Lost Vibration Test Data Recovery Using Convolutional Neural Network: A Case Study

• URL: http://arxiv.org/abs/2204.05440v1
• Date: Mon, 11 Apr 2022 23:24:03 GMT
• Title: Lost Vibration Test Data Recovery Using Convolutional Neural Network: A Case Study
• Authors: Pouya Moeinifard, Mohammad Sadra Rajabi, Maryam Bitaraf
• Abstract summary: This paper proposes a CNN algorithm for the Alamosa Canyon Bridge as a real structure. Three different CNN models were considered to predict one and two malfunctioned sensors. The accuracy of the model was increased by adding a convolutional layer.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data loss in Structural Health Monitoring (SHM) networks has recently become one of the main challenges for engineers. Therefore, a data recovery method for SHM, generally an expensive procedure, is essential. Lately, some techniques offered to recover this valuable raw data using Neural Network (NN) algorithms. Among them, the convolutional neural network (CNN) based on convolution, a mathematical operation, can be applied to non-image datasets such as signals to extract important features without human supervision. However, the effect of different parameters has not been studied and optimized for SHM applications. Therefore, this paper aims to propose different architectures and investigate the effects of different hyperparameters for one of the newest proposed methods, which is based on a CNN algorithm for the Alamosa Canyon Bridge as a real structure. For this purpose, three different CNN models were considered to predict one and two malfunctioned sensors by finding the correlation between other sensors, respectively. Then the CNN algorithm was trained by experimental data, and the results showed that the method had a reliable performance in predicting Alamosa Canyon Bridge's missed data. The accuracy of the model was increased by adding a convolutional layer. Also, a standard neural network with two hidden layers was trained with the same inputs and outputs of the CNN models. Based on the results, the CNN model had higher accuracy, lower computational cost, and was faster than the standard neural network.

Related papers

• Few-shot Learning using Data Augmentation and Time-Frequency Transformation for Time Series Classification [6.830148185797109]
  We propose a novel few-shot learning framework through data augmentation. We also develop a sequence-spectrogram neural network (SSNN) Our methodology demonstrates its applicability of addressing the few-shot problems for time series classification.
  arXiv  Detail & Related papers  (2023-11-06T15:32:50Z)
• Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data [24.86314525762012]
  We show that ReLU CNN trained by gradient descent can achieve near Bayes-optimal accuracy. Our result demonstrates that CNNs have a remarkable capacity to efficiently learn XOR problems, even in the presence of highly correlated features.
  arXiv  Detail & Related papers  (2023-10-03T11:31:37Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• Deep Learning Architectures for FSCV, a Comparison [0.0]
  Suitability is determined by the predictive performance in the "out-of-probe" case, the response to artificially induced electrical noise, and the ability to predict when the model will be errant for a given probe. The InceptionTime architecture, a deep convolutional neural network, has the best absolute predictive performance of the models tested but was more susceptible to noise. A naive multilayer perceptron architecture had the second lowest prediction error and was less affected by the artificial noise, suggesting that convolutions may not be as important for this task as one might suspect.
  arXiv  Detail & Related papers  (2022-12-05T00:20:10Z)
• Learning to Learn with Generative Models of Neural Network Checkpoints [71.06722933442956]
  We construct a dataset of neural network checkpoints and train a generative model on the parameters. We find that our approach successfully generates parameters for a wide range of loss prompts. We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.
  arXiv  Detail & Related papers  (2022-09-26T17:59:58Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• A Novel Hand Gesture Detection and Recognition system based on ensemble-based Convolutional Neural Network [3.5665681694253903]
  Detection of hand portion has become a challenging task in computer vision and pattern recognition communities. Deep learning algorithm like convolutional neural network (CNN) architecture has become a very popular choice for classification tasks. In this paper, an ensemble of CNN-based approaches is presented to overcome some problems like high variance during prediction, overfitting problem and also prediction errors.
  arXiv  Detail & Related papers  (2022-02-25T06:46:58Z)
• Neural Architecture Dilation for Adversarial Robustness [56.18555072877193]
  A shortcoming of convolutional neural networks is that they are vulnerable to adversarial attacks. This paper aims to improve the adversarial robustness of the backbone CNNs that have a satisfactory accuracy. Under a minimal computational overhead, a dilation architecture is expected to be friendly with the standard performance of the backbone CNN.
  arXiv  Detail & Related papers  (2021-08-16T03:58:00Z)
• BreakingBED -- Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks [65.2021953284622]
  We study robustness of CNNs against white-box and black-box adversarial attacks. Results are shown for distilled CNNs, agent-based state-of-the-art pruned models, and binarized neural networks.
  arXiv  Detail & Related papers  (2021-03-14T20:43:19Z)
• Transfer Learning with Convolutional Networks for Atmospheric Parameter Retrieval [14.131127382785973]
  The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series provides important measurements for Numerical Weather Prediction (NWP) Retrieving accurate atmospheric parameters from the raw data provided by IASI is a large challenge, but necessary in order to use the data in NWP models. We show how features extracted from the IASI data by a CNN trained to predict a physical variable can be used as inputs to another statistical method designed to predict a different physical variable at low altitude.
  arXiv  Detail & Related papers  (2020-12-09T09:28:42Z)
• Deep learning for gravitational-wave data analysis: A resampling white-box approach [62.997667081978825]
  We apply Convolutional Neural Networks (CNNs) to detect gravitational wave (GW) signals of compact binary coalescences, using single-interferometer data from LIGO detectors. CNNs were quite precise to detect noise but not sensitive enough to recall GW signals, meaning that CNNs are better for noise reduction than generation of GW triggers.
  arXiv  Detail & Related papers  (2020-09-09T03:28:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.