scholarly journals Roll motion prediction using a hybrid deep learning and ARIMA model

2018 ◽  
Vol 144 ◽  
pp. 251-258 ◽  
Author(s):  
Novri Suhermi ◽  
Suhartono ◽  
Dedy Dwi Prastyo ◽  
Baharuddin Ali
Keyword(s):  
Deep Learning ◽  
Arima Model ◽  
Motion Prediction ◽  
Roll Motion

scholarly journals Ship roll motion prediction based on ℓ1 regularized extreme learning machine

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0206476 ◽  
Author(s):  
Binglei Guan ◽  
Wei Yang ◽  
Zhibin Wang ◽  
Yinggan Tang
Keyword(s):  
Extreme Learning Machine ◽  
Motion Prediction ◽  
Roll Motion ◽  
Ship Roll ◽  
Learning Machine ◽  
Ship Roll Motion

scholarly journals Assessing the Performance of Deep Learning Algorithms for Short-Term Surface Water Quality Prediction

2021 ◽  
Vol 13 (19) ◽  
pp. 10690
Author(s):  
Heelak Choi ◽  
Sang-Ik Suh ◽  
Su-Hee Kim ◽  
Eun Jin Han ◽  
Seo Jin Ki
Keyword(s):  
Water Quality ◽  
Deep Learning ◽  
Surface Water ◽  
Learning Algorithms ◽  
Arima Model ◽  
Surface Water Quality ◽  
Percentage Error ◽  
Data Sets ◽  
Learning Models ◽  
Dependent Variables

This study aimed to investigate the applicability of deep learning algorithms to (monthly) surface water quality forecasting. A comparison was made between the performance of an autoregressive integrated moving average (ARIMA) model and four deep learning models. All prediction algorithms, except for the ARIMA model working on a single variable, were tested with univariate inputs consisting of one of two dependent variables as well as multivariate inputs containing both dependent and independent variables. We found that deep learning models (6.31–18.78%, in terms of the mean absolute percentage error) showed better performance than the ARIMA model (27.32–404.54%) in univariate data sets, regardless of dependent variables. However, the accuracy of prediction was not improved for all dependent variables in the presence of other associated water quality variables. In addition, changes in the number of input variables, sliding window size (i.e., input and output time steps), and relevant variables (e.g., meteorological and discharge parameters) resulted in wide variation of the predictive accuracy of deep learning models, reaching as high as 377.97%. Therefore, a refined search identifying the optimal values on such influencing factors is recommended to achieve the best performance of any deep learning model in given multivariate data sets.

scholarly journals FuseAD: Unsupervised Anomaly Detection in Streaming Sensors Data by Fusing Statistical and Deep Learning Models

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2451 ◽  
Author(s):  
Mohsin Munir ◽  
Shoaib Ahmed Siddiqui ◽  
Muhammad Ali Chattha ◽  
Andreas Dengel ◽  
Sheraz Ahmed
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Internal State ◽  
Arima Model ◽  
Streaming Data ◽  
Detection Technique ◽  
Detection Methods ◽  
Smart Devices ◽  
Detection Techniques ◽  
Unsupervised Anomaly Detection

The need for robust unsupervised anomaly detection in streaming data is increasing rapidly in the current era of smart devices, where enormous data are gathered from numerous sensors. These sensors record the internal state of a machine, the external environment, and the interaction of machines with other machines and humans. It is of prime importance to leverage this information in order to minimize downtime of machines, or even avoid downtime completely by constant monitoring. Since each device generates a different type of streaming data, it is normally the case that a specific kind of anomaly detection technique performs better than the others depending on the data type. For some types of data and use-cases, statistical anomaly detection techniques work better, whereas for others, deep learning-based techniques are preferred. In this paper, we present a novel anomaly detection technique, FuseAD, which takes advantage of both statistical and deep-learning-based approaches by fusing them together in a residual fashion. The obtained results show an increase in area under the curve (AUC) as compared to state-of-the-art anomaly detection methods when FuseAD is tested on a publicly available dataset (Yahoo Webscope benchmark). The obtained results advocate that this fusion-based technique can obtain the best of both worlds by combining their strengths and complementing their weaknesses. We also perform an ablation study to quantify the contribution of the individual components in FuseAD, i.e., the statistical ARIMA model as well as the deep-learning-based convolutional neural network (CNN) model.

scholarly journals Prediction of Short-Time Cloud Motion Using a Deep-Learning Model

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1151
Author(s):  
Xinyue Su ◽  
Tiejian Li ◽  
Chenge An ◽  
Guangqian Wang
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Block Matching ◽  
Solar Irradiation ◽  
Motion Prediction ◽  
Significant Information ◽  
Cloud Motion ◽  
Short Time ◽  
Gated Recurrent Unit ◽  
Deep Learning Model

A cloud image can provide significant information, such as precipitation and solar irradiation. Predicting short-time cloud motion from images is the primary means of making intra-hour irradiation forecasts for solar-energy production and is also important for precipitation forecasts. However, it is very challenging to predict cloud motion (especially nonlinear motion) accurately. Traditional methods of cloud-motion prediction are based on block matching and the linear extrapolation of cloud features; they largely ignore nonstationary processes, such as inversion and deformation, and the boundary conditions of the prediction region. In this paper, the prediction of cloud motion is regarded as a spatiotemporal sequence-forecasting problem, for which an end-to-end deep-learning model is established; both the input and output are spatiotemporal sequences. The model is based on gated recurrent unit (GRU)- recurrent convolutional network (RCN), a variant of the gated recurrent unit (GRU), which has convolutional structures to deal with spatiotemporal features. We further introduce surrounding context into the prediction task. We apply our proposed Multi-GRU-RCN model to FengYun-2G satellite infrared data and compare the results to those of the state-of-the-art method of cloud-motion prediction, the variational optical flow (VOF) method, and two well-known deep-learning models, namely, the convolutional long short-term memory (ConvLSTM) and GRU. The Multi-GRU-RCN model predicts intra-hour cloud motion better than the other methods, with the largest peak signal-to-noise ratio and structural similarity index. The results prove the applicability of the GRU-RCN method for solving the spatiotemporal data prediction problem and indicate the advantages of our model for further applications.

Motion Prediction for Autonomous Vehicles from Lyft Dataset using Deep Learning

2020 ◽  
Author(s):  
Sampurna Mandal ◽  
Swagatam Biswas ◽  
Valentina E. Balas ◽  
Rabindra Nath Shaw ◽  
Ankush Ghosh
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Motion Prediction

scholarly journals Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach

2022 ◽  
Vol 19 (2) ◽  
pp. 738
Author(s):  
Abdelgader Alamrouni ◽  
Fidan Aslanova ◽  
Sagiru Mati ◽  
Hamza Sabo Maccido ◽  
Afaf. A. Jibril ◽  
...  
Keyword(s):  
Deep Learning ◽  
Autocorrelation Function ◽  
Short Term Memory ◽  
Least Squares Method ◽  
Arima Model ◽  
Information Criterion ◽  
Generalized Least Squares ◽  
Performance Criteria ◽  
Regional Modeling ◽  
Estimation Model

Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. For this purpose, augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, autocorrelation function (ACF), partial autocorrelation function (PACF), Schwarz information criterion (SIC), and residual diagnostics were considered in determining the best ARIMA model for cumulative COVID-19 cases (CCC) across multi-region countries. Seven different performance criteria were used to evaluate the accuracy of the models. The obtained results justified both types of ARIMA model, with ARIMAGLS and ensemble ARIMA demonstrating superiority to the other models. Among the DL models analyzed, LSTM-M1 emerged as the best and most reliable estimation model, with both RF and LSTM attaining more than 80% prediction accuracy. While the EML of the DL proved merit with 96% accuracy. The outcomes of the two scenarios indicate the superiority of ARIMA time series and DL models in further decision making for FK.

A Comparison of Time Domain Methods for Asymmetric Roll Predictions

2014 ◽  
Author(s):  
Robert Seah ◽  
Fabien Bigot ◽  
Nathan Tom ◽  
Dominique Roddier
Keyword(s):  
Time Domain ◽  
Hydrodynamic Model ◽  
The Other ◽  
Motion Prediction ◽  
Roll Motion ◽  
Analysis Tool ◽  
Dynamics Analysis ◽  
Prediction Tool ◽  
Environmental Condition ◽  
Asymmetric Configuration

Unlike ocean going vessels, FPSOs often have appendages, such as bilge keels or riser porches, at or below the waterline in an asymmetric configuration. In addition, the riser and mooring systems impose asymmetric loads on the hull. As a result, the expected roll motion response to a wave environment is asymmetric and traditional methodologies cannot be used to predict it. Morison drag elements can be incorporated to represent the asymmetric condition and are easily implemented in time domain simulations. The limitation to this engineering approach is that the drag coefficient can only be calibrated to produce accurate motions or accurate appendage loads but not both. In this paper we compare the response using two time domain approaches, the first being adapted from a commercial marine dynamics analysis tool [9] and the other being a specialized hydrodynamics motion prediction tool [4]. Here, the commercial tool utilizes constant coefficient drag elements in conjunction with traditional linear equivalent roll damping to model the effect of unequal port and starboard bilge keels as is typical when a riser balcony are present. In contrast, the newly developed hydrodynamic model relies solely on a Keulegan-Carpenter (KC) number dependent drag relation to represent the asymmetric drag contributions. The different calibration procedures will be discussed and a comparison for a design environmental condition between the two methodologies will be presented.

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