A Multi-Convolutional Autoencoder Approach to Multivariate Geochemical Anomaly Recognition

Chen, Lirong; Guan, Qingfeng; Feng, Bin; Yue, Hanqiu; Wang, Junyi; Zhang, Fan

doi:10.3390/min9050270

Cited by 37 publications

(8 citation statements)

References 59 publications

(71 reference statements)

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“…In the case of image data composed of horizontal and vertical pixels, compressing the image is performed at the encoder, and the feature values of the compressed image are shown in the latent. With the information of the latent feature values of the compressed image, the decoder can reconstruct the image (Chandar AP et al, 2014;Chen et al, 2019;Lyons et al, 2014;Ma et al, 2016;Wen & Zhang, 2018;Zhang & Ye, 2019). The AE's learning result is shown as an output identical to the input based on SL.…”

Section: Operation Of Caementioning

confidence: 99%

“…Figure 6 shows the architectures for various types of AE. A typical AE places the same number of layers and nodes for the encoder and the decoder (Chen et al, 2019;Happel & Murre, 1994;LeCun et al, 2015;Wen & Zhang, 2018). Through FNN, which compressed the image data in AE to extract a feature value, this study predicted the latent value and used the AE's decoder to reconstruct it as an image.…”

Section: Reconstruction Of Autoencoder Schemesmentioning

confidence: 99%

“…The influence on the respective reconstruction method is investigated by changing the number of layers, and the method and architecture that exhibited optimal reconstruction performance were selected. (Chen et al, 2019;Happel & Murre, 1994;Zuo & Xiong, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Model on 2-Dimensional Image Data using Convolutional Autoencoder and Fully Connected Neural Networks: Application to Computational Fluid Dynamics

Yoon

Doh

2022

Preprint

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This study proposes a deep-learning-based image prediction meta-modeling method to develop an image-based approximate optimized design using 2D image data. An image-based meta-model is generated with an autoencoder (AE) and fully connected neural networks (FNN). To create this meta-model, we suggested three methods as FNN-based AE, convolutional autoencoder (CAE) based on convolution neural networks (CNN), and hybrid-convolutional autoencoder (H-CAE) combining the FNN and CAE. To verify the proposed methods, we applied them to predict the pressure distribution for around a 2-dimensional airfoil to replace the computational fluid dynamics simulation. As a result, the H-CAE among the proposed methods shows the high prediction accuracy, 99.9811% of the best image reconstruction rate for the pressure distribution around the airfoil. Therefore, H-CAE offers the best learning capability. There is an advantage that the latent can be predicted using shape parameters of an airfoil as inputs via the FNN based on the latent with the compressed image data.

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Section: Operation Of Caementioning

confidence: 99%

Section: Reconstruction Of Autoencoder Schemesmentioning

confidence: 99%

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Deep Learning Model on 2-Dimensional Image Data using Convolutional Autoencoder and Fully Connected Neural Networks: Application to Computational Fluid Dynamics

Yoon

Doh

2022

Preprint

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“…In order to use the advantages of both autoencoders and CNNs, CAEs are used in this study, which usually use convolution and pooling layers to extract the key features of the input data and compress them (encoding) and deconvolution and unpooling layers to reconstruct the original data from the compressed form (decoding) [36]. Figure 2 shows an illustrative example of the structure of a two-dimensional (2D) CAE.…”

Section: Overview Of Autoencodersmentioning

confidence: 99%

Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder

Rastin

Amiri

Darvishan

2021

Shock and Vibration

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Structural health monitoring (SHM) is a hot research topic with the main purpose of damage detection in a structure and assessing its health state. The major focus of SHM studies in recent years has been on developing vibration-based damage detection algorithms and using machine learning, especially deep learning-based approaches. Most of the deep learning-based methods proposed for damage detection in civil structures are based on supervised algorithms that require data from the healthy state and different damaged states of the structure in the training phase. As it is not usually possible to collect data from damaged states of a large civil structure, using such algorithms for these structures may be impractical. This paper proposes a new unsupervised deep learning-based method for structural damage detection based on convolutional autoencoders (CAEs). The main objective of the proposed method is to identify and quantify structural damage using a CAE network that employs raw vibration signals from the structure and is trained by the signals solely acquired from the healthy state of the structure. The CAE is chosen to take advantage of high feature extraction capability of convolution layers and at the same time use the advantages of an autoencoder as an unsupervised algorithm that does not need data from damaged states in the training phase. Applications on the two numerical models of IASC-ASCE benchmark structure and a grid structure located at the University of Central Florida, as well as the full-scale Tianjin Yonghe Bridge, prove the efficiency of the proposed algorithm in assessing the global health state of the structures and quantifying the damage.

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“…Applying the deep learning algorithms as a subcategory of machine learning algorithms can lead to improving the accuracy of classification or prediction by replacing the manual selection [45]. Such techniques have been employed in recognizing geochemical anomalies related to mineralization via deep autoencoder networks [46], deep variational autoencoder network [45], convolutional autoencoder networks [91], and combining deep learning with other anomaly detection methods [54,56].…”

Section: The Outputs Predicted/modeled Using ML In the Selected Literature And The Inputs Utilizedmentioning

confidence: 99%

A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

2021

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Machine learning is a subcategory of artificial intelligence, which aims to make computers capable of solving complex problems without being explicitly programmed. Availability of large datasets, development of effective algorithms, and access to the powerful computers have resulted in the unprecedented success of machine learning in recent years. This powerful tool has been employed in a plethora of science and engineering domains including mining and minerals industry. Considering the ever-increasing global demand for raw materials, complexities of the geological structure of ore deposits, and decreasing ore grade, high-quality and extensive mineralogical information is required. Comprehensive analyses of such invaluable information call for advanced and powerful techniques including machine learning. This paper presents a systematic review of the efforts that have been dedicated to the development of machine learning-based solutions for better utilizing mineralogical data in mining and mineral studies. To that end, we investigate the main reasons behind the superiority of machine learning in the relevant literature, machine learning algorithms that have been deployed, input data, concerned outputs, as well as the general trends in the subject area.

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A Multi-Convolutional Autoencoder Approach to Multivariate Geochemical Anomaly Recognition

Cited by 37 publications

References 59 publications

Deep Learning Model on 2-Dimensional Image Data using Convolutional Autoencoder and Fully Connected Neural Networks: Application to Computational Fluid Dynamics

Deep Learning Model on 2-Dimensional Image Data using Convolutional Autoencoder and Fully Connected Neural Networks: Application to Computational Fluid Dynamics

Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder

A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

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