scholarly journals Using Improved XGBoost Algorithm to Obtain Modified Atmospheric Refractive Index

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yanbo Mai ◽  
Zheng Sheng ◽  
Hanqing Shi ◽  
Qixiang Liao
Keyword(s):  
Neural Network ◽  
Refractive Index ◽  
Bp Neural Network ◽  
Missing Values ◽  
Mean Squared Error ◽  
Atmospheric Environment ◽  
Gradient Boosting ◽  
Percentage Error ◽  
Atmospheric Refraction ◽  
Extreme Gradient Boosting

Atmospheric refraction is a special meteorological phenomenon mainly caused by gas molecules and aerosol particles in the atmosphere, which can change the propagation direction of electromagnetic waves in the atmospheric environment. Atmospheric refractive index, an index to measure atmospheric refraction, is an important parameter for electromagnetic wave. Given that it is difficult to obtain the atmospheric refractive index of 100 meters (m)–3000°m over the ocean, this paper proposes an improved extreme gradient boosting (XGBoost) algorithm based on comprehensive learning particle swarm optimization (CLPSO) operator to obtain them. Finally, the mean absolute percentage error (MAPE) and root mean-squared error (RMSE) are used as evaluation criteria to compare the prediction results of improved XGBoost algorithm with backpropagation (BP) neural network and traditional XGBoost algorithm. The results show that the MAPE and RMSE of the improved XGBoost algorithm are 39% less than those of BP neural network and 32% less than those of the traditional XGBoost. Besides, the improved XGBoost algorithm has the strongest learning and generalization capability to calculate missing values of atmospheric refractive index among the three algorithms. The results of this paper provide a new method to obtain atmospheric refractive index, which will be of great reference significance to further study the atmospheric refraction.

scholarly journals An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data based on the GRNN-SGTM Ensemble

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2625 ◽  
Author(s):  
Roman Tkachenko ◽  
Ivan Izonin ◽  
Natalia Kryvinska ◽  
Ivanna Dronyuk ◽  
Khrystyna Zub
Keyword(s):  
Neural Network ◽  
Missing Values ◽  
Mean Squared Error ◽  
Ensemble Method ◽  
Transformation Model ◽  
General Regression Neural Network ◽  
Geometric Transformation ◽  
Percentage Error ◽  
General Regression Neural Networks ◽  
General Regression

The purpose of this paper is to improve the accuracy of solving prediction tasks of the missing IoT data recovery. To achieve this, the authors have developed a new ensemble of neural network tools. It consists of two successive General Regression Neural Network (GRNN) networks and one neural-like structure of the Successive Geometric Transformation Model (SGTM). The principle of ensemble topology construction on two successively connected general regression neural networks, supplemented with an SGTM neural-like structure, is mathematically substantiated, which improves the accuracy of prediction results. The effectiveness of the method is based on the replacement of the summation of the results of the two GRNNs with a weighted summation, which improves the accuracy of the ensemble operation in general. A detailed algorithmic implementation of the ensemble method as well as a flowchart of its operation is presented. The parameters of the ensemble operation are determined by optimization using the brute-force method. Based on the developed ensemble method, the solution of the task of completing the partially missing values in the real monitoring dataset of the air environment collected by the IoT device is presented. By comparing the performance of the developed ensemble with the existing methods, the highest accuracy of its performance (by the parameters of Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) accuracy) among the most similar in this class has been proved.

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.

scholarly journals Influence of Dextransucrase of Weissella Cibaria Nitcsk4 on Low Molecular Weight Dextran Yield: a Statistical Approach using Mixed Level Taguchi Design and Artificial Neural Network

2019 ◽  
Vol 9 (2S2) ◽  
pp. 657-664
Keyword(s):  
Neural Network ◽  
Molecular Weight ◽  
Mean Squared Error ◽  
Sucrose Concentration ◽  
Average Molecular Weight ◽  
Taguchi Design ◽  
Percentage Error ◽  
Low Molecular Weight ◽  
Weight Average Molecular Weight ◽  
Weissella Cibaria

In the present study, the influence of dextransucrase of Weissella cibaria NITCSK4 (DSWc4), sucrose concentration, and reaction temperature on the yield of low molecular weight dextran (LMWD-DexWc4) was investigated using mixed level Taguchi design and back propagation neural network (BPNN). BPNN model with three neurons in a hidden layer generated a low mean squared error (MSE). The determination coefficients (R2 -value) for ANN and Taguchi models were 0.991 and 0.998, respectively. Considering absolute average deviation (AAD) and MSE, Taguchi model is more adequate. Among three factors, the percentage yield of low molecular weight of dextran is invariably dependent on the sucrose concentration. The study suggested that a low sucrose concentration (3% w/v), DSWc4 (0.25 IU/ml) and slightly high temperature (35°C) ultimately favored the production of LMWD-DexWc4 (91.639%). LMW-DexWc4 produced by DSWc4 at optimized conditions was analyzed. The weight average molecular weight of LMW-DexWc4 was calculated using M-H expression, found to be 85775 (≈90 kDa). The relative percentage error between the number and weight average molecular weight was found to be less (4.42%). The polydispersity (PD) index of the LMW-DexWc4 was found to be 0.9576 and the value is close to 1. The PD value depicted that the molecular weight distribution of dextran was narrowly dispersed.

scholarly journals Prediction of Electropulse-Induced Nonlinear Temperature Variation of Mg Alloy Based on Machine Learning

2020 ◽  
Vol 58 (6) ◽  
pp. 413-422
Author(s):  
Jinyeong Yu ◽  
Myoungjae Lee ◽  
Young Hoon Moon ◽  
Yoojeong Noh ◽  
Taekyung Lee
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Temperature Variation ◽  
High Energy ◽  
Mg Alloy ◽  
Model Complexity ◽  
Gradient Boosting ◽  
Learning Technology ◽  
Extreme Gradient Boosting ◽  
Nonlinear Temperature

Electropulse-induced heating has attracted attention due to its high energy efficiency. However, the process gives rise to a nonlinear temperature variation, which is difficult to predict using a traditional physics model. As an alternative, this study employed machine-learning technology to predict such temperature variation for the first time. Mg alloy was exposed to a single electropulse with a variety of pulse magnitudes and durations for this purpose. Nine machine-learning models were established from algorithms from artificial neural network (ANN), deep neural network (DNN), and extreme gradient boosting (XGBoost). The ANN models showed an insufficient predicting capability with respect to the region of peak temperature, where temperature varied most significantly. The DNN models were built by increasing model complexity, enhancing architectures, and tuning hyperparameters. They exhibited a remarkable improvement in predicting capability at the heating-cooling boundary as well as overall estimation. As a result, the DNN-2 model in this group showed the best prediction of nonlinear temperature variation among the machinelearning models built in this study. The XGBoost model exhibited poor predicting performance when default hyperparameters were applied. However, hyperparameter tuning of learning rates and maximum depths resulted in a decent predicting capability with this algorithm. Furthermore, XGBoost models exhibited an extreme reduction in learning time compared with the ANN and DNN models. This advantage is expected to be useful for predicting more complicated cases including various materials, multi-step electropulses, and electrically-assisted forming.

scholarly journals Interactive Genetic Algorithm Oriented toward the Novel Design of Traditional Patterns

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 36 ◽  
Author(s):  
Jian Lv ◽  
Miaomiao Zhu ◽  
Weijie Pan ◽  
Xiang Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Surrogate Model ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Interactive Evolutionary Computation ◽  
Interactive Genetic Algorithm ◽  
Novel Design ◽  
Fuzzy Interval

To create alternative complex patterns, a novel design method is introduced in this study based on the error back propagation (BP) neural network user cognitive surrogate model of an interactive genetic algorithm with individual fuzzy interval fitness (IGA-BPFIF). First, the quantitative rules of aesthetic evaluation and the user’s hesitation are used to construct the Gaussian blur tool to form the individual’s fuzzy interval fitness. Then, the user’s cognitive surrogate model based on the BP neural network is constructed, and a new fitness estimation strategy is presented. By measuring the mean squared error, the surrogate model is well managed during the evolution of the population. According to the users’ demands and preferences, the features are extracted for the interactive evolutionary computation. The experiments show that IGA-BPFIF can effectively design innovative patterns matching users’ preferences and can contribute to the heritage of traditional national patterns.

scholarly journals Ensemble Neural Network Approach for Accurate Load Forecasting in a Power System

2009 ◽  
Vol 19 (2) ◽  
pp. 303-315 ◽  
Author(s):  
Krzysztof Siwek ◽  
Stanisław Osowski ◽  
Ryszard Szupiluk
Keyword(s):  
Neural Network ◽  
Power System ◽  
Mean Squared Error ◽  
Load Forecasting ◽  
Weighted Averaging ◽  
Percentage Error ◽  
Network Approach ◽  
Three Solutions ◽  
Neural Network Approach ◽  
Ensemble Neural Network

Ensemble Neural Network Approach for Accurate Load Forecasting in a Power SystemThe paper presents an improved method for 1-24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an ensemble system. We will integrate the results of partial predictions made by three solutions, out of which one relies on a multilayer perceptron and two others on self-organizing networks of the competitive type. As the expert system we will apply different integration methods: simple averaging, SVD based weighted averaging, principal component analysis and blind source separation. The results of numerical experiments, concerning forecasting the hourly load for the next 24 hours of the Polish power system, will be presented and discussed. We will compare the performance of different ensemble methods on the basis of the mean absolute percentage error, mean squared error and maximum percentage error. They show a significant improvement of the proposed ensemble method in comparison to the individual results of prediction. The comparison of our work with the results of other papers for the same data proves the superiority of our approach.

scholarly journals Hierarchical attention networks for information extraction from cancer pathology reports

2017 ◽  
Vol 25 (3) ◽  
pp. 321-330 ◽  
Author(s):  
Shang Gao ◽  
Michael T Young ◽  
John X Qiu ◽  
Hong-Jun Yoon ◽  
James B Christian ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Information Extraction ◽  
Model Performance ◽  
Gradient Boosting ◽  
Support Vector ◽  
Attention Networks ◽  
Cancer Pathology ◽  
Extreme Gradient Boosting ◽  
Pathology Reports

Abstract Objective We explored how a deep learning (DL) approach based on hierarchical attention networks (HANs) can improve model performance for multiple information extraction tasks from unstructured cancer pathology reports compared to conventional methods that do not sufficiently capture syntactic and semantic contexts from free-text documents. Materials and Methods Data for our analyses were obtained from 942 deidentified pathology reports collected by the National Cancer Institute Surveillance, Epidemiology, and End Results program. The HAN was implemented for 2 information extraction tasks: (1) primary site, matched to 12 International Classification of Diseases for Oncology topography codes (7 breast, 5 lung primary sites), and (2) histological grade classification, matched to G1–G4. Model performance metrics were compared to conventional machine learning (ML) approaches including naive Bayes, logistic regression, support vector machine, random forest, and extreme gradient boosting, and other DL models, including a recurrent neural network (RNN), a recurrent neural network with attention (RNN w/A), and a convolutional neural network. Results Our results demonstrate that for both information tasks, HAN performed significantly better compared to the conventional ML and DL techniques. In particular, across the 2 tasks, the mean micro and macroF-scores for the HAN with pretraining were (0.852,0.708), compared to naive Bayes (0.518, 0.213), logistic regression (0.682, 0.453), support vector machine (0.634, 0.434), random forest (0.698, 0.508), extreme gradient boosting (0.696, 0.522), RNN (0.505, 0.301), RNN w/A (0.637, 0.471), and convolutional neural network (0.714, 0.460). Conclusions HAN-based DL models show promise in information abstraction tasks within unstructured clinical pathology reports.

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