Research on a Time Difference Model Method Based on Satellite Common - view and Optical Fiber Comparison Method

2017 ◽  
Author(s):  
Yuwei Li ◽  
Juan Du ◽  
Wengli Wang ◽  
Lingda Meng ◽  
Yanrong Xue
Keyword(s):  
Optical Fiber ◽  
Comparison Method ◽  
Time Difference ◽  
Difference Model ◽  
Common View ◽  
Model Method

scholarly journals Strip-pair comparison method for building threshold color-difference model: theoretical model validation

2020 ◽  
Vol 28 (14) ◽  
pp. 21336
Author(s):  
Fernando Brusola ◽  
Ignacio Tortajada ◽  
Ismael Lengua ◽  
Begoña Jordá ◽  
Guillermo Peris-Fajarnés
Keyword(s):  
Theoretical Model ◽  
Model Validation ◽  
Color Difference ◽  
Comparison Method ◽  
Difference Model ◽  
Pair Comparison

Optimized Space  Station CV Method and Its Differences from GNSS CV

2020 ◽  
Author(s):  
Yinhua Liu ◽  
Xiaohui Li
Keyword(s):  
Atomic Clock ◽  
Space Station ◽  
Satellite System ◽  
Comparison Method ◽  
Space System ◽  
Common View ◽  
Near Earth Space ◽  
Clock System ◽  
Blind Area ◽  
Global Navigation Satellite

Abstract There will be better atomic clock system and micro-wave time comparison link in the near earth space station, like Chinese Space Station and European ACES(Atomic Clock Ensemble in Space) system, than those in the GNSS(Global Navigation satellite System) satellites. Therefore, the space station common-view (CV) will realize more accurate time comparison than GNSS CV in theory. But due to the orbit characteristic of the space station, there are some limitations if traditional GNSS CV time comparison method is applied to the space station. In order to solve these problems, the GNSS CV method is optimized and the method that is appropriate for the space station is proposed. First, the basic CV principle is analyzed, and the delay items which are needed to be considered for GNSS and space station CV are compared and analyzed. Then, the differences between GNSS and space station CV are studied, and the influences of orbit error on these two CV methods are analyzed in detail. The GNSS CV method is optimized to be fit for the space station next. Finally, the performance of the optimized method is validated by simulated experiments. The simulation results show that the space station time comparison accuracy of several tens of picoseconds can be obtained by the optimized method. Furthermore, the problem of CV blind area is solved by the optimized method effectively.

Origin-Aware Location Prediction Based on Historical Vehicle Trajectories

2022 ◽  
Vol 13 (1) ◽  
pp. 1-18
Author(s):  
Meng Chen ◽  
Qingjie Liu ◽  
Weiming Huang ◽  
Teng Zhang ◽  
Yixuan Zuo ◽  
...  
Keyword(s):  
Travel Time ◽  
Markov Models ◽  
Prediction Method ◽  
Time Difference ◽  
Location Prediction ◽  
Trajectory Data ◽  
Difference Model ◽  
The Difference ◽  
Real World Datasets ◽  
Joint Prediction

Next location prediction is of great importance for many location-based applications and provides essential intelligence to various businesses. In previous studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Nevertheless, due to the time and space complexity, these methods (e.g., Markov models) only utilize the just passed locations to predict next locations, neglecting earlier passed locations in the trajectory. In this work, we seek to enhance the prediction performance by incorporating the travel time from all the passed locations in the query trajectory to each candidate next location. To this end, we propose a novel prediction method, namely the Travel Time Difference Model, which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Moreover, we integrate the Travel Time Difference Model with a Sequential and Temporal Predictor to yield a joint model. The joint prediction model integrates local sequential transitions, temporal regularity, and global travel time information in the trajectory for the next location prediction problem. We have conducted extensive experiments on two real-world datasets: the vehicle passage record data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over baseline methods.

Virtual Measurement of Machining Errors Based on Comparison Method of Three Models

2011 ◽  
Vol 189-193 ◽  
pp. 1571-1574
Author(s):  
Li Zhi Gu ◽  
Li Peng ◽  
Lei Peng ◽  
Li Qiong
Keyword(s):  
Measurement System ◽  
Random Number ◽  
Comparison Method ◽  
Measuring System ◽  
Probe Technique ◽  
Model Method ◽  
Function Extension ◽  
Machining Errors ◽  
Sampling Points ◽  
Good Agreement

A virtual measurement system for geometric surfaces of some specific mechanical components was created with Cage Method and Virtual Probe technique based on Three Model Method. The fundamental constituents of the system, the key part of the Three model Method, measured model, were presented in detail with weighed random number technique. Measuring accuracy may properly be controlled by alteration of the number of sampling points of the measured model. Principle of the measuring system, with extendable function, was put forward and explained. The system was operated by VC++ and OpenGL with characteristics of lifelike, function-extension, and immersion. Virtual measurement on a bar-shaft for cylindricalness is given as an example, and it has been shown that the results have much good agreement with those of experiments and that the system worked stably.

Method of Common View Based on Digital Satellite Television

2014 ◽  
Vol 721 ◽  
pp. 666-669
Author(s):  
Xiao Long Liu ◽  
Yu Hua ◽  
Yu Xiang ◽  
Hui Qing Liu
Keyword(s):  
Local Time ◽  
Time Scale ◽  
Time Difference ◽  
Digital Television ◽  
Common View ◽  
Satellite Television ◽  
Television Signal

This paper apply satellite digital television signal for common view based on the DVB-S standard. By choosing some information from the signal as timing signal, we can get the arriving time of timing signal on local time scale. At last, we use the arriving time and some necessary correct massage to get the time difference between two locations.

Disturbance Detection of Optical Fiber Sensing Electronic System and Its Loss Prediction Using Big Data Technology

2021 ◽  
Vol 16 (3) ◽  
pp. 452-459
Author(s):  
De Wu
Keyword(s):  
Big Data ◽  
Optical Fiber ◽  
Electronic System ◽  
Time Difference ◽  
Abstract Model ◽  
Backscatter Signal ◽  
Elman Neural Network ◽  
Fiber Sensing ◽  
Loss Prediction ◽  
Optical Fiber Sensing

Optical fiber sensing technology performs quite well in large-scale positioning, real-time monitoring and other fields, while its stability and environmental adaptability of the system need to be improved in the application of engineering. There are two aspects that need to be improved. The first is that the optical fiber signal disturbance needs to be located. The second is that the loss of fiber fusion joint needs to be predicted to replace the fiber components in time. Based on this, first, the transmission matrix model is established for interference optical path transmission of optical fiber sensing electronic system in the case of signal disturbance. The abstract model of backscatter signal and disturbance signal location in the case of signal disturbance are proposed respectively; then, many correlative experiments are carried out for the loss problem of optical fiber fusion points, and big data technologies such as grey model (GM (1,1)), ARIMA model and Elman neural network are introduced to predict the loss of fusion joint. In the experiment, the time difference between the singular signal and the optical pulse, or the corresponding time difference between singular signal and the beginning of the sensing region, can be obtained from the backscattering trajectory. Then, the disturbance location can be obtained based on the disturbance location abstract model; deviation maintaining welding machine is used, its loss of fusion joint is low, and the corresponding high temperature resistance and humidity resistance are good. The loss prediction performance of different models is compared, and the combined prediction model has smaller prediction error and higher prediction accuracy.

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