Calibration of polyvinylidene fluoride (PVDF) stress gauges under high-impact dynamic compression by machine learning

2022 ◽  
Vol 131 (2) ◽  
pp. 024502
Author(s):  
Shuang Qin ◽  
Zheng Yu ◽  
Xu Zhang ◽  
Shuqi Yang ◽  
Wenyang Peng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Polyvinylidene Fluoride ◽  
Dynamic Compression ◽  
High Impact

scholarly journals Machine Learning Use for Prognostic Purposes in Multiple Sclerosis

Life ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 122
Author(s):  
Ruggiero Seccia ◽  
Silvia Romano ◽  
Marco Salvetti ◽  
Andrea Crisanti ◽  
Laura Palagi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
High Impact ◽  
Prognostic Models ◽  
Early Prediction ◽  
Disease Course ◽  
Slow Progression ◽  
Relapsing Remitting ◽  
Long Time ◽  
Effective Drugs

The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge.

scholarly journals Facing Erosion Identification in Railway Lines Using Pixel-Wise Deep-Based Approaches

2020 ◽  
Vol 12 (4) ◽  
pp. 739
Author(s):  
Keiller Nogueira ◽  
Gabriel L. S. Machado ◽  
Pedro H. T. Gama ◽  
Caio C. V. da Silva ◽  
Remis Balaniuk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Impact ◽  
Automatic Machine ◽  
Feature Representation ◽  
Data Driven ◽  
Maintenance Costs ◽  
Crucial Step ◽  
Machine Learning Methods ◽  
High Resolution Images

Soil erosion is considered one of the most expensive natural hazards with a high impact on several infrastructure assets. Among them, railway lines are one of the most likely constructions for the appearance of erosion and, consequently, one of the most troublesome due to the maintenance costs, risks of derailments, and so on. Therefore, it is fundamental to identify and monitor erosion in railway lines to prevent major consequences. Currently, erosion identification is manually performed by humans using huge image sets, a time-consuming and slow task. Hence, automatic machine learning methods appear as an appealing alternative. A crucial step for automatic erosion identification is to create a good feature representation. Towards such objective, deep learning can learn data-driven features and classifiers. In this paper, we propose a novel deep learning-based framework capable of performing erosion identification in railway lines. Six techniques were evaluated and the best one, Dynamic Dilated ConvNet, was integrated into this framework that was then encapsulated into a new ArcGIS plugin to facilitate its use by non-programmer users. To analyze such techniques, we also propose a new dataset, composed of almost 2000 high-resolution images.

scholarly journals Articles with impact: insights into 10 years of research with machine learning

2020 ◽  
Vol 129 (4) ◽  
pp. 967-979
Author(s):  
Stephan van der Zwaard ◽  
Arie-Willem de Leeuw ◽  
L. (Rens) A. Meerhoff ◽  
Sue C. Bodine ◽  
Arno Knobbe
Keyword(s):  
Machine Learning ◽  
High Impact ◽  
Research Articles ◽  
Comprehensive Collection ◽  
Number Of Citations ◽  
Article Impact

Common measures of article impact are the Altmetric Attention Scores, number of downloads, and number of citations. To our knowledge, this is the first study that applies machine learning on a comprehensive collection of article characteristics to predict article attention scores, downloads, and citations. Using 10 years of research articles, we obtained accurate predictions of high-impact articles and discovered important article characteristics related to article impact.

scholarly journals Coupling physics in machine learning to predict properties of high-temperatures alloys

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Jian Peng ◽  
Yukinori Yamamoto ◽  
Jeffrey A. Hawk ◽  
Edgar Lara-Curzio ◽  
Dongwon Shin
Keyword(s):  
Machine Learning ◽  
High Temperature ◽  
Yield Strength ◽  
Strengthening Mechanism ◽  
Current Approach ◽  
High Impact ◽  
Data Driven ◽  
High Temperature Alloy ◽  
High Temperature Alloys ◽  
Verification Process

Abstract High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex high-temperature alloys with an example of the 9–12 wt% Cr steels yield strength. We have incorporated synthetic alloy features that capture microstructure and phase transformations into the dataset. Identified high impact features that affect yield strength of 9Cr from correlation analysis agree well with the generally accepted strengthening mechanism. As a part of the verification process, the consistency of sub-datasets has been extensively evaluated with respect to temperature and then refined for the boundary conditions of trained ML models. The predicted yield strength of 9Cr steels using the ML models is in excellent agreement with experiments. The current approach introduces physically meaningful constraints in interrogating the trained ML models to predict properties of hypothetical alloys when applied to data-driven materials.

scholarly journals A machine-learning scraping tool for data fusion in the analysis of sentiments about pandemics for supporting business decisions with human-centric AI explanations

2021 ◽  
Vol 7 ◽  
pp. e713
Author(s):  
Swarn Avinash Kumar ◽  
Moustafa M. Nasralla ◽  
Iván García-Magariño ◽  
Harsh Kumar
Keyword(s):  
Machine Learning ◽  
Expert System ◽  
Data Fusion ◽  
High Impact ◽  
Business Decisions ◽  
Daily Routines ◽  
Web Scraping ◽  
Small Medium Enterprises ◽  
Medium Enterprises ◽  
Different Sources

The COVID-19 pandemic is changing daily routines for many citizens with a high impact on the economy in some sectors. Small-medium enterprises of some sectors need to be aware of both the pandemic evolution and the corresponding sentiments of customers in order to figure out which are the best commercialization techniques. This article proposes an expert system based on the combination of machine learning and sentiment analysis in order to support business decisions with data fusion through web scraping. The system uses human-centric artificial intelligence for automatically generating explanations. The expert system feeds from online content from different sources using a scraping module. It allows users to interact with the expert system providing feedback, and the system uses this feedback to improve its recommendations with supervised learning.

e-WOM Analysis Methods

2020 ◽  
pp. 138-159
Author(s):  
Ioannis Stivaktakis ◽  
Angelika Kokkinaki
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Regression Analysis ◽  
Sentiment Analysis ◽  
Analytical Methods ◽  
Word Of Mouth ◽  
Scientific Work ◽  
High Impact ◽  
Work Related ◽  
Before And After

Electronic word of mouth (e-WOM) is rapidly becoming an empowering tool for consumers to express their experiences on services or products, on social media or other platforms. Beyond the obvious implications of such content to potential consumers, interest is also high among researchers, industry players, and other stakeholders who strive to analyze before-and-after sales expectations, emotions, and perceptions of customers. The need to find efficient ways of extracting and then analyzing online content rendered the reuse of tools and methodologies initially applied in other fields as well as the development of new approaches. In this chapter, the authors identify high-impact scientific work related to e-WOM and point out the analytical methods for analyzing e-WOM content. Furthermore, this chapter refers to the most relevant studies employing such methods and their findings. More specifically, it discusses clustering, sentiment analysis, supervised and unsupervised machine learning, lexicon-based approaches, corpus-based approach, summarization and predicting, and regression analysis.

scholarly journals Machine Learning of committor functions for predicting high impact climate events

2020 ◽  
Author(s):  
Dario Lucente ◽  
Freddy Bouchet ◽  
Corentin Herbert
Keyword(s):  
Machine Learning ◽  
Time Scales ◽  
Southern Oscillation ◽  
High Impact ◽  
Learning Approach ◽  
Observation Data ◽  
Model Errors ◽  
Lyapunov Time ◽  
Machine Learning Approach ◽  
Climate Events

<p>There is a growing interest in the climate community to improve the prediction of high impact climate events, for instance ENSO (El-Ni\~no--Southern Oscillation) or extreme events, using a combination of model and observation data. In this talk we present a machine learning approach for predicting the committor function, the relevant concept.<span> </span></p><p>Because the dynamics of the climate system is chaotic, one usually distinguishes between time scales much shorter than a Lyapunov time for which a deterministic weather forecast is relevant, and time scales much longer than a mixing times beyond which any deterministic forecast is irrelevant and only climate averaged or probabilistic quantities can be predicted. However, for most applications, the largest interest is for intermediate time scales for which some information, more precise than the climate averages, might be predicted, but for which a deterministic forecast is not relevant. We call this range of time scales \it{the predictability margin}. We stress in this talk that the prediction problem at the predictability margin is of a probabilistic nature. Indeed, such time scales might typically be of the order of the Lyapunov time scale or larger, where errors on the initial condition and model errors limit our ability to compute deterministically the evolution. In this talk we explain that, in a dynamical context, the relevant quantity for predicting a future event at the predictability margin is a committor function. A committor function is the probability that an event will occur or not in the future, as a function of the current state of the system.<span> </span></p><p>We compute and discuss the committor function from data, either through a direct approach or through a machine learning approach using neural networks. We discuss two examples: a) the computation of the Jin and Timmerman model, a low dimensional model proposed to explain the decadal amplitude changes of El-Ni\~no, b) the computation of committor function for extreme heat waves. We compare several machine learning approaches, using neural network or using kernel-based analogue methods.</p><p>From the point of view of the climate extremes, our main conclusion is that one should generically distinguish between states with either intrinsic predictability or intrinsic unpredictability. This predictability concept is markedly different from the deterministic unpredictability arising because of chaotic dynamics and exponential sensivity to initial conditions.<span> </span></p>

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