Additive manufacturing(AM)has made significant progress in recent years and has been successfully applied in various fields owing to its ability to manufacture complex geometries.This method efficiently expands the de...Additive manufacturing(AM)has made significant progress in recent years and has been successfully applied in various fields owing to its ability to manufacture complex geometries.This method efficiently expands the design space,allowing for the creation of products with better performance than ever before.With the emergence of new manufacturing technologies,new design methods are required to efficiently utilize the expanded design space.Therefore,topology optimization methods have attracted the attention of researchers because of their ability to generate new and optimized designs without requiring prior experience.The combination of AM and topology optimization has proven to be a powerful tool for structural innovation in design and manufacturing.However,it is important to note that AM does not eliminate all manufacturing restrictions but instead replaces them with a different set of design considerations that designers must consider for the successful implementation of these technologies.This has motivated research on topology optimization methods that incorporate manufacturable constraints for AM structures.In this paper,we present a survey of the latest studies in this research area,with a particular focus on developments in China.Additionally,we discuss the existing research gaps and future development trends.展开更多
The integration of remote sensing and artificial intelligence technologies into photovoltaic(PV)power generation has significantly enhanced the efficiency and precision of monitoring and evaluating PV station construc...The integration of remote sensing and artificial intelligence technologies into photovoltaic(PV)power generation has significantly enhanced the efficiency and precision of monitoring and evaluating PV station construction.However,most semantic segmentation models are primarily developed for natural scenes,often neglecting the distinctive visual attributes of PV panels.We introduce a visual feature constraint method designed to tailor the segmentation network to the unique aspects of PV panels,including their texture,color,and shape.The method incorporates a constraint module,comprised of three adversarial autoencoders,into a conventional segmentation model.This technique represents a versatile training framework that can be seamlessly integrated with state-of-theart models,providing clear insights into the learning process.Experimental results with UperNet,SegFormer,DeepLabV3+,TransUNet,CorrMatch,SCSM and UKAN as baseline models show a maximum IoU improvement of 2.16%.Notably,UperNet attains the superior segmentation outcomes,whereas DeepLabV3+exhibits the greatest benefit from the imposed constraints.Furthermore,our findings reveal that various models exhibit distinct sensitivities to different visual features,and employing multiple constraints typically yields better results than relying on single-feature constraints.Collectively,our proposed method showcases its potential to advance PV panel segmentation in remote sensing applications,presenting a scalable and effective solution.展开更多
This paper presents a feature modeling approach to address the 3D structural topology design optimization withfeature constraints. In the proposed algorithm, various features are formed into searchable shape features ...This paper presents a feature modeling approach to address the 3D structural topology design optimization withfeature constraints. In the proposed algorithm, various features are formed into searchable shape features bythe feature modeling technology, and the models of feature elements are established. The feature elements thatmeet the design requirements are found by employing a feature matching technology, and the constraint factorscombined with the pseudo density of elements are initialized according to the optimized feature elements. Then,through controlling the constraint factors and utilizing the optimization criterion method along with the filteringtechnology of independent mesh, the structural design optimization is implemented. The present feature modelingapproach is applied to the feature-based structural topology optimization using empirical data. Meanwhile, theimproved mathematical model based on the density method with the constraint factors and the correspondingsolution processes are also presented. Compared with the traditional method which requires complicated constraintprocessing, the present approach is flexibly applied to the 3D structural design optimization with added holesby changing the constraint factors, thus it can design a structure with predetermined features more directly andeasily. Numerical examples show effectiveness of the proposed feature modeling approach, which is suitable for thepractical engineering design.展开更多
Example-based super-resolution algorithms,which predict unknown high-resolution image information using a relationship model learnt from known high- and low-resolution image pairs, have attracted considerable interest...Example-based super-resolution algorithms,which predict unknown high-resolution image information using a relationship model learnt from known high- and low-resolution image pairs, have attracted considerable interest in the field of image processing. In this paper, we propose a multi-example feature-constrained back-projection method for image super-resolution. Firstly, we take advantage of a feature-constrained polynomial interpolation method to enlarge the low-resolution image. Next, we consider low-frequency images of different resolutions to provide an example pair. Then, we use adaptive k NN search to find similar patches in the low-resolution image for every image patch in the high-resolution low-frequency image, leading to a regression model between similar patches to be learnt. The learnt model is applied to the low-resolution high-frequency image to produce high-resolution high-frequency information. An iterative back-projection algorithm is used as the final step to determine the final high-resolution image.Experimental results demonstrate that our method improves the visual quality of the high-resolution image.展开更多
基金supported by National Natural Science Foundation of China(Grant Nos.12272076,U2341232,11332004,and U1808215)the 111 Project of China(Grant No.B14013).
文摘Additive manufacturing(AM)has made significant progress in recent years and has been successfully applied in various fields owing to its ability to manufacture complex geometries.This method efficiently expands the design space,allowing for the creation of products with better performance than ever before.With the emergence of new manufacturing technologies,new design methods are required to efficiently utilize the expanded design space.Therefore,topology optimization methods have attracted the attention of researchers because of their ability to generate new and optimized designs without requiring prior experience.The combination of AM and topology optimization has proven to be a powerful tool for structural innovation in design and manufacturing.However,it is important to note that AM does not eliminate all manufacturing restrictions but instead replaces them with a different set of design considerations that designers must consider for the successful implementation of these technologies.This has motivated research on topology optimization methods that incorporate manufacturable constraints for AM structures.In this paper,we present a survey of the latest studies in this research area,with a particular focus on developments in China.Additionally,we discuss the existing research gaps and future development trends.
基金supported by the Key Research and Development Program of Ordos[grant number YF20232306].
文摘The integration of remote sensing and artificial intelligence technologies into photovoltaic(PV)power generation has significantly enhanced the efficiency and precision of monitoring and evaluating PV station construction.However,most semantic segmentation models are primarily developed for natural scenes,often neglecting the distinctive visual attributes of PV panels.We introduce a visual feature constraint method designed to tailor the segmentation network to the unique aspects of PV panels,including their texture,color,and shape.The method incorporates a constraint module,comprised of three adversarial autoencoders,into a conventional segmentation model.This technique represents a versatile training framework that can be seamlessly integrated with state-of-theart models,providing clear insights into the learning process.Experimental results with UperNet,SegFormer,DeepLabV3+,TransUNet,CorrMatch,SCSM and UKAN as baseline models show a maximum IoU improvement of 2.16%.Notably,UperNet attains the superior segmentation outcomes,whereas DeepLabV3+exhibits the greatest benefit from the imposed constraints.Furthermore,our findings reveal that various models exhibit distinct sensitivities to different visual features,and employing multiple constraints typically yields better results than relying on single-feature constraints.Collectively,our proposed method showcases its potential to advance PV panel segmentation in remote sensing applications,presenting a scalable and effective solution.
基金This work is supported by the National Natural Science Foundation of China(12002218)the Youth Foundation of Education Department of Liaoning Province(JYT19034).These supports are gratefully acknowledged.
文摘This paper presents a feature modeling approach to address the 3D structural topology design optimization withfeature constraints. In the proposed algorithm, various features are formed into searchable shape features bythe feature modeling technology, and the models of feature elements are established. The feature elements thatmeet the design requirements are found by employing a feature matching technology, and the constraint factorscombined with the pseudo density of elements are initialized according to the optimized feature elements. Then,through controlling the constraint factors and utilizing the optimization criterion method along with the filteringtechnology of independent mesh, the structural design optimization is implemented. The present feature modelingapproach is applied to the feature-based structural topology optimization using empirical data. Meanwhile, theimproved mathematical model based on the density method with the constraint factors and the correspondingsolution processes are also presented. Compared with the traditional method which requires complicated constraintprocessing, the present approach is flexibly applied to the 3D structural design optimization with added holesby changing the constraint factors, thus it can design a structure with predetermined features more directly andeasily. Numerical examples show effectiveness of the proposed feature modeling approach, which is suitable for thepractical engineering design.
基金supported by the National Natural Science Foundation of China(Grant Nos.61572292,61332015,61373078,and 61272430)the National Research Foundation for the Doctoral Program of Higher Education of China(Grant No.20110131130004)
文摘Example-based super-resolution algorithms,which predict unknown high-resolution image information using a relationship model learnt from known high- and low-resolution image pairs, have attracted considerable interest in the field of image processing. In this paper, we propose a multi-example feature-constrained back-projection method for image super-resolution. Firstly, we take advantage of a feature-constrained polynomial interpolation method to enlarge the low-resolution image. Next, we consider low-frequency images of different resolutions to provide an example pair. Then, we use adaptive k NN search to find similar patches in the low-resolution image for every image patch in the high-resolution low-frequency image, leading to a regression model between similar patches to be learnt. The learnt model is applied to the low-resolution high-frequency image to produce high-resolution high-frequency information. An iterative back-projection algorithm is used as the final step to determine the final high-resolution image.Experimental results demonstrate that our method improves the visual quality of the high-resolution image.
