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Patent
o9 Solutions | Date: 2016-02-25

An unstructured data input is accessed that includes an electronic communication. Content of the unstructured data is parsed to determine one or more terms in the unstructured data input. It is determined that one or more particular elements defined in a structured business data model correspond to the terms. Tags are assigned to the unstructured data based on the terms corresponding to the one or more particular elements, where the tags define an association between the unstructured data and the structured data model.

Claims which contain your search:

1. A method comprising: accessing a unstructured data input, wherein the unstructured data input comprises an electronic communication; parsing content of the unstructured data to determine one or more terms in the unstructured data input; determining that one or more particular elements defined in a structured data model correspond to the terms, wherein the structured data model comprises a business model; and assigning tags to the unstructured data based on the terms corresponding to the one or more particular elements, wherein the tags define an association between the unstructured data and the structured data model.

3. The method of claim 1, wherein the structured data model is one of a plurality of data models and the tags define an association between the unstructured data input and a particular one of the plurality of data models.

4. The method of claim 3, wherein the plurality of data models comprise an interconnected network of r plan models

5. The method of claim 4, wherein each of the plan models respective future outcomes of an organization.

6. The method of claim 5, wherein each of the plan models comprises a respective outcome measures model, a respective input drivers model, a respective sensitivity model defining dependencies of outcome measures modeled in the corresponding outcome measures model on input drivers modeled in the corresponding input drivers model, and a respective scope model.

7. The method of claim 6, wherein each scope model comprises a respective included entities model, one or more respective included member models, one or more respective included hierarchies models, one or more respective entity models, one or more member type models, and one or more member models.

8. The method of claim 1, wherein the one or more particular elements of the structured data model are included in a plurality of elements of the structured data model and the structured data model comprises a plurality of sub-models corresponding to the plurality of elements.

14. The method of claim 13, wherein determining that one or more particular elements defined in a structured data model correspond to the terms comprises searching the structured data model for elements corresponding to the meanings of the terms.

17. The method of claim 1, further comprising causing one or more values included in the unstructured data input to populate data in the structured data model corresponding to an attribute associated with one of the particular elements.

18. The method of claim 1, further comprising causing the unstructured data input to be presented together with a visualization of the structured data model based on the assigned tags.

21. At least one machine accessible storage medium having instructions stored thereon, the instructions when executed on a machine, cause the machine to: access a unstructured data input, wherein the unstructured data input comprises an electronic communication; parse content of the unstructured data to determine one or more terms in the unstructured data input; determine that one or more particular elements defined in a structured data model correspond to the terms, wherein the structured data model comprises a business model; and assign tags to the unstructured data based on the terms corresponding to the one or more particular elements, wherein the tags define an association between the unstructured data and the structured data model.

22. A system comprising: at least one processor; at least one memory element; and a planning system to:host a plurality of structured data models, wherein the structured data models comprise a network of plan models; andreceive particular unstructured data comprising an electronic communication, wherein the planning system comprises a data scanner to:parse content of the unstructured data to determine one or more terms in the unstructured data;determine that one or more particular elements defined in a structured data model correspond to the terms, wherein the structured data model comprises a business model; andassign tags to the unstructured data based on the terms corresponding to the one or more particular elements, wherein the tags define an association between the particular unstructured data and the structured data model.

23. The system of claim 22, wherein the planning system is further to populate a particular attribute in the structured data model with one or more values determined from the parsing of the unstructured data input based on the association between the particular unstructured data and the structured data model, wherein the particular attribute is associated with one of the particular elements.

24. The system of claim 22, wherein the planning system is further to: generate a graphical representation of data in the structured data model; and present the unstructured data input with the graphical representation based on the assigned tags.

25. The system of claim 22, wherein: the business model comprises a plurality of interconnected plan models, each of the plan models a respective future outcome of an organization, each of the plan models comprises a respective outcome measures model, a respective input drivers model, a respective sensitivity model defining dependencies of outcome measures modeled in the corresponding outcome measures model on input drivers modeled in the corresponding input drivers model, and a respective scope model, and each scope model comprises a respective included entities model, one or more respective included member models, one or more respective included hierarchies models, one or more respective entity models, one or more member type models, and one or more member models.

...

According to the invention, for the purpose of presenting structure information (SI) about a technical object (TO), an apparatus (SP) having an identification module (IDM), a modelling module (MM), an image generation module (IGEN) and having a projection module (PM) is provided. The identification module (IDM) is used for identifying the technical object. The modelling module (MM) is set up to provide a structure model (SM) of the identified technical object and also to specifically ascertain, in each case, an outwardly recognisable object structure (OS) for the technical object and a piece of inner structure information (SI), which is physically associated with the object structure, about the technical object on the basis of the structure model (SM). The image generation module (IGEN) is used to produce a structure image (SIMG) of the technical object, to insert the object structure (OS) into the structure image (SIMG) in a manner true to projection and to insert the structure information (SI) into the structure image (SIMG) in a graphical association with the object structure (OS) in a manner of presentation that differs from the manner of presentation of the object structure (OS). Finally, the projection module (PM) is used to project the structure image (SIMG) onto the technical object (TO).

...
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Weight of records per source
Name Score Publications Conferences Grants Patents Trademarks News Webs
567.7 10 10 10 10 10 10 10
499.3 10 10 10 10 10 10 10
473.4 10 10 10 10 10 10 10
462.7 10 10 10 10 10 10 10
424.1 10 10 10 10 10 10 10
IBM
420.2 10 10 10 10 10 10 10
402.5 10 10 10 10 10 10 10
402.0 10 10 10 10 10 10 10
394.6 10 10 10 10 10 10 10
389.5 10 10 10 10 10 10 10
381.9 10 10 10 10 10 10 10
375.3 10 10 10 10 10 10 10
375.0 10 10 10 10 10 10 10
342.4 10 10 10 10 10 10 10
337.8 10 10 10 10 10 10 10
336.4 10 10 10 10 10 10 10
332.7 10 10 10 10 10 10 10
323.7 10 10 10 10 10 10 10
320.9 10 10 10 10 10 10 10
314.0 10 10 10 10 10 10 10
310.4 10 10 10 10 10 10 10
309.4 10 10 10 10 10 10 10
299.6 10 10 10 10 10 10 10
297.6 10 10 10 10 10 10 10
296.3 10 10 10 10 10 10 10
296.0 10 10 10 10 10 10 10
286.8 10 10 10 10 10 10 10
286.1 10 10 10 10 10 10 10
284.3 10 10 10 10 10 10 10
278.5 10 10 10 10 10 10 10
276.0 10 10 10 10 10 10 10
270.7 10 10 10 10 10 10 10
266.8 10 10 10 10 10 10 10
262.3 10 10 10 10 10 10 10
259.9 10 10 10 10 10 10 10
257.8 10 10 10 10 10 10 10
257.2 10 10 10 10 10 10 10
248.7 10 10 10 10 10 10 10
247.0 10 10 10 10 10 10 10
245.6 10 10 10 10 10 10 10
241.2 10 10 10 10 10 10 10
236.7 10 10 10 10 10 10 10
233.8 10 10 10 10 10 10 10
232.1 10 10 10 10 10 10 10
229.0 10 10 10 10 10 10 10
228.6 10 10 10 10 10 10 10
227.5 10 10 10 10 10 10 10
224.9 10 10 10 10 10 10 10
222.6 10 10 10 10 10 10 10
220.6 10 10 10 10 10 10 10
217.8 10 10 10 10 10 10 10
217.4 10 10 10 10 10 10 10
217.3 10 10 10 10 10 10 10
215.9 10 10 10 10 10 10 10
215.1 10 10 10 10 10 10 10
213.2 10 10 10 10 10 10 10
212.5 10 10 10 10 10 10 10
212.1 10 10 10 10 10 10 10
209.9 10 10 10 10 10 10 10
208.6 10 10 10 10 10 10 10
Columbia University
208.1 740 92 67 10 10 10 10
Technical University of Denmark
205.1 597 164 23 10 10 10 10
Cornell University
203.5 723 85 61 10 10 10 10
Nanjing Southeast University
203.4 970 266 - 10 10 10 10
Rutgers University
201.3 555 94 53 10 10 10 10
University of Southern California
201.0 610 185 46 10 10 10 10
Carnegie Mellon University
200.1 425 265 62 10 10 10 10
University of British Columbia
199.5 912 150 1 10 10 10 10
Virginia Polytechnic Institute and State University
192.3 514 217 57 10 10 10 10
National University of Singapore
189.1 716 204 - 10 10 10 10
Beijing Institute of Technology
188.3 764 389 1 10 10 10 10
Peking University
186.8 783 219 1 10 10 10 10
California Institute of Technology
186.1 561 92 37 10 10 10 10
Rice University
183.6 378 59 29 10 10 10 10
University of Pittsburgh
183.2 665 55 40 10 10 10 10
Polytechnic of Milan
181.8 539 289 13 10 10 10 10
Seoul National University
181.0 699 113 - 10 10 10 10
Jilin University
180.7 805 307 - 10 10 10 10
Nanjing University of Aeronautics and Astronautics
180.0 816 210 - 10 10 10 10
North Carolina State University
178.0 577 118 65 10 10 10 10
University of Lisbon
177.5 670 242 8 10 10 10 10
University of Missouri
175.7 403 47 13 10 10 10 10
Iowa State University
175.1 482 90 35 10 10 10 10
Chongqing University
175.0 836 173 - 10 10 10 10
University of New South Wales
173.1 742 187 1 10 10 10 10
Beijing Jiaotong University
172.4 676 303 - 10 10 10 10
Princeton University
172.2 625 85 32 10 10 10 10
Wuhan University
171.8 827 268 - 10 10 10 10
Arizona State University
169.7 545 154 62 10 10 10 10
University of Edinburgh
168.3 639 112 62 10 10 10 10
University of California at Santa Barbara
165.0 482 60 54 10 10 10 10
University of Alberta
164.5 695 118 - 10 10 10 10
University of Sao Paulo
164.2 1,052 174 1 10 10 10 10
Shandong University
163.8 657 206 - 10 10 10 10
University of Leeds
163.2 542 64 62 10 10 10 10
KTH Royal Institute of Technology
163.1 521 168 - 10 10 10 10
University of Colorado at Boulder
162.2 619 71 62 10 10 10 10
Nanyang Technological University
161.1 630 188 - 10 10 10 10
Lawrence Berkeley National Laboratory
159.1 739 42 - 10 10 10 10
RWTH Aachen
158.0 511 240 7 10 10 10 10

Patent
o9 Solutions | Date: 2016-02-25

An unstructured data input is accessed that includes an electronic communication. Content of the unstructured data is parsed to determine one or more terms in the unstructured data input. It is determined that one or more particular elements defined in a structured business data model correspond to the terms. Tags are assigned to the unstructured data based on the terms corresponding to the one or more particular elements, where the tags define an association between the unstructured data and the structured data model.

Claims which contain your search:

1. A method comprising: accessing a unstructured data input, wherein the unstructured data input comprises an electronic communication; parsing content of the unstructured data to determine one or more terms in the unstructured data input; determining that one or more particular elements defined in a structured data model correspond to the terms, wherein the structured data model comprises a business model; and assigning tags to the unstructured data based on the terms corresponding to the one or more particular elements, wherein the tags define an association between the unstructured data and the structured data model.

3. The method of claim 1, wherein the structured data model is one of a plurality of data models and the tags define an association between the unstructured data input and a particular one of the plurality of data models.

4. The method of claim 3, wherein the plurality of data models comprise an interconnected network of r plan models

5. The method of claim 4, wherein each of the plan models respective future outcomes of an organization.

6. The method of claim 5, wherein each of the plan models comprises a respective outcome measures model, a respective input drivers model, a respective sensitivity model defining dependencies of outcome measures modeled in the corresponding outcome measures model on input drivers modeled in the corresponding input drivers model, and a respective scope model.

7. The method of claim 6, wherein each scope model comprises a respective included entities model, one or more respective included member models, one or more respective included hierarchies models, one or more respective entity models, one or more member type models, and one or more member models.

8. The method of claim 1, wherein the one or more particular elements of the structured data model are included in a plurality of elements of the structured data model and the structured data model comprises a plurality of sub-models corresponding to the plurality of elements.

14. The method of claim 13, wherein determining that one or more particular elements defined in a structured data model correspond to the terms comprises searching the structured data model for elements corresponding to the meanings of the terms.

17. The method of claim 1, further comprising causing one or more values included in the unstructured data input to populate data in the structured data model corresponding to an attribute associated with one of the particular elements.

18. The method of claim 1, further comprising causing the unstructured data input to be presented together with a visualization of the structured data model based on the assigned tags.

21. At least one machine accessible storage medium having instructions stored thereon, the instructions when executed on a machine, cause the machine to: access a unstructured data input, wherein the unstructured data input comprises an electronic communication; parse content of the unstructured data to determine one or more terms in the unstructured data input; determine that one or more particular elements defined in a structured data model correspond to the terms, wherein the structured data model comprises a business model; and assign tags to the unstructured data based on the terms corresponding to the one or more particular elements, wherein the tags define an association between the unstructured data and the structured data model.

22. A system comprising: at least one processor; at least one memory element; and a planning system to:host a plurality of structured data models, wherein the structured data models comprise a network of plan models; andreceive particular unstructured data comprising an electronic communication, wherein the planning system comprises a data scanner to:parse content of the unstructured data to determine one or more terms in the unstructured data;determine that one or more particular elements defined in a structured data model correspond to the terms, wherein the structured data model comprises a business model; andassign tags to the unstructured data based on the terms corresponding to the one or more particular elements, wherein the tags define an association between the particular unstructured data and the structured data model.

23. The system of claim 22, wherein the planning system is further to populate a particular attribute in the structured data model with one or more values determined from the parsing of the unstructured data input based on the association between the particular unstructured data and the structured data model, wherein the particular attribute is associated with one of the particular elements.

24. The system of claim 22, wherein the planning system is further to: generate a graphical representation of data in the structured data model; and present the unstructured data input with the graphical representation based on the assigned tags.

25. The system of claim 22, wherein: the business model comprises a plurality of interconnected plan models, each of the plan models a respective future outcome of an organization, each of the plan models comprises a respective outcome measures model, a respective input drivers model, a respective sensitivity model defining dependencies of outcome measures modeled in the corresponding outcome measures model on input drivers modeled in the corresponding input drivers model, and a respective scope model, and each scope model comprises a respective included entities model, one or more respective included member models, one or more respective included hierarchies models, one or more respective entity models, one or more member type models, and one or more member models.


According to the invention, for the purpose of presenting structure information (SI) about a technical object (TO), an apparatus (SP) having an identification module (IDM), a modelling module (MM), an image generation module (IGEN) and having a projection module (PM) is provided. The identification module (IDM) is used for identifying the technical object. The modelling module (MM) is set up to provide a structure model (SM) of the identified technical object and also to specifically ascertain, in each case, an outwardly recognisable object structure (OS) for the technical object and a piece of inner structure information (SI), which is physically associated with the object structure, about the technical object on the basis of the structure model (SM). The image generation module (IGEN) is used to produce a structure image (SIMG) of the technical object, to insert the object structure (OS) into the structure image (SIMG) in a manner true to projection and to insert the structure information (SI) into the structure image (SIMG) in a graphical association with the object structure (OS) in a manner of presentation that differs from the manner of presentation of the object structure (OS). Finally, the projection module (PM) is used to project the structure image (SIMG) onto the technical object (TO).


Patent
Honeywell | Date: 2017-04-07

Tuning model structures of dynamic systems are described herein. One method for tuning model structures of a dynamic system includes predicting a variable for each of a number of models associated with a number of model structures of a dynamic system, calculating a rate of error of the predicted variable for each of the number of models compared to an observed variable, determining a best model structure among the number of model structures based on the calculated rate of error, and creating a revised model structure using the best model structure to tune the number of model structures of the dynamic system.

Claims which contain your search:

1. A non-transitory computer readable medium having computer readable instructions stored thereon that are executable by a processor to: determine, based on a rate of error associated with each of a plurality of models associated with a plurality of model structures, a best model structure and a worst model structure among the plurality of model structures; generate a revised model structure using the best model structure by combining a sub-group of the plurality of model structures; replace the worst model structure with the revised model structure; and control a heating, ventilation, and air-conditioning (HVAC) system using the best model structure.

2. The computer readable medium of claim 1, wherein the computer readable instructions are executable by the processor to define a plurality of new model structures eligible to be the best model structure.

3. The computer readable medium of claim 1, wherein generating the revised model structure includes migrating a random model structure towards the best model structure.

4. The computer readable medium of claim 1, wherein the revised model structure includes a model structure with a calculated rate of error associated therewith that is less than a calculated rate of error associated with the best model structure.

5. The computer readable medium of claim 1, wherein the computer readable instructions are executable by the processor to calculate the rate of error by comparing a plurality of predicted variables to a plurality of observed variables for each of the plurality of models associated with the plurality of model structures.

7. The computer readable medium of claim 1, wherein the computer readable instructions are executable by the processor to update the rate of error associated with each of the plurality of models over time.

8. The computer readable medium of claim 1, wherein generating the revised model structure includes creating a new model structure using surrogate modeling.

9. A computing device for tuning model structures, comprising: a memory; and a processor configured to execute executable instructions stored in the memory to:determine, based on an evaluation of a plurality of model structures associated with a plurality of models, a best model structure and a worst model structure among the plurality of model structures;generate a revised model structure using the best model structure by combining a sub-group of the plurality of model structures;replace the worst model structure with the revised model structure; andcontrol a heating, ventilation, and air-conditioning (HVAC) system by using the best model structure.

10. The computing device of claim 9, wherein the instructions are executable by the processor to evaluate the plurality of model structures.

11. The computing device of claim 9, wherein the worst model structure is a model structure with a higher calculated rate of error than the remaining plurality of model structures.

12. The computing device of claim 9, wherein the worst model structure is a model structure with less than a threshold plurality of evaluations.

13. The computing device of claim 9, wherein the plurality of model structures include dependencies between the plurality of variables for each of the plurality of models.

15. A method for tuning model structures, comprising: identifying, by a computing device, a plurality of models associated with a plurality of model structures; evaluating, by the computing device, the plurality of model structures; identifying, by the computing device, a best model structure and a worst model structure based on the evaluation of the plurality of model structures; generating, by the computing device, a revised model structure using the best model structure by combining a sub-group of the plurality of model structures; replacing, by the computing device, the worst model structure with the revised model structure; tuning, by the computing device, the plurality of model structures by defining a plurality of new model structures eligible to be the best model structure; and controlling, by the computing device, a heating, ventilation, and air-conditioning (HVAC) system by using the best model structure.

16. The method of claim 15, wherein evaluating the plurality of model structures includes: calculating a predicted value for a plurality of variables in a model associated with a model structure over a time period, wherein the plurality of model structures have dependencies between the plurality of variables for each of the plurality of models; comparing the predicted value for the plurality of variables to an observed value for each of the plurality of variables over the time period; and calculating a rate of error for the model structure based on the comparison of the predicted values and the observed values.

17. The method of claim 15, wherein the method includes saving a plurality of well performing model structures, wherein the plurality of well performing model structures include a plurality of model structures with a lower calculated rate of error than a remaining plurality of model structures.

18. The method of claim 17, wherein generating the revised model structure includes migrating a well performing model structure among the plurality of well performing model structures towards the best model structure.

19. The method of claim 15, wherein the method includes saving a plurality of immature model structures, wherein the plurality of immature model structures include a plurality of model structures with less than a threshold plurality of evaluations.

20. The method of claim 19, wherein the method includes refraining from replacing the plurality of immature model structures.


A method for analyzing a structure includes processing nondestructive inspection (NDI) data (112) for a multi-layer structure to define areas of inconsistency at an internal layer or an interface between adjacent layers. The method includes mapping (106) the areas of inconsistency to finite elements of a finite element model (114) of a nominal of the structure. These finite elements are thereby identified as affected finite elements and include finite elements for the affected internal layer or interface. The method includes producing a reconstructed finite element model (116) of the affected structure from the nominal finite element model (114), and a modified property or state value assigned to respective element datasets of the affected finite elements. The method includes performing a finite element method (FEM) failure analysis (110) of the reconstructed finite element model (116) under a load, which indicates an extent of residual integrity of the affected structure.

Claims which contain your search:

8. A method (500) of analyzing a structure, the method comprising:processing nondestructive inspection (NDI) data (112) for an affected structure (200) composed of a plurality of layers (202), the NDI data (112) being processed to define an area of inconsistency at an internal layer of the plurality of layers (202), or an interface between a particular pair of adjacent layers in the plurality of layers (202);receiving a finite element model (114) of a nominal of the affected structure (200), the finite element model (114) being composed of a plurality of finite elements (404) having respective element datasets, the plurality of finite elements (404) including a mesh of finite elements (404) for each of the plurality of layers (202), and finite elements (404) at an interface between each pair of adjacent layers in the plurality of layers (202);mapping the area of inconsistency to at least some of the plurality of finite elements (404) that are thereby identified as affected finite elements (404), and that include finite elements (404) of the mesh of finite elements (404) for the internal layer, or the finite elements (404) at the interface between the particular pair of adjacent layers;producing a reconstructed finite element model (116) of the affected structure (200) from the finite element model (114) of the nominal, and a modified property or state value assigned to respective element datasets of the affected finite elements (404); andperforming a finite element method (FEM) failure analysis (110) of the reconstructed finite element model (116) under a load, the FEM failure analysis (110) producing an output that indicates an extent of residual integrity of the affected structure (200).

1. An apparatus (800) for implementation of a system (100) for analyzing a structure, the apparatus (800) comprising a processor (802) and a memory (804) storing executable instructions that, in response to execution by the processor (802), cause the apparatus (800) to implement at least:a nondestructive inspection (NDI) process module (102) configured to process NDI data (112) for an affected structure composed of a plurality of layers (202), the NDI data (112) being processed to define an area of inconsistency at an internal layer of the plurality of layers (202), or an interface between a particular pair of adjacent layers in the plurality of layers (202);a finite element model input interface (104) configured to receive a finite element model (114) of a nominal of the affected structure (200), the finite element model (114) being composed of a plurality of finite elements (404) having respective element datasets, the plurality of finite elements (404) including a mesh of finite elements (404) for each of the plurality of layers (202), and finite elements (404) at an interface between each pair of adjacent layers in the plurality of layers (202);a mapping module (106) configured to map the area of inconsistency to at least some of the plurality of finite elements (404) that are thereby identified as affected finite elements (404), and that include finite elements (404) of the mesh of finite elements (404) for the internal layer, or the finite elements (404) at the interface between the particular pair of adjacent layers;a reconstitution module (108) configured to produce a reconstructed finite element model (116) of the affected structure (200) from the finite element model (114) of the nominal, and a modified property or state value assigned to respective element datasets of the affected finite elements (404); anda finite element method (FEM) failure analyzer (110) configured to perform a FEM failure analysis of the reconstructed finite element model (116) under a load, the FEM failure analysis producing an output that indicates an extent of residual integrity of the affected structure (200).


Patent
Beihang University | Date: 2017-01-12

The invention provides a structure self-adaptive 3D model editing method, which includes: given a 3D model library, clustering 3D models of same category according to structures; learning a design knowledge prior between components of 3D models in same group; learning a structure switching rule between 3D models in different groups; after user edits a 3D model component, determining a final group of the model according to inter-group design knowledge prior, and editing other components of the model according to intra-group design knowledge prior, so that the model as a whole satisfies design knowledge priors of a category of 3D models. Through editing few components by the user, other components of the model can be optimized automatically and the edited 3D model satisfying prior designs of the model library can be obtained. The invention can be applied to fields of 3D model editing and constructing, computer aided design etc.

Claims which contain your search:

1. A structure self-adaptive three-dimensional (3D) model editing method, comprising: step S100, clustering 3D models of a same category according to structures: inputting 3D models of the same category, clustering the 3D models into different groups according to difference in components contained in the 3D models, wherein structures of models in the same group are required to be as similar as possible and structures of models between different groups are required to be as different as possible; step S200, learning a design knowledge prior of intra-group 3D models: compiling statistics of relationships between the components of models in the same group using a multivariate linear regression model according to a result of the clustering of the 3D models of the same category, to guide a 3D model editing procedure with structures thereof being preserved; step S300, learning a structure switching rule of inter-group 3D models: analyzing geometrical parameter distribution of common components of the models in different groups according to the result of the clustering of the 3D models of the same category, and obtaining the structure switching rule of inter-group 3D models; and step S400, optimizing a user-edited 3D model: editing, by a user using an interactive tool, a size, a position, and/or an angle parameter of a component of a 3D model, adjusting a structure of the user-edited 3D model and automatically optimizing geometrical parameters of other components of the user-edited 3D model according to the learned design knowledge prior of intra-group 3D models and the learned structure switching rule of inter-group 3D models so that the optimized 3D model satisfies a design knowledge prior of a model library, the structure self-adaptive 3D model editing method is used to increase a 3D model editing speed and improve degree of automation of the model editing.

2. The method according to claim 1, wherein the clustering 3D models of a same category according to structures in step S 100 comprises: step S110, normalizing sizes and positions of 3D models in the model library, and pre-dividing the models in the model library into a component level, wherein the method does not require a semantic corresponding relationship between components of different models, the corresponding relationship between components of different models is obtained automatically by clustering the components according to a position relationship between the components; step S120, after clustering the components of the 3D models, determining a quantity N of component types in the models of the same category and defining a set of the components as {P_(1), P_(2), . . . P_(N)}, for a model S_(i )in the model library, a vector x_(i )containing N elements is obtained, if the model S_(i )contains a component P_(n), then x_(i)(n)=1, otherwise, x_(i)(n)=0, given any two 3D models S_(i )and S_(j), vectors x_(i )and x_(j )are obtained, wherein a distance ( ) between the two vectors is defined as:

3. The method according to claim 1, wherein the learning a design knowledge prior of intra-group 3D models in step 200 comprises: step S210, obtaining an orientated bounding box (OBB) for a component of a 3D model, which comprises following three steps: first, obtaining an approximate convex hull for coordinate points of the 3D model, taking a random plane on the approximate convex hull as a projection plane and projecting all the points of the model to the projection plane, calculating a 2D OBB of the projected points and stretching the 2D OBB along a plane normal direction until all the points of the model are included, which forms a candidate OBB; then, calculating a quantity of symmetric planes of respective OBBs, defining three candidate planes for any OBB i, wherein these candidate planes are determined by a center C_(i )and three axial directions (a_(i)^(1), a_(i)^(2), a_(i)^(3)) of the OBB, obtaining uniform sampling points on a surface of the model, calculating reflected points of the sampling points when reflected by a random candidate plane, calculating distances from these reflected points to the surface of the model, if a distance is smaller than 0.0001, determining the corresponding sampling point as a symmetric point, if a percentage of the symmetric points exceeds 90%, determining the candidate plane as a symmetric plane; eventually, determining an optimal OBB which has the most symmetric planes, if a plurality of candidate OBBs contain a same quantity of symmetric planes, determining the OBB with a smallest volume as a final OBB; step 220, extracting parameters of the component of the 3D model, given a random model, OBBs of a plurality of components are obtained, for component j of model i, a center C_(j)^(i )of the OBB, three axial directions (a_(j,1)^(i), a_(j,2)^(i), a_(j,3)^(i)), and the lengths (e_(j,1)^(i), e_(j,2)^(i), e_(j,3)^(i)) of the OBB in the respective axial directions are obtained, these parameters are used to extract nine-dimensional parameters, for the component of model i, parameters F_(j)^(i)=(f_(j,1)^(i), f_(j,2)^(i), f_(j,3)^(i ). . . f_(j,9)^(i)) are obtained, wherein first three parameters represent the center of the OBB, middle three parameters represent projection angles between the respective axial directions and corresponding world coordinate axes, and last three parameters represent lengths of the OBB on the three directions, the obtained nine-dimensional parameters are successively used in the learning of the design knowledge prior of the model, which are inputs of an intra-group design knowledge prior learning module, and are also candidate threshold values of an inter-group structure switching parameter of an inter-group design knowledge prior learning module; and step 230, learning an intra-group design knowledge prior, the intra-group design knowledge prior emphasizes on learning a deformation rule of the model while the structure thereof is preserved, in a same structure group, defining a component that all the modules contain as a common component, a total quantity of the common components is M, defining a multivariate regression coefficient matrix as {A^((m))|m=1, . . . M}, wherein A^((m) )contains regression coefficients of all the common components, a_(i)=_(0), . . . _(n )represents an i-th row of the matrix, which is computed as:

4. The method according to claim 1, wherein the learning a structure switching rule of inter-group 3D models in step S 300 comprises: step S310, determining a candidate threshold value of structure switching, given two structure groups S_(i )and S_(j), wherein the two structure groups contain components having a corresponding relationship, then a parameter of a common component can be threshold values of structure switching; and step S320, determining a final threshold value of structure switching, using an MM matrix to represent a structure switching rule of inter-group 3D models, the matrix is expressed as {T_(n)^(t)|t=1, . . . 9}_(n=1)^(N), wherein T_(n)^(t )represents a correlative value of any two structure groups with respective to a t-th parameter of a component P_(n), T_(n)^(t)(i, j) represents whether the parameter should be the threshold value between the structure groups S_(i )and S_(j), assuming the component P_(n )is the common component of the structure groups S_(i )and S_(j), the t-th parameters of the components P_(n )of all the models in the two structure groups is represented as {B_(i)|b_(1)^(i), . . . b_(N)^(i)} and {B_(j)|b_(1)^(j), . . . b_(N)^(j)}, then d(B) and d(b, B) are defined as:

5. The method according to claim 1, wherein the optimizing a user-edited 3D model in step S 400 comprises: step S410, editing a component of a 3D model, wherein a user selects a component of a 3D model using a mouse, and conducts translation, rotation, scaling, deleting and adding operations to the corresponding component; step S420, performing self-adaptive structure conversion: if the user selects the component of the model and operates a parameter, then setting the parameter as b, if the parameter is a threshold value of inter-group structure switching and a current structure group is S_(i), the following formula is used to determine if the structure needs to be converted to S_(j):


In one embodiment, an enclosure generator automatically generates an enclosure for a device based on a three-dimensional (3D) model of a target surface and component instances that are associated with different positions within the device. In operation, the enclosure generator computes a surface region based on the target surface and the component instances. Subsequently, the enclosure generator computes a front panel model and a back structure model based on the surface region. Notably, the back structure model includes support structure geometries. Together, the front panel model and the back structure model comprise an enclosure model. The enclosure generator then stores the enclosure model or transmits the enclosure model to a 3D fabrication device. Advantageously, unlike conventional, primarily manual approaches to enclosure generation, the enclosure generator does not rely on the user possessing any significant technical expertise.

Claims which contain your search:

1. A computer-implemented method for generating an enclosure for a device, the method comprising: computing a surface region based on a target surface model and one or more component instances, wherein each component instance is associated with a different position within the device; generating a front panel model based on the surface region; generating a back structure model based on the surface region and the one or more component instances, wherein the back structure model includes one or more support structure geometries; and storing the front panel model and the back structure model or transmitting the front panel model and the back structure model to a three-dimensional (3D) fabrication device.

2. The computer-implemented method of claim 1, further comprising generating a mounting bracket model based on the target surface model and one or more mounting positions.

3. The computer-implemented method of claim 1, wherein generating the front panel model comprises enlarging the surface region based on a predetermined thickness to generate a first geometry, and generating a 3D model that includes the first geometry.

4. The computer-implement method of claim 1, wherein generating the front panel model comprises: enlarging the surface region based on a predetermined thickness to generate a first geometry; determining that a first position of a first component instance that is not included in the one or more component instances resides within the first geometry; performing a Boolean difference operation between the first component instance and the first geometry to generate a second geometry; and generating a 3D model that includes the second geometry.

5. The computer-implemented method of claim 1, wherein generating the back structure model comprises: generating a side geometry with a predefined thickness based on the surface region and the one or more component instances; generating the one or more support structure geometries based on the side geometry and at least one component instance included in the one or more component instances; and generating a 3D model that includes the side geometry and the support structure geometries.

8. The computer-implemented method of claim 1, wherein computing the surface region comprises: projecting the one or more component instances along an extrusion direction onto the target surface model to generate one or more bounding boxes; generating a convex hull based on the one or more bounding boxes; and selecting one or more faces inside the convex hull.

9. The computer-implemented method of claim 1, further comprising, prior to computing the surface region: computing a preliminary surface region based on the target surface model and a first set of component instances that includes the one or more component instances and at least one other component instance; determining that a dimension of the preliminary surface region exceeds a predetermined threshold and, in response, displaying a multi-enclosure option to a user; receiving a selection of the multi-enclosure option and, in response, generating an enclosure model based on the target surface model and the at least one other component instance; and storing the enclosure model or transmitting the enclosure model to a three-dimensional (3D) fabrication device.

10. A computer-readable storage medium including instructions that, when executed by a processing unit, cause the processing unit to generate an enclosure for a device by performing the steps of: computing a surface region based on a target surface model and one or more component instances, wherein each component instance is associated with a different position within the device; generating a front panel model based on the surface region; generating a back structure model based on the surface region and the one or more component instances, wherein the back structure model includes one or more support structure geometries; and storing the front panel model and the back structure model or transmitting the front panel model and the back structure model to a three-dimensional (3D) fabrication device.

11. The computer-readable storage medium of claim 10, further comprising generating a mounting bracket model based on the target surface model and one or more mounting positions.

12. The computer-readable storage medium of claim 10, wherein generating the front panel model comprises enlarging the surface region based on a predetermined thickness to generate a first geometry, and generating a 3D model that includes the first geometry.

13. The computer-readable storage medium of claim 10, wherein generating the front panel model comprises: enlarging the surface region based on a predetermined thickness to generate a first geometry; determining that a first position of a first component instance that is not included in the one or more component instances resides within the first geometry; performing a Boolean difference operation between the first component instance and the first geometry to generate a second geometry; and generating a 3D model that includes the second geometry.

14. The computer-readable storage medium of claim 10, wherein generating the back structure model comprises: generating a side geometry with a predefined thickness based on the surface region and the one or more component instances; generating the one or more support structure geometries based on the side geometry and at least one component instance included in the one or more component instances; and generating a 3D model that includes the side geometry and the support structure geometries.

19. A system comprising: a memory storing an enclosure generator; and a processor that is coupled to the memory and, when executing the enclosure generator, is configured to:compute a surface region based on a target surface model and one or more component instances, wherein each component instance is associated with a different position within the device;generate a front panel model based on the surface region;generate a back structure model based on the surface region and the one or more component instances, wherein the back structure model includes one or more support structure geometries; andstore the front panel model and the back structure model or transmit the front panel model and the back structure model to a three-dimensional (3D) fabrication device.


A method for analyzing a structure includes processing nondestructive inspection (NDI) data for a multi-layer structure to define areas of inconsistency at an internal layer or an interface between adjacent layers. The method includes mapping the areas of inconsistency to finite elements of a finite element model of a nominal of the structure. These finite elements are thereby identified as affected finite elements and include finite elements for the affected internal layer or interface. The method includes producing a reconstructed finite element model of the affected structure from the nominal finite element model, and a modified property or state value assigned to respective element datasets of the affected finite elements. The method includes performing a finite element method (FEM) failure analysis of the reconstructed finite element model under a load, which indicates an extent of residual integrity of the affected structure.

Claims which contain your search:

8. A method of analyzing a structure, the method comprising: processing nondestructive inspection (NDI) data for an affected structure composed of a plurality of layers, the NDI data being processed to define an area of inconsistency at an internal layer of the plurality of layers, or an interface between a particular pair of adjacent layers in the plurality of layers; receiving a finite element model of a nominal of the affected structure, the finite element model being composed of a plurality of finite elements having respective element datasets, the plurality of finite elements including a mesh of finite elements for each of the plurality of layers, and finite elements at an interface between each pair of adjacent layers in the plurality of layers; mapping the area of inconsistency to at least some of the plurality of finite elements that are thereby identified as affected finite elements, and that include finite elements of the mesh of finite elements for the internal layer, or the finite elements at the interface between the particular pair of adjacent layers; producing a reconstructed finite element model of the affected structure from the finite element model of the nominal, and a modified property or state value assigned to respective element datasets of the affected finite elements; and performing a finite element method (FEM) failure analysis of the reconstructed finite element model under a load, the FEM failure analysis producing an output that indicates an extent of residual integrity of the affected structure.

1. An apparatus for implementation of a system for analyzing a structure, the apparatus comprising a processor and a memory storing executable instructions that, in response to execution by the processor, cause the apparatus to implement at least: a nondestructive inspection (NDI) process module configured to process NDI data for an affected structure composed of a plurality of layers, the NDI data being processed to define an area of inconsistency at an internal layer of the plurality of layers, or an interface between a particular pair of adjacent layers in the plurality of layers; a finite element model input interface configured to receive a finite element model of a nominal of the affected structure, the finite element model being composed of a plurality of finite elements having respective element datasets, the plurality of finite elements including a mesh of finite elements for each of the plurality of layers, and finite elements at an interface between each pair of adjacent layers in the plurality of layers; a mapping module configured to map the area of inconsistency to at least some of the plurality of finite elements that are thereby identified as affected finite elements, and that include finite elements of the mesh of finite elements for the internal layer, or the finite elements at the interface between the particular pair of adjacent layers; a reconstitution module configured to produce a reconstructed finite element model of the affected structure from the finite element model of the nominal, and a modified property or state value assigned to respective element datasets of the affected finite elements; and a finite element method (FEM) failure analyzer configured to perform a FEM failure analysis of the reconstructed finite element model under a load, the FEM failure analysis producing an output that indicates an extent of residual integrity of the affected structure.

15. A computer-readable storage medium for analyzing a structure, the computer-readable storage medium being non-transitory and having computer-readable program code portions stored therein that in response to execution by a processor, cause an apparatus to at least: process NDI data for an affected structure composed of a plurality of layers, the NDI data being processed to define an area of inconsistency at an internal layer of the plurality of layers, or an interface between a particular pair of adjacent layers in the plurality of layers; receive a finite element model of a nominal of the affected structure, the finite element model being composed of a plurality of finite elements having respective element datasets, the plurality of finite elements including a mesh of finite elements for each of the plurality of layers, and finite elements at an interface between each pair of adjacent layers in the plurality of layers; map the area of inconsistency to at least some of the plurality of finite elements that are thereby identified as affected finite elements, and that include finite elements of the mesh of finite elements for the internal layer, or the finite elements at the interface between the particular pair of adjacent layers; produce a reconstructed finite element model of the affected structure from the finite element model of the nominal, and a modified property or state value assigned to respective element datasets of the affected finite elements; and perform a finite element method (FEM) failure analysis of the reconstructed finite element model under a load, the FEM failure analysis producing an output that indicates an extent of residual integrity of the affected structure.


Patent
Adobe Systems | Date: 2015-12-22

Techniques and systems are described to model and extract knowledge from images. A digital medium environment is configured to learn and use a model to compute a descriptive summarization of an input image automatically and without user intervention. Training data is obtained to train a model using machine learning in order to generate a structured image representation that serves as the descriptive summarization of an input image. The images and associated text are processed to extract structured semantic knowledge from the text, which is then associated with the images. The structured semantic knowledge is processed along with corresponding images to train a model using machine learning such that the model describes a relationship between text features within the structured semantic knowledge. Once the model is learned, the model is usable to process input images to generate a structured image representation of the image.

Claims which contain your search:

1. In a digital medium environment to learn a model that is usable to explicitly correlate image features of an input image with text features automatically and without user intervention, a method implemented by at least one computing device comprising: obtaining training data by the at least one computing device, the training data including images and associated text; extracting text features as structured semantic knowledge from the associated text of the training data using natural language processing by the at least one computing device; training a model using the structured semantic knowledge and the images as part of machine learning by the at least one computing device, the model once trained is configured to form a structured image representation to explicitly correlate the image features of the input image with at least one of the extracted text features.

2. The method as described in claim 1, further comprising generating the structured image representation as a descriptive summarization of the input image by associating structured tags with the input image using the trained model, the associating including computing probabilities that the extracted text features and the image features of the input image describe a same concept.

5. The method as described in claim 1, wherein the structured semantic knowledge is in a form of or a tuple.

6. The method as described in claim 1, wherein the extracting includes: extracting a plurality of the text features of the structured semenatic knowledge using of a plurality of different tuple extraction techniques; and based on at least partial consensus in the plurality of the text features of the structured semantic knowledge extracted using the plurality of different tuple extraction techniques to::determine whether to correct at least one of the text features of the structured semantic knowledge;remove the at least one of the text features of the structured semantic knowledge, oridentify a degree of confidence in the extracting of respective said text features of the structured semantic knowledge by the at least one computing device.

7. The method as described in claim 1, wherein the extracting includes: jointly extracting the structured semantic knowledge for a plurality of individual said text features of associated with a single respective said image; and based on at least partial consensus from to the plurality of individual said text features of the text associated with the single respective said image to;determine whether to correct at least one of the plurality of individual text features of the structured semantic knowledge;remove the at least one of the plurality of individual said text features of the structured semantic knowledge; oridentify a degree of confidence in the extracting of respective said text feature of the structured semantic knowledge by the at least one computing device.

8. The method as described in claim 1, wherein the extracting includes localizing at least part of the structured semantic knowledge as corresponding to respective subjects, predicates, or objects, in respective said images of the training data.

9. The method as described in claim 1, wherein the training includes adapting the text features or the image features of the image information, one to another, such that similar structured knowledge concepts have nearby and related representations within a vector space as part of the structured semantic knowledge.

10. The method as described in claim 1, wherein the model explicitly correlates the image features of the input image with the text features such that at least one of the image features is explicitly correlated with a first one of the text features but not a second one of the text features.

11. The method as described in claim 1, wherein the structured image representation of the input image explicitly correlates structured semantic knowledge obtained from the trained model with the input image and is performed without using text otherwise associated with the input image.

12. In a digital medium environment to use a model to explicitly correlate text features with image features of an input image automatically and without user intervention, a system by at least one computing device comprising: an extractor module to extract structured semantic knowledge from text associated with images in training data using natural language processing, the structured semantic knowledge describing the text features; a model training module to train a model using the structured semantic knowledge and image features of the images in the training data as part of machine learning by the at least one computing device, the model configured for determining probabilities to predict how well image features of the input image correlate to the extracted text features.

14. The system as described in claim 12, wherein the structured semantic knowledge is in a form of or a tuple.

15. The system as described in claim 12, wherein the extractor module is configured to localize at least part of the structured semantic knowledge as corresponding to respective objects in respective said images.

16. The system as described in claim 12, further comprising a structured image representation use module configured to use the structured image representation to locate the input image as part of an image search as part of a determination of how well the structured image representation corresponds to a search query of the image search.

17. The system as described in claim 12, further comprising a structured image representation use module configured to generate a caption for the image based on the structured image representation.

18. The system as described in claim 12, further comprising a structured image representation use module configured to deduce, based on the structured image representation, scene properties of the input image.

19. In a digital medium environment to use a model as part of an image search to locate an input image automatically and without user intervention, a method implemented by at least one computing device comprising: extracting structured semantic knowledge from text associated with images in training data using natural language processing by the at least one computing device, the structured semantic knowledge describing text features; training a model using the structured semantic knowledge and the images from the training data as part of machine learning by the at least one computing device; forming a structured image representation of the input image, by the at least one computing device, that explicitly correlates at least part of the text features of the training data with image features of the input image; and locating the input image as part of an image search by the at least one computing device, using the structured image representation.


A structure of interest is irradiated with radiation for example in the x-ray or EUV waveband, and scattered radiation is detected by a detector (306). A processor (308) calculates a property such as linewidth (CD) by simulating interaction of radiation with a structure and comparing the simulated interaction with the detected radiation. A layered structure model (600, 610) is used to represent the structure in a numerical method. The structure model defines for each layer of the structure a homogeneous background permittivity and for at least one layer a non-homogeneous contrast permittivity. The method uses Maxwells equation in Born approximation, whereby a product of the contrast permittivity and the total field is approximated by a product of the contrast permittivity and the background field. A computation complexity is reduced by several orders of magnitude compared with known methods.

Claims which contain your search:

1. A method of simulating interaction of radiation with a structure, the method comprising the steps of: (a) defining a layered structure model to represent the structure in a two- or three-dimensional model space, the structure model defining for each layer of the structure a homogeneous background permittivity and for at least one layer a non-homogeneous contrast permittivity; and (b) using the layered structure model in a modal method to simulate interaction of radiation with the structure, a total field being calculated in terms of a background field and a contrast field, the background field within each layer being independent of the non-homogeneous contrast permittivity,wherein step (b) uses Maxwells equation in a Born approximation, whereby a product of the non-homogeneous contrast permittivity and the total field is approximated by a product of the non-homogeneous contrast permittivity and the background field.

2. The method of claim 1, wherein said structure model defines a unit cell of a structure that is periodic in one or more directions.

4. The method of claim 1, wherein said structure model defines said structure as a series of layers in a first direction, the modal method being performed by solving the Maxwell equation analytically in said first direction within each layer and connecting the solutions obtained for the series of layers to obtain a solution for the structure as a whole.

12. A method of determining parameters of a structure, the method comprising performing the steps of: (a) defining a layered structure model to represent the structure in a two- or three-dimensional model space, the layered structure model defining for each layer of the structure a homogeneous background permittivity and for at least one layer a non-homogeneous contrast permittivity; and (b) using the structure model in a modal method to simulate interaction of radiation with the structure, a total field being calculated in terms of a background field and a contrast field, the background field within each layer being independent of the non-homogeneous contrast permittivity, and (c) repeating step (b) while varying parameters of the structure model.

13. The method of claim 12, wherein step (c) comprises: (c1) comparing the interaction simulated in step (b) with a real interaction observed in a metrology apparatus with said target structure; (c2) varying one or more parameters of the structure model based on the result of the comparison; and (c3) repeating step (b) using the varied parameters,and wherein the method further comprises: (d) after a number of iterations of step (c) reporting parameters of the structure model as a measurement of parameters of the target structure.

20. A processing apparatus for use in simulating interaction of radiation with a structure, the processing apparatus comprising: storage for a layered structure model to represent the structure in a two- or three-dimensional model space, the structure model defining for each layer of the structure a homogeneous background permittivity and for at least one layer a non-homogeneous contrast permittivity; and a processor for using the structure model in a modal method to simulate interaction of radiation with the structure, a total field being calculated in terms of a background field and a contrast field, the background field within each layer being independent of the non-homogeneous contrast permittivity, wherein the processor is arranged to use Maxwells equation in a Born approximation, whereby a product of the non-homogeneous contrast permittivity and the total field is approximated by a product of the non-homogeneous contrast permittivity and the background field.

22. The processing apparatus of claim 20, wherein said structure model defines said structure as a series of layers in a first direction, the modal method being performed by solving the Maxwell equation analytically in said first direction within each layer and connecting the solutions obtained for the series of layers to obtain a solution for the structure as a whole.

25. An apparatus for determining parameters of a structure, the apparatus comprising a processing apparatus that simulates interaction of radiation with the structure, and repeats simulation while varying parameters of the structure model, wherein the processing apparatus comprises: storage for a layered structure model to represent the structure in a two- or three-dimensional model space, the structure model defining for each layer of the structure a homogeneous background permittivity and for at least one layer a non-homogeneous contrast permittivity; and a processor for using the structure model in a modal method to simulate interaction of radiation with the structure, a total field being calculated in terms of a background field and a contrast field, the background field within each layer being independent of the non-homogeneous contrast permittivity, and wherein the processor is arranged to use Maxwells equation in a Born approximation, whereby a product of the non-homogeneous contrast permittivity and the total field is approximated by a product of the non-homogeneous contrast permittivity and the background field.

26. The apparatus of claim 25, wherein the apparatus is arranged to repeat operation of the processor by: comparing the interaction simulated with a real interaction observed in a metrology apparatus with said target structure; varying one or more parameters of the structure model based on the result of the comparison; repeating operation of the processor using the varied parameters; and after a number of operations of the processor to report parameters of the structure model as a measurement of parameters of the target structure.

27. A metrology apparatus for use in determining parameters of a structure, the metrology apparatus comprising: an irradiation system for generating a beam of radiation; a substrate support operable with the irradiation system for irradiating a structure formed on the substrate with radiation; a detection system for detecting radiation after interaction with the structure; andan apparatus arranged to determine a property of the structure based on the detected radiation, the apparatus comprising:storage for a layered structure model to represent the structure in a two- or three-dimensional model space, the structure model defining for each layer of the structure a homogeneous background permittivity and for at least one layer a non-homogeneous contrast permittivity; anda processor for using the structure model in a modal method to simulate interaction of radiation with the structure, a total field being calculated in terms of a background field and a contrast field, the background field within each layer being independent of the non-homogeneous contrast permittivity, and wherein the processor is arranged to use Maxwells equation in a Born approximation, whereby a product of the non-homogeneous contrast permittivity and the total field is approximated by a product of the non-homogeneous contrast permittivity and the background field.

28. A device manufacturing method comprising: transferring a pattern from a patterning device onto a substrate using a lithographic process, the pattern defining at least one structure; measuring one or more properties of the structure to determine a value for one or more parameters of the lithographic process; and applying a correction in subsequent operations of the lithographic process in accordance with the measured property, wherein the step of measuring the properties of the structure includes determining a property by: (a) defining a layered structure model to represent the structure in a two- or three-dimensional model space, the layered structure model defining for each layer of the structure a homogeneous background permittivity and for at least one layer a non-homogeneous contrast permittivity; and (b) using the structure model in a modal method to simulate interaction of radiation with the structure, a total field being calculated in terms of a background field and a contrast field, the background field within each layer being independent of the non-homogeneous contrast permittivity, and (c) repeating step (b) while varying parameters of the structure model.


There is enabled an accurate fluid-structure interaction simulation on a biological organ having a deformable site that has an interface difficult to track. An operation unit (12) represents a structure domain in which tissues of a biological organ exist by using a structure mesh model (2) based on a Lagrange description method and a fluid domain in which fluid inside the biological organ exists by using an ALE fluid mesh model (3) based on an ALE description method. In addition, when performing a fluid-structure interaction simulation, the operation unit (12) deforms the structure mesh model (2) along with the progress of the simulation. Specifically, the operation unit (12) deforms the ALE fluid mesh model (3) in such a manner that no gap is formed on a first interface (4) located between a domain in which a site other than a certain site (2a) of the biological organ in the structure domain exists and the fluid domain or no overlap is formed with the structure domain, to track the first interface (4). The operation unit (12) captures a position of a second interface (5) located between a domain in which the certain site (2a) in the structure domain exists and the fluid domain by using the ALE fluid mesh model (3) as a reference.

Claims which contain your search:

1. A biological simulation apparatus comprising:a storage unit configured to hold a geometric model that represents a structure of a biological organ; andan operation unit configured to perform a fluid-structure interaction simulation that obtains ever-changing equilibrium conditions by representing, among domains in the geometric model, a structure domain in which tissues of the biological organ exist by using a structure mesh model based on a Lagrange description method and a fluid domain in which fluid inside the biological organ exists by using an ALE (Arbitrary Lagrangian Eulerian) fluid mesh model based on an ALE description method, deforming the structure mesh model in accordance with a motion of the biological organ along with a progress of the simulation, generating a deformed ALE fluid mesh model by deforming the ALE fluid mesh model in such a manner that no gap is formed on a first interface located between a domain in which a site other than a certain site of the biological organ in the structure domain exists and the fluid domain or no overlap is formed with the structure domain, tracking the first interface by using the deformed ALE fluid mesh model, capturing a position of a second interface located between a domain in which the certain site in the structure domain exists and the fluid domain by using the deformed ALE fluid mesh model as a reference, and simultaneously solving both motions of the biological organ and the fluid therein, including the interaction of the biological organ and the fluid.

2. The biological simulation apparatus according to claim 1, wherein the storage unit holds a plurality of postoperative geometric models which represent structures of the biological organ that are obtained by performing a plurality of virtual operations using different operative procedures, respectively, on the biological organ, andwherein the operation unit performs a fluid-structure interaction simulation on each of the plurality of postoperative geometric models and displays simulation results of the plurality of virtual operations.

3. The biological simulation apparatus according to claim 2, wherein the operation unit displays possible techniques usable in a virtual operation, generates a postoperative geometric model by deforming the geometric model in accordance with a selected technique, and stores the generated postoperative geometric model in the storage unit.

4. The biological simulation apparatus according to claim 2 or 3, wherein the operation unit evaluates a simulation result about each of the plurality of postoperative geometric models in view of a predetermined reference and arranges and displays the plurality of virtual operations in view of the evaluation results.

5. The biological simulation apparatus according to any one of claims 1 to 4, wherein the operation unit deforms the ALE fluid mesh model by using an ALE method and captures the position of the second interface using the ALE fluid mesh model as a reference by using a Lagrange multiplier method.

7. A biological simulation apparatus control method executed by a biological simulation apparatus, the method comprising:representing, among domains in a geometric model that represents a structure of a biological organ, a structure domain in which tissues of the biological organ exist by using a structure mesh model based on a Lagrange description method and a fluid domain in which fluid inside the biological organ exists by using an ALE fluid mesh model based on an ALE description method; andperforming a fluid-structure interaction simulation that obtains ever-changing equilibrium conditions by deforming the structure mesh model in accordance with a motion of the biological organ along with a progress of the simulation, generating a deformed ALE fluid mesh model by deforming the ALE fluid mesh model in such a manner that no gap is formed on a first interface located between a domain in which a site other than a certain site of the biological organ in the structure domain exists and the fluid domain or no overlap is formed with the structure domain, tracking the first interface by using the deformed ALE fluid mesh model, capturing a position of a second interface located between a domain in which the certain site in the structure domain exists and the fluid domain by using the deformed ALE fluid mesh model as a reference, and simultaneously solving both motions of the biological organ and the fluid therein, including the interaction of the biological organ and the fluid.

8. A biological simulation apparatus control program which causes a biological simulation apparatus to perform a procedure comprising:representing, among domains in a geometric model that represents a structure of a biological organ, a structure domain in which tissues of the biological organ exist by using a structure mesh model based on a Lagrange description method and a fluid domain in which fluid inside the biological organ exists by using an ALE fluid mesh model based on an ALE description method; andperforming a fluid-structure interaction simulation that obtains ever-changing equilibrium conditions by deforming the structure mesh model in accordance with a motion of the biological organ along with a progress of the simulation, generating a deformed ALE fluid mesh model by deforming the ALE fluid mesh model in such a manner that no gap is formed on a first interface located between a domain in which a site other than a certain site of the biological organ in the structure domain exists and the fluid domain or no overlap is formed with the structure domain, tracking the first interface by using the deformed ALE fluid mesh model, capturing a position of a second interface located between a domain in which the certain site in the structure domain exists and the fluid domain by using the deformed ALE fluid mesh model as a reference, and simultaneously solving both motions of the biological organ and the fluid therein, including the interaction of the biological organ and the fluid.