scholarly journals Numerical Forecast Correction of Temperature and Wind Using a Single-Station Single-Time Spatial LightGBM Method

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 193
Author(s):  
Rongnian Tang ◽  
Yuke Ning ◽  
Chuang Li ◽  
Wen Feng ◽  
Youlong Chen ◽  
...  
Keyword(s):  
High Performance ◽  
Socioeconomic Development ◽  
Spatial Information ◽  
Weather Prediction ◽  
Gradient Boosting ◽  
Machine Model ◽  
Single Time ◽  
Light Gradient ◽  
Single Station ◽  
Numerical Forecast

Achieving high-performance numerical weather prediction (NWP) is important for people’s livelihoods and for socioeconomic development. However, NWP is obtained by solving differential equations with globally observed data without capturing enough local and spatial information at the observed station. To improve the forecasting performance, we propose a novel spatial lightGBM (Light Gradient Boosting Machine) model to correct the numerical forecast results at each observation station. By capturing the local spatial information of stations and using a single-station single-time strategy, the proposed method can incorporate the observed data and model data to achieve high-performance correction of medium-range predictions. Experimental results for temperature and wind prediction in Hainan Province show that the proposed correction method performs well compared with the ECWMF model and outperforms other competing methods.

scholarly journals Artificial Intelligence-Based Prediction of Key Textural Properties from LUCAS and ICRAF Spectral Libraries

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1550
Author(s):  
Mohamed Zakaria Gouda ◽  
El Mehdi Nagihi ◽  
Lotfi Khiari ◽  
Jacques Gallichand ◽  
Mahmoud Ismail
Keyword(s):  
Soil Texture ◽  
High Performance ◽  
Management Practices ◽  
Textural Properties ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Accurate Estimation ◽  
Light Gradient ◽  
Extreme Gradient Boosting ◽  
Spectral Libraries

Soil texture is a key soil property influencing many agronomic practices including fertilization and liming. Therefore, an accurate estimation of soil texture is essential for adopting sustainable soil management practices. In this study, we used different machine learning algorithms trained on vis–NIR spectra from existing soil spectral libraries (ICRAF and LUCAS) to predict soil textural fractions (sand–silt–clay %). In addition, we predicted the soil textural groups (G1: Fine, G2: Medium, and G3: Coarse) using routine chemical characteristics as auxiliary. With the ICRAF dataset, multilayer perceptron resulted in good predictions for sand and clay (R2 = 0.78 and 0.85, respectively) and categorical boosting outperformed the other algorithms (random forest, extreme gradient boosting, linear regression) for silt prediction (R2 = 0.81). For the LUCAS dataset, categorical boosting consistently showed a high performance for sand, silt, and clay predictions (R2 = 0.79, 0.76, and 0.85, respectively). Furthermore, the soil texture groups (G1, G2, and G3) were classified using the light gradient boosted machine algorithm with a high accuracy (83% and 84% for ICRAF and LUCAS, respectively). These results, using spectral data, are very promising for rapid diagnosis of soil texture and group in order to adjust agricultural practices.

scholarly journals Robust Spatiotemporal Estimation of PM Concentrations Using Boosting-Based Ensemble Models

2021 ◽  
Vol 13 (24) ◽  
pp. 13782
Author(s):  
Soyoung Park ◽  
Sanghun Son ◽  
Jaegu Bae ◽  
Doi Lee ◽  
Jae-Jin Kim ◽  
...  
Keyword(s):  
High Performance ◽  
Mean Squared Error ◽  
Spatial Prediction ◽  
Absolute Error ◽  
Air Pollutant ◽  
Gradient Boosting ◽  
Percentage Error ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
Daily Pm10

Particulate matter (PM) as an air pollutant is harmful to the human body as well as to the ecosystem. It is crucial to understand the spatiotemporal PM distribution in order to effectively implement reduction methods. However, ground-based air quality monitoring sites are limited in providing reliable concentration values owing to their patchy distribution. Here, we aimed to predict daily PM10 concentrations using boosting algorithms such as gradient boosting machine (GBM), extreme gradient boost (XGB), and light gradient boosting machine (LightGBM). The three models performed well in estimating the spatial contrasts and temporal variability in daily PM10 concentrations. In particular, the LightGBM model outperformed the GBM and XGM models, with an adjusted R2 of 0.84, a root mean squared error of 12.108 μg/m2, a mean absolute error of 8.543 μg/m2, and a mean absolute percentage error of 16%. Despite having high performance, the LightGBM model showed low spatial prediction accuracy near the southwest part of the study area. Additionally, temporal differences were found between the observed and predicted values at high concentrations. These outcomes indicate that such methods can provide intuitive and reliable PM10 concentration values for the management, prevention, and mitigation of air pollution. In the future, performance accuracy could be improved through consideration of different variables related to spatial and seasonal characteristics.

scholarly journals A Novel High Performance Discrete Flux Integrator for Control Algorithms of Fast Rotating AC Machines

2021 ◽  
Vol 11 (5) ◽  
pp. 2150
Author(s):  
Claudio Rossi ◽  
Alessio Pilati ◽  
Marco Bertoldi
Keyword(s):  
High Performance ◽  
Permanent Magnets ◽  
Machine Model ◽  
Ac Machines ◽  
Fast Calculation ◽  
Calculation Time ◽  
Digital Implementation ◽  
Rotating Speed ◽  
Interior Permanent Magnets ◽  
Accuracy Performance

This paper deals with the digital implementation of a motor control algorithm based on a unified machine model, thus usable with every traditional electric machine type (induction, brushless with interior permanent magnets, surface permanent magnets or pure reluctance). Starting from the machine equations in matrix form in continuous time, the paper exposes their discrete time transformation, suitable for digital implementation. Since the solution of these equations requires integration, the virtual division of the calculation time in sub-intervals is proposed to make the calculations more accurate. Optimization of this solver enables faster runs and higher precision especially when high rotating speed requires fast calculation time. The proposed solver is presented at different implementation levels, and its speed and accuracy performance are compared with standard solvers.

scholarly journals A Multi-Class Automatic Sleep Staging Method Based on Photoplethysmography Signals

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 116
Author(s):  
Xiangfa Zhao ◽  
Guobing Sun
Keyword(s):  
Time Domain ◽  
Single Channel ◽  
Kappa Statistic ◽  
Gradient Boosting ◽  
Sleep Staging ◽  
Challenging Problem ◽  
Sleep State ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
The Time Domain

Automatic sleep staging with only one channel is a challenging problem in sleep-related research. In this paper, a simple and efficient method named PPG-based multi-class automatic sleep staging (PMSS) is proposed using only a photoplethysmography (PPG) signal. Single-channel PPG data were obtained from four categories of subjects in the CAP sleep database. After the preprocessing of PPG data, feature extraction was performed from the time domain, frequency domain, and nonlinear domain, and a total of 21 features were extracted. Finally, the Light Gradient Boosting Machine (LightGBM) classifier was used for multi-class sleep staging. The accuracy of the multi-class automatic sleep staging was over 70%, and the Cohen’s kappa statistic k was over 0.6. This also showed that the PMSS method can also be applied to stage the sleep state for patients with sleep disorders.

scholarly journals Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jong Ho Kim ◽  
Haewon Kim ◽  
Ji Su Jang ◽  
Sung Mi Hwang ◽  
So Young Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Confidence Interval ◽  
Neck Circumference ◽  
Difficult Laryngoscopy ◽  
Gradient Boosting ◽  
Test Set ◽  
Equal Distribution ◽  
Light Gradient ◽  
Extreme Gradient Boosting

Abstract Background Predicting difficult airway is challengeable in patients with limited airway evaluation. The aim of this study is to develop and validate a model that predicts difficult laryngoscopy by machine learning of neck circumference and thyromental height as predictors that can be used even for patients with limited airway evaluation. Methods Variables for prediction of difficulty laryngoscopy included age, sex, height, weight, body mass index, neck circumference, and thyromental distance. Difficult laryngoscopy was defined as Grade 3 and 4 by the Cormack-Lehane classification. The preanesthesia and anesthesia data of 1677 patients who had undergone general anesthesia at a single center were collected. The data set was randomly stratified into a training set (80%) and a test set (20%), with equal distribution of difficulty laryngoscopy. The training data sets were trained with five algorithms (logistic regression, multilayer perceptron, random forest, extreme gradient boosting, and light gradient boosting machine). The prediction models were validated through a test set. Results The model’s performance using random forest was best (area under receiver operating characteristic curve = 0.79 [95% confidence interval: 0.72–0.86], area under precision-recall curve = 0.32 [95% confidence interval: 0.27–0.37]). Conclusions Machine learning can predict difficult laryngoscopy through a combination of several predictors including neck circumference and thyromental height. The performance of the model can be improved with more data, a new variable and combination of models.

Modeling and Parameter Identification for Condition Monitoring of Surface-Mount Permanent Magnet Machines Under Magnet Demagnetization

2020 ◽  
Author(s):  
Fanny Pinto Delgado ◽  
Ziyou Song ◽  
Heath F. Hofmann ◽  
Jing Sun
Keyword(s):  
Permanent Magnet ◽  
Condition Monitoring ◽  
High Performance ◽  
High Efficiency ◽  
Health Condition ◽  
Maintenance Cost ◽  
Parameter Estimator ◽  
Two Phase ◽  
Machine Model ◽  
Surface Mount

Abstract Permanent Magnet Synchronous Machines (PMSMs) have been preferred for high-performance applications due to their high torque density, high power density, high control accuracy, and high efficiency over a wide operating range. During operation, monitoring the PMSM’s health condition is crucial for detecting any anomalies so that performance degradation, maintenance/downtime costs, and safety hazards can be avoided. In particular, demagnetization of PMSMs can lead to not only degraded performance but also high maintenance cost as they are the most expensive components in a PMSM. In this paper, an equivalent two-phase model for surface-mount permanent magnet (SMPM) machines under permanent magnet demagnetization is formulated and a parameter estimator is proposed for condition monitoring purposes. The performance of the proposed estimator is investigated through analysis and simulation under different conditions, and compared with a parameter estimator based on the standard SMPM machine model. In terms of information that can be extracted for fault diagnosis and condition monitoring, the proposed estimator exhibits advantages over the standard-model-based estimator as it can differentiate between uniform demagnetization over all poles and asymmetric demagnetization between north and south poles.

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