[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120253-en":3,"doc-seo-120253-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120253,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Crystal Structure Detection in Microgravity Experiments using Machine Learning Methods - Bachelor Thesis","This bachelor thesis analyzes machine learning methods for crystal structure detection in microgravity experiments. The objective is to identify crystal structures from particles using a 2D projection, while extending and modifying an existing algorithm originally designed for 3D data. Extensive testing validates accuracy and computational efficiency across multiple experimental conditions, including variations in experimental setup parameters. The work also investigates how these methods could be integrated to improve an overall experimental workflow. Finally, the thesis compares modified implementations and discusses alternative approaches and future research directions.","Faculty of Applied Computer Sciences and Biosciences  \nBACHELOR THESIS  \nCrystal Structure Detection in Microgravity Experiments using Machine Learning Methods  \nAuthor:  \nEkaterina Filonenko  \nCourse of Study: Applied Mathematics  \nSeminar Group: MA20w1-B  \nFirst Examiner:  \nProf. Dr. rer. nat. Dr.-Ing. habil. Florian Zaussinger  \nSecond Examiner: Dr. Marika Kaden  \nSubmission:  \nMittweida, 22.09.2024  \nDefense/Evaluation: Mittweida, 2024  \nBibliographic Description  \nFilonenko, Ekaterina:  \nCrystal Structure Detection in Microgravity Experiments using Machine Learning Methods.– 2024.– 24 S.  \nMittweida, Hochschule Mittweida – University of Applied Sciences, Faculty of Applied Computer Sciences and Biosciences, Bachelor Thesis, 2024 .  \nReferat  \nIn this thesis, we analyze Machine Learning methods for crystal structure detection in microgravity experiments. Our objective is to identify crystal structures of the particles by a 2D projection. We modify an already existing algorithm for 3D structures [4] . Through extensive testing, we validate the accuracy and efficiency of our approach in various experimental conditions. Additionally, we explore the potential for integrating these methods to enhance the overall experimental workflow. Finally, we demonstrate the advantages of our modified implementations and discuss other possible approaches.  \nContents  \nContents I  \nList of Figures III  \nSource code IV  \nAcknowledgment V  \n1 Introduction 1  \n1.1 Structure ........................................... 1  \n1.2 Crystalline Solids and Crystal Structures ......................... 2  \n1.3 Description of Crystal Structures ............................. 3  \n1.3.1 Body-Centered Cubic (BCC) Structure .......................... 3  \n1.3.2 Face-Centered Cubic (FCC) Structure ........................... 4  \n1.3.3 Hexagonal Close-Packed (HCP) Structure ........................ 4  \n1.4 Microgravity Experiments ................................. 5  \n1.5 Physical Experiment for a 2D Projection ......................... 6  \n2 Methods 7  \n2.1 Feature Vector ....................................... 7  \n2.2 Multilayer Perceptron ................................... 8  \n2.3 Sample Data Generation .................................. 8  \n2.4 Procedure to obtain 2D from 3D Data .......................... 9  \n2.5 Training and Evaluation .................................. 14  \n2.6 Parameters of the Experimental Setup .......................... 15  \n3 Results 16  \n3.1 Slice Thickness ....................................... 16  \n3.2 Slice Rotation ........................................ 17  \n3.3 Circle Range ......................................... 19  \n4 Discussion 22  \n4.1 Experimental Setup Parameters ............................. 22  \n4.2 Comparison of Neighborhood Definitions ........................ 22  \n4.3 Challenges and Limitations ................................ 23  \n4.4 Future Research ...................................... 23  \n4.5 Conclusion .......................................... 23  \nAppendix 25  \nA Mixed Crystal Signature (Refactored) 25  \nA.1 Signaure ........................................... 25  \nA.2 Data Generator ....................................... 28  \nA.3 Crystal Analyzer ....................................... 31  \nB Projection 35  \nB.1 Plane ............................................. 35  \nB.2 Slice ............................................. 36  \nC Notebooks 38  \nC.1 Slice ............................................. 39  \nC.2 Experimental Parameters ................................. 47  \nC.3 Experimental Parameters for Area Neighborhood ................... 52  \nBibliography 62  \nStatutory Declaration in Lieu of an Oath 65  \nList of Figures  \n1.1 Schematic diagram highlighting the differences between the solid (left), liquid (centre) and gaseous (right) states of matter [25] ....................... 2  \n1.2 The bcc unit cell that consists of a net total of two atoms (left) and a schematic diagram of the bcc unit cell that has ","cbCaiu71lf3i30JV","https://ap.wps.com/l/cbCaiu71lf3i30JV","pdf",7077011,1,73,"English","en",105,"# Introduction\n## Structure\n## Crystalline Solids and Crystal Structures\n## Description of Crystal Structures\n## Microgravity Experiments\n## Physical Experiment for a 2D Projection\n# Methods\n## Feature Vector\n## Multilayer Perceptron\n## Sample Data Generation\n## Procedure to obtain 2D from 3D Data\n## Training and Evaluation\n## Parameters of the Experimental Setup\n# Results\n## Slice Thickness\n## Slice Rotation\n## Circle Range\n# Discussion\n## Experimental Setup Parameters\n## Comparison of Neighborhood Definitions\n## Challenges and Limitations\n## Future Research\n## Conclusion\n# Appendix\n## Mixed Crystal Signature (Refactored)\n## Projection\n## Notebooks","[{\"question\":\"How does the thesis detect crystal structures in microgravity experiments?\",\"answer\":\"It applies machine learning to infer crystal structures from particle observations represented as a 2D projection, rather than requiring full 3D information. The approach modifies an existing 3D algorithm to support this 2D setting.\"},{\"question\":\"What methods and model are used for the detection task?\",\"answer\":\"The thesis builds a feature vector representation and uses a multilayer perceptron. It also includes procedures for generating sample data and transforming 3D data into 2D slices for training and evaluation.\"},{\"question\":\"Which experimental setup factors are studied in the results?\",\"answer\":\"The results focus on slice thickness, slice rotation, and the circle range. These parameters are used to test how changes in experimental conditions affect detection accuracy and efficiency.\"}]","Crystal Structure Detection in Microgravity Experiments using Machine Learning Methods - Bachelor Thesis | PDF",1785729039,184,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"crystal-structure-detection-in-microgravity-experiments-using-machine-learning-methods-bachelor-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/crystal-structure-detection-in-microgravity-experiments-using-machine-learning-methods-bachelor-thesis/120253/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the thesis detect crystal structures in microgravity experiments?","Question",{"text":75,"@type":76},"It applies machine learning to infer crystal structures from particle observations represented as a 2D projection, rather than requiring full 3D information. The approach modifies an existing 3D algorithm to support this 2D setting.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What methods and model are used for the detection task?",{"text":80,"@type":76},"The thesis builds a feature vector representation and uses a multilayer perceptron. It also includes procedures for generating sample data and transforming 3D data into 2D slices for training and evaluation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which experimental setup factors are studied in the results?",{"text":84,"@type":76},"The results focus on slice thickness, slice rotation, and the circle range. 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