453,113 research outputs found
Application of the linear matching method to creep-fatigue failure analysis of cruciform weldment manufactured of the austenitic steel AISI type 316N(L)
This paper demonstrates the recent extension of the Linear Matching Method (LMM) to include cyclic creep assessment [1] in application to a creep-fatigue analysis of a cruciform weldment made of the stainless steel AISI type 316N(L). The obtained results are compared with the results of experimental studies implemented by Bretherton et al. [2] with the overall objective to identify fatigue strength reduction factors (FSRF) of austenitic weldments for further design application. These studies included a series of strain-controlled tests at 550°C with different combinations of reversed bending moment and dwell time Δt. Five levels of reversed bending moment histories corresponding to defined values of total strain range Δεtot in remote parent material (1%, 0.6%, 0.4%, 0.3%, 0.25%) were used in combination with three variants of creep-fatigue conditions: pure fatigue, 1 hour and 5 hours of dwell period Δt of hold in tension. An overview of previous works devoted to analysis and simulation of these experiments [2] and highlight of the LMM development progress could be found in [3]
Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates
We establish the first nonasymptotic error bounds for Kaplan-Meier-based
nearest neighbor and kernel survival probability estimators where feature
vectors reside in metric spaces. Our bounds imply rates of strong consistency
for these nonparametric estimators and, up to a log factor, match an existing
lower bound for conditional CDF estimation. Our proof strategy also yields
nonasymptotic guarantees for nearest neighbor and kernel variants of the
Nelson-Aalen cumulative hazards estimator. We experimentally compare these
methods on four datasets. We find that for the kernel survival estimator, a
good choice of kernel is one learned using random survival forests.Comment: International Conference on Machine Learning (ICML 2019
Factorizations of Matrices Over Projective-free Rings
An element of a ring is called strongly -clean provided that it
can be written as the sum of an idempotent and an element in that
commute. We characterize, in this article, the strongly -cleanness of
matrices over projective-free rings. These extend many known results on
strongly clean matrices over commutative local rings
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