scholarly journals Improved Strategies for BeiDou Ultrarapid Satellites’ Clock Bias Prediction Using BDS-2 and BDS-3 Integrated Processing

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Chao Hu ◽  
Zhongyuan Wang
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Atomic Clock ◽  
Weight Matrix ◽  
Temporal Correlations ◽  
Variogram Model ◽  
Integrated Processing ◽  
One Step ◽  
Model Residuals ◽  
Sparse Modelling

GNSS ultrarapid clock biases are key inputs of rapid high-accuracy applications, especially for its prediction parts. With the fast development of the BeiDou system (BDS), the system performances are mainly represented by orbit and clock products. However, it is suggested that the BDS-predicted clock biases cannot meet the requirement of real-time or near real-time services. In this research, the BDS satellite-predicted ultrarapid clock bias products are optimized with three methods, namely, one-step strategy, intersatellite correlation, and variogram model, using the combined estimation of BDS-2 and BDS-3 satellites. Firstly, considering the traditional two-step strategy for modelling clock bias prediction, we take all terms (including trend and periodic terms) into one-step solution of model estimation based on the sparse modelling in machine learning. Secondly, because of the much more stable on-board atomic clock of BDS-3 satellites, the intersatellite correlations between BDS-2 and BDS-3 are utilized to enhance the solution of model coefficients. Thirdly, to further improve the model, the temporal correlations in model residuals are used to reconstruct the stochastic function obtained by variogram. In addition, to verify the proposed improved strategies, 12 schemes of BDS clock bias prediction experiments are designed and analyzed with different conditions. According to the results of predicted clock biases, it is indicted that (1) the stability of BDS-3 on-board clocks is more optimal compared with BDS-2, which can be used to strengthen the solution of the clock bias prediction model; (2) the one-step estimation of the clock bias model by sparse modelling can slightly increase the accuracy of prediction results; (3) both BDS-2- and BDS-3-predicted clock biases benefited each other by inserting the intersatellite correlations into the weight matrix, in which the accuracy of 18-hour period with one-step strategy can be improved by 28.6% and 27.2% for BDS-2 and BDS-3, respectively; and (4) after the introduction of the variogram model in updating the weight matrix, the clock bias prediction model is further corrected by 8.0% and 11.1% for BDS-2 and BDS-3. In summary, improved strategies for BDS ultrarapid satellites’ clock bias prediction using BDS-2 and BDS-3 integrated processing are meaningful for the current BDS ultrarapid satellites’ clock bias prediction products.

An Interactive Minicomputer Program for the Indexing and Simulation of Electron Diffraction Patterns

1976 ◽  
Vol 34 ◽  
pp. 542-543
Author(s):  
R.P. Goehner ◽  
W.T. Hatfield ◽  
Prakash Rao
Keyword(s):  
Electron Diffraction ◽  
Real Time ◽  
Pattern Analysis ◽  
Flow Diagram ◽  
Hard Copy ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Transmission Electron ◽  
One Step ◽  
Diffraction Patterns

Computer programs are now available in various laboratories for the indexing and simulation of transmission electron diffraction patterns. Although these programs address themselves to the solution of various aspects of the indexing and simulation process, the ultimate goal is to perform real time diffraction pattern analysis directly off of the imaging screen of the transmission electron microscope. The program to be described in this paper represents one step prior to real time analysis. It involves the combination of two programs, described in an earlier paper(l), into a single program for use on an interactive basis with a minicomputer. In our case, the minicomputer is an INTERDATA 70 equipped with a Tektronix 4010-1 graphical display terminal and hard copy unit.A simplified flow diagram of the combined program, written in Fortran IV, is shown in Figure 1. It consists of two programs INDEX and TEDP which index and simulate electron diffraction patterns respectively. The user has the option of choosing either the indexing or simulating aspects of the combined program.

scholarly journals Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3496
Author(s):  
Haijun Wang ◽  
Diqiu He ◽  
Mingjian Liao ◽  
Peng Liu ◽  
Ruilin Lai
Keyword(s):  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Prediction Model ◽  
Real Time ◽  
Support Vector ◽  
Weld Quality ◽  
Prediction Ability ◽  
Friction Stir ◽  
Temperature Signal ◽  
2219 Aluminum Alloy

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

scholarly journals Current Trends in Random Walks on Random Lattices

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1148
Author(s):  
Jewgeni H. Dshalalow ◽  
Ryan T. White
Keyword(s):  
Random Walk ◽  
Random Walks ◽  
Real Time ◽  
Real Space ◽  
Historical Background ◽  
Escape Time ◽  
The Real ◽  
Current Trends ◽  
One Step ◽  
Time Position

In a classical random walk model, a walker moves through a deterministic d-dimensional integer lattice in one step at a time, without drifting in any direction. In a more advanced setting, a walker randomly moves over a randomly configured (non equidistant) lattice jumping a random number of steps. In some further variants, there is a limited access walker’s moves. That is, the walker’s movements are not available in real time. Instead, the observations are limited to some random epochs resulting in a delayed information about the real-time position of the walker, its escape time, and location outside a bounded subset of the real space. In this case we target the virtual first passage (or escape) time. Thus, unlike standard random walk problems, rather than crossing the boundary, we deal with the walker’s escape location arbitrarily distant from the boundary. In this paper, we give a short historical background on random walk, discuss various directions in the development of random walk theory, and survey most of our results obtained in the last 25–30 years, including the very recent ones dated 2020–21. Among different applications of such random walks, we discuss stock markets, stochastic networks, games, and queueing.

scholarly journals Analysis and validation of a highly sensitive one-step nested quantitative real-time polymerase chain reaction assay for specific detection of severe acute respiratory syndrome coronavirus 2

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Yang Zhang ◽  
Chunyang Dai ◽  
Huiyan Wang ◽  
Yong Gao ◽  
Tuantuan Li ◽  
...  
Keyword(s):  
Polymerase Chain Reaction ◽  
Real Time ◽  
Quantitative Polymerase Chain Reaction ◽  
Clinical Samples ◽  
Chain Reaction ◽  
Pcr Assay ◽  
Qrt Pcr ◽  
Highly Sensitive ◽  
One Step ◽  
Polymerase Chain

Abstract Background Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, is posing a serious threat to global public health. Reverse transcriptase real-time quantitative polymerase chain reaction (qRT-PCR) is widely used as the gold standard for clinical detection of SARS-CoV-2. Due to technical limitations, the reported positive rates of qRT-PCR assay of throat swab samples vary from 30 to 60%. Therefore, the evaluation of alternative strategies to overcome the limitations of qRT-PCR is required. A previous study reported that one-step nested (OSN)-qRT-PCR revealed better suitability for detecting SARS-CoV-2. However, information on the analytical performance of OSN-qRT-PCR is insufficient. Method In this study, we aimed to analyze OSN-qRT-PCR by comparing it with droplet digital PCR (ddPCR) and qRT-PCR by using a dilution series of SARS-CoV-2 pseudoviral RNA and a quality assessment panel. The clinical performance of OSN-qRT-PCR was also validated and compared with ddPCR and qRT-PCR using specimens from COVID-19 patients. Result The limit of detection (copies/ml) of qRT-PCR, ddPCR, and OSN-qRT-PCR were 520.1 (95% CI: 363.23–1145.69) for ORF1ab and 528.1 (95% CI: 347.7–1248.7) for N, 401.8 (95% CI: 284.8–938.3) for ORF1ab and 336.8 (95% CI: 244.6–792.5) for N, and 194.74 (95% CI: 139.7–430.9) for ORF1ab and 189.1 (95% CI: 130.9–433.9) for N, respectively. Of the 34 clinical samples from COVID-19 patients, the positive rates of OSN-qRT-PCR, ddPCR, and qRT-PCR were 82.35% (28/34), 67.65% (23/34), and 58.82% (20/34), respectively. Conclusion In conclusion, the highly sensitive and specific OSN-qRT-PCR assay is superior to ddPCR and qRT-PCR assays, showing great potential as a technique for detection of SARS-CoV-2 in patients with low viral loads.

scholarly journals An Adaptive RBF-NMPC Architecture for Trajectory Tracking Control of Underwater Vehicles

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 105
Author(s):  
Zhenzhong Chu ◽  
Da Wang ◽  
Fei Meng
Keyword(s):  
Model Predictive Control ◽  
Prediction Model ◽  
Real Time ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Working Environment ◽  
Gray Wolf ◽  
Trajectory Tracking Control ◽  
System Output

An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction output to adapt to the complex and uncertain working environment. This provides an accurate and real-time prediction model for model predictive control (MPC). For optimization, an improved adaptive gray wolf optimization (AGWO) algorithm is proposed to obtain the trajectory tracking control law. Finally, the tracking control performance of the proposed algorithm is verified by simulation. The simulation results show that the proposed RBF-NMPC can not only achieve the same level of real-time performance as the linear model predictive control (LMPC) but also has a superior anti-interference ability. Compared with LMPC, the tracking performance of RBF-NMPC is improved by at least 43% and 25% in the case of no interference and interference, respectively.

Sign in / Sign up

Export Citation Format

Share Document