Abstract: Large Language Models (LLMs) recently demonstrated extraordinary capability in various natural language processing (NLP) tasks including language translation, text generation, question ...

Abstract: An important issue in multiobjective optimization is the quantitative comparison of the performance of different algorithms. In the case of multiobjective evolutionary algorithms, the ...

Abstract: In spite of the increasing use of machine learning techniques, in-memory computing and hardware have increased the interest to accelerate neural network operation. Henceforth, novel embedded ...

Abstract: This paper proposes a step-by-step procedure for designing the LCL filter of a front-end three-phase active rectifier. The primary goal is to reduce the switching frequency ripple at a ...

Abstract: Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN), such as Graph Attention Networks (GAT), are two classic neural network models, which are applied to the processing of ...

Abstract: The purpose of this study was to assess the impact of Artificial Intelligence (AI) on education. Premised on a narrative and framework for assessing AI identified from a preliminary analysis ...

Abstract: In this brief, adaptive neural control is presented for a class of output feedback nonlinear systems in the presence of unknown functions. The unknown functions are handled via on-line ...

Book Abstract: "Power quality problems have increasingly become a substantial concern over the last decade, but surprisingly few analytical techniques have been developed to overcome these ...

Abstract: This review paper is to give a full picture of fault detection and diagnosis (FDD) in complex systems from the perspective of data processing. As a matter of fact, an FDD system is a ...

Abstract: Recently, Convolutional Neural Network (CNN) based models have achieved great success in Single Image Super-Resolution (SISR). Owing to the strength of deep networks, these CNN models learn ...

Abstract: Explainability has become one of the most discussed topics in machine learning research in recent years, and although a lot of methodologies that try to provide explanations to black-box ...

Abstract: Distinct feature extraction methods are simultaneously used to describe bearing faults. This approach produces a large number of heterogeneous features that augment discriminative ...