Summary
At present with the massive induction of distributed renewable energy sources (RES), energy storage systems (ESS) have the potential to curb the intermittent nature of micro sources and provide a steady supply of power to the load. It gives an optimum solution and considers as a major part of intelligent grids. For making a green environment, Electric Vehicle (EV) is the best option that emits zero exhaust gases, cleaner, less noisy and eco‐friendly compared to engine‐based vehicles. It could embark power sanctuary by allowing open access to RES. Nonetheless, EVs presently face encounters in the deployment of ESSs, inroad to their reliability, capacity, price, and online management issues. This study comprehensive review about technical advancements of ESSs, its detailed taxonomy, features, implementation, possibilities with system differences, and additional features of particularly EV applications. Hence, in this current study, technical analysis of Energy storage systems, its leading technologies, core assets, global energy stakeholders, economic merits and techniques on energy conversion is provided. Besides, the way of deploying energy storage techniques, the barriers and assessments are also presented to give a wider scope in this particular area.
This paper is NOT THE PUBLISHED VERSION; but the author's final, peer-reviewed manuscript. The published version may be accessed by following the link in the citation below.
Deep learning has achieved a high classification accuracy on image classification tasks, including emotion categorization. However, deep learning models are highly vulnerable to adversarial attacks. Even a small change, imperceptible to a human (e.g. one-pixel attack), can decrease the classification accuracy of deep models. One reason could be their homogeneous representation of knowledge that considers all pixels in an image to be equally important is easily fooled. Enabling multiple representations of the same object, e.g. at the constituent and holistic viewpoints provides robustness against attacking a single view. This heterogeneity is provided by lateralization in biological systems. Lateral asymmetry of biological intelligence suggests heterogeneous learning of objects. This heterogeneity allows information to be learned at different levels of abstraction, i.e. at the constituent and the holistic level, enabling multiple representations of the same object. This work aims to create a novel system that can consider heterogeneous features e.g. mouth, eyes, nose, and jaw in a face image for emotion categorization. The experimental results show that the lateralized system successfully considers constituent and holistic features to exhibit robustness to unimportant and irrelevant changes to emotion in an image, demonstrating performance accuracy better than (or similar) to the deep learning system (VGG19). Overall, the novel lateralized method shows a stronger resistance to changes (10.86 − 47.72% decrease) than the deep model (25.15 − 83.43% decrease). The advances arise by allowing heterogeneous features, which enable constituent and holistic representations of image components.
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