scholarly journals Adaptive Surrogate Estimation with Spatial Features Using a Deep Convolutional Autoencoder for CO2 Geological Sequestration

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 413
Author(s):  
Suryeom Jo ◽  
Changhyup Park ◽  
Dong-Woo Ryu ◽  
Seongin Ahn
Keyword(s):  
Deep Learning ◽  
Spatial Data ◽  
Flow Behavior ◽  
Percentage Error ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Spatial Properties ◽  
Reconstruction Performance ◽  
Latent Features ◽  
Convolutional Autoencoder

This paper develops a reliable deep-learning framework to extract latent features from spatial properties and investigates adaptive surrogate estimation to sequester CO2 into heterogeneous deep saline aquifers. Our deep-learning architecture includes a deep convolutional autoencoder (DCAE) and a fully-convolutional network to not only reduce computational costs but also to extract dimensionality-reduced features to conserve spatial characteristics. The workflow integrates two different spatial properties within a single convolutional system, and it also achieves accurate reconstruction performance. This approach significantly reduces the number of parameters to 4.3% of the original number required, e.g., the number of three-dimensional spatial properties needed decreases from 44,460 to 1920. The successful dimensionality reduction is accomplished by the DCAE system regarding all inputs as image channels from the initial stage of learning using the fully-convolutional network instead of fully-connected layers. The DCAE reconstructs spatial parameters such as permeability and porosity while conserving their statistical values, i.e., their mean and standard deviation, achieving R-squared values of over 0.972 with a mean absolute percentage error of their mean values of less than 1.79%. The adaptive surrogate model using the latent features extracted by DCAE, well operations, and modeling parameters is able to accurately estimate CO2 sequestration performances. The model shows R-squared values of over 0.892 for testing data not used in training and validation. The DCAE-based surrogate estimation exploits the reliable integration of various spatial data within the fully-convolutional network and allows us to evaluate flow behavior occurring in a subsurface domain.

scholarly journals Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks

2018 ◽  
Vol 10 (11) ◽  
pp. 1827 ◽  
Author(s):  
Ahram Song ◽  
Jaewan Choi ◽  
Youkyung Han ◽  
Yongil Kim
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Spatial Information ◽  
Short Term Memory ◽  
Hyperspectral Images ◽  
Convolutional Network ◽  
Ground Truth Data ◽  
Fully Convolutional Network ◽  
Training Samples ◽  
Multi Temporal

Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such problems, our study proposed a novel CD method for hyperspectral images (HSIs), including sample generation and a deep-learning network, called the recurrent three-dimensional (3D) fully convolutional network (Re3FCN), which merged the advantages of a 3D fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM). Principal component analysis (PCA) and the spectral correlation angle (SCA) were used to generate training samples with high probabilities of being changed or unchanged. The strategy assisted in training fewer samples of representative feature expression. The Re3FCN was mainly comprised of spectral–spatial and temporal modules. Particularly, a spectral–spatial module with a 3D convolutional layer extracts the spectral–spatial features from the HSIs simultaneously, whilst a temporal module with ConvLSTM records and analyzes the multi-temporal HSI change information. The study first proposed a simple and effective method to generate samples for network training. This method can be applied effectively to cases with no training samples. Re3FCN can perform end-to-end detection for binary and multiple changes. Moreover, Re3FCN can receive multi-temporal HSIs directly as input without learning the characteristics of multiple changes. Finally, the network could extract joint spectral–spatial–temporal features and it preserved the spatial structure during the learning process through the fully convolutional structure. This study was the first to use a 3D FCN and a ConvLSTM for the remote-sensing CD. To demonstrate the effectiveness of the proposed CD method, we performed binary and multi-class CD experiments. Results revealed that the Re3FCN outperformed the other conventional methods, such as change vector analysis, iteratively reweighted multivariate alteration detection, PCA-SCA, FCN, and the combination of 2D convolutional layers-fully connected LSTM.

scholarly journals Investigasi Pengaruh Skema Stride dan Step Training untuk Deteksi Jari Pada Region-based Fully Convolutional Network (R-FCN) dalam Teknologi Augmented Reality

2021 ◽  
Vol 12 (2) ◽  
pp. 138
Author(s):  
Hashfi Fadhillah ◽  
Suryo Adhi Wibowo ◽  
Rita Purnamasari
Keyword(s):  
Deep Learning ◽  
Augmented Reality ◽  
Virtual World ◽  
Training Data ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Connected Network ◽  
Learning Region ◽  
Centroid Point ◽  
Fully Connected

Abstract  Combining the real world with the virtual world and then modeling it in 3D is an effort carried on Augmented Reality (AR) technology. Using fingers for computer operations on multi-devices makes the system more interactive. Marker-based AR is one type of AR that uses markers in its detection. This study designed the AR system by detecting fingertips as markers. This system is designed using the Region-based Deep Fully Convolutional Network (R-FCN) deep learning method. This method develops detection results obtained from the Fully Connected Network (FCN). Detection results will be integrated with a computer pointer for basic operations. This study uses a predetermined step scheme to get the best IoU parameters, precision and accuracy. The scheme in this study uses a step scheme, namely: 25K, 50K and 75K step. High precision creates centroid point changes that are not too far away. High accuracy can improve AR performance under conditions of rapid movement and improper finger conditions. The system design uses a dataset in the form of an index finger image with a configuration of 10,800 training data and 3,600 test data. The model will be tested on each scheme using video at different distances, locations and times. This study produced the best results on the 25K step scheme with IoU of 69%, precision of 5.56 and accuracy of 96%.Keyword: Augmented Reality, Region-based Convolutional Network, Fully Convolutional Network, Pointer, Step training Abstrak Menggabungkan dunia nyata dengan dunia virtual lalu memodelkannya bentuk 3D merupakan upaya yang diusung pada teknologi Augmented Reality (AR). Menggunakan jari untuk operasi komputer pada multi-device membuat sistem yang lebih interaktif. Marker-based AR merupakan salah satu jenis AR yang menggunakan marker dalam deteksinya. Penelitian ini merancang sistem AR dengan mendeteksi ujung jari sebagai marker. Sistem ini dirancang menggunakan metode deep learning Region-based Fully Convolutional Network (R-FCN). Metode ini mengembangkan hasil deteksi yang didapat dari Fully Connected Network (FCN). Hasil deteksi akan diintegrasikan dengan pointer komputer untuk operasi dasar. Penelitian ini menggunakan skema step training yang telah ditentukan untuk mendapatkan parameter IoU, presisi dan akurasi yang terbaik. Skema pada penelitian ini menggunakan skema step yaitu: 25K, 50K dan 75K step. Presisi tinggi menciptakan perubahan titik centroid yang tidak terlalu jauh. Akurasi  yang tinggi dapat meningkatkan kinerja AR dalam kondisi pergerakan yang cepat dan kondisi jari yang tidak tepat. Perancangan sistem menggunakan dataset berupa citra jari telunjuk dengan konfigurasi 10.800 data latih dan 3.600 data uji. Model akan diuji pada tiap skema dilakukan menggunakan video pada jarak, lokasi dan waktu yang berbeda. Penelitian ini menghasilkan hasil terbaik pada skema step 25K dengan IoU sebesar 69%, presisi sebesar 5,56 dan akurasi sebesar 96%.Kata kunci: Augmented Reality, Region-based Convolutional Network, Fully Convolutional Network, Pointer, Step training 

scholarly journals Noncontact super-resolution guided wave array imaging of subwavelength defects using a multiscale deep learning approach

2020 ◽  
pp. 147592172094295
Author(s):  
Homin Song ◽  
Yongchao Yang
Keyword(s):  
Deep Learning ◽  
Guided Waves ◽  
Laser Doppler Vibrometer ◽  
Super Resolution ◽  
Guided Wave ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Structural Details ◽  
Imaging Approach ◽  
Array Imaging

Subwavelength defect imaging using guided waves has been known to be a difficult task mainly due to the diffraction limit and dispersion of guided waves. In this article, we present a noncontact super-resolution guided wave array imaging approach based on deep learning to visualize subwavelength defects in plate-like structures. The proposed approach is a novel hierarchical multiscale imaging approach that combines two distinct fully convolutional networks. The first fully convolutional network, the global detection network, globally detects subwavelength defects in a raw low-resolution guided wave beamforming image. Then, the subsequent second fully convolutional network, the local super-resolution network, locally resolves subwavelength-scale fine structural details of the detected defects. We conduct a series of numerical simulations and laboratory-scale experiments using a noncontact guided wave array enabled by a scanning laser Doppler vibrometer on aluminate plates with various subwavelength defects. The results demonstrate that the proposed super-resolution guided wave array imaging approach not only locates subwavelength defects but also visualizes super-resolution fine structural details of these defects, thus enabling further estimation of the size and shape of the detected subwavelength defects. We discuss several key aspects of the performance of our approach, compare with an existing super-resolution algorithm, and make recommendations for its successful implementations.

Deep Learning Models for Semantic Multi-Modal Medical Image Segmentation

2020 ◽  
pp. 12-30
Author(s):  
V. R. S. Mani
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image Segmentation ◽  
Learning Models ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Comprehensive Picture ◽  
Application Methods ◽  
Segmentation Task

In this chapter, the author paints a comprehensive picture of different deep learning models used in different multi-modal image segmentation tasks. This chapter is an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different types of multi-modal images and the corresponding types of convolution neural networks used in the segmentation task. The chapter starts with an introduction to CNN topology and describes various models like Hyper Dense Net, Organ Attention Net, UNet, VNet, Dilated Fully Convolutional Network, Transfer Learning, etc.

scholarly journals Image Cropping with Composition and Saliency Aware Aesthetic Score Map

2020 ◽  
Vol 34 (07) ◽  
pp. 12104-12111
Author(s):  
Yi Tu ◽  
Li Niu ◽  
Weijie Zhao ◽  
Dawei Cheng ◽  
Liqing Zhang
Keyword(s):  
Deep Learning ◽  
Convolutional Network ◽  
Aesthetic Quality ◽  
Aesthetic Evaluation ◽  
Fully Convolutional Network ◽  
Intrinsic Mechanism ◽  
Real World Applications ◽  
Benchmark Datasets ◽  
Score Map ◽  
The Aesthetic

Aesthetic image cropping is a practical but challenging task which aims at finding the best crops with the highest aesthetic quality in an image. Recently, many deep learning methods have been proposed to address this problem, but they did not reveal the intrinsic mechanism of aesthetic evaluation. In this paper, we propose an interpretable image cropping model to unveil the mystery. For each image, we use a fully convolutional network to produce an aesthetic score map, which is shared among all candidate crops during crop-level aesthetic evaluation. Then, we require the aesthetic score map to be both composition-aware and saliency-aware. In particular, the same region is assigned with different aesthetic scores based on its relative positions in different crops. Moreover, a visually salient region is supposed to have more sensitive aesthetic scores so that our network can learn to place salient objects at more proper positions. Such an aesthetic score map can be used to localize aesthetically important regions in an image, which sheds light on the composition rules learned by our model. We show the competitive performance of our model in the image cropping task on several benchmark datasets, and also demonstrate its generality in real-world applications.

scholarly journals Lithological Mapping Based on Fully Convolutional Network and Multi-Source Geological Data

2021 ◽  
Vol 13 (23) ◽  
pp. 4860
Author(s):  
Ziye Wang ◽  
Renguang Zuo ◽  
Hao Liu
Keyword(s):  
Deep Learning ◽  
Data Fusion ◽  
Classification Accuracy ◽  
Learning Algorithms ◽  
Geological Mapping ◽  
Fusion Technology ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Geological Features ◽  
Source Data

Deep learning algorithms have found numerous applications in the field of geological mapping to assist in mineral exploration and benefit from capabilities such as high-dimensional feature learning and processing through multi-layer networks. However, there are two challenges associated with identifying geological features using deep learning methods. On the one hand, a single type of data resource cannot diagnose the characteristics of all geological units; on the other hand, deep learning models are commonly designed to output a certain class for the whole input rather than segmenting it into several parts, which is necessary for geological mapping tasks. To address such concerns, a framework that comprises a multi-source data fusion technology and a fully convolutional network (FCN) model is proposed in this study, aiming to improve the classification accuracy for geological mapping. Furthermore, multi-source data fusion technology is first applied to integrate geochemical, geophysical, and remote sensing data for comprehensive analysis. A semantic segmentation-based FCN model is then constructed to determine the lithological units per pixel by exploring the relationships among multi-source data. The FCN is trained end-to-end and performs dense pixel-wise prediction with an arbitrary input size, which is ideal for targeting geological features such as lithological units. The framework is finally proven by a comparative study in discriminating seven lithological units in the Cuonadong dome, Tibet, China. A total classification accuracy of 0.96 and a high mean intersection over union value of 0.9 were achieved, indicating that the proposed model would be an innovative alternative to traditional machine learning algorithms for geological feature mapping.

scholarly journals A Deep Learning-Based Automatic Mosquito Sensing and Control System for Urban Mosquito Habitats

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2785 ◽  
Author(s):  
Kyukwang Kim ◽  
Jieum Hyun ◽  
Hyeongkeun Kim ◽  
Hwijoon Lim ◽  
Hyun Myung
Keyword(s):  
Deep Learning ◽  
Processing Time ◽  
Mosquito Control ◽  
Automated System ◽  
Learning Networks ◽  
Single Image ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Static Water ◽  
And Control

Mosquito control is important as mosquitoes are extremely harmful pests that spread various infectious diseases. In this research, we present the preliminary results of an automated system that detects the presence of mosquitoes via image processing using multiple deep learning networks. The Fully Convolutional Network (FCN) and neural network-based regression demonstrated an accuracy of 84%. Meanwhile, the single image classifier demonstrated an accuracy of only 52%. The overall processing time also decreased from 4.64 to 2.47 s compared to the conventional classifying network. After detection, a larvicide made from toxic protein crystals of the Bacillus thuringiensis serotype israelensis bacteria was injected into static water to stop the proliferation of mosquitoes. This system demonstrates a higher efficiency than hunting adult mosquitos while avoiding damage to other insects.

scholarly journals Accurate brain age prediction with lightweight deep neural networks

2019 ◽  
Author(s):  
Han Peng ◽  
Weikang Gong ◽  
Christian F. Beckmann ◽  
Andrea Vedaldi ◽  
Stephen M. Smith
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Uk Biobank ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Prediction Bias ◽  
Dataset Size ◽  
Art Performance ◽  
Brain Age ◽  
Age Prediction

AbstractDeep learning has huge potential for accurate disease prediction with neuroimaging data, but the prediction performance is often limited by training-dataset size and computing memory requirements. To address this, we propose a deep convolutional neural network model, Simple Fully Convolutional Network (SFCN), for accurate prediction of brain age using T1-weighted structural MRI data. Compared with other popular deep network architectures, SFCN has fewer parameters, so is more compatible with small dataset size and 3D volume data. The network architecture was combined with several techniques for boosting performance, including data augmentation, pre-training, model regularization, model ensemble and prediction bias correction. We compared our overall SFCN approach with several widely-used machine learning models. It achieved state-of-the-art performance in UK Biobank data (N = 14,503), with mean absolute error (MAE) = 2.14y in brain age prediction and 99.5% in sex classification. SFCN also won (both parts of) the 2019 Predictive Analysis Challenge for brain age prediction, involving 79 competing teams (N = 2,638, MAE = 2.90y). We describe here the details of our approach, and its optimisation and validation. Our approach can easily be generalised to other tasks using different image modalities, and is released on GitHub.HighlightsA lightweight deep learning model, Simple Fully Convolutional Network (SFCN), is presented, achieving state-of-the-art brain age prediction and sex classification performance in UK Biobank MRI brain imaging data.Even with limited number of training subjects (e.g., 50), SFCN performs better than widely-used regression models.A semi-multimodal ensemble strategy is proposed and achieved first place in the PAC 2019 brain age prediction challenge.Linear regression can remove brain age prediction bias (even on unlabelled data) while maintaining state-of-the-art performance.

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