scholarly journals Real-Time Modeling of Regional Tropospheric Delay Based on Multicore Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xu Yang ◽  
Xinyuan Jiang ◽  
Chuang Jiang ◽  
Lei Xu
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Root Mean Square ◽  
Average Level ◽  
Support Vector ◽  
Tropospheric Delay ◽  
Mean Square ◽  
Survey Area ◽  
Time Modeling ◽  
Modeling Experiment

Real-time modeling of regional troposphere has attracted considerable research attention in the current GNSS field, and its modeling products play an important role in global navigation satellite system (GNSS) real-time precise positioning and real-time inversion of atmospheric water vapor. Multicore support vector machine (MS) based on genetic optimization algorithm, single-core support vector machine (SVM), four-parameter method (FP), neural network method (BP), and root mean square fusion method (SUM) are used for real-time and final zenith tropospheric delay (ZTD) modeling of Hong Kong CORS network in this study. Real-time ZTD modeling experiment results for five consecutive days showed that the average deviation (bias) and root mean square (RMS) of FP, BP, SVM, and SUM reduced by 48.25%, 54.46%, 41.82%, and 51.82% and 43.16%, 48.46%, 30.09%, and 33.86%, respectively, compared with MS. The final ZTD modeling experiment results showed that the bias and RMS of FP, BP, SVM, and SUM reduced by 3.80%, 49.78%, 25.71%, and 49.35% and 43.16%, 48.46%, 30.09%, and 33.86%, respectively, compared with MS. Accuracy of the five methods generally reaches millimeter level in most of the time periods. MS demonstrates higher precision and stability in the modeling of stations with an elevation at the average level of the survey area and higher elevation than that of other models. MS, SVM, and SUM exhibit higher precision and stability in the modeling of the station with an elevation at the average level of the survey area than FP. Meanwhile, real-time modeling error distribution of the five methods is significantly better than the final modeling. Standard deviation and average real-time modeling improved by 43.19% and 24.04%, respectively.

scholarly journals Perbandingan Algoritma Support Vector Machine Biasa dan Support Vector Machine berbasis Particle Swarm Optimization untuk Prediksi Gempa Bumi

2018 ◽  
Vol 4 (1) ◽  
Author(s):  
Agustian Noor
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Particle Swarm ◽  
Support Vector ◽  
Mean Square ◽  
Swarm Optimization

Gempa merupakan fenomena alam secara periodik yang terjadi di seluruh belahan bumi akibat adanya gaya pembangkit pasang surut yang utamanya berasal dari matahari dan bulan. Tujuan penelitian ini adalah untuk menganalisa hasil gempa bumi di Sumara Utara. Metode yang diusulkan adalahmembandingkan SVM dan SVM-PSO yang menggunakan data dari instansi terkait khususnya di daerah Sumatra Utara, Masing-masing algoritma akan implementasikan dengan menggunakan RapidMiner 5.1 Pengukuran kinerja dilakukan dengan menghitung rata-rata error yang terjadi melalui besaran Root Mean Square Error (RMSE). Semakin kecil nilai dari masing-masing parameter kinerja ini menyatakan semakin dekat nilai prediksi dengan nilai sebenarnya. Dengan demikian dapat diketahui algoritma yang lebih akurat.

Accuracy Improvement of Solar Power Estimation Using Real-Time Degradation Computation of PV Panels

2021 ◽  
pp. 2150242
Author(s):  
Parveen Bhola ◽  
Saurabh Bhardwaj
Keyword(s):  
Root Mean Square Error ◽  
Real Time ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Meteorological Parameters ◽  
Solar Power ◽  
Performance Ratio ◽  
Accurate Estimation ◽  
Support Vector ◽  
Mean Square

Many applications including power trading and planning require the accurate estimation of solar power in real time. As the power output of the solar panels degrades over the time period, so its real-time estimation is tough without the degradation parameter. In the proposed method, the effect of degradation in terms of performance ratio is incorporated along with other meteorological parameters. The degradation is calculated in real time using the clustering-based technique without physical inspection on site. Initially, the power is estimated using Support Vector Regression (SVR) model with the meteorological parameters. The estimation is further fine-tuned in sync with the degradation rate. The model is validated on the real data (Meteorological parameters and Solar power) procured from the solar plant. After refinement, the estimation results show significant improvement in terms of statistical measures. Now, the estimation accuracy in terms of coefficient of determination R2 is 92% and the error metrics normalized root mean square error (NMRSE), mean absolute percentage error (MAPE), root mean square error (RMSE) are 7.13, 5.92 and 14.54, respectively.

PEMODELAN PREDIKTIF KONSUMSI ENERGI BANGUNAN GEDUNG KOMERSIAL DENGAN ALGORITMA SUPPORT VECTOR MACHINE

2018 ◽  
Vol 14 (2) ◽  
pp. 225
Author(s):  
Indriyanti Indriyanti ◽  
Agus Subekti
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Support Vector ◽  
Mean Square

Konsumsi energi bangunan yang semakin meningkat mendorong para peneliti untuk membangun sebuah model prediksi dengan menerapkan metode machine learning, namun masih belum diketahui model yang paling akurat. Model prediktif untuk konsumsi energi bangunan komersial penting untuk konservasi energi. Dengan menggunakan model yang tepat, kita dapat membuat desain bangunan yang lebih efisien dalam penggunaan energi. Dalam tulisan ini, kami mengusulkan model prediktif berdasarkan metode pembelajaran mesin untuk mendapatkan model terbaik dalam memprediksi total konsumsi energi. Algoritma yang digunakan yaitu SMOreg dan LibSVM dari kelas Support Vector Machine, kemudian untuk evaluasi model berdasarkan nilai Mean Absolute Error dan Root Mean Square Error. Dengan menggunakan dataset publik yang tersedia, kami mengembangkan model berdasarkan pada mesin vektor pendukung untuk regresi. Hasil pengujian kedua algoritma tersebut diketahui bahwa algoritma SMOreg memiliki akurasi lebih baik karena memiliki nilai MAE dan RMSE sebesar 4,70 dan 10,15, sedangkan untuk model LibSVM memiliki nilai MAE dan RMSE sebesar 9,37 dan 14,45. Kami mengusulkan metode berdasarkan algoritma SMOreg karena kinerjanya lebih baik.

Assessment of EGNOS Model over Asia Area Using IGS_ZTD

2014 ◽  
Vol 501-504 ◽  
pp. 2182-2186
Author(s):  
Li Long Liu ◽  
Miao Zhou ◽  
Teng Xu Zhang ◽  
Wei Wang ◽  
Liang Ke Huang
Keyword(s):  
Real Time ◽  
Root Mean Square ◽  
Tropospheric Delay ◽  
Mean Square ◽  
Correction Model ◽  
The Real ◽  
Zenith Tropospheric Delay ◽  
Temporal And Spatial ◽  
Delay Correction

In this study, three years of the zenith tropospheric delay (ZTD) data observed from 46 International GNSS system (IGS) sites distributed in Asian area used to assess the effectiveness and accuracy of ZTD calculated from EGNOS model, and the application of the EGNOS model are also analyzed in Asian area. Relative to IGS observed ZTD, the bias and root mean square (RMS) for ZTD calculated from EGNOS model presents an obvious variation in temporal and spatial. These results provide a reference for the study of the tropospheric delay correction model, the real-time GNSS navigation and positioning.

scholarly journals Real-Time Lime Quality Control through Process Automation

2021 ◽  
Vol 7 (2) ◽  
pp. 1-10
Author(s):  
Vipul Kumar Tiwari* ◽  
Abhishek Choudhary ◽  
Umesh Kr. Singh ◽  
Anil Kumar Kothari ◽  
Manish Kr. Singh
Keyword(s):  
Support Vector Machine ◽  
Predictive Model ◽  
Real Time ◽  
Control Model ◽  
Support Vector ◽  
Mean Square ◽  
Process Automation ◽  
Steel Making ◽  
And Control

In the steel industry - Tata steel, India, most of the lime produced in the lime plant is used in the steel-making process at LD shops. The quality of steel produced at LD shops depends on the quality of lime used. Moreover, the lime also helps in the crucial dephosphorization process during steel-making. The calcined lime produced in the lime plant goes to the laboratory for testing its final quality (CaO%), which is very difficult to control. To predict, control and enhance the quality of lime during lime making process, five machine-learning-based models such as multivariate linear regression, support vector machine, decision tree, random forest and extreme gradient boosting have been developed using different algorithms. Python has been used as a tool to integrate the algorithms in the models. Each model has been trained on the past 14 months’ data of process parameters, collected from level 1 sensor devices, to predict the future quality of lime. To boost the model’s prediction performance, hyper-parameter tuning has been performed using grid-search algorithm. A comparative study has been done among all the models to select a final model with the least root mean square error in predicting and control future lime quality. After the comparison, results show that the model incorporating support vector machine algorithm has least value of root mean square error of 1.23 in predicting future lime quality. In addition to this, a self-learning approach has also been incorporated into support vector machine model to enhance its performance further in realtime. The result shows that the performance has been boosted from 85% strike-rate in +/-2 error range to 90% of strike-rate in +/-1 error range in real-time. Further, the above predictive model has been extended to build a control model which gives prescriptions as output to control the future quality of lime. For this purpose, a golden batch of good data has been fetched which has shown the best quality of lime (≥ 94% of CaO%). A good range of process parameters has been extracted in the form of upper control limit and lower control limit to tune the set-points and to give the prescriptions to the user. The integration of these two models (Predictive model and control model) helps in controlling the quality of lime 12 hours before its final production of lime in lime plant. Results show that both models (Predictive model and control model) have 90% of strike-rate within +/-1 of error in real-time. Finally, a human machine interface has been developed to facilitate the user to take action based on control model’s output. Eventually this work is deployed as a lime making process automation to predict and control the lime quality.

scholarly journals KOMPARASI PENGGUNAAN ALGORITMA SUPPORT VECTOR MACHINE DENGAN PARTICLE SWARM OPTIMIZATION DALAM MEMPREDIKSI SUHU UDARA

2017 ◽  
Vol 8 (4) ◽  
pp. 277
Author(s):  
Muhammad Rusdi
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Support Vector ◽  
Mean Square ◽  
Swarm Optimization ◽  
Vector Machines

Algoritma yang dapat dipakai untuk memprediksi data suhu udara,ada yang sebagian yang sudah  diketahui algoritma mana yang memiliki kinerja lebih akurat dan sebagian lagi belum di uji kinerja akurasi dari algoritma tersebut. Untuk hal tersebut  algoritma perlu diuji untuk mengetahuinya. Metode yang diusulkan adalah SVM-PSO .metode ini di bandingkan dengan algoritma SVM,SVM-PSO yang sudah di uji akurasinya untuk prediksi data suhu udara. Algoritma yang akan diuji adalahSVM-PSO dan SVM, yang digunakan untuk prediksi suhu udara. Masing-masing algoritma akan implementasikan dengan menggunakan RapidMiner 5.3.Pengukuran kinerja dilakukan dengan menghitung rata-rata error yang terjadi melalui besaran Root Mean Square Error (RMSE). Semakin kecil nilai dari masing-masing parameter kinerja ini menyatakan semakin dekat nilai prediksi dengan nilai sebenarnya. Dengan demikian dapat diketahui algoritma yang lebih akurat. Kata Kunci: Suhu Udara, RMSE, support vector machines,svm-pso prediksi suhu udara.

scholarly journals Regional Zenith Tropospheric Delay Modeling Based on Least Squares Support Vector Machine Using GNSS and ERA5 Data

2021 ◽  
Vol 13 (5) ◽  
pp. 1004
Author(s):  
Song Li ◽  
Tianhe Xu ◽  
Nan Jiang ◽  
Honglei Yang ◽  
Shuaimin Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
High Efficiency ◽  
Rapid Development ◽  
Support Vector ◽  
Tropospheric Delay ◽  
Improvement Rate ◽  
Zenith Tropospheric Delay ◽  
Background Data ◽  
Mean Square Errors

The meteorological reanalysis data has been widely applied to derive zenith tropospheric delay (ZTD) with a high spatial and temporal resolution. With the rapid development of artificial intelligence, machine learning also begins as a high-efficiency tool to be employed in modeling and predicting ZTD. In this paper, we develop three new regional ZTD models based on the least squares support vector machine (LSSVM), using both the International GNSS Service (IGS)-ZTD products and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data over Europe throughout 2018. Among them, the ERA5 data is extended to ERA5S-ZTD and ERA5P-ZTD as the background data by the model method and integral method, respectively. Depending on different background data, three schemes are designed to construct ZTD models based on the LSSVM algorithm, including the without background data, with the ERA5S-ZTD, and with the ERA5P-ZTD. To investigate the advantage and feasibility of the proposed ZTD models, we evaluate the accuracy of two background data and three schemes by segmental comparison with the IGS-ZTD of 85 IGS stations in Europe. The results show that the overall average Root Mean Square Errors (RMSE) value of all sites is 30.1 mm for the ERA5S-ZTD, and 10.7 mm for the ERA5P-ZTD. The overall average RMSE is 25.8 mm, 22.9 mm, and 9 mm for the three schemes, respectively. Moreover, the overall improvement rate is 19.1% and 1.6% for the ZTD model with ERA5S-ZTD and ERA5P-ZTD, respectively. In order to explore the reason of the lower improvement for the ZTD model with ERA5P-ZTD, the loop verification is performed by estimating the ZTD values of each available IGS station. In actuality, the monthly improvement rate of estimated ZTD is positive for most stations, and the biggest improvement rate can even reach about 40%. The negative rate mainly comes from specific stations, these stations are located on the edge of the region, near the coast, as well as the lower similarity between the individual verified station and training stations.

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