Augmented Stochastic Multiple Imputation Model for Airport Pavement Missing Data Imputation

2014 ◽  
Vol 2449 (1) ◽  
pp. 96-104 ◽  
Author(s):  
J. Farhan ◽  
T. F. Fwa
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Data Imputation ◽  
Imputation Model ◽  
Missing Data Imputation ◽  
Airport Pavement

Adaptive Deep Incremental Learning — Assisted Missing Data Imputation for Streaming Data

2021 ◽  
Author(s):  
C. V. S. R. Syavasya ◽  
M. A. Lakshmi
Keyword(s):  
Missing Data ◽  
Incremental Learning ◽  
Missing Values ◽  
Learning Algorithm ◽  
Streaming Data ◽  
Stochastic Gradient Descent ◽  
Data Imputation ◽  
Imputation Model ◽  
Missing Data Imputation ◽  
Hidden Neurons

With the rapid explosion of the data streams from the applications, ensuring accurate data analysis is essential for effective real-time decision making. Nowadays, data stream applications often confront the missing values that affect the performance of the classification models. Several imputation models have adopted the deep learning algorithms for estimating the missing values; however, the lack of parameter and structure tuning in classification, degrade the performance for data imputation. This work presents the missing data imputation model using the adaptive deep incremental learning algorithm for streaming applications. The proposed approach incorporates two main processes: enhancing the deep incremental learning algorithm and enhancing deep incremental learning-based imputation. Initially, the proposed approach focuses on tuning the learning rate with both the Adaptive Moment Estimation (Adam) along with Stochastic Gradient Descent (SGD) optimizers and tuning the hidden neurons. Secondly, the proposed approach applies the enhanced deep incremental learning algorithm to estimate the imputed values in two steps: (i) imputation process to predict the missing values based on the temporal-proximity and (ii) generation of complete IoT dataset by imputing the missing values from both the predicted values. The experimental outcomes illustrate that the proposed imputation model effectively transforms the incomplete dataset into a complete dataset with minimal error.

scholarly journals Data-driven missing data imputation in cluster monitoring system based on deep neural network

2019 ◽  
Vol 50 (3) ◽  
pp. 860-877 ◽  
Author(s):  
Jie Lin ◽  
NianHua Li ◽  
Md Ashraful Alam ◽  
Yuqing Ma
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Multiple Imputation ◽  
Monitoring System ◽  
Deep Neural Network ◽  
Original Data ◽  
Data Driven ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Estimation Of Missing Data

Abstract Due to cluster instability, not in the cluster monitoring system. This paper focuses on the missing data imputation processing for the cluster monitoring application and proposes a new hybrid multiple imputation framework. This new imputation approach is different from the conventional multiple imputation technologies in the fact that it attempts to impute the missing data for an arbitrary missing pattern with a model-based and data-driven combination architecture. Essentially, the deep neural network, as the data model, extracts deep features from the data and deep features are further calculated then by a regression or data-driven strategies and used to create the estimation of missing data with the arbitrary missing pattern. This paper gives evidence that if we can train a deep neural network to construct the deep features of the data, imputation based on deep features is better than that directly on the original data. In the experiments, we compare the proposed method with other conventional multiple imputation approaches for varying missing data patterns, missing ratios, and different datasets including real cluster data. The result illustrates that when data encounters larger missing ratio and various missing patterns, the proposed algorithm has the ability to achieve more accurate and stable imputation performance.

Continuous missing data imputation with incomplete dataset by generative adversarial networks–based unsupervised learning for long-term bridge health monitoring

2021 ◽  
pp. 147592172110219
Author(s):  
Huachen Jiang ◽  
Chunfeng Wan ◽  
Kang Yang ◽  
Youliang Ding ◽  
Songtao Xue
Keyword(s):  
Missing Data ◽  
Health Monitoring ◽  
Signal Transmission ◽  
Imputation Accuracy ◽  
Generative Adversarial Networks ◽  
Data Imputation ◽  
Sensor Failure ◽  
Generative Adversarial Network ◽  
Missing Data Imputation ◽  
Adversarial Network

Wireless sensors are the key components of structural health monitoring systems. During the signal transmission, sensor failure is inevitable, among which, data loss is the most common type. Missing data problem poses a huge challenge to the consequent damage detection and condition assessment, and therefore, great importance should be attached. Conventional missing data imputation basically adopts the correlation-based method, especially for strain monitoring data. However, such methods often require delicate model selection, and the correlations for vehicle-induced strains are much harder to be captured compared with temperature-induced strains. In this article, a novel data-driven generative adversarial network (GAN) for imputing missing strain response is proposed. As opposed to traditional ways where correlations for inter-strains are explicitly modeled, the proposed method directly imputes the missing data considering the spatial–temporal relationships with other strain sensors based on the remaining observed data. Furthermore, the intact and complete dataset is not even necessary during the training process, which shows another great superiority over the model-based imputation method. The proposed method is implemented and verified on a real concrete bridge. In order to demonstrate the applicability and robustness of the GAN, imputation for single and multiple sensors is studied. Results show the proposed method provides an excellent performance of imputation accuracy and efficiency.

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