Results 41 to 50 of about 798,795 (221)

Linear spectral statistics of eigenvectors of anisotropic sample covariance matrices

, 2020
Consider sample covariance matrices of the form $Q:=\Sigma^{1/2} X X^* \Sigma^{1/2}$, where $X=(x_{ij})$ is an $n\times N$ random matrix whose entries are independent random variables with mean zero and variance $N^{-1}$, and $\Sigma$ is a deterministic ...
Yang, Fan
core  

Setup‐Optimized Sequencing in Job Shops: Modeling Workstation Productivity and Lateness Behavior

Advanced Engineering Materials, EarlyView.
Setup‐optimized sequencing in job‐shop production creates a trade‐off between productivity improvement and schedule reliability. A WIP‐explicit modeling framework links sequencing‐induced productivity gains and lateness dispersion through the production operating curve.
Friederike Stefanowski, Matthias Schmidt, Peter Nyhuis   +2 more
wiley   +1 more source

A Novel Application of Fractional Order Derivative Moth Flame Optimization Algorithm for Solving the Problem of Optimal Coordination of Directional Overcurrent Relays

Fractal and Fractional
The proper coordination of directional overcurrent relays (DOCRs) is crucial in electrical power systems. The coordination of DOCRs in a multi-loop power system is expressed as an optimization problem.
Abdul Wadood, Herie Park
doaj   +1 more source

Two-dimensional Kolmogorov-type Goodness-of-fit Tests Based on Characterizations and their Asymptotic Efficiencies

, 2015
In this paper new two-dimensional goodness of fit tests are proposed. They are of supremum-type and are based on different types of characterizations. For the first time a characterization based on independence of two statistics is used for goodness-of ...
Milošević, Bojana, Obradović, Marko
core   +1 more source

Testing for Homogeneity in Mixture Models [PDF]

, 2016
Statistical models of unobserved heterogeneity are typically formalized as mixtures of simple parametric models and interest naturally focuses on testing for homogeneity versus general mixture alternatives.
Gu, Jiaying, Koenker, Roger, Volgushev, Stanislav   +2 more
core   +1 more source

A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann, Sarath Menon, Raphael Röske, Johannes Steinhülb, Michael Selzer, Jan Janssen, Tilmann Hickel, Ruth Schwaiger, Jörg Neugebauer   +8 more
wiley   +1 more source

Optimization of the Production of Rubber Compounds Using Mathematical Models

Advanced Engineering Materials, EarlyView.
Rubber compounds were mixed in a batch internal mixer, and symbolic regression was used to derive mathematical models linking recipe and process parameters to ram path, torque, and mixing quality (incorporation, dispersion, distribution). Subsequent optimization with evolutionary algorithms identified operating conditions that reduce specific energy ...
Anke Bardehle, Andreas Limper, Ulrich Giese, Carlos Lucke, Mario Beiner, Christoph Gögelein, Lisa Leuchtenberger‐Engel, Christian Hopmann   +7 more
wiley   +1 more source

Soil Classification from Cone Penetration Test Profiles Based on XGBoost

Applied Sciences
This study develops a machine-learning-based framework for multiclass soil classification using Cone Penetration Test (CPT) data, aiming to overcome the limitations of traditional empirical Soil Behavior Type (SBT) charts and improve the automation ...
Jinzhang Zhang, Jiaze Ni, Feiyang Wang, Hongwei Huang, Dongming Zhang   +4 more
doaj   +1 more source

Strong Approximation of Empirical Copula Processes by Gaussian Processes

, 2011
We provide the strong approximation of empirical copula processes by a Gaussian process. In addition we establish a strong approximation of the smoothed empirical copula processes and a law of iterated ...
Adler R. J., Bouzebda S., Chung K.-L., Csörgő M., Csörgő M., Cui S., DasGupta A., Deheuvels P., Deheuvels P., Deheuvels P., Fermanian J.-D., Joe H., McNeil A. J., Moore D. S., Nelsen R. B., Piterbarg V. I., Salim Bouzebda, Shorack G. R., Sklar A., Sklar A., Tarek Zari, Tsukahara H.   +21 more
core   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source