From cf9c71be72a2e95603c3fe0899ac0e1d2bb51ae1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Sun, 2 Jul 2023 07:24:37 +0200 Subject: [PATCH] Suggested change to JOSS submission (#104) * Citation should be in-text, not parenthesized. * Capitalize Jupyter Notebook --- docs/JOSS/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/JOSS/paper.md b/docs/JOSS/paper.md index 58aa90d4..c08e06ad 100644 --- a/docs/JOSS/paper.md +++ b/docs/JOSS/paper.md @@ -106,7 +106,7 @@ Already provided are classic controllers (i.e., industry standard contollers) li Many basic auxiliary functionalities for the essential operation of electric power grids are provided too such as coordinate transformations for basic controller classes, data logging, measurement of real and imaginary powers, and phase-locked loops for frequency and phase angle extraction. -The interface provided by [@Tian2020Reinforcement] is also available for training +The interface provided by @Tian2020Reinforcement is also available for training data-driven control approaches like RL. This enables users who want to integrate contemporary open-source Julia-based RL toolboxes such as ``ReinforcementLearning.jl`` [@Tian2020Reinforcement]. @@ -141,7 +141,7 @@ The ``ElectricGrid.jl`` toolbox provides the following key features: * Interesting use cases applying data-driven learning. # Examples -For illustration and interactive introduction, jupyter notebooks are available for each topic. +For illustration and interactive introduction, Jupyter Notebooks are available for each topic. These provide clear and easy-to-expand examples of: - [Utilising ElectricGrid.jl to build an energy grid](https://github.com/upb-lea/JuliaElectricGrid.jl/blob/main/examples/notebooks/Env_Create_DEMO.ipynb), - [Theoretical principles behind the calculations](https://github.com/upb-lea/JuliaElectricGrid.jl/blob/main/examples/notebooks/NodeConstructor_Theory_DEMO.ipynb),