FinFETs: From devices to architectures
D Bhattacharya, NK Jha - Advances in Electronics, 2014 - Wiley Online Library
Since Moore's law driven scaling of planar MOSFETs faces formidable challenges in the
nanometer regime, FinFETs and Trigate FETs have emerged as their successors. Owing to …
nanometer regime, FinFETs and Trigate FETs have emerged as their successors. Owing to …
Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives
Convolutional neural networks, are one of the most representative deep learning models.
CNNs were extensively used in many aspects of medical image analysis, allowing for great …
CNNs were extensively used in many aspects of medical image analysis, allowing for great …
River water quality index prediction and uncertainty analysis: A comparative study of machine learning models
SBHS Asadollah, A Sharafati, D Motta… - Journal of environmental …, 2021 - Elsevier
Abstract The Water Quality Index (WQI) is the most common indicator to characterize surface
water quality. This study introduces a new ensemble machine learning model called Extra …
water quality. This study introduces a new ensemble machine learning model called Extra …
State of the art Monte Carlo method applied to power system analysis with distributed generation
Traditionally, electric power systems are subject to uncertainties related to equipment
availability, topological changes, faults, disturbances, behaviour of load, etc. In particular …
availability, topological changes, faults, disturbances, behaviour of load, etc. In particular …
Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence
H Janssen - Reliability Engineering & System Safety, 2013 - Elsevier
Monte Carlo analysis has become nearly ubiquitous since its introduction, now over 65
years ago. It is an important tool in many assessments of the reliability and robustness of …
years ago. It is an important tool in many assessments of the reliability and robustness of …
Plug-in electric vehicle behavior modeling in energy market: A novel deep learning-based approach with clustering technique
Growing penetration of Plug-in Electric Vehicles (PEVs) in the transportation fleet and their
subsequent charging demands introduce substantial intermittency to the electric load profile …
subsequent charging demands introduce substantial intermittency to the electric load profile …
Stochastic testing method for transistor-level uncertainty quantification based on generalized polynomial chaos
Uncertainties have become a major concern in integrated circuit design. In order to avoid the
huge number of repeated simulations in conventional Monte Carlo flows, this paper presents …
huge number of repeated simulations in conventional Monte Carlo flows, this paper presents …
Quasi-Monte Carlo based probabilistic small signal stability analysis for power systems with plug-in electric vehicle and wind power integration
WC Wong, CY Chung, KW Chan… - IEEE Transactions on …, 2013 - ieeexplore.ieee.org
This work presents a new quasi-Monte Carlo (QMC) based probabilistic small signal stability
analysis (PSSSA) method to assess the dynamic effects of plug-in electric vehicles (PEVs) …
analysis (PSSSA) method to assess the dynamic effects of plug-in electric vehicles (PEVs) …
[HTML][HTML] AI-driven modelling approaches for predicting oxygen levels in aquatic environments
Reliable water quality models are crucial for better water management and pollution control.
Biochemical oxygen demand (BOD) and dissolved oxygen (DO) are the widely recognized …
Biochemical oxygen demand (BOD) and dissolved oxygen (DO) are the widely recognized …
Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications
In this paper, the potential benefits of quasi-Monte Carlo (QMC) methods for uncertainty
propagation are assessed via two applications: a numerical case study and a realistic and …
propagation are assessed via two applications: a numerical case study and a realistic and …