Rodriguez et al 2021c - Revision history

Rimni at 14:39, 14 December 2021

2021-12-14T14:39:11Z

Rimni at 14:38, 14 December 2021

2021-12-14T14:38:31Z

Rimni at 14:34, 19 October 2021

2021-10-19T14:34:08Z

Rimni: /* References */

2021-10-19T14:33:33Z

‎References

Rimni: /* References */

2021-10-19T14:32:02Z

‎References

Srriverar at 14:00, 19 October 2021

2021-10-19T14:00:50Z

Rimni at 11:09, 19 October 2021

2021-10-19T11:09:49Z

Rimni: /* =Abstract */

2021-10-19T11:08:59Z

‎=Abstract

Rimni at 11:08, 19 October 2021

2021-10-19T11:08:34Z

Show changes

Onate: Onate moved page Review 800592388678 to Rodriguez et al 2021c

2021-10-02T09:02:37Z

Onate moved page Review 800592388678 to Rodriguez et al 2021c

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 :* plotly [25]: We did not explore the inclusion of interactive graphs deeply. The use of libraries such as plotly or the classic matplotlib can provide interesting pedagogical approaches as they allow users to interact with the mathematical objects they are faced with.
-==Conclussion==
+==5. Conclussion==
 The development of Google Colaboratory notebooks to complement classroom learning can be included in the category of development of educational and didactic resources, particularly university educational resources. Their preparation is not very different from the preparation of textbooks, books, or lecture notes, which are generally used; the process of preparation should be subject to rigorous phases of planning, development, and evaluation. During the development of this novel resource, the bibliography to be used was previously analyzed; this bibliography will guide the contents of the Google Colab notebooks. In this way, the next step was to find analogous material, either on Python or other languages, related to optimization techniques.
 ==References==

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 '''Google Colab''' or short for Colaboratory is a product developed by Google Research, which is a free to use platform and can be used to execute arbitrary Python codes through the browser. This tool is especially suited for machine learning, data analysis as well as educational purposes. Colab is a hosted Jupyter Notebook service which requires no setup and provides free access to computing power like GPUs on the cloud. This is ofcourse extended if the user pays for the pro version called Colab Pro.
-===1.1. Why Jupyter Notebook?===
+===1.1 Why Jupyter Notebook?===
 Some of the features provided by Jupyter are as follows [1-2]:
@@ Line 21: / Line 21: @@
 :* Animations and dynamic visualizations or interactive visuals are available on Jupyter Notebook which helps users interact with visuals and understand patterns in-depth
-===1.2. Why Google Colab?===
+===1.2 Why Google Colab?===
 Just as we discussed about some primary features that Jupyter provides to its users, here are some features that Google Colab provides its users as well [3-4]

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 [15] Luo F.-L. Machine learning for digital front‐end. In Machine Learning for Future Wireless Communications , IEEE, pp. 327-381, 2020.
-[16] Pani S.K., Tripathy S., Jandieri G., Kundu S., Butt T.A. Applications of machine learning in big-data analytics and cloud computing. In Applications of Machine Learning in Big-Data Analytics and Cloud Computing, River Publishers, pp.i-xxxii, 2021.
+[16] Pani S.K., Tripathy S., Jandieri G., Kundu S., Butt T.A. Applications of machine learning in big-data analytics and cloud computing. In Applications of Machine Learning in Big-Data Analytics and Cloud Computing, River Publishers, pp. i-xxxii, 2021.
 [17] De Graaf E., Kolmos A. Characteristics of problem-based learning. International Journal of Engineering Education, 19(5):657-662, 2003.

@@ Line 325: / Line 325: @@
 [10] Thumann A.,  Mehta D.P. 4 electrical system optimization. In Handbook of Energy Engineering , River Publishers, pp.61-116, 2013.
-[11] Morais H., Vale Z.A., Soares J.,  Sousa T., Nara K.,  Theologi A.-M., Rueda J., Ndreko M.,  Erlich I.,  Mishra S., Pullaguram D., Faria P., Mori H., Takahashi M., Qamar Raza M., Nadarajah M., Shi R.,  Lee K.Y., Rivera S., Romero A. Integration of renewable energy in smart grid. In Applications of Modern Heuristic Optimization Methods in Power and Energy Systems , IEEE, pp. 613-773, 2020.
+[11] Morais H., Vale Z.A., Soares J.,  Sousa T., Nara K.,  Theologi A.-M., Rueda J., Ndreko M.,  Erlich I.,  Mishra S., Pullaguram D., Faria P., Mori H., Takahashi M., Qamar Raza M., Nadarajah M., Shi R.,  Lee K.Y., Rivera S.R., Romero A. Integration of renewable energy in smart grid. In Applications of Modern Heuristic Optimization Methods in Power and Energy Systems , IEEE, pp. 613-773, 2020.
-[12]   Koutsoukis N.C.,  Georgilakis P.S.,  Hatziargyriou N.D.,  Mohammed O., Elsayed A., An K., Hur K., Luong N.H.,  Bosman P.A.N.,  Grond M.O.W., La Poutré H., Chiang H.-D., Cui J., Xu T., Asada E.N., London J.B.A.,  Saraiva F.O., Sun W., Venayagamoorthy K., Zhou Q., Wang S., Zhang Y.-F., Gusain D., Rueda J.,  Boemer J.C., Palensky P., Alvarez D.L., Rivera S. Distribution system. In Applications of Modern Heuristic Optimization Methods in Power and Energy Systems , IEEE, pp. 381-611, 2020.
+[12]   Koutsoukis N.C.,  Georgilakis P.S.,  Hatziargyriou N.D.,  Mohammed O., Elsayed A., An K., Hur K., Luong N.H.,  Bosman P.A.N.,  Grond M.O.W., La Poutré H., Chiang H.-D., Cui J., Xu T., Asada E.N., London J.B.A.,  Saraiva F.O., Sun W., Venayagamoorthy K., Zhou Q., Wang S., Zhang Y.-F., Gusain D., Rueda J.,  Boemer J.C., Palensky P., Alvarez D.L., Rivera S.R. Distribution system. In Applications of Modern Heuristic Optimization Methods in Power and Energy Systems , IEEE, pp. 381-611, 2020.
 [13] Boyd S.,  Parrilo P. 6.079 Introduction to convex optimization. Fall 2009, Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA, 2009.
-[14] Caro-Ruiz C.,  Al-Sumaiti A.S., Rivera S.,  Mojica-Nava E. A MDP-based vulnerability analysis of power networks considering network topology and transmission capacity. In IEEE Access, 8:2032-2041, 2020.
+[14] Caro-Ruiz C.,  Al-Sumaiti A.S., Rivera S.R.,  Mojica-Nava E. A MDP-based vulnerability analysis of power networks considering network topology and transmission capacity. In IEEE Access, 8:2032-2041, 2020.
 [15] Luo F.-L. Machine learning for digital front‐end. In Machine Learning for Future Wireless Communications , IEEE, pp. 327-381, 2020.

@@ Line 321: / Line 321: @@
 [8] [https://analyticsindiamag.com/explained-5-drawback-of-google-colab/ https://analyticsindiamag.com/explained-5-drawback-of-google-colab/]
-[9] H. M. E. Abdelsalam and H. P. Bao, "A simulation-based optimization framework for product development cycle time reduction," in IEEE Transactions on Engineering Management, vol. 53, no. 1, pp. 69-85, Feb. 2006, doi: 10.1109/TEM.2005.861805.
+[9] Abdelsalam H.M.E.,   Bao H.P. A simulation-based optimization framework for product development cycle time reduction. In IEEE Transactions on Engineering Management, 53(1):69-85, 2006.
-[10] Albert Thumann; D. Paul Mehta, "4 Electrical System Optimization," in Handbook of Energy Engineering , River Publishers, 2013, pp.61-116.
+[10] Thumann A.,  Mehta D.P. 4 electrical system optimization. In Handbook of Energy Engineering , River Publishers, pp.61-116, 2013.
-[11] H. Morais; Zita A. Vale; João Soares; T. Sousa; Koichi Nara; Aimilia‐Myrsini Theologi; Jose Rueda; Mario Ndreko; István Erlich; Sukumar Mishra; Deepak Pullaguram; Pedro Faria; Hiroyuki Mori; Masato Takahashi; Muhammad Qamar Raza; Mithulananthan Nadarajah; Ruifeng Shi; Kwang Y. Lee; Sergio Rivera; Andres Romero, "INTEGRATION OF RENEWABLE ENERGY IN SMART GRID," in Applications of Modern Heuristic Optimization Methods in Power and Energy Systems , IEEE, 2020, pp.613-773, doi: 10.1002/9781119602286.ch6.
+[11] Morais H., Vale Z.A., Soares J.,  Sousa T., Nara K.,  Theologi A.-M., Rueda J., Ndreko M.,  Erlich I.,  Mishra S., Pullaguram D., Faria P., Mori H., Takahashi M., Qamar Raza M., Nadarajah M., Shi R.,  Lee K.Y., Rivera S., Romero A. Integration of renewable energy in smart grid. In Applications of Modern Heuristic Optimization Methods in Power and Energy Systems , IEEE, pp. 613-773, 2020.
-[12]  Nikolaos C. Koutsoukis; Pavlos S. Georgilakis; Nikos D. Hatziargyriou; Osama Mohammed; Ahmed Elsayed; Kyungsung An; Kyeon Hur; Ngoc Hoang Luong; Peter A. N. Bosman; Marinus O. W. Grond; Han La Poutré; Hsiao‐Dong Chiang; Jinda Cui; Tianshi Xu; Eduardo N. Asada; João Bosco A. London; Filipe O. Saraiva; Wei Sun; Kumar Venayagamoorthy; Qun Zhou; Shuo Wang; Yong‐Feng Zhang; Digvijay Gusain; Jose Rueda; Jens C. Boemer; Peter Palensky; David L. Alvarez; Sergio Rivera, "DISTRIBUTION SYSTEM," in Applications of Modern Heuristic Optimization Methods in Power and Energy Systems , IEEE, 2020, pp.381-611, doi: 10.1002/9781119602286.ch5.
+[12]   Koutsoukis N.C.,  Georgilakis P.S.,  Hatziargyriou N.D.,  Mohammed O., Elsayed A., An K., Hur K., Luong N.H.,  Bosman P.A.N.,  Grond M.O.W., La Poutré H., Chiang H.-D., Cui J., Xu T., Asada E.N., London J.B.A.,  Saraiva F.O., Sun W., Venayagamoorthy K., Zhou Q., Wang S., Zhang Y.-F., Gusain D., Rueda J.,  Boemer J.C., Palensky P., Alvarez D.L., Rivera S. Distribution system. In Applications of Modern Heuristic Optimization Methods in Power and Energy Systems , IEEE, pp. 381-611, 2020.
 [13] Boyd S.,  Parrilo P. 6.079 Introduction to convex optimization. Fall 2009, Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA, 2009.
-[14] C. Caro-Ruiz, A. S. Al-Sumaiti, S. Rivera and E. Mojica-Nava, "A MDP-Based Vulnerability Analysis of Power Networks Considering Network Topology and Transmission Capacity," in IEEE Access, vol. 8, pp. 2032-2041, 2020, doi: 10.1109/ACCESS.2019.2962139.
+[14] Caro-Ruiz C.,  Al-Sumaiti A.S., Rivera S.,  Mojica-Nava E. A MDP-based vulnerability analysis of power networks considering network topology and transmission capacity. In IEEE Access, 8:2032-2041, 2020.
-[15] Fa-Long Luo, "Machine Learning for Digital Front‐End," in Machine Learning for Future Wireless Communications , IEEE, 2020, pp.327-381, doi: 10.1002/9781119562306.ch17.
+[15] Luo F.-L. Machine learning for digital front‐end. In Machine Learning for Future Wireless Communications , IEEE, pp. 327-381, 2020.
-[16] Subhendu Kumar Pani; Somanath Tripathy; George Jandieri; Sumit Kundu; Talal Ashraf Butt, "Applications of Machine Learning in Big-Data Analytics and Cloud Computing," in Applications of Machine Learning in Big-Data Analytics and Cloud Computing , River Publishers, 2021, pp.i-xxxii.
+[16] Pani S.K., Tripathy S., Jandieri G., Kundu S., Butt T.A. Applications of machine learning in big-data analytics and cloud computing. In Applications of Machine Learning in Big-Data Analytics and Cloud Computing, River Publishers, pp.i-xxxii, 2021.
-[17] De Graaf E., Kolmos A. Characteristics of problem-based learning. International Journal of Engineering Education, 2003 - Citeseer.
+[17] De Graaf E., Kolmos A. Characteristics of problem-based learning. International Journal of Engineering Education, 19(5):657-662, 2003.
-[18] L. Gonzalez, "The Problem-Based Learning Model," 2019 Eighth International Conference on Educational Innovation through Technology (EITT), 2019, pp. 180-183, doi: 10.1109/EITT.2019.00042.
+[18] Gonzalez L. The problem-based learning model. 2019 Eighth International Conference on Educational Innovation through Technology (EITT), pp. 180-183, 2019.
-[19] D. L. Alvarez, S. R. Rivera and E. E. Mombello, "Transformer Thermal Capacity Estimation and Prediction Using Dynamic Rating Monitoring," in IEEE Transactions on Power Delivery, vol. 34, no. 4, pp. 1695-1705, Aug. 2019, doi: 10.1109/TPWRD.2019.2918243.
+[19]  Alvarez D.L.,  Rivera S.R.,  Mombello E.E. Transformer thermal capacity estimation and prediction using dynamic rating monitoring. in IEEE Transactions on Power Delivery, 34(4):1695-1705, 2019.
-[20] D. L. Alvarez, A. S. Al-Sumaiti and S. R. Rivera, "Estimation of an Optimal PV Panel Cleaning Strategy Based on Both Annual Radiation Profile and Module Degradation," in IEEE Access, vol. 8, pp. 63832-63839, 2020, doi: 10.1109/ACCESS.2020.2983322.
+[20] Alvarez D.L.,  Al-Sumaiti A.S.,   Rivera S.R. Estimation of an optimal PV panel cleaning strategy based on both annual radiation profile and module degradation. In IEEE Access, 8:63832-63839, 2020.
 [21] https://www.canva.com/es_es/about/

@@ Line 302: / Line 302: @@
 ==References==
 [1] [https://towardsdatascience.com/4-reasons-why-you-should-use-google-colab-for-your-next-project-b0c4aaad39ed https://towardsdatascience.com/4-reasons-why-you-should-use-google-colab-for-your-next-project-b0c4aaad39ed]
@@ Line 366: / Line 368: @@
 [32] https://www.cvxpy.org/

← Older revision		Revision as of 11:08, 19 October 2021
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−	==Abstract=	+	==Abstract==

	Online code sharing and documentation platforms have become an essential part of every analyst and data scientist’s lives. Google Colab and Jupyter Notebook are some of the well named platforms which are used quite extensively to work on datasets. These platforms allow the user to showcase and manipulate data along with the capabilities of adding beautiful visualizations and essential narrative texts. They offer high customization abilities for the users along with Collaboration and sharing capabilities. These notebooks also offer a variety of languages to choose from, like R, Python, PySpark etc. if the necessary packages are installed. Other platforms available for working on big data includes R Markdown, Kaggle, Spark Notebook and many other popular tools. With the recent developments in Data Science technologies and Machine learning, there has been an increased adoption for these platforms, which has eventually led to the addition of new features to these tools. In this way, this paper present a case study (optimization techniques course) with the python notebook usefullness'''		Online code sharing and documentation platforms have become an essential part of every analyst and data scientist’s lives. Google Colab and Jupyter Notebook are some of the well named platforms which are used quite extensively to work on datasets. These platforms allow the user to showcase and manipulate data along with the capabilities of adding beautiful visualizations and essential narrative texts. They offer high customization abilities for the users along with Collaboration and sharing capabilities. These notebooks also offer a variety of languages to choose from, like R, Python, PySpark etc. if the necessary packages are installed. Other platforms available for working on big data includes R Markdown, Kaggle, Spark Notebook and many other popular tools. With the recent developments in Data Science technologies and Machine learning, there has been an increased adoption for these platforms, which has eventually led to the addition of new features to these tools. In this way, this paper present a case study (optimization techniques course) with the python notebook usefullness'''

← Older revision	Revision as of 09:02, 2 October 2021
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