Parameter Extraction for Equivalent Circuit of Common Mode Choke Based on Deep Neural Network, Random Forest Tree and Extreme Gradient Boosting Algorithm

2019 ◽  
Author(s):  
Zheng Li ◽  
Jian Guo ◽  
Wenchang Xu ◽  
Yu Guo ◽  
Fengmei Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Equivalent Circuit ◽  
Deep Neural Network ◽  
Parameter Extraction ◽  
Forest Tree ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Random Forest Tree ◽  
Boosting Algorithm

scholarly journals Retrieval of Total Precipitable Water from Himawari-8 AHI Data: A Comparison of Random Forest, Extreme Gradient Boosting, and Deep Neural Network

2019 ◽  
Vol 11 (15) ◽  
pp. 1741 ◽  
Author(s):  
Yeonjin Lee ◽  
Daehyeon Han ◽  
Myoung-Hwan Ahn ◽  
Jungho Im ◽  
Su Jeong Lee
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
High Resolution ◽  
Random Forest ◽  
Correlation Coefficient ◽  
Deep Neural Network ◽  
Precipitable Water ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Resolution Data

Total precipitable water (TPW), a column of water vapor content in the atmosphere, provides information on the spatial distribution of moisture. The high-resolution TPW, together with atmospheric stability indices such as convective available potential energy (CAPE), is an effective indicator of severe weather phenomena in the pre-convective atmospheric condition. With the advent of high performing imaging instrument onboard geostationary satellites such as Advanced Himawari Imager (AHI) onboard Himawari-8 of Japan and Advanced Meteorological Imager (AMI) onboard GeoKompsat-2A of Korea, it is expected that unprecedented spatiotemporal resolution data (e.g., AMI plans to provide 2 km resolution data at every 2 min over the northeast part of East Asia) will be provided. To derive TPW from such high-resolution data in a timely fashion, an efficient algorithm is highly required. Here, machine learning approaches—random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN)—are assessed for the TPW retrieved from AHI over the clear sky in Northeast Asia area. For the training dataset, the nine infrared brightness temperatures (BT) of AHI (BT8 to 16 centered at 6.2, 6.9, 7.3, 8.6, 9.6, 10.4, 11.2, 12.4, and 13.3 μ m , respectively), six dual channel differences and observation conditions such as time, latitude, longitude, and satellite zenith angle for two years (September 2016 to August 2018) are used. The corresponding TPW is prepared by integrating the water vapor profiles from InterimEuropean Centre for Medium-Range Weather Forecasts Re-Analysis data (ERA-Interim). The algorithm performances are assessed using the ERA-Interim and radiosonde observations (RAOB) as the reference data. The results show that the DNN model performs better than RF and XGB with a correlation coefficient of 0.96, a mean bias of 0.90 mm, and a root mean square error (RMSE) of 4.65 mm when compared to the ERA-Interim. Similarly, DNN results in a correlation coefficient of 0.95, a mean bias of 1.25 mm, and an RMSE of 5.03 mm when compared to RAOB. Contributing variables to retrieve the TPW in each model and the spatial and temporal analysis of the retrieved TPW are carefully examined and discussed.

scholarly journals Development of an IoT Architecture Based on a Deep Neural Network against Cyber Attacks for Automated Guided Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8467
Author(s):  
Mahmoud Elsisi ◽  
Minh-Quang Tran
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Online Monitoring ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
Automated Guided Vehicles ◽  
Gradient Boosting ◽  
Machine Model ◽  
Extreme Gradient Boosting

This paper introduces an integrated IoT architecture to handle the problem of cyber attacks based on a developed deep neural network (DNN) with a rectified linear unit in order to provide reliable and secure online monitoring for automated guided vehicles (AGVs). The developed IoT architecture based on a DNN introduces a new approach for the online monitoring of AGVs against cyber attacks with a cheap and easy implementation instead of the traditional cyber attack detection schemes in the literature. The proposed DNN is trained based on experimental AGV data that represent the real state of the AGV and different types of cyber attacks including a random attack, ramp attack, pulse attack, and sinusoidal attack that is injected by the attacker into the internet network. The proposed DNN is compared with different deep learning and machine learning algorithms such as a one dimension convolutional neural network (1D-CNN), a supported vector machine model (SVM), random forest, extreme gradient boosting (XGBoost), and a decision tree for greater validation. Furthermore, the proposed IoT architecture based on a DNN can provide an effective detection for the AGV status with an excellent accuracy of 96.77% that is significantly greater than the accuracy based on the traditional schemes. The AGV status based on the proposed IoT architecture with a DNN is visualized by an advanced IoT platform named CONTACT Elements for IoT. Different test scenarios with a practical setup of an AGV with IoT are carried out to emphasize the performance of the suggested IoT architecture based on a DNN. The results approve the usefulness of the proposed IoT to provide effective cybersecurity for data visualization and tracking of the AGV status that enhances decision-making and improves industrial productivity.

scholarly journals Treinamento de Classificadores de Aprendizagem de Máquina para o Diagnóstico do Glaucoma com o Uso de Dados de Perimetria Automatizada Padrão (SAP)

2021 ◽  
Author(s):  
Bruno F. Hashimoto ◽  
Leonardo S. Shigueoka ◽  
Vital P. Costa ◽  
Edson S. Gomi
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Deep Neural Network ◽  
Gradient Boosting ◽  
Automated Perimetry ◽  
Standard Automated Perimetry

O glaucoma é uma doença ocular que provoca danos ao nervo óptico e consequente perda do campo visual. Este projeto de pesquisa tem como objetivo investigar o desempenho de Classificadores de Aprendizagem de Máquina no diagnóstico do glaucoma usando dados do exame SAP (Standard Automated Perimetry). Por conta do dataset com dados de pacientes reais ser pequeno, foi gerado um dataset sintético para aumentar a quantidade de dados disponíveis. Foram testados os classificadores Random Forest, Gradient Boosting e DNN (Deep Neural Network). Os resultados obtidos mostram que esses classificadores apresentam desempenho aceitável para o diagnóstico do glaucoma, todos com AUC acima de 83,9% e acurácia acima de 72,0%.

scholarly journals Multi-scale Two-way Deep Neural Network for Stock Trend Prediction

2020 ◽  
Author(s):  
Guang Liu ◽  
Yuzhao Mao ◽  
Qi Sun ◽  
Hailong Huang ◽  
Weiguo Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Gradient Boosting ◽  
Trend Prediction ◽  
Future Studies ◽  
Multi Scale ◽  
Single Scale ◽  
Extreme Gradient Boosting ◽  
Scale Behavior

Stock Trend Prediction(STP) has drawn wide attention from various fields, especially Artificial Intelligence. Most previous studies are single-scale oriented which results in information loss from a multi-scale perspective. In fact, multi-scale behavior is vital for making intelligent investment decisions. A mature investor will thoroughly investigate the state of a stock market at various time scales. To automatically learn the multi-scale information in stock data, we propose a Multi-scale Two-way Deep Neural Network. It learns multi-scale patterns from two types of scale-information, wavelet-based and downsampling-based, by eXtreme Gradient Boosting and Recurrent Convolutional Neural Network, respectively. After combining the learned patterns from the two-way, our model achieves state-of-the-art performance on FI-2010 and CSI-2016, where the latter is our published long-range stock dataset to help future studies for STP task. Extensive experimental results on the two datasets indicate that multi-scale information can significantly improve the STP performance and our model is superior in capturing such information.

scholarly journals Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China

2021 ◽  
Vol 13 (5) ◽  
pp. 1021
Author(s):  
Hu Ding ◽  
Jiaming Na ◽  
Shangjing Jiang ◽  
Jie Zhu ◽  
Kai Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Loess Plateau ◽  
Water Conservation ◽  
Nearest Neighbor ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
The Loess Plateau ◽  
Object Based ◽  
Extreme Gradient Boosting

Artificial terraces are of great importance for agricultural production and soil and water conservation. Automatic high-accuracy mapping of artificial terraces is the basis of monitoring and related studies. Previous research achieved artificial terrace mapping based on high-resolution digital elevation models (DEMs) or imagery. As a result of the importance of the contextual information for terrace mapping, object-based image analysis (OBIA) combined with machine learning (ML) technologies are widely used. However, the selection of an appropriate classifier is of great importance for the terrace mapping task. In this study, the performance of an integrated framework using OBIA and ML for terrace mapping was tested. A catchment, Zhifanggou, in the Loess Plateau, China, was used as the study area. First, optimized image segmentation was conducted. Then, features from the DEMs and imagery were extracted, and the correlations between the features were analyzed and ranked for classification. Finally, three different commonly-used ML classifiers, namely, extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor (KNN), were used for terrace mapping. The comparison with the ground truth, as delineated by field survey, indicated that random forest performed best, with a 95.60% overall accuracy (followed by 94.16% and 92.33% for XGBoost and KNN, respectively). The influence of class imbalance and feature selection is discussed. This work provides a credible framework for mapping artificial terraces.

scholarly journals Machine Learning-Based Energy System Model for Tissue Paper Machines

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 655
Author(s):  
Huanhuan Zhang ◽  
Jigeng Li ◽  
Mengna Hong
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Gradient Boosting ◽  
Paper Machine ◽  
Steam Consumption ◽  
Tissue Paper ◽  
Paper Machines ◽  
Energy Consumption Model ◽  
Extreme Gradient Boosting ◽  
Consumption Model

With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines.

An intelligent evolutionary extreme gradient boosting algorithm development for modeling scour depths under submerged weir

2021 ◽  
Author(s):  
Hai Tao ◽  
Maria Habib ◽  
Ibrahim Aljarah ◽  
Hossam Faris ◽  
Haitham Abdulmohsin Afan ◽  
...  
Keyword(s):  
Gradient Boosting ◽  
Algorithm Development ◽  
Extreme Gradient Boosting ◽  
Boosting Algorithm
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