Real-time identification of time-varying tension in stay cables by monitoring cable transversal acceleration

2013 ◽  
Vol 21 (7) ◽  
pp. 1100-1117 ◽  
Author(s):  
Hui Li ◽  
Fujian Zhang ◽  
Yizhou Jin
Keyword(s):  
Real Time ◽  
Time Varying ◽  
Stay Cables ◽  
Real Time Identification

scholarly journals Modal control using real-time identification for time-varying structures

2010 ◽  
Vol 11 (6) ◽  
pp. 513-520
Author(s):  
Fengyan Deng ◽  
Didier Rémond ◽  
Luc Gaudiller
Keyword(s):  
Real Time ◽  
Modal Control ◽  
Time Varying ◽  
Real Time Identification

scholarly journals Identification of a non-stationary system using the Multi-Model approach

2019 ◽  
Vol 281 ◽  
pp. 04004
Author(s):  
Khaled FAWAZ
Keyword(s):  
Dynamic System ◽  
Real Time ◽  
Linear Systems ◽  
Time Varying ◽  
Time Varying Parameters ◽  
Identification Technique ◽  
Industrial Boiler ◽  
Non Linear Systems ◽  
Real Time Identification ◽  
Model Approach

In this paper, we will present a real-time identification of a dynamic system with time-varying parameters: the Steam Generator (Precisely the Industrial Boiler), which is located at the LAGIS (Laboratory of Automation, Computer and Signal Engineering). ) of the Lille University. The approach used to identify the system is the Multimodel, which is an identification technique of Non-linear systems.

scholarly journals Real-Time Identification of Patients Included in the CMS Bundled Payment Care Improvement (BPCI) Program

2020 ◽  
Vol 41 (S1) ◽  
pp. s367-s368
Author(s):  
Michael Korvink ◽  
John Martin ◽  
Michael Long
Keyword(s):  
Performance Evaluation ◽  
Real Time ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Care Providers ◽  
Meta Model ◽  
Bundled Payment ◽  
Clinical Cohort ◽  
Real Time Identification ◽  
Care Improvement

Background: The Bundled Payment Care Improvement Program is a CMS initiative designed to encourage greater collaboration across settings of care, especially as it relates to an initial set of targeted clinical episodes, which include sepsis and pneumonia. As with many CMS incentive programs, performance evaluation is retrospective in nature, resulting in after-the-fact changes in operational processes to improve both efficiency and quality. Although retrospective performance evaluation is informative, care providers would ideally identify a patient’s potential clinical cohort during the index stay and implement care management procedures as necessary to prevent or reduce the severity of the condition. The primary challenges for real-time identification of a patient’s clinical cohort are CMS-targeted cohorts are based on either MS-DRG (grouping of ICD-10 codes) or HCPCS coding—coding that occurs after discharge by clinical abstractors. Additionally, many informative data elements in the EHR lack standardization and no simple and reliable heuristic rules can be employed to meaningfully identify those cohorts without human review. Objective: To share the results of an ensemble statistical model to predict patient risks of sepsis and pneumonia during their hospital (ie, index) stay. Methods: The predictive model uses a combination of Bernoulli Naïve Bayes natural language processing (NLP) classifiers, to reduce text dimensionality into a single probability value, and an eXtreme Gradient Boosting (XGBoost) algorithm as a meta-model to collectively evaluate both standardized clinical elements alongside the NLP-based text probabilities. Results: Bernoulli Naïve Bayes classifiers have proven to perform well on short text strings and allow for highly explanatory unstructured or semistructured text fields (eg, reason for visit, culture results), to be used in a both comparative and generalizable way within the larger XGBoost model. Conclusions: The choice of XGBoost as the meta-model has the benefits of mitigating concerns of nonlinearity among clinical features, reducing potential of overfitting, while allowing missing values to exist within the data. Both the Bayesian classifier and meta-model were trained using a patient-level integrated dataset extracted from both a patient-billing and EHR data warehouse maintained by Premier. The data set, joined by patient admission-date, medical record number, date of birth, and hospital entity code, allows the presence of both the coded clinical cohort (derived from the MS-DRG) and the explanatory features in the EHR to exist within a single patient encounter record. The resulting model produced F1 performance scores of .65 for the sepsis population and .61 for the pneumonia population.Funding: NoneDisclosures: None

Study on real-time thermal–mechanical–frictional coupling characteristics of ball bearings based on the inverse thermal network method

2021 ◽  
pp. 135065012199356
Author(s):  
Tie-Jun Li ◽  
Meng-Zhuo Wang ◽  
Chun-Yu Zhao
Keyword(s):  
Real Time ◽  
Thermal Contact Resistance ◽  
Thermal Contact ◽  
Time Varying ◽  
Ball Bearings ◽  
The Real ◽  
Thermal Network ◽  
Network Method ◽  
Thermal Network Method ◽  
Coupling Characteristics

The real-time thermal–mechanical–frictional coupling characteristics of bearings are critical to the accuracy, reliability, and life of entire machines. To obtain the real-time dynamic characteristics of ball bearings, a novel model to calculate point contact dynamic friction in mixed lubrication was firstly presented in this work. The model of time-varying thermal contact resistance under fit between the ring and the ball, between the ring and the housing, and between the ring and the shaft was established using the fractal theory and the heat transfer theory. Furthermore, an inverse thermal network method with time-varying thermal contact resistance was presented. Using these models, the real-time thermal–mechanical–frictional coupling characteristics of ball bearings were obtained. The effectiveness of the presented models was verified by experiment and comparison.

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