scholarly journals PROBABILISTIC ANALYSIS OF THE MAXIMAL LOAD OF INDUSTRIAL MACHINES

2021 ◽  
Vol 2021 (6) ◽  
pp. 5391-5395
Author(s):  
ZDENEK FOLTA ◽  
◽  
PAVEL SKALNY ◽  
PETR MATEJKA ◽  
MIROSLAV TROCHTA ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Engineering Design ◽  
Probabilistic Analysis ◽  
Expectation Maximization ◽  
Lognormal Distribution ◽  
Convex Combination ◽  
Probability Distributions ◽  
Expectation Maximization Algorithm ◽  
Maximal Load ◽  
Data Source

This study describes the statistical analysis of peak forces of industrial washing machines. The data source comes from twelve different machines. The measurements are done using a force gauge installed in places for fastening screws. A new software based on LabView has been developed to gauge the acting forces. To determine extreme force values, various probability distributions are applied. Furthermore, a convex combination of lognormal distribution is used in more complicated cases. The parameters of the lognormal mixtures are determined using modified an Expectation-maximization algorithm. Finally, the achieved results are interpreted with regard to the engineering design and to the operating reliability.

scholarly journals Accurate and scalable social recommendation using mixed-membership stochastic block models

2016 ◽  
Vol 113 (50) ◽  
pp. 14207-14212 ◽  
Author(s):  
Antonia Godoy-Lorite ◽  
Roger Guimerà ◽  
Cristopher Moore ◽  
Marta Sales-Pardo
Keyword(s):  
Expectation Maximization ◽  
Probability Distributions ◽  
Expectation Maximization Algorithm ◽  
Large Datasets ◽  
User Preferences ◽  
Social Recommendation ◽  
Scalable Algorithm ◽  
Measure Of Similarity ◽  
Block Models ◽  
Group Memberships

With increasing amounts of information available, modeling and predicting user preferences—for books or articles, for example—are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users’ ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user’s and item’s groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets.

Ordered subset expectation maximization algorithm for image reconstruction applied to zero-flow or low-flow mimicking brain phantom

2005 ◽  
Vol 25 (1_suppl) ◽  
pp. S678-S678
Author(s):  
Yasuhiro Akazawa ◽  
Yasuhiro Katsura ◽  
Ryohei Matsuura ◽  
Piao Rishu ◽  
Ansar M D Ashik ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Low Flow ◽  
Brain Phantom ◽  
Zero Flow
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