A machine-learning approach for damage detection in aircraft structures using self-powered sensor data

2017 ◽  
Author(s):  
Hadi Salehi ◽  
Saptarshi Das ◽  
Shantanu Chakrabartty ◽  
Subir Biswas ◽  
Rigoberto Burgueño
Keyword(s):  
Machine Learning ◽  
Damage Detection ◽  
Sensor Data ◽  
Learning Approach ◽  
Aircraft Structures ◽  
Machine Learning Approach ◽  
Self Powered

scholarly journals Damage identification in aircraft structures with self-powered sensing technology: A machine learning approach

2018 ◽  
Vol 25 (12) ◽  
pp. e2262 ◽  
Author(s):  
Hadi Salehi ◽  
Saptarshi Das ◽  
Shantanu Chakrabartty ◽  
Subir Biswas ◽  
Rigoberto Burgueño
Keyword(s):  
Machine Learning ◽  
Damage Identification ◽  
Learning Approach ◽  
Aircraft Structures ◽  
Sensing Technology ◽  
Machine Learning Approach ◽  
Self Powered

P 120 A machine learning approach to detecting sleep and sleep disorders in acceleration sensor data

2017 ◽  
Vol 128 (10) ◽  
pp. e388
Author(s):  
R. Leenings ◽  
C. Glatz ◽  
A. Heidbreder ◽  
M. Boentert ◽  
G. Pipa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Disorders ◽  
Sensor Data ◽  
Learning Approach ◽  
Acceleration Sensor ◽  
Machine Learning Approach

scholarly journals Outlier detection in WSN by entropy based machine learning approach

2020 ◽  
Vol 20 (3) ◽  
pp. 1435
Author(s):  
Manmohan Singh Yadav ◽  
Shish Ahamad
Keyword(s):  
Machine Learning ◽  
Outlier Detection ◽  
Sensor Data ◽  
Learning Approach ◽  
Random Subspace ◽  
Environmental Disasters ◽  
Machine Learning Approach ◽  
The World ◽  
Prediction Capability

<p>Environmental disasters like flooding, earthquake etc. causes catastrophic effects all over the world. WSN based techniques have become popular in susceptibility modelling of such disaster due to their greater strength and efficiency in the prediction of such threats. This paper demonstrates the machine learning-based approach to predict outlier in sensor data with bagging, boosting, random subspace, SVM and KNN based frameworks for outlier prediction using a WSN data. First of all database is pre processed with 14 sensor motes with presence of outlier due to intrusion. Subsequently segmented database is created from sensor pairs. Finally, the data entropy is calculated and used as a feature to determine the presence of outlier used different approach. Results show that the KNN model has the highest prediction capability for outlier assessment.</p>

scholarly journals Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2828 ◽  
Author(s):  
Dylan Kobsar ◽  
Reed Ferber
Keyword(s):  
Machine Learning ◽  
Clinical Outcomes ◽  
Movement Patterns ◽  
Movement Pattern ◽  
Human Movement ◽  
Sensor Data ◽  
Learning Approach ◽  
Wearable Sensor ◽  
Machine Learning Approach ◽  
Subject Specific

Wearable sensors can provide detailed information on human movement but the clinical impact of this information remains limited. We propose a machine learning approach, using wearable sensor data, to identify subject-specific changes in gait patterns related to improvements in clinical outcomes. Eight patients with knee osteoarthritis (OA) completed two gait trials before and one following an exercise intervention. Wearable sensor data (e.g., 3-dimensional (3D) linear accelerations) were collected from a sensor located near the lower back, lateral thigh and lateral shank during level treadmill walking at a preferred speed. Wearable sensor data from the 2 pre-intervention gait trials were used to define each individual’s typical movement pattern using a one-class support vector machine (OCSVM). The percentage of strides defined as outliers, based on the pre-intervention gait data and the OCSVM, were used to define the overall change in an individual’s movement pattern. The correlation between the change in movement patterns following the intervention (i.e., percentage of outliers) and improvement in self-reported clinical outcomes (e.g., pain and function) was assessed using a Spearman rank correlation. The number of outliers observed post-intervention exhibited a large association (ρ = 0.78) with improvements in self-reported clinical outcomes. These findings demonstrate a proof-of-concept and a novel methodological approach for integrating machine learning and wearable sensor data. This approach provides an objective and evidence-informed way to understand clinically important changes in human movement patterns in response to exercise therapy.

A Machine Learning approach for the damage detection in composite structures under quasi-random loads

2021 ◽  
Author(s):  
Alberto Ciampaglia ◽  
Antonio Mastropietro ◽  
Francesco Vaccarino ◽  
Giovanni Belingardi ◽  
Alessandro De Gregorio
Keyword(s):  
Machine Learning ◽  
Damage Detection ◽  
Composite Structures ◽  
Learning Approach ◽  
Random Loads ◽  
Machine Learning Approach
Sign in / Sign up

Export Citation Format

Share Document