scholarly journals Damage Identification of Vehicle Brake Disks by the use of Impedance-Based SHM and Unsupervised Machine Learning Method

2020 ◽  
Vol 7 (6) ◽  
pp. 324-330
Author(s):  
Stanley Washington Ferreira de Rezende ◽  
Bruno Pereira Barella ◽  
Jose dos Reis Vieira de Moura Jr
Keyword(s):  
Machine Learning ◽  
Damage Identification ◽  
Machine Learning Method ◽  
Learning Method ◽  
Unsupervised Machine Learning

Channel head extraction based on fuzzy unsupervised machine learning method

Geomorphology ◽  
2021 ◽  
pp. 107888
Author(s):  
Jian Wu ◽  
Haixing Liu ◽  
Zhe Wang ◽  
Lei Ye ◽  
Min Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Unsupervised Machine Learning

scholarly journals An unsupervised machine learning method for assessing quality of tandem mass spectra

2012 ◽  
Vol 10 (Suppl 1) ◽  
pp. S12 ◽  
Author(s):  
Wenjun Lin ◽  
Jianxin Wang ◽  
Wen-Jun Zhang ◽  
Fang-Xiang Wu
Keyword(s):  
Machine Learning ◽  
Mass Spectra ◽  
Tandem Mass ◽  
Machine Learning Method ◽  
Learning Method ◽  
Unsupervised Machine Learning ◽  
Tandem Mass Spectra

scholarly journals Statistically Representative Metrology of Nanoparticles via Unsupervised Machine Learning of TEM Images

Nanomaterials ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2706
Author(s):  
Haotian Wen ◽  
José María Luna-Romera ◽  
José C. Riquelme ◽  
Christian Dwyer ◽  
Shery L. Y. Chang
Keyword(s):  
Machine Learning ◽  
Morphological Characterization ◽  
Atomic Resolution ◽  
Machine Learning Method ◽  
Learning Method ◽  
Unsupervised Machine Learning ◽  
Transmission Electron ◽  
Property Control ◽  
Quality Imaging

The morphology of nanoparticles governs their properties for a range of important applications. Thus, the ability to statistically correlate this key particle performance parameter is paramount in achieving accurate control of nanoparticle properties. Among several effective techniques for morphological characterization of nanoparticles, transmission electron microscopy (TEM) can provide a direct, accurate characterization of the details of nanoparticle structures and morphology at atomic resolution. However, manually analyzing a large number of TEM images is laborious. In this work, we demonstrate an efficient, robust and highly automated unsupervised machine learning method for the metrology of nanoparticle systems based on TEM images. Our method not only can achieve statistically significant analysis, but it is also robust against variable image quality, imaging modalities, and particle dispersions. The ability to efficiently gain statistically significant particle metrology is critical in advancing precise particle synthesis and accurate property control.

scholarly journals Exploring gene–gene interaction in family‐based data with an unsupervised machine learning method: EPISFA

2020 ◽  
Vol 44 (8) ◽  
pp. 811-824
Author(s):  
Xiao Xiang ◽  
Siyue Wang ◽  
Tianyi Liu ◽  
Mengying Wang ◽  
Jiawen Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gene Interaction ◽  
Machine Learning Method ◽  
Learning Method ◽  
Unsupervised Machine Learning ◽  
Family Based

Applying an Unsupervised Machine Learning Method for Defining Maintenance Significant Items

2021 ◽  
Author(s):  
Miguel Angelo de Carvalho Michalski ◽  
Arthur Henrique de Andrade Melani ◽  
Renan Favarão da Silva ◽  
Gilberto Francisco Martha de Souza
Keyword(s):  
Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Unsupervised Machine Learning

scholarly journals Cognitive phenotypes 1 month after ICU discharge in mechanically ventilated patients: a prospective observational cohort study

Critical Care ◽  
2020 ◽  
Vol 24 (1) ◽  
Author(s):  
Sol Fernández-Gonzalo ◽  
Guillem Navarra-Ventura ◽  
Neus Bacardit ◽  
Gemma Gomà Fernández ◽  
Candelaria de Haro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Cognitive Reserve ◽  
Predisposing Factors ◽  
Machine Learning Method ◽  
Learning Method ◽  
Icu Patients ◽  
Unsupervised Machine Learning ◽  
Mechanically Ventilated ◽  
Icu Discharge

Abstract Background ICU patients undergoing invasive mechanical ventilation experience cognitive decline associated with their critical illness and its management. The early detection of different cognitive phenotypes might reveal the involvement of diverse pathophysiological mechanisms and help to clarify the role of the precipitating and predisposing factors. Our main objective is to identify cognitive phenotypes in critically ill survivors 1 month after ICU discharge using an unsupervised machine learning method, and to contrast them with the classical approach of cognitive impairment assessment. For descriptive purposes, precipitating and predisposing factors for cognitive impairment were explored. Methods A total of 156 mechanically ventilated critically ill patients from two medical/surgical ICUs were prospectively studied. Patients with previous cognitive impairment, neurological or psychiatric diagnosis were excluded. Clinical variables were registered during ICU stay, and 100 patients were cognitively assessed 1 month after ICU discharge. The unsupervised machine learning K-means clustering algorithm was applied to detect cognitive phenotypes. Exploratory analyses were used to study precipitating and predisposing factors for cognitive impairment. Results K-means testing identified three clusters (K) of patients with different cognitive phenotypes: K1 (n = 13), severe cognitive impairment in speed of processing (92%) and executive function (85%); K2 (n = 33), moderate-to-severe deficits in learning-memory (55%), memory retrieval (67%), speed of processing (36.4%) and executive function (33.3%); and K3 (n = 46), normal cognitive profile in 89% of patients. Using the classical approach, moderate-to-severe cognitive decline was recorded in 47% of patients, while the K-means method accurately classified 85.9%. The descriptive analysis showed significant differences in days (p = 0.016) and doses (p = 0.039) with opioid treatment in K1 vs. K2 and K3. In K2, there were more women, patients were older and had more comorbidities (p = 0.001) than in K1 or K3. Cognitive reserve was significantly (p = 0.001) higher in K3 than in K1 or K2. Conclusion One month after ICU discharge, three groups of patients with different cognitive phenotypes were identified through an unsupervised machine learning method. This novel approach improved the classical classification of cognitive impairment in ICU survivors. In the exploratory analysis, gender, age and the level of cognitive reserve emerged as relevant predisposing factors for cognitive impairment in ICU patients. Trial registration ClinicalTrials.gov Identifier:NCT02390024; March 17,2015.

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