Comprehensive Study on Transformer Fault Detection via Frequency Response Analysis

The sudden outage of a transformer due to a fault can cause irreparable damage to the electricity industry. Hence, by conducting momentarily inspections of the transformer's condition, faults can be promptly detected, and the transformer can be disconnected from the power grid to prevent subsequent failures in this equipment. Detecting faults...

Advances in Modeling of Noisy Quantum Computers: Spin Qubits in Semiconductor Quantum Dots

The new quantum era is expected to have an unprecedented social impact, enabling the research of tomorrow in several pivotal fields. These perspectives require a physical system able to encode, process and store for a sufficiently long amount of time the quantum information. However, the optimal engineering of currently available quantum...

DeepPick: A Deep Learning Approach to Unveil Outstanding Users Ranking with Public Attainable Features

Outstanding users (OUs) denote the influential, 'core' or 'bridge' users in online social networks. How to accurately detect and rank them is an important problem for third-party online service providers and researchers. Conventional efforts, ranging from early graph-based algorithms to recent machine learning-based approaches, typically rely on...

The TRV Improvement of Fast Circuit Breakers Using Solid-State Series Superconducting Reactor

Utilizing superconducting technology in fault current limiters for power grid applications is a practical solution to achieve a more modern and efficient power system. This is due to their low-loss profile, high efficiency, and ability to transmit about 5x more power in the same footprint compared to conventional counterparts. In addition,...

MFA-MTJ Model: Magnetic-Field-Aware Compact Model of pMTJ for Robust STT-MRAM Design

The popularity of perpendicular magnetic tunnel junction (pMTJ)-based spin-transfer torque magnetic random access memories (STT-MRAMs) is growing very fast. The performance of such memories is very sensitive to magnetic fields, including both internal and external ones. This article presents a magnetic-field-aware compact model of pMTJ, named...

Design of a Fan-Out Panel-Level SiC MOSFET Power Module Using Ant Colony Optimization-Back Propagation Neural Network

A new panel-level silicon carbide (SiC) metal oxide semiconductor field effect transistor (MOSFET) power module was developed by using the fan-out and embedded chip technologies. To achieve the more effective thermal management and higher reliability under thermal cycling, a new optimization method called Ant colony optimization-back...

Symmetrical Component Decomposition of DC Distribution Systems

Employing bipolar dc distribution systems introduces the possibility of imbalance in the system. To analyze these systems it is crucial to create novel modelling techniques. This paper presents a method to decompose dc distribution systems into symmetrical components. The presented method simplifies the analysis of balanced, unbalanced, and...

A probabilistic physics-of-failure reliability assessment approach for integrated LED lamps

This work studies the effect of randomness of LED's lumen depreciation on reliability of the entire LED lamp. An integrated LED light bulb is selected as carrier of the proposed method. A PoF based lumen depreciation model and electronic-thermal simulations are introduced for reliability prediction. The normal distribution is used to describe...

Characterization and Compact Modeling of Nanometer CMOS Transistors at Deep-Cryogenic Temperatures

Cryogenic characterization and modeling of two nanometer bulk CMOS technologies (0.16-μm and 40-nm) are presented in this paper. Several devices from both technologies were extensively characterized at temperatures of 4 K and below. Based on a detailed understanding of the device physics at deep-cryogenic temperatures, a compact...

Railway track circuit fault diagnosis using recurrent neural networks

Timely detection and identification of faults in railway track circuits are crucial for the safety and availability of railway networks. In this paper, the use of the long-short-term memory (LSTM) recurrent neural network is proposed to accomplish these tasks based on the commonly available measurement signals. By considering the signals from...

Thermal Expansion and Aging Effects in Neuromorphic Signal Processor

In this paper, we propose an efficient methodology based on a real-time estimator and predictor-corrector scheme for accurate thermal expansion profile and aging evaluation of a neuromorphic signal processor circuit components. As the experimental results indicate, for comparable mesh size, the proposed method is 1~2 order of magnitude more...