Characterization of soil electrical conductivity from Chicken Creek Catchment using deep learning inversion of geophysical data

2020 ◽  
Author(s):  
Davood Moghadas ◽  
Annika Badorreck
Keyword(s):  
Electrical Conductivity ◽  
Deep Learning ◽  
Spatial Scales ◽  
Single Step ◽  
Accurate Estimation ◽  
Soil Electrical Conductivity ◽  
Convolutional Network ◽  
Data Set ◽  
Widespread Application

<p>Exploring hydrological and ecological processes plays a key role in understanding ecosystem developments. In this respect, the constructed catchment, Chicken Creek (Brandenburg, Germany), has been established for fundamental and interdisciplinary scientific research. The main components of the site include a base soil which is followed by a Tertiary clay layer (aquiclude) and sand layer (aquifer) on the top of the domain. In general, the soil mediates many of the processes that govern water resources and quality, such as the partition of precipitation into infiltration and runoff, groundwater recharge, contaminant transport, plant growth, evaporation and energy exchanges between the Earth’s surface and its atmosphere. In this respect, characterization of the soil electrical conductivity (EC) is important, since it is highly correlated with different chemical and physical soil properties.</p><p>Low frequency loop-loop electromagnetic induction (EMI) techniques have found widespread application for non-invasive delineation of the subsurface EC structures at different spatial scales. However, successful inversion of EMI data has been a major challenge for decades, due to the non-linearity, non-uniqueness and dimensionality of the inverse problem. Here, a novel approach based on deep learning inversion via convolutional neural networks is proposed to instantaneously estimate subsurface EC layering from EMI data. In this respect, a fully convolutional network was trained on a large synthetic data set generated based on one-dimensional EMI forward model. The trained network was used to find subsurface electromagnetic conductivity images from EMI data measured along two transect from Chicken Creek catchment. Dipole-dipole electrical resistivity tomography data were measured as well to obtain reference subsurface EC distributions down to a 6 m depth. The inversely estimated models were juxtaposed and compared with their counterparts obtained from a spatially constrained deterministic algorithm as a standard code. Application of the deep learning inversion for subsurface imaging from Chicken Creek catchment manifested the accuracy and robustness of the proposed approach for EMI inversion. This approach returns subsurface EC distribution directly from EMI data in a single step without any iterations. The proposed strategy simplifies considerably EMI inversion and allows for rapid and accurate estimation of subsurface electromagnetic conductivity images from multi-configuration EMI data.</p>

One-dimensional deep learning inversion of electromagnetic induction data using convolutional neural network

2020 ◽  
Vol 222 (1) ◽  
pp. 247-259 ◽  
Author(s):  
Davood Moghadas
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Electromagnetic Induction ◽  
Computational Cost ◽  
Single Step ◽  
Accurate Estimation ◽  
Well Performance ◽  
Convolutional Network ◽  
Data Set

SUMMARY Conventional geophysical inversion techniques suffer from several limitations including computational cost, nonlinearity, non-uniqueness and dimensionality of the inverse problem. Successful inversion of geophysical data has been a major challenge for decades. Here, a novel approach based on deep learning (DL) inversion via convolutional neural network (CNN) is proposed to instantaneously estimate subsurface electrical conductivity (σ) layering from electromagnetic induction (EMI) data. In this respect, a fully convolutional network was trained on a large synthetic data set generated based on 1-D EMI forward model. The accuracy of the proposed approach was examined using several synthetic scenarios. Moreover, the trained network was used to find subsurface electromagnetic conductivity images (EMCIs) from EMI data measured along two transects from Chicken Creek catchment (Brandenburg, Germany). Dipole–dipole electrical resistivity tomography data were measured as well to obtain reference subsurface σ distributions down to a 6 m depth. The inversely estimated models were juxtaposed and compared with their counterparts obtained from a spatially constrained deterministic algorithm as a standard code. Theoretical simulations demonstrated a well performance of the algorithm even in the presence of noise in data. Moreover, application of the DL inversion for subsurface imaging from Chicken Creek catchment manifested the accuracy and robustness of the proposed approach for EMI inversion. This approach returns subsurface σ distribution directly from EMI data in a single step without any iterations. The proposed strategy simplifies considerably EMI inversion and allows for rapid and accurate estimation of subsurface EMCI from multiconfiguration EMI data.

CHARACTERIZATION OF SPATIAL VARIABILITY OF SOIL ELECTRICAL CONDUCTIVITY AND CONE INDEX USING COULTER AND PENETROMETER-TYPE SENSORS

Soil Science ◽  
2006 ◽  
Vol 171 (8) ◽  
pp. 627-637 ◽  
Author(s):  
Jay David Jabro ◽  
Robert G. Evans ◽  
Yunseup Kim ◽  
William B. Stevens ◽  
William M. Iversen
Keyword(s):  
Electrical Conductivity ◽  
Spatial Variability ◽  
Soil Electrical Conductivity ◽  
Cone Index

scholarly journals Characterizing Malignant Melanoma Clinically Resembling Seborrheic Keratosis Using Deep Knowledge Transfer

Cancers ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 6300
Author(s):  
Panagiota Spyridonos ◽  
George Gaitanis ◽  
Aristidis Likas ◽  
Ioannis Bassukas
Keyword(s):  
Public Access ◽  
Classification Model ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Seborrheic Keratosis ◽  
Convolutional Network ◽  
Data Set ◽  
Computer Based ◽  
Hyperparameter Selection

Malignant melanomas resembling seborrheic keratosis (SK-like MMs) are atypical, challenging to diagnose melanoma cases that carry the risk of delayed diagnosis and inadequate treatment. On the other hand, SK may mimic melanoma, producing a ‘false positive’ with unnecessary lesion excisions. The present study proposes a computer-based approach using dermoscopy images for the characterization of SΚ-like MMs. Dermoscopic images were retrieved from the International Skin Imaging Collaboration archive. Exploiting image embeddings from pretrained convolutional network VGG16, we trained a support vector machine (SVM) classification model on a data set of 667 images. SVM optimal hyperparameter selection was carried out using the Bayesian optimization method. The classifier was tested on an independent data set of 311 images with atypical appearance: MMs had an absence of pigmented network and had an existence of milia-like cysts. SK lacked milia-like cysts and had a pigmented network. Atypical MMs were characterized with a sensitivity and specificity of 78.6% and 84.5%, respectively. The advent of deep learning in image recognition has attracted the interest of computer science towards improved skin lesion diagnosis. Open-source, public access archives of skin images empower further the implementation and validation of computer-based systems that might contribute significantly to complex clinical diagnostic problems such as the characterization of SK-like MMs.

scholarly journals Chinese Character Boxes: Single Shot Detector Network for Chinese Character Detection

2019 ◽  
Vol 9 (2) ◽  
pp. 315 ◽  
Author(s):  
Junhwan Ryu ◽  
Sungho Kim
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Chinese Character ◽  
Layer Structure ◽  
Single Step ◽  
Single Shot ◽  
Data Set ◽  
Pyramid Structure ◽  
Feature Pyramid ◽  
Translation Systems

This paper proposes a deep learning-based Chinese character detection network which is important for character recognition and translation. Detecting the correct character area is an important part of recognition and translation. Previous studies have focused on methods using projection through image pre-processing and recognition methods based on segmentation and methods using hand-crafted features such as analyzing and using features. Unfortunately, the results are vulnerable to noise. Recently, recognition or translation systems based on deep learning were dealt with as a single step from detection to translation but they failed to consider the inaccurate localization problem that arises in detectors. This paper proposes a Chinese character boxes (CCB) network that deals with a method to detect the character area more accurately using the single-shot multibox detector (SSD) as the baseline and called CCB-SSD. The proposed CCB-SSD network has a single prediction layer structure in which unnecessary layers are removed from the feature-pyramid structure. The augmentation method for training is introduced and the problem caused by the use of default boxes is solved by using the proposed non-maximum suppression (NMS). The experimental results revealed a 96.1% detection rate and 0.89 performance against the false positives per character (FPPC) which is the proposed false positive index for the character data-set and caoshu data-set used in this paper. This method showed better performance than the conventional SSD with 69.4% and 6.57 FPPC.

Landfill investigation using geophysical methods for an improved characterization of the landfill geometry and for assessing microbiological activity

2020 ◽  
Author(s):  
Lukas Aigner ◽  
Jakob Gallistl ◽  
Matthias Steiner ◽  
Christian Brandstätter ◽  
Johann Fellner ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Greenhouse Gas ◽  
Spatial Scales ◽  
Landfill Gas ◽  
Geophysical Methods ◽  
Gas Production ◽  
Construction And Demolition Waste ◽  
Demolition Waste ◽  
Transient Electromagnetic

<p>The release of landfill gas is responsible for approximately 3 % of the global greenhouse gas emissions. Especially a high content of organic matter in municipal solid waste (MSW) in wet areas may enhance the microbial activity and the production of landfill gas and leachate as metabolic products. Accordingly, the delineation of saturated zones and biogeochemically active and inactive areas is critical for designing adequate stabilization systems to limit the environmental impact of landfills on greenhouse gas production. Therefore, landfill investigations with high spatial resolution are critical for environmental protection. Geophysical methods are a cost-efficient possibility to obtain almost continuous information about subsurface properties at various spatial scales, which can help to identify biogeochemical active zones. Within this case study we investigate the applicability of three geophysical methods, namely (i) the electrical resistivity tomography (ERT), (ii) the induced polarization (IP) method and (iii) the transient electromagnetic (TEM) method to characterize the landfill geometry and to discriminate between biogeochemically active and inactive areas. The investigated landfill is located close to Vienna (Austria) and consists of a mixture of MSW, construction and demolition waste (CDW) and excavated soil. We conducted ERT and IP measurements along 17 profiles distributed over the area of the landfill to provide high resolution images of the subsurface down to 8 m depth. Additionally, we used transient electromagnetic measurements along selected profiles to provide information on deeper structures of the landfill as well as to evaluate the electrical conductivity obtained with ERT. Our results show that the electrical conductivity obtained by both ERT and TEM is mainly sensitive to the increase in the fluid conductivity associated to leachate production and migration. Additionally, a decrease in electrical conductivity is associated to CDW and dry MSW and can help to distinguish between different waste types. However, images of the polarization effect obtained with the IP method, expressed in terms of the phase of the complex conductivity, revealed an improved contrast to characterize variations in the architecture and biogeochemical activity of the landfill. Hence, our study demonstrates that the geophysical methods we applied are well-suited for landfill investigations permitting an improved characterization of landfill geometry and variation in waste composition. In particular, the IP method can delineate between biogeochemically active and inactive zones.</p>

scholarly journals Generative Adversarial Domain Adaptation for Nucleus Quantification in Images of Tissue Immunohistochemically Stained for Ki-67

2020 ◽  
pp. 666-679 ◽  
Author(s):  
Xuhong Zhang ◽  
Toby C. Cornish ◽  
Lin Yang ◽  
Tellen D. Bennett ◽  
Debashis Ghosh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Domain Adaptation ◽  
Training Data ◽  
Data Sets ◽  
Learning Models ◽  
Convolutional Network ◽  
Ki 67 ◽  
Data Set ◽  
Target Data ◽  
Real Target

PURPOSE We focus on the problem of scarcity of annotated training data for nucleus recognition in Ki-67 immunohistochemistry (IHC)–stained pancreatic neuroendocrine tumor (NET) images. We hypothesize that deep learning–based domain adaptation is helpful for nucleus recognition when image annotations are unavailable in target data sets. METHODS We considered 2 different institutional pancreatic NET data sets: one (ie, source) containing 38 cases with 114 annotated images and the other (ie, target) containing 72 cases with 20 annotated images. The gold standards were manually annotated by 1 pathologist. We developed a novel deep learning–based domain adaptation framework to count different types of nuclei (ie, immunopositive tumor, immunonegative tumor, nontumor nuclei). We compared the proposed method with several recent fully supervised deep learning models, such as fully convolutional network-8s (FCN-8s), U-Net, fully convolutional regression network (FCRN) A, FCRNB, and fully residual convolutional network (FRCN). We also evaluated the proposed method by learning with a mixture of converted source images and real target annotations. RESULTS Our method achieved an F1 score of 81.3% and 62.3% for nucleus detection and classification in the target data set, respectively. Our method outperformed FCN-8s (53.6% and 43.6% for nucleus detection and classification, respectively), U-Net (61.1% and 47.6%), FCRNA (63.4% and 55.8%), and FCRNB (68.2% and 60.6%) in terms of F1 score and was competitive with FRCN (81.7% and 70.7%). In addition, learning with a mixture of converted source images and only a small set of real target labels could further boost the performance. CONCLUSION This study demonstrates that deep learning–based domain adaptation is helpful for nucleus recognition in Ki-67 IHC stained images when target data annotations are not available. It would improve the applicability of deep learning models designed for downstream supervised learning tasks on different data sets.

An end-to-end learning method for industrial defect detection

2019 ◽  
Vol 40 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Yupei Wu ◽  
Di Guo ◽  
Huaping Liu ◽  
Yao Huang
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Network Architecture ◽  
Learning Method ◽  
Unified Framework ◽  
Convolutional Network ◽  
Data Set ◽  
Content Type ◽  
End To End ◽  
Dual Arm

Purpose Automatic defect detection is a fundamental and vital topic in the research field of industrial intelligence. In this work, the authors develop a more flexible deep learning method for the industrial defect detection. Design/methodology/approach The authors propose a unified framework for detecting defects in industrial products or planar surfaces based on an end-to-end learning strategy. A lightweight deep learning architecture for blade defect detection is specifically demonstrated. In addition, a blade defect data set is collected with the dual-arm image collection system. Findings Numerous experiments are conducted on the collected data set, and experimental results demonstrate that the proposed system can achieve satisfactory performance over other methods. Furthermore, the data equalization operation helps for a better defect detection result. Originality/value An end-to-end learning framework is established for defect detection. Although the adopted fully convolutional network has been extensively used for semantic segmentation in images, to the best knowledge of the authors, it has not been used for industrial defect detection. To remedy the difficulties of blade defect detection which has been analyzed above, the authors develop a new network architecture which integrates the residue learning to perform the efficient defect detection. A dual-arm data collection platform is constructed and extensive experimental validation are conducted.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

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