Weight Fusion Orbit Determination Method with Multi-Source at Injection Stage

2014 ◽  
Vol 568-570 ◽  
pp. 987-993
Author(s):  
Yong Xing Mao ◽  
Jun Shan Mu ◽  
Xiao Qiu Ni ◽  
De An Zhong ◽  
Jie Xiang
Keyword(s):  
Data Processing ◽  
Orbit Determination ◽  
Real Data ◽  
Measure Data ◽  
Determination Method ◽  
Source Data ◽  
Different Types ◽  
Precise Measure ◽  
Weight Method ◽  
Orbit Improvement

In this paper, according to the theory of differential orbit improvement, the dynamics equations are deduced for the data of different types, and the weight method is introduced to fuse the multi-source data with different precision and form according to the RMS of condition function associated to different data. The results of simulations and real data processing show that the high precise measure data of different orbit measurement have been brought into full play, and the orbit determination precision is improved obviously.

scholarly journals GPR-based Detection of Voids and Evaluation of Grouting Under Semi-rigid Basement

2018 ◽  
Author(s):  
Jiaqi Zheng
Keyword(s):  
Theoretical Analysis ◽  
Data Processing ◽  
Least Squares Method ◽  
Fdtd Method ◽  
Real Data ◽  
Rigid Base ◽  
The Road ◽  
Image Characteristics ◽  
Different Types ◽  
Processing Techniques

The void underneath semi-rigid base is a common defect in roads. There are some difficulties in the detection and repair for this kind of hidden damage, as well as in the evaluation of the effects of grouting treatment. For the detection and maintenance of roads, it is essential to study the detection and judging for voids underneath base and the evaluation of the spread of grout. Through theoretical analysis, numerical simulation and analysis of real data, this research generated the characteristics of under-base voids of different types and dimensions on GPR images, proposed the detecting and dimension-measuring methods for under-base voids, and studied the process and effects of data analysis techniques. (1) The characteristics of under-base voids of different types (air-filled, water-filled or grout-treated) and dimensions (height and horizontal dimensions), on A-scan and B-scan GPR image respectively, were analyzed theoretically. The gprMax software which is based on the FDTD method was employed to simulate the transmission of GPR wave within the road structure, which certified the conclusion of theoretical analysis of the image characteristics of voids. In addition, the influence of antenna frequency on the detection for voids are also analyzed.(2) Approaches for detecting voids and for estimating its height were studied, focusing on voids with a height ranging from 0.01m to 0.3m. The Least Squares Method of System Identification and the Tikhonov Regularized Deconvolution were both successfully applied to the detection and dimension estimation of air-filled voids, and their application conditions were discussed. As for water-filled and grout-treated voids, the reflection-amplitude-based dielectric constant method was used for void detection.(3) The approach for estimating the horizontal dimension of voids was studied, focusing on voids with a length ranging from 0.04m to 0.52m. According to the simulating results of air-filled voids, the estimation index was selected, and the linear calculation formula for length of voids was generated by regression analysis. (4) The data processing process was discussed. Also, the effects of different data processing techniques were studied in terms of noise filtering and attenuation compensation, and their influence on the image characteristics was also discussed.

ANALYSIS OF FEEDBACK IN PERSONAL LEARNING ENVIRONMENT

2018 ◽  
pp. 36-41
Author(s):  
Yu. Yu. Yakunin ◽  
A. K. Pogrebnikov
Keyword(s):  
Learning Environment ◽  
Data Processing ◽  
Information Technologies ◽  
Real Data ◽  
Educational Process ◽  
Personal Learning ◽  
Personal Learning Environment ◽  
Control Actions ◽  
Source Data

The article discusses the approach to the use of personal learning environment for obtaining and analyzing feedback from students. The study was conducted on the basis of assessments and feedback from students, collected over six semesters. A total of 1200 students from the Institute of Space and Information Technologies of the Siberian Federal University took part in the research. An algorithm is proposed for selecting reliable estimates of students, allowing to clear the source data from interference and emissions, as well as to increase the relevance of these estimates. The results of the analysis of data from the students, showing the possibility of their use for the formation of control actions on the educational process are presented. Examples of real data reflecting interference, numerical characteristics of disciplines formed from the results of the survey, as well as the effect of interference on the results of data processing, are considered.

syris: a flexible and efficient framework for X-ray imaging experiments simulation

2017 ◽  
Vol 24 (6) ◽  
pp. 1283-1295 ◽  
Author(s):  
Tomáš Faragó ◽  
Petr Mikulík ◽  
Alexey Ershov ◽  
Matthias Vogelgesang ◽  
Daniel Hänschke ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Data Processing ◽  
Graphics Processing Units ◽  
High Speed ◽  
Real Data ◽  
Estimation Accuracy ◽  
Processing Parameter ◽  
X Ray ◽  
X Ray Imaging ◽  
Imaging Conditions

An open-source framework for conducting a broad range of virtual X-ray imaging experiments,syris, is presented. The simulated wavefield created by a source propagates through an arbitrary number of objects until it reaches a detector. The objects in the light path and the source are time-dependent, which enables simulations of dynamic experiments,e.g.four-dimensional time-resolved tomography and laminography. The high-level interface ofsyrisis written in Python and its modularity makes the framework very flexible. The computationally demanding parts behind this interface are implemented in OpenCL, which enables fast calculations on modern graphics processing units. The combination of flexibility and speed opens new possibilities for studying novel imaging methods and systematic search of optimal combinations of measurement conditions and data processing parameters. This can help to increase the success rates and efficiency of valuable synchrotron beam time. To demonstrate the capabilities of the framework, various experiments have been simulated and compared with real data. To show the use case of measurement and data processing parameter optimization based on simulation, a virtual counterpart of a high-speed radiography experiment was created and the simulated data were used to select a suitable motion estimation algorithm; one of its parameters was optimized in order to achieve the best motion estimation accuracy when applied on the real data.syriswas also used to simulate tomographic data sets under various imaging conditions which impact the tomographic reconstruction accuracy, and it is shown how the accuracy may guide the selection of imaging conditions for particular use cases.

Comparison of two different types of fractional-order COVID-19 distributed time-delay models with real data application

2021 ◽  
pp. 2150219
Author(s):  
Liangli Yang ◽  
Yongmei Su ◽  
Xinjian Zhuo
Keyword(s):  
Time Delay ◽  
Fractional Order ◽  
Distributed Delay ◽  
Real Data ◽  
Least Square ◽  
Order Model ◽  
The Real ◽  
Fractional Order Model ◽  
Different Types ◽  
Distributed Time Delay

The outbreak of COVID-19 has a great impact on the world. Considering that there are different infection delays among different populations, which can be expressed as distributed delay, and the distributed time-delay is rarely used in fractional-order model to simulate the real data, here we establish two different types of fractional order (Caputo and Caputo–Fabrizio) COVID-19 models with distributed time-delay. Parameters are estimated by the least-square method according to the report data of China and other 12 countries. The results of Caputo and Caputo–Fabrizio model with distributed time-delay and without delay, the integer-order model with distributed delay are compared. These show that the fractional-order model can be better in fitting the real data. Moreover, Caputo order is better in short-term time fitting, Caputo–Fabrizio order is better in long-term fitting and prediction. Finally, the influence of several parameters is simulated in Caputo order model, which further verifies the importance of taking strict quarantine measures and paying close attention to the incubation period population.

scholarly journals Inverse Source Data-Processing Strategies for Radio-Frequency Localization in Indoor Environments

Sensors ◽  
2017 ◽  
Vol 17 (11) ◽  
pp. 2469 ◽  
Author(s):  
Gianluca Gennarelli ◽  
Obada Al Khatib ◽  
Francesco Soldovieri
Keyword(s):  
Data Processing ◽  
Radio Frequency ◽  
Indoor Environments ◽  
Processing Strategies ◽  
Source Data

scholarly journals BARcode DEmixing through Non-negative Spatial Regression (BarDensr)

2021 ◽  
Vol 17 (3) ◽  
pp. e1008256
Author(s):  
Shuonan Chen ◽  
Jackson Loper ◽  
Xiaoyin Chen ◽  
Alex Vaughan ◽  
Anthony M. Zador ◽  
...  
Keyword(s):  
State Of The Art ◽  
Spatial Regression ◽  
Image Data ◽  
Real Data ◽  
Optimization Methods ◽  
Spatial Density ◽  
Signal Recovery ◽  
Cloud Platform ◽  
Rna Transcripts ◽  
Different Types

Modern spatial transcriptomics methods can target thousands of different types of RNA transcripts in a single slice of tissue. Many biological applications demand a high spatial density of transcripts relative to the imaging resolution, leading to partial mixing of transcript rolonies in many voxels; unfortunately, current analysis methods do not perform robustly in this highly-mixed setting. Here we develop a new analysis approach, BARcode DEmixing through Non-negative Spatial Regression (BarDensr): we start with a generative model of the physical process that leads to the observed image data and then apply sparse convex optimization methods to estimate the underlying (demixed) rolony densities. We apply BarDensr to simulated and real data and find that it achieves state of the art signal recovery, particularly in densely-labeled regions or data with low spatial resolution. Finally, BarDensr is fast and parallelizable. We provide open-source code as well as an implementation for the ‘NeuroCAAS’ cloud platform.

scholarly journals Formulation and Solution of an Inverse Reliability Problem to Simulate the Dynamic Behavior of COVID-19 Pandemic

2021 ◽  
Vol 22 (1) ◽  
pp. 91-107
Author(s):  
F. S. Lobato ◽  
G. M. Platt ◽  
G. B. Libotte ◽  
A. J. Silva Neto
Keyword(s):  
Dynamic Behavior ◽  
Differential Evolution Algorithm ◽  
Iteration Process ◽  
Real Data ◽  
The Novel ◽  
Design Variables ◽  
Different Types ◽  
Evolution Algorithm ◽  
Novel Coronavirus ◽  
Optimization Loop

Different types of mathematical models have been used to predict the dynamic behavior of the novel coronavirus (COVID-19). Many of them involve the formulation and solution of inverse problems. This kind of problem is generally carried out by considering the model, the vector of design variables, and system parameters as deterministic values. In this contribution, a methodology based on a double loop iteration process and devoted to evaluate the influence of uncertainties on inverse problem is evaluated. The inner optimization loop is used to find the solution associated with the highest probability value, and the outer loop is the regular optimization loop used to determine the vector of design variables. For this task, we use an inverse reliability approach and Differential Evolution algorithm. For illustration purposes, the proposed methodology is applied to estimate the parameters of SIRD (Susceptible-Infectious-Recovery-Dead) model associated with dynamic behavior of COVID-19 pandemic considering real data from China's epidemic and uncertainties in the basic reproduction number (R0). The obtained results demonstrate, as expected, that the increase of reliability implies the increase of the objective function value.

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