Chatter Detection Based on ARMAX Model-Based Monitoring Method in Thin Wall Turning Operation

2018 ◽  
pp. 319-330
Author(s):  
Yang Liu ◽  
Zhenhua Xiong
Keyword(s):  
Thin Wall ◽  
Chatter Detection ◽  
Monitoring Method ◽  
Turning Operation ◽  
Model Based ◽  
Armax Model

scholarly journals Evaluation of a Model-Based Hemodynamic Monitoring Method in a Porcine Study of Septic Shock

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
James A. Revie ◽  
David Stevenson ◽  
J. Geoffrey Chase ◽  
Chris J. Pretty ◽  
Bernard C. Lambermont ◽  
...  
Keyword(s):  
Septic Shock ◽  
Right Ventricular ◽  
Temporal Trends ◽  
Systemic Resistance ◽  
Monitoring Method ◽  
Average Decrease ◽  
Model Based ◽  
Cardiovascular Models ◽  
Left And Right ◽  
Improved Model

Introduction. The accuracy and clinical applicability of an improved model-based system for tracking hemodynamic changes is assessed in an animal study on septic shock.Methods. This study used cardiovascular measurements recorded during a porcine trial studying the efficacy of large-pore hemofiltration for treating septic shock. Four Pietrain pigs were instrumented and induced with septic shock. A subset of the measured data, representing clinically available measurements, was used to identify subject-specific cardiovascular models. These models were then validated against the remaining measurements.Results. The system accurately matched independent measures of left and right ventricle end diastolic volumes and maximum left and right ventricular pressures to percentage errors less than 20% (except for the 95th percentile error in maximum right ventricular pressure) and allR2>0.76. An average decrease of 42% in systemic resistance, a main cardiovascular consequence of septic shock, was observed 120 minutes after the infusion of the endotoxin, consistent with experimentally measured trends. Moreover, modelled temporal trends in right ventricular end systolic elastance and afterload tracked changes in corresponding experimentally derived metrics.Conclusions. These results demonstrate that this model-based method can monitor disease-dependent changes in preload, afterload, and contractility in porcine study of septic shock.

An enhanced dynamic Gaussian mixture model–based damage monitoring method of aircraft structures under environmental and operational conditions

2018 ◽  
Vol 18 (2) ◽  
pp. 524-545 ◽  
Author(s):  
Lei Qiu ◽  
Fang Fang ◽  
Shenfang Yuan ◽  
Christian Boller ◽  
Yuanqiang Ren
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Health Monitoring ◽  
Gaussian Mixture ◽  
Monitoring Method ◽  
Structural Health ◽  
Model Based ◽  
Damage Monitoring

Gaussian mixture model–based structural health monitoring methods have been studied in recent years to improve the reliability of damage monitoring under environmental and operational conditions. However, most of these methods only use the ordinary expectation maximization algorithm to construct the Gaussian mixture model but the expectation maximization algorithm can easily lead to a local optimal solution and a singular solution, which also results in unreliable and unstable damage monitoring especially for complex structures. This article proposes an enhanced dynamic Gaussian mixture model–based damage monitoring method. First, an enhanced Gaussian mixture model constructing algorithm based on a Gaussian mixture model merge-split operation and a singularity inhibition mechanism is developed to keep the stability of the Gaussian mixture model and to obtain a unique optimal solution. Then, a probability similarity–based damage detection index is proposed to realize a normalized and general damage detection. The method combined with guided wave structural health monitoring technique is validated by the hole-edge cracks monitoring of an aluminum plate and a real aircraft wing spar. The results indicate that the method is efficient to improve the reliability and the stability of damage detection under fatigue load and varying structural boundary conditions. The method is simple and reliable regarding aviation application. It is a data-driven statistical method which is model-independent and less experience-dependent. It can be used by combining with different kinds of structural health monitoring techniques.

scholarly journals An Integrated Wildlife Recognition Model Based on Multi-Branch Aggregation and Squeeze-And-Excitation Network

2019 ◽  
Vol 9 (14) ◽  
pp. 2794 ◽  
Author(s):  
Jiangjian Xie ◽  
Anqi Li ◽  
Junguo Zhang ◽  
Zhean Cheng
Keyword(s):  
Recognition Accuracy ◽  
Infrared Camera ◽  
Integrated Model ◽  
Experimental Results ◽  
Residual Network ◽  
Monitoring Method ◽  
Image Identification ◽  
Wildlife Species ◽  
Model Based ◽  
Maximum Improvement

Infrared camera trapping, which helps capture large volumes of wildlife images, is a widely-used, non-intrusive monitoring method in wildlife surveillance. This method can greatly reduce the workload of zoologists through automatic image identification. To achieve higher accuracy in wildlife recognition, the integrated model based on multi-branch aggregation and Squeeze-and-Excitation network is introduced. This model adopts multi-branch aggregation transformation to extract features, and uses Squeeze-and-Excitation block to adaptively recalibrate channel-wise feature responses based on explicit self-mapped interdependencies between channels. The efficacy of the integrated model is tested on two datasets: the Snapshot Serengeti dataset and our own dataset. From experimental results on the Snapshot Serengeti dataset, the integrated model applies to the recognition of 26 wildlife species, with the highest accuracies in Top-1 (when the correct class is the most probable class) and Top-5 (when the correct class is within the five most probable classes) at 95.3% and 98.8%, respectively. Compared with the ROI-CNN algorithm and ResNet (Deep Residual Network), on our own dataset, the integrated model, shows a maximum improvement of 4.4% in recognition accuracy.

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