CN115859485A - Streamline seed point selection method based on ship appearance characteristics - Google Patents
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Abstract
The invention provides a streamline seed point selection method based on ship appearance characteristics, and belongs to the field of scientific calculation visualization. The method comprises the following steps of collecting and constructing a sample data set, constructing a BP (back propagation) neural network, predicting all points on a next layer boundary line from all points of a longitudinal and middle section contour line or all points on one region boundary line, respectively inputting constructed test set data into the BP neural network for training, constructing a first region boundary line network model, a second region boundary line network model, a third region boundary line network model and a fourth region boundary line network model, and respectively inputting test set data into the trained boundary line network models to obtain corresponding boundary lines. By constructing a streamline seed point distribution area boundary line screening model and dividing a streamline seed point distribution area, the flow field characteristics can be accurately reflected through the streamline on the premise that fewer seed points are selected and fewer streamlines are generated.
Description
Technical Field
The invention relates to a streamline seed point selection method based on ship appearance characteristics, and belongs to the technical field of scientific calculation visualization.
Background
The generation of the streamline requires to know the velocity vector of each point in the flow field, then select a seed point, and start integration from the seed point according to the positive direction or the negative direction of the velocity at the point. Before the integration, an appropriate integration step size is set, such as a distance for each step of integration is specified. That is, the streamline starts from the seed point position, integrates with a proper step length along the velocity vector of the seed point position, and extends to the positive direction or the negative direction, so as to obtain the complete streamline.
In the streamline generation process, the selected positions of the seed points influence the effect of streamline distribution, theoretically, one seed point can generate a streamline through calculation, and the more densely the seed points are distributed, the more densely the streamline is distributed.
Taking the generation of a basin streamline near a ship as an example, the manual selection of streamline seed points in actual engineering follows a principle: in areas with large deformation, such as a bow and a stern, the streamlines are distributed densely in areas close to the ship. The invention also provides a streamline seed point selection method based on ship appearance characteristics, which can automatically divide a flow field area of a river basin near a ship into a characteristic area needing to select more kinds of seed points and a non-characteristic area needing to select a proper amount of seed points.
In order to better meet the actual requirements, in the process of specifically realizing the method, not only a characteristic region and a non-characteristic region are divided, but also the region is divided in multiple levels by combining factors such as the distance from the surface of the ship, the angle of water flow when impacting the ship and the like, so that the flow field characteristics of a watershed near the ship can be better displayed.
Disclosure of Invention
The invention aims to provide a streamline seed point selection method based on ship appearance characteristics.
In order to achieve the purpose, the invention is realized by the following technical scheme:
step 1: and collecting and constructing a sample data set.
Step 1-1: selecting a ship model to obtain a longitudinal section of the ship model, selecting a first area boundary line, a second area boundary line, a third area boundary line and a fourth area boundary line, dividing an area between a ship longitudinal section profile line and the first boundary line into a first area, dividing an area between the first boundary line and the second boundary line into a second area, dividing an area between the second boundary line and the third boundary line into a third area, and dividing an area between the third boundary line and the fourth boundary line into a fourth area.
Step 1-2: the vertical middle section contour line is taken as a boundary line 0, the first zone boundary line is taken as a boundary line 1, the second zone boundary line is taken as a boundary line 2, the third zone boundary line is taken as a boundary line 3, and the fourth zone boundary line is taken as a boundary line 4.
Step 1-3: constructing a first sample data set by taking the boundary line 0 as an input and the boundary line 1 as an output; constructing a second sample data set by taking the boundary line 1 as input, the boundary line 2 as output, the boundary line 2 as input and the boundary line 3 as output; constructing a fourth sample data set by taking the boundary line 3 as input and the boundary line 4 as output; and divided into a test set and a training set.
Step 2: constructing a BP neural network, and predicting all points on a boundary line of a next layer from all points of a profile line of a longitudinal-medial section or all points on a boundary line of a region; the BP neural network hidden layer has 3 layers, the number of input and output neurons is m, and m is equal to the number of points taken on the boundary line.
And 3, step 3: and respectively inputting the constructed test set data into BP neural network training to construct a first area boundary line network model, a second area boundary line network model, a third area boundary line network model and a fourth area boundary line network model.
And 4, step 4: and respectively inputting the test set data into the trained boundary line network model to obtain corresponding boundary lines.
Preferably, the selecting step of the first area boundary line is as follows:
the method comprises the steps of collecting streamline seed points at set intervals according to the longitudinal and middle profile contour line of a ship in the total length of the ship, wherein the total length of the ship is the maximum horizontal distance between the head end and the tail end of the surface of the ship, and the maximum horizontal distance is parallel to a design waterline.
And calculating the minimum included angle between a connecting line between two adjacent points of each point on the profile line of the longitudinal-middle section and a horizontal line, and calculating the slope of the vertical line of the connecting line between two adjacent points.
According to the slope of the vertical line obtained by the connecting line of the two adjacent points of the point, the slope extends a distance from the point to the direction far away from the profile contour line of the ship; connecting all end points of the extended vertical line to form a first area boundary line; and the area between the first area boundary line and the longitudinal and middle section contour lines is a first area.
Preferably, the selecting steps of the second area boundary line, the third area boundary line and the fourth area boundary line are as follows:
acquiring streamline seed points at a set distance on the boundary line of the previous area according to the maximum horizontal distance between the head end point and the tail end point of the previous area; said spacing isIn which>Representing the overall length of the vessel.
And calculating the minimum included angle between a connecting line between two adjacent points of each point on the boundary line of the previous area and the horizontal line, and calculating the slope of the perpendicular line of the connecting line between two adjacent points.
According to the slope of the vertical line obtained by the connecting line of the two adjacent points of the point, the slope extends a distance from the point to the direction far away from the middle section contour line of the ship; connecting all the end points of the extended vertical line to form a boundary line of the current area; and the area between the current area boundary line and the previous area boundary line is the current area.
Preferably, the vertical line is extended as follows:
if the minimum angle between the line connecting two adjacent points of the point and the horizontal line is within the interval of 0 degrees and 30 degrees, the perpendicular line connecting the adjacent points through the point extends outwards by the distance of t.
If the minimum angle between the line connecting two adjacent points of the point and the horizontal line is within the interval of (30 DEG, 60 DEG), the line connecting the adjacent points through the point is extended outward by a distance of 1.25 x t in the perpendicular direction.
If the minimum angle between the line connecting two adjacent points of the point and the horizontal line is within the interval (60 DEG, 90 DEG), the line connecting the adjacent points is extended outward by a distance of 1.5 x t through the point.
When each point of the first area boundary line is selected, T =0.05 × T, when the second area boundary line is selected, T =0.1 × T, when the third area boundary line is selected, T =0.15 × T, and when the fourth area boundary line is selected, T =0.2 × T, wherein T represents the draught depth of the ship.
Preferably, the specific steps of step 3 are as follows:
step 3-1: initializing BP neural network parameters, and setting learning rate of BP neural network as,/>Is->The weight matrix of the BP neural network is compared with>And a bias matrix pick>Is set to->In between, a random matrix, is selected>Is the BP neural network->The fifth on the hidden layer>Weights on individual neurons, based on the neural signal>Is the number ^ greater or lesser of the bp neural network>The fifth on the hidden layer>Bias values on individual neurons.
Step 3-2: inputting the constructed training set into a BP neural network, iterating the sample data in the BP neural network for P times, and obtaining the MSE function with the minimum error value asWill >>And a target value->By contrast, if->Less than the target value>Then the iteration is stopped, if->Greater than the target value>Then the iteration continues until pick>Less than target value。
Preferably, the activation function of the BP neural network adopts a sigmoid function, and the mean square error function of the loss function adopts an MSE function.
The sigmoid function is:
in the formula,represents the value of the neuron after calculation by the activation function, is/are>Representing the jth input value of the ith hidden layer.
The MSE function is:
in the formula,mis the number of points on the boundary line,is the coordinate of the ith point of the set of points on the current solution boundary line c, and>the coordinates of the ith point in the set of points on the current solved boundary line c are shown, the value of c is 1,2,3 and 4,is the variance value of the two point coordinate values, i.e.>The value of (c).
in the formula,representing the total length of the ship, and c is the boundary line of the current solution.
The invention has the advantages that: the invention provides a streamline seed point distribution strategy from the aspect of ship surface characteristics, aiming at the situation that the flow field is possibly influenced by the particularity of the ship appearance to generate complex characteristics. The flow field characteristics can be accurately reflected through the flow lines on the premise of selecting fewer seed points and generating fewer flow lines.
The lightweight generation of the streamline is realized: on one hand, the number of the seed points is increased for the characteristic region, and on the premise of ensuring that the streamline effect is not influenced for the non-characteristic region, the number of the seed points is properly reduced, so that the reasonable utilization rate of computing resources is improved; on the other hand, when the number of the flow lines is reduced, researchers can capture flow field characteristics more when observing the flow lines, and sight confusion caused by huge number of the flow lines is avoided.
The automatic generation of the streamline is realized: aiming at the means of manually selecting the characteristic regions commonly used by researchers at present, the automatic selection of the characteristic regions is realized. A BP neural network model is set up, so that the seed point distribution area selection and the seed point selection are self-learning, self-organizing and self-adapting.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a schematic view of the flow structure of the present invention.
FIG. 2 is a schematic diagram of the zone division according to the present invention.
FIG. 3 is a schematic diagram of the minimum angle between the line connecting two adjacent points and the horizontal line.
FIG. 4 is a schematic diagram of the process of the present invention for taking the point on the boundary line of the No. 1 region from the point on the profile line of the vertical section.
FIG. 5 is a schematic diagram of streamline effects under a general selection method.
Fig. 6 is a streamline schematic diagram based on the ship-type feature selection method of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
A streamline seed point selecting method based on ship appearance characteristics comprises the following steps:
step 1: and collecting and constructing a sample data set.
Step 1-1: selecting a ship model to obtain a longitudinal section of the ship model, selecting a first area boundary line, a second area boundary line, a third area boundary line and a fourth area boundary line, dividing an area between a ship longitudinal section profile line and the first boundary line into a first area, dividing an area between the first boundary line and the second boundary line into a second area, dividing an area between the second boundary line and the third boundary line into a third area, and dividing an area between the third boundary line and the fourth boundary line into a fourth area.
The selection step of the first area boundary line is as follows:
the method comprises the steps of collecting streamline seed points at set intervals according to the profile contour line of the ship overall length in the ship longitudinal and middle direction, wherein the overall length of the ship is the maximum horizontal distance which is parallel to a design waterline between the head end and the tail end of the surface of the ship.
And calculating the minimum included angle between a connecting line between two adjacent points of each point on the profile line of the longitudinal-middle section and the horizontal line, and calculating the slope of the perpendicular line of the connecting line between two adjacent points.
According to the slope of the vertical line obtained by the connecting line of the two adjacent points of the point, the slope extends a distance from the point to the direction far away from the profile contour line of the ship; connecting all end points of the extended vertical line to form a first area boundary line; and the area between the first area boundary line and the longitudinal and middle section contour lines is a first area.
The second area boundary line, the third area boundary line and the fourth area boundary line are selected as follows:
acquiring streamline seed points at a set interval on the boundary line of the previous area according to the maximum horizontal distance between the head end and the tail end of the previous area; said spacing isWherein is present>Representing the overall length of the vessel.
And calculating the minimum included angle between a connecting line between two adjacent points of each point on the boundary line of the previous area and the horizontal line, and calculating the slope of the perpendicular line of the connecting line between two adjacent points.
According to the slope of the vertical line obtained by the connecting line of the two adjacent points of the point, the slope extends a distance from the point to the direction far away from the profile contour line of the ship; connecting all the end points of the extended vertical line to form a boundary line of the current area; and the area between the current area boundary line and the previous area boundary line is the current area.
The vertical line extending mode is as follows:
if the minimum angle between the line connecting two adjacent points of the point and the horizontal line is within the interval of 0 degrees and 30 degrees, the perpendicular line connecting the adjacent points through the point extends outwards by the distance of t.
If the minimum angle between the line connecting two adjacent points of the point and the horizontal line is within the interval (30 degrees, 60 degrees), the perpendicular line connecting the adjacent points through the point is extended outwards by a distance of 1.25 x t.
If the minimum angle between the line connecting two adjacent points of the point and the horizontal line is within the interval of (60 DEG, 90 DEG), the line connecting the adjacent points through the point is extended outward by a distance of 1.5 x t from the perpendicular line.
When each point of the first area boundary line is selected, T =0.05 × T, when the second area boundary line is selected, T =0.1 × T, when the third area boundary line is selected, T =0.15 × T, and when the fourth area boundary line is selected, T =0.2 × T, wherein T represents the draught depth of the ship.
According to the effect of the region division, the region areas are sorted from small to large as follows: region 1, region 2, region 3, and region 4. On the premise that the number of the seed points in the four regions is the same, the density of the seed points is sorted from small to large as follows: zone 4, zone 3, zone 2, zone 1. This effect satisfies the expression "the closer the area to the surface of the vessel, the denser the distribution of the seed points".
Step 1-2: the vertical middle section contour line is taken as a boundary line 0, the first zone boundary line is taken as a boundary line 1, the second zone boundary line is taken as a boundary line 2, the third zone boundary line is taken as a boundary line 3, and the fourth zone boundary line is taken as a boundary line 4.
Step 1-3: constructing a first sample data set by taking the boundary line 0 as an input and the boundary line 1 as an output; constructing a second sample data set by taking the boundary line 1 as input, the boundary line 2 as output, the boundary line 2 as input and the boundary line 3 as output; constructing a fourth sample data set by taking the boundary line 3 as an input and the boundary line 4 as an output; and divided into a test set and a training set.
And 2, step: constructing a BP neural network, and predicting all points on a boundary line of a next layer from all points of a profile line of a longitudinal-medial section or all points on a boundary line of a region; the BP neural network hidden layer has 3 layers, the number of input and output neurons is m, and m is equal to the number of streamline seed points on the boundary line.
And 3, step 3: and respectively inputting the constructed test set data into BP neural network training to construct a first area boundary line network model, a second area boundary line network model, a third area boundary line network model and a fourth area boundary line network model.
The specific steps of the step 3 are as follows:
step 3-1: initializing BP neural network parameters and setting BP neural network learning rate as,/>Is->The weight matrix of the BP neural network is compared with>And a bias matrix pick>Is set to->In between, a random matrix, is selected>For BP neural network ^ th->The fifth on the hidden layer>Weight on each neuron->Is the number ^ greater or lesser of the bp neural network>The fifth on the hidden layer>Bias values on individual neurons.
In the formula,is a bias matrix->Is/are>A sub-status value, <' > or>Is->The first derivative of (a). />
Step 3-2: inputting the constructed training set into a BP neural network, iterating P times of sample data in the BP neural network, and obtaining the minimum value of the error of the MSE function asWill >>And a target value->By contrast, if->Less than the target value>Then the iteration is stopped, if->Greater than the target value>Then the iteration continues until pick>Less than target value。
in the formula,representing the total length of the ship, and c is the boundary line of the current solution.
The activation function of the BP neural network adopts a sigmoid function, and the mean square error function of the loss function adopts an MSE function.
The sigmoid function is:
in the formula,represents the value of the neuron after calculation by the activation function, is/are>Representing the jth input value of the ith hidden layer.
The MSE function is:
in the formula,mis the number of points on the boundary line,is the coordinate of the ith point of the set of points on the current solution boundary line c, < >>Representing the coordinates of the ith point in the set of points on the current solution boundary line c,the value of c is 1,2,3,4,is the variance value of the two point coordinate values, i.e.>The value of (c).
And 4, step 4: and respectively inputting the test set data into the trained boundary line network model to obtain corresponding boundary lines.
In a flow field area of a watershed near the same ship, seed points are selected by using a general method and a ship-type characteristic-based method respectively, and a streamline is generated. The following table compares the two methods in terms of seed point number, computation time, and streamline effect.
TABLE 1 comparison of the results of the experiments of the present invention and the general method
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A streamline seed point selecting method based on ship appearance characteristics is characterized by comprising the following steps:
step 1: collecting and constructing a sample data set;
step 1-1: selecting a ship model to obtain a longitudinal section of the ship model, selecting a first area boundary line, a second area boundary line, a third area boundary line and a fourth area boundary line, dividing an area between a ship longitudinal section profile line and the first boundary line into a first area, dividing an area between the first boundary line and the second boundary line into a second area, dividing an area between the second boundary line and the third boundary line into a third area, and dividing an area between the third boundary line and the fourth boundary line into a fourth area;
step 1-2: recording a longitudinal-middle section contour line as a boundary line 0, a first area boundary line as a boundary line 1, a second area boundary line as a boundary line 2, a third area boundary line as a boundary line 3 and a fourth area boundary line as a boundary line 4;
step 1-3: constructing a first sample data set by taking the boundary line 0 as an input and the boundary line 1 as an output; constructing a second sample data set by taking the boundary line 1 as input, the boundary line 2 as output, the boundary line 2 as input and the boundary line 3 as output; constructing a fourth sample data set by taking the boundary line 3 as an input and the boundary line 4 as an output; and divided into a test set and a training set;
step 2: constructing a BP neural network, and predicting all points on a boundary line of a next layer from all points of a profile line of a longitudinal-medial section or all points on a boundary line of a region; the BP neural network hidden layer has 3 layers, the number of input and output neurons is m, and m is equal to the number of points taken on the boundary line;
and step 3: respectively inputting the constructed test set data into a BP neural network for training, and constructing a first area boundary line network model, a second area boundary line network model, a third area boundary line network model and a fourth area boundary line network model;
and 4, step 4: and respectively inputting the test set data into the trained boundary line network model to obtain corresponding boundary lines.
2. The streamline seed point selection method based on ship appearance characteristics according to claim 1, wherein the first region boundary line is selected by the following steps:
acquiring streamline seed points at set intervals on a profile line of a longitudinal and middle section of a ship according to the total length of the ship, wherein the total length of the ship is the maximum horizontal distance between the head end and the tail end of the surface of the ship, which is parallel to a designed waterline;
calculating the minimum included angle between a connecting line between two adjacent points and a horizontal line of each point on the profile line of the longitudinal-middle section, and calculating the slope of a vertical line of the connecting line between two adjacent points;
according to the slope of the vertical line obtained by the connecting line of the two adjacent points of the point, the slope extends a distance from the point to the direction far away from the profile contour line of the ship; connecting all end points of the extended vertical line to form a first area boundary line; and the area between the first area boundary line and the longitudinal and middle section contour lines is a first area.
3. The streamline seed point selection method based on ship appearance characteristics according to claim 2,
the second area boundary line, the third area boundary line and the fourth area boundary line are selected as follows:
acquiring streamline seed points at a set interval on the boundary line of the previous area according to the maximum horizontal distance between the head end and the tail end of the previous area; said spacing isWherein is present>Represents the overall length of the ship;
calculating the minimum included angle between a connecting line between two adjacent points of each point on the boundary line of the previous area and a horizontal line, and calculating the slope of a perpendicular line of the connecting line between two adjacent points;
according to the slope of a vertical line obtained by connecting two adjacent points of the point, extending a distance from the point to the direction far away from the contour line of the middle section of the ship by using the slope; connecting all the end points of the extended vertical line to form a boundary line of the current area; and the area between the current area boundary line and the previous area boundary line is the current area.
4. The streamline seed point selection method based on ship appearance characteristics according to claim 3, wherein the vertical line is extended as follows:
if the minimum included angle between the connecting line between two adjacent points of the point and the horizontal line is positioned in the interval of [0 degrees and 30 degrees ], the perpendicular line connecting the adjacent points passing through the point extends outwards for a distance of t;
if the minimum included angle between the connecting line between two adjacent points of the point and the horizontal line is within the interval of (30 degrees and 60 degrees), the perpendicular line between the connecting lines of the adjacent points is extended outwards by the distance of 1.25 x t through the point;
if the minimum included angle between the connecting line between two adjacent points of the point and the horizontal line is within the interval of (60 degrees and 90 degrees), the perpendicular line between the connecting lines of the adjacent points is extended outwards by the distance of 1.5 x t through the point;
when each point of the first area boundary line is selected, T =0.05 × T, when the second area boundary line is selected, T =0.1 × T, when the third area boundary line is selected, T =0.15 × T, and when the fourth area boundary line is selected, T =0.2 × T, wherein T represents the draught depth of the ship.
5. The streamline seed point selection method based on ship appearance characteristics according to claim 1, wherein the specific steps in step 3 are as follows:
step 3-1: initializing BP neural network parameters, and setting learning rate of BP neural network as,/>Is->The weight matrix of the BP neural network is compared with>And a bias matrix pick>Is set to->In between, a random matrix, is selected>Is the BP neural network->The fifth on the hidden layer>Weight on each neuron->Is the number ^ greater or lesser of the bp neural network>The fifth on the hidden layer>A bias value on an individual neuron;
step 3-2: inputting the constructed training set into a BP neural network, iterating P times of sample data in the BP neural network, and obtaining the minimum value of the error of the MSE function asWill >>And a target value->By contrast, if->Less than the target value>Then the iteration is stopped, if->Greater than the target value>Then the iteration continues until pick>Less than the target value>。
6. The method for selecting streamline seed points based on ship appearance characteristics according to claim 5, wherein the weight matrix;
7. The streamline seed point selection method based on ship appearance characteristics according to claim 5, wherein a sigmoid function is adopted as an activation function of a BP neural network, and a MSE function is adopted as a loss function mean square error function;
the sigmoid function is:
in the formula,represents the value of the neuron after calculation by the activation function, is/are>A jth input value representing an ith hidden layer;
the MSE function is:
in the formula,mis the number of points on the boundary line,is the coordinate of the ith point of the set of points on the current solution boundary line c, and>the coordinates of the ith point in the set of points on the current solved boundary line c are shown, the value of c is 1,2,3 and 4,is the variance value of the two point coordinate values, i.e.>The value of (c).
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Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090150097A1 (en) * | 2007-12-07 | 2009-06-11 | Landmark Graphics Corporation, A Halliburton Company | Systems and Methods For Utilizing Cell Based Flow Simulation Results to Calculate Streamline Trajectories |
WO2014049346A1 (en) * | 2012-09-26 | 2014-04-03 | Bergen Teknologioverføring As | Image filtering |
CN109559369A (en) * | 2018-12-04 | 2019-04-02 | 中国科学院地理科学与资源研究所 | It is a kind of towards the ocean current vector field visualization method based on angleonly tracking for flowing to feature |
US20200160061A1 (en) * | 2017-12-11 | 2020-05-21 | Zhuhai Da Hengqin Technology Development Co., Ltd. | Automatic ship tracking method and system based on deep learning network and mean shift |
CN111832660A (en) * | 2020-07-23 | 2020-10-27 | 南京联云智能***有限公司 | Multi-target real-time detection method for mobile ship |
US20210117796A1 (en) * | 2020-12-01 | 2021-04-22 | Harbin Engineering University | Ship Motion Prediction Method Based on Long Short-Term Memory Network and Gaussian Process Regression |
CN113159117A (en) * | 2021-03-11 | 2021-07-23 | 中国科学院计算机网络信息中心 | Streamline generation method and device |
CN114936413A (en) * | 2022-04-21 | 2022-08-23 | 哈尔滨工程大学 | Ship body shape optimization neural network modeling method and ship body shape optimization method |
CN115511752A (en) * | 2022-10-31 | 2022-12-23 | 合肥埃科光电科技股份有限公司 | BP neural network-based point coordinate distortion removal method and storage medium |
US20220414891A1 (en) * | 2019-09-09 | 2022-12-29 | South China University Of Technology | Method for automatic segmentation of fuzzy boundary image based on active contour and deep learning |
-
2023
- 2023-02-27 CN CN202310165021.8A patent/CN115859485B/en active Active
Patent Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090150097A1 (en) * | 2007-12-07 | 2009-06-11 | Landmark Graphics Corporation, A Halliburton Company | Systems and Methods For Utilizing Cell Based Flow Simulation Results to Calculate Streamline Trajectories |
WO2014049346A1 (en) * | 2012-09-26 | 2014-04-03 | Bergen Teknologioverføring As | Image filtering |
US20200160061A1 (en) * | 2017-12-11 | 2020-05-21 | Zhuhai Da Hengqin Technology Development Co., Ltd. | Automatic ship tracking method and system based on deep learning network and mean shift |
CN109559369A (en) * | 2018-12-04 | 2019-04-02 | 中国科学院地理科学与资源研究所 | It is a kind of towards the ocean current vector field visualization method based on angleonly tracking for flowing to feature |
US20220414891A1 (en) * | 2019-09-09 | 2022-12-29 | South China University Of Technology | Method for automatic segmentation of fuzzy boundary image based on active contour and deep learning |
CN111832660A (en) * | 2020-07-23 | 2020-10-27 | 南京联云智能***有限公司 | Multi-target real-time detection method for mobile ship |
US20210117796A1 (en) * | 2020-12-01 | 2021-04-22 | Harbin Engineering University | Ship Motion Prediction Method Based on Long Short-Term Memory Network and Gaussian Process Regression |
CN113159117A (en) * | 2021-03-11 | 2021-07-23 | 中国科学院计算机网络信息中心 | Streamline generation method and device |
CN114936413A (en) * | 2022-04-21 | 2022-08-23 | 哈尔滨工程大学 | Ship body shape optimization neural network modeling method and ship body shape optimization method |
CN115511752A (en) * | 2022-10-31 | 2022-12-23 | 合肥埃科光电科技股份有限公司 | BP neural network-based point coordinate distortion removal method and storage medium |
Non-Patent Citations (5)
Title |
---|
QIN,XJ等: "Streamline Uniform Placement Algorithm With Dynamic Seed Points", 《IEEE ACCESS》 * |
吕天耀: "基于流线相似性的流场并行可视化方法研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
熊光正;黄智濒;戴志涛;杨武兵;: "一种数据驱动的三维流场流线特征化筛选方法", 北京邮电大学学报 * |
苏萁;王逸斌;赵宁;: "基于机器学习算法的舰面流场预警***研究", 测控技术 * |
高明正;张火明;金尚忠;: "BP神经网络在船舶与海洋工程中的应用研究", 舰船科学技术 * |
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