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Assessing the Improvements Brought by Artificial Intelligence on the Prediction of Aerodynamic Coefficients

Assessing the Improvements Brought by Artificial Intelligenc...

引用

International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Zerouaoui, Jad Alaoui, Altaf Ettaki, Badia Chakir, ElMahjoub Laboratory of Material and Subatomic Physics Faculty of Sciences Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco LyRICA: Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering School of Information Sciences Rabat Rabat Morocco

ISBN: (纸本)9783031262531

The implementation of Artificial Intelligence in the aerospace field is fairly new and various domains of the aerospace discipline are to be explored. This paper provides a general review on the upbringing and improvements of Artificial Intelligence as a pilar of the next generation of scientific research. Applications and developments brought by Artificial Intelligence and Deep Learning to the aerospace industry generally, mainly the prediction of aerodynamic coefficients and design optimization using intricate neural networks replicating the process of the human brain. Scientific developments participated in improving major areas in the aerospace field. Due to the expensive and time-consuming process related to the complexity of the geometry and the precision of the results required, researchers integrated surrogate models based on Deep Learning in order to improve the calculation process of the aerodynamic coefficients. On one hand, this paper introduces and discusses the general architecture of several networks used to enhance the calculation ability of the machine, while presenting the process of each work along with recounting the different methods employed. On the other hand, a discussion regarding the challenges presented with utilizing Computational Fluid Dynamics while assessing and comparing the results brought by surrogate models based on efficiency and time consumption is also tackled. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Evaluating the Impact of Dataset Size on Univariate Prediction Techniques for Moroccan Agriculture

Evaluating the Impact of Dataset Size on Univariate Predicti...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Ed-daoudi, Rachid Alaoui, Altaf Zerouaoui, Jad Ettaki, Badia Zerouaoui, Jamal Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco LyRICA: Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering School of Information Sciences Rabat Rabat Morocco

ISBN: (纸本)9783031262531

Learning models used for prediction are mostly developed without taking into account the size of datasets that can produce models of high accuracy and better performance. Although, the general believe is that, large dataset is needed to construct a predictive learning model. To describe a data set as large in size depends on the circumstances and context of prediction. This means that what makes a dataset to be considered as being big or small is controversial. In this paper, the ability of the predictive model to adapt to a particular size of data in training is examined. The study experiments on three different sizes of Moroccan agricultural data using a variety of statistical and Machine Learning techniques, to create predictive models with a view to establishing if the size of data has any effect on the accuracy of a model. The output of each model is measured using the Mean Absolute Error (MAE) and r-squared, and comparisons are made. The results of training the models through the three partitioned dataset show that, the models trained with the smallest and largest size of training data appear to be less accurate, while the models trained with a medium sized dataset delivers a much better results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Energy transition and digitalization: Small modular reactors and smart grids 7

Energy transition and digitalization: Small modular reactors...

引用

7th IEEE International Congress on Information science and Technology, CiSt 2023

作者： Housni, Hafsa Majit, Nissrine Amrous, Naila Daoudi, Najima Malzi, Mohamed Jaoud CIAS: Competitive Intelligence Analysis and Strategy School of Information Sciences Laboratory MIKS: Mediation Information Knowledge Society Rabat Morocco Laboratory LyRICA: Computer Science Data Science and Artificial Intelligence ITQAN: Innovative Technologies Data Quality and Artificial Intelligence School of Information Sciences Rabat Morocco Research Laboratory in Energy Environment and Resource Economics Cadi Ayyad University Rabat Morocco

ISBN: (纸本)9781665461337

This article aims to critically evaluate the current state of digitalization in SGs and SMRs and explore their implications for sustainable energy systems. Herein, we have compiled a table of information sources specifically focused on the integration of digitalization in Smart Grids (SG) and Small Modular Reactors (SMRs). The selected articles were chosen based on specific eligibility, inclusion, and exclusion criteria, ensuring their relevance and quality for our analysis. By conducting an in-depth review of the application of digital technologies in the context of SGs and SMRs, key trends, challenges, and opportunities are identified. The review encompasses various aspects, including advanced sensors, data analytics, optimization techniques, communication technologies, and cybersecurity considerations. The results highlight the potential of deep learning algorithms, data-enabled physics-informed machine learning, and digital twin technology in enhancing the safety, performance, and efficiency of SMRs. Moreover, digital technologies are found to contribute to improved grid management, increased renewable energy integration, and effective demand-side management. However, challenges related to data uncertainty, quality, security, and complexity need to be addressed. The article concludes by emphasizing the transformative potential of digitalization in shaping the future of energy systems and the importance of continued research and collaboration to overcome challenges and fully realize the benefits of digital technologies in the energy transition. © 2023 IEEE.

关键词： Smart power grids

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Predicting Monthly Wheat Crop Coefficients in Morocco Using ANN Models with Limited Meteorological Data: A Study in the Draa-Tafilalet Region 9th

Predicting Monthly Wheat Crop Coefficients in Morocco Using ...

引用

9th International Conference on Smart City Applications, SCA 2024

作者： Ed-Daoudi, Rachid Ettaki, Badia Zerouaoui, Jamal LyRICA: Research Laboratory in Computer Science Data Sciences and Artificial Intelligence School of Information Sciences B.P: 6204 Rabat-Instituts 10100 Rabat Morocco Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco

ISBN: (纸本)9783031886522

The increasing population and water demand in North Africa, particularly in semi-arid and arid regions like Morocco, necessitate efficient water resource management. Morocco experiences significant water scarcity due to persistent droughts and rising agricultural water demand. Accurate estimation of crop water requirements, particularly crop evapotranspiration, influenced by climatic and crop-specific conditions, is vital for improving water use efficiency. This study focuses on wheat, a staple crop in Morocco's Draa-tafilalet region, encompassing Ouarzazate, Zagora, and Tinghir. Traditional methods for estimating the crop coefficient require comprehensive climatic datasets, often unavailable. Therefore, this study aims to develop and validate artificial neural network models to predict monthly Kc values for wheat using limited climatic data. The results showed that the optimal model for predicting crop coefficients used a minimum and maximum temperatures, along with solar radiation, applying different configurations of hidden neuron layers for each region: (7, 5) for Ouarzazate, (8, 6) for Zagora, and (9, 6) for Tinghir. The best model's accuracy was close to 1, showing a statistically significant accordance between observed and predicted values. This study highlights the model's high accuracy and recommends its use for predicting crop coefficients values with limited climatic factors. It also supports water users in developing and updating Kc databases for each region based on climatic conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Resource allocation

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Energy transition and digitalization: Small modular reactors and smart grids

Energy transition and digitalization: Small modular reactors...

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International Conference on Information science and Technology (ICIST)

作者： Hafsa Housni Nissrine Majit Naila Amrous Najima Daoudi Mohamed Jaoud Malzi Laboratory MIKS: Mediation Information Knowledge Society CIAS: Competitive Intelligence Analysis and Strategy School of Information Sciences Rabat Morocco Laboratory LyRICA: Computer Science Data Science and Artificial Intelligence ITQAN: Innovative Technologies data Quality and Artificial Intelligence School of Information Sciences Rabat Morocco Research Laboratory in Energy Environment and Resource Economics Cadi Ayyad University Rabat Morocco Laboratory MIKS: Mediation Information Knowledge Society CIAS: Competitive Intelligence Analysis and Strategy School of Information Sciences Rabat Morocco

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