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Systems Neuro Browser (SNUB) 系统神经浏览器(SNUB)
Pub Date : 2024-03-24 DOI: 10.21105/joss.06187
Caleb Weinreb, Mohammed Abdal Monium Osman, M. Jay, S. R. Datta
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引用次数: 0
checkpoint_schedules: schedules for incrementalcheckpointing of adjoint simulations checkpoint_schedules:用于增量检查邻接模拟的时间表
Pub Date : 2024-03-22 DOI: 10.21105/joss.06148
Daiane I. Dolci, James R. Maddison, David A. Ham, Guillaume Pallez, Julien Herrmann
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引用次数: 0
MRdataset : A unified and user-friendly interface tomedical imaging datasets MRdataset : 统一且用户友好的医学成像数据集界面
Pub Date : 2024-03-21 DOI: 10.21105/joss.06269
Harsh Sinha, P. Raamana
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引用次数: 0
Automatic Computation for Robot Design (ACRoD): APython package for numerical calculation of Jacobian of a robot at agiven configuration around a specified end-effector point 机器人设计自动计算 (ACRoD):APython 软件包,用于围绕指定的末端执行器点,对机器人在给定配置下的雅各布系数进行数值计算
Pub Date : 2024-03-21 DOI: 10.21105/joss.05927
A. Jacob, Rituparna Datta
The Jacobian of a robot refers to the matrix that linearly maps the velocity components of the end-effector and the velocities at the actuated joints. The Jacobian is extensively used in dimensional synthesis for Jacobian-based optimal performances of robotic manipulators, in which the optimal dimensional parameters of robots are computed. Determination of accurate mobility (Yang et al., 2008) of planar and spatial mechanisms can also be performed by using Jacobian in cases where Chebychev–Grübler–Kutzbach criterion cannot accurately determine the mobility (Gogu, 2005). As a result, Jacobian is a significant part for both kinematic analysis, dimensional synthesis and mobility determination of a mechanism. Hence, the formulation of Jacobian has its key importance in the literature and in the application of performance optimisation along with mobility computation. Formulation of Jacobian for serial manipulators can be computed easily, however, it is increasingly complicated to formulate Jacobian for parallel manipulators due to the existence of passive joint velocities and the nature in which these are related to active joint velocities. Several studies (Altuzarra et al., 2006; Dutre et al., 1997; D. Kim et al., 2000; S.-G. Kim &
机器人的雅各比是指将末端执行器的速度分量与执行关节的速度进行线性映射的矩阵。雅各布矩阵被广泛应用于基于雅各布矩阵的机器人机械手最佳性能的尺寸合成中,其中计算了机器人的最佳尺寸参数。在切比切夫-格吕布勒-库茨巴赫准则无法准确确定移动性的情况下,也可以使用雅各布函数来确定平面和空间机构的准确移动性(Yang 等人,2008 年)(Gogu,2005 年)。因此,雅各布函数对于机构的运动学分析、尺寸合成和流动性确定都具有重要意义。因此,雅各布公式在文献中以及在性能优化和机动性计算的应用中都具有重要意义。串联机械手的雅各布公式可以轻松计算,但由于存在被动关节速度以及这些速度与主动关节速度之间的关系,并联机械手的雅各布公式计算变得越来越复杂。一些研究(Altuzarra 等人,2006 年;Dutre 等人,1997 年;D. Kim 等人,2000 年;S.-G.Kim &
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引用次数: 0
Pywaterflood: Well connectivity analysis throughcapacitance-resistance modeling Pywaterflood:通过电容电阻模型分析水井连通性
Pub Date : 2024-03-20 DOI: 10.21105/joss.06191
Frank Male
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引用次数: 0
greeks: Sensitivities of Prices of Financial Optionsand Implied Volatilities 希腊:金融期权价格的敏感性和隐含波动率
Pub Date : 2024-03-19 DOI: 10.21105/joss.05987
Anselm Hudde
Summary The greeks R package leverages the Black-Scholes model and more general jump diffusion models to compute sensitivities of financial option prices for European, geometric and arithmetic Asian, as well as American options, with various payoff functions (for a treatment see Hull (2022), and Angus (1999) for the case of geometric Asian options). The Black-Scholes model is the standard approach for modelling stock prices, while jump diffusion models aim to offer a more realistic representation of market movements, see Kou (2002). Furthermore, methods to compute implied volatilities are provided for a wide range of option types and custom payoff functions. Classical formulas are implemented for European options in the Black-Scholes model, as is presented in Hull (2022). In the case of Asian options, Malliavin Monte Carlo Greeks are implemented, see Hudde & Rüschendorf (2023), or Lyuu et al. (2019). For American options, the Binomial Tree method is implemented, as is presented in Hull (2022). greeks includes a Shiny app to interactively plot the results.
摘要 greeks R 软件包利用布莱克-斯科尔斯(Black-Scholes)模型和更一般的跃迁扩散模型来 计算具有不同报酬函数的欧式、几何和算术式亚洲以及美式期权的金融期权价格的敏感性(有 关处理方法见 Hull(2022)和 Angus(1999)对几何式亚洲期权的处理方法)。布莱克-斯科尔斯(Black-Scholes)模型是股票价格建模的标准方法,而跃迁扩散模型旨在更真实地反映市场走势,见 Kou(2002)。此外,还为多种期权类型和自定义报酬函数提供了计算隐含波动率的方法。对于 Black-Scholes 模型中的欧式期权,采用了 Hull(2022)中的经典公式。对于亚洲期权,则采用马利亚文蒙特卡罗希腊公式,参见 Hudde & Rüschendorf (2023) 或 Lyuu 等人 (2019)。对于美式期权,则采用二叉树方法,见 Hull(2022)。
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引用次数: 1
GTFS Segments: A Fast and Efficient Library to GenerateBus Stop Spacings GTFS 区段:快速高效的总线站间距生成库
Pub Date : 2024-03-19 DOI: 10.21105/joss.06306
Sairpaneeth Devunuri, Lewis J. Lehe
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引用次数: 3
Shapelets: A Python package implementing shapeletfunctions and their applications 小形状实现 shapelet 函数及其应用的 Python 软件包
Pub Date : 2024-03-18 DOI: 10.21105/joss.06058
Matthew Peres Tino, Abbas Yusuf Abdulaziz, R. Suderman, Thomas J Akdeniz, N. Abukhdeir
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引用次数: 0
Imbalance: A comprehensive multi-interface Juliatoolbox to address class imbalance 失衡:解决阶级失衡问题的综合性多界面朱利亚工具箱
Pub Date : 2024-03-18 DOI: 10.21105/joss.06310
Essam Wisam, Anthony Blaom
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引用次数: 0
FeenoX: a cloud-first finite-element(ish) computationalengineering tool FeenoX:云端优先的有限元计算工程工具
Pub Date : 2024-03-16 DOI: 10.21105/joss.05846
Jeremy Theler
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引用次数: 0
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Journal of Open Source Software
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