This document demonstrates how to use advanced mesh regions in Phase2 8.0 to combine graded and uniform meshing. It allows defining a dense uniform mesh region, like around an excavation, within an overall graded mesh, combining their advantages. The tutorial shows how to add a boundary around an excavation, define an advanced mesh region there, and set its element length to generate a uniform mesh in that area while keeping the outside graded.
User guide of numerical simulation gridding v2.2.0Bo Sun
This is the user guide of DepthInsight™ numerical simulation gridding module. For corresponding video tutorials , please visit and subscribe our Youtube channel: https://www.youtube.com/channel/UCjHyG-mG7NQofUWTZgpBT2w
DepthInsight™ software products include modules as follows:
Structure Interpretation
Well and Data Management
Plan Module
Profile Module
Attribute Modeling
Velocity Modeling
Structural Modeling
Reservoir Geological Modeling
Numerical Simulation Gridding
Rock Modeling
Geo-mechanical Modeling
Paleo-Structural Modeling
Enormous Modeling Platform
For more information about our company, Beijing GridWorld Software Technology Co., Ltd., please visit our website: http://gridworld.com.cn/en/
This file contains a simple description about what I have created about how to detect object motion and track whatever moving as a computer vision project when being undergraduate student at 2014.
The MATLAB code of the system is also available in the document.
Find me on:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://www.academia.edu/
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/
Texture Segmentation Based on Multifractal Dimensionijsc
Texture segmentation can be considered the most important problem, since human can distinguish different
textures quit easily, but the automatic segmentation is quit complex and it is still an open problem for
research. In this paper focus on implement novel supervised algorithm for multitexture segmentation and
this algorithm based on blocking procedure where each image divide into block (16×16 pixels) and extract
vector feature for each block to classification these block based on these feature. These feature extract
using Box Counting Method (BCM). BCM generate single feature for each block and this feature not
enough to characterize each block ,therefore, must be implement algorithm provide more than one slide for
the image based on new method produce multithresolding, after this use BCM to generate single feature for
each slide.
A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...csandit
3-dimensional object modelling of real world objects in steady state by means of multiple point
cloud (pcl) depth scans taken by using sensing camera and application of smoothing algorithm
are suggested in this study. Polygon structure, which is constituted by coordinates of point
cloud (x,y,z) corresponding to the position of 3D model in space and obtained by nodal points
and connection of these points by means of triangulation, is utilized for the demonstration of 3D
models. Gaussian smoothing and developed methods are applied to the mesh consisting of
merge of these polygons, and a new mesh simplification and augmentation algorithm are
suggested for the over the 3D modelling. Mesh consisting of merge of polygons can be
demonstrated in a more packed, smooth and fluent way. In this study is shown that applied the
triangulation and smoothing method for 3D modelling, perform to a fast and robust mesh
structures compared to existing methods therewithal no remeshing is necessary for refinement
and reduction.
Derivation of Convolutional Neural Network (ConvNet) from Fully Connected Net...Ahmed Gad
In image analysis, convolutional neural networks (CNNs or ConvNets for short) are time and memory efficient than fully connected (FC) networks. But why? What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article.
Image analysis has a number of challenges such as classification, object detection, recognition, description, etc. If an image classifier, for example, is to be created, it should be able to work with a high accuracy even with variations such as occlusion, illumination changes, viewing angles, and others. The traditional pipeline of image classification with its main step of feature engineering is not suitable for working in rich environments. Even experts in the field won’t be able to give a single or a group of features that are able to reach high accuracy under different variations. Motivated by this problem, the idea of feature learning came out. The suitable features to work with images are learned automatically. This is the reason why artificial neural networks (ANNs) are one of the robust ways of image analysis. Based on a learning algorithm such as gradient descent (GD), ANN learns the image features automatically. The raw image is applied to the ANN and ANN is responsible for generating the features describing it.
-Reference
Aghdam, Hamed Habibi, and Elnaz Jahani Heravi. Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification. Springer, 2017.
Two-dimensional Block of Spatial Convolution Algorithm and SimulationCSCJournals
This paper proposes an algorithm based on sub image-segmentation strategy. The proposed scheme divides a grayscale image into overlapped 6×6 blocks each of which is segmented into four small 3x3 non-overlapped sub-images. A new spatial approach for efficiently computing 2-dimensional linear convolution or cross-correlation between suitable flipped and fixed filter coefficients (sub image for cross-correlation) and corresponding input sub image is presented. Computation of convolution is iterated vertically and horizontally for each of the four input sub-images. The convolution outputs of these four sub-images are processed to be converted from 6×6 arrays to 4×4 arrays so that the core of the original image is reproduced. The present algorithm proposes a simplified processing technique based on a particular arrangement of the input samples, spatial filtering and small sub-images. This results in reducing the computational complexity as compared with other well known FFT-based techniques. This algorithm lends itself for partitioned small sub-images, local image spatial filtering and noise reduction. The effectiveness of the algorithm is demonstrated through some simulation examples.
User guide of numerical simulation gridding v2.2.0Bo Sun
This is the user guide of DepthInsight™ numerical simulation gridding module. For corresponding video tutorials , please visit and subscribe our Youtube channel: https://www.youtube.com/channel/UCjHyG-mG7NQofUWTZgpBT2w
DepthInsight™ software products include modules as follows:
Structure Interpretation
Well and Data Management
Plan Module
Profile Module
Attribute Modeling
Velocity Modeling
Structural Modeling
Reservoir Geological Modeling
Numerical Simulation Gridding
Rock Modeling
Geo-mechanical Modeling
Paleo-Structural Modeling
Enormous Modeling Platform
For more information about our company, Beijing GridWorld Software Technology Co., Ltd., please visit our website: http://gridworld.com.cn/en/
This file contains a simple description about what I have created about how to detect object motion and track whatever moving as a computer vision project when being undergraduate student at 2014.
The MATLAB code of the system is also available in the document.
Find me on:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://www.academia.edu/
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/
Texture Segmentation Based on Multifractal Dimensionijsc
Texture segmentation can be considered the most important problem, since human can distinguish different
textures quit easily, but the automatic segmentation is quit complex and it is still an open problem for
research. In this paper focus on implement novel supervised algorithm for multitexture segmentation and
this algorithm based on blocking procedure where each image divide into block (16×16 pixels) and extract
vector feature for each block to classification these block based on these feature. These feature extract
using Box Counting Method (BCM). BCM generate single feature for each block and this feature not
enough to characterize each block ,therefore, must be implement algorithm provide more than one slide for
the image based on new method produce multithresolding, after this use BCM to generate single feature for
each slide.
A NOVEL APPROACH TO SMOOTHING ON 3D STRUCTURED ADAPTIVE MESH OF THE KINECT-BA...csandit
3-dimensional object modelling of real world objects in steady state by means of multiple point
cloud (pcl) depth scans taken by using sensing camera and application of smoothing algorithm
are suggested in this study. Polygon structure, which is constituted by coordinates of point
cloud (x,y,z) corresponding to the position of 3D model in space and obtained by nodal points
and connection of these points by means of triangulation, is utilized for the demonstration of 3D
models. Gaussian smoothing and developed methods are applied to the mesh consisting of
merge of these polygons, and a new mesh simplification and augmentation algorithm are
suggested for the over the 3D modelling. Mesh consisting of merge of polygons can be
demonstrated in a more packed, smooth and fluent way. In this study is shown that applied the
triangulation and smoothing method for 3D modelling, perform to a fast and robust mesh
structures compared to existing methods therewithal no remeshing is necessary for refinement
and reduction.
Derivation of Convolutional Neural Network (ConvNet) from Fully Connected Net...Ahmed Gad
In image analysis, convolutional neural networks (CNNs or ConvNets for short) are time and memory efficient than fully connected (FC) networks. But why? What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article.
Image analysis has a number of challenges such as classification, object detection, recognition, description, etc. If an image classifier, for example, is to be created, it should be able to work with a high accuracy even with variations such as occlusion, illumination changes, viewing angles, and others. The traditional pipeline of image classification with its main step of feature engineering is not suitable for working in rich environments. Even experts in the field won’t be able to give a single or a group of features that are able to reach high accuracy under different variations. Motivated by this problem, the idea of feature learning came out. The suitable features to work with images are learned automatically. This is the reason why artificial neural networks (ANNs) are one of the robust ways of image analysis. Based on a learning algorithm such as gradient descent (GD), ANN learns the image features automatically. The raw image is applied to the ANN and ANN is responsible for generating the features describing it.
-Reference
Aghdam, Hamed Habibi, and Elnaz Jahani Heravi. Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification. Springer, 2017.
Two-dimensional Block of Spatial Convolution Algorithm and SimulationCSCJournals
This paper proposes an algorithm based on sub image-segmentation strategy. The proposed scheme divides a grayscale image into overlapped 6×6 blocks each of which is segmented into four small 3x3 non-overlapped sub-images. A new spatial approach for efficiently computing 2-dimensional linear convolution or cross-correlation between suitable flipped and fixed filter coefficients (sub image for cross-correlation) and corresponding input sub image is presented. Computation of convolution is iterated vertically and horizontally for each of the four input sub-images. The convolution outputs of these four sub-images are processed to be converted from 6×6 arrays to 4×4 arrays so that the core of the original image is reproduced. The present algorithm proposes a simplified processing technique based on a particular arrangement of the input samples, spatial filtering and small sub-images. This results in reducing the computational complexity as compared with other well known FFT-based techniques. This algorithm lends itself for partitioned small sub-images, local image spatial filtering and noise reduction. The effectiveness of the algorithm is demonstrated through some simulation examples.
One of the purposes of meshing is to actually make the problem solvable using Finite Element. By meshing, you break up the domain into pieces, each piece representing an element.
CONCEPT OF FINITE ELEMENT MODELLING FOR TRUSSES AND BEAMS USING ABAQUSIAEME Publication
Abaqus is one of the powerful engineering software programs which are based on the finite element method. The Abaqus can solve wide range of pr oblems from linear to nonlinear analyses. Abaqus is widely used in many sectors like automotive and mechanical industries for design and development of FEM products. The finite element method is a numerical technique for finding approximate solutions for d ifferential and integral equations. The finite element word was coined by Clough in 1960. In 1960s, engineers used the method for solving the problems in stress analysis, strain analysis, heat and fluid transfer, and other region. Abaqus CAE can provide a simple creating model, submitting the modal, monitoring, and evaluating result and then can also compare with theoretical calculation.
یکی از فایل های سایت آلفا پروژه که در مورد نحوه انجام پروژه با استفاده از نرم افزار بسیار محبوب متلب می باشد. جهت کسب اطلاعات بیشتر به آدرس زیر مراجعه فرمایید
https://alfaproject.ir/do-matlab-project/
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Phase2 developers tip_-_advanced_mesh_regions
1. Developer’s Tip
Using advanced mesh regions in Phase2 8.0 to combine
graded and uniform meshing
Ease of mesh generation and the ability to customize the mesh has always
been a hallmark of Phase2. In Phase2 version 8, an important new capability
has been added to the mesh customization options: it is now possible to define
regions of uniform mesh density within a graded mesh.
This allows you to create
a dense uniform mesh in
critical areas of the model
(e.g. around excavations),
while maintaining a graded
mesh outside of these
regions. This combines the
advantages of uniform and
graded meshing - you can
easily maximize the mesh
density where it is needed,
while keeping the overall
number of elements low.
Application of uniform mesh region within a graded mesh
This developer’s tip will demonstrate this new meshing option. The option is
well hidden in the Mesh Setup dialog, so you might not find it without some
encouragement. Once you learn how to use it, you may find it a very useful
tool for controlling the density of the mesh in particular regions.
2. To start with we will read in the Tutorial 01 Quick Start.fez file. Start the Phase2 Model
program.
1. Select File > Recent Folders > Tutorials Folder.
2. Open the Tutorial 01 Quick Start.fez file.
3. Since we will be modifying this file, save it with a new file name. Select File > Save As
and save it with a new file name (e.g. advanced mesh region 01.fez).
4. Now let’s add a closed material boundary around the excavation, to define a separate
region which surrounds the excavation.
5. Select Add Material from the toolbar or the Boundaries menu. If the Reset Mesh dialog
appears select OK. In the prompt line enter the following coordinates to define a closed
boundary which surrounds the excavation.
-10, -5
-10, 20
10, 20
10 , -5
-10, -5
The material boundary will be added to the model.
6. Select Discretize and Mesh from the toolbar or the Mesh menu. The model should
appear as follows.
Figure 2 – boundary added to define region around excavation
3. 7. Select Mesh Setup from the toolbar or the Mesh menu.
8. Select the Advanced button in the Mesh Setup dialog. The dialog will expand to reveal
advanced mesh setup options.
9. Select the Use Advanced Mesh Regions checkbox. Select the Add button at the right of
the dialog.
Figure 3 – mesh setup dialog with advanced options displayed
10. The dialog will minimize (roll up) and you will be prompted to select the region of the
model in which you would like to customize the mesh.
11. Click the mouse anywhere inside the region between the excavation boundary and the
material boundary.
12. The mesh setup dialog will automatically maximize again. Note:
You have defined one advanced mesh region.
By default the Mesh Type = Uniform for the advanced mesh region.
Note that the overall Mesh Type = Graded (at the top of the dialog).
13. For the advanced mesh region, a default element length is initially calculated based on
the current geometry. Set the Element Length = 1 as shown in the following figure.
4. Figure 4 – advanced mesh region defined in mesh setup
14. Select OK. The model should now appear as follows:
Figure 5 – uniform mesh region defined around excavation
5. 15. Zoom in to the excavation to get a better look. You have defined a region of uniform
mesh density with average element length approximately equal to 1. Outside of this
region the mesh is graded with larger elements towards the external boundary.
Figure 6 – close-up of uniform mesh region
16. Save this model and we will demonstrate another method of selecting the custom mesh
region.
6. There are two methods of selecting an advanced mesh region:
You can click the mouse inside a closed region (as demonstrated above).
You can click and drag a rectangular window over any region of the model to create an
advanced mesh region.
We will now demonstrate the second method.
1. Select Mesh Setup from the toolbar or the Mesh menu.
2. First delete the existing custom mesh region. In the dialog, click on the grid row
representing the existing advanced mesh region and select the Delete button.
3. Select the Add button.
4. The dialog will minimize (roll up) and you will be prompted to select the region of the
model in which you would like to customize the mesh. Notice that the prompt line
instruction indicates: “Use mouse to click inside a closed region or draw a rectangular
region”.
5. Click and drag a rectangular window which is larger than the green material boundary,
say from about (15, 25) to about (-15, -10).
6. The Mesh Setup dialog will automatically maximize again.
7. You have defined a new advanced mesh region. Set the Element Length = 1.
8. Select OK. If you see a message dialog about re-setting the mesh select Yes. The model
should look similar to the following figure.
Figure 7 – mesh region selected by a rectangular window
7. Notice that the entire region within the rectangular window on the previous page has been remeshed with a uniform mesh with average element length approximately equal to 1.
When you use a rectangular selection window, you can enclose any number of different
boundaries or boundary types (e.g. excavation, material, stage) and the entire region will be
discretized and meshed according to the average element length you have defined for the
region. (See the note below).
You are encouraged to experiment further with this option to get familiar with its capabilities
and results.
For example, if you open Tutorial 02 Materials and Staging.fez, and add a customized mesh
region around the entire excavated area with an average element length = 5, you will get
results similar to the figure below.
Figure 8 – uniform mesh region applied to Tutorial 02
NOTE: the uniformity of the mesh depends on the geometry of your boundaries and the spacing
of vertices on the boundaries with respect to the element length. If you have closely spaced
vertices (i.e. vertex spacing is less than the element length for the advanced mesh region) then
the mesh will not be exactly uniform. Phase2 will do its best to generate a uniform mesh within
the constraints imposed by the boundary geometry and the element length.
8. Users of Phase2 version 7 may recall that a very similar option already existed in the Mesh
Setup dialog. In version 7 the option was referred to as “Discretization Regions” while in version
8 the new option is referred to as “Advanced Mesh Regions”.
Figure 9 – comparison of version 7 (left) and version 8 (right) advanced mesh setup options
The primary difference between the version 7 discretization region and version 8 advanced
mesh region is as follows:
In version 7 only the discretization of the model boundaries was affected. The mesh
within the selected region still remained a graded mesh. You could not directly control
the element size except on the boundaries.
In version 8, the discretization of the model boundaries AND the size of the finite
elements within the region are simultaneously determined by the average element
length, allowing you to define a true uniform mesh within the selected region.
There are also some other differences between version 7 and 8 with respect to this option, but
we will not detail those here. You can obtain the version 7 behaviour using version 8 by
choosing Mesh Type = Graded for an advanced mesh region.
Although both version 7 and version 8 have many different options for customizing the
discretization and the mesh density in selected areas, only version 8 allows you to
simultaneously change both discretization and mesh density in order to obtain a uniform mesh
region within a graded mesh.
9. In the Mesh Setup dialog you may have noticed the following checkbox: “Do not discretize arc
or circle segments” shown below.
Figure 10 – arc and circle discretization option
This checkbox is on by default. If your model includes boundaries which were created using the
Arc or Circle options, the effect of this checkbox is to only allow ONE discretization per line
segment, on a boundary which was originally defined as an arc or circle. (The purpose of this
option is to improve analysis results for liners placed on these boundaries).
If you are using the Advanced Mesh regions option, you should keep this in mind. If the “Do not
discretize arc or circle segments” checkbox is selected, this means that the element size along
circular or arc boundaries, will be determined by the length of the segments of the arc or circle.
This will override the value of Element Length which you enter for an Advanced Mesh region,
and you may notice non-uniform mesh results along arc or circular boundaries, unless the
element length and arc line segment length are approximately equal.
10. The Advanced Mesh Regions option is not something that you will need to use all of the time.
For the majority of models, a good quality graded mesh will give excellent results with a
minimum of user input.
However there are cases where it may be beneficial to create a dense uniform mesh in critical
areas of the model (e.g. around excavations), while maintaining a graded mesh outside of these
regions. For example:
Highly stressed areas supported by rock bolts
Models in which the results may be mesh-dependent
Studies of brittle spalling around excavations, where fine uniform meshes are required
in order to capture the material behaviour
The new Advanced Mesh Regions option in Phase2 8 is a powerful new tool which gives the user
a great deal of flexibility to customize the mesh density in any region of the model with a
minimum of effort. It combines the advantages of uniform and graded meshing - you can easily
maximize the mesh density where it is needed, while keeping the overall number of elements
low.