scholarly journals Detection of Plunging Breaking Waves Based on Machine Learning

2018 ◽  
Author(s):  
Ying Tu ◽  
Zhengshun Cheng ◽  
Michael Muskulus
Keyword(s):  
Machine Learning ◽  
Wave Height ◽  
Water Depth ◽  
Experimental Studies ◽  
Classification Problem ◽  
Breaking Waves ◽  
Offshore Structures ◽  
Trained Classifier ◽  
Structural Loading ◽  
Logistic Regression Algorithm

Plunging breaking waves that occur in the vicinity of offshore structures can lead to high impulsive slamming loads, which are significant for the structural loading. The occurrence of plunging breaking waves is usually identified based on criteria that are derived from theoretical analyses and experimental studies. Given a large amount of data, detecting plunging breaking waves can be treated as a typical classification problem, which can be solved by a machine learning approach. In this study, logistic regression algorithm is used together with the experimental data from the WaveSlam project to train a classifier for the detection. Three normalized dimensionless features are introduced based on the measured data for the training. A classifier with respect to four wave parameters (i.e. water depth, wave height, crest height and wave period) is then explicitly developed for detecting plunging breaking waves. It is found that the trained classifier has an accuracy of 98.7% and F1 score of 99.2% for the tested data. Among the three dimensionless parameters, the ratio of wave height to water depth, H/d, is the most decisive factor for the detection of plunging breaking waves.

scholarly journals RANDOM BREAKING WAVES HORIZONTAL SEABED

1986 ◽  
Vol 1 (20) ◽  
pp. 68 ◽  
Author(s):  
Hans Peter Riedel ◽  
Anthony Paul Byrne
Keyword(s):  
Wave Height ◽  
Water Depth ◽  
Breaking Waves ◽  
Random Wave ◽  
Random Waves ◽  
Flume Experiments ◽  
Depth Ratio ◽  
Wave Trains ◽  
Monochromatic Waves

According to wave theories the depth limited wave height over a horizontal seabed has a wave height to water depth ratio (H/d) of about 0.8. Flume experiments with monochromatic waves over a horizontal seabed have failed to produce H/d ratios greater than 0.55. However designers still tend to use H/d 0.8 for their design waves. Experiments have been carried out using random wave trains in the flume over a horizontal seabed. These experiments have shown that the limiting H/d ratio of 0.55 applies equally well to random waves.

scholarly journals SENTIMENT ANALYSIS OF CUSTOMER REVIEWS

2021 ◽  
Vol 4 (1) ◽  
pp. 113-125
Author(s):  
Syed Rashiq Nazar ◽  
◽  
Tapalina Bhattasali
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Binary Classification ◽  
Classification Problem ◽  
Supervised Machine Learning ◽  
Frequency Model ◽  
Customer Reviews ◽  
Machine Learning Model ◽  
Logistic Regression Algorithm

Sentiment analysis is a process in which we classify text data as positive, negative, or neutral or into some other category, which helps understand the sentiment behind the data. Mainly machine learning and natural language processing methods are combined in this process. One can find customer sentiment in reviews, tweets, comments, etc. A company needs to evaluate the sentiment behind the reviews of its product. Customer sentiment can be a valuable asset to the company. This ultimately helps the company make better decisions regarding its product marketing and improving product quality. This paper focuses on the sentiment analysis of customer reviews from Amazon. The reviews contain textual feedback along with a rating system. The aim is to build a supervised machine learning model to classify the review as positive or negative. As reviews are in the text format, there is a need to vectorize the text to numerical format for the computer to process the data. To do this, we use the Bag-of-words model and the TF-IDF (Term Frequency-Inverse Document Frequency) model. These two models are related to each other, and the aim is to find which model performs better in our case. The problem in our case is a binary classification problem; the logistic regression algorithm is used. Finally, the performance of the model is calculated using a metric called the F1 score.

Focused Plunging Breaking Waves Impact on Pile Group in Finite Water Depth

2021 ◽  
pp. 1-17
Author(s):  
Ting Cui ◽  
Arun Kamath ◽  
Weizhi Wang ◽  
Lihao Yuan ◽  
Duanfeng Han ◽  
...  
Keyword(s):  
Water Depth ◽  
Pile Group ◽  
Breaking Waves ◽  
Offshore Structures ◽  
Structural Safety ◽  
Wave Forces ◽  
Pile Groups ◽  
Navier Stokes Equation ◽  
Finite Water Depth ◽  
Numerical Wave

Abstract Accuracy estimation of wave loading on cylinders in a pile group under different impact scenarios is essential for both the structural safety and cost of coastal and offshore structures. Differing from the interaction of waves with a single cylinder, less attention has been paid to pile groups under different arrangements. Numerical simulations of interactions between plunging breaking waves and pile group in finite water depth are performed using the two-phase flow model in REEF3D, an open-source computational fluid dynamics program to investigate the wave loads and flow kinematics characteristics. The Reynolds-averaged Navier-Stokes equation with the two equation k − ω turbulence model is adopted to resolve the numerical wave tank. The model is validated by comparing the numerical wave forces and free surface elevation with measurements from experiments. The computational results show fairly good agreement with experimental data. Four cases are simulated with different relative distances, numbers of cylinders and arrangements. Results show that the wave forces on cylinders in the pile group are effected by the relative distance between cylinders. The staggered arrangement has a significant influence on the wave forces on the first and second cylinder. The interaction inside a pile group mostly happens between the neighboring cylinders.

scholarly journals EXPERIMENTAL RESULTS OF BREAKING WAVE IMPACT ON A VERTICAL WALL WITH AN OVERHANGING HORIZONTAL CANTILEVER SLAB

2011 ◽  
Vol 1 (32) ◽  
pp. 26
Author(s):  
Dogan Kisacik ◽  
Peter Troch ◽  
Philippe Van Bogaert
Keyword(s):  
Wave Height ◽  
Water Depth ◽  
Vertical Wall ◽  
Breaking Waves ◽  
Wave Period ◽  
Breaking Wave ◽  
Wave Impact ◽  
Scaled Model ◽  
Physical Experiments ◽  
Horizontal Part

Physical experiments (at a scale of 1/20) are carried out using a vertical wall with horizontal cantilevering slab. Tests are conducted for a range of values of water depth, wave period and wave height. A parametric analysis of measured forces (Fh and Fv) both on the vertical and horizontal part of the scaled model respectively is conducted. The highest impact pressure and forces are measured in the case of breaking waves with a small air trap. Maximum pressures are measured around SWL and at the corner of the scaled model. The horizontal part of the scaled model is more exposed to impact waves than the vertical part. Fh and Fv are very sensitive for the variation of water depth (hs) and wave height (H) while variation of wave period (T) has a rather limited effect.

Bulk drag coefficient of a subaquatic vegetation subjected to irregular waves: Influence of Reynolds and Keulegan-Carpenter numbers

2020 ◽  
pp. 34-42
Author(s):  
Thibault Chastel ◽  
Kevin Botten ◽  
Nathalie Durand ◽  
Nicole Goutal
Keyword(s):  
Drag Coefficient ◽  
Wave Height ◽  
Wave Attenuation ◽  
Empirical Relationship ◽  
Breaking Waves ◽  
Irregular Waves ◽  
Geometrical Parameters ◽  
Flume Experiments ◽  
Seagrass Meadows ◽  
Artificial Vegetation

Seagrass meadows are essential for protection of coastal erosion by damping wave and stabilizing the seabed. Seagrass are considered as a source of water resistance which modifies strongly the wave dynamics. As a part of EDF R & D seagrass restoration project in the Berre lagoon, we quantify the wave attenuation due to artificial vegetation distributed in a flume. Experiments have been conducted at Saint-Venant Hydraulics Laboratory wave flume (Chatou, France). We measure the wave damping with 13 resistive waves gauges along a distance L = 22.5 m for the “low” density and L = 12.15 m for the “high” density of vegetation mimics. A JONSWAP spectrum is used for the generation of irregular waves with significant wave height Hs ranging from 0.10 to 0.23 m and peak period Tp ranging from 1 to 3 s. Artificial vegetation is a model of Posidonia oceanica seagrass species represented by slightly flexible polypropylene shoots with 8 artificial leaves of 0.28 and 0.16 m height. Different hydrodynamics conditions (Hs, Tp, water depth hw) and geometrical parameters (submergence ratio α, shoot density N) have been tested to see their influence on wave attenuation. For a high submergence ratio (typically 0.7), the wave attenuation can reach 67% of the incident wave height whereas for a low submergence ratio (< 0.2) the wave attenuation is negligible. From each experiment, a bulk drag coefficient has been extracted following the energy dissipation model for irregular non-breaking waves developed by Mendez and Losada (2004). This model, based on the assumption that the energy loss over the species meadow is essentially due to the drag force, takes into account both wave and vegetation parameter. Finally, we found an empirical relationship for Cd depending on 2 dimensionless parameters: the Reynolds and Keulegan-Carpenter numbers. These relationships are compared with other similar studies.

scholarly journals Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1071
Author(s):  
Lucia Billeci ◽  
Asia Badolato ◽  
Lorenzo Bachi ◽  
Alessandro Tonacci
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Classification Problem ◽  
Computer Algorithms ◽  
Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

scholarly journals Advancing Stress Detection Methodology with Deep Learning Techniques Targeting UX Evaluation in AAL Scenarios: Applying Embeddings for Categorical Variables

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1550
Author(s):  
Alexandros Liapis ◽  
Evanthia Faliagka ◽  
Christos P. Antonopoulos ◽  
Georgios Keramidas ◽  
Nikolaos Voros
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
User Experience ◽  
Electrodermal Activity ◽  
Binary Classification ◽  
Research Question ◽  
Classification Problem ◽  
Categorical Variables ◽  
Stress Detection ◽  
Software Failures

Physiological measurements have been widely used by researchers and practitioners in order to address the stress detection challenge. So far, various datasets for stress detection have been recorded and are available to the research community for testing and benchmarking. The majority of the stress-related available datasets have been recorded while users were exposed to intense stressors, such as songs, movie clips, major hardware/software failures, image datasets, and gaming scenarios. However, it remains an open research question if such datasets can be used for creating models that will effectively detect stress in different contexts. This paper investigates the performance of the publicly available physiological dataset named WESAD (wearable stress and affect detection) in the context of user experience (UX) evaluation. More specifically, electrodermal activity (EDA) and skin temperature (ST) signals from WESAD were used in order to train three traditional machine learning classifiers and a simple feed forward deep learning artificial neural network combining continues variables and entity embeddings. Regarding the binary classification problem (stress vs. no stress), high accuracy (up to 97.4%), for both training approaches (deep-learning, machine learning), was achieved. Regarding the stress detection effectiveness of the created models in another context, such as user experience (UX) evaluation, the results were quite impressive. More specifically, the deep-learning model achieved a rather high agreement when a user-annotated dataset was used for validation.

scholarly journals Cross-Predicting Essential Genes between Two Model Eukaryotic Species Using Machine Learning

2021 ◽  
Vol 22 (10) ◽  
pp. 5056
Author(s):  
Tulio L. Campos ◽  
Pasi K. Korhonen ◽  
Neil D. Young
Keyword(s):  
Machine Learning ◽  
Experimental Studies ◽  
Essential Genes ◽  
Subcellular Localisation ◽  
Cross Prediction ◽  
Gene Essentiality ◽  
C Elegans ◽  
Cellular Processes ◽  
Eukaryotic Species ◽  
The Cross

Experimental studies of Caenorhabditis elegans and Drosophila melanogaster have contributed substantially to our understanding of molecular and cellular processes in metazoans at large. Since the publication of their genomes, functional genomic investigations have identified genes that are essential or non-essential for survival in each species. Recently, a range of features linked to gene essentiality have been inferred using a machine learning (ML)-based approach, allowing essentiality predictions within a species. Nevertheless, predictions between species are still elusive. Here, we undertake a comprehensive study using ML to discover and validate features of essential genes common to both C. elegans and D. melanogaster. We demonstrate that the cross-species prediction of gene essentiality is possible using a subset of features linked to nucleotide/protein sequences, protein orthology and subcellular localisation, single-cell RNA-seq, and histone methylation markers. Complementary analyses showed that essential genes are enriched for transcription and translation functions and are preferentially located away from heterochromatin regions of C. elegans and D. melanogaster chromosomes. The present work should enable the cross-prediction of essential genes between model and non-model metazoans.

EXPRESS: When Algorithms Fail: Consumers’ Responses to Brand Harm Crises Caused by Algorithm Errors

2021 ◽  
pp. 002224292199708
Author(s):  
Raji Srinivasan ◽  
Gülen Sarial-Abi
Keyword(s):  
Machine Learning ◽  
Theory Of Mind ◽  
Human Error ◽  
Learning Algorithm ◽  
Experimental Studies ◽  
Task Characteristics ◽  
Machine Learning Algorithm ◽  
Incentive Compatible ◽  
Mind Perception ◽  
Interactive Task

Algorithms increasingly used by brands sometimes fail to perform as expected or even worse, cause harm, causing brand harm crises. Unfortunately, algorithm failures are increasing in frequency. Yet, we know little about consumers’ responses to brands following such brand harm crises. Extending developments in the theory of mind perception, we hypothesize that following a brand harm crisis caused by an algorithm error (vs. human error), consumers will respond less negatively to the brand. We further hypothesize that consumers’ lower mind perception of agency of the algorithm (vs. human) for the error that lowers their perceptions of the algorithm’s responsibility for the harm caused by the error will mediate this relationship. We also hypothesize four moderators of this relationship: two algorithm characteristics, anthropomorphized algorithm and machine learning algorithm and two task characteristics where the algorithm is deployed, subjective (vs. objective) task and interactive (vs. non-interactive) task. We find support for the hypotheses in eight experimental studies including two incentive-compatible studies. We examine the effects of two managerial interventions to manage the aftermath of brand harm crises caused by algorithm errors. The research’s findings advance the literature on brand harm crises, algorithm usage, and algorithmic marketing and generate managerial guidelines to address the aftermath of such brand harm crises.

scholarly journals Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

2021 ◽  
Vol 11 (7) ◽  
pp. 885
Author(s):  
Maher Abujelala ◽  
Rohith Karthikeyan ◽  
Oshin Tyagi ◽  
Jing Du ◽  
Ranjana K. Mehta
Keyword(s):  
Machine Learning ◽  
Environmental Conditions ◽  
Brain Activity ◽  
Memory Task ◽  
Classification Problem ◽  
Brain Regions ◽  
Training Data ◽  
Information Encoding ◽  
Machine Learning Approach ◽  
Encoding And Retrieval

The nature of firefighters` duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.

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