Welding penetration monitoring for pulsed GTAW using visual sensor based on AAM and random forests

2020 ◽  
Author(s):  
Chao Chen ◽  
Na Lv ◽  
Shanben Chen
Keyword(s):  
Random Forests ◽  
Visual Sensor ◽  
Pulsed Gtaw

Research on Measurement of Pulsed-GTAW Process by Multiple Sensors

2012 ◽  
Vol 157-158 ◽  
pp. 185-188 ◽  
Author(s):  
Bo Chen ◽  
Hong Tao Zhang ◽  
Shan Ben Chen ◽  
Ji Cai Feng
Keyword(s):  
Information Fusion ◽  
Welding Process ◽  
Accurate Information ◽  
Time Varying ◽  
Visual Sensor ◽  
Multiple Sensors ◽  
Arc Sensor ◽  
Pulsed Gtaw ◽  
Gtaw Process

Welding process is a non-linear, multi-variable and time-varying process accompanied by many interference factors such as dust, intense arc and electromagnetism, so it is difficult to obtain accurate information reflecting the welding process status, let alone controlling the process precisely. This paper used arc sensor, sound sensor and visual sensor simultaneously to obtain different information of the pulsed-GTAW process, and SVM was used to fuse the information to predict the quality of the welding process, experiment results showed multi-sensor information fusion could predict the process more precisely. This laid the foundation for further controlling the welding quality automatically and intelligently.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

Effects of nitrogen and hydrogen in argon shielding gas on bead profile, delta-ferrite and nitrogen contents of the pulsed GTAW welds of AISI 316L stainless steel

2016 ◽  
Vol 58 (6) ◽  
pp. 489-494 ◽  
Author(s):  
Panyasak Phakpeetinan ◽  
Amnuysak Chianpairot ◽  
Ekkarut Viyanit ◽  
Fritz Hartung ◽  
Gobboon Lothongkum
Keyword(s):  
Stainless Steel ◽  
316L Stainless Steel ◽  
Aisi 316L ◽  
Shielding Gas ◽  
Delta Ferrite ◽  
Aisi 316L Stainless Steel ◽  
Pulsed Gtaw ◽  
Nitrogen Contents ◽  
Bead Profile

A Diverse Assimilation of Sequence and Structure Dependent Features for Amyloid Plaque Prediction Using Random Forests

2013 ◽  
Vol 10 (1) ◽  
pp. 38-44
Author(s):  
Smitha Sunil Nair ◽  
N. V. Reddy ◽  
K. Hareesha ◽  
S. Balaji
Keyword(s):  
Random Forests ◽  
Amyloid Plaque
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