Results 91 to 100 of about 5,065 (216)
Future InternetThis article investigates and compares four unsupervised anomaly detection algorithms: the Autoencoder (AE), LSTM-Autoencoder (LSTM-AE), One-Class SVM (OCSVM), and the Isolation Forest (IF). The analysis focuses on SCADA telemetry data from an urban wind
Lukasz Pawlik
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Design of experimental study for the AE-DNNs.
, 2019 Steps 1-7 (green line) represent model training, and steps 8-13 (blue line) show model evaluation. KNHANES, Korea national health and nutrition examination survey; AE, autoencoder; RE, reconstruction error; DNN, deep neural network; CHD, coronary heart ...
Jong Yun Lee (8087834) +3 more
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Frontiers in Sustainable Food SystemsIntroductionEarly detection of crop stress in precision agriculture is hindered by complex multivariate sensor interactions, spatial variability across fields, and the scarcity of labeled anomaly data.
S. V. Sharvani +5 more
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Advanced Flame front Detection in Combustion Processes Using Autoencoder Approach
This research explores the detection of flame front evolution in spark-ignition engines using an innovative neural network, the autoencoder. High-speed camera images from an optical access engine were analyzed under different air excess coefficient ...
Federico Ricci, Francesco Mariani
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An Autoencoder Gated Recurrent Unit for Remaining Useful Life Prediction
, 2020 With the development of smart manufacturing, in order to detect abnormal conditions of the equipment, a large number of sensors have been used to record the variables associated with production equipment. This study focuses on the prediction of Remaining
Yi-Wei Lu +2 more
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Ae$$^2$$I: A Double Autoencoder for Imputation of Missing Values
The most common strategy of imputing missing values in a table is to study either the column-column relationship or the row-row relationship of the data table, then use the relationship to impute the missing values based on the non-missing values from other columns of the same row, or from the other rows of the same column.
openaire +2 more sources
Unsupervised anomaly detection is well known for its ability to effectively identify and discern anomalies in data containing rare anomalies or diverse patterns, leading to broad applications across various research fields.
Dohyung Kwon, Jeongmin Yu
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Exploring Autoencoder-based Error-bounded Compression for Scientific Data
, 2023 Error-bounded lossy compression is becoming an indispensable technique for the success of today's scientific projects with vast volumes of data produced during simulations or instrument data acquisitions.
Liu, Jinyang +7 more
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SensorsThis paper discusses the problem of recognizing defective epoxy drop images for the purpose of performing vision-based die attachment inspection in integrated circuit (IC) manufacturing based on deep neural networks.
Lamia Alam, Nasser Kehtarnavaz
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Variational Autoencoder to Obtain High Resolution Wind Fields from Reanalysis Data
WindAccurate wind flow prediction is essential for various applications, including the placement of wind turbines and a multitude of environmental assessments.
Bernhard Rösch +5 more
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