scholarly journals Deep neural networks for active wave breaking classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Caio Eadi Stringari ◽  
Pedro Veras Guimarães ◽  
Jean-François Filipot ◽  
Fabien Leckler ◽  
Rui Duarte
Keyword(s):  
Machine Learning ◽  
Wave Breaking ◽  
Present Method ◽  
Breaking Waves ◽  
Future Research ◽  
Accuracy Score ◽  
Offshore Structure ◽  
Management Tools ◽  
Active Wave ◽  
Ocean Properties

AbstractWave breaking is an important process for energy dissipation in the open ocean and coastal seas. It drives beach morphodynamics, controls air-sea interactions, determines when ship and offshore structure operations can occur safely, and influences on the retrieval of ocean properties from satellites. Still, wave breaking lacks a proper physical understanding mainly due to scarce observational field data. Consequently, new methods and data are required to improve our current understanding of this process. In this paper we present a novel machine learning method to detect active wave breaking, that is, waves that are actively generating visible bubble entrainment in video imagery data. The present method is based on classical machine learning and deep learning techniques and is made freely available to the community alongside this publication. The results indicate that our best performing model had a balanced classification accuracy score of $$\approx$$ ≈ 90% when classifying active wave breaking in the test dataset. An example of a direct application of the method includes a statistical description of geometrical and kinematic properties of breaking waves. We expect that the present method and the associated dataset will be crucial for future research related to wave breaking in several areas of research, which include but are not limited to: improving operational forecast models, developing risk assessment and coastal management tools, and refining the retrieval of remotely sensed ocean properties.

scholarly journals Slamming Force Contribution due to Plunging Breakers on Circular, Square and Diamond Cylinders

2019 ◽  
Author(s):  
Xin Wang ◽  
Arun Kr Dev ◽  
Longbin Tao ◽  
De Wang Chia ◽  
Yali Zhang
Keyword(s):  
Cross Section ◽  
Wave Breaking ◽  
Extreme Event ◽  
Air Entrainment ◽  
Breaking Waves ◽  
Offshore Structures ◽  
Design Guidelines ◽  
Cylindrical Shape ◽  
Offshore Structure ◽  
Slamming Load

Abstract Plunging breakers, unlike non-breaking waves, impose additional slamming load on the offshore structures. This additional slamming load is considered an extreme event and is one of the most devastating forces that an offshore structure could encounter during its operational lifecycle. Whilst there are design guidelines for offshore structures pertaining to breaking waves, however it is limited to only cylindrical shape. The amount of slamming load contribution by the plunging jet is also dependent on the cross section geometries of the offshore structures. Different geometries would give rise to different air entrainment phenomenon during wave breaking and therefore affecting the slamming load contributions. In this research, JONSWAP spectrum was used to create plunging breakers via the focusing method at Newcastle University’s Wind Wave and Current tank. The crux of this research is to investigate the wave-breaking impact load on cylindrical structures with different cross section geometries commonly used in the offshore industry.

scholarly journals Experimental investigation of intermittent airflow separation and microscale wave breaking in wavy two-phase pipe flow

2019 ◽  
Vol 878 ◽  
pp. 796-819
Author(s):  
P. Vollestad ◽  
A. A. Ayati ◽  
A. Jensen
Keyword(s):  
Flow Rate ◽  
Pipe Flow ◽  
Wave Breaking ◽  
Gas Flow ◽  
Breaking Waves ◽  
Wave Crest ◽  
Leeward Side ◽  
Gas Flow Rate ◽  
Active Wave ◽  
The Waves

We perform an experimental analysis of co-current, stratified wavy pipe flow, with the aim of investigating the effect of small scale wave breaking (microscale breaking) on the airflow. Particle image velocimetry is applied simultaneously in the gas and liquid phases. Active wave breaking is identified by high levels of vorticity on the leeward side of individual waves, and the statistics of the airflow above breaking and non-breaking waves are extracted from the gas-phase velocity fields. Keeping the liquid superficial velocity constant ($U_{sl}=0.1~\text{m}~\text{s}^{-1}$), we consider two experimental cases of different gas flow rates. The lowest flow rate ($U_{sg}=1.85~\text{m}~\text{s}^{-1}$) is slightly higher than the onset of microscale breaking, while the higher flow rate ($U_{sg}=2.20~\text{m}~\text{s}^{-1}$) is within the regime where wave breaking is observed to be frequent, and the root-mean-square interface elevation $\unicode[STIX]{x1D702}_{rms}$ is independent of gas flow rate. Results show that for the lowest gas flow rate considered, active wave breaking has a stabilizing effect on the airflow above the waves, reducing the sheltered region on the leeward side of the wave and the turbulence above the wave crest compared with non-breaking waves at similar steepness. At the higher gas flow rate the effect of active wave breaking is found to be small, and the main geometrical properties of the waves are found to dominate the evolution of the separated flow region.

PHYSICAL INTERPRETATION OF WAVE BREAKING CRITERIA

2017 ◽  
Author(s):  
Sergey Kuznetsov ◽  
Sergey Kuznetsov ◽  
Yana Saprykina ◽  
Yana Saprykina ◽  
Boris Divinskiy ◽  
...  
Keyword(s):  
Nonlinear Wave ◽  
Wave Breaking ◽  
Second Harmonic ◽  
Vertical Axis ◽  
Breaking Waves ◽  
Wave Transformation ◽  
Spectral Structure ◽  
Similarity Parameter ◽  
Nonlinear Harmonics ◽  
The Waves

On the base of experimental data it was revealed that type of wave breaking depends on wave asymmetry against the vertical axis at wave breaking point. The asymmetry of waves is defined by spectral structure of waves: by the ratio between amplitudes of first and second nonlinear harmonics and by phase shift between them. The relative position of nonlinear harmonics is defined by a stage of nonlinear wave transformation and the direction of energy transfer between the first and second harmonics. The value of amplitude of the second nonlinear harmonic in comparing with first harmonic is significantly more in waves, breaking by spilling type, than in waves breaking by plunging type. The waves, breaking by plunging type, have the crest of second harmonic shifted forward to one of the first harmonic, so the waves have "saw-tooth" shape asymmetrical to vertical axis. In the waves, breaking by spilling type, the crests of harmonic coincides and these waves are symmetric against the vertical axis. It was found that limit height of breaking waves in empirical criteria depends on type of wave breaking, spectral peak period and a relation between wave energy of main and second nonlinear wave harmonics. It also depends on surf similarity parameter defining conditions of nonlinear wave transformations above inclined bottom.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Android Malware Detection Techniques: A Literature Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Meghna Dhalaria ◽  
Ekta Gandotra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Future Research ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Future Research Directions ◽  
To Come ◽  
Tools And Techniques

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

scholarly journals Recommendation Systems for Education: Systematic Review

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1611
Author(s):  
María Cora Urdaneta-Ponte ◽  
Amaia Mendez-Zorrilla ◽  
Ibon Oleagordia-Ruiz
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Formal Education ◽  
Research Work ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Educational Resources ◽  
Future Research ◽  
Collaborative Approach ◽  
Developmental Approach

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

scholarly journals Characteristics of Breaking Wave Forces on Piles over a Permeable Seabed

2021 ◽  
Vol 9 (5) ◽  
pp. 520
Author(s):  
Zhenyu Liu ◽  
Zhen Guo ◽  
Yuzhe Dou ◽  
Fanyu Zeng
Keyword(s):  
Wave Breaking ◽  
Stokes Equations ◽  
Slope Angle ◽  
Wave Force ◽  
Breaking Waves ◽  
Offshore Wind ◽  
Secondary Wave ◽  
Wave Forces ◽  
Breaking Wave ◽  
Breaking Wave Forces

Most offshore wind turbines are installed in shallow water and exposed to breaking waves. Previous numerical studies focusing on breaking wave forces generally ignored the seabed permeability. In this paper, a numerical model based on Volume-Averaged Reynolds Averaged Navier–Stokes equations (VARANS) is employed to reveal the process of a solitary wave interacting with a rigid pile over a permeable slope. Through applying the Forchheimer saturated drag equation, effects of seabed permeability on fluid motions are simulated. The reliability of the present model is verified by comparisons between experimentally obtained data and the numerical results. Further, 190 cases are simulated and the effects of different parameters on breaking wave forces on the pile are studied systematically. Results indicate that over a permeable seabed, the maximum breaking wave forces can occur not only when waves break just before the pile, but also when a “secondary wave wall” slams against the pile, after wave breaking. With the initial wave height increasing, breaking wave forces will increase, but the growth can decrease as the slope angle and permeability increase. For inclined piles around the wave breaking point, the maximum breaking wave force usually occurs with an inclination angle of α = −22.5° or 0°.

scholarly journals A review of infant cry analysis and classification

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunyan Ji ◽  
Thosini Bamunu Mudiyanselage ◽  
Yutong Gao ◽  
Yi Pan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Signal Analysis ◽  
Future Research ◽  
Prosodic Features ◽  
Infant Cry ◽  
Machine Learning Classification ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Processing Techniques

AbstractThis paper reviews recent research works in infant cry signal analysis and classification tasks. A broad range of literatures are reviewed mainly from the aspects of data acquisition, cross domain signal processing techniques, and machine learning classification methods. We introduce pre-processing approaches and describe a diversity of features such as MFCC, spectrogram, and fundamental frequency, etc. Both acoustic features and prosodic features extracted from different domains can discriminate frame-based signals from one another and can be used to train machine learning classifiers. Together with traditional machine learning classifiers such as KNN, SVM, and GMM, newly developed neural network architectures such as CNN and RNN are applied in infant cry research. We present some significant experimental results on pathological cry identification, cry reason classification, and cry sound detection with some typical databases. This survey systematically studies the previous research in all relevant areas of infant cry and provides an insight on the current cutting-edge works in infant cry signal analysis and classification. We also propose future research directions in data processing, feature extraction, and neural network classification fields to better understand, interpret, and process infant cry signals.

scholarly journals A Review on Human–AI Interaction in Machine Learning and Insights for Medical Applications

2021 ◽  
Vol 18 (4) ◽  
pp. 2121
Author(s):  
Mansoureh Maadi ◽  
Hadi Akbarzadeh Khorshidi ◽  
Uwe Aickelin
Keyword(s):  
Machine Learning ◽  
Future Research ◽  
Computational Power ◽  
Medical Field ◽  
Interactive Machine Learning ◽  
Human In The Loop ◽  
Human Interactions ◽  
Scoping Literature Review ◽  
Domain Expertise ◽  
Expertise Level

Objective: To provide a human–Artificial Intelligence (AI) interaction review for Machine Learning (ML) applications to inform how to best combine both human domain expertise and computational power of ML methods. The review focuses on the medical field, as the medical ML application literature highlights a special necessity of medical experts collaborating with ML approaches. Methods: A scoping literature review is performed on Scopus and Google Scholar using the terms “human in the loop”, “human in the loop machine learning”, and “interactive machine learning”. Peer-reviewed papers published from 2015 to 2020 are included in our review. Results: We design four questions to investigate and describe human–AI interaction in ML applications. These questions are “Why should humans be in the loop?”, “Where does human–AI interaction occur in the ML processes?”, “Who are the humans in the loop?”, and “How do humans interact with ML in Human-In-the-Loop ML (HILML)?”. To answer the first question, we describe three main reasons regarding the importance of human involvement in ML applications. To address the second question, human–AI interaction is investigated in three main algorithmic stages: 1. data producing and pre-processing; 2. ML modelling; and 3. ML evaluation and refinement. The importance of the expertise level of the humans in human–AI interaction is described to answer the third question. The number of human interactions in HILML is grouped into three categories to address the fourth question. We conclude the paper by offering a discussion on open opportunities for future research in HILML.

scholarly journals Combining Virtual Reality and Organizational Neuroscience for Leadership Assessment

2021 ◽  
Vol 11 (13) ◽  
pp. 5956
Author(s):  
Elena Parra ◽  
Irene Alice Chicchi Giglioli ◽  
Jestine Philip ◽  
Lucia Amalia Carrasco-Ribelles ◽  
Javier Marín-Morales ◽  
...  
Keyword(s):  
Machine Learning ◽  
Eye Tracking ◽  
Ecological Validity ◽  
Objective Assessment ◽  
Leadership Skills ◽  
Three Dimensional ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Promising Tool ◽  
Future Research Agenda

In this article, we introduce three-dimensional Serious Games (3DSGs) under an evidence-centered design (ECD) framework and use an organizational neuroscience-based eye-tracking measure to capture implicit behavioral signals associated with leadership skills. While ECD is a well-established framework used in the design and development of assessments, it has rarely been utilized in organizational research. The study proposes a novel 3DSG combined with organizational neuroscience methods as a promising tool to assess and recognize leadership-related behavioral patterns that manifest during complex and realistic social situations. We offer a research protocol for assessing task- and relationship-oriented leadership skills that uses ECD, eye-tracking measures, and machine learning. Seamlessly embedding biological measures into 3DSGs enables objective assessment methods that are based on machine learning techniques to achieve high ecological validity. We conclude by describing a future research agenda for the combined use of 3DSGs and organizational neuroscience methods for leadership and human resources.

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