scholarly journals Data-Driven Discovery in Mineralogy: Recent Advances in Data Resources, Analysis, and Visualization

Engineering ◽  
2019 ◽  
Vol 5 (3) ◽  
pp. 397-405 ◽  
Author(s):  
Robert M. Hazen ◽  
Robert T. Downs ◽  
Ahmed Eleish ◽  
Peter Fox ◽  
Olivier C. Gagné ◽  
...  
Keyword(s):  
Data Driven ◽  
Recent Advances

scholarly journals Recent Advances in Anomaly Detection Methods Applied to Aviation

Aerospace ◽  
2019 ◽  
Vol 6 (11) ◽  
pp. 117 ◽  
Author(s):  
Luis Basora ◽  
Xavier Olive ◽  
Thomas Dubot
Keyword(s):  
Anomaly Detection ◽  
Time Series Data ◽  
Data Driven ◽  
Sensor Data ◽  
Detection Methods ◽  
Series Data ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Recent Advances ◽  
Flight Operations

Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

Special Issue on Recent Advances in Data Driven Modeling

2019 ◽  
pp. -1--1
Author(s):  
Wen-Hsiang Hsieh ◽  
Jerzy Rozenblit ◽  
Minvydas Ragulskis
Keyword(s):  
Data Driven ◽  
Special Issue ◽  
Recent Advances ◽  
Data Driven Modeling

A Survey on Failure Prognosis Research: Theory and Applications

2006 ◽  
Author(s):  
Javad Mohammadpour ◽  
Karolos M. Grigoriadis ◽  
Matthew A. Franchek ◽  
Benjamin J. Zwissler
Keyword(s):  
Industrial Applications ◽  
Data Driven ◽  
Remaining Life ◽  
Damage Modeling ◽  
Engineering Applications ◽  
Model Based ◽  
Recent Advances ◽  
Prognosis Research ◽  
Failure Prognosis

The paper presents a survey of recent advances in the area of failure prognosis and highlights some of the engineering applications in this area. The theoretical aspects of data-driven and model-based prognosis and damage modeling are summarized. The paper also reviews a recently proposed intelligent modelbased diagnosis/prognosis structure and its components. Finally, some of the important industrial applications, in which prognosis techniques have been employed for anticipating failure occurrence and determining useful remaining life are discussed.

Recent Advances and Trends of Predictive Maintenance from Data-driven Machine Prognostics Perspective

Measurement ◽  
2021 ◽  
pp. 110276
Author(s):  
Yuxin Wen ◽  
Md. Fashiar Rahman ◽  
Honglun Xu ◽  
Tzu-Liang Bill Tseng
Keyword(s):  
Predictive Maintenance ◽  
Data Driven ◽  
Recent Advances

Recent advances on industrial data-driven energy savings: Digital twins and infrastructures

2021 ◽  
Vol 135 ◽  
pp. 110208 ◽  
Author(s):  
Sin Yong Teng ◽  
Michal Touš ◽  
Wei Dong Leong ◽  
Bing Shen How ◽  
Hon Loong Lam ◽  
...  
Keyword(s):  
Energy Savings ◽  
Data Driven ◽  
Industrial Data ◽  
Recent Advances ◽  
Digital Twins

Recent advances in data-driven dynamics and control

2020 ◽  
Vol 8 (4) ◽  
pp. 1200-1221
Author(s):  
Zhi-Sai Ma ◽  
Xiang Li ◽  
Meng-Xin He ◽  
Su Jia ◽  
Qiang Yin ◽  
...  
Keyword(s):  
Data Driven ◽  
Recent Advances ◽  
Dynamics And Control ◽  
And Control

scholarly journals Recent Advances in Anomaly Detection Methods applied to Aviation

2019 ◽  
Author(s):  
Luis Basora ◽  
Xavier Olive ◽  
Thomas Dubot
Keyword(s):  
Anomaly Detection ◽  
Time Series Data ◽  
Data Driven ◽  
Sensor Data ◽  
Detection Methods ◽  
Series Data ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Recent Advances ◽  
Flight Operations

Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. We cover especially unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

scholarly journals Machine learning and fault rupture: a review

2020 ◽  
Author(s):  
Christopher Ren ◽  
Claudia Hulbert ◽  
Paul Johnson ◽  
Bertrand Rouet‐Leduc
Keyword(s):  
Machine Learning ◽  
Solid Earth ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Fault Rupture ◽  
Exponential Increase ◽  
Recent Advances ◽  
Learning Techniques

Geophysics has historically been a data-driven field, however in recent years the exponential increase of available data has lead to increased adoption of machine learning techniques and algorithm for analysis, detection and forecasting applications to faulting. This work reviews recent advances in the application of machine learning in the study of fault rupture ranging from the laboratory to Solid Earth.

scholarly journals Probing the overarching continuum theory: Data-driven phenotypic clustering of children with ASD or ADHD

2021 ◽  
Author(s):  
Marie Katharina Deserno ◽  
Joe Bathelt ◽  
Annabeth Groenman ◽  
Hilde Geurts
Keyword(s):  
Continuum Theory ◽  
Data Driven ◽  
Integrative Approach ◽  
Typically Developing ◽  
Typically Developing Children ◽  
Random Forest Classification ◽  
Symptom Profile ◽  
Recent Advances ◽  
Forest Classification ◽  
The Difference

The clinical validity of the distinction between ADHD and ASD is a longstanding discussion. Recent advances in the realm of data-driven analytic techniques now enable us to formally investigate theories aiming to explain the frequent co-occurrence of these neurodevelopmental conditions. In this study, we probe different theoretical positions by means of a pre-registered integrative approach of novel classification, subgrouping and taxometric techniques in a representative sample (N=434) and replicate the results in an independent sample (N=219) of children (ADHD, ASD, and typically developing) aged 7 to 14 years. First, Random Forest Classification could predict diagnostic groups based on questionnaire data with limited accuracy - suggesting some remaining overlap in behavioural symptoms between them. Second, community detection identified four distinct groups, but none of them showed a symptom profile clearly related to either ADHD or ASD in neither the original sample nor the replication sample. Third, taxometric analyses showed evidence for a categorical distinction between ASD and typically developing children, a dimensional characterization of the difference between ADHD and typically developing children and mixed results for the distinction between the diagnostic groups. We present a novel framework of cutting-edge statistical techniques which represent recent advances in both the models and the data used for research in psychiatric nosology. Our results suggest that that ASD and ADHD cannot be unambiguously characterised as either two separate clinical entities or opposite ends of a spectrum and highlight the need to study ADHD and ASD traits in tandem.

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