Heterostructures based on ultrawide-bandgap (UWBG) semiconductors (bandgap >4.0 eV), such as boron nitride (BN) and diamond, hold significant importance for the development of high-power electronics in the next gen...
详细信息
We examine ultrastrong coupling of an ensemble of paramagnetic spins in Gd 3 Ga 5 O 12 with terahertz photons in the presence of a high external magnetic field both in the bulk and thin-film limits.
ISBN:
(纸本)9781957171258
We examine ultrastrong coupling of an ensemble of paramagnetic spins in Gd 3 Ga 5 O 12 with terahertz photons in the presence of a high external magnetic field both in the bulk and thin-film limits.
This paper deals with the problem of monitoring of induction motors (IM) through the development of fault detection and diagnosis (FDD) approach. The developed FDD technique is addressed such that, the principal compo...
详细信息
ISBN:
(数字)9781728110806
ISBN:
(纸本)9781728110813
This paper deals with the problem of monitoring of induction motors (IM) through the development of fault detection and diagnosis (FDD) approach. The developed FDD technique is addressed such that, the principal component analysis (PCA) technique is used for features extraction purposes and the machine learning (ML) classifiers are applied for fault diagnosis. In the proposed FDD approach the most efficient features are extracted and selected through PCA scheme using induction motor data. Besides, their statistical characteristics (mean and variance) are also included. The ML classifiers are applied using the extracted and selected features to perform the FDD problem. The obtained results indicate that the proposed techniques have a wide application area, fast fault detection and diagnosis, making them more reliable for induction motors monitoring.
Fault detection and diagnosis for modern wind turbines converter (WTC) systems have been received an important measure for improving the operation of these systems, in such a way to increase their reliability, availab...
详细信息
ISBN:
(数字)9781728110806
ISBN:
(纸本)9781728110813
Fault detection and diagnosis for modern wind turbines converter (WTC) systems have been received an important measure for improving the operation of these systems, in such a way to increase their reliability, availability and required safety. Therefore, this paper deals with the problem of fault detection and diagnosis (FDD) in WTC systems. The developed FDD approach uses feature extraction and selection, and fault classification tools for monitoring WTC system under different operating conditions. The developed FDD approach is addressed such that, the principal component analysis (PCA) technique is used for feature extraction purposes and the machine learning (ML) classifiers are applied for fault diagnosis. In the proposed FDD approach, an efficient features in PCA subspace that extract and select the most informative features from WTC data are provided. Besides, their statistical characteristics are also included. The ML classifiers are applied to the extracted and selected features to perform the fault diagnosis problem. The effectiveness and higher classification accuracy of the developed approach are demonstrated using simulated data extracted from different operating conditions of the wind turbine.
To ensure high reliability of the Grid-Connected Photovoltaic (GCPV) systems, promptly faults detection, diagnosis and automatic process monitoring are essential tools to keep the PV and the grid network under optimal...
详细信息
ISBN:
(数字)9781728110806
ISBN:
(纸本)9781728110813
To ensure high reliability of the Grid-Connected Photovoltaic (GCPV) systems, promptly faults detection, diagnosis and automatic process monitoring are essential tools to keep the PV and the grid network under optimal functioning. Regardless of fault types, incipient faults are usually more difficult to detect and accurately isolate. As an alternative and effective method, the principal components analysis (PCA) is proposed to extract and select more relevant features and support vector machines (SVM) technique is applied to quickly detect the faults that occur in a GCPV system. The T2 and squared weighted errors (SWE) statistics, generally used as fault detection indices, are appropriately extracted and selected within the PCA framework. Both of these features are fed to a SVM classifier handling the incipient fault detection. This task is carried out on a simulated GCPV operating under maximum power point trackers (MPPT) and matching realistic outdoor to demonstrate the effectiveness and robustness of the proposed technique.
Hard-to-predict bursts of COVID-19 pandemic revealed significance of statistical modeling which would resolve spatio-temporal correlations over geographical areas, for example spread of the infection over a city with ...
详细信息
Hard-to-predict bursts of COVID-19 pandemic revealed significance of statistical modeling which would resolve spatio-temporal correlations over geographical areas, for example spread of the infection over a city with census tract granularity. In this manuscript, we provide algorithmic answers to the following two inter-related public health challenges of immense social impact which have not been adequately addressed (1) Inference Challenge: assuming that there are N census blocks (nodes) in the city, and given an initial infection at any set of nodes, e.g. any N of possible single node infections, any N(N-1)=2 of possible two node infections, etc, what is the probability for a subset of census blocks to become infected by the time the spread of the infection burst is stabilized? (2) Prevention Challenge: What is the minimal control action one can take to minimize the infected part of the stabilized state footprint? To answer the challenges, we build a Graphical Model of pandemic of the attractive Ising (pair-wise, binary) type, where each node represents a census tract and each edge factor represents the strength of the pairwise interaction between a pair of nodes, e.g. representing the inter-node travel, road closure and related, and each local bias/field represents the community level of immunization, acceptance of the social distance and mask wearing practice, etc. Resolving the Inference Challenge requires finding the Maximum-A-Posteriory (MAP), i.e. most probable, state of the Ising Model constrained to the set of initially infected nodes. (An infected node is in the +1 state and a node which remained safe is in the-1 state.) We show that almost all attractive Ising Models on dense graphs result in either of the two possibilities (modes) for the MAP state: either all nodes which were not infected initially became infected, or all the initially uninfected nodes remain uninfected (susceptible). This bi-modal solution of the Inference Challenge allows us to re-sta
We fabricated a full-dielectric three-dimensional photonic-crystal cavity containing an ultrahigh-mobility two-dimensional electron gas. By applying a strong perpendicular magnetic field, we created Landau polaritons ...
详细信息
In recent years, tremendous progress has been made on numerical algorithms for solving partial differential equations (PDEs) in a very high dimension, using ideas from either nonlinear (multilevel) Monte Carlo or deep...
详细信息
Intelligent computing techniques have a paramount importance to the treatment of cybersecurity incidents. In such Artificial Intelligence (AI) context, while most of the algorithms explored in the cybersecurity domain...
详细信息
暂无评论