scholarly journals Bagged Decision Trees Based Scheme of Microgrid Protection Using Windowed Fast Fourier and Wavelet Transforms

Electronics ◽  
2018 ◽  
Vol 7 (5) ◽  
pp. 61 ◽  
Author(s):  
Solomon Netsanet ◽  
Jianhua Zhang ◽  
Dehua Zheng
Keyword(s):  
Decision Trees ◽  
Wavelet Transforms ◽  
Bagged Decision Trees

scholarly journals Bagged Decision Trees Based Scheme of Microgrid Protection Using Windowed Fast Fourier and Wavelet Transforms

2018 ◽  
Author(s):  
Solomon Netsanet Alemu ◽  
Jianhua Zhang ◽  
Dehua Zheng
Keyword(s):  
Decision Trees ◽  
Wavelet Transforms ◽  
Distribution Systems ◽  
Circuit Breaker ◽  
Great Level ◽  
Detection Scheme ◽  
Novel Approach ◽  
Bagged Decision Trees ◽  
The Right ◽  
Beijing China

Microgrids of varying size and applications are regarded as a key feature of modernizing the power system. The protection of those systems, however, has become a major challenge and a popular research topic for the reason that it involves greater complexity than traditional distribution systems. This paper addresses the issue through a novel approach which utilizes detailed analysis of current and voltage waveforms through windowed fast Fourier and wavelet transforms. The fault detection scheme involves bagged decision trees which use input features extracted from the signal processing stage and selected by correlation analysis. The technique was tested on a microgrid model developed using PSCAD/EMTDS, which is inspired from an operational microgrid in Goldwind Sc. Tech. Co. Ltd, in Beijing, China. The results showed great level of effectiveness to accurately identify faults from other non-fault disturbances, precisely locate the fault and trigger opening of the right circuit breaker/s under different operation modes, fault resistances and other system disturbances.

scholarly journals Automatic stent detection in intravascular OCT images using bagged decision trees

2012 ◽  
Vol 3 (11) ◽  
pp. 2809 ◽  
Author(s):  
Hong Lu ◽  
Madhusudhana Gargesha ◽  
Zhao Wang ◽  
Daniel Chamie ◽  
Guilherme F. Attizani ◽  
...  
Keyword(s):  
Decision Trees ◽  
Bagged Decision Trees

scholarly journals Integration of Deep and Ensemble Learning for Detecting COVID-19 in Computed Tomography Images

2020 ◽  
Author(s):  
Ali Haidar ◽  
Lois Holloway
Keyword(s):  
Computed Tomography ◽  
Decision Trees ◽  
Ct Images ◽  
Gradient Boosting ◽  
Deep Convolutional Neural Networks ◽  
Adaptive Boosting ◽  
Computed Tomography Images ◽  
Extreme Gradient Boosting ◽  
Bagged Decision Trees ◽  
Additive Regression

Abstract This paper presents an approach for detecting covid-19 in Computed Tomography (CT) images by integrating deep convolutional neural networks and ensembles of decision trees. The proposed approach consisted of three steps. In the first step, the CT images slices were collected and processed. In the second step, a deep convolutional neural network was trained to predict covid-19 in the CT images. In the third step, deep features were extracted and were used to train an ensemble of decision trees. Six types/packages of ensembles of decision trees were investigated: extreme gradient boosting (XGBoost), bagged decision trees (BDT), random forest (RF), adaptive boosting decision trees (Adaboost), gradient boosting decision trees (GBDT), and dropouts meet multiple additive regression trees (DART). The accuracy, sensitivity, specificity, f1-score, precision, and area under the ROC curve (AUC) were calculated to compare the models against each other. The proposed approach revealed the highest performance with a RF that reported 0.87 accuracy, 0.87 f1-score, and 0.90 AUC. The developed models revealed similar performance when compared to previously published models. This highlights the efficiency of combining deep networks with ensembles of decision trees for detecting covid-19.

scholarly journals Integration of Deep and Ensemble Learning for Detecting COVID-19 in Computed Tomography Images

2020 ◽  
Author(s):  
Ali Haidar ◽  
Lois Holloway
Keyword(s):  
Computed Tomography ◽  
Decision Trees ◽  
Ct Images ◽  
Gradient Boosting ◽  
Deep Convolutional Neural Networks ◽  
Adaptive Boosting ◽  
Computed Tomography Images ◽  
Extreme Gradient Boosting ◽  
Bagged Decision Trees ◽  
Additive Regression

Abstract This paper presents an approach for detecting covid-19 in Computed Tomography (CT) images by integrating deep convolutional neural networks and ensembles of decision trees. The proposed approach consisted of three steps. In the first step, the CT images slices were collected and processed. In the second step, a deep convolutional neural network was trained to predict covid-19 in the CT images. In the third step, deep features were extracted and were used to train an ensemble of decision trees. Six types/packages of ensembles of decision trees were investigated: extreme gradient boosting (XGBoost), bagged decision trees (BDT), random forest (RF), adaptive boosting decision trees (Adaboost), gradient boosting decision trees (GBDT), and dropouts meet multiple additive regression trees (DART). The accuracy, sensitivity, specificity, f1-score, precision, and area under the ROC curve (AUC) were calculated to compare the models against each other. The proposed approach revealed the highest performance with a RF that reported 0.87 accuracy, 0.87 f1-score, and 0.90 AUC. The developed models revealed similar performance when compared to previously published models. This highlights the efficiency of combining deep networks with ensembles of decision trees for detecting covid-19.

scholarly journals Contribution of clouds radiative forcing to the local surface temperature variability

2020 ◽  
Author(s):  
Oscar Rojas ◽  
Marjolaine Chiriaco ◽  
Sophie Bastin ◽  
Justine Ringard
Keyword(s):  
Heat Exchange ◽  
Decision Trees ◽  
Radiative Forcing ◽  
Temperature Variability ◽  
Local Climate ◽  
Hourly Temperature ◽  
Local Contribution ◽  
Bagged Decision Trees ◽  
The One ◽  
Local Surface Temperature

<p>The local contribution of clouds to the surface energy balance and temperature variability is an important topic in order to apprehend how this intake affects local climate variability and extreme events, how this contribution varies from one place to another, and how it evolves in a warming climate. The scope of this study is to understand how clouds impact temperature variability, to quantify their contribution, and to compare their effects to other surface processes. To do so, we develop a method to estimate the different terms that control temperature variability at the surface (∂T<sub>2m</sub> /∂t) by using this equation: <strong>∂T<sub>2m</sub> /∂t=R+HA+HG+Adv</strong> where R is the radiation that is separated into the cloud term (R<sub>cloud</sub>) and the clear sky one (R<sub>CS</sub>), HA the atmospheric heat exchange, HG the ground heat exchange, and Adv the advection. These terms are estimated hourly, almost only using direct measurements from SIRTA-ReOBS dataset (an hourly long-term multi-variables dataset retrieved from SIRTA, an observatory located in a semi-urban area 20-km South-West of Paris; Chiriaco et al., 2019) for a five-years period. The method gives good results for the hourly temperature variability, with a 0.8 correlation coefficient and a weak residual term between left part (directly measured) and right part of the equation.</p><p>A bagged decision trees analysis of this equation shows that R<sub>CS</sub> dominates temperature variability during daytime and is mainly modulated by cloud radiative effect (R<sub>cloud</sub>). During nighttime, the bagged decision trees analysis determines that R<sub>cloud</sub> is the term controlling temperature changes. When a diurnal cycle analysis (split into seasons) is performed for each term, HA becomes an important negative modulator in the late afternoon, chiefly in spring and summer, when evaporation and thermal conduction are increased. In contrast, HG and Adv terms do not play an essential role on temperature variability at this temporal scale and their contribution is barely considerable in the one-hour variability, but still they remain necessary in order to obtain the best coefficient estimator between the directly measured observations and the method estimated. All terms except advection have a marked monthly-hourly cycle.</p><p>Next steps consist in characterize the types of clouds and study their physical properties corresponding to the cases where R<sub>cloud</sub> is significant, using the Lidar profiles also available in the SIRTA-ReOBS dataset.</p>

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