Laser Cladding Quality Monitoring Using Coaxial Image Based on Machine Learning

Kao, I-Hsi; Hsu, Ya-Wen; Lai, Yi; Perng, Jau-Woei

doi:10.1109/tim.2019.2926878

Cited by 22 publications

(7 citation statements)

References 48 publications

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“…These systems further demonstrate that CNN + SoftMax has the best performance in our experiment. Other research experiments also show that CNN + SoftMax has a better performance than the other machine learning algorithms [41,42].…”

Section: Discussionmentioning

confidence: 96%

Mortality Prediction of Septic Patients in the Emergency Department Based on Machine Learning

Perng

Kao

Kung

et al. 2019

JCM

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In emergency departments, the most common cause of death associated with suspected infected patients is sepsis. In this study, deep learning algorithms were used to predict the mortality of suspected infected patients in a hospital emergency department. During January 2007 and December 2013, 42,220 patients considered in this study were admitted to the emergency department due to suspected infection. In the present study, a deep learning structure for mortality prediction of septic patients was developed and compared with several machine learning methods as well as two sepsis screening tools: the systemic inflammatory response syndrome (SIRS) and quick sepsis-related organ failure assessment (qSOFA). The mortality predictions were explored for septic patients who died within 72 h and 28 days. Results demonstrated that the accuracy rate of deep learning methods, especially Convolutional Neural Network plus SoftMax (87.01% in 72 h and 81.59% in 28 d), exceeds that of the other machine learning methods, SIRS, and qSOFA. We expect that deep learning can effectively assist medical staff in early identification of critical patients.

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Section: Discussionmentioning

confidence: 96%

Mortality Prediction of Septic Patients in the Emergency Department Based on Machine Learning

Perng

Kao

Kung

et al. 2019

JCM

Self Cite

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“…α was possible from zero to C. It was tuned to find the best state between the complexity of SVM model and empirical error. If too large, the model became very complex which could fall into the overfitting situation [32] . Overfitting of a model was an error occurring when the function of the model was too closely fit to training data.…”

Section: Resultsmentioning

confidence: 99%

Support Vector Method with Radial Basis Function and Multi-Segment of Electrocardiogram for Paroxysmal Atrial Fibrillation Recognition

Nuryani

2020

Indonesian J Appl Phys

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Paroxysmal Atrial fibrillation (PAF) is a heart problem relating to irregular and rapid beating of the heart atria. It has risk of stroke and is independently associated with risk of mortality. Early information of PAF episode is important for a patient to have appropriate treatment to reduce atrial fibrillation complications. This article presents a new strategy to detect PAF with base of statistical electrocardiographic features and a support vector machine (SVM). R-peak series of electrocardiogram were segmented and were extracted to find the statistics of RR intervals. Different approaches in relation with the segmentation were investigated. Two-class SVM with radial basis function (RBF) and the statistics of RR intervals were examined for PAF detection. Using clinical data of patients with PAF, the proposed strategy showed excellent performance of 99.17% in terms of accuracy. The experimental result indicated that the appropriate statistics of RR intervals and SVM-RBF with its suitable parameters can perform well for PAF detection.

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“…The ANN model established a precise linkage between density and acoustic signals. Image-based approaches have gained increasing attention in processing monitoring due to both the convenience of the use of cameras and the abundant information contained in images during the process [16]. Wang et al [17] developed a vision-based surface monitoring system using convolutional neural network (CNN) for fused deposition modeling which could achieve efficient defect classification with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Process Monitoring of Laser-Induced Graphene Production With Deep Transfer Learning

Xia

Shao

Huang

et al. 2022

IEEE Trans. Instrum. Meas.

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Three-dimensional (3D) graphene has been increasingly used in many applications due to its superior properties. The laser-induced graphene (LIG) technique is an effective way to produce 3D graphene by combining graphene preparation and patterning into a single step using direct laser writing. However, the variation in process parameters and environment could largely affect the formation and crystallization quality of 3D graphene. This paper develops a vision and deep transfer learning-based processing monitoring system for LIG production. To solve the problem of limited labeled data, novel convolutional de-noising auto-encoder (CDAE)-based unsupervised learning is developed to utilize the available unlabeled images. The learned weights from CDAE are then transferred to a gaussian convolutional deep belief network (GCDBN) model for further fine-tuning with a very small amount of labeled images. The experimental results show the proposed method can achieve the state-of-art performance of precise and robust monitoring for the quality of the LIG formation.

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Laser Cladding Quality Monitoring Using Coaxial Image Based on Machine Learning

Cited by 22 publications

References 48 publications

Mortality Prediction of Septic Patients in the Emergency Department Based on Machine Learning

Mortality Prediction of Septic Patients in the Emergency Department Based on Machine Learning

Support Vector Method with Radial Basis Function and Multi-Segment of Electrocardiogram for Paroxysmal Atrial Fibrillation Recognition

Intelligent Process Monitoring of Laser-Induced Graphene Production With Deep Transfer Learning

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