scholarly journals A Feed Forward Neural Network Based on Model Output Statistics for Short-Term Hurricane Intensity Prediction

2019 ◽  
Vol 34 (4) ◽  
pp. 985-997 ◽  
Author(s):  
Kirkwood A. Cloud ◽  
Brian J. Reich ◽  
Christopher M. Rozoff ◽  
Stefano Alessandrini ◽  
William E. Lewis ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Training Data ◽  
Lead Times ◽  
Time Data ◽  
Feed Forward Neural Network ◽  
Hurricane Intensity ◽  
Feed Forward ◽  
Intensity Prediction ◽  
Skill Scores

Abstract A feed forward neural network (FFNN) is developed for tropical cyclone (TC) intensity prediction, where intensity is defined as the maximum 1-min average 10-m wind speed. This deep learning model incorporates a real-time operational estimate of the current intensity and predictors derived from Hurricane Weather Research and Forecasting (HWRF; 2017 version) Model forecasts. The FFNN model is developed with the operational constraint of being restricted to 6-h-old HWRF data. Best track intensity data are used for observational verification. The forecast training data are from 2014 to 2016 HWRF reforecast data and cover a wide variety of TCs from both the Atlantic and eastern Pacific Ocean basins. Cross validation shows that the FFNN increasingly outperforms the operational observation-adjusted HWRF (HWFI) in terms of mean absolute error (MAE) at forecast lead times from 3 to 57 h. Out-of-sample testing on real-time data from 2017 shows the HWFI produces lower MAE than the FFNN at lead times of 24 h or less and similar MAEs at later lead times. On the other hand, the 2017 data indicate significant potential for the FFNN in the prediction of rapid intensification (RI), with RI defined here as an intensification of at least 30 kt (1 kt ≈ 0.51 m s−1) in a 24-h period. The FFNN produces 4 times the number of hits in HWFI for RI. While the FFNN has more false alarms than the HWFI, Brier skill scores show that, in the Atlantic, the FFNN has significantly greater skill than the HWFI and probabilistic Statistical Hurricane Intensity Prediction System RI index.

SENTENCE ALIGNMENT USING FEED FORWARD NEURAL NETWORK

2006 ◽  
Vol 16 (06) ◽  
pp. 423-434 ◽  
Author(s):  
MOHAMED ABDEL FATTAH ◽  
FUJI REN ◽  
SHINGO KUROIWA
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Training Data ◽  
Parameter Vector ◽  
Feed Forward Neural Network ◽  
New Approach ◽  
Feed Forward ◽  
Parallel Corpora ◽  
Feature Parameter ◽  
Essential Resource

Parallel corpora have become an essential resource for work in multi lingual natural language processing. However, sentence aligned parallel corpora are more efficient than non-aligned parallel corpora for cross language information retrieval and machine translation applications. In this paper, we present a new approach to align sentences in bilingual parallel corpora based on feed forward neural network classifier. A feature parameter vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuate score, and cognate score values. A set of manually prepared training data has been assigned to train the feed forward neural network. Another set of data was used for testing. Using this new approach, we could achieve an error reduction of 60% over length based approach when applied on English–Arabic parallel documents. Moreover this new approach is valid for any language pair and it is quite flexible approach since the feature parameter vector may contain more/less or different features than that we used in our system such as lexical match feature.

scholarly journals Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery

Electrochem ◽  
2022 ◽  
Vol 3 (1) ◽  
pp. 42-57
Author(s):  
Devendrasinh Darbar ◽  
Indranil Bhattacharya
Keyword(s):  
Neural Network ◽  
Equivalent Circuit Model ◽  
Reference Electrode ◽  
State Of Charge ◽  
Training Data ◽  
Model Parameters ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Non Linear ◽  
Battery State Of Charge

Estimating the accurate State of Charge (SOC) of a battery is important to avoid the over/undercharging and protect the battery pack from low cycle life. Current methods of SOC estimation use complex equations in the Extended Kalman Filter (EKF) and the equivalent circuit model. In this paper, we used a Feed Forward Neural Network (FNN) to estimate the SOC value accurately where battery parameters such as current, voltage, and charge are mapped directly to the SOC value at the output. A FNN could self-learn the weights with each training data point and update the model parameters such as weights and bias using a combination of two gradient descents (Adam). This model comprises the Dropout technique, which can have many neural network architectures by dropping the neuron/mode at each epoch/training cycle using the same weights and biases. Our FNN model was trained with data comprising different current rates and tested for different cycling data, for example, 5th, 10th, 20th, and 50th cycles and at a different cutoff voltage (4.5 V). The battery used for estimating the SOC value was a Na-ion based battery, which is highly non-linear, and it was fabricated in a house using Na0.67Fe0.5Mn0.5O2 (NFM) as a cathode and Na metal as a reference electrode. The FNN successfully estimated the SOC value for the highly non-linear nature of the Na-ion battery at different current rates (0.05 C, 0.1 C, 0.5 C, 1 C, 2 C), for different cycling data, and at higher cut-off voltage of –4.5 V Na+, reaching the R2 value of ~0.97–~0.99, ~0.99, and ~0.98, respectively.

Estimating adsorption isotherm parameters in chromatography via a virtual injection promoting double feed-forward neural network

2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Chen Xu ◽  
Ye Zhang
Keyword(s):  
Neural Network ◽  
Adsorption Isotherms ◽  
Regularization Parameter ◽  
Real Data ◽  
Training Data ◽  
Inversion Method ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Ill Posed ◽  
Physics Model

Abstract The means to obtain the adsorption isotherms is a fundamental open problem in competitive chromatography. A modern technique of estimating adsorption isotherms is to solve a nonlinear inverse problem in a partial differential equation so that the simulated batch separation coincides with actual experimental results. However, this identification process is usually ill-posed in the sense that the uniqueness of adsorption isotherms cannot be guaranteed, and moreover, the small noise in the measured response can lead to a large fluctuation in the traditional estimation of adsorption isotherms. The conventional mathematical method of solving this problem is the variational regularization, which is formulated as a non-convex minimization problem with a regularized objective functional. However, in this method, the choice of regularization parameter and the design of a convergent solution algorithm are quite difficult in practice. Moreover, due to the restricted number of injection profiles in experiments, the types of measured data are extremely limited, which may lead to a biased estimation. In order to overcome these difficulties, in this paper, we develop a new inversion method – the virtual injection promoting double feed-forward neural network (VIP-DFNN). In this approach, the training data contain various types of artificial injections and synthetic noisy measurement at outlet, generated by a conventional physics model – a time-dependent convection-diffusion system. Numerical experiments with both artificial and real data from laboratory experiments show that the proposed VIP-DFNN is an efficient and robust algorithm.

scholarly journals Abnormality Detection Using LBP Features and K-Means Labelling based Feed-Forward Neural Network in Video Sequence

2019 ◽  
Vol 8 (9S) ◽  
pp. 629-633
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Video Surveillance ◽  
Local Binary Pattern ◽  
Video Sequence ◽  
Detection Method ◽  
Experimental Results ◽  
Abnormality Detection ◽  
Feed Forward Neural Network ◽  
Feed Forward

Video surveillance is widely used in various domains like military, commercial and consumer areas. One of the objectives in video surveillance is the detection of normal and abnormal behavior.It has always been a challenge to accurately identify such events in any real time video sequence. In this paper, abnormality detection method using Local Binary Pattern and k-means labeling basedfeed-forward neural network has been proposed. The performance of the proposed method has also been compared with four other techniques in literature to show its worthiness. It can be seen in the experimental results that an accuracy of up to 98% has been achieved for the proposed technique.

scholarly journals Hybrid neural networks in rainfall-inundation forecasting based on a synthetic potential inundation database

2011 ◽  
Vol 11 (3) ◽  
pp. 771-787 ◽  
Author(s):  
T.-Y. Pan ◽  
J.-S. Lai ◽  
T.-J. Chang ◽  
H.-K. Chang ◽  
K.-C. Chang ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Overland Flow ◽  
Principal Component ◽  
Hydraulic Modeling ◽  
Ann Model ◽  
Feed Forward Neural Network ◽  
Time Saving ◽  
Feed Forward ◽  
Inundation Depth

Abstract. This study attempts to achieve real-time rainfall-inundation forecasting in lowland regions, based on a synthetic potential inundation database. With the principal component analysis and a feed-forward neural network, a rainfall-inundation hybrid neural network (RiHNN) is proposed to forecast 1-h-ahead inundation depth as hydrographs at specific representative locations using spatial rainfall intensities and accumulations. A systematic procedure is presented to construct the RiHNN, which combines the merits of detailed hydraulic modeling in flood-prone lowlands via a two-dimensional overland-flow model and time-saving calculation in a real-time rainfall-inundation forecasting via ANN model. Analytical results from the RiHNNs with various principal components indicate that the RiHNNs with fewer weights can have about the same performance as a feed-forward neural network. The RiHNNs evaluated through four types of real/synthetic rainfall events also show to fit inundation-depth hydrographs well with high rainfall. Moreover, the results of real-time rainfall-inundation forecasting help the emergency manager set operational responses, which are beneficial for flood warning preparations.

scholarly journals Power quality disturbances classification using complex wavelet phasor space reconstruction and fully connected feed forward neural network

2021 ◽  
Vol 10 (6) ◽  
pp. 2980-2988
Author(s):  
R. Likhitha ◽  
A. Manjunatha
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Power Quality ◽  
Classification Accuracy ◽  
Feed Forward Neural Network ◽  
Time Activity ◽  
Feed Forward ◽  
Power Quality Disturbances ◽  
Complex Wavelet ◽  
Fully Connected

Power quality disturbances (PQD) degrades the quality of power. Detection of these PQDs in real time using smart systems connected to the power grid is a challenge due to the integration of energy generation units and electronic devices. Deep learning methods have shown advantages for PQD classification accurately. PQD events are non-stationary and occur at discrete events. Pre-processing of power signal using dual tree complex wavelet transform in localizing the disturbances according to time-frequency-phase information improves classification accuracy.Phase space reconstruction of complex wavelet sub bands to 2D data and use of fully connected feed forward neural network improves classification accuracy. In this work, a combination of DTCWT-PSR and FC-FFNN is used to classify different complex PSDs accurately.The proposed algorithm is evaluated for its performance considering different network configurations and the most optimum structure is developed. The classification accuracy is demonstrated to be 99.71% for complex PQDs and is suitable for real time activity with reduced complexity.

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