Prediction of Characteristics of Wave Breaking in Shallow Water Using Neural Network Techniques

2017 ◽  
Author(s):  
Nicholas Kouvaras ◽  
Manhar R. Dhanak
Keyword(s):  
Neural Network ◽  
Wave Height ◽  
Predictive Models ◽  
Wave Breaking ◽  
Laboratory Experiments ◽  
Network Models ◽  
Classification Model ◽  
Neural Network Models ◽  
Wave Setup ◽  
Fringing Reef

The characteristics of wave breaking over a fringing reef are considered using a set of laboratory experiments and the results are used to develop associated predictive models. Various methods are typically used to estimate the characteristics of nearshore wave breaking, mostly based on empirical, analytical and numerical techniques. Deo et al. (2003) used an artificial neural network approach to predict the breaking wave height and breaking depth for waves transforming over a range of simply sloped bottoms. The approach is based on using available representative data to train appropriate neural network models. The Deo et al. (2003) approach is extended here to predict other characteristics of wave breaking, including the type of wave breaking, and the position of breaking over a fringing reef, as well as the associated wave setup, and the rate of dissipation of wave energy, based on observations from a series of laboratory experiments involving monochromatic waves impacting on an idealized reef. Yao et al. (2013) showed that for such geometry, the critical parameter is the ratio of deep-water wave height to the depth of the shallow reef flat downstream of the position of wave breaking, H1/hs, rather than the slope of the reef. H1/hs, and the wave frequency parameter, fH1/g, are provided as inputs to the neural network models of the feed-forward type that are developed to predict the above characteristics of wave breaking. The models are trained using the experimental data. The breaker type classification model has a success rate of over 95%, implying that the neural networks method outperforms previously used criteria for classifying breaker types. The numeric prediction model for the dimensionless position of wave breaking also performs well, with a high degree of correlation between the predicted and actual positions of wave breaking. The performance is higher when only the plunging breaker instances are considered, but lower when only the spilling breaker instances are considered. The corresponding neural network models for wave setup within the surf zone and the difference in energy flux between the incident and broken wave have success rates of approximately 89% and 94% respectively. The method may be extended to provide predictive models for consideration of a range of natural coastal conditions, random waves, and various bottom profiles and complex geometry, based on training and testing of the models using representative field and laboratory observational data, in support of accurate prediction of near-shore wave phenomena.

scholarly journals Constructive Learning of Deep Neural Networks for Bigdata Analysis

2020 ◽  
Vol 9 (12) ◽  
pp. 311-322
Author(s):  
Soha Abd Mohamed El-Moamen ◽  
Marghany Hassan Mohamed ◽  
Mohammed F. Farghally
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Binary Classification ◽  
Network Models ◽  
Classification Model ◽  
Neural Network Models ◽  
Constructive Learning ◽  
The Neural Network ◽  
Rapid Pace ◽  
Better Than

The need for tracking and evaluation of patients in real-time has contributed to an increase in knowing people’s actions to enhance care facilities. Deep learning is good at both a rapid pace in collecting frameworks of big data healthcare and good predictions for detection the lung cancer early. In this paper, we proposed a constructive deep neural network with Apache Spark to classify images and levels of lung cancer. We developed a binary classification model using threshold technique classifying nodules to benign or malignant. At the proposed framework, the neural network models training, defined using the Keras API, is performed using BigDL in a distributed Spark clusters. The proposed algorithm has metrics AUC-0.9810, a misclassifying rate from which it has been shown that our suggested classifiers perform better than other classifiers.

scholarly journals Aspect-Level Sentiment Analysis Based on Position Features Using Multilevel Interactive Bidirectional GRU and Attention Mechanism

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiaodi Wang ◽  
Xiaoliang Chen ◽  
Mingwei Tang ◽  
Tian Yang ◽  
Zhen Wang
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Network Models ◽  
Attention Mechanism ◽  
Sentiment Classification ◽  
Classification Model ◽  
Neural Network Models ◽  
Position Information ◽  
Gated Recurrent Unit ◽  
Relative Position Information

The aim of aspect-level sentiment analysis is to identify the sentiment polarity of a given target term in sentences. Existing neural network models provide a useful account of how to judge the polarity. However, context relative position information for the target terms is adversely ignored under the limitation of training datasets. Considering position features between words into the models can improve the accuracy of sentiment classification. Hence, this study proposes an improved classification model by combining multilevel interactive bidirectional Gated Recurrent Unit (GRU), attention mechanisms, and position features (MI-biGRU). Firstly, the position features of words in a sentence are initialized to enrich word embedding. Secondly, the approach extracts the features of target terms and context by using a well-constructed multilevel interactive bidirectional neural network. Thirdly, an attention mechanism is introduced so that the model can pay greater attention to those words that are important for sentiment analysis. Finally, four classic sentiment classification datasets are used to deal with aspect-level tasks. Experimental results indicate that there is a correlation between the multilevel interactive attention network and the position features. MI-biGRU can obviously improve the performance of classification.

Double Multi-Head Attention-Based Capsule Network for Relation Classification

2021 ◽  
Author(s):  
Hongjun Heng ◽  
Renjie Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Language Processing ◽  
Layer Structure ◽  
Single Layer ◽  
Network Models ◽  
Classification Model ◽  
Neural Network Models ◽  
Comparable Performance ◽  
Relation Classification

Semantic relation classification is an important task in the field of nature language processing. The existing neural network relation classification models introduce attention mechanism to increase the importance of significant features, but part of these attention models only have one head which is not enough to capture more distinctive fine-grained features. Models based on RNN (Recurrent Neural Network) usually use single-layer structure and have limited feature extraction capability. Current RNN-based capsule networks have problem of improper handling of noise which increase complexity of network. Therefore, we propose a capsule network relation classification model based on double multi-head attention. In this model, we introduce an auxiliary BiGRU (Bidirectional Gated Recurrent Unit) to make up for the lack of feature extraction performance of single BiGRU, improve the bilinear attention through double multihead mechanism to enable the model to obtain more information of sentence from different representation subspace and instantiate capsules with sentence-level features to alleviate noise impact. Experiments on the SemEval-2010 Task 8 benchmark dataset show that our model outperforms most of previous state-of-the-art neural network models and achieves the comparable performance with F1 score of 85.3% in capsule network.

scholarly journals Enhanced Weight-Optimized Recurrent Neural Networks Based on Sine Cosine Algorithm for Wave Height Prediction

2021 ◽  
Vol 9 (5) ◽  
pp. 524
Author(s):  
Alawi Alqushaibi ◽  
Said Jadid Abdulkadir ◽  
Helmi Md Rais ◽  
Qasem Al-Tashi ◽  
Mohammed G. Ragab ◽  
...  
Keyword(s):  
Neural Network ◽  
Wave Height ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Network Models ◽  
Absolute Error ◽  
Recurrent Network ◽  
Neural Network Models ◽  
Height Prediction ◽  
Sine Cosine Algorithm

Constructing offshore and coastal structures with the highest level of stability and lowest cost, as well as the prevention of faulty risk, is the desired plan that stakeholders seek to obtain. The successful construction plans of such projects mostly rely on well-analyzed and modeled metocean data that yield high prediction accuracy for the ocean environmental conditions including waves and wind. Over the past decades, planning and designing coastal projects have been accomplished by traditional static analytic, which requires tremendous efforts and high-cost resources to validate the data and determine the transformation of metocean data conditions. Therefore, the wind plays an essential role in the oceanic atmosphere and contributes to the formation of waves. This paper proposes an enhanced weight-optimized neural network based on Sine Cosine Algorithm (SCA) to accurately predict the wave height. Three neural network models named: Long Short-Term Memory (LSTM), Vanilla Recurrent Neural Network (VRNN), and Gated Recurrent Network (GRU) are enhanced, instead of random weight initialization, SCA generates weight values that are adaptable to the nature of the data and model structure. Besides, a Grid Search (GS) is utilized to automatically find the best models’ configurations. To validate the performance of the proposed models, metocean datasets have been used. The original LSTM, VRNN, and GRU are implemented and used as benchmarking models. The results show that the optimized models outperform the original three benchmarking models in terms of mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE).

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

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