Approach For Automatic Extraction Research Articles

Extraction of Feature Information from Point Cloud of Large Volume Steel Truss Members

In order to address the issues pertaining to the subjective nature and limited precision associated with selecting feature points in the point cloud of a large steel truss structure, this study proposes a batch automatic extraction approach for identifying key feature information, including boundaries, corner points, and bolt holes of large steel truss components. This method relies on the nested application of established processing algorithms such as Euclidean clusters, regional growth clusters, and random sampling consensus. In addition, a novel approach is suggested for validating the precision of feature information extraction through the utilization of standard theoretical models. The results of the experimental and large-scale lower chord tests demonstrate that our approach is not dependent on specialized software, exhibits excellent efficiency, and possesses an acceptable degree of automation. The findings of this study can provide accurate data support for reverse modeling, virtual trial assembly, and dimensional inspection of steel truss components.

Scraping Relevant Images from Web Pages without Download

Automatically scraping relevant images from web pages is an error-prone and time-consuming task, leading experts to prefer manually preparing extraction patterns for a website. Existing web scraping tools are built on these patterns. However, this manual approach is laborious and requires specialized knowledge. Automatic extraction approaches, while a potential solution, require large training datasets and numerous features, including width, height, pixels, and file size, that can be difficult and time-consuming to obtain. To address these challenges, we propose a semi-automatic approach that does not require an expert, utilizes small training datasets, and has a low error rate while saving time and storage. Our approach involves clustering web pages from a website and suggesting several pages for a non-expert to annotate relevant images. The approach then uses these annotations to construct a learning model based on textual data from the HTML elements. In the experiments, we used a dataset of 635,015 images from 200 news websites, each containing 100 pages, with 22,632 relevant images. When comparing several machine learning methods for both automatic approaches and our proposed approach, the AdaBoost method yields the best performance results. When using automatic extraction approaches, the best f-Measure that can be achieved is 0.805 with a learning model constructed from a large training dataset consisting of 120 websites (12,000 web pages). In contrast, our approach achieved an average f-Measure of 0.958 for 200 websites with only six web pages annotated per website. This means that a non-expert only needs to examine 1,200 web pages to determine the relevant images for 200 websites. Our approach also saves time and storage space by not requiring the download of images and can be easily integrated into currently available web scraping tools, because it is based on textual data.

Quantifying shoreline dynamics in the Indian Sundarban delta with Google Earth Engine (GEE)-based automatic extraction approach

Quantifying shoreline dynamics in the Indian Sundarban delta with Google Earth Engine (GEE)-based automatic extraction approach

An ANN-based advancing double-front method for automatic isotropic triangle generation

The advancing front method (AFM) is one of the widely used unstructured grid generation techniques. However, the efficiency is relatively low because only one cell is generated in the advancing procedure. In this work, a novel automatic isotropic triangle generation technique is developed by introducing an artificial neural network (ANN) based advancing double-front method (ADFM) to improve the mesh generation efficiency. First, a variety of different patterns are extracted from the AFM mesh generation method and extended to the ADFM method. The mesh generation process in each pattern is discussed in detail. Second, an initial isotropic triangular mesh is generated by the traditional mesh generation method, and then an approach for automatic extraction of the training dataset is proposed. The preprocessed dataset is input into the ANN to train the network, then some typical patterns are obtained through learning. Third, after inputting the initial discrete boundary as initial fronts, the grid is generated from the shortest front and adjacent front. The coordinates of the points contained in the dual fronts and the adjacent points are sent into the neural network as the grid generation environment to obtain the most possible mesh generation pattern, the corresponding methods are used to update the advancing front until the whole computational domain is covered by initial grids, and finally, some smoothing techniques are carried out to improve the quality initial grids. Several typical cases are tested to validate the effectiveness. The experimental results show that the ANN can accurately identify mesh generation patterns, and the mesh generation efficiency is 50% higher than that of the traditional single-front AFM.

Spatial search and a three level model based water layer extraction from C-band SAR image

ABSTRACT This paper describes a spatial search and a three-level model-based approach for automatic extraction of surface water layers from Sentinel-1 C-band SAR images at 10 m spatial resolution. The technique incorporates a connected component spatial search for segmenting low backscatter regions and uses the segmented image object for characterizing the segments. The water body is described here as a collection of different spatially connected segments. A three-level model is used to describe the connected segments of a water body in SAR data. Noise tolerance is achieved in this method by incorporating a speckle noise level into the model. The segmentation process further calculates contextual information which includes shadow estimated from DEM, polarization angle of the segment, and a boundary co-occurrence in both polarization to qualify the detected segments as a water body. The proposed method is found to have an accuracy of 94% in terms of f1 score. The algorithm, estimation of different parameters, and the results obtained in selected regions are explained in this paper.

MMWD: An efficient mobile malicious webpage detection framework based on deep learning and edge cloud

AbstractIn recent years, with the rapid development of mobile social networks and services, the research of mobile malicious webpage detection has become a hot topic. Most of the existing malicious webpage detection systems are deployed on desktop systems and servers. Due to the limitation of network transmission delay and computing resources, these existing solutions fail to provide the real‐time and lightweight properties for mobile webpage detection. In this paper, we propose an advanced mobile malicious webpage detection framework based on deep learning and edge cloud. Inspired by the idea of edge computing, a multidevice load optimization approach is first introduced to improve detection efficiency. Second, an automatic extraction approach based on deep learning model features is presented to enhance detection accuracy. Furthermore, detection systems can be flexibly deployed on edge nodes and servers, thus providing the properties of resource optimization deployment and real‐time detection. Finally, comparative analysis and performance evaluation are presented to show the detection efficiency and accuracy of the proposed framework.

Parametric modeling method for integrated design and manufacturing of radial compressor impeller

Radial compressor impeller (RCI) manufacturing is moving toward to improve competitiveness through smart manufacturing. Although current CAD (computer-aided design) and CAM (computer-aided manufacturing) techniques provide multiple tools to construct and optimize complicated geometries of RCI, an adjustment of blade shape often leads to nonparametric geometric reconstruction. As the key geometric elements in RCI, set of streamlines (SSL) and meridional section (MS) play vital roles in modeling and aerodynamic optimization. This paper presents a novel approach for automatic extraction of SSL and MS from a RCI 3D model or workpiece. Furthermore, the presented method can interactively construct a parameterized RCI platform, which inputs and outputs the presented RCI parameters to existing CFD and CAM systems rapidly and effectively. An integrated acquisition is employed. The straight generatrix vectors (SGVs) are identified and their boundary points are defined. After the uniform partition of SGV segments, the sequent equant points along spanwise direction are packaged to fit SSL. To manipulate the smoothness and approximation, a double-fitting method is performed to generate SSL. A parameterized system for extracting SSL and modeling RCI is developed. The validities of the presented method in different systems are demonstrated. Furthermore, the presented method breaks conventional RCI development procedure and shortens RCI product cycles, which is desirable and significative for integrated RCI design and manufacturing.

An Integrated Multiscale Geometric Analysis Approach for Automatic Extraction of Power Lines From High Resolution Remote Sensing Images

High resolution remote sensing systems provide cheaper and fast way of acquiring images of power lines. However, such images depicting the details of other complex background objects, noises, and complicated brightness measurements, make separate extraction of the power lines challenging. This paper addresses the problem of automatic extraction of power lines from high resolution remote sensing images obtained from different sources. In order to automatically extract the power lines, we proposed an integrated Multiscale Geometric Analysis (MGA) approach. First, complementary Gabor and matched filters (MF) were employed over an image to suppress unnecessary background and noises, and initial discrimination of the power lines. Then, the filtering output was decomposed in to scale and orientation based subband coefficients using the Fast Discrete Curvelet Transform (FDCT) so as to access and modify different image features separately. By employing selective modification operations, well-established power line structures ready for extraction were derived. Finally the powerlines were extracted with hysteresis thresholding. The approach was successful in extracting power lines from high resolution images captured in any orientation. It is robust even when the source image is cluttered, and degraded due to noise and brightness effects. Power lines represented by weak intensities, crossing bright image regions, changing direction, closer power lines and those crossing each other, disconnected/broken power lines due to noise and occlusions were all inferred and extracted successfully. The approach was validated using real test images and the performance measures showed over 90% average accuracy fitting the ground truth.

Standard Statistical and Graph based Automatic Keyword Extraction

Automatic extraction of terms from a document is essential in the current digital era to summarize the documents. For instance, instead of go through the full documents, some of the author's keywords partially explain the discussions of the documents. However, the author's keywords are not sufficient to identify the whole concept of the document. Hence the requirement of automatic term extraction methods is necessary. The major categories of automatic extraction approaches falls mainly on some techniques such as Natural Language Processing, Statistical approaches, Graph Based approaches, Natural Inspired algorithmic approaches, etc. Even though there are numerous approaches available the exact automatic keyword extraction is a major challenge in areas, that reveals around documents. In this paper, a comparative analysis of Keyword extraction between standard Statistical approaches and Graph based approaches has been conducted. In standard statistical approaches, the terms are extracted on the basis of physical counts and in the Graph based approach, the documents are automatically constructed as graphs by applying centrality measures during the keyword extraction process. The results of both approaches were compared and analyzed.

Towards machine-assisted meta-studies: the Hubble constant

ABSTRACT We present an approach for automatic extraction of measured values from the astrophysical literature, using the Hubble constant for our pilot study. Our rules-based model – a classical technique in natural language processing – has successfully extracted 298 measurements of the Hubble constant, with uncertainties, from the 208 541 available arXiv astrophysics papers. We have also created an artificial neural network classifier to identify papers in arXiv which report novel measurements. From the analysis of our results we find that reporting measurements with uncertainties and the correct units is critical information when distinguishing novel measurements in free text. Our results correctly highlight the current tension for measurements of the Hubble constant and recover the 3.5σ discrepancy – demonstrating that the tool presented in this paper is useful for meta-studies of astrophysical measurements from a large number of publications.

Nearshore sandbars crest position dynamics analysed based on Earth Observation data

Understanding long-term underwater sandbars dynamics is a key element for optimal coastal zone management, but this endeavour is often held back by data availability. In order to increase the spatial and temporal resolution, availability and quality of sandbars observations, this study proposes and evaluates a new approach for automatic extraction of sandbars positions from freely available satellite imagery, using a case study of complex waters located on the Danube Delta coast, Black Sea. Validation of the satellite extracted sandbars locations against 4 in-situ measurements between 2016 and 2018 shows a very good correlation between the two datasets, demonstrating the high accuracy of satellite-derived data in revealing the underwater morphology of the upper shoreface along large areas and with high temporal resolution. The obtained sandbars positions were then integrated into an automatic workflow able to accurately represent the seasonal and multi-annual sandbars dynamics and also to give an insight about the sandbars alongshore behavioural changes. The results are similar to the ones previously suggested by other studies, which empowered different methods. These prove the high potential of the newly proposed algorithm to be used for the automatic extraction of the positions of underwater features in the surf zone on other sites worldwide, after proper tuning of parameters in accordance with local conditions. The main constraints of the proposed approach are represented by the omission of sandbars position that imprint a low signal on the satellite image due to increased water depth (especially for the outer bar), high turbidity (mostly for the inner bar) or any other noise generation situation (sun glint, small wind waves). The main advantages of the described algorithm are related to the simplicity of implementation and adaptability to other areas of interest and other types of satellite data.

Identifying Linear Traces of the Han Dynasty Great Wall in Dunhuang Using Gaofen-1 Satellite Remote Sensing Imagery and the Hough Transform

The Han Dynasty Great Wall (GH), one of the largest and most significant ancient defense projects in the whole of northern China, has been studied increasingly not only because it provides important information about the diplomatic and military strategies of the Han Empire (206 B.C.–220 A.D.), but also because it is considered to be a cultural and national symbol of modern China as well as a valuable archaeological monument. Thus, it is crucial to obtain the spatial pattern and preservation situation of the GH for next-step archaeological analysis and conservation management. Nowadays, remote sensing specialists and archaeologists have given priority to manual visualization and a (semi-) automatic extraction approach is lacking. Based on the very high-resolution (VHR) satellite remote sensing imagery, this paper aims to identify automatically the archaeological features of the GH located in ancient Dunhuang, northwest China. Gaofen-1 (GF-1) data were first processed and enhanced after image correction and mathematical morphology, and the M-statistic was then used to analyze the spectral characteristics of GF-1 multispectral (MS) data. In addition, based on GF-1 panchromatic (PAN) data, an auto-identification method that integrates an improved Otsu segmentation algorithm with a Linear Hough Transform (LHT) is proposed. Finally, by making a comparison with visual extraction results, the proposed method was assessed qualitatively and semi-quantitatively to have an accuracy of 80% for the homogenous background in Dunhuang. These automatic identification results could be used to map and evaluate the preservation state of the GH in Dunhuang. Also, the proposed automatic approach was applied to identify similar linear traces of other generations of the Great Wall of China (Western Xia Dynasty (581 A.D.–618 A.D.) and Ming Dynasty (1368 A.D.–1644 A.D.)) in various geographic regions. Moreover, the results indicate that the computer-based automatic identification has great potential in archaeological research, and the proposed method can be generalized and applied to monitor and evaluate the state of preservation of the Great Wall of China in the future.

Extended Vulnerability Feature Extraction Based on Public Resources

The focus of this research is to define a framework that automatically analyses Common Vulnerabilities and Exposures (CVE) from public and disclosed resources and makes mapping to the target computer system. The current framework calculates risk assessment and estimates vulnerabilities impact according to the features of the target platform. In this paper, we describe the main vulnerability feature set, provide approaches for automatic extraction from databases and open resources. We evaluated and improved each obtaining approaches on the recent set of security vulnerabilities (2018 year database). Comparison obtained results with results of manual expert analysis is proved.

Optimization Based Liver Contour Extraction of Abdominal CT Images

This paper introduces computer aided analysis system for diagnosis of liver abnormality in abdominal CT images. Segmenting the liver and visualizing the region of interest is a most challenging task in the field of cancer imaging, due to small observable changes between healthy and unhealthy liver. In this paper, hybrid approach for automatic extraction of liver contour is proposed. To obtain optimal threshold, the proposed work integrates segmentation method with optimization technique in order to provide better accuracy. This method uses bilateral filter for preprocessing and Fuzzy C means clustering (FCM) for segmentation. Mean Grey Wolf Optimization technique (mGWO) has been used to get the optimal threshold. This threshold is used for segmenting the region of interest. From the segmented output, largest connected region are identified using Label Connected Component (LCC) algorithm. The effectiveness of proposed method is quantitatively evaluated by comparing with ground truth obtained from radiologists. The performance criteria like dice coefficient, true positive error and misclassification rate are taken for evaluation.

Evaluation of in vivo digital root reconstruction based on anatomical characteristics of the periodontal ligament using cone beam computed tomography

This study’s aim was to develop and validate an approach to automatically extract and reconstruct three-dimensional (3D) digital root models from in vivo teeth based on the anatomical characteristics of the periodontal ligament using cone beam computed tomography (CBCT) data. Prior to undergoing dental extractions for orthodontic purposes, the CBCT data of each study participant were collected and imported into Mimics software to reconstruct 3D in vivo digital root models (test models). Twenty roots of 17 teeth extracted from the study’s participants were scanned using a dental scanner to obtain 3D in vitro digital root models (reference models). The 3D morphological deviation between the reference and test models was compared; the 3D size of the bucco-lingual, mesio-distal, and root length dimensions were calculated. This approach achieved an average 3D morphological deviation of 0.21 mm, and the average size error in the bucco-lingual, mesio-distal, and root length dimensions were −0.35 mm, −0.17 mm, and 0.47 mm, respectively. This new automatic extraction approach rapidly and accurately reconstructs 3D in vivo root models with detailed morphological information, and has the potential to improve diagnostic and treatment work flow in orthodontic clinics, as well as in other areas of dentistry.

Automatic building footprint extraction from high-resolution satellite image using mathematical morphology

ABSTRACTAutomatic building extraction from High-Resolution Satellite (HRS) image has been an important field of research in the area of remote sensing. Different techniques related to radiometric, geometric, edge detection and object based have already been discussed and used by various researchers for building extraction. However, faithfulness of extraction is highly dependent on user intervention. This study proposes a novel morphological based automatic approach for extraction of buildings using HRS image. Moreover, using such an automatic approach, buildings can be detected having different size and shape. The proposed technique integrates morphological Top-hat filter, and K-means algorithm to extract buildings having bright and dark rooftops. Further, extracted bright and dark rooftop building segments have been combined together to obtain the final output that contains final extracted building segments. In order to eliminate false-detected buildings, different parameters like area, eccentricity, and axis ratio (major/minor axis) have been used. The suitability of the technique has been judged using different indicators such as completeness, correctness and quality.

Ensemble EMD-Based Automatic Extraction of the Catenary Structure Wavelength From the Pantograph–Catenary Contact Force

This paper explores the use of pantograph–catenary contact force (PCCF) for monitoring of the current collection quality and detection of anomalies in the interaction between pantograph and catenary. The concept of catenary structure wavelength (CSW) is proposed as the dominant component of PCCF. It describes the signal components caused by the cyclical catenary structure in span and interdropper distance. To obtain the CSWs and non-CSW residual of PCCF, an automatic extraction approach based on the ensemble empirical mode decomposition (EEMD) is proposed. In the approach, the instantaneous frequency of each intrinsic mode function generated by the EEMD is employed for the extraction of CSWs. Some selected trials on the PCCF data from simulation and measurement are performed and indicate that the extraction approach is adaptive to the PCCF under various circumstances, including different operation speeds, pantograph type, and catenary structure. Analyses on the extracted CSWs and non-CSW residual show that, with certain tolerance against measurement noise, the approach can preserve intact the characterizations of current collection quality and make anomalies easier to detect.

Automatic building extraction from very high-resolution image and LiDAR data with SVM algorithm

Recent developments in the field of remote sensing have introduced new sensor technologies in usage of LiDAR, SAR, and high-resolution optical data. Classification performance is expected to increase through combining these various data sources. The purpose of this study is to develop a new approach for automatic extraction of buildings in urbanized and suburbanized areas. For this purpose, multi-feature extraction process including the spatial, spectral, and textural features were conducted on the very high spatial resolution multispectral aerial images and the LiDAR data set. SVM algorithm was trained by using this multi-feature data, and the classification was performed. After the classification of building and non-building, objects were extracted with high accuracy for the test areas. As a result, it has been proven that multi-features derived from combination of optical and LiDAR data can be successfully applied to solve the problem of automatic detection of buildings by using the proposed approach.

CD-REST: a system for extracting chemical-induced disease relation in literature.

Mining chemical-induced disease relations embedded in the vast biomedical literature could facilitate a wide range of computational biomedical applications, such as pharmacovigilance. The BioCreative V organized a Chemical Disease Relation (CDR) Track regarding chemical-induced disease relation extraction from biomedical literature in 2015. We participated in all subtasks of this challenge. In this article, we present our participation system Chemical Disease Relation Extraction SysTem (CD-REST), an end-to-end system for extracting chemical-induced disease relations in biomedical literature. CD-REST consists of two main components: (1) a chemical and disease named entity recognition and normalization module, which employs the Conditional Random Fields algorithm for entity recognition and a Vector Space Model-based approach for normalization; and (2) a relation extraction module that classifies both sentence-level and document-level candidate drug–disease pairs by support vector machines. Our system achieved the best performance on the chemical-induced disease relation extraction subtask in the BioCreative V CDR Track, demonstrating the effectiveness of our proposed machine learning-based approaches for automatic extraction of chemical-induced disease relations in biomedical literature. The CD-REST system provides web services using HTTP POST request. The web services can be accessed from http://clinicalnlptool.com/cdr. The online CD-REST demonstration system is available at http://clinicalnlptool.com/cdr/cdr.html.Database URL: http://clinicalnlptool.com/cdr; http://clinicalnlptool.com/cdr/cdr.html

A syntactic component for Vietnamese language processing

This paper presents the development of a syntactic component for the Vietnamese language. We first discuss the construction of a lexicalized tree-adjoining grammar using an automatic extraction approach. We then present the construction and evaluation of a deep syntactic parser based on the extracted grammar. This is a complete system integrating necessary tools to process Vietnamese text, which permits to take as input raw texts and produce syntactic structures. A dependency annotation scheme for Vietnamese and an algorithm for extracting dependency structures from derivation trees are also proposed. At present, this is the first Vietnamese parsing system capable of producing both constituency and dependency analyses with encouraging performances: 69.33% and 73.21% for constituency and dependency analysis accuracy, respectively. The parser also compares favourably to a statistical parser which is trained and tested on the same data sets.