Results 1 to 10 of about 2,499,784 (284)

Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data [PDF]

open access: yesSensors, 2021
Fault detection and diagnosis (FDD) has received considerable attention with the advent of big data. Many data-driven FDD procedures have been proposed, but most of them may not be accurate when data missing occurs.
Yuequn Zhang, Lei Luo, Xu Ji, Yiyang Dai
doaj   +2 more sources

Scalable Tensor Factorizations for Incomplete Data [PDF]

open access: yesChemometrics and Intelligent Laboratory Systems, 2010
The problem of incomplete data - i.e., data with missing or unknown values - in multi-way arrays is ubiquitous in biomedical signal processing, network traffic analysis, bibliometrics, social network analysis, chemometrics, computer vision, communication
Acar   +35 more
core   +3 more sources

MODIFIED POSSIBILISTIC FUZZY C-MEANS ALGORITHM FOR CLUSTERING INCOMPLETE DATA SETS

open access: yesActa Polytechnica, 2021
A possibilistic fuzzy c-means (PFCM) algorithm is a reliable algorithm proposed to deal with the weaknesses associated with handling noise sensitivity and coincidence clusters in fuzzy c-means (FCM) and possibilistic c-means (PCM).
Rustam   +7 more
doaj   +1 more source

A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data [PDF]

open access: yesJournal of Artificial Intelligence and Data Mining, 2020
The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data.
J. Tayyebi, E. Hosseinzadeh
doaj   +1 more source

Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

open access: yesForecasting, 2021
This article considers the estimation of Approximate Dynamic Factor Models with homoscedastic, cross-sectionally correlated errors for incomplete panel data.
Monica Defend   +5 more
doaj   +1 more source

Relating incomplete data and incomplete theory [PDF]

open access: yesPhysical Review D, 2004
43 pages, 1 ...
Binetruy, P.   +4 more
openaire   +4 more sources

A Combined Residual Detection Method of Reaction Wheel for Fault Detection

open access: yesShanghai Jiaotong Daxue xuebao, 2021
A fault detection method of combined residual is proposed to effectively master the health state of the reaction wheels of in-orbit satellite according to the telemetry data. Based on the characteristics of in-orbit telemetry data, in the proposed method,
HE Xiawei, CAI Yunze, YAN Lingling
doaj   +1 more source

Partial Convolutional LSTM for Spatiotemporal Prediction of Incomplete Data

open access: yesIEEE Access, 2020
Advanced data analysis techniques facilitate data-driven spatiotemporal prediction in various fields. However, in real-world data, missing values are inevitable, which causes the data incomplete and makes predictions more challenging.
Hyesook Son, Yun Jang
doaj   +1 more source

RELIABILITY MODELING BASED ON INCOMPLETE DATA: OIL PUMP APPLICATION [PDF]

open access: yesManagement Systems in Production Engineering, 2014
The reliability analysis for industrial maintenance is now increasingly demanded by the industrialists in the world. Indeed, the modern manufacturing facilities are equipped by data acquisition and monitoring system, these systems generates a large ...
Ahmed HAFAIFA   +2 more
doaj   +1 more source

A Hybrid Deep Learning Method for Early and Late Mild Cognitive Impairment Diagnosis With Incomplete Multimodal Data

open access: yesFrontiers in Neuroinformatics, 2022
Multimodality neuroimages have been widely applied to diagnose mild cognitive impairment (MCI). However, the missing data problem is unavoidable. Most previously developed methods first train a generative adversarial network (GAN) to synthesize missing ...
Leiming Jin   +5 more
doaj   +1 more source

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