? 2022 Elsevier B.V.In this paper, geometric deep learning techniques are applied to learn approximate models for power system estimation and calculation tasks. Nine different graph neural network architectures from literature are compared for this purpose. The underlying graph and known physical algebr...
Developing audio applications with deep learning typically includes creating and accessing data sets, preprocessing and exploring data, developing predictive models, and deploying and sharing applications. MATLAB® provides toolboxes to support each stage of the development.While...
The current development in deep learning is witnessing an exponential transition into automation applications. This automation transition can provide a promising framework for higher performance and lower complexity. This ongoing transition undergoes several rapid changes, resulting in the processing of the d...
Deep learning is largely feature-engineering-free:This is perhaps the biggest advantage of deep learning. It is well-known that the efficacy of a ML model depends a lot on how the data are represented for the ML algorithm to learn patterns from. Usually for a scientific or engineering applic...
VAEs integrated with deep learning have been widely utilized in different power system applications such as fault and anomaly detection in time series energy data [34], [35], [36]. Recently, VAE is implemented in [37] to forecast the solar power generation. VAE is also used as a ...
Deep reinforcement learning for power system applications: An overview. CSEE J. Power Energy Syst. 2019, 6, 213–225. [Google Scholar] Glavic, M. (Deep) reinforcement learning for electric power system control and related problems: A short review and perspectives. Annu. Rev. Control 2019, ...
To address these shortcomings, recent advances have introduced deep reinforcement learning (DRL)-based approaches for enhancing the resilience of power and energy systems. Within power and energy systems, DRL, a combination of reinforcement learning (RL) and deep learning, has emerged as an ...
applications of deep learning is in the early detection and course-correction of these problems associated with infants and children. This is a major difference between machine learning and deep learning where machine learning is often just used for specific tasks and deep learning, on the other ...
Learn how deep learning works and how to use deep learning to design smart systems in a variety of applications. Resources include videos, examples, and documentation.
land-cover -> Model Generalization in Deep Learning Applications for Land Cover Mapping generalizablersc -> Cross-dataset Learning for Generalizable Land Use Scene Classification Large-scale-Automatic-Identification-of-Urban-Vacant-Land -> Large-scale automatic identification of urban vacant land using...