In this study,cylindrical sandstone samples were imaged by CT scanning technique,and the pore structure images of sandstone samples were analyzed and generated by combining with StyleGAN2-ADA generative adversarial ne...In this study,cylindrical sandstone samples were imaged by CT scanning technique,and the pore structure images of sandstone samples were analyzed and generated by combining with StyleGAN2-ADA generative adversarial network(GAN)model.Firstly,nine small column samples with a diameter of 4 mm were drilled from sandstone samples with a diameter of 2.5 cm,and their CT scanning results were preprocessed.Because the change between adjacent slices was little,using all slices directly may lead to the problem of pattern collapse in the process of model generation.In order to solve this problem,one slice was selected as training data every 30 slices,and the diversity of slices was verified by calculating the LPIPS values of these slices.The results showed that the strategy of selecting one slice every 30 slices could effectively improve the diversity of images generated by the model and avoid the phenomenon of pattern collapse.Through this process,a total of 295 discontinuous two-dimensional slices were generated for the generation and segmentation analysis of sandstone pore structures.This study can provide effective data support for accurate segmentation of porous medium structures,and simultaneously improves the stability and diversity of generative adversarial network under the condition of small samples.展开更多
In this paper we present a research-through-design study where we employed text-to-text,text-to-image,and image-to-image generative tools for a conceptual architecture project for the eVolo skyscraper competition.We t...In this paper we present a research-through-design study where we employed text-to-text,text-to-image,and image-to-image generative tools for a conceptual architecture project for the eVolo skyscraper competition.We trained these algorithms on a dataset that we collected and curated,consisting of texts about and images of architecture.We describe our design process,present the final proposal,reflect on the usefulness of such tools for early-stage design,and discuss implications for future research and practice.By analysing the results from training the text-to-text generators we could establish a specific design brief that informed the final concept.The results from the image-to-image generator gave an overview of the shape grammars of previous submissions.All results were intriguing and can assist creativity and in this way,the tools were useful for gaining insight into historical architectural data,helped shape a specific design brief,and provoked new ideas.By reflecting on our design process,we argue that the use of language when employing such tools takes a new role and that three layers of language intertwined in our work:architectural discourse,programming languages,and annotations.We present a map that unfolds how these layers came together as a contribution to making machine learning more explainable for creatives.展开更多
文摘In this study,cylindrical sandstone samples were imaged by CT scanning technique,and the pore structure images of sandstone samples were analyzed and generated by combining with StyleGAN2-ADA generative adversarial network(GAN)model.Firstly,nine small column samples with a diameter of 4 mm were drilled from sandstone samples with a diameter of 2.5 cm,and their CT scanning results were preprocessed.Because the change between adjacent slices was little,using all slices directly may lead to the problem of pattern collapse in the process of model generation.In order to solve this problem,one slice was selected as training data every 30 slices,and the diversity of slices was verified by calculating the LPIPS values of these slices.The results showed that the strategy of selecting one slice every 30 slices could effectively improve the diversity of images generated by the model and avoid the phenomenon of pattern collapse.Through this process,a total of 295 discontinuous two-dimensional slices were generated for the generation and segmentation analysis of sandstone pore structures.This study can provide effective data support for accurate segmentation of porous medium structures,and simultaneously improves the stability and diversity of generative adversarial network under the condition of small samples.
基金This research was partly funded by the Human-Centered Al cluster at the Department of Communication and Psychology at Aalborg University in Denmark.Johanna Lauritzen and Matthias Klages have contributed to data collection and processing for this work in its initial stages.Additionally,we would like to thank the editor and the reviewers for their valuable feedback which helped refine and clarify many aspects of the manuscript.
文摘In this paper we present a research-through-design study where we employed text-to-text,text-to-image,and image-to-image generative tools for a conceptual architecture project for the eVolo skyscraper competition.We trained these algorithms on a dataset that we collected and curated,consisting of texts about and images of architecture.We describe our design process,present the final proposal,reflect on the usefulness of such tools for early-stage design,and discuss implications for future research and practice.By analysing the results from training the text-to-text generators we could establish a specific design brief that informed the final concept.The results from the image-to-image generator gave an overview of the shape grammars of previous submissions.All results were intriguing and can assist creativity and in this way,the tools were useful for gaining insight into historical architectural data,helped shape a specific design brief,and provoked new ideas.By reflecting on our design process,we argue that the use of language when employing such tools takes a new role and that three layers of language intertwined in our work:architectural discourse,programming languages,and annotations.We present a map that unfolds how these layers came together as a contribution to making machine learning more explainable for creatives.
