Results 71 to 80 of about 559,504 (336)
DFTCalc: A Tool for Efficient Fault Tree Analysis [PDF]
, 2013 Effective risk management is a key to ensure that our nuclear power plants, medical equipment, and power grids are dependable; and it is often required by law. Fault Tree Analysis (FTA) is a widely used methodology here, computing important dependability measures like system reliability.
Arnold F. +4 more
openaire +2 more sources
THE FAULT TREE ANALYSIS OF AIRCRAFT HYDRAULIC BRAKE SYSTEM BASED ON HYPER-ELLIPSOIDAL MODEL
Jixie qiangdu, 2017 In fault tree analysis of aircraft hydraulic brake system,the probability statistical information of failure rate for bottom event is usually not sufficient.To solve this problem,a fault tree analysis method based on hyper-ellipsoidal model was given ...
ZHANG LeiLei +4 more
doaj
Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics
Advanced Engineering Materials, EarlyView.Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani +2 more
wiley +1 more source
Simulation of Inhomogeneous Refractive Index Fields Induced by Hot Tailored Forming Components
Advanced Engineering Materials, EarlyView.This article presents a simulation model for simulating inhomogeneous refractive index fields (IRIF) in hot‐forged components, accounting for thermal influences and complex geometries. Through this simulation, a priori knowledge about the propagation of the IRIF can be obtained, allowing for the positioning of the component or an optical measurement ...
Pascal Kern +3 more
wiley +1 more source
Nonlinearity and Domain Switching in a 3D‐Printed Architected Ferroelectric
Advanced Engineering Materials, EarlyView.By combining functional properties measurement with in situ 2D X‐ray microdiffraction experiments, it is shown that nonlinear polarization and strain responses of a 3D‐printed architected ferroelectric are driven by localized progression of domain switching, which depends on nonuniform electric‐field distribution as well as evolving stress fields.
Abhijit Pramanick +7 more
wiley +1 more source
Advanced Materials Interfaces, EarlyView., 2023 This review discusses the use of Surface‐Enhanced Raman Spectroscopy (SERS) combined with Artificial Intelligence (AI) for detecting antimicrobial resistance (AMR). Various SERS studies used with AI techniques, including machine learning and deep learning, are analyzed for their advantages and limitations.
Zakarya Al‐Shaebi +4 more
wiley +1 more source
IEEE Access, 2020 In order to solve the problem of inaccurate fault location in the transmission network under some abnormal conditions, such as traveling wave location device faults, startup failure and time recording error, a novel traveling wave fault location method ...
Kun Yu +5 more
doaj +1 more source
Efficient Inference on Generalized Fault Diagrams [PDF]
arXiv, 2013 The generalized fault diagram, a data structure for failure analysis based on the influence diagram, is defined. Unlike the fault tree, this structure allows for dependence among the basic events and replicated logical elements. A heuristic procedure is developed for efficient processing of these structures.
arxiv
Fault-Tolerant Formation Tracking of Heterogeneous Multi-Agent Systems with Time-Varying Actuator Faults and Its Application to Task-Space Cooperative Tracking of Manipulators [PDF]
arXiv, 2020 This paper addresses a formation tracking problem for nonlinear multi-agent systems with time-varying actuator faults, in which only a subset of agents has access to the leader's information over the directed leader-follower network with a spanning tree.
arxiv
Advanced Engineering Materials, EarlyView.In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi +4 more
wiley +1 more source