INTRODUCTION OF EIO MODEL IN TLS METHOD TO CALCULATE THE COORDINATES TRANSFORMATION BY HELMERT’S FORMULA

The EIO (Errors In Observations) model is used in the total least squares method to calculate, process geodetic data. Next to the classical least squares method, it is applied to solve more solutions. When we use the EIO model in calculus and process, performing a matrix inverse has a large dimension will be avoided. Moreover, the calculation and accuracy evaluation steps are based on the iterative algorithm to get the results. In this paper, the authors use the procedure of calculating and evaluating the accuracy of the EIO model in the experimental calculation of the coordinate transformation according to the Helmert formula

Convergence of daily mean coordinates of precise positioning methods

As part of the research of modern movements of the Earth’s crust, an analysis of 7 high-precision methods for calculating GNSS positions was carried out for the convergence of their daily mean coordinates. Based on Euclidean distances, regular and maximal discrepancies between coordinates of different methods are given. According to the coordinates in the ITRF, 5 methods are stood out with regular coordinate discrepancies <1 mm, and individual maximum discrepancies up to 30 mm. The other two methods have regular discrepancies in coordinates up to 2 cm, and the maximum differences reach 1 m. For a group of stations global coordinates transformation into a local reference frame leads to the effect of coordinate stabilization and increases their relative precision in the time series. As a result of such procedure, the level of maximum coordinate discrepancies between the methods decreased to 46%. Moreover, one of the methods of calculating coordinates has improved its convergence with the other methods by 80%. Based on the Euclidean distance method, the quality of the raw data for each station was evaluated. Thus, there is a group of 8 stations, for which the convergence of coordinates in different methods are approximately at the same level, and 2-3 times better than for the other 2 stations.

Crosslink: An R Package for Network Visualization of Grouped Nodes

The demand for network visualization of relationships between nodes attributed to different categories grows in various biomedical research scenarios, such as gene regulatory networks, drug-target networks, ligand-receptor interactions and association networks of multi-omics elements. Elegantly visualizing the relationships between nodes with complex metadata of nodes and edges appended may inspire new insights. Here, we developed the crosslink R package, tailored for network visualization of grouped nodes, to provide a series of flexible functions for generating network diagrams. We first designed a CrossLink class for storage of metadata about nodes and edges and manipulation of node coordinates. Then affine transformation and function mapping transformation are implemented to perform fundamental node coordinates transformation by groups, based on which various network layouts can be defined easily. For convenience, we predefined several commonly used layouts, including row, column, arc, polygon and hive, which also can be combined in one layout. Finally, we designed a user-friendly wrapper function to draw network connections, aesthetic mappings of metadata and decoration with related annotation graphs in one interface by taking advantage of the powerful ggplot2 system. Overall, the crosslink R package is easy-to-use for achieving complex visualization of a network diagram of grouped nodes surrounded by associated annotation graphs.Availability and ImplementationCosslink is an open-source R package, freely available from github: https://github.com/zzwch/crosslink; A detailed user documentation can be found in https://zzwch.github.io/crosslink/.

Output regulation for switched discrete-time systems with output signal quantization

This paper investigates the output regulation problem for switched discrete-time systems with output quantization. We adopt the quantized output in feedback controllers and allow each subsystem to have its own quantization density, so that the communication network can be efficiently utilized. By using the different coordinates transformation, the solvability of the output regulation problem is guaranteed under deigned output feedback controllers with the switching signals satisfying a dwell time constraint. In the simulation, a pulse-width modulation driven boost converter model is employed to validate the result.

Molecular free energy optimization on a computational graph

GSFE-refinement is a super efficient protein refinement method that integrates the GSFE theory, coordinates transformation, neural network and auto differentiation, and maps molecular free energy optimization onto a computational graph.

Burn images segmentation based on Burn-GAN

Introduction Burn injuries are severe problems for human. Accurate segmentation for burn wounds in patient surface can improve the calculation precision of %TBSA (Total burn surface area), which is helpful in determining treatment plan. Recently, deep learning methods have been used to automatically segment wounds. However, owing to the difficulty of collecting relevant images as training data, those methods cannot often achieve fine segmentation. A burn image generating framework is proposed in this paper to generate burn image datasets with annotations automatically. Those datasets can be used to increase segmentation accuracy and save the time of annotating. Method This paper brings forward an advanced burn image generation framework called Burn-GAN. The framework consists of four parts: Generating burn wounds based on the mainstream Style-GAN network; Fusing wounds with human skins by Color Adjusted Seamless Cloning (CASC); Simulating real burn scene in three-dimensional space; Acquiring annotated dataset through three-dimensional and local burn coordinates transformation. Using this framework, a large variety of burn image datasets can be obtained. Finally, standard metrics like precision, Pixel Accuracy (PA) and Dice Coefficient (DC) were utilized to assess the framework. Results With non-saturating loss with R2 regularization (NSLR2) and CASC, the segmentation network gains the best results. The framework achieved precision at 90.75%, PA at 96.88% and improved the DC from 84.5% to 89.3%. Conclusions A burn data generating framework have been built to improve the segmentation network, which can automatically segment burn images with higher accuracy and less time than traditional methods.

Towards computing a new kinematic coordinates transformation parameters for Egypt

ITRF (International Terrestrial Reference Frame) determines the origin, alignment of the system ’s fundamental planes or axes, scale, physical constants, and models such as the size, shape, and alignment of the reference ellipsoid. The ITRF is regularly updated to take into account the Earth’s dynamics and is now sufficiently re-fined to ensure that the change between successive ITRF versions is in the order of 1-2 cm. The Egyptian Survey Authority (ESA) established the Egyptian’s HARN (High Accuracy Reference Network) and linked it to the international frame (ITRF1994 epoch1996) as a static frame. As this datum is static, coordinates of stations do not change with time, ignoring both the tectonic motion and the different definitions for all following ITRF realizations. With the continuous increasing progress of using the IGS and CORS stations as well as the merging of the concept of tectonic plate motion, and the Precise Point Positioning technique has a well know precise technique, there were offset in the current coordinates of the HARN network about 40 cm, so a set of simple transformation parameters (T x , T y , T z ) calculated. In the current study, Plate Motion Models (PPM) and Egyptian Deformation Model (EGY-DM) were investigated based on ITRF2008 epoch2015.4 to choose the best model in calculating the computed parameters. The evaluation process of the computed transformation parameters on chosen points of HARN & NACN (Notational Agricultural Cadastral network) demonstrates that the estimated transformation parameters by EGY-DM give the lowest horizontal and vertical differences 16 mm and 17mm, respectively, and with standard deviation does not exceed 2 cm, except one station due to deficiency of observation time.