting the deterministic value of the quantities of interest. The second one focuses on predicting the uncertainty present in the deep learning model while estimating the quantities of interest. Deep ensemble and Monte Carlo Dropout methods are close representatives of maximum likelihood and Bayesian inference approach respectively, and they are used to derive the uncertainty present in the model. The results of this study prove that the models used here are capable of predicting the quantities of interest accurately and are capable of estimating the uncertainty present. The models are capable of accurately reproducing the physics on unseen data and show the degree of uncertainty when there is a shift of physics in the boiling regime.

scholarly journals Application of Machine Learning Method of Data-Driven Deep Learning Model to Predict Well Production Rate in the Shale Gas Reservoirs

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3629
Author(s):  
Dongkwon Han ◽  
Sunil Kwon
Keyword(s):  
Deep Learning ◽  
Hydraulic Fracturing ◽  
Physical Phenomenon ◽  
Principal Component ◽  
Learning Model ◽  
Gas Production ◽  
Data Driven ◽  
Percentage Error ◽  
Proxy Model ◽  
Deep Learning Model

Reservoir modeling to predict shale reservoir productivity is considerably uncertain and time consuming. Since we need to simulate the physical phenomenon of multi-stage hydraulic fracturing. To overcome these limitations, this paper presents an alternative proxy model based on data-driven deep learning model. Furthermore, this study not only proposes the development process of a proxy model, but also verifies using field data for 1239 horizontal wells from the Montney shale formation in Alberta, Canada. A deep neural network (DNN) based on multi-layer perceptron was applied to predict the cumulative gas production as the dependent variable. The independent variable is largely divided into four types: well information, completion and hydraulic fracturing and production data. It was found that the prediction performance was better when using a principal component with a cumulative contribution of 85% using principal component analysis that extracts important information from multivariate data, and when predicting with a DNN model using 6 variables calculated through variable importance analysis. Hence, to develop a reliable deep learning model, sensitivity analysis of hyperparameters was performed to determine one-hot encoding, dropout, activation function, learning rate, hidden layer number and neuron number. As a result, the best prediction of the mean absolute percentage error of the cumulative gas production improved to at least 0.2% and up to 9.1%. The novel approach of this study can also be applied to other shale formations. Furthermore, a useful guide for economic analysis and future development plans of nearby reservoirs.

scholarly journals Finding epic moments in live content through deep learning on collective decisions

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Hyeonho Song ◽  
Kunwoo Park ◽  
Meeyoung Cha
Keyword(s):  
Deep Learning ◽  
User Study ◽  
Data Driven ◽  
Live Streaming ◽  
Video Content ◽  
Live Video ◽  
Collective Decisions ◽  
Data Driven Approach ◽  
Streaming Services ◽  
Deep Learning Model

AbstractLive streaming services enable the audience to interact with one another and the streamer over live content. The surging popularity of live streaming platforms has created a competitive environment. To retain existing viewers and attract newcomers, streamers and fans often create a well-condensed summary of the streamed content. However, this process is manual and costly due to the length of online live streaming events. The current study identifies enjoyable moments in user-generated live video content by examining the audiences’ collective evaluation of its epicness. We characterize what features “epic” moments and present a deep learning model to extract them based on analyzing two million user-recommended clips and the associated chat conversations. The evaluation shows that our data-driven approach can identify epic moments from user-generated streamed content that cover various contexts (e.g., victory, funny, awkward, embarrassing). Our user study further demonstrates that the proposed automatic model performs comparably to expert suggestions. We discuss implications of the collective decision-driven extraction in identifying diverse epic moments in a scalable way.

scholarly journals Purely satellite data–driven deep learning forecast of complicated tropical instability waves

2020 ◽  
Vol 6 (29) ◽  
pp. eaba1482
Author(s):  
Gang Zheng ◽  
Xiaofeng Li ◽  
Rong-Hua Zhang ◽  
Bin Liu
Keyword(s):  
Deep Learning ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Data ◽  
Numerical Models ◽  
Learning Model ◽  
Sea Surface ◽  
Data Driven ◽  
Instability Wave ◽  
Deep Learning Model

Forecasting fields of oceanic phenomena has long been dependent on physical equation–based numerical models. The challenge is that many natural processes need to be considered for understanding complicated phenomena. In contrast, rules of the processes are already embedded in the time-series observation itself. Thus, inspired by largely available satellite remote sensing data and the advance of deep learning technology, we developed a purely satellite data–driven deep learning model for forecasting the sea surface temperature evolution associated with a typical phenomenon: a tropical instability wave. During the testing period of 9 years (2010–2019), our model accurately and efficiently forecasts the sea surface temperature field. This study demonstrates the strong potential of the satellite data–driven deep learning model as an alternative to traditional numerical models for forecasting oceanic phenomena.

Using artificial neural networks with joint muon-gravity datasets for shallow subsurface density prediction at volcanoes

2021 ◽  
Author(s):  
Katherine Cosburn ◽  
Mousumi Roy
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Synthetic Data ◽  
Cosmic Ray ◽  
Learning Model ◽  
Data Driven ◽  
Shallow Subsurface ◽  
Artificial Neural ◽  
Deep Learning Model

<p>The ability to accurately and reliably obtain images of shallow subsurface anomalies within the Earth is important for hazard monitoring at many geologic structures, such as volcanic edifices. In recent years, the use of machine learning as a novel, data-driven approach to addressing complex inverse problems in the geosciences has gained increasing attention, particularly in the field of seismology. Here we present a physics-based, machine learning method to integrate disparate geophysical datasets for shallow subsurface imaging. We develop a methodology for imaging static density variations at a volcano with well-characterized topography by pairing synthetic cosmic-ray muon and gravity datasets. We use an artificial neural network (ANN) to interpret noisy synthetic datasets generated using theoretical knowledge of the forward kernels that relate these datasets to density. The deep learning model is trained with synthetic data from a suite of possible anomalous density structures and its accuracy is determined by comparing against the known forward calculation.<span> </span></p><p>In essence, we have converted a traditional inversion problem into a pattern recognition tool, where the ANN learns to predict discrete anomalous patterns within a target structure. Given a comprehensive suite of possible patterns and an appropriate amount of added noise to the synthetic data, the ANN can then interpolate the best-fit anomalous pattern given data it has never seen before, such as those obtained from field measurements. The power of this approach is its generality, and our methodology may be applied to a range of observables, such as seismic travel times and electrical conductivity. Our method relies on physics-based forward kernels that connect observations to physical parameters, such as density, temperature, composition, porosity, and saturation. The key benefit in using a physics-based approach as opposed to a data-driven one is the ability to get accurate predictions in cases where the amount of data may be too sparse or difficult to obtain to reliably train a neural network. We compare our approach to a traditional inversion, where appropriate, and highlight the (dis)advantages of the deep learning model.</p>

scholarly journals A novel data-driven model for real-time influenza forecasting

2017 ◽  
Author(s):  
Siva R. Venna ◽  
Amirhossein Tavanaei ◽  
Raju N. Gottumukkala ◽  
Vijay V. Raghavan ◽  
Anthony Maida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Model ◽  
Data Driven ◽  
Geographical Proximity ◽  
Learning Methods ◽  
Forecasting Methods ◽  
Geographical Regions ◽  
Spatio Temporal ◽  
Deep Learning Model

AbstractWe provide data-driven machine learning methods that are capable of making real-time influenza forecasts that integrate the impacts of climatic factors and geographical proximity to achieve better forecasting performance. The key contributions of our approach are both applying deep learning methods and incorporation of environmental and spatio-temporal factors to improve the performance of the influenza forecasting models. We evaluate the method on Influenza Like Illness (ILI) counts and climatic data, both publicly available data sets. Our proposed method outperforms existing known influenza forecasting methods in terms of their Mean Absolute Percentage Error and Root Mean Square Error. The key advantages of the proposed data-driven methods are as following: (1) The deep-learning model was able to effectively capture the temporal dynamics of flu spread in different geographical regions, (2) The extensions to the deep-learning model capture the influence of external variables that include the geographical proximity and climatic variables such as humidity, temperature, precipitation and sun exposure in future stages, (3) The model consistently performs well for both the city scale and the regional scale on the Google Flu Trends (GFT) and Center for Disease Control (CDC) flu counts. The results offer a promising direction in terms of both data-driven forecasting methods and capturing the influence of spatio-temporal and environmental factors for influenza forecasting methods.

scholarly journals A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications

2021 ◽  
Vol 11 (23) ◽  
pp. 11191
Author(s):  
Prayitno ◽  
Chi-Ren Shyu ◽  
Karisma Trinanda Putra ◽  
Hsing-Chung Chen ◽  
Yuan-Yu Tsai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Data Partitioning ◽  
Data Driven ◽  
Future Research ◽  
Healthcare Applications ◽  
Smart Healthcare ◽  
Protection Mechanisms ◽  
Benchmark Datasets ◽  
Deep Learning Model

Recent advances in deep learning have shown many successful stories in smart healthcare applications with data-driven insight into improving clinical institutions’ quality of care. Excellent deep learning models are heavily data-driven. The more data trained, the more robust and more generalizable the performance of the deep learning model. However, pooling the medical data into centralized storage to train a robust deep learning model faces privacy, ownership, and strict regulation challenges. Federated learning resolves the previous challenges with a shared global deep learning model using a central aggregator server. At the same time, patient data remain with the local party, maintaining data anonymity and security. In this study, first, we provide a comprehensive, up-to-date review of research employing federated learning in healthcare applications. Second, we evaluate a set of recent challenges from a data-centric perspective in federated learning, such as data partitioning characteristics, data distributions, data protection mechanisms, and benchmark datasets. Finally, we point out several potential challenges and future research directions in healthcare applications.

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