StressNet - Deep learning to predict stress with fracture propagation in brittle materials

AbstractCatastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal stress is critical to predicting time to failure and improving the fracture resistance and reliability of materials. Existing high-fidelity methods, such as the Finite-Discrete Element Model (FDEM), are limited by their high computational cost. Therefore, to reduce computational cost while preserving accuracy, a deep learning model, StressNet, is proposed to predict the entire sequence of maximum internal stress based on fracture propagation and the initial stress data. More specifically, the Temporal Independent Convolutional Neural Network (TI-CNN) is designed to capture the spatial features of fractures like fracture path and spall regions, and the Bidirectional Long Short-term Memory (Bi-LSTM) Network is adapted to capture the temporal features. By fusing these features, the evolution in time of the maximum internal stress can be accurately predicted. Moreover, an adaptive loss function is designed by dynamically integrating the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE), to reflect the fluctuations in maximum internal stress. After training, the proposed model is able to compute accurate multi-step predictions of maximum internal stress in approximately 20 seconds, as compared to the FDEM run time of 4 h, with an average MAPE of 2% relative to test data.

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Wind power forecasting for the Villonaco wind farm

Wind Engineering ◽

10.1177/0309524x20968817 ◽

2020 ◽

pp. 0309524X2096881

Author(s):

Jorge Maldonado-Correa ◽

Marcelo Valdiviezo-Condolo ◽

Marlon Santiago Viñan-Ludeña ◽

Carlos Samaniego-Ojeda ◽

Marco Rojas-Moncayo

Keyword(s):

Wind Energy ◽

Wind Power ◽

Hybrid Model ◽

Wind Farm ◽

Short Term Memory ◽

Mean Squared Error ◽

Active Power ◽

Percentage Error ◽

Efficient Operation ◽

The Mean

Wind energy is a non-programmable form of generation, hence, accurate and reliable wind energy prediction is of great importance for the efficient operation of wind farms. This article presents a study for the prediction of active power for the Villonaco Wind Farm (VWF), located in southern Ecuador at approximately 2700 m above sea level. Through the use of artificial neural networks, experimental tests are developed based on the models of Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) to obtain a hybrid model that fits the best characteristics of the individual models. Data from the active power SCADA (Supervisory Control and Data Acquisition) system for the years 2014 to 2018 are used to train and validate the models. Hybrid model is presented as the most appropriate option by the values obtained, viz., the mean absolute error (MAE), the mean squared error (MSE), and mean absolute percentage error (MAPE) that were 0.1365, 0.0974, and 144.26, respectively, outperforming to the others wind power forecast models.

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Improving Sentiment Analysis using Hybrid Deep Learning Model

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190328200012 ◽

2020 ◽

Vol 13 (4) ◽

pp. 627-640 ◽

Cited By ~ 1

Author(s):

Avinash Chandra Pandey ◽

Dharmveer Singh Rajpoot

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sentiment Analysis ◽

Classification Accuracy ◽

Short Term Memory ◽

Computational Cost ◽

Extraction Process ◽

Learning Model ◽

Sentiment Classification ◽

Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning

Sensors ◽

10.3390/s21227716 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7716

Author(s):

Krzysztof K. Cwalina ◽

Piotr Rajchowski ◽

Alicja Olejniczak ◽

Olga Błaszkiewicz ◽

Robert Burczyk

Keyword(s):

Deep Learning ◽

Interface Design ◽

Mean Squared Error ◽

Radio Channel ◽

Computational Cost ◽

Urban Environments ◽

Data Allocation ◽

Urban Networks ◽

Radio Interface ◽

Channel State Estimation

Following the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides a significant gain (almost 40%) with 10.7% compared to the linear model with the lowest RMSE (Root Mean Squared Error) 17.01%. The solution can be adopted as a part of the data allocation algorithm implemented in the telemetry devices equipped with the 4G radio interface, or, after the adjustment, the NB-IoT (Narrowband Internet of Things), to maximize the reliability of the services in harsh indoor or urban environments. Presented results also prove the existence of the inverse proportional dependence between the number of hidden layers and the number of historical samples in terms of the obtained RMSE. The increase of the historical data memory allows using models with fewer hidden layers while maintaining a comparable RMSE value for each scenario, which reduces the total computational cost.

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PP15 Predicting variations of calls to an ambulance service in the UK caused by circulating infections using-deep learning methods

Emergency Medicine Journal ◽

10.1136/emermed-2019-999abs.15 ◽

2019 ◽

Vol 36 (10) ◽

pp. e7.2-e7

Author(s):

Thilo Reich ◽

Marcin Budka

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Prehospital Care ◽

Machine Learning Algorithms ◽

Ambulance Service ◽

Percentage Error ◽

Learning Methods ◽

Average Percentage ◽

Average Percentage Error ◽

The Uk

BackgroundDigital patient records in the ambulance service have opened up new opportunities for prehospital care. Previously it was demonstrated that prehospital pyrexia numbers are linked to an increase in overall calls to the ambulance service. This study aims to predict the future number of calls using deep-learning methods.MethodsTemperature readings for 280,447 patients were generously provided by the South Western Ambulance Service Trust. The data covered the time between 05/01/2016 and 30/04/2017 with overall 44,472 patients being pyretic. A rolling window of 10 days was applied to daily sums for both pyretic and apyretic patients. These windows were used as input features to train machine-learning algorithms predicting the number of calls 10 days ahead. Algorithms tested include Linear Regression (LR), basic Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. A genetic approach was used to optimise the architecture, in which parameters were randomly modified and over several generations the best performing algorithm will be selected to be further manipulated. To assess performance the Mean Average Percentage Error (MAPE) was used.ResultsThe initial analysis showed that the total patient number and pyretic patient numbers are correlated. The best performing algorithms with varying numbers of hidden units had the following MAPE in comparison to simple LR: LR=19.4%, LSTM (104 units) = 6.1%, RNN (79 units)=6.01%, GRU (80 units)=5.97%.ConclusionsThese preliminary results suggest that deep-learning methods allow to predict the variations in total number of calls caused by circulating infections. Further investigations will aim to confirm these findings. Once fully verified these algorithms could play a major role in operational planning of any ambulance service by predicting increases in demand.

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Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction

Symmetry ◽

10.3390/sym13101942 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1942

Author(s):

Pyae Pyae Phyo ◽

Yung-Cheol Byun

Keyword(s):

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Absolute Error ◽

Percentage Error ◽

Ensemble Model ◽

Load Prediction ◽

Energy Prediction ◽

The Times ◽

One Year

The energy manufacturers are required to produce an accurate amount of energy by meeting the energy requirements at the end-user side. Consequently, energy prediction becomes an essential role in the electric industrial zone. In this paper, we propose the hybrid ensemble deep learning model, which combines multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN-LSTM to improve the forecasting performance. These DL architectures are more popular and better than other machine learning (ML) models for time series electrical load prediction. Therefore, hourly-based energy data are collected from Jeju Island, South Korea, and applied for forecasting. We considered external features associated with meteorological conditions affecting energy. Two-year training and one-year testing data are preprocessed and arranged to reform the times series, which are then trained in each DL model. The forecasting results of the proposed ensemble model are evaluated by using mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Error metrics are compared with DL stand-alone models such as MLP, CNN, LSTM, and CNN-LSTM. Our ensemble model provides better performance than other forecasting models, providing minimum MAPE at 0.75%, and was proven to be inherently symmetric for forecasting time-series energy and demand data, which is of utmost concern to the power system sector.

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A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling

Forecasting ◽

10.3390/forecast4010001 ◽

2021 ◽

Vol 4 (1) ◽

pp. 1-25

Author(s):

Thabang Mathonsi ◽

Terence L. van Zyl

Keyword(s):

Neural Network ◽

Deep Learning ◽

Recurrent Neural Network ◽

Short Term Memory ◽

Hybrid Methods ◽

Prediction Interval ◽

Hybrid Approach ◽

Computational Cost ◽

Exponential Smoothing ◽

Learning Methods

Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at forecasting tasks and quantifying the associated uncertainty with those forecasts (prediction intervals). One example is Exponential Smoothing Recurrent Neural Network (ES-RNN), a hybrid between a statistical forecasting model and a recurrent neural network variant. ES-RNN achieves a 9.4% improvement in absolute error in the Makridakis-4 Forecasting Competition. This improvement and similar outperformance from other hybrid models have primarily been demonstrated only on univariate datasets. Difficulties with applying hybrid forecast methods to multivariate data include (i) the high computational cost involved in hyperparameter tuning for models that are not parsimonious, (ii) challenges associated with auto-correlation inherent in the data, as well as (iii) complex dependency (cross-correlation) between the covariates that may be hard to capture. This paper presents Multivariate Exponential Smoothing Long Short Term Memory (MES-LSTM), a generalized multivariate extension to ES-RNN, that overcomes these challenges. MES-LSTM utilizes a vectorized implementation. We test MES-LSTM on several aggregated coronavirus disease of 2019 (COVID-19) morbidity datasets and find our hybrid approach shows consistent, significant improvement over pure statistical and deep learning methods at forecast accuracy and prediction interval construction.

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RNN-based Dimensional Speech Emotion Recognition

10.31227/osf.io/wa3vp ◽

2020 ◽

Author(s):

Bagus Tris Atmaja

Keyword(s):

Emotion Recognition ◽

Short Term Memory ◽

Mean Squared Error ◽

Absolute Error ◽

Recognition System ◽

Speech Emotion Recognition ◽

Percentage Error ◽

Concordance Correlation ◽

Acoustic Feature ◽

Dense System

◆ A speech emotion recognition system based on recurrent neural networks is developed using long short-term memory networks.◆ Two of acoustic feature sets are evaluated: 31 Features (3 time-domain features, 5 frequency-domain features, 13 MFCCs, 5 F0s, and 5 Harmonics) and eGeMaps feature set (23 features).◆ To evaluate the performance, some metrics are used i.e. mean squared error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and concordance correlation coefficient (CCC). Among those metrics, CCC is main focus as it is used by other researchers.◆ The developed system used multi-task learning to maximize arousal, valence, and dominance at the same time using CCC loss (1 - CCC). The result shows using LSTM networks improve the CCC score compared to baseline dense system. The best CCC score isobtained on arousal followed by dominance and valence.

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Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model

Sensors ◽

10.3390/s20216126 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6126

Author(s):

Tae Hyong Kim ◽

Ahnryul Choi ◽

Hyun Mu Heo ◽

Hyunggun Kim ◽

Joung Hwan Mun

Keyword(s):

Deep Learning ◽

Data Augmentation ◽

Short Term Memory ◽

Fall Detection ◽

Learning Model ◽

Measurement Unit ◽

Percentage Error ◽

Loss Of Balance ◽

Impact Acceleration ◽

Deep Learning Model

Pre-impact fall detection can detect a fall before a body segment hits the ground. When it is integrated with a protective system, it can directly prevent an injury due to hitting the ground. An impact acceleration peak magnitude is one of key measurement factors that can affect the severity of an injury. It can be used as a design parameter for wearable protective devices to prevent injuries. In our study, a novel method is proposed to predict an impact acceleration magnitude after loss of balance using a single inertial measurement unit (IMU) sensor and a sequential-based deep learning model. Twenty-four healthy participants participated in this study for fall experiments. Each participant worn a single IMU sensor on the waist to collect tri-axial accelerometer and angular velocity data. A deep learning method, bi-directional long short-term memory (LSTM) regression, is applied to predict a fall’s impact acceleration magnitude prior to fall impact (a fall in five directions). To improve prediction performance, a data augmentation technique with increment of dataset is applied. Our proposed model showed a mean absolute percentage error (MAPE) of 6.69 ± 0.33% with r value of 0.93 when all three different types of data augmentation techniques are applied. Additionally, there was a significant reduction of MAPE by 45.2% when the number of training datasets was increased by 4-fold. These results show that impact acceleration magnitude can be used as an activation parameter for fall prevention such as in a wearable airbag system by optimizing deployment process to minimize fall injury in real time.

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Short-Term Load Forecasting Using an Attended Sequential Encoder-Stacked Decoder Model with Online Training

Applied Sciences ◽

10.3390/app11114927 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4927

Author(s):

Sylwia Henselmeyer ◽

Marcin Grzegorzek

Keyword(s):

New York ◽

Short Term Memory ◽

Percentage Error ◽

Short Term ◽

New Approach ◽

Mohawk Valley ◽

Small Load ◽

Short Term Load Forecasting ◽

System Operator ◽

The Mean

The paper presents a new approach for the prediction of load active power 24 h ahead using an attended sequential encoder and stacked decoder model with Long Short-Term Memory cells. The load data are owned by the New York Independent System Operator (NYISO) and is dated from the years 2014–2017. Due to dynamics in the load patterns, multiple short pieces of training on pre-filtered data are executed in combination with the transfer learning concept. The evaluation is done by direct comparison with the results of the NYISO forecast and additionally under consideration of several benchmark methods. The results in terms of the Mean Absolute Percentage Error range from 1.5% for the highly loaded New York City zone to 3% for the Mohawk Valley zone with rather small load consumption. The execution time of a day ahead forecast including the training on a personal computer without GPU accounts to 10 s on average.

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249 Predicting body weight of finishing pigs using machine and deep learning algorithms

Journal of Animal Science ◽

10.1093/jas/skaa278.324 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 176-176

Author(s):

Yuqing He ◽

Francesco Tiezzi ◽

Christian Maltecca

Keyword(s):

Body Weight ◽

Deep Learning ◽

Feed Intake ◽

Short Term Memory ◽

Mean Squared Error ◽

Learning Algorithm ◽

Occupation Time ◽

Feed Consumption ◽

Lasso Regression ◽

Recording Equipment

Abstract Understanding and exploiting feeding patterns in swine could allow a reduced feed waste and minimized sorting losses. The objectives of this study were to evaluate the ability to predict whether a pig reached a target weight at finishing by using several algorithms and to compare the prediction using varying amounts of data during the growing period. Data were collected on 655 pigs from 75 to 166 days of age. Pigs were housed with 8 to 15 pigs and a Feed Intake Recording Equipment in each pen. Feed consumption, occupation time, and body weight per visit were recorded when a pig visited the feeder. Lasso Regression (LS), a machine learning algorithm: Random Forest (RF), and a deep learning algorithm: Long-short Term Memory (LSTM) network, were used to forecast whether pigs can reach 129 kg at the finishing stage (159–166 d). Times of visits, a sum of feed consumption, a sum of occupation time in the feeder every day, and age were used as predictors. Data were split into 6 slices by 14 days and used to calibrate the models and their predictive ability was tested with data corresponding to the last 8 days of the study period. The greatest correlation coefficients were 0.799, 0.828, and 0.868 using slice 6 (145–158 d) to train the LS, RF, and LSTM, respectively. The LS and LSTM algorithms had a smaller root mean squared error, 0.863 and 0.895 compared to the RF with 1.375 in the prediction. Overall, LS and LSTM performed best. Predictions using data closest to the finishing stage proved better. This study connects the dynamics of feeding behavior and feed intake data to growth using prediction methods that will hopefully accelerate the mainstream application of electronic feeders in pig production systems.

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