Adaptive prediction model of gas concentration based on EMD and GPR

2021 ◽  
Author(s):  
Dingwen Dong
Keyword(s):  
Prediction Model ◽  
Gas Concentration ◽  
Adaptive Prediction

scholarly journals Adaptive Prediction Model in Prospective Molecular Signature–Based Clinical Studies

2013 ◽  
Vol 20 (3) ◽  
pp. 531-539 ◽  
Author(s):  
Guanghua Xiao ◽  
Shuangge Ma ◽  
John Minna ◽  
Yang Xie
Keyword(s):  
Prediction Model ◽  
Clinical Studies ◽  
Molecular Signature ◽  
Adaptive Prediction

scholarly journals Prediction Model for Dissolved Gas Concentration in Transformer Oil Based on Modified Grey Wolf Optimizer and LSSVM with Grey Relational Analysis and Empirical Mode Decomposition

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 422 ◽  
Author(s):  
Bing Zeng ◽  
Jiang Guo ◽  
Fangqing Zhang ◽  
Wenqiang Zhu ◽  
Zhihuai Xiao ◽  
...  
Keyword(s):  
Prediction Model ◽  
Ambient Temperature ◽  
Grey Relational Analysis ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Dissolved Gas ◽  
Gas Concentration ◽  
Relational Analysis ◽  
Mode Decomposition ◽  
Grey Relational

Oil-immersed transformer is one of the most important components in the power system. The dissolved gas concentration prediction in oil is vital for early incipient fault detection of transformer. In this paper, a model for predicting the dissolved gas concentration in power transformer based on the modified grey wolf optimizer and least squares support vector machine (MGWO-LSSVM) with grey relational analysis (GRA) and empirical mode decomposition (EMD) is proposed, in which the influence of transformer load, oil temperature and ambient temperature on gas concentration is taken into consideration. Firstly, GRA is used to analyze the correlation between dissolved gas concentration and transformer load, oil temperature and ambient temperature, and the optimal feature set affecting gas concentration is extracted and selected as the input of the prediction model. Then, EMD is used to decompose the non-stationary series data of dissolved gas concentration into stationary subsequences with different scales. Finally, the MGWO-LSSVM is used to predict each subsequence, and the prediction values of all subsequences are combined to get the final result. DGA samples from two transformers are used to verify the proposed method, which shows high prediction accuracy, stronger generalization ability and robustness by comparing with LSSVM, particle swarm optimization (PSO)-LSSVM, GWO-LSSVM, MGWO-LSSVM, EMD-PSO-LSSVM, EMD-GWO-LSSVM, EMD-MGWO-LSSVM, GRA-EMD-PSO-LSSVM and GRA-EMD-GWO-LSSVM.

Research on Predictive Model of Gas Concentration for Driving Ventilation in Coal Mine Based on RBF and BP

2013 ◽  
Vol 706-708 ◽  
pp. 1805-1809
Author(s):  
Xiao Yan Gong ◽  
Jun Guo ◽  
He Xue ◽  
Dong Hui Yan ◽  
Zhe Wu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Coal Mine ◽  
Rbf Neural Network ◽  
Coal Mines ◽  
Observation Data ◽  
Prediction Errors ◽  
Application Research ◽  
Gas Concentration ◽  
Mine Sites

In order to predict accurately gas concentration and design ventilation scheme in driving ventilation process under different gas emission in coal mine, based on the analysis of various ventilation factors, the prediction model structure of gas concentration for driving ventilation was designed based on RBF and BP neural network in this paper. Then MATLAB software and the observation data obtained from the coal mine sites were used to compare and analyze the prediction errors of two models, and a RBF neural network model with higher prediction precision was obtained. After that, the prediction model was used for practical application research on the gas concentration of the heading face in concrete coal mines. The research shows that the settled prediction model can not only predict the gas concentration precisely of driving ventilation, but also provide a certain theory basis for different driving ventilation equipment layout and parameters configuration in the driving ventilation process of coal mines.

Abstract 315: Time Adaptive Model for Real-time Cardiopulmonary Resuscitation Outcome Prediction: A Nationwide Out-of-hospital Cardiac Arrest Study

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_4) ◽  
Author(s):  
Ji Woong Kim ◽  
Juhyung Ha ◽  
Sung Yeon Hwang ◽  
Taerim Kim ◽  
Wonchul Cha
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Prediction Model ◽  
Real Time ◽  
Outcome Prediction ◽  
Training Dataset ◽  
Adaptive Model ◽  
Entire Study ◽  
Adaptive Prediction ◽  
Prediction Probability

Background: OHCA patients use lots of hospital resources, and predicting prognosis is important in decision making for patient treatment. However, no algorithm can predict survival to hospital discharge rates in real-time. Aim: This study aimed to develop and validate the time adaptive model for real-time outcome prediction of OHCA patients. Methods: We performed a retrospective observational study using data from the Korea OHCA Registry in South Korea. In this study, we exclude patients with trauma, experienced ROSC before arriving in the ED, and patient who did not execute CPR in ED to select patients who executed CPR in ED. To develop the time adaptive prediction model, we organize training dataset as ongoing CPR patients by the minute. We used XGBoost as a machine-learning method and find the area under the receiver operating characteristic curve (AUROC) and predict the probability of the time adaptive prediction model. Results: The entire study population is 67270 and the majority were male patients (64%) with a median age of 70 years (IQR 23 years); 2632 (4.0%) had a shockable first documented rhythm at the ED. The subject was split into derivation and validation datasets at a ratio of 8 to 2. The AUROC of the model is 0.72 when the CPR starts, 0.68 after 30 minutes, and 0.62 after 60 minutes. Prediction probability of the time adaptive prediction model is shown in Fig. 1. Conclusions: We developed and validated the time adaptive prediction model by training ongoing CPR patients by minute to predict the CPR outcome of OHCA patients in real-time. This study showed the potential of a machine-learning-based algorithm model for decision making of patients about the termination of resuscitation.

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