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 $R$ is called strongly $J^{\#}$-clean provided that it can be written as the sum of an idempotent and an element in $J^{\#}(R)$ that commute. We characterize, in this article, the strongly $J^{\#}$-cleanness of matrices over projective-free rings. These extend many known results on strongly clean matrices over commutative local rings

A VLSI architecture of JPEG2000 encoder

Copyright @ 2004 IEEEThis paper proposes a VLSI architecture of JPEG2000 encoder, which functionally consists of two parts: discrete wavelet transform (DWT) and embedded block coding with optimized truncation (EBCOT). For DWT, a spatial combinative lifting algorithm (SCLA)-based scheme with both 5/3 reversible and 9/7 irreversible filters is adopted to reduce 50% and 42% multiplication computations, respectively, compared with the conventional lifting-based implementation (LBI). For EBCOT, a dynamic memory control (DMC) strategy of Tier-1 encoding is adopted to reduce 60% scale of the on-chip wavelet coefficient storage and a subband parallel-processing method is employed to speed up the EBCOT context formation (CF) process; an architecture of Tier-2 encoding is presented to reduce the scale of on-chip bitstream buffering from full-tile size down to three-code-block size and considerably eliminate the iterations of the rate-distortion (RD) truncation.This work was supported in part by the China National High Technologies Research Program (863) under Grant 2002AA1Z142