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 An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3108 ◽  
Author(s):  
Shing-Hong Liu ◽  
Chuan-Bi Lin ◽  
Ying Chen ◽  
Wenxi Chen ◽  
Tai-Shen Huang ◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Real Time ◽  
Root Mean Square ◽  
The Body ◽  
Median Frequency ◽  
Mean Square ◽  
Sport Injuries ◽  
Physiological Measurement ◽  
The Real ◽  
Emg Signal

In recent years, wearable monitoring devices have been very popular in the health care field and are being used to avoid sport injuries during exercise. They are usually worn on the wrist, the same as sport watches, or on the chest, like an electrocardiogram patch. Common functions of these wearable devices are that they use real time to display the state of health of the body, and they are all small sized. The electromyogram (EMG) signal is usually used to show muscle activity. Thus, the EMG signal could be used to determine the muscle-fatigue conditions. In this study, the goal is to develop an EMG patch which could be worn on the lower leg, the gastrocnemius muscle, to detect real-time muscle fatigue while exercising. A micro controller unit (MCU) in the EMG patch is part of an ARM Cortex-M4 processor, which is used to measure the median frequency (MF) of an EMG signal in real time. When the muscle starts showing tiredness, the median frequency will shift to a low frequency. In order to delete the noise of the isotonic EMG signal, the EMG patch has to run the empirical mode decomposition algorithm. A two-electrode circuit was designed to measure the EMG signal. The maximum power consumption of the EMG patch was about 39.5 mAh. In order to verify that the real-time MF values measured by the EMG patch were close to the off-line MF values measured by the computer system, we used the root-mean-square value to estimate the difference in the real-time MF values and the off-line MF values. There were 20 participants that rode an exercise bicycle at different speeds. Their EMG signals were recorded with an EMG patch and a physiological measurement system at the same time. Every participant rode the exercise bicycle twice. The averaged root-mean-square values were 2.86 ± 0.86 Hz and 2.56 ± 0.47 Hz for the first and second time, respectively. Moreover, we also developed an application program implemented on a smart phone to display the participants’ muscle-fatigue conditions and information while exercising. Therefore, the EMG patch designed in this study could monitor the muscle-fatigue conditions to avoid sport injuries while exercising.

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 An empirical zenith wet delay correction model using piecewise height functions

2018 ◽  
Vol 36 (6) ◽  
pp. 1507-1519 ◽  
Author(s):  
YiBin Yao ◽  
YuFeng Hu
Keyword(s):  
Tropospheric Delay ◽  
Test Results ◽  
Correction Model ◽  
Height Functions ◽  
Zenith Wet Delay ◽  
Space Geodetic Techniques ◽  
Vertical Variations ◽  
Temporal And Spatial ◽  
Gpt2w Model ◽  
Delay Correction

Abstract. Tropospheric delay is an important error source in space geodetic techniques. The temporal and spatial variations of the zenith wet delay (ZWD) are very large and thus limit the accuracy of tropospheric delay modelling. Thus, it is worthwhile undertaking research aimed at constructing a precise ZWD model. Based on the analysis of vertical variations of ZWD, we divided the troposphere into three height intervals (below 2 km, 2 to 5 km, and 5 to 10 km) and determined the fitting functions for the ZWD within these height intervals. The global empirical ZWD model HZWD, which considers the periodic variations of ZWD with a spatial resolution of 5∘×5∘, is established using the ECMWF ZWD profiles from 2001 to 2010. Validated by the ECMWF ZWD data in 2015, the precision of the ZWD estimation in the HZWD model over the three height intervals are improved by 1.4, 0.9, and 1.2 mm, respectively, compared to that of the currently best GPT2w model (23.8, 13.1, and 2.6 mm). The test results from ZWD data from 318 radiosonde stations show that the root mean square error (RMSE) in the HZWD model over the three height intervals was reduced by 2 % (0.6 mm), 5 % (0.9 mm), and 33 % (1.7 mm), respectively, compared to the GPT2w model (30.1, 15.8, and 3.5 mm) over the three height intervals. In addition, the spatial and temporal stabilities of the HZWD model are higher than those of GPT2w and UNB3m.

scholarly journals A fractional order epidemic model for the simulation of outbreaks of Ebola

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Weiqiu Pan ◽  
Tianzeng Li ◽  
Safdar Ali
Keyword(s):  
Numerical Solution ◽  
Root Mean Square ◽  
Fractional Order ◽  
Relative Error ◽  
Numerical Solutions ◽  
Real Data ◽  
Mean Square ◽  
Order Model ◽  
The Real ◽  
Fractional Orders

AbstractThe Ebola outbreak in 2014 caused many infections and deaths. Some literature works have proposed some models to study Ebola virus, such as SIR, SIS, SEIR, etc. It is proved that the fractional order model can describe epidemic dynamics better than the integer order model. In this paper, we propose a fractional order Ebola system and analyze the nonnegative solution, the basic reproduction number $R_{0}$ R 0 , and the stabilities of equilibrium points for the system firstly. In many studies, the numerical solutions of some models cannot fit very well with the real data. Thus, to show the dynamics of the Ebola epidemic, the Gorenflo–Mainardi–Moretti–Paradisi scheme (GMMP) is taken to get the numerical solution of the SEIR fractional order Ebola system and the modified grid approximation method (MGAM) is used to acquire the parameters of the SEIR fractional order Ebola system. We consider that the GMMP method may lead to absurd numerical solutions, so its stability and convergence are given. Then, the new fractional orders, parameters, and the root-mean-square relative error $g(U^{*})=0.4146$ g ( U ∗ ) = 0.4146 are obtained. With the new fractional orders and parameters, the numerical solution of the SEIR fractional order Ebola system is closer to the real data than those models in other literature works. Meanwhile, we find that most of the fractional order Ebola systems have the same order. Hence, the fractional order Ebola system with different orders using the Caputo derivatives is also studied. We also adopt the MGAM algorithm to obtain the new orders, parameters, and the root-mean-square relative error which is $g(U^{*})=0.2744$ g ( U ∗ ) = 0.2744 . With the new parameters and orders, the fractional order Ebola systems with different orders fit very well with the real data.

scholarly journals Power factor correction using capacitors & filters

2018 ◽  
Vol 7 (2.12) ◽  
pp. 234
Author(s):  
Karthik Subramanian ◽  
Shantam Tandon
Keyword(s):  
Root Mean Square ◽  
Phase Difference ◽  
Power Factor ◽  
Reactive Power ◽  
Power Factor Correction ◽  
Active Filters ◽  
Minimum Amount ◽  
Mean Square ◽  
The Real ◽  
Electrical Consumption

Power factor is the ratio of the real current or voltage received by a load to the root mean square (rms) value of the current or voltage that was supposed to be acquired by the same load. The fact that the two become different is due to the presence of reactive power in the circuit which gets dissipated.Improving the power factor means reducing the phase difference between voltage and current. Since majority of the loads are of inductive nature, they require some amount of reactive power for them to function. Therefore, for the better use of electrical appliances with minimum amount of electrical consumption, the power factor should necessarily be increased and should be brought near to 1. This can be easily done by the help of Automatic Power Factor Correction Capacitors and Active filters.  

scholarly journals Multiresolution Network Temporal and Spatial Scheduling Model of Scenic Spot

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Peng Ge ◽  
Zhixue Liao ◽  
Chang Liu ◽  
Peiyu Ren ◽  
Zhaoxia Guo
Keyword(s):  
Real Time ◽  
Distribution System ◽  
Low Carbon ◽  
The Real ◽  
Scheduling Model ◽  
Scenic Spot ◽  
Full Capacity ◽  
Develop Economy ◽  
Temporal And Spatial ◽  
Demand And Capacity

Tourism is one of pillar industries of the world economy. Low-carbon tourism will be the mainstream direction of the scenic spots' development, and theωpath of low-carbon tourism development is to develop economy and protect environment simultaneously. However, as the tourists' quantity is increasing, the loads of scenic spots are out of control. And the instantaneous overload in some spots caused the image phenomenon of full capacity of the whole scenic spot. Therefore, realizing the real-time schedule becomes the primary purpose of scenic spot’s management. This paper divides the tourism distribution system into several logically related subsystems and constructs a temporal and spatial multiresolution network scheduling model according to the regularity of scenic spots’ overload phenomenon in time and space. It also defines dynamic distribution probability and equivalent dynamic demand to realize the real-time prediction. We define gravitational function between fields and takes it as the utility of schedule, after resolving the transportation model of each resolution, it achieves hierarchical balance between demand and capacity of the system. The last part of the paper analyzes the time complexity of constructing a multiresolution distribution system.

scholarly journals A New Zenith Tropospheric Delay Grid Product for Real-Time PPP Applications over China

Sensors ◽  
2017 ◽  
Vol 18 (2) ◽  
pp. 65 ◽  
Author(s):  
Yidong Lou ◽  
Jinfang Huang ◽  
Weixing Zhang ◽  
Hong Liang ◽  
Fu Zheng ◽  
...  
Keyword(s):  
Real Time ◽  
Tropospheric Delay ◽  
Zenith Tropospheric Delay

Klasifikasi Gerakan Jatuh Berbasis Accelerometer dan Gyroscope Menggunakan K-Nearest Neighbors

2020 ◽  
Vol 4 (2) ◽  
pp. 24-29
Author(s):  
Adlian Jefiza ◽  
Indra Daulay ◽  
Jhon Hericson Purba
Keyword(s):  
Real Time ◽  
Root Mean Square ◽  
Nearest Neighbors ◽  
Training Data ◽  
Mean Square ◽  
K Nearest Neighbors ◽  
Testing Data ◽  
Total Data

Permasalahan utama pada penelitian ini merujuk kepada semakin menurunnya daya tahan tubuh lanjut usia (lansia). Hal ini membutuhkan sistem monitoring aktivitas lansia secara real time. Untuk mendeteksi kegiatan para lansia, dirancang sebuah perangkat monitoring dengan accelerometer 3-sumbu dan gyroscope 3-sumbu. Data sensor diperoleh dari lima partisipan. Setiap partisipan melakukan lima gerakan yaitu terjatuh, duduk, tidur, rukuk dan sujud. Gerakan yang dipilih adalah gerakan yang menyerupai gerakan jatuh. Total data yang diperoleh dari partisipan adalah 75 data yang terbagi menjadi training data dan testing data. Penelitian ini menggunakan metode transformasi Wavelet untuk mengenali fitur dari gerakan. Untuk pengklasifikasian setiap gerakan, digunakan metode K-nearest neighbors (KNN). Hasil klasifikasi gerakan menggunakan lima kelas menghasilkan nilai root mean square sebesar 0.0074 dengan akurasi 100%.

scholarly journals Indian COVID-19 dynamics: Prediction using autoregressive integrated moving average modelling

2021 ◽  
Vol 52 (1) ◽  
pp. 6-14
Author(s):  
Amit Tak ◽  
Sunita Dia ◽  
Mahendra Dia ◽  
Todd Wehner
Keyword(s):  
Root Mean Square Error ◽  
Real Time ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Moving Average ◽  
Information Criteria ◽  
Evidence Based ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average ◽  
Akaike Information Criteria

Background: The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centrepiece in evidence-based disease management. Numerous approaches that use mathematical modelling have been used to predict the outcome of the pandemic, including data-driven models, empirical and hybrid models. This study was aimed at prediction of COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Material and Methods: Real-time Indian data of cumulative cases and deaths of COVID-19 was retrieved from the Johns Hopkins dashboard. The dataset from 11 March 2020 to 25 June 2020 (n = 107 time points) was used to fit the autoregressive integrated moving average model. The model with minimum Akaike Information Criteria was used for forecasting. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) were used to validate the model. Results: The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths, respectively, with minimum Akaike Information Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 June 2020 to 5 July 2020 showed a trend toward continuous increment. The PredRMSE and BaseRMSE of ARIMA (1,3,2) model were 21,137 and 166,330, respectively. Similarly, PredRMSE and BaseRMSE of ARIMA (3,3,1) model were 668.7 and 5,431, respectively. Conclusion: It is proposed that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimisation and evidence-based decision making for a subsequent state of affairs.

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