This research work is motivated by the need for effective demand response management (DRM) in smart grid systems. DRM is critical in optimizing the energy load by adjusting the energy prices and shifting high electric...
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This paper proposes a non-isolated high-gain DC-DC converter capable of delivering a lOx voltage gain with a 50 percent duty cycle. The topology represents an enhanced version of the Zeta converter and aims to address...
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Machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies on labeled measurement data for training and testing. The majority of existing detectors are developed assuming that the a...
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Machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies on labeled measurement data for training and testing. The majority of existing detectors are developed assuming that the adopted datasets for training have correct labeling information. However, such an assumption is not always valid as training data might include measurement samples that are incorrectly labeled as benign, namely, adversarial data poisoning samples, which have not been detected before. Neglecting such an aspect makes detectors susceptible to data poisoning. Our investigations revealed that detection rates (DRs) of existing detectors significantly deteriorate by up to 9-29% when subject to data poisoning in generalized and topology-specific settings. Thus, we propose a generalized graph neural network-based anomaly detector that is robust against FDIAs and data poisoning. It requires only benign datasets for training and employs an autoencoder with Chebyshev graph convolutional recurrent layers with attention mechanism to capture the spatial and temporal correlations within measurement data. The proposed convolutional recurrent graph autoencoder model is trained and tested on various topologies (from 14, 39, and 118-bus systems). Due to such factors, it yields stable generalized detection performance that is degraded by only 1.6-3.7% in DR against high levels of data poisoning and unseen FDIAs in unobserved topologies. Impact Statement-Artificial Intelligence (AI) systems are used in smart grids to detect cyberattacks. They can automatically detect malicious actions carried out bymalicious entities that falsifymeasurement data within power grids. Themajority of such systems are data-driven and rely on labeled data for model training and testing. However, datasets are not always correctly labeled since malicious entities might be carrying out cyberattacks without being detected, which leads to training on mislabeled datasets. Such actions might degrade the d
A 3D clock tree topology generation algorithm is presented based on ternary trees. In addition, a method that leads to an improved stopping criterion for algorithms based on nearest neighbor is proposed. A new grid as...
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Data augmentation is a critical component in building modern deep-learning systems. In this article, we propose MFG Augment, a novel data augmentation method based on the mean-field game (MFG) theory that can synthesi...
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Deep neural networks are commonly used for histopathology image analysis. However, such data-driven models are sensitive to style variances across scanners and suffer a significant performance degradation as a result....
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Industrial drying is one of the most energy intensive manufacturing processes and it is utilized across various industries, making it an ideal target for optimization. To achieve this condition based drying can be imp...
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With the aim of exploring the integration of federated learning (FL) into wireless networks, particularly in light of the challenge of spectrum resource limitations, this paper considers the integration of over-the-ai...
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In this work, we introduce two variants of the U-shaped error correction code transformer (U-ECCT) in combination with weight-sharing to improve the decoding performance of the error correction code transformer (ECCT)...
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Online nonconvex optimization has been an active area of research recently. Previous studies either considered the global regret with full information about the objective functions, or studied the local regret with wi...
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