{"title":"D3mirt: Descriptive Three-Dimensional Multidimensional\u0000Item Response Theory for R","authors":"Erik Forsberg","doi":"10.21105/joss.06523","DOIUrl":"https://doi.org/10.21105/joss.06523","url":null,"abstract":"","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141646407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julian Ceddia, Jack Hellerstedt, Benjamin Lowe, A. Schiffrin
{"title":"Scanbot: An STM Automation Bot","authors":"Julian Ceddia, Jack Hellerstedt, Benjamin Lowe, A. Schiffrin","doi":"10.21105/joss.06028","DOIUrl":"https://doi.org/10.21105/joss.06028","url":null,"abstract":"","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":"37 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141645517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jonathan Knirsch, Felix Frölich, Lukas Hof, Florian Wittemann, Luise Kärger
The Machine instructions for material extrusion processes (MEX), such as the fused filament fabrication (FFF) process, are typically provided as GCode, which can be generated by a variety of slicer programs. The 3D model of the part is sliced into multiple layers and a tool path is created for each according to the parameters for infill, perimeters supports and other structures
{"title":"pyGCodeDecode: A Python package for time-accurate GCode\u0000simulation in material extrusion processes","authors":"Jonathan Knirsch, Felix Frölich, Lukas Hof, Florian Wittemann, Luise Kärger","doi":"10.21105/joss.06465","DOIUrl":"https://doi.org/10.21105/joss.06465","url":null,"abstract":"The Machine instructions for material extrusion processes (MEX), such as the fused filament fabrication (FFF) process, are typically provided as GCode, which can be generated by a variety of slicer programs. The 3D model of the part is sliced into multiple layers and a tool path is created for each according to the parameters for infill, perimeters supports and other structures","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":"29 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141645704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Ong, Martin B. Nielsen, Emily J. Hatt, Guy R. Davies
The upcoming second release of PBJam -- a software instrument for fitting normal modes ("peakbagging") -- supplements the simple power-spectrum model used in the first version to additionally constrain other features. Dipole ($ell = 1$) modes, which had been excluded in the initial version of the tool, are now specifically included. The primary samples of the PLATO mission consist mainly of main-sequence and subgiant stars, so PBjam implements a single parameterisation of dipole mixed-mode frequencies that reduces to pure p-modes in the former, and is suitable for use with the latter, outside the red-giant"asymptotic"regime. In keeping with the overall philosophy of PBjam's design, PBjam 2 will specify prior distributions on these parameters empirically, through predetermined values found for existing samples of solar-like oscillators. While the red-giant asymptotic regime has been extensively characterised observationally, the nonasymptotic construction for subgiants here has not, requiring us to construct this prior sample ourselves. To assist in this task, we built a tool -- Reggae -- to manually fine-tune and fit the dipole-mode model, and check the quality of both our initial guesses and fitted solutions. We have found it very helpful both for these tuning and visualisation tasks, and also as a didactic aid to understanding the dipole mixed-mode parameters. Moreover, no other tools currently exist for performing these tasks in the nonasymptotic parameterisation considered here. As such, we release Reggae publicly in advance of this update to PBjam, as we believe the community will benefit from access to such a visualisation tool. This will also assist future users of PBjam in devising ad-hoc prior constraints on the mixed-mode parameters, should they wish to perform mode identification for anomalous stars.
{"title":"Reggae: A Parametric Tuner for PBJam, and a\u0000Visualization Tool for Red Giant Oscillation Spectra","authors":"J. Ong, Martin B. Nielsen, Emily J. Hatt, Guy R. Davies","doi":"10.21105/joss.06588","DOIUrl":"https://doi.org/10.21105/joss.06588","url":null,"abstract":"The upcoming second release of PBJam -- a software instrument for fitting normal modes (\"peakbagging\") -- supplements the simple power-spectrum model used in the first version to additionally constrain other features. Dipole ($ell = 1$) modes, which had been excluded in the initial version of the tool, are now specifically included. The primary samples of the PLATO mission consist mainly of main-sequence and subgiant stars, so PBjam implements a single parameterisation of dipole mixed-mode frequencies that reduces to pure p-modes in the former, and is suitable for use with the latter, outside the red-giant\"asymptotic\"regime. In keeping with the overall philosophy of PBjam's design, PBjam 2 will specify prior distributions on these parameters empirically, through predetermined values found for existing samples of solar-like oscillators. While the red-giant asymptotic regime has been extensively characterised observationally, the nonasymptotic construction for subgiants here has not, requiring us to construct this prior sample ourselves. To assist in this task, we built a tool -- Reggae -- to manually fine-tune and fit the dipole-mode model, and check the quality of both our initial guesses and fitted solutions. We have found it very helpful both for these tuning and visualisation tasks, and also as a didactic aid to understanding the dipole mixed-mode parameters. Moreover, no other tools currently exist for performing these tasks in the nonasymptotic parameterisation considered here. As such, we release Reggae publicly in advance of this update to PBjam, as we believe the community will benefit from access to such a visualisation tool. This will also assist future users of PBjam in devising ad-hoc prior constraints on the mixed-mode parameters, should they wish to perform mode identification for anomalous stars.","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":"67 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141651552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"TransitionsInTimeseries.jl: A performant, extensible\u0000and reliable software for reproducible detection and prediction of\u0000transitions in timeseries","authors":"Jan Swierczek-Jereczek, George Datseris","doi":"10.21105/joss.06464","DOIUrl":"https://doi.org/10.21105/joss.06464","url":null,"abstract":"","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":"51 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141651868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thiago Glissoi Lopes, Paulo Monteiro de Carvalho Monson, P. R. de Aguiar, Reinaldo Götz de Oliveira Junior, P. O. C. Júnior
{"title":"fff_segmenter: A signal segmentation script for\u0000acoustic FFF fabrication data in MATLAB","authors":"Thiago Glissoi Lopes, Paulo Monteiro de Carvalho Monson, P. R. de Aguiar, Reinaldo Götz de Oliveira Junior, P. O. C. Júnior","doi":"10.21105/joss.06620","DOIUrl":"https://doi.org/10.21105/joss.06620","url":null,"abstract":"","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":"58 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141654543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Unjhawala, Ishaan Mahajan, R. Serban, D. Negrut
{"title":"A Library of Lower Fidelity Dynamics Models (LFDMs) For\u0000On-Road Vehicle Dynamics Targeting Faster Than Real-Time\u0000Applications","authors":"H. Unjhawala, Ishaan Mahajan, R. Serban, D. Negrut","doi":"10.21105/joss.06548","DOIUrl":"https://doi.org/10.21105/joss.06548","url":null,"abstract":"","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":"47 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141654483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Renato Laffranchi Falcão, Jorás Custódio Campos de Oliveira, Pedro Henrique Britto Aragão Andrade, Ricardo Ribeiro Rodrigues, Fabrício Jailson Barth, J. F. B. Brancalion
The goal of this project is to advance research in maritime search and rescue missions using Reinforcement Learning techniques. The software provides researchers with two distinct environments: one simulates shipwrecked people drifting with maritime currents, creating a stochastic setting for training and evaluating autonomous agents; the other features a realistic particle simulation for mapping and optimizing search area coverage by autonomous agents. Both environments adhere to open-source standards and offer extensive customization options, allowing users to tailor them to specific research needs. These tools enable Reinforcement Learning agents to learn efficient policies for locating shipwrecked individuals or maximizing search area coverage, thereby enhancing the effectiveness of maritime rescue operations
{"title":"DSSE: An environment for simulation of reinforcement\u0000learning-empowered drone swarm maritime search and rescue\u0000missions","authors":"Renato Laffranchi Falcão, Jorás Custódio Campos de Oliveira, Pedro Henrique Britto Aragão Andrade, Ricardo Ribeiro Rodrigues, Fabrício Jailson Barth, J. F. B. Brancalion","doi":"10.21105/joss.06746","DOIUrl":"https://doi.org/10.21105/joss.06746","url":null,"abstract":"The goal of this project is to advance research in maritime search and rescue missions using Reinforcement Learning techniques. The software provides researchers with two distinct environments: one simulates shipwrecked people drifting with maritime currents, creating a stochastic setting for training and evaluating autonomous agents; the other features a realistic particle simulation for mapping and optimizing search area coverage by autonomous agents. Both environments adhere to open-source standards and offer extensive customization options, allowing users to tailor them to specific research needs. These tools enable Reinforcement Learning agents to learn efficient policies for locating shipwrecked individuals or maximizing search area coverage, thereby enhancing the effectiveness of maritime rescue operations","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":"131 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141656442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
effects in experimental measurements and model simulations
实验测量和模型模拟的影响
{"title":"rgfrosh: A Python package for calculating shock\u0000conditions using real gas equations of state","authors":"Cory Kinney, Subith S. Vasu","doi":"10.21105/joss.06855","DOIUrl":"https://doi.org/10.21105/joss.06855","url":null,"abstract":"effects in experimental measurements and model simulations","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":"65 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"servir-aces: A Python Package for Training Machine\u0000Learning Models for Remote Sensing Applications","authors":"B. Bhandari, Timothy Mayer","doi":"10.21105/joss.06729","DOIUrl":"https://doi.org/10.21105/joss.06729","url":null,"abstract":"","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":"28 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141662462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：