Deep Spatio-Temporal Neural Networks for Risk Prediction and Decision Support in Aviation Operations

2021 ◽  
Vol 21 (4) ◽  
Author(s):  
HyunKi Lee ◽  
Tejas G. Puranik ◽  
Dimitri N. Mavris
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Short Term Memory ◽  
Upper And Lower Bounds ◽  
Civil Aviation ◽  
Data Set ◽  
Ground Speed ◽  
Spatio Temporal ◽  
Aircraft Trajectory ◽  
Aviation Operations

Abstract The maintenance and improvement of safety are among the most critical concerns in civil aviation operations. Due to the increased availability of data and improvements in computing power, applying artificial intelligence technologies to reduce risk in aviation safety has gained momentum. In this paper, a framework is developed to build a predictive model of future aircraft trajectory that can be utilized online to assist air crews in their decision-making during approach. Flight data parameters from the approach phase between certain approach altitudes (also called gates) are utilized for training an offline model that predicts the aircraft’s ground speed at future points. This model is developed by combining convolutional neural networks (CNNs) and long short-term memory (LSTM) layers. Due to the myriad of model combinations possible, hyperband algorithm is used to automate the hyperparameter tuning process to choose the best possible model. The validated offline model can then be used to predict the aircraft’s future states and provide decision-support to air crews. The method is demonstrated using publicly available Flight Operations Quality Assurance (FOQA) data from the National Aeronautics and Space Administration (NASA). The developed model can predict the ground speed at an accuracy between 1.27% and 2.69% relative root-mean-square error. A safety score is also evaluated considering the upper and lower bounds of variation observed within the available data set. Thus, the developed model represents an improved performance over existing techniques in literature and shows significant promise for decision-support in aviation operations.

scholarly journals Natural language processing systems for data extraction and mapping on the basis of unstructured text blocks

2020 ◽  
Vol 26 (3) ◽  
pp. 53-61
Author(s):  
Pavel Kikin ◽  
Alexey Kolesnikov ◽  
Alexey Portnov ◽  
Denis Grischenko
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Mathematical Models ◽  
Optimal Algorithm ◽  
The State ◽  
Gradient Boosting ◽  
Learning Methods ◽  
Data Set ◽  
Machine Learning Methods ◽  
Spatio Temporal

The state of ecological systems, along with their general characteristics, is almost always described by indicators that vary in space and time, which leads to a significant complication of constructing mathematical models for predicting the state of such systems. One of the ways to simplify and automate the construction of mathematical models for predicting the state of such systems is the use of machine learning methods. The article provides a comparison of traditional and based on neural networks, algorithms and machine learning methods for predicting spatio-temporal series representing ecosystem data. Analysis and comparison were carried out among the following algorithms and methods: logistic regression, random forest, gradient boosting on decision trees, SARIMAX, neural networks of long-term short-term memory (LSTM) and controlled recurrent blocks (GRU). To conduct the study, data sets were selected that have both spatial and temporal components: the values of the number of mosquitoes, the number of dengue infections, the physical condition of tropical grove trees, and the water level in the river. The article discusses the necessary steps for preliminary data processing, depending on the algorithm used. Also, Kolmogorov complexity was calculated as one of the parameters that can help formalize the choice of the most optimal algorithm when constructing mathematical models of spatio-temporal data for the sets used. Based on the results of the analysis, recommendations are given on the application of certain methods and specific technical solutions, depending on the characteristics of the data set that describes a particular ecosystem

scholarly journals Predicting of air pollutant concentrations based on spatio-temporal attention convolutional LSTM networks

2021 ◽  
Author(s):  
P. Jiang ◽  
I. Bychkov ◽  
J. Liu ◽  
A. Hmelnov
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Support Vector ◽  
Temporal Attention ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Spatio Temporal

Forecasting of air pollutant concentration, which is influenced by air pollution accumulation, traffic flow and industrial emissions, has attracted extensive attention for decades. In this paper, we propose a spatio-temporal attention convolutional long short term memory neural networks (Attention-CNN-LSTM) for air pollutant concentration forecasting. Firstly, we analyze the Granger causalities between different stations and establish a hyperparametric Gaussian vector weight function to determine spatial autocorrelation variables, which is used as part of the input feature. Secondly, convolutional neural networks (CNN) is employed to extract the temporal dependence and spatial correlation of the input, while feature maps and channels are weighted by attention mechanism, so as to improve the effectiveness of the features. Finally, a depth long short term memory (LSTM) based time series predictor is established for learning the long-term and short-term dependence of pollutant concentration. In order to reduce the effect of diverse complex factors on LSTM, inherent features are extracted from historical air pollutant concentration data meteorological data and timestamp information are incorporated into the proposed model. Extensive experiments were performed using the Attention-CNNLSTM, autoregressive integrated moving average (ARIMA), support vector regression (SVR), traditional LSTM and CNN, respectively. The results demonstrated that the feasibility and practicability of Attention-CNN-LSTM on estimating CO and NO concentration.

scholarly journals Groundwater Level Forecasting with Artificial Neural Networks: A Comparison of LSTM, CNN and NARX

2020 ◽  
Author(s):  
Andreas Wunsch ◽  
Tanja Liesch ◽  
Stefan Broda
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Groundwater Level ◽  
Short Term Memory ◽  
State Of The Art ◽  
Predictive Ability ◽  
Data Set ◽  
Groundwater Levels ◽  
Narx Models ◽  
Artificial Neural

Abstract. It is now well established to use shallow artificial neural networks (ANN) to obtain accurate and reliable groundwater level forecasts, which are an important tool for sustainable groundwater management. However, we observe an increasing shift from conventional shallow ANNs to state-of-the-art deep learning (DL) techniques, but a direct comparison of the performance is often lacking. Although they have already clearly proven their suitability, especially shallow recurrent networks frequently seem to be excluded from the study design despite the euphoria about new DL techniques and its successes in various disciplines. Therefore, we aim to provide an overview on the predictive ability in terms of groundwater levels of shallow conventional recurrent ANN namely nonlinear autoregressive networks with exogenous inputs (NARX), and popular state-of-the-art DL-techniques such as long short-term memory (LSTM) and convolutional neural networks (CNN). We compare both the performance on sequence-to-value (seq2val) and sequence-to-sequence (seq2seq) forecasting on a 4-year period, while using only few, widely available and easy to measure meteorological input parameters, which makes our approach widely applicable. We observe that for seq2val forecasts NARX models on average perform best, however, CNNs are much faster and only slightly worse in terms of accuracy. For seq2seq forecasts, mostly NARX outperform both DL-models and even almost reach the speed of CNNs. However, NARX are the least robust against initialization effects, which nevertheless can be handled easily using ensemble forecasting. We showed that shallow neural networks, such as NARX, should not be neglected in comparison to DL-techniques; however, LSTMs and CNNs might perform substantially better with a larger data set, where DL really can demonstrate its strengths, which is rarely available in the groundwater domain though.

scholarly journals Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru

2021 ◽  
Author(s):  
Chardin Hoyos Cordova ◽  
Manuel Niño Lopez Portocarrero ◽  
Rodrigo Salas ◽  
Romina Torres ◽  
Paulo Canas Rodrigues ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Short Term Memory ◽  
Environmental Data ◽  
Air Quality Assessment ◽  
Artificial Neural ◽  
Spatio Temporal ◽  
Lstm Network ◽  
Recurrent Artificial Neural Networks

Abstract The prediction of air pollution is of great importance in highly populated areas because it has a direct impact on both the management of the city's economic activity and the health of its inhabitants. In this work, the spatio-temporal behavior of air quality in Metropolitan Lima was evaluated and predicted using the recurrent artificial neural network known as Long-Short Term Memory networks (LSTM). The LSTM was implemented for the hourly prediction of PM10 based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The model was evaluated under two validation schemes: the hold-out (HO) and the blocked-nested cross-validation (BNCV). The simulation results show that periods of low PM10 concentration are predicted with high precision. Whereas, for periods of high contamination, the LSTM network with BNCV has better predictability performance. In conclusion, recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better performance to forecast this type of environmental data, and can also be extrapolated to other pollutants.

scholarly journals Performance Evaluation of Deep Neural Networks Applied to Speech Recognition: RNN, LSTM and GRU

2019 ◽  
Vol 9 (4) ◽  
pp. 235-245 ◽  
Author(s):  
Apeksha Shewalkar ◽  
Deepika Nyavanandi ◽  
Simone A. Ludwig
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Language Model ◽  
Error Rates ◽  
Short Term ◽  
Data Set ◽  
Speech Data

Abstract Deep Neural Networks (DNN) are nothing but neural networks with many hidden layers. DNNs are becoming popular in automatic speech recognition tasks which combines a good acoustic with a language model. Standard feedforward neural networks cannot handle speech data well since they do not have a way to feed information from a later layer back to an earlier layer. Thus, Recurrent Neural Networks (RNNs) have been introduced to take temporal dependencies into account. However, the shortcoming of RNNs is that long-term dependencies due to the vanishing/exploding gradient problem cannot be handled. Therefore, Long Short-Term Memory (LSTM) networks were introduced, which are a special case of RNNs, that takes long-term dependencies in a speech in addition to short-term dependencies into account. Similarily, GRU (Gated Recurrent Unit) networks are an improvement of LSTM networks also taking long-term dependencies into consideration. Thus, in this paper, we evaluate RNN, LSTM, and GRU to compare their performances on a reduced TED-LIUM speech data set. The results show that LSTM achieves the best word error rates, however, the GRU optimization is faster while achieving word error rates close to LSTM.

LSTM multichannel neural networks in mental task classification

2019 ◽  
Vol 38 (4) ◽  
pp. 1204-1213
Author(s):  
Sławomir Opałka ◽  
Dominik Szajerman ◽  
Adam Wojciechowski
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
State Of The Art ◽  
Mental Task ◽  
Data Set ◽  
Content Type ◽  
Architecture Model ◽  
Task Classification ◽  
Mental Tasks Classification ◽  
Mental Tasks

Purpose The purpose of this paper is to apply recurrent neural networks (RNNs) and more specifically long-short term memory (LSTM)-based ones for mental task classification in terms of BCI systems. The authors have introduced novel LSTM-based multichannel architecture model which proved to be highly promising in other fields, yet was not used for mental tasks classification. Design/methodology/approach Validity of the multichannel LSTM-based solution was confronted with the results achieved by a non-multichannel state-of-the-art solutions on a well-recognized data set. Findings The results demonstrated evident advantage of the introduced method. The best of the provided variants outperformed most of the RNNs approaches and was comparable with the best state-of-the-art methods. Practical implications The approach presented in the manuscript enables more detailed investigation of the electroencephalography analysis methods, invaluable for BCI mental tasks classification. Originality/value The new approach to mental task classification, exploiting LSTM-based RNNs with multichannel architecture, operating on spatial features retrieving filters, has been adapted to mental tasks with noticeable results. To the best of the authors’ knowledge, such an approach was not present in the literature before.

scholarly journals Human Action Recognition in Videos using Convolution Long Short-Term Memory Network with Spatio-Temporal Networks

2021 ◽  
Vol 5 (1) ◽  
pp. 25-33
Author(s):  
Ashok Sarabu ◽  
Ajit Kumar Santra
Keyword(s):  
Neural Networks ◽  
Action Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Human Action Recognition ◽  
Human Action ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Spatio Temporal

Two-stream convolutional networks plays an essential role as a powerful feature extractor in human action recognition in videos. Recent studies have shown the importance of two-stream Convolutional Neural Networks (CNN) to recognize human action recognition. Recurrent Neural Networks (RNN) has achieved the best performance in video activity recognition combining CNN. Encouraged by CNN's results with RNN, we present a two-stream network with two CNNs and Convolution Long-Short Term Memory (CLSTM). First, we extricate Spatio-temporal features using two CNNs using pre-trained ImageNet models. Second, the results of two CNNs from step one are combined and fed as input to the CLSTM to get the overall classification score. We also explored the various fusion function performance that combines two CNNs and the effects of feature mapping at different layers. And, conclude the best fusion function along with layer number. To avoid the problem of overfitting, we adopt the data augmentation techniques. Our proposed model demonstrates a substantial improvement compared to the current two-stream methods on the benchmark datasets with 70.4% on HMDB-51 and 95.4% on UCF-101 using the pre-trained ImageNet model. Doi: 10.28991/esj-2021-01254 Full Text: PDF

scholarly journals Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chardin Hoyos Cordova ◽  
Manuel Niño Lopez Portocarrero ◽  
Rodrigo Salas ◽  
Romina Torres ◽  
Paulo Canas Rodrigues ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Short Term Memory ◽  
Environmental Data ◽  
Simulation Results ◽  
Artificial Neural ◽  
Spatio Temporal ◽  
Pm 10 ◽  
Monitoring Stations

AbstractThe prediction of air pollution is of great importance in highly populated areas because it directly impacts both the management of the city’s economic activity and the health of its inhabitants. This work evaluates and predicts the Spatio-temporal behavior of air quality in Metropolitan Lima, Peru, using artificial neural networks. The conventional feedforward backpropagation known as Multilayer Perceptron (MLP) and the Recurrent Artificial Neural network known as Long Short-Term Memory networks (LSTM) were implemented for the hourly prediction of $$\hbox {PM}_{10}$$ PM 10 based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The models were validated using two schemes: The Hold-Out and the Blocked-Nested Cross-Validation (BNCV). The simulation results show that periods of moderate $$\hbox {PM}_{10}$$ PM 10 concentration are predicted with high precision. Whereas, for periods of high contamination, the performance of both models, the MLP and LSTM, were diminished. On the other hand, the prediction performance improved slightly when the models were trained and validated with the BNCV scheme. The simulation results showed that the models obtained a good performance for the CDM, CRB, and SMP monitoring stations, characterized by a moderate to low level of contamination. However, the results show the difficulty of predicting this contaminant in those stations that present critical contamination episodes, such as ATE and HCH. In conclusion, the LSTM recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better predictability performance for this type of environmental data.

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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