Modelling Subjectivity in Visual Perception of Orientation for Image Retrieval.

ERIC Educational Resources Information Center

Sanchez, D.; Chamorro-Martinez, J.; Vila, M. A.

2003-01-01

Discussion of multimedia libraries and the need for storage, indexing, and retrieval techniques focuses on the combination of computer vision and data mining techniques to model high-level concepts for image retrieval based on perceptual features of the human visual system. Uses fuzzy set theory to measure users' assessments and to capture users'…

Exploitation of multi-temporal Earth Observation imagery for monitoring land cover change in mining sites

NASA Astrophysics Data System (ADS)

Petropoulos, G.; Partsinevelos, P.; Mitraka, Z.

2012-04-01

Surface mining has been shown to cause intensive environmental degradation in terms of landscape, vegetation and biological communities. Nowadays, the commercial availability of remote sensing imagery at high spatiotemporal scales, has improved dramatically our ability to monitor surface mining activity and evaluate its impact on the environment and society. In this study we investigate the potential use of Landsat TM imagery combined with diverse classification techniques, namely artificial neural networks and support vector machines for delineating mining exploration and assessing its effect on vegetation in various surface mining sites in the Greek island of Milos. Assessment of the mining impact in the study area is validated through the analysis of available QuickBird imagery acquired nearly concurrently to the TM overpasses. Results indicate the capability of the TM sensor combined with the image analysis applied herein as a potential economically viable solution to provide rapidly and at regular time intervals information on mining activity and its impact to the local environment. KEYWORDS: mining environmental impact, remote sensing, image classification, change detection, land reclamation, support vector machines, neural networks

Using remote sensing imagery to monitoring sea surface pollution cause by abandoned gold-copper mine

NASA Astrophysics Data System (ADS)

Kao, H. M.; Ren, H.; Lee, Y. T.

2010-08-01

The Chinkuashih Benshen mine was the largest gold-copper mine in Taiwan before the owner had abandoned the mine in 1987. However, even the mine had been closed, the mineral still interacts with rain and underground water and flowed into the sea. The polluted sea surface had appeared yellow, green and even white color, and the pollutants had carried by the coast current. In this study, we used the optical satellite images to monitoring the sea surface. Several image processing algorithms are employed especial the subpixel technique and linear mixture model to estimate the concentration of pollutants. The change detection approach is also applied to track them. We also conduct the chemical analysis of the polluted water to provide the ground truth validation. By the correlation analysis between the satellite observation and the ground truth chemical analysis, an effective approach to monitoring water pollution could be established.

Direct imaging of explosives.

PubMed

Knapp, E A; Moler, R B; Saunders, A W; Trower, W P

2000-01-01

Any technique that can detect nitrogen concentrations can screen for concealed explosives. However, such a technique would have to be insensitive to metal, both encasing and incidental. If images of the nitrogen concentrations could be captured, then, since form follows function, a robust screening technology could be developed. However these images would have to be sensitive to the surface densities at or below that of the nitrogen contained in buried anti-personnel mines or of the SEMTEX that brought down Pan Am 103, approximately 200 g. Although the ability to image in three-dimensions would somewhat reduce false positives, capturing collateral images of carbon and oxygen would virtually assure that nitrogenous non-explosive material like fertilizer, Melmac dinnerware, and salami could be eliminated. We are developing such an instrument, the Nitrogen Camera, which has met experimentally these criteria with the exception of providing oxygen images, which awaits the availability of a sufficiently energetic light source. Our Nitrogen Camera technique uses an electron accelerator to produce photonuclear reactions whose unique decays it registers. Clearly if our Nitrogen Camera is made mobile, it could be effective in detecting buried mines, either in an active battlefield situation or in the clearing of abandoned military munitions. Combat operations require that a swathe the width of an armored vehicle, 5 miles deep, be screened in an hour, which is within our camera's scanning speed. Detecting abandoned munitions is technically easier as it is free from the onerous speed requirement. We describe here our Nitrogen Camera and show its 180 pixel intensity images of elemental nitrogen in a 200 g mine simulant and in a 125 g stick of SEMTEX. We also report on our progress in creating a lorry transportable 70 MeV electron racetrack microtron, the principal enabling technology that will allow our Nitrogen Camera to be deployed in the field.

Localization-based super-resolution imaging meets high-content screening.

PubMed

Beghin, Anne; Kechkar, Adel; Butler, Corey; Levet, Florian; Cabillic, Marine; Rossier, Olivier; Giannone, Gregory; Galland, Rémi; Choquet, Daniel; Sibarita, Jean-Baptiste

2017-12-01

Single-molecule localization microscopy techniques have proven to be essential tools for quantitatively monitoring biological processes at unprecedented spatial resolution. However, these techniques are very low throughput and are not yet compatible with fully automated, multiparametric cellular assays. This shortcoming is primarily due to the huge amount of data generated during imaging and the lack of software for automation and dedicated data mining. We describe an automated quantitative single-molecule-based super-resolution methodology that operates in standard multiwell plates and uses analysis based on high-content screening and data-mining software. The workflow is compatible with fixed- and live-cell imaging and allows extraction of quantitative data like fluorophore photophysics, protein clustering or dynamic behavior of biomolecules. We demonstrate that the method is compatible with high-content screening using 3D dSTORM and DNA-PAINT based super-resolution microscopy as well as single-particle tracking.

Content based image retrieval using local binary pattern operator and data mining techniques.

PubMed

Vatamanu, Oana Astrid; Frandeş, Mirela; Lungeanu, Diana; Mihalaş, Gheorghe-Ioan

2015-01-01

Content based image retrieval (CBIR) concerns the retrieval of similar images from image databases, using feature vectors extracted from images. These feature vectors globally define the visual content present in an image, defined by e.g., texture, colour, shape, and spatial relations between vectors. Herein, we propose the definition of feature vectors using the Local Binary Pattern (LBP) operator. A study was performed in order to determine the optimum LBP variant for the general definition of image feature vectors. The chosen LBP variant is then subsequently used to build an ultrasound image database, and a database with images obtained from Wireless Capsule Endoscopy. The image indexing process is optimized using data clustering techniques for images belonging to the same class. Finally, the proposed indexing method is compared to the classical indexing technique, which is nowadays widely used.

Segmentation of fluorescence microscopy cell images using unsupervised mining.

PubMed

Du, Xian; Dua, Sumeet

2010-05-28

The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu's threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu's threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications.

Lamb wave detection of limpet mines on ship hulls.

PubMed

Bingham, Jill; Hinders, Mark; Friedman, Adam

2009-12-01

This paper describes the use of ultrasonic guided waves for identifying the mass loading due to underwater limpet mines on ship hulls. The Dynamic Wavelet Fingerprint Technique (DFWT) is used to render the guided wave mode information in two-dimensional binary images because the waveform features of interest are too subtle to identify in time domain. The use of wavelets allows both time and scale features from the original signals to be retained, and image processing can be used to automatically extract features that correspond to the arrival times of the guided wave modes. For further understanding of how the guided wave modes propagate through the real structures, a parallel processing, 3D elastic wave simulation is developed using the finite integration technique (EFIT). This full field, technique models situations that are too complex for analytical solutions, such as built up 3D structures. The simulations have produced informative visualizations of the guided wave modes in the structures as well as mimicking directly the output from sensors placed in the simulation space for direct comparison to experiments. Results from both drydock and in-water experiments with dummy mines are also shown.

Image/Time Series Mining Algorithms: Applications to Developmental Biology, Document Processing and Data Streams

ERIC Educational Resources Information Center

Tataw, Oben Moses

2013-01-01

Interdisciplinary research in computer science requires the development of computational techniques for practical application in different domains. This usually requires careful integration of different areas of technical expertise. This dissertation presents image and time series analysis algorithms, with practical interdisciplinary applications…

Brain tumor classification using AFM in combination with data mining techniques.

PubMed

Huml, Marlene; Silye, René; Zauner, Gerald; Hutterer, Stephan; Schilcher, Kurt

2013-01-01

Although classification of astrocytic tumors is standardized by the WHO grading system, which is mainly based on microscopy-derived, histomorphological features, there is great interobserver variability. The main causes are thought to be the complexity of morphological details varying from tumor to tumor and from patient to patient, variations in the technical histopathological procedures like staining protocols, and finally the individual experience of the diagnosing pathologist. Thus, to raise astrocytoma grading to a more objective standard, this paper proposes a methodology based on atomic force microscopy (AFM) derived images made from histopathological samples in combination with data mining techniques. By comparing AFM images with corresponding light microscopy images of the same area, the progressive formation of cavities due to cell necrosis was identified as a typical morphological marker for a computer-assisted analysis. Using genetic programming as a tool for feature analysis, a best model was created that achieved 94.74% classification accuracy in distinguishing grade II tumors from grade IV ones. While utilizing modern image analysis techniques, AFM may become an important tool in astrocytic tumor diagnosis. By this way patients suffering from grade II tumors are identified unambiguously, having a less risk for malignant transformation. They would benefit from early adjuvant therapies.

Utilization of Envisat/ers SAR Data Over the Jharia Coalfield, India for Subsidence Monitoring

NASA Astrophysics Data System (ADS)

Srivastava, Vinay Kumar

2012-07-01

Extended abstract Jharia coalfield the prime coking coal-producing belt in India, started commercial production in 1894. Mining in Jharia coalfield (JCF) is in form of both opencast and underground mining. The area is affected by various environmental hazards such as, coal fire, subsidence, land degradation and toxic gas emissions. Currently, coal fire and subsidence are the major problems in the coalfield and causes continuous changes in topography. Monitoring of such dynamic topographic changes in a hazard-prone mining belt is a critical input for land environmental management. Such temporal topographic changes over span of the time and even short term mining activity within a year could be done from Digital Elevation Model (DEM) generated using various space-borne techniques.. Among all techniques available for generating DEM, SAR Interferometry technique has been successful and effective which offers high resolution spatial detail to a level of few cm. DEM obtained from processing of SAR Interferometry (InSAR) technique using ERS SAR data of April 12 and 13, 1995 provides high spatial resolution images which is useful for monitoring and measuring dynamic changes in land topography. Several workers have successfully InSAR this technique for mapping and monitoring of changes in land surface due to various causes. Using ERS tandem data sets of 16 and 17 May 1996 passes, DInSAR map over the Jharia coal field has been obtained from the interferogram generated by integrating information from ground control points and precise high coherence orbital parameters. Further, using ENVISAT/ ASAR data of June 5 and 6, 2007 and integrating GPS measurements at 4 ground points where corner reflectors were preinstalled for getting bright spots on images and using orbital parameters, a slant range corrected image over the study area has been obtained. shows the plot of differential phases along a particular profile l over a subsidence region in Jharia coal field and the corresponding correlation coefficients. . Further an attempt has been made to delineate subsidence area in Jharia coal field using SAR Interoferometry technique..

Non-destructive analysis of sensory traits of dry-cured loins by MRI-computer vision techniques and data mining.

PubMed

Caballero, Daniel; Antequera, Teresa; Caro, Andrés; Ávila, María Del Mar; G Rodríguez, Pablo; Perez-Palacios, Trinidad

2017-07-01

Magnetic resonance imaging (MRI) combined with computer vision techniques have been proposed as an alternative or complementary technique to determine the quality parameters of food in a non-destructive way. The aim of this work was to analyze the sensory attributes of dry-cured loins using this technique. For that, different MRI acquisition sequences (spin echo, gradient echo and turbo 3D), algorithms for MRI analysis (GLCM, NGLDM, GLRLM and GLCM-NGLDM-GLRLM) and predictive data mining techniques (multiple linear regression and isotonic regression) were tested. The correlation coefficient (R) and mean absolute error (MAE) were used to validate the prediction results. The combination of spin echo, GLCM and isotonic regression produced the most accurate results. In addition, the MRI data from dry-cured loins seems to be more suitable than the data from fresh loins. The application of predictive data mining techniques on computational texture features from the MRI data of loins enables the determination of the sensory traits of dry-cured loins in a non-destructive way. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Emerging technology becomes an opportunity for EOS

NASA Astrophysics Data System (ADS)

Fargion, Giulietta S.; Harberts, Robert; Masek, Jeffrey G.

1996-11-01

During the last decade, we have seen an explosive growth in our ability to collect and generate data. When implemented, NASA's Earth observing system data information system (EOSDIS) will receive about 50 gigabytes of remotely sensed image data per hour. This will generate an urgent need for new techniques and tools that can automatically and intelligently assist in transforming this abundance of data into useful knowledge. Some emerging technologies that address these challenges include data mining and knowledge discovery in databases (KDD). The most basic data mining application is a content-based search (examples include finding images of particular meteorological phenomena or identifying data that have been previously mined or interpreted). In order that these technologies be effectively exploited for EOSDIS development, a better understanding of data mining and the requirements for using this technology is necessary. The authors are currently undertaking a project exploring the requirements and options of content-based search and data mining for use on EOSDIS. The scope of the project is to develop a prototype with which to investigate user interface concepts, requirements, and designs relevant for EOSDIS core system (ECS) subsystem utilizing these techniques. The goal is to identify a generic handling of these functions. This prototype will help identify opportunities which the earth science community and EOSDIS can use to meet the challenges of collecting, searching, retrieving, and interacting with abundant data resources in highly productive ways.

Grid-Enabled Quantitative Analysis of Breast Cancer

DTIC Science & Technology

2009-10-01

large-scale, multi-modality computerized image analysis . The central hypothesis of this research is that large-scale image analysis for breast cancer...pilot study to utilize large scale parallel Grid computing to harness the nationwide cluster infrastructure for optimization of medical image ... analysis parameters. Additionally, we investigated the use of cutting edge dataanalysis/ mining techniques as applied to Ultrasound, FFDM, and DCE-MRI Breast

Elevated rates of gold mining in the Amazon revealed through high-resolution monitoring.

PubMed

Asner, Gregory P; Llactayo, William; Tupayachi, Raul; Luna, Ernesto Ráez

2013-11-12

Gold mining has rapidly increased in western Amazonia, but the rates and ecological impacts of mining remain poorly known and potentially underestimated. We combined field surveys, airborne mapping, and high-resolution satellite imaging to assess road- and river-based gold mining in the Madre de Dios region of the Peruvian Amazon from 1999 to 2012. In this period, the geographic extent of gold mining increased 400%. The average annual rate of forest loss as a result of gold mining tripled in 2008 following the global economic recession, closely associated with increased gold prices. Small clandestine operations now comprise more than half of all gold mining activities throughout the region. These rates of gold mining are far higher than previous estimates that were based on traditional satellite mapping techniques. Our results prove that gold mining is growing more rapidly than previously thought, and that high-resolution monitoring approaches are required to accurately quantify human impacts on tropical forests.

Elevated rates of gold mining in the Amazon revealed through high-resolution monitoring

PubMed Central

Asner, Gregory P.; Llactayo, William; Tupayachi, Raul; Luna, Ernesto Ráez

2013-01-01

Gold mining has rapidly increased in western Amazonia, but the rates and ecological impacts of mining remain poorly known and potentially underestimated. We combined field surveys, airborne mapping, and high-resolution satellite imaging to assess road- and river-based gold mining in the Madre de Dios region of the Peruvian Amazon from 1999 to 2012. In this period, the geographic extent of gold mining increased 400%. The average annual rate of forest loss as a result of gold mining tripled in 2008 following the global economic recession, closely associated with increased gold prices. Small clandestine operations now comprise more than half of all gold mining activities throughout the region. These rates of gold mining are far higher than previous estimates that were based on traditional satellite mapping techniques. Our results prove that gold mining is growing more rapidly than previously thought, and that high-resolution monitoring approaches are required to accurately quantify human impacts on tropical forests. PMID:24167281

The exploration and prevention of mine water invasion in Feicheng area based on RS

NASA Astrophysics Data System (ADS)

Zheng, Yong-Guo; Wang, Ping; Ting, He

2004-10-01

Recently, when the ninth and tenth were mined in Feiching city mining area, several mine wells occurred on water invasion. Based on systematic interpretation of TMimages in Fei Cheng mining area, authors find that there are five zones of NS trending lineaments, which nearly distribute in radial in TM images. Image processing can be divided into three types, they are spectrum enhancement, spatial filtering and data fusion, the useful methods in this area are auto-adaptive enhancement, density slicing and K-L transform. With ninth and tenth seam coals mined, three mines of east area have broken out serious accidents of water. Statistical materials and the test of water quality drawing off five limestone indicates water-yielding zone near NS, NNE, and NW trending faults, or near intersection point of its and others. In order to solve the problem, using remote sensing and other techniques, we try to find some influential factors on mine flow. Further analyses, such as, the exploration of geology on earth, and microcosmic from rock slice, the authors find that there are some reasons which lead to water invasion such as geological structure, karsts, index and so on, in which the main reason might be north-south deep fracture which is the pathway of well water's distribution, migration and recharge of mine water. There being more complicate geologic structure in the west of mine area, at last, with RS authors point out important zone of mine water invasion which the prevention-control of hazards from mine water and some measures to avoid water blast in future.

Assessment of the capability of remote sensing and GIS techniques for monitoring reclamation success in coal mine degraded lands.

PubMed

Karan, Shivesh Kishore; Samadder, Sukha Ranjan; Maiti, Subodh Kumar

2016-11-01

The objective of the present study is to monitor reclamation activity in mining areas. Monitoring of these reclaimed sites in the vicinity of mining areas and on closed Over Burden (OB) dumps is critical for improving the overall environmental condition, especially in developing countries where area around the mines are densely populated. The present study evaluated the reclamation success in the Block II area of Jharia coal field, India, using Landsat satellite images for the years 2000 and 2015. Four image processing methods (support vector machine, ratio vegetation index, enhanced vegetation index, and normalized difference vegetation index) were used to quantify the change in vegetation cover between the years 2000 and 2015. The study also evaluated the relationship between vegetation health and moisture content of the study area using remote sensing techniques. Statistical linear regression analysis revealed that Normalized Difference Vegetation Index (NDVI) coupled with Normalized Difference Moisture Index (NDMI) is the best method for vegetation monitoring in the study area when compared to other indices. A strong linear relationship (r(2) > 0.86) was found between NDVI and NDMI. An increase of 21% from 213.88 ha in 2000 to 258.9 ha in 2015 was observed in the vegetation cover of the reclaimed sites for an open cast mine, indicating satisfactory reclamation activity. NDVI results indicated that vegetation health also improved over the years. Copyright © 2016 Elsevier Ltd. All rights reserved.

Annotating images by mining image search results.

PubMed

Wang, Xin-Jing; Zhang, Lei; Li, Xirong; Ma, Wei-Ying

2008-11-01

Although it has been studied for years by the computer vision and machine learning communities, image annotation is still far from practical. In this paper, we propose a novel attempt at model-free image annotation, which is a data-driven approach that annotates images by mining their search results. Some 2.4 million images with their surrounding text are collected from a few photo forums to support this approach. The entire process is formulated in a divide-and-conquer framework where a query keyword is provided along with the uncaptioned image to improve both the effectiveness and efficiency. This is helpful when the collected data set is not dense everywhere. In this sense, our approach contains three steps: 1) the search process to discover visually and semantically similar search results, 2) the mining process to identify salient terms from textual descriptions of the search results, and 3) the annotation rejection process to filter out noisy terms yielded by Step 2. To ensure real-time annotation, two key techniques are leveraged-one is to map the high-dimensional image visual features into hash codes, the other is to implement it as a distributed system, of which the search and mining processes are provided as Web services. As a typical result, the entire process finishes in less than 1 second. Since no training data set is required, our approach enables annotating with unlimited vocabulary and is highly scalable and robust to outliers. Experimental results on both real Web images and a benchmark image data set show the effectiveness and efficiency of the proposed algorithm. It is also worth noting that, although the entire approach is illustrated within the divide-and conquer framework, a query keyword is not crucial to our current implementation. We provide experimental results to prove this.

REMOTE LAND MINE(FIELD) DETECTION. An Overview of Techniques (DETECTIE VAN LANDMIJNEN EN MIJNENVELDEN OP AFSTAND. Een Overzicht van de technieken),

DTIC Science & Technology

1994-09-01

titel DETECTIE VAN LANDMIJNEN EN MIJNENVELDEN OP AFSTAND, een overzicht van de technieken auteur (s) Drs. J.S. Groot, Ir. Y.H.L. Janssen datum september...functions based on set theory . The fundamental theory is developed in the sixties. This theory was applicable to binary images (black-and-white images...held at TNO-FEL. Various subjects related to fusion techniques: Dempster Shafer theory , Bayesian inference, Kalman filtering, fuzzy logic. [A15], [B4

The Application of LANDSAT Multi-Temporal Thermal Infrared Data to Identify Coal Fire in the Khanh Hoa Coal Mine, Thai Nguyen province, Vietnam

NASA Astrophysics Data System (ADS)

Trinh, Le Hung; Zablotskii, V. R.

2017-12-01

The Khanh Hoa coal mine is a surface coal mine in the Thai Nguyen province, which is one of the largest deposits of coal in the Vietnam. Numerous reasons such as improper mining techniques and policy, as well as unauthorized mining caused surface and subsurface coal fire in this area. Coal fire is a dangerous phenomenon which affects the environment seriously by releasing toxic fumes which causes forest fires, and subsidence of infrastructure surface. This article presents study on the application of LANDSAT multi-temporal thermal infrared images, which help to detect coal fire. The results obtained in this study can be used to monitor fire zones so as to give warnings and solutions to prevent coal fire.

Intelligent Interfaces for Mining Large-Scale RNAi-HCS Image Databases

PubMed Central

Lin, Chen; Mak, Wayne; Hong, Pengyu; Sepp, Katharine; Perrimon, Norbert

2010-01-01

Recently, High-content screening (HCS) has been combined with RNA interference (RNAi) to become an essential image-based high-throughput method for studying genes and biological networks through RNAi-induced cellular phenotype analyses. However, a genome-wide RNAi-HCS screen typically generates tens of thousands of images, most of which remain uncategorized due to the inadequacies of existing HCS image analysis tools. Until now, it still requires highly trained scientists to browse a prohibitively large RNAi-HCS image database and produce only a handful of qualitative results regarding cellular morphological phenotypes. For this reason we have developed intelligent interfaces to facilitate the application of the HCS technology in biomedical research. Our new interfaces empower biologists with computational power not only to effectively and efficiently explore large-scale RNAi-HCS image databases, but also to apply their knowledge and experience to interactive mining of cellular phenotypes using Content-Based Image Retrieval (CBIR) with Relevance Feedback (RF) techniques. PMID:21278820

Automated image processing and analysis of cartilage MRI: enabling technology for data mining applied to osteoarthritis

PubMed Central

Tameem, Hussain Z.; Sinha, Usha S.

2011-01-01

Osteoarthritis (OA) is a heterogeneous and multi-factorial disease characterized by the progressive loss of articular cartilage. Magnetic Resonance Imaging has been established as an accurate technique to assess cartilage damage through both cartilage morphology (volume and thickness) and cartilage water mobility (Spin-lattice relaxation, T2). The Osteoarthritis Initiative, OAI, is a large scale serial assessment of subjects at different stages of OA including those with pre-clinical symptoms. The electronic availability of the comprehensive data collected as part of the initiative provides an unprecedented opportunity to discover new relationships in complex diseases such as OA. However, imaging data, which provides the most accurate non-invasive assessment of OA, is not directly amenable for data mining. Changes in morphometry and relaxivity with OA disease are both complex and subtle, making manual methods extremely difficult. This chapter focuses on the image analysis techniques to automatically localize the differences in morphometry and relaxivity changes in different population sub-groups (normal and OA subjects segregated by age, gender, and race). The image analysis infrastructure will enable automatic extraction of cartilage features at the voxel level; the ultimate goal is to integrate this infrastructure to discover relationships between the image findings and other clinical features. PMID:21785520

Automated image processing and analysis of cartilage MRI: enabling technology for data mining applied to osteoarthritis

NASA Astrophysics Data System (ADS)

Tameem, Hussain Z.; Sinha, Usha S.

2007-11-01

Osteoarthritis (OA) is a heterogeneous and multi-factorial disease characterized by the progressive loss of articular cartilage. Magnetic Resonance Imaging has been established as an accurate technique to assess cartilage damage through both cartilage morphology (volume and thickness) and cartilage water mobility (Spin-lattice relaxation, T2). The Osteoarthritis Initiative, OAI, is a large scale serial assessment of subjects at different stages of OA including those with pre-clinical symptoms. The electronic availability of the comprehensive data collected as part of the initiative provides an unprecedented opportunity to discover new relationships in complex diseases such as OA. However, imaging data, which provides the most accurate non-invasive assessment of OA, is not directly amenable for data mining. Changes in morphometry and relaxivity with OA disease are both complex and subtle, making manual methods extremely difficult. This chapter focuses on the image analysis techniques to automatically localize the differences in morphometry and relaxivity changes in different population sub-groups (normal and OA subjects segregated by age, gender, and race). The image analysis infrastructure will enable automatic extraction of cartilage features at the voxel level; the ultimate goal is to integrate this infrastructure to discover relationships between the image findings and other clinical features.

GyneScan

PubMed Central

Acharya, U. Rajendra; Sree, S. Vinitha; Kulshreshtha, Sanjeev; Molinari, Filippo; Koh, Joel En Wei; Saba, Luca; Suri, Jasjit S.

2014-01-01

Ovarian cancer is the fifth highest cause of cancer in women and the leading cause of death from gynecological cancers. Accurate diagnosis of ovarian cancer from acquired images is dependent on the expertise and experience of ultrasonographers or physicians, and is therefore, associated with inter observer variabilities. Computer Aided Diagnostic (CAD) techniques use a number of different data mining techniques to automatically predict the presence or absence of cancer, and therefore, are more reliable and accurate. A review of published literature in the field of CAD based ovarian cancer detection indicates that many studies use ultrasound images as the base for analysis. The key objective of this work is to propose an effective adjunct CAD technique called GyneScan for ovarian tumor detection in ultrasound images. In our proposed data mining framework, we extract several texture features based on first order statistics, Gray Level Co-occurrence Matrix and run length matrix. The significant features selected using t-test are then used to train and test several supervised learning based classifiers such as Probabilistic Neural Networks (PNN), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbor (KNN), and Naïve Bayes (NB). We evaluated the developed framework using 1300 benign and 1300 malignant images. Using 11 significant features in KNN/PNN classifiers, we were able to achieve 100% classification accuracy, sensitivity, specificity, and positive predictive value in detecting ovarian tumor. Even though more validation using larger databases would better establish the robustness of our technique, the preliminary results are promising. This technique could be used as a reliable adjunct method to existing imaging modalities to provide a more confident second opinion on the presence/absence of ovarian tumor. PMID:24325128

Machine learning for a Toolkit for Image Mining

NASA Technical Reports Server (NTRS)

Delanoy, Richard L.

1995-01-01

A prototype user environment is described that enables a user with very limited computer skills to collaborate with a computer algorithm to develop search tools (agents) that can be used for image analysis, creating metadata for tagging images, searching for images in an image database on the basis of image content, or as a component of computer vision algorithms. Agents are learned in an ongoing, two-way dialogue between the user and the algorithm. The user points to mistakes made in classification. The algorithm, in response, attempts to discover which image attributes are discriminating between objects of interest and clutter. It then builds a candidate agent and applies it to an input image, producing an 'interest' image highlighting features that are consistent with the set of objects and clutter indicated by the user. The dialogue repeats until the user is satisfied. The prototype environment, called the Toolkit for Image Mining (TIM) is currently capable of learning spectral and textural patterns. Learning exhibits rapid convergence to reasonable levels of performance and, when thoroughly trained, Fo appears to be competitive in discrimination accuracy with other classification techniques.

An AK-LDMeans algorithm based on image clustering

NASA Astrophysics Data System (ADS)

Chen, Huimin; Li, Xingwei; Zhang, Yongbin; Chen, Nan

2018-03-01

Clustering is an effective analytical technique for handling unmarked data for value mining. Its ultimate goal is to mark unclassified data quickly and correctly. We use the roadmap for the current image processing as the experimental background. In this paper, we propose an AK-LDMeans algorithm to automatically lock the K value by designing the Kcost fold line, and then use the long-distance high-density method to select the clustering centers to further replace the traditional initial clustering center selection method, which further improves the efficiency and accuracy of the traditional K-Means Algorithm. And the experimental results are compared with the current clustering algorithm and the results are obtained. The algorithm can provide effective reference value in the fields of image processing, machine vision and data mining.

EU-FP7-iMARS: Analysis of Mars Multi-Resolution Images Using Auto-Coregistration Data Mining and Crowd Source Techniques: Processed Results - a First Look

NASA Astrophysics Data System (ADS)

Muller, Jan-Peter; Tao, Yu; Sidiropoulos, Panagiotis; Gwinner, Klaus; Willner, Konrad; Fanara, Lida; Waehlisch, Marita; van Gasselt, Stephan; Walter, Sebastian; Steikert, Ralf; Schreiner, Bjoern; Ivanov, Anton; Cantini, Federico; Wardlaw, Jessica; Morley, Jeremy; Sprinks, James; Giordano, Michele; Marsh, Stuart; Kim, Jungrack; Houghton, Robert; Bamford, Steven

2016-06-01

Understanding planetary atmosphere-surface exchange and extra-terrestrial-surface formation processes within our Solar System is one of the fundamental goals of planetary science research. There has been a revolution in planetary surface observations over the last 15 years, especially in 3D imaging of surface shape. This has led to the ability to overlay image data and derived information from different epochs, back in time to the mid 1970s, to examine changes through time, such as the recent discovery of mass movement, tracking inter-year seasonal changes and looking for occurrences of fresh craters. Within the EU FP-7 iMars project, we have developed a fully automated multi-resolution DTM processing chain, called the Coregistration ASP-Gotcha Optimised (CASP-GO), based on the open source NASA Ames Stereo Pipeline (ASP) [Tao et al., this conference], which is being applied to the production of planetwide DTMs and ORIs (OrthoRectified Images) from CTX and HiRISE. Alongside the production of individual strip CTX & HiRISE DTMs & ORIs, DLR [Gwinner et al., 2015] have processed HRSC mosaics of ORIs and DTMs for complete areas in a consistent manner using photogrammetric bundle block adjustment techniques. A novel automated co-registration and orthorectification chain has been developed by [Sidiropoulos & Muller, this conference]. Using the HRSC map products (both mosaics and orbital strips) as a map-base it is being applied to many of the 400,000 level-1 EDR images taken by the 4 NASA orbital cameras. In particular, the NASA Viking Orbiter camera (VO), Mars Orbiter Camera (MOC), Context Camera (CTX) as well as the High Resolution Imaging Science Experiment (HiRISE) back to 1976. A webGIS has been developed [van Gasselt et al., this conference] for displaying this time sequence of imagery and will be demonstrated showing an example from one of the HRSC quadrangle map-sheets. Automated quality control [Sidiropoulos & Muller, 2015] techniques are applied to screen for suitable images and these are extended to detect temporal changes in features on the surface such as mass movements, streaks, spiders, impact craters, CO2 geysers and Swiss Cheese terrain. For result verification these data mining techniques are then being employed within a citizen science project within the Zooniverse family. Examples of data mining and its verification will be presented.

Detecting Underground Mine Voids Using Complex Geophysical Techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaminski, V. F.; Harbert, W. P.; Hammack, R. W.

2006-12-01

In July 2006, the National Energy Technology Laboratory in collaboration with Department of Geology and Planetary Science, University of Pittsburgh conducted complex ground geophysical surveys of an area known to be underlain by shallow coal mines. Geophysical methods including electromagnetic induction, DC resistivity and seismic reflection were conducted. The purpose of these surveys was to: 1) verify underground mine voids based on a century-old mine map that showed subsurface mine workings georeferenced to match with present location of geophysical test-site located on the territory of Bruceton research center in Pittsburgh, PA, 2) deliniate mine workings that may be potentially filledmore » with electrically conductive water filtrate emerging from adjacent groundwater collectors and 3) establish an equipment calibration site for geophysical instruments. Data from electromagnetic and resistivity surveys were further processed and inverted using EM1DFM, EMIGMA or Earthimager 2D capablilities in order to generate conductivity/depth images. Anomaly maps were generated, that revealed the locations of potential mine openings.« less

Employing image processing techniques for cancer detection using microarray images.

PubMed

Dehghan Khalilabad, Nastaran; Hassanpour, Hamid

2017-02-01

Microarray technology is a powerful genomic tool for simultaneously studying and analyzing the behavior of thousands of genes. The analysis of images obtained from this technology plays a critical role in the detection and treatment of diseases. The aim of the current study is to develop an automated system for analyzing data from microarray images in order to detect cancerous cases. The proposed system consists of three main phases, namely image processing, data mining, and the detection of the disease. The image processing phase performs operations such as refining image rotation, gridding (locating genes) and extracting raw data from images the data mining includes normalizing the extracted data and selecting the more effective genes. Finally, via the extracted data, cancerous cell is recognized. To evaluate the performance of the proposed system, microarray database is employed which includes Breast cancer, Myeloid Leukemia and Lymphomas from the Stanford Microarray Database. The results indicate that the proposed system is able to identify the type of cancer from the data set with an accuracy of 95.45%, 94.11%, and 100%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

International Congress on High Speed Photography and Photonics, 17th, Pretoria, Republic of South Africa, Sept. 1-5, 1986, Proceedings. Volumes 1 & 2

NASA Astrophysics Data System (ADS)

McDowell, M. W.; Hollingworth, D.

1986-01-01

The present conference discusses topics in mining applications of high speed photography, ballistic, shock wave and detonation studies employing high speed photography, laser and X-ray diagnostics, biomechanical photography, millisec-microsec-nanosec-picosec-femtosec photographic methods, holographic, schlieren, and interferometric techniques, and videography. Attention is given to such issues as the pulse-shaping of ultrashort optical pulses, the performance of soft X-ray streak cameras, multiple-frame image tube operation, moire-enlargement motion-raster photography, two-dimensional imaging with tomographic techniques, photochron TV streak cameras, and streak techniques in detonics.

Image Information Mining Utilizing Hierarchical Segmentation

NASA Technical Reports Server (NTRS)

Tilton, James C.; Marchisio, Giovanni; Koperski, Krzysztof; Datcu, Mihai

2002-01-01

The Hierarchical Segmentation (HSEG) algorithm is an approach for producing high quality, hierarchically related image segmentations. The VisiMine image information mining system utilizes clustering and segmentation algorithms for reducing visual information in multispectral images to a manageable size. The project discussed herein seeks to enhance the VisiMine system through incorporating hierarchical segmentations from HSEG into the VisiMine system.

EU-FP7-iMARS: analysis of Mars multi-resolution images using auto-coregistration, data mining and crowd source techniques

NASA Astrophysics Data System (ADS)

Ivanov, Anton; Muller, Jan-Peter; Tao, Yu; Kim, Jung-Rack; Gwinner, Klaus; Van Gasselt, Stephan; Morley, Jeremy; Houghton, Robert; Bamford, Steven; Sidiropoulos, Panagiotis; Fanara, Lida; Waenlish, Marita; Walter, Sebastian; Steinkert, Ralf; Schreiner, Bjorn; Cantini, Federico; Wardlaw, Jessica; Sprinks, James; Giordano, Michele; Marsh, Stuart

2016-07-01

Understanding planetary atmosphere-surface and extra-terrestrial-surface formation processes within our Solar System is one of the fundamental goals of planetary science research. There has been a revolution in planetary surface observations over the last 15 years, especially in 3D imaging of surface shape. This has led to the ability to be able to overlay different epochs back in time to the mid 1970s, to examine time-varying changes, such as the recent discovery of mass movement, tracking inter-year seasonal changes and looking for occurrences of fresh craters. Within the EU FP-7 iMars project, UCL have developed a fully automated multi-resolution DTM processing chain, called the Co-registration ASP-Gotcha Optimised (CASP-GO), based on the open source NASA Ames Stereo Pipeline (ASP), which is being applied to the production of planetwide DTMs and ORIs (OrthoRectified Images) from CTX and HiRISE. Alongside the production of individual strip CTX & HiRISE DTMs & ORIs, DLR have processed HRSC mosaics of ORIs and DTMs for complete areas in a consistent manner using photogrammetric bundle block adjustment techniques. A novel automated co-registration and orthorectification chain has been developed and is being applied to level-1 EDR images taken by the 4 NASA orbital cameras since 1976 using the HRSC map products (both mosaics and orbital strips) as a map-base. The project has also included Mars Radar profiles from Mars Express and Mars Reconnaissance Orbiter missions. A webGIS has been developed for displaying this time sequence of imagery and a demonstration will be shown applied to one of the map-sheets. Automated quality control techniques are applied to screen for suitable images and these are extended to detect temporal changes in features on the surface such as mass movements, streaks, spiders, impact craters, CO2 geysers and Swiss Cheese terrain. These data mining techniques are then being employed within a citizen science project within the Zooniverse family to verify the results of these data mining techniques. Examples of data mining and its verification will be presented. We will present a software tool to ease access to co-registered MARSIS and SHARAD radargrams and geometry data such as probing point latitude and longitude and spacecraft altitude. Data are extracted from official ESA and NASA released data using self-developed python classes. Geometrical data and metadata are exposed as WFS layers using a QGIS server, which can be further integrated with other data. Radar geometry data will be available as a part of the iMars WebGIS framework and images will be available via PDS and PSA archives. Acknowledgements The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under iMars grant agreement n˚ 607379 as well as partial funding from the STFC "MSSL Consolidated Grant" ST/K000977/1.

Evaluation of airborne thermal infrared imagery for locating mine drainage sites in the Lower Youghiogheny River Basin, Pennsylvania, USA

USGS Publications Warehouse

Sams, James I.; Veloski, Garret; Ackman, T.E.

2003-01-01

Nighttime high-resolution airborne thermal infrared imagery (TIR) data were collected in the predawn hours during Feb 5-8 and March 11-12, 1999, from a helicopter platform for 72.4 km of the Youghiogheny River, from Connellsville to McKeesport, in southwestern Pennsylvania. The TIR data were used to identify sources of mine drainage from abandoned mines that discharge directly into the Youghiogheny River. Image-processing and geographic information systems (GIS) techniques were used to identify 70 sites within the study area as possible mine drainage sources. The combination of GIS datasets and the airborne TIR data provided a fast and accurate method to target the possible sources. After field reconnaissance, it was determined that 24 of the 70 sites were mine drainage. This paper summarizes: the procedures used to process the TIR data and extract potential mine-drainage sites; methods used for verification of the TIR data; a discussion of factors affecting the TIR data; and a brief summary of water quality.

Close-range sensors for small unmanned bottom vehicles: update

NASA Astrophysics Data System (ADS)

Bernstein, Charles L.

2000-07-01

The Surf Zone Reconnaissance Project is developing sensors for small, autonomous, Underwater Bottom-crawling Vehicles. The objective is to enable small, crawling robots to autonomously detect and classify mines and obstacles on the ocean bottom in depths between 0 and 10 feet. We have identified a promising set of techniques that will exploit the electromagnetic, shape, texture, image, and vibratory- modal features of this images. During FY99 and FY00 we have worked toward refining these techniques. Signature data sets have been collected for a standard target set to facilitate the development of sensor fusion and target detection and classification algorithms. Specific behaviors, termed microbehaviors, are developed to utilize the robot's mobility to position and operate the sensors. A first generation, close-range sensor suite, composed of 5 sensors, will be completed and tested on a crawling platform in FY00, and will be further refined and demonstrated in FY01 as part of the Mine Countermeasures 6.3 core program sponsored by the Office of Naval Research.

Quantitative analysis and feature recognition in 3-D microstructural data sets

NASA Astrophysics Data System (ADS)

Lewis, A. C.; Suh, C.; Stukowski, M.; Geltmacher, A. B.; Spanos, G.; Rajan, K.

2006-12-01

A three-dimensional (3-D) reconstruction of an austenitic stainless-steel microstructure was used as input for an image-based finite-element model to simulate the anisotropic elastic mechanical response of the microstructure. The quantitative data-mining and data-warehousing techniques used to correlate regions of high stress with critical microstructural features are discussed. Initial analysis of elastic stresses near grain boundaries due to mechanical loading revealed low overall correlation with their location in the microstructure. However, the use of data-mining and feature-tracking techniques to identify high-stress outliers revealed that many of these high-stress points are generated near grain boundaries and grain edges (triple junctions). These techniques also allowed for the differentiation between high stresses due to boundary conditions of the finite volume reconstructed, and those due to 3-D microstructural features.

Multisensor fusion for the detection of mines and minelike targets

NASA Astrophysics Data System (ADS)

Hanshaw, Terilee

1995-06-01

The US Army's Communications and Electronics Command through the auspices of its Night Vision and Electronics Sensors Directorate (CECOM-NVESD) is actively applying multisensor techniques to the detection of mine targets. This multisensor research results from the 'detection activity' with its broad range of operational conditions and targets. Multisensor operation justifies significant attention by yielding high target detection and low false alarm statistics. Furthermore, recent advances in sensor and computing technologies make its practical application realistic and affordable. The mine detection field-of-endeavor has since its WWI baptismal investigated the known spectra for applicable mine observation phenomena. Countless sensors, algorithms, processors, networks, and other techniques have been investigated to determine candidacy for mine detection. CECOM-NVESD efforts have addressed a wide range of sensors spanning the spectrum from gravity field perturbations, magentic field disturbances, seismic sounding, electromagnetic fields, earth penetrating radar imagery, and infrared/visible/ultraviolet surface imaging technologies. Supplementary analysis has considered sensor candidate applicability by testing under field conditions (versus laboratory), in determination of fieldability. As these field conditions directly effect the probability of detection and false alarms, sensor employment and design must be considered. Consequently, as a given sensor's performance is influenced directly by the operational conditions, tradeoffs are necessary. At present, mass produced and fielded mine detection techniques are limited to those incorporating a single sensor/processor methodology such as, pulse induction and megnetometry, as found in hand held detectors. The most sensitive fielded systems can detect minute metal components in small mine targets but result in very high false alarm rates reducing velocity in operation environments. Furthermore, the actual speed of advance for the entire mission (convoy, movement to engagement, etc.) is determined by the level of difficulty presented in clearance or avoidance activities required in response to the potential 'targets' marked throughout a detection activity. Therefore the application of fielded hand held systems to convoy operations in clearly impractical. CECOM-NVESD efforts are presently seeking to overcome these operational limitations by substantially increasing speed of detection while reducing the false alarm rate through the application of multisensor techniques. The CECOM-NVESD application of multisensor techniques through integration/fusion methods will be defined in this paper.

Web Mining for Web Image Retrieval.

ERIC Educational Resources Information Center

Chen, Zheng; Wenyin, Liu; Zhang, Feng; Li, Mingjing; Zhang, Hongjiang

2001-01-01

Presents a prototype system for image retrieval from the Internet using Web mining. Discusses the architecture of the Web image retrieval prototype; document space modeling; user log mining; and image retrieval experiments to evaluate the proposed system. (AEF)

Techniques of Photometry and Astrometry with APASS, Gaia, and Pan-STARRs Results (Abstract)

NASA Astrophysics Data System (ADS)

Green, W.

2017-12-01

(Abstract only) The databases with the APASS DR9, Gaia DR1, and the Pan-STARRs 3pi DR1 data releases are publicly available for use. There is a bit of data-mining involved to download and manage these reference stars. This paper discusses the use of these databases to acquire accurate photometric references as well as techniques for improving results. Images are prepared in the usual way: zero, dark, flat-fields, and WCS solutions with Astrometry.net. Images are then processed with Sextractor to produce an ASCII table of identifying photometric features. The database manages photometics catalogs and images converted to ASCII tables. Scripts convert the files into SQL and assimilate them into database tables. Using SQL techniques, each image star is merged with reference data to produce publishable results. The VYSOS has over 13,000 images of the ONC5 field to process with roughly 100 total fields in the campaign. This paper provides the overview for this daunting task.

The application of LANDSAT-1 imagery for monitoring strip mines in the new river watershed in northeast Tennessee, part 2

NASA Technical Reports Server (NTRS)

Shahrokhi, F. (Principal Investigator); Sharber, L. A.

1977-01-01

The author has identified the following significant results. LANDSAT imagery and supplementary aircraft photography of the New River drainage basin were subjected to a multilevel analysis using conventional photointerpretation methods, densitometric techniques, multispectral analysis, and statistical tests to determine the accuracy of LANDSAT-1 imagery for measuring strip mines of common size. The LANDSAT areas were compared with low altitude measurements. The average accuracy over all the mined land sample areas mapped from LANDSAT-1 was 90%. The discrimination of strip mine subcategories is somewhat limited on LANDSAT imagery. A mine site, whether active or inactive, can be inferred by lack of vegetation, by shape, or image texture. Mine ponds are difficult or impossible to detect because of their small size and turbidity. Unless bordered and contrasted with vegetation, haulage roads are impossible to delineate. Preparation plants and refuge areas are not detectable. Density slicing of LANDSAT band 7 proved most useful in the detection of reclamation progress within the mined areas. For most state requirements for year-round monitoring of surface mined land, LANDSAT is of limited value. However, for periodic updating of regional surface maps, LANDSAT may provide sufficient accuracies for some users.

Use of a remotely piloted aircraft system for hazard assessment in a rocky mining area (Lucca, Italy)

NASA Astrophysics Data System (ADS)

Salvini, Riccardo; Mastrorocco, Giovanni; Esposito, Giuseppe; Di Bartolo, Silvia; Coggan, John; Vanneschi, Claudio

2018-01-01

The use of remote sensing techniques is now common practice in different working environments, including engineering geology. Moreover, in recent years the development of structure from motion (SfM) methods, together with rapid technological improvement, has allowed the widespread use of cost-effective remotely piloted aircraft systems (RPAS) for acquiring detailed and accurate geometrical information even in evolving environments, such as mining contexts. Indeed, the acquisition of remotely sensed data from hazardous areas provides accurate 3-D models and high-resolution orthophotos minimizing the risk for operators. The quality and quantity of the data obtainable from RPAS surveys can then be used for inspection of mining areas, audit of mining design, rock mass characterizations, stability analysis investigations and monitoring activities. Despite the widespread use of RPAS, its potential and limitations still have to be fully understood.In this paper a case study is shown where a RPAS was used for the engineering geological investigation of a closed marble mine area in Italy; direct ground-based techniques could not be applied for safety reasons. In view of the re-activation of mining operations, high-resolution images taken from different positions and heights were acquired and processed using SfM techniques to obtain an accurate and detailed 3-D model of the area. The geometrical and radiometrical information was subsequently used for a deterministic rock mass characterization, which led to the identification of two large marble blocks that pose a potential significant hazard issue for the future workforce. A preliminary stability analysis, with a focus on investigating the contribution of potential rock bridges, was then performed in order to demonstrate the potential use of RPAS information in engineering geological contexts for geohazard identification, awareness and reduction.

Developing image processing meta-algorithms with data mining of multiple metrics.

PubMed

Leung, Kelvin; Cunha, Alexandre; Toga, A W; Parker, D Stott

2014-01-01

People often use multiple metrics in image processing, but here we take a novel approach of mining the values of batteries of metrics on image processing results. We present a case for extending image processing methods to incorporate automated mining of multiple image metric values. Here by a metric we mean any image similarity or distance measure, and in this paper we consider intensity-based and statistical image measures and focus on registration as an image processing problem. We show how it is possible to develop meta-algorithms that evaluate different image processing results with a number of different metrics and mine the results in an automated fashion so as to select the best results. We show that the mining of multiple metrics offers a variety of potential benefits for many image processing problems, including improved robustness and validation.

Wideband radar for airborne minefield detection

NASA Astrophysics Data System (ADS)

Clark, William W.; Burns, Brian; Dorff, Gary; Plasky, Brian; Moussally, George; Soumekh, Mehrdad

2006-05-01

Ground Penetrating Radar (GPR) has been applied for several years to the problem of detecting both antipersonnel and anti-tank landmines. RDECOM CERDEC NVESD is developing an airborne wideband GPR sensor for the detection of minefields including surface and buried mines. In this paper, we describe the as-built system, data and image processing techniques to generate imagery, and current issues with this type of radar. Further, we will display images from a recent field test.

LLNL electro-optical mine detection program

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson, C.; Aimonetti, W.; Barth, M.

1994-09-30

Under funding from the Advanced Research Projects Agency (ARPA) and the US Marine Corps (USMC), Lawrence Livermore National Laboratory (LLNL) has directed a program aimed at improving detection capabilities against buried mines and munitions. The program has provided a national test facility for buried mines in arid environments, compiled and distributed an extensive data base of infrared (IR), ground penetrating radar (GPR), and other measurements made at that site, served as a host for other organizations wishing to make measurements, made considerable progress in the use of ground penetrating radar for mine detection, and worked on the difficult problem ofmore » sensor fusion as applied to buried mine detection. While the majority of our effort has been concentrated on the buried mine problem, LLNL has worked with the U.S.M.C. on surface mine problems as well, providing data and analysis to support the COBRA (Coastal Battlefield Reconnaissance and Analysis) program. The original aim of the experimental aspect of the program was the utilization of multiband infrared approaches for the detection of buried mines. Later the work was extended to a multisensor investigation, including sensors other than infrared imagers. After an early series of measurements, it was determined that further progress would require a larger test facility in a natural environment, so the Buried Object Test Facility (BOTF) was constructed at the Nevada Test Site. After extensive testing, with sensors spanning the electromagnetic spectrum from the near ultraviolet to radio frequencies, possible paths for improvement were: improved spatial resolution providing better ground texture discrimination; analysis which involves more complicated spatial queueing and filtering; additional IR bands using imaging spectroscopy; the use of additional sensors other than IR and the use of data fusion techniques with multi-sensor data; and utilizing time dependent observables like temperature.« less

Object-based image analysis for the assessment of mineral extraction in conflict regions - a case study in the Democratic Republic of the Congo

NASA Astrophysics Data System (ADS)

Kranz, Olaf; Schoepfer, Elisabeth; Spröhnle, Kristin; Lang, Stefan

2016-06-01

In this study object-based image analysis (OBIA) techniques were applied to assess land cover changes related to mineral extraction in a conflict-affected area of the eastern Democratic Republic of the Congo (DRC) over a period of five years based on very high resolution (VHR) satellite data of different sensors. Object-based approaches explicitly consider spatio-temporal aspects which allow extracting important information to document mining activities. The use of remote sensing data as an independent, up-to-date and reliable data source provided hints on the general development of the mining sector in relation to socio-economic and political decisions. While in early 2010, the situation was still characterised by an intensification of mineral extraction, a mining ban between autumn 2010 and spring 2011 marked the starting point for a continuous decrease of mining activities. The latter can be substantiated through a decrease in the extend of the mining area as well as of the number of dwellings in the nearby settlement. A following demilitarisation and the mentioned need for accountability with respect to the origin of certain minerals led to organised, more industrialized exploitation. This development is likewise visible on satellite imagery as typical clearings within forested areas. The results of the continuous monitoring in turn facilitate non-governmental organisations (NGOs) to further foster the mentioned establishment of responsible supply chains by the mining industry throughout the entire period of investigation.

Fundus Image Features Extraction for Exudate Mining in Coordination with Content Based Image Retrieval: A Study

NASA Astrophysics Data System (ADS)

Gururaj, C.; Jayadevappa, D.; Tunga, Satish

2018-02-01

Medical field has seen a phenomenal improvement over the previous years. The invention of computers with appropriate increase in the processing and internet speed has changed the face of the medical technology. However there is still scope for improvement of the technologies in use today. One of the many such technologies of medical aid is the detection of afflictions of the eye. Although a repertoire of research has been accomplished in this field, most of them fail to address how to take the detection forward to a stage where it will be beneficial to the society at large. An automated system that can predict the current medical condition of a patient after taking the fundus image of his eye is yet to see the light of the day. Such a system is explored in this paper by summarizing a number of techniques for fundus image features extraction, predominantly hard exudate mining, coupled with Content Based Image Retrieval to develop an automation tool. The knowledge of the same would bring about worthy changes in the domain of exudates extraction of the eye. This is essential in cases where the patients may not have access to the best of technologies. This paper attempts at a comprehensive summary of the techniques for Content Based Image Retrieval (CBIR) or fundus features image extraction, and few choice methods of both, and an exploration which aims to find ways to combine these two attractive features, and combine them so that it is beneficial to all.

Fundus Image Features Extraction for Exudate Mining in Coordination with Content Based Image Retrieval: A Study

NASA Astrophysics Data System (ADS)

Gururaj, C.; Jayadevappa, D.; Tunga, Satish

2018-06-01

Medical field has seen a phenomenal improvement over the previous years. The invention of computers with appropriate increase in the processing and internet speed has changed the face of the medical technology. However there is still scope for improvement of the technologies in use today. One of the many such technologies of medical aid is the detection of afflictions of the eye. Although a repertoire of research has been accomplished in this field, most of them fail to address how to take the detection forward to a stage where it will be beneficial to the society at large. An automated system that can predict the current medical condition of a patient after taking the fundus image of his eye is yet to see the light of the day. Such a system is explored in this paper by summarizing a number of techniques for fundus image features extraction, predominantly hard exudate mining, coupled with Content Based Image Retrieval to develop an automation tool. The knowledge of the same would bring about worthy changes in the domain of exudates extraction of the eye. This is essential in cases where the patients may not have access to the best of technologies. This paper attempts at a comprehensive summary of the techniques for Content Based Image Retrieval (CBIR) or fundus features image extraction, and few choice methods of both, and an exploration which aims to find ways to combine these two attractive features, and combine them so that it is beneficial to all.

Developing Image Processing Meta-Algorithms with Data Mining of Multiple Metrics

PubMed Central

Cunha, Alexandre; Toga, A. W.; Parker, D. Stott

2014-01-01

People often use multiple metrics in image processing, but here we take a novel approach of mining the values of batteries of metrics on image processing results. We present a case for extending image processing methods to incorporate automated mining of multiple image metric values. Here by a metric we mean any image similarity or distance measure, and in this paper we consider intensity-based and statistical image measures and focus on registration as an image processing problem. We show how it is possible to develop meta-algorithms that evaluate different image processing results with a number of different metrics and mine the results in an automated fashion so as to select the best results. We show that the mining of multiple metrics offers a variety of potential benefits for many image processing problems, including improved robustness and validation. PMID:24653748

An electromagnetic noncontacting sensor for thickness measurement in a dispersive medium

NASA Technical Reports Server (NTRS)

Chufo, Robert L.

1994-01-01

This paper describes a general purpose imaging technology developed by the U.S. Bureau of Mines (USBM) that, when fully implemented, will solve the general problem of 'seeing into the earth.' A first-generation radar coal thickness sensor, the RCTS-1, has been developed and field-tested in both underground and highwall mines. The noncontacting electromagnetic technique uses spatial modulation created by moving a simple sensor antenna in a direction along each axis to be measured while the complex reflection coefficient is measured at multiple frequencies over a two-to-one bandwidth. The antenna motion imparts spatial modulation to the data that enables signal processing to solve the problems of media, target, and antenna dispersion. Knowledge of the dielectric constant of the media is not necessary because the electrical properties of the media are determined automatically along with the distance to the target and thickness of each layer of the target. The sensor was developed as a navigation guidance sensor to accurately detect the coal/noncoal interface required for the USBM computer-assisted mining machine program. Other mining applications include the location of rock fractures, water-filled voids, and abandoned gas wells. These hazards can be detected in advance of the mining operation. This initiating technology is being expanded into a full three-dimensional (3-D) imaging system that will have applications in both the underground and surface environment.

Detecting and monitoring UCG subsidence with InSAR

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mellors, R J; Foxall, W; Yang, X

2012-03-23

The use of interferometric synthetic aperture radar (InSAR) to measure surface subsidence caused by Underground Coal Gasification (UCG) is tested. InSAR is a remote sensing technique that uses Synthetic Aperture Radar images to make spatial images of surface deformation and may be deployed from satellite or an airplane. With current commercial satellite data, the technique works best in areas with little vegetation or farming activity. UCG subsidence is generally caused by roof collapse, which adversely affects UCG operations due to gas loss and is therefore important to monitor. Previous studies have demonstrated the usefulness of InSAR in measuring surface subsidencemore » related to coal mining and surface deformation caused by a coal mining roof collapse in Crandall Canyon, Utah is imaged as a proof-of-concept. InSAR data is collected and processed over three known UCG operations including two pilot plants (Majuba, South Africa and Wulanchabu, China) and an operational plant (Angren, Uzbekistan). A clear f eature showing approximately 7 cm of subsidence is observed in the UCG field in Angren. Subsidence is not observed in the other two areas, which produce from deeper coal seams and processed a smaller volume. The results show that in some cases, InSAR is a useful tool to image UCG related subsidence. Data from newer satellites and improved algorithms will improve effectiveness.« less

An integrtated approach to the use of Landsat TM data for gold exploration in west central Nevada

NASA Technical Reports Server (NTRS)

Mouat, D. A.; Myers, J. S.; Miller, N. L.

1987-01-01

This paper represents an integration of several Landsat TM image processing techniques with other data to discriminate the lithologies and associated areas of hydrothermal alteration in the vicinity of the Paradise Peak gold mine in west central Nevada. A microprocessor-based image processing system and an IDIMS system were used to analyze data from a 512 X 512 window of a Landsat-5 TM scene collected on June 30, 1984. Image processing techniques included simple band composites, band ratio composites, principal components composites, and baseline-based composites. These techniques were chosen based on their ability to discriminate the spectral characteristics of the products of hydrothermal alteration as well as of the associated regional lithologies. The simple band composite, ratio composite, two principal components composites, and the baseline-based composites separately can define the principal areas of alteration. Combined, they provide a very powerful exploration tool.

Utility of hyperspectral imagers in the mining industry: Italy's gypsum reserves

NASA Astrophysics Data System (ADS)

Wilson, Janette H.; Greenberger, Rebecca N.

2014-05-01

The mining industry is plagued with socioeconomic and safety roadblocks with not many solutions in the midst of a demanding market. As more and more geologic research using hyperspectral technology has been performed, along with an affordable price point for commercial use of hyperspectral technology, the benefits of hyperspectral imaging to the mining industry has become apparent. This study identifies the key areas of use for hyperspectral imaging in the mining industry through a case study of gypsum mine samples obtained from a mine in central Tuscany.

Underground Mining Method Selection Using WPM and PROMETHEE

NASA Astrophysics Data System (ADS)

Balusa, Bhanu Chander; Singam, Jayanthu

2018-04-01

The aim of this paper is to represent the solution to the problem of selecting suitable underground mining method for the mining industry. It is achieved by using two multi-attribute decision making techniques. These two techniques are weighted product method (WPM) and preference ranking organization method for enrichment evaluation (PROMETHEE). In this paper, analytic hierarchy process is used for weight's calculation of the attributes (i.e. parameters which are used in this paper). Mining method selection depends on physical parameters, mechanical parameters, economical parameters and technical parameters. WPM and PROMETHEE techniques have the ability to consider the relationship between the parameters and mining methods. The proposed techniques give higher accuracy and faster computation capability when compared with other decision making techniques. The proposed techniques are presented to determine the effective mining method for bauxite mine. The results of these techniques are compared with methods used in the earlier research works. The results show, conventional cut and fill method is the most suitable mining method.

Flight State Information Inference with Application to Helicopter Cockpit Video Data Analysis Using Data Mining Techniques

NASA Astrophysics Data System (ADS)

Shin, Sanghyun

The National Transportation Safety Board (NTSB) has recently emphasized the importance of analyzing flight data as one of the most effective methods to improve eciency and safety of helicopter operations. By analyzing flight data with Flight Data Monitoring (FDM) programs, the safety and performance of helicopter operations can be evaluated and improved. In spite of the NTSB's effort, the safety of helicopter operations has not improved at the same rate as the safety of worldwide airlines, and the accident rate of helicopters continues to be much higher than that of fixed-wing aircraft. One of the main reasons is that the participation rates of the rotorcraft industry in the FDM programs are low due to the high costs of the Flight Data Recorder (FDR), the need of a special readout device to decode the FDR, anxiety of punitive action, etc. Since a video camera is easily installed, accessible, and inexpensively maintained, cockpit video data could complement the FDR in the presence of the FDR or possibly replace the role of the FDR in the absence of the FDR. Cockpit video data is composed of image and audio data: image data contains outside views through cockpit windows and activities on the flight instrument panels, whereas audio data contains sounds of the alarms within the cockpit. The goal of this research is to develop, test, and demonstrate a cockpit video data analysis algorithm based on data mining and signal processing techniques that can help better understand situations in the cockpit and the state of a helicopter by efficiently and accurately inferring the useful flight information from cockpit video data. Image processing algorithms based on data mining techniques are proposed to estimate a helicopter's attitude such as the bank and pitch angles, identify indicators from a flight instrument panel, and read the gauges and the numbers in the analogue gauge indicators and digital displays from cockpit image data. In addition, an audio processing algorithm based on signal processing and abrupt change detection techniques is proposed to identify types of warning alarms and to detect the occurrence times of individual alarms from cockpit audio data. Those proposed algorithms are then successfully applied to simulated and real helicopter cockpit video data to demonstrate and validate their performance.

Preliminary study of detection of buried landmines using a programmable hyperspectral imager

NASA Astrophysics Data System (ADS)

McFee, John E.; Ripley, Herb T.; Buxton, Roger; Thriscutt, Andrew M.

1996-05-01

Experiments were conducted to determine if buried mines could be detected by measuring the change in reflectance spectra of vegetation above mine burial sites. Mines were laid using hand methods and simulated mechanical methods and spectral images were obtained over a three month period using a casi hyperspectral imager scanned from a personnel lift. Mines were not detectable by measurement of the shift of the red edge of vegetative spectra. By calculating the linear correlation coefficient image, some mines in light vegetative cover (grass, grass/blueberries) were apparently detected, but mines buried in heavy vegetation cover (deep ferns) were not detectable. Due to problems with ground truthing, accurate probabilities of detection and false alarm rates were not obtained.

Seeing is believing: on the use of image databases for visually exploring plant organelle dynamics.

PubMed

Mano, Shoji; Miwa, Tomoki; Nishikawa, Shuh-ichi; Mimura, Tetsuro; Nishimura, Mikio

2009-12-01

Organelle dynamics vary dramatically depending on cell type, developmental stage and environmental stimuli, so that various parameters, such as size, number and behavior, are required for the description of the dynamics of each organelle. Imaging techniques are superior to other techniques for describing organelle dynamics because these parameters are visually exhibited. Therefore, as the results can be seen immediately, investigators can more easily grasp organelle dynamics. At present, imaging techniques are emerging as fundamental tools in plant organelle research, and the development of new methodologies to visualize organelles and the improvement of analytical tools and equipment have allowed the large-scale generation of image and movie data. Accordingly, image databases that accumulate information on organelle dynamics are an increasingly indispensable part of modern plant organelle research. In addition, image databases are potentially rich data sources for computational analyses, as image and movie data reposited in the databases contain valuable and significant information, such as size, number, length and velocity. Computational analytical tools support image-based data mining, such as segmentation, quantification and statistical analyses, to extract biologically meaningful information from each database and combine them to construct models. In this review, we outline the image databases that are dedicated to plant organelle research and present their potential as resources for image-based computational analyses.

Mapping Environmental Contaminants at Ray Mine, AZ

NASA Technical Reports Server (NTRS)

McCubbin, Ian; Lang, Harold

2000-01-01

Airborne Visible and InfraRed Imaging Spectrometer (AVIRIS) data was collected over Ray Mine as part of a demonstration project for the Environmental Protection Agency (EPA) through the Advanced Measurement Initiative (AMI). The overall goal of AMI is to accelerate adoption and application of advanced measurement technologies for cost effective environmental monitoring. The site was selected to demonstrate the benefit to EPA in using advanced remote sensing technologies for the detection of environmental contaminants due to the mineral extraction industry. The role of the Jet Propulsion Laboratory in this pilot study is to provide data as well as performing calibration, data analysis, and validation of the AVIRIS results. EPA is also interested in developing protocols that use commercial software to perform such work on other high priority EPA sites. Reflectance retrieval was performed using outputs generated by the MODTRAN radiative transfer model and field spectra collected for the purpose of calibration. We are presenting advanced applications of the ENVI software package using n-Dimensional Partial Unmixing to identify image-derived endmembers that best match target materials reference spectra from multiple spectral libraries. Upon identification of the image endmembers the Mixture Tuned Match Filter algorithm was applied to map the endmembers within each scene. Using this technique it was possible to map four different mineral classes that are associated with mine generated acid waste.

Application of Data Mining and Knowledge Discovery Techniques to Enhance Binary Target Detection and Decision-Making for Compromised Visual Images

DTIC Science & Technology

2004-11-01

affords exciting opportunities in target detection. The input signal may be a sum of sine waves, it could be an auditory signal, or possibly a visual...rendering of a scene. Since image processing is an area in which the original data are stationary in some sense ( auditory signals suffer from...11 Example 1 of SR - Identification of a Subliminal Signal below a Threshold .......................... 13 Example 2 of SR

Constructing and Classifying Email Networks from Raw Forensic Images

DTIC Science & Technology

2016-09-01

data mining for sequence and pattern mining ; in medical imaging for image segmentation; and in computer vision for object recognition” [28]. 2.3.1...machine learning and data mining suite that is written in Python. It provides a platform for experiment selection, recommendation systems, and...predictivemod- eling. The Orange library is a hierarchically-organized toolbox of data mining components. Data filtering and probability assessment are at the

An evolution of image source camera attribution approaches.

PubMed

Jahanirad, Mehdi; Wahab, Ainuddin Wahid Abdul; Anuar, Nor Badrul

2016-05-01

Camera attribution plays an important role in digital image forensics by providing the evidence and distinguishing characteristics of the origin of the digital image. It allows the forensic analyser to find the possible source camera which captured the image under investigation. However, in real-world applications, these approaches have faced many challenges due to the large set of multimedia data publicly available through photo sharing and social network sites, captured with uncontrolled conditions and undergone variety of hardware and software post-processing operations. Moreover, the legal system only accepts the forensic analysis of the digital image evidence if the applied camera attribution techniques are unbiased, reliable, nondestructive and widely accepted by the experts in the field. The aim of this paper is to investigate the evolutionary trend of image source camera attribution approaches from fundamental to practice, in particular, with the application of image processing and data mining techniques. Extracting implicit knowledge from images using intrinsic image artifacts for source camera attribution requires a structured image mining process. In this paper, we attempt to provide an introductory tutorial on the image processing pipeline, to determine the general classification of the features corresponding to different components for source camera attribution. The article also reviews techniques of the source camera attribution more comprehensively in the domain of the image forensics in conjunction with the presentation of classifying ongoing developments within the specified area. The classification of the existing source camera attribution approaches is presented based on the specific parameters, such as colour image processing pipeline, hardware- and software-related artifacts and the methods to extract such artifacts. The more recent source camera attribution approaches, which have not yet gained sufficient attention among image forensics researchers, are also critically analysed and further categorised into four different classes, namely, optical aberrations based, sensor camera fingerprints based, processing statistics based and processing regularities based, to present a classification. Furthermore, this paper aims to investigate the challenging problems, and the proposed strategies of such schemes based on the suggested taxonomy to plot an evolution of the source camera attribution approaches with respect to the subjective optimisation criteria over the last decade. The optimisation criteria were determined based on the strategies proposed to increase the detection accuracy, robustness and computational efficiency of source camera brand, model or device attribution. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

43 CFR 3420.1-4 - General requirements for land use planning.

Code of Federal Regulations, 2011 CFR

2011-10-01

... mining by other than underground mining techniques. (ii) For the purposes of this paragraph, any surface... techniques shall be deemed to have expressed a preference in favor of mining. Where a significant number of... underground mining techniques, that area shall be considered acceptable for further consideration only for...

43 CFR 3420.1-4 - General requirements for land use planning.

Code of Federal Regulations, 2013 CFR

2013-10-01

... mining by other than underground mining techniques. (ii) For the purposes of this paragraph, any surface... techniques shall be deemed to have expressed a preference in favor of mining. Where a significant number of... underground mining techniques, that area shall be considered acceptable for further consideration only for...

43 CFR 3420.1-4 - General requirements for land use planning.

Code of Federal Regulations, 2014 CFR

2014-10-01

... mining by other than underground mining techniques. (ii) For the purposes of this paragraph, any surface... techniques shall be deemed to have expressed a preference in favor of mining. Where a significant number of... underground mining techniques, that area shall be considered acceptable for further consideration only for...

43 CFR 3420.1-4 - General requirements for land use planning.

Code of Federal Regulations, 2012 CFR

2012-10-01

... mining by other than underground mining techniques. (ii) For the purposes of this paragraph, any surface... techniques shall be deemed to have expressed a preference in favor of mining. Where a significant number of... underground mining techniques, that area shall be considered acceptable for further consideration only for...

CoBOP: Electro-Optic Identification Laser Line Sean Sensors

DTIC Science & Technology

1998-01-01

Electro - Optic Identification Sensors Project[1] is to develop and demonstrate high resolution underwater electro - optic (EO) imaging sensors, and associated image processing/analysis methods, for rapid visual identification of mines and mine-like contacts (MLCs). Identification of MLCs is a pressing Fleet need. During MCM operations, sonar contacts are classified as mine-like if they are sufficiently similar to signatures of mines. Each contact classified as mine-like must be identified as a mine or not a mine. During MCM operations in littoral areas,

Mind your crossings: Mining GIS imagery for crosswalk localization.

PubMed

Ahmetovic, Dragan; Manduchi, Roberto; Coughlan, James M; Mascetti, Sergio

2017-04-01

For blind travelers, finding crosswalks and remaining within their borders while traversing them is a crucial part of any trip involving street crossings. While standard Orientation & Mobility (O&M) techniques allow blind travelers to safely negotiate street crossings, additional information about crosswalks and other important features at intersections would be helpful in many situations, resulting in greater safety and/or comfort during independent travel. For instance, in planning a trip a blind pedestrian may wish to be informed of the presence of all marked crossings near a desired route. We have conducted a survey of several O&M experts from the United States and Italy to determine the role that crosswalks play in travel by blind pedestrians. The results show stark differences between survey respondents from the U.S. compared with Italy: the former group emphasized the importance of following standard O&M techniques at all legal crossings (marked or unmarked), while the latter group strongly recommended crossing at marked crossings whenever possible. These contrasting opinions reflect differences in the traffic regulations of the two countries and highlight the diversity of needs that travelers in different regions may have. To address the challenges faced by blind pedestrians in negotiating street crossings, we devised a computer vision-based technique that mines existing spatial image databases for discovery of zebra crosswalks in urban settings. Our algorithm first searches for zebra crosswalks in satellite images; all candidates thus found are validated against spatially registered Google Street View images. This cascaded approach enables fast and reliable discovery and localization of zebra crosswalks in large image datasets. While fully automatic, our algorithm can be improved by a final crowdsourcing validation. To this end, we developed a Pedestrian Crossing Human Validation (PCHV) web service, which supports crowdsourcing to rule out false positives and identify false negatives.

Mind your crossings: Mining GIS imagery for crosswalk localization

PubMed Central

Ahmetovic, Dragan; Manduchi, Roberto; Coughlan, James M.; Mascetti, Sergio

2017-01-01

For blind travelers, finding crosswalks and remaining within their borders while traversing them is a crucial part of any trip involving street crossings. While standard Orientation & Mobility (O&M) techniques allow blind travelers to safely negotiate street crossings, additional information about crosswalks and other important features at intersections would be helpful in many situations, resulting in greater safety and/or comfort during independent travel. For instance, in planning a trip a blind pedestrian may wish to be informed of the presence of all marked crossings near a desired route. We have conducted a survey of several O&M experts from the United States and Italy to determine the role that crosswalks play in travel by blind pedestrians. The results show stark differences between survey respondents from the U.S. compared with Italy: the former group emphasized the importance of following standard O&M techniques at all legal crossings (marked or unmarked), while the latter group strongly recommended crossing at marked crossings whenever possible. These contrasting opinions reflect differences in the traffic regulations of the two countries and highlight the diversity of needs that travelers in different regions may have. To address the challenges faced by blind pedestrians in negotiating street crossings, we devised a computer vision-based technique that mines existing spatial image databases for discovery of zebra crosswalks in urban settings. Our algorithm first searches for zebra crosswalks in satellite images; all candidates thus found are validated against spatially registered Google Street View images. This cascaded approach enables fast and reliable discovery and localization of zebra crosswalks in large image datasets. While fully automatic, our algorithm can be improved by a final crowdsourcing validation. To this end, we developed a Pedestrian Crossing Human Validation (PCHV) web service, which supports crowdsourcing to rule out false positives and identify false negatives. PMID:28757907

Big data analytics in hyperspectral imaging for detection of microbial colonies on agar plates (Conference Presentation)

NASA Astrophysics Data System (ADS)

Yoon, Seung-Chul; Park, Bosoon; Lawrence, Kurt C.

2017-05-01

Various types of optical imaging techniques measuring light reflectivity and scattering can detect microbial colonies of foodborne pathogens on agar plates. Until recently, these techniques were developed to provide solutions for hypothesis-driven studies, which focused on developing tools and batch/offline machine learning methods with well defined sets of data. These have relatively high accuracy and rapid response time because the tools and methods are often optimized for the collected data. However, they often need to be retrained or recalibrated when new untrained data and/or features are added. A big-data driven technique is more suitable for online learning of new/ambiguous samples and for mining unknown or hidden features. Although big data research in hyperspectral imaging is emerging in remote sensing and many tools and methods have been developed so far in many other applications such as bioinformatics, the tools and methods still need to be evaluated and adjusted in applications where the conventional batch machine learning algorithms were dominant. The primary objective of this study is to evaluate appropriate big data analytic tools and methods for online learning and mining of foodborne pathogens on agar plates. After the tools and methods are successfully identified, they will be applied to rapidly search big color and hyperspectral image data of microbial colonies collected over the past 5 years in house and find the most probable colony or a group of colonies in the collected big data. The meta-data, such as collection time and any unstructured data (e.g. comments), will also be analyzed and presented with output results. The expected results will be novel, big data-driven technology to correctly detect and recognize microbial colonies of various foodborne pathogens on agar plates.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nurhandoko, Bagus Endar B.; Wely, Woen; Setiadi, Herlan

It is already known that tomography has a great impact for analyzing and mapping unknown objects based on inversion, travel time as well as waveform inversion. Therefore, tomography has used in wide area, not only in medical but also in petroleum as well as mining. Recently, tomography method is being applied in several mining industries. A case study of tomography imaging has been carried out in DOZ ( Deep Ore Zone ) block caving mine, Tembagapura, Papua. Many researchers are undergoing to investigate the properties of DOZ cave not only outside but also inside which is unknown. Tomography takes amore » part for determining this objective.The sources are natural from the seismic events that caused by mining induced seismicity and rocks deformation activity, therefore it is called as passive seismic. These microseismic travel time data are processed by Simultaneous Iterative Reconstruction Technique (SIRT). The result of the inversion can be used for DOZ cave monitoring. These information must be used for identifying weak zone inside the cave. In addition, these results of tomography can be used to determine DOZ and cave information to support mine activity in PT. Freeport Indonesia.« less

3D Texture Features Mining for MRI Brain Tumor Identification

NASA Astrophysics Data System (ADS)

Rahim, Mohd Shafry Mohd; Saba, Tanzila; Nayer, Fatima; Syed, Afraz Zahra

2014-03-01

Medical image segmentation is a process to extract region of interest and to divide an image into its individual meaningful, homogeneous components. Actually, these components will have a strong relationship with the objects of interest in an image. For computer-aided diagnosis and therapy process, medical image segmentation is an initial mandatory step. Medical image segmentation is a sophisticated and challenging task because of the sophisticated nature of the medical images. Indeed, successful medical image analysis heavily dependent on the segmentation accuracy. Texture is one of the major features to identify region of interests in an image or to classify an object. 2D textures features yields poor classification results. Hence, this paper represents 3D features extraction using texture analysis and SVM as segmentation technique in the testing methodologies.

Identification of mine collapses, explosions and earthquakes using INSAR: a preliminary investigation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Foxall, B; Sweeney, J J; Walter, W R

1998-07-07

Interferograms constmcted from satellite-borne synthetic aperture radar images have the capability of mapping sub-cm ground surface deformation over areas on the order of 100 x 100 km with a spatial resolution on the order of 10 meters. We investigate the utility of synthetic aperture radar interferomehy (InSAR) used in conjunction with regional seismic methods in detecting and discriminating different types of seismic events in the context of special event analysis for the CTBT. For this initial study, we carried out elastic dislocation modeling of underground explosions, mine collapses and small (M<5.5) shallow earthquakes to produce synthetic interferograms and then analyzedmore » satellite radar data for a large mine collapse. The synthetic modeling shows that, for a given magnitude each type of event produces a distinctive pattern of ground deformation that can be recognized in, and recovered from, the corresponding interferogram. These diagnostic characteristics include not only differences in the polarities of surface displacements but also differences in displacement amplitudes from the different sources. The technique is especially sensitive to source depth, a parameter that is crucial in discriminating earthquakes from the other event types but is often very poorly constrained by regional seismic data alone. The ERS radar data analyzed is from a M L 5.2 seismic event that occurred in southwestern Wyoming on February 3,1995. Although seismic data from the event have some characteristics of an underground explosion, based on seismological and geodetic data it has been identified as being caused by a large underground collapse in the Solvay Mine. Several pairs of before-collapse and after-collapse radar images were phase processed to obtain interferograms. The minimum time separation for a before-collapse and after-collapse pair was 548 days. Even with this long time separation, phase coherence between the image pairs was acceptable and a deformation map was successfully obtained. Two images, separated by 1 day and occurring after the mine collapse, were used to form a digital elevation map (DEM) that was used to correct for topography. The interferograms identify the large deformation at the Solvay Mine as well as some areas of lesser deformation near other mines in the area. The large amount of deformation at the Solvay Mine was identified, but (as predicted by our dislocation modeling) could not be quantified absolutely because of the incoherent interference pattern it produced« less

Hierarchical content-based image retrieval by dynamic indexing and guided search

NASA Astrophysics Data System (ADS)

You, Jane; Cheung, King H.; Liu, James; Guo, Linong

2003-12-01

This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include: a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing, an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features.

Study of application of ERTS-A imagery to fracture-related mine safety hazards in the coal mining industry

NASA Technical Reports Server (NTRS)

Wier, C. E.; Wobber, F. J.; Russell, O. R.; Martin, K. R. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Mined land reclamation analysis procedures developed within the Indiana portion of the Illinois Coal Basin were independently tested in Ohio utilizing 1:80,000 scale enlargements of ERTS-1 image 1029-15361-7 (dated August 21, 1972). An area in Belmont County was selected for analysis due to the extensive surface mining and the different degrees of reclamation occurring in this area. Contour mining in this area provided the opportunity to extend techniques developed for analysis of relatively flat mining areas in Indiana to areas of rolling topography in Ohio. The analysts had no previous experience in the area. Field investigations largely confirmed office analysis results although in a few areas estimates of vegetation percentages were found to be too high. In one area this error approximated 25%. These results suggest that systematic ERTS-1 analysis in combination with selective field sampling can provide reliable vegetation percentage estimates in excess of 25% accuracy with minimum equipment investment and training. The utility of ERTS-1 for practical and reasonably reliable update of mined lands information for groups with budget limitations is suggested. Many states can benefit from low cost updates using ERTS-1 imagery from public sources.

Using temporarily coherent point interferometric synthetic aperture radar for land subsidence monitoring in a mining region of western China

NASA Astrophysics Data System (ADS)

Fan, Hongdong; Xu, Qiang; Hu, Zhongbo; Du, Sen

2017-04-01

Yuyang mine is located in the semiarid western region of China where, due to serious land subsidence caused by underground coal exploitation, the local ecological environment has become more fragile. An advanced interferometric synthetic aperture radar (InSAR) technique, temporarily coherent point InSAR, is applied to measure surface movements caused by different mining conditions. Fifteen high-resolution TerraSAR-X images acquired between October 2, 2012, and March 27, 2013, were processed to generate time-series data for ground deformation. The results show that the maximum accumulated values of subsidence and velocity were 86 mm and 162 mm/year, respectively; these measurements were taken above the fully mechanized longwall caving faces. Based on the dynamic land subsidence caused by the exploitation of one working face, the land subsidence range was deduced to have increased 38 m in the mining direction with 11 days' coal extraction. Although some mining faces were ceased in 2009, they could also have contributed to a small residual deformation of overlying strata. Surface subsidence of the backfill mining region was quite small, the maximum only 21 mm, so backfill exploitation is an effective method for reducing the land subsidence while coal is mined.

EU-FP7-iMARS: analysis of Mars multi-resolution images using auto-coregistration, data mining and crowd source techniques: A Mid-term Report

NASA Astrophysics Data System (ADS)

Muller, J.-P.; Yershov, V.; Sidiropoulos, P.; Gwinner, K.; Willner, K.; Fanara, L.; Waelisch, M.; van Gasselt, S.; Walter, S.; Ivanov, A.; Cantini, F.; Morley, J. G.; Sprinks, J.; Giordano, M.; Wardlaw, J.; Kim, J.-R.; Chen, W.-T.; Houghton, R.; Bamford, S.

2015-10-01

Understanding the role of different solid surface formation processes within our Solar System is one of the fundamental goals of planetary science research. There has been a revolution in planetary surface observations over the last 8 years, especially in 3D imaging of surface shape (down to resolutions of 10s of cms) and subsequent terrain correction of imagery from orbiting spacecraft. This has led to the potential to be able to overlay different epochs back to the mid-1970s. Within iMars, a processing system has been developed to generate 3D Digital Terrain Models (DTMs) and corresponding OrthoRectified Images (ORIs) fully automatically from NASA MRO HiRISE and CTX stereo-pairs which are coregistered to corresponding HRSC ORI/DTMs. In parallel, iMars has developed a fully automated processing chain for co-registering level-1 (EDR) images from all previous NASA orbital missions to these HRSC ORIs and in the case of HiRISE these are further co-registered to previously co-registered CTX-to-HRSC ORIs. Examples will be shown of these multi-resolution ORIs and the application of different data mining algorithms to change detection using these co-registered images. iMars has recently launched a citizen science experiment to evaluate best practices for future citizen scientist validation of such data mining processed results. An example of the iMars website will be shown along with an embedded Version 0 prototype of a webGIS based on OGC standards.

Comparative data mining analysis for information retrieval of MODIS images: monitoring lake turbidity changes at Lake Okeechobee, Florida

NASA Astrophysics Data System (ADS)

Chang, Ni-Bin; Daranpob, Ammarin; Yang, Y. Jeffrey; Jin, Kang-Ren

2009-09-01

In the remote sensing field, a frequently recurring question is: Which computational intelligence or data mining algorithms are most suitable for the retrieval of essential information given that most natural systems exhibit very high non-linearity. Among potential candidates might be empirical regression, neural network model, support vector machine, genetic algorithm/genetic programming, analytical equation, etc. This paper compares three types of data mining techniques, including multiple non-linear regression, artificial neural networks, and genetic programming, for estimating multi-temporal turbidity changes following hurricane events at Lake Okeechobee, Florida. This retrospective analysis aims to identify how the major hurricanes impacted the water quality management in 2003-2004. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra 8-day composite imageries were used to retrieve the spatial patterns of turbidity distributions for comparison against the visual patterns discernible in the in-situ observations. By evaluating four statistical parameters, the genetic programming model was finally selected as the most suitable data mining tool for classification in which the MODIS band 1 image and wind speed were recognized as the major determinants by the model. The multi-temporal turbidity maps generated before and after the major hurricane events in 2003-2004 showed that turbidity levels were substantially higher after hurricane episodes. The spatial patterns of turbidity confirm that sediment-laden water travels to the shore where it reduces the intensity of the light necessary to submerged plants for photosynthesis. This reduction results in substantial loss of biomass during the post-hurricane period.

Data mining in radiology

PubMed Central

Kharat, Amit T; Singh, Amarjit; Kulkarni, Vilas M; Shah, Digish

2014-01-01

Data mining facilitates the study of radiology data in various dimensions. It converts large patient image and text datasets into useful information that helps in improving patient care and provides informative reports. Data mining technology analyzes data within the Radiology Information System and Hospital Information System using specialized software which assesses relationships and agreement in available information. By using similar data analysis tools, radiologists can make informed decisions and predict the future outcome of a particular imaging finding. Data, information and knowledge are the components of data mining. Classes, Clusters, Associations, Sequential patterns, Classification, Prediction and Decision tree are the various types of data mining. Data mining has the potential to make delivery of health care affordable and ensure that the best imaging practices are followed. It is a tool for academic research. Data mining is considered to be ethically neutral, however concerns regarding privacy and legality exists which need to be addressed to ensure success of data mining. PMID:25024513

Imaging Preferential Flow Pathways of Contaminants from Passive Acid Mine Drainage Mitigation Sites Using Electrical Resistivity

NASA Astrophysics Data System (ADS)

Kelley, N.; Mount, G.; Terry, N.; Herndon, E.; Singer, D. M.

2017-12-01

The Critical Zone represents the surficial and shallow layer of rock, air, water, and soil where most interactions between living organisms and the Earth occur. Acid mine drainage (AMD) resulting from coal extraction can influence both biological and geochemical processes across this zone. Conservative estimates suggest that more than 300 million gallons of AMD are released daily, making this acidic solution of water and contaminants a common issue in areas with legacy or current coal extraction. Electrical resistivity imaging (ERI) provides a rapid and minimally invasive method to identify and monitor contaminant pathways from AMD remediation systems in the subsurface of the Critical Zone. The technique yields spatially continuous data of subsurface resistivity that can be inverted to determine electrical conductivity as a function of depth. Since elevated concentrations of heavy metals can directly influence soil conductivity, ERI data can be used to trace the flow pathways or perhaps unknown mine conduits and transport of heavy metals through the subsurface near acid mine drainage sources. This study aims to examine preferential contaminant migration from those sources through substrate pores, fractures, and shallow mine workings in the near subsurface surrounding AMD sites in eastern Ohio and western Pennsylvania. We utilize time lapse ERI measures during different hydrologic conditions to better understand the variability of preferential flow pathways in relation to changes in stage and discharge within the remediation systems. To confirm ERI findings, and provide constraint to geochemical reactions occurring in the shallow subsurface, we conducted Inductively Coupled Plasma (ICP) spectrometry analysis of groundwater samples from boreholes along the survey transects. Through these combined methods, we can provide insight into the ability of engineered systems to contain and isolate metals in passive acid mine drainage treatment systems.

Image Mining in Remote Sensing for Coastal Wetlands Mapping: from Pixel Based to Object Based Approach

NASA Astrophysics Data System (ADS)

Farda, N. M.; Danoedoro, P.; Hartono; Harjoko, A.

2016-11-01

The availably of remote sensing image data is numerous now, and with a large amount of data it makes “knowledge gap” in extraction of selected information, especially coastal wetlands. Coastal wetlands provide ecosystem services essential to people and the environment. The aim of this research is to extract coastal wetlands information from satellite data using pixel based and object based image mining approach. Landsat MSS, Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI images located in Segara Anakan lagoon are selected to represent data at various multi temporal images. The input for image mining are visible and near infrared bands, PCA band, invers PCA bands, mean shift segmentation bands, bare soil index, vegetation index, wetness index, elevation from SRTM and ASTER GDEM, and GLCM (Harralick) or variability texture. There is three methods were applied to extract coastal wetlands using image mining: pixel based - Decision Tree C4.5, pixel based - Back Propagation Neural Network, and object based - Mean Shift segmentation and Decision Tree C4.5. The results show that remote sensing image mining can be used to map coastal wetlands ecosystem. Decision Tree C4.5 can be mapped with highest accuracy (0.75 overall kappa). The availability of remote sensing image mining for mapping coastal wetlands is very important to provide better understanding about their spatiotemporal coastal wetlands dynamics distribution.

An open data mining framework for the analysis of medical images: application on obstructive nephropathy microscopy images.

PubMed

Doukas, Charalampos; Goudas, Theodosis; Fischer, Simon; Mierswa, Ingo; Chatziioannou, Aristotle; Maglogiannis, Ilias

2010-01-01

This paper presents an open image-mining framework that provides access to tools and methods for the characterization of medical images. Several image processing and feature extraction operators have been implemented and exposed through Web Services. Rapid-Miner, an open source data mining system has been utilized for applying classification operators and creating the essential processing workflows. The proposed framework has been applied for the detection of salient objects in Obstructive Nephropathy microscopy images. Initial classification results are quite promising demonstrating the feasibility of automated characterization of kidney biopsy images.

Knowledge discovery from structured mammography reports using inductive logic programming.

PubMed

Burnside, Elizabeth S; Davis, Jesse; Costa, Victor Santos; Dutra, Inês de Castro; Kahn, Charles E; Fine, Jason; Page, David

2005-01-01

The development of large mammography databases provides an opportunity for knowledge discovery and data mining techniques to recognize patterns not previously appreciated. Using a database from a breast imaging practice containing patient risk factors, imaging findings, and biopsy results, we tested whether inductive logic programming (ILP) could discover interesting hypotheses that could subsequently be tested and validated. The ILP algorithm discovered two hypotheses from the data that were 1) judged as interesting by a subspecialty trained mammographer and 2) validated by analysis of the data itself.

[Application of text mining approach to pre-education prior to clinical practice].

PubMed

Koinuma, Masayoshi; Koike, Katsuya; Nakamura, Hitoshi

2008-06-01

We developed a new survey analysis technique to understand students' actual aims for effective pretraining prior to clinical practice. We asked third-year undergraduate students to write fixed-style complete and free sentences on "preparation of drug dispensing." Then, we converted their sentence data in to text style and performed Japanese-language morphologic analysis on the data using language analysis software. We classified key words, which were created on the basis of the word class information of the Japanese language morphologic analysis, into categories based on causes and characteristics. In addition to this, we classified the characteristics into six categories consisting of those concepts including "knowledge," "skill and attitude," "image," etc. with the KJ method technique. The results showed that the awareness of students of "preparation of drug dispensing" tended to be approximately three-fold more frequent in "skill and attitude," "risk," etc. than in "knowledge." Regarding the characteristics in the category of the "image," words like "hard," "challenging," "responsibility," "life," etc. frequently occurred. The results of corresponding analysis showed that the characteristics of the words "knowledge" and "skills and attitude" were independent. As the result of developing a cause-and-effect diagram, it was demonstrated that the phase "hanging tough" described most of the various factors. We thus could understand students' actual feelings by applying text-mining as a new survey analysis technique.

Changes in the Extent of Surface Mining and Reclamation in the Central Appalachians Detected Using a 1976-2006 Landsat Time Series

NASA Technical Reports Server (NTRS)

Townsend, Philip A.; Helmers, David P.; Kingdon, Clayton C.; McNeil, Brenden E.; de Beurs, Kirsten M.; Eshleman, Keith N.

2009-01-01

Surface mining and reclamation is the dominant driver of land cover land use change (LCLUC) in the Central Appalachian Mountain region of the Eastern U.S. Accurate quantification of the extent of mining activities is important for assessing how this LCLUC affects ecosystem services such as aesthetics, biodiversity, and mitigation of flooding.We used Landsat imagery from 1976, 1987, 1999 and 2006 to map the extent of surface mines and mine reclamation for eight large watersheds in the Central Appalachian region of West Virginia, Maryland and Pennsylvania. We employed standard image processing techniques in conjunction with a temporal decision tree and GIS maps of mine permits and wetlands to map active and reclaimed mines and track changes through time. For the entire study area, active surface mine extent was highest in 1976, prior to implementation of the Surface Mine Control and Reclamation Act in 1977, with 1.76% of the study area in active mines, declining to 0.44% in 2006. The most extensively mined watershed, Georges Creek in Maryland, was 5.45% active mines in 1976, declining to 1.83% in 2006. For the entire study area, the area of reclaimed mines increased from 1.35% to 4.99% from 1976 to 2006, and from 4.71% to 15.42% in Georges Creek. Land cover conversion to mines and then reclaimed mines after 1976 was almost exclusively from forest. Accuracy levels for mined and reclaimed cover was above 85% for all time periods, and was generally above 80% for mapping active and reclaimed mines separately, especially for the later time periods in which good accuracy assessment data were available. Among other implications, the mapped patterns of LCLUC are likely to significantly affect watershed hydrology, as mined and reclaimed areas have lower infiltration capacity and thus more rapid runoff than unmined forest watersheds, leading to greater potential for extreme flooding during heavy rainfall events.

An efficient data mining framework for the characterization of symptomatic and asymptomatic carotid plaque using bidimensional empirical mode decomposition technique.

PubMed

Molinari, Filippo; Raghavendra, U; Gudigar, Anjan; Meiburger, Kristen M; Rajendra Acharya, U

2018-02-23

Atherosclerosis is a type of cardiovascular disease which may cause stroke. It is due to the deposition of fatty plaque in the artery walls resulting in the reduction of elasticity gradually and hence restricting the blood flow to the heart. Hence, an early prediction of carotid plaque deposition is important, as it can save lives. This paper proposes a novel data mining framework for the assessment of atherosclerosis in its early stage using ultrasound images. In this work, we are using 1353 symptomatic and 420 asymptomatic carotid plaque ultrasound images. Our proposed method classifies the symptomatic and asymptomatic carotid plaques using bidimensional empirical mode decomposition (BEMD) and entropy features. The unbalanced data samples are compensated using adaptive synthetic sampling (ADASYN), and the developed method yielded a promising accuracy of 91.43%, sensitivity of 97.26%, and specificity of 83.22% using fourteen features. Hence, the proposed method can be used as an assisting tool during the regular screening of carotid arteries in hospitals. Graphical abstract Outline for our efficient data mining framework for the characterization of symptomatic and asymptomatic carotid plaques.

Application of EREP, LANDSAT, and aircraft image data to environmental problems related to coal mining

NASA Technical Reports Server (NTRS)

Amato, R. V.; Russell, O. R.; Martin, K. R.; Wier, C. E.

1975-01-01

Remote sensing techniques were used to study coal mining sites within the Eastern Interior Coal Basin (Indiana, Illinois, and western Kentucky), the Appalachian Coal Basin (Ohio, West Virginia, and Pennsylvania) and the anthracite coal basins of northeastern Pennsylvania. Remote sensor data evaluated during these studies were acquired by LANDSAT, Skylab and both high and low altitude aircraft. Airborne sensors included multispectral scanners, multiband cameras and standard mapping cameras loaded with panchromatic, color and color infrared films. The research conducted in these areas is a useful prerequisite to the development of an operational monitoring system that can be peridically employed to supply state and federal regulatory agencies with supportive data. Further research, however, must be undertaken to systematically examine those mining processes and features that can be monitored cost effectively using remote sensors and for determining what combination of sensors and ground sampling processes provide the optimum combination for an operational system.

Analysis of Mining-Induced Subsidence Prediction by Exponent Knothe Model Combined with Insar and Leveling

NASA Astrophysics Data System (ADS)

Chen, Lei; Zhang, Liguo; Tang, Yixian; Zhang, Hong

2018-04-01

The principle of exponent Knothe model was introduced in detail and the variation process of mining subsidence with time was analysed based on the formulas of subsidence, subsidence velocity and subsidence acceleration in the paper. Five scenes of radar images and six levelling measurements were collected to extract ground deformation characteristics in one coal mining area in this study. Then the unknown parameters of exponent Knothe model were estimated by combined levelling data with deformation information along the line of sight obtained by InSAR technique. By compared the fitting and prediction results obtained by InSAR and levelling with that obtained only by levelling, it was shown that the accuracy of fitting and prediction combined with InSAR and levelling was obviously better than the other that. Therefore, the InSAR measurements can significantly improve the fitting and prediction accuracy of exponent Knothe model.

Science and Technology Text Mining: Near-Earth Space

DTIC Science & Technology

2003-07-21

TRANSFER; 177SATELLITE IMAGES; 175 SPATIAL RESOLUTION ; 174 SEA ICE; 166 SYSTEM GPS; 166 TOPEX POSEIDON; 165 SATELLITE MEASUREMENTS; 163 RADIATION BUDGET...1073 ICE; 1065 SATELLITES; 1062 PAPER; 1009 EARTH; 1008 RESOLUTION ; 1000 MODELS; 962 RADIATION; 943 DERIVED; 938 OCEAN; 928 CURRENT; 925 SPATIAL ; 899...PARAMETERS; 729 TECHNIQUE; 714 OPTICAL; 714 SPACECRAFT; 711 DEGREE; 702 TRANSMISSION; 696 LARGE; 693 TEST; 686 NUMBER; 671 EFFECTS ; 662 SPECTRAL ; 661

Data Mining: The Art of Automated Knowledge Extraction

NASA Astrophysics Data System (ADS)

Karimabadi, H.; Sipes, T.

2012-12-01

Data mining algorithms are used routinely in a wide variety of fields and they are gaining adoption in sciences. The realities of real world data analysis are that (a) data has flaws, and (b) the models and assumptions that we bring to the data are inevitably flawed, and/or biased and misspecified in some way. Data mining can improve data analysis by detecting anomalies in the data, check for consistency of the user model assumptions, and decipher complex patterns and relationships that would not be possible otherwise. The common form of data collected from in situ spacecraft measurements is multi-variate time series which represents one of the most challenging problems in data mining. We have successfully developed algorithms to deal with such data and have extended the algorithms to handle streaming data. In this talk, we illustrate the utility of our algorithms through several examples including automated detection of reconnection exhausts in the solar wind and flux ropes in the magnetotail. We also show examples from successful applications of our technique to analysis of 3D kinetic simulations. With an eye to the future, we provide an overview of our upcoming plans that include collaborative data mining, expert outsourcing data mining, computer vision for image analysis, among others. Finally, we discuss the integration of data mining algorithms with web-based services such as VxOs and other Heliophysics data centers and the resulting capabilities that it would enable.

Combining knowledge discovery from databases (KDD) and case-based reasoning (CBR) to support diagnosis of medical images

NASA Astrophysics Data System (ADS)

Stranieri, Andrew; Yearwood, John; Pham, Binh

1999-07-01

The development of data warehouses for the storage and analysis of very large corpora of medical image data represents a significant trend in health care and research. Amongst other benefits, the trend toward warehousing enables the use of techniques for automatically discovering knowledge from large and distributed databases. In this paper, we present an application design for knowledge discovery from databases (KDD) techniques that enhance the performance of the problem solving strategy known as case- based reasoning (CBR) for the diagnosis of radiological images. The problem of diagnosing the abnormality of the cervical spine is used to illustrate the method. The design of a case-based medical image diagnostic support system has three essential characteristics. The first is a case representation that comprises textual descriptions of the image, visual features that are known to be useful for indexing images, and additional visual features to be discovered by data mining many existing images. The second characteristic of the approach presented here involves the development of a case base that comprises an optimal number and distribution of cases. The third characteristic involves the automatic discovery, using KDD techniques, of adaptation knowledge to enhance the performance of the case based reasoner. Together, the three characteristics of our approach can overcome real time efficiency obstacles that otherwise mitigate against the use of CBR to the domain of medical image analysis.

Application of high-resolution subsurface imaging techniques to water resource investigations. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sternberg, B.K.; Thomas, S.J.

1992-12-01

The overall objective of the project was to apply a new high-resolution imaging system to water resource investigations. This imaging system measures the ellipticity of received magnetic-field components. The source of the magnetic field is a long-line transmitter emitting frequencies from 30 Hz to 30 kHz. A new high-accuracy calibration method was used to enhance the resolution of the measurements. The specific objectives included: (1) refine the system hardware and software based on these investigations, (2) learn the limitations of this technology in practical water resource investigations, and (3) improve interpretation techniques to extract the highest possible resolution. Successful fieldmore » surveys were run at: (1) San Xavier Mine, Arizona - flow of injected fluid was monitored with the system. (2) Avra Valley, Arizona - subsurface stratigraphy was imaged. A survey at a third site was less successful; interpreted resistivity section does not agree with nearby well logs. Surveys are continuing at this site.« less

Poker Flats Mine - Div. of Mining, Land, and Water

Science.gov Websites

Lands Coal Regulatory Program Large Mine Permits Mineral Property and Rights Mining Index Land Fishery Water Resources Factsheets Forms banner image of landscape Poker Flats Mine Home Mining Coal Regulatory Program Poker Flats Mine Mining Coal Regulatory Program Info Chickaloon Chuit Watershed Chuitna

Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review.

PubMed

Maione, Camila; Barbosa, Rommel Melgaço

2018-01-24

Rice is one of the most important staple foods around the world. Authentication of rice is one of the most addressed concerns in the present literature, which includes recognition of its geographical origin and variety, certification of organic rice and many other issues. Good results have been achieved by multivariate data analysis and data mining techniques when combined with specific parameters for ascertaining authenticity and many other useful characteristics of rice, such as quality, yield and others. This paper brings a review of the recent research projects on discrimination and authentication of rice using multivariate data analysis and data mining techniques. We found that data obtained from image processing, molecular and atomic spectroscopy, elemental fingerprinting, genetic markers, molecular content and others are promising sources of information regarding geographical origin, variety and other aspects of rice, being widely used combined with multivariate data analysis techniques. Principal component analysis and linear discriminant analysis are the preferred methods, but several other data classification techniques such as support vector machines, artificial neural networks and others are also frequently present in some studies and show high performance for discrimination of rice.

MSL: Facilitating automatic and physical analysis of published scientific literature in PDF format.

PubMed

Ahmed, Zeeshan; Dandekar, Thomas

2015-01-01

Published scientific literature contains millions of figures, including information about the results obtained from different scientific experiments e.g. PCR-ELISA data, microarray analysis, gel electrophoresis, mass spectrometry data, DNA/RNA sequencing, diagnostic imaging (CT/MRI and ultrasound scans), and medicinal imaging like electroencephalography (EEG), magnetoencephalography (MEG), echocardiography  (ECG), positron-emission tomography (PET) images. The importance of biomedical figures has been widely recognized in scientific and medicine communities, as they play a vital role in providing major original data, experimental and computational results in concise form. One major challenge for implementing a system for scientific literature analysis is extracting and analyzing text and figures from published PDF files by physical and logical document analysis. Here we present a product line architecture based bioinformatics tool 'Mining Scientific Literature (MSL)', which supports the extraction of text and images by interpreting all kinds of published PDF files using advanced data mining and image processing techniques. It provides modules for the marginalization of extracted text based on different coordinates and keywords, visualization of extracted figures and extraction of embedded text from all kinds of biological and biomedical figures using applied Optimal Character Recognition (OCR). Moreover, for further analysis and usage, it generates the system's output in different formats including text, PDF, XML and images files. Hence, MSL is an easy to install and use analysis tool to interpret published scientific literature in PDF format.

A primer to frequent itemset mining for bioinformatics

PubMed Central

Naulaerts, Stefan; Meysman, Pieter; Bittremieux, Wout; Vu, Trung Nghia; Vanden Berghe, Wim; Goethals, Bart

2015-01-01

Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences. PMID:24162173

APPLYING DATA MINING APPROACHES TO FURTHER ...

EPA Pesticide Factsheets

This dataset will be used to illustrate various data mining techniques to biologically profile the chemical space. This dataset will be used to illustrate various data mining techniques to biologically profile the chemical space.

A New Approach in Coal Mine Exploration Using Cosmic Ray Muons

NASA Astrophysics Data System (ADS)

Darijani, Reza; Negarestani, Ali; Rezaie, Mohammad Reza; Fatemi, Syed Jalil; Akhond, Ahmad

2016-08-01

Muon radiography is a technique that uses cosmic ray muons to image the interior of large scale geological structures. The muon absorption in matter is the most important parameter in cosmic ray muon radiography. Cosmic ray muon radiography is similar to X-ray radiography. The main aim in this survey is the simulation of the muon radiography for exploration of mines. So, the production source, tracking, and detection of cosmic ray muons were simulated by MCNPX code. For this purpose, the input data of the source card in MCNPX code were extracted from the muon energy spectrum at sea level. In addition, the other input data such as average density and thickness of layers that were used in this code are the measured data from Pabdana (Kerman, Iran) coal mines. The average thickness and density of these layers in the coal mines are from 2 to 4 m and 1.3 gr/c3, respectively. To increase the spatial resolution, a detector was placed inside the mountain. The results indicated that using this approach, the layers with minimum thickness about 2.5 m can be identified.

Diagnostic support for glaucoma using retinal images: a hybrid image analysis and data mining approach.

PubMed

Yu, Jin; Abidi, Syed Sibte Raza; Artes, Paul; McIntyre, Andy; Heywood, Malcolm

2005-01-01

The availability of modern imaging techniques such as Confocal Scanning Laser Tomography (CSLT) for capturing high-quality optic nerve images offer the potential for developing automatic and objective methods for diagnosing glaucoma. We present a hybrid approach that features the analysis of CSLT images using moment methods to derive abstract image defining features. The features are then used to train classifers for automatically distinguishing CSLT images of normal and glaucoma patient. As a first, in this paper, we present investigations in feature subset selction methods for reducing the relatively large input space produced by the moment methods. We use neural networks and support vector machines to determine a sub-set of moments that offer high classification accuracy. We demonstratee the efficacy of our methods to discriminate between healthy and glaucomatous optic disks based on shape information automatically derived from optic disk topography and reflectance images.

Orapa Diamond Mine, Botswana

NASA Image and Video Library

2015-11-16

This image from NASA Terra spacecraft shows the Orapa diamond mine, the world largest diamond mine by area. The mine is located in Botswana. It is the oldest of four mines operated by the same company, having begun operations in 1971. Orapa is an open pit style of mine, located on two kimberlite pipes. Currently, the Orapa mine annually produces approximately 11 million carats (2200 kg) of diamonds. The Letlhakane diamond mine is also an open pit construction. In 2003, the Letlhakane mine produced 1.06 million carats of diamonds. The Damtshaa diamond mine is the newest of four mines, located on top of four distinct kimberlite pipes of varying ore grade. The mine is forecast to produce about 5 million carats of diamond over the projected 31 year life of the mine. The image was acquired October 5, 2014, covers an area of 28 by 45 km, and is located at 21.3 degrees south, 25.4 degrees east. http://photojournal.jpl.nasa.gov/catalog/PIA20104

Generative Topic Modeling in Image Data Mining and Bioinformatics Studies

ERIC Educational Resources Information Center

Chen, Xin

2012-01-01

Probabilistic topic models have been developed for applications in various domains such as text mining, information retrieval and computer vision and bioinformatics domain. In this thesis, we focus on developing novel probabilistic topic models for image mining and bioinformatics studies. Specifically, a probabilistic topic-connection (PTC) model…

Photometry Using Kepler "Superstamps" of Open Clusters NGC 6791 & NGC 6819

NASA Astrophysics Data System (ADS)

Kuehn, Charles A.; Drury, Jason A.; Bellamy, Beau R.; Stello, Dennis; Bedding, Timothy R.; Reed, Mike; Quick, Breanna

2015-09-01

The Kepler space telescope has proven to be a gold mine for the study of variable stars. Usually, Kepler only reads out a handful of pixels around each pre-selected target star, omitting a large number of stars in the Kepler field. Fortunately, for the open clusters NGC 6791 and NGC 6819, Kepler also read out larger "superstamps" which contained complete images of the central region of each cluster. These cluster images can be used to study additional stars in the open clusters that were not originally on Kepler's target list. We discuss our work on using two photometric techniques to analyze these superstamps and present sample results from this project to demonstrate the value of this technique for a wide variety of variable stars.

A systematic mapping study of process mining

NASA Astrophysics Data System (ADS)

Maita, Ana Rocío Cárdenas; Martins, Lucas Corrêa; López Paz, Carlos Ramón; Rafferty, Laura; Hung, Patrick C. K.; Peres, Sarajane Marques; Fantinato, Marcelo

2018-05-01

This study systematically assesses the process mining scenario from 2005 to 2014. The analysis of 705 papers evidenced 'discovery' (71%) as the main type of process mining addressed and 'categorical prediction' (25%) as the main mining task solved. The most applied traditional technique is the 'graph structure-based' ones (38%). Specifically concerning computational intelligence and machine learning techniques, we concluded that little relevance has been given to them. The most applied are 'evolutionary computation' (9%) and 'decision tree' (6%), respectively. Process mining challenges, such as balancing among robustness, simplicity, accuracy and generalization, could benefit from a larger use of such techniques.

Privacy Preserving Sequential Pattern Mining in Data Stream

NASA Astrophysics Data System (ADS)

Huang, Qin-Hua

The privacy preserving data mining technique researches have gained much attention in recent years. For data stream systems, wireless networks and mobile devices, the related stream data mining techniques research is still in its' early stage. In this paper, an data mining algorithm dealing with privacy preserving problem in data stream is presented.

Science and Technology Review June 2005

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aufderheide, M

2005-05-03

This is the articles in this month's issue: (1) Close Collaborations Advance Progress in Genomic Research--Commentary by Elbert Branscomb; (2) Mining Genomes--Livermore computer programs help locate the stretches of DNA in gene deserts that regulate protein-making genes; (3) Shedding Light on Quantum Physics--Laboratory laser research builds from the foundation of Einstein's description of the quantization of light. (4) The Sharper Image for Surveillance--Speckle imaging-an image-processing technique used in astronomy is bringing long-distance surveillance into sharper focus. (5) Keeping Cool Close to the Sun--The specially coated gamma-ray spectrometer aboard the MESSENGER spacecraft will help scientists determine the abundance of elements inmore » Mercury's crust.« less

Lithologic and structural mapping of the Abiete-Toko gold district in southern Cameroon, using Landsat 7 ETM+/SRTM

NASA Astrophysics Data System (ADS)

Binam Mandeng, Eugène Pascal; Bondjè Bidjeck, Louise Marie; Takodjou Wambo, Jonas Didero; Taku, Agbor; Bineli Betsi, Thierry; Solange Ipan, Antoinette; Tchami Nfada, Lionel; Bitom Dieudonné, Lucien

2018-03-01

The geology of the Abiete-Toko gold district in South Cameroon is investigated using a combination of Landsat 7 ETM+/SRTM image processing techniques, conventional geologic field mapping and geostatistical analysis. The satellite images were treated using Principal Component Analysis and Sobel filters to separate the background noise from lithotectonic structures which were matched with field data. The results show that this area has been affected by a polyphase deformation represented by S1 foliation, Sc1 schistosity, L1 lineation, S2 foliation, F2 folds, and F3 shear zones and faults. A detailed analysis of all the structures led to the identification of two major networks of dextral and sinistral shear zones oriented WNW-ESE and NE-SW, respectively. These results may serve in mining prospection, especially in the search for tectonically controlled primary mineralization and so may significantly guide the exploration of primary gold mineralization in the Abiete-Toko area subjected to years of artisanal gold mining.

Comparative analysis of data mining techniques for business data

NASA Astrophysics Data System (ADS)

Jamil, Jastini Mohd; Shaharanee, Izwan Nizal Mohd

2014-12-01

Data mining is the process of employing one or more computer learning techniques to automatically analyze and extract knowledge from data contained within a database. Companies are using this tool to further understand their customers, to design targeted sales and marketing campaigns, to predict what product customers will buy and the frequency of purchase, and to spot trends in customer preferences that can lead to new product development. In this paper, we conduct a systematic approach to explore several of data mining techniques in business application. The experimental result reveals that all data mining techniques accomplish their goals perfectly, but each of the technique has its own characteristics and specification that demonstrate their accuracy, proficiency and preference.

Runtime support for parallelizing data mining algorithms

NASA Astrophysics Data System (ADS)

Jin, Ruoming; Agrawal, Gagan

2002-03-01

With recent technological advances, shared memory parallel machines have become more scalable, and offer large main memories and high bus bandwidths. They are emerging as good platforms for data warehousing and data mining. In this paper, we focus on shared memory parallelization of data mining algorithms. We have developed a series of techniques for parallelization of data mining algorithms, including full replication, full locking, fixed locking, optimized full locking, and cache-sensitive locking. Unlike previous work on shared memory parallelization of specific data mining algorithms, all of our techniques apply to a large number of common data mining algorithms. In addition, we propose a reduction-object based interface for specifying a data mining algorithm. We show how our runtime system can apply any of the technique we have developed starting from a common specification of the algorithm.

D Reconstruction and Modeling of Subterranean Landscapes in Collaborative Mining Archeology Projects: Techniques, Applications and Experiences

NASA Astrophysics Data System (ADS)

Arles, A.; Clerc, P.; Sarah, G.; Téreygeol, F.; Bonnamour, G.; Heckes, J.; Klein, A.

2013-07-01

Mining and underground archaeology are two domains of expertise where three-dimensional data take an important part in the associated researches. Up to now, archaeologists study mines and underground networks from line-plot surveys, cross-section of galleries, and from tool marks surveys. All this kind of information can be clearly recorded back from the field from threedimensional models with a more cautious and extensive approach. Besides, the volumes of the underground structures that are very important data to explain the mining activities are difficult to evaluate from "traditional" hand-made recordings. They can now be calculated more accurately from a 3D model. Finally, reconstructed scenes are a powerful tool as thinking aid to look back again to a structure in the office or in future times. And the recorded models, rendered photo-realistically, can also be used for cultural heritage documentation presenting inaccessible and sometimes dangerous places to the public. Nowadays, thanks to modern computer technologies and highly developed software tools paired with sophisticated digital camera equipment, complex photogrammetric processes are available for moderate costs for research teams. Recognizing these advantages the authors develop and utilize image-based workflows in order to document ancient mining monuments and underground sites as a basis for further historical and archaeological researches, performed in collaborative partnership during recent projects on medieval silver mines and preventive excavations of undergrounds in France.

Data Mining Techniques Applied to Hydrogen Lactose Breath Test.

PubMed

Rubio-Escudero, Cristina; Valverde-Fernández, Justo; Nepomuceno-Chamorro, Isabel; Pontes-Balanza, Beatriz; Hernández-Mendoza, Yoedusvany; Rodríguez-Herrera, Alfonso

2017-01-01

Analyze a set of data of hydrogen breath tests by use of data mining tools. Identify new patterns of H2 production. Hydrogen breath tests data sets as well as k-means clustering as the data mining technique to a dataset of 2571 patients. Six different patterns have been extracted upon analysis of the hydrogen breath test data. We have also shown the relevance of each of the samples taken throughout the test. Analysis of the hydrogen breath test data sets using data mining techniques has identified new patterns of hydrogen generation upon lactose absorption. We can see the potential of application of data mining techniques to clinical data sets. These results offer promising data for future research on the relations between gut microbiota produced hydrogen and its link to clinical symptoms.

Monitoring of ground movement in open pit iron mines of Carajás Province (Amazon region) based on A-DInSAR techniques using TerraSAR-X data

NASA Astrophysics Data System (ADS)

Silva, Guilherme Gregório; Mura, José Claudio; Paradella, Waldir Renato; Gama, Fabio Furlan; Temporim, Filipe Altoé

2017-04-01

Persistent scatterer interferometry (PSI) analysis of a large area is always a challenging task regarding the removal of the atmospheric phase component. This work presents an investigation of ground movement measurements based on a combination of differential SAR interferometry time-series (DTS) and PSI techniques, applied on a large area of extent with open pit iron mines located in Carajás (Brazilian Amazon Region), aiming at detecting linear and nonlinear ground movement. These mines have presented a history of instability, and surface monitoring measurements over sectors of the mines (pit walls) have been carried out based on ground-based radar and total station (prisms). Using a priori information regarding the topographic phase error and a phase displacement model derived from DTS, temporal phase unwrapping in the PSI processing and the removal of the atmospheric phases can be performed more efficiently. A set of 33 TerraSAR-X (TSX-1) images, acquired during the period from March 2012 to April 2013, was used to perform this investigation. The DTS analysis was carried out on a stack of multilook unwrapped interferograms using an extension of SVD to obtain the least-square solution. The height errors and deformation rates provided by the DTS approach were subtracted from the stack of interferograms to perform the PSI analysis. This procedure improved the capability of the PSI analysis for detecting high rates of deformation, as well as increased the numbers of point density of the final results. The proposed methodology showed good results for monitoring surface displacement in a large mining area, which is located in a rain forest environment, providing very useful information about the ground movement for planning and risk control.

Monitoring of surface movement in a large area of the open pit iron mines (Carajás, Brazil) based on A-DInSAR techniques using TerraSAR-X data

NASA Astrophysics Data System (ADS)

Mura, José C.; Paradella, Waldir R.; Gama, Fabio F.; Silva, Guilherme G.

2016-10-01

PSI (Persistent Scatterer Interferometry) analysis of large area is always a challenging task regarding the removal of the atmospheric phase component. This work presents an investigation of ground deformation measurements based on a combination of DInSAR Time-Series (DTS) and PSI techniques, applied in a large area of open pit iron mines located in Carajás (Brazilian Amazon Region), aiming at detect high rates of linear and nonlinear ground deformation. These mines have presented a historical of instability and surface monitoring measurements over sectors of the mines (pit walls) have been carried out based on ground based radar and total station (prisms). By using a priori information regarding the topographic phase error and phase displacement model derived from DTS, temporal phase unwrapping in the PSI processing and the removal of the atmospheric phases can be performed more efficiently. A set of 33 TerraSAR-X-1 images, acquired during the period from March 2012 to April 2013, was used to perform this investigation. The DTS analysis was carried out on a stack of multi-look unwrapped interferogram using an extension of SVD to obtain the Least-Square solution. The height errors and deformation rates provided by the DTS approach were subtracted from the stack of interferogram to perform the PSI analysis. This procedure improved the capability of the PSI analysis to detect high rates of deformation as well as increased the numbers of point density of the final results. The proposed methodology showed good results for monitoring surface displacement in a large mining area, which is located in a rain forest environment, providing very useful information about the ground movement for planning and risks control.

A Review of Financial Accounting Fraud Detection based on Data Mining Techniques

NASA Astrophysics Data System (ADS)

Sharma, Anuj; Kumar Panigrahi, Prabin

2012-02-01

With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. The failure of internal auditing system of the organization in identifying the accounting frauds has lead to use of specialized procedures to detect financial accounting fraud, collective known as forensic accounting. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. This paper presents a comprehensive review of the literature on the application of data mining techniques for the detection of financial accounting fraud and proposes a framework for data mining techniques based accounting fraud detection. The systematic and comprehensive literature review of the data mining techniques applicable to financial accounting fraud detection may provide a foundation to future research in this field. The findings of this review show that data mining techniques like logistic models, neural networks, Bayesian belief network, and decision trees have been applied most extensively to provide primary solutions to the problems inherent in the detection and classification of fraudulent data.

The Geomatics Contribution for the Valorisation Project in the Rocca of San Silvestro Landscape Site

NASA Astrophysics Data System (ADS)

Brocchini, D.; Chiabrando, F.; Colucci, E.; Sammartano, G.; Spanò, A.; Teppati Losè, L.; Villa, A.

2017-05-01

This paper proposes an emblematic project where several multi-sensor strategies for spatial data acquisition and management, range based and image based, were combined to create a series of integrated territorial and architectural scale products characterized by a rich multi-content nature. The work presented here was finalized in a test site that is composed by an ensemble of diversified cultural deposits; the objects that were surveyed and modelled range from the landscape with its widespread mining sites, the main tower with its defensive role, the urban configuration of the settlement, the building systems and techniques, a medieval mine. For this reason, the Rocca of San Silvestro represented a perfect test case, due to its complex and multi-stratified character. This archaeological site is a medieval fortified village near the municipality of Campiglia Marittima (LI), Italy. The Rocca is part of an Archaeological Mines Park and is included in the Parchi della Val di Cornia (a system of archaeological parks, natural parks and museums in the south-west of Tuscany). The fundamental role of a deep knowledge about a cultural artefact before the planning of a restoration and valorisation project is globally recognized; the qualitative and quantitative knowledge provided by geomatics techniques is part of this process. The paper will present the different techniques that were used, the products that were obtained and will focus on some mapping and WEB GIS applications and analyses that were performed and considerations that were made.

Strip mine reclamation: criteria and methods for measurement of revegetation success. Progress report, April 1, 1980-March 31, 1981

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carrel, J.E.; Kucera, C.L.; Johannsen, C.J.

1980-12-01

During this contract period research was continued at finding suitable methods and criteria for determining the success of revegetation in Midwestern prime ag lands strip mined for coal. Particularly important to the experimental design was the concept of reference areas, which were nearby fields from which the performance standards for reclaimed areas were derived. Direct and remote sensing techniques for measuring plant ground cover, production, and species composition were tested. 15 mine sites were worked in which were permitted under interim permanent surface mine regulations and in 4 adjoining reference sites. Studies at 9 prelaw sites were continued. All sitesmore » were either in Missouri or Illinois. Data gathered in the 1980 growing season showed that 13 unmanaged or young mineland pastures generally had lower average ground cover and production than 2 reference pastures. In contrast, yields at approximately 40% of 11 recently reclaimed mine sites planted with winter wheat, soybeans, or milo were statistically similar to 3 reference values. Digital computer image analysis of color infrared aerial photographs, when compared to ground level measurements, was a fast, accurate, and inexpensive way to determine plant ground cover and areas. But the remote sensing approach was inferior to standard surface methods for detailing plant species abundance and composition.« less

Exploring context and content links in social media: a latent space method.

PubMed

Qi, Guo-Jun; Aggarwal, Charu; Tian, Qi; Ji, Heng; Huang, Thomas S

2012-05-01

Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques.

Validation of Airborne Visible-Infrared Imaging Spectrometer Data at Ray Mine, AZ

NASA Technical Reports Server (NTRS)

Lang, H.; Baloga, S.

1999-01-01

We validate 1997 Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) reflectance spectra covering 0.4 meu - 2.4 meu from a stable, flat mineralogically characterized man-made target at Ray Mine, AZ, the site for an EPA/NASA assessment of the utility of remote sensing for monitoring acid drainage from an active open pit mine.

MO-C-BRCD-03: The Role of Informatics in Medical Physics and Vice Versa.

PubMed

Andriole, K

2012-06-01

Like Medical Physics, Imaging Informatics encompasses concepts touching every aspect of the imaging chain from image creation, acquisition, management and archival, to image processing, analysis, display and interpretation. The two disciplines are in fact quite complementary, with similar goals to improve the quality of care provided to patients using an evidence-based approach, to assure safety in the clinical and research environments, to facilitate efficiency in the workplace, and to accelerate knowledge discovery. Use-cases describing several areas of informatics activity will be given to illustrate current limitations that would benefit from medical physicist participation, and conversely areas in which informaticists may contribute to the solution. Topics to be discussed include radiation dose monitoring, process management and quality control, display technologies, business analytics techniques, and quantitative imaging. Quantitative imaging is increasingly becoming an essential part of biomedicalresearch as well as being incorporated into clinical diagnostic activities. Referring clinicians are asking for more objective information to be gleaned from the imaging tests that they order so that they may make the best clinical management decisions for their patients. Medical Physicists may be called upon to identify existing issues as well as develop, validate and implement new approaches and technologies to help move the field further toward quantitative imaging methods for the future. Biomedical imaging informatics tools and techniques such as standards, integration, data mining, cloud computing and new systems architectures, ontologies and lexicons, data visualization and navigation tools, and business analytics applications can be used to overcome some of the existing limitations. 1. Describe what is meant by Medical Imaging Informatics and understand why the medical physicist should care. 2. Identify existing limitations in information technologies with respect to Medical Physics, and conversely see how Informatics may assist the medical physicist in filling some of the current gaps in their activities. 3. Understand general informatics concepts and areas of investigation including imaging and workflow standards, systems integration, computing architectures, ontologies, data mining and business analytics, data visualization and human-computer interface tools, and the importance of quantitative imaging for the future of Medical Physics and Imaging Informatics. 4. Become familiar with on-going efforts to address current challenges facing future research into and clinical implementation of quantitative imaging applications. © 2012 American Association of Physicists in Medicine.

Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods.

PubMed

Patel, Tejal A; Puppala, Mamta; Ogunti, Richard O; Ensor, Joe E; He, Tiancheng; Shewale, Jitesh B; Ankerst, Donna P; Kaklamani, Virginia G; Rodriguez, Angel A; Wong, Stephen T C; Chang, Jenny C

2017-01-01

A key challenge to mining electronic health records for mammography research is the preponderance of unstructured narrative text, which strikingly limits usable output. The imaging characteristics of breast cancer subtypes have been described previously, but without standardization of parameters for data mining. The authors searched the enterprise-wide data warehouse at the Houston Methodist Hospital, the Methodist Environment for Translational Enhancement and Outcomes Research (METEOR), for patients with Breast Imaging Reporting and Data System (BI-RADS) category 5 mammogram readings performed between January 2006 and May 2015 and an available pathology report. The authors developed natural language processing (NLP) software algorithms to automatically extract mammographic and pathologic findings from free text mammogram and pathology reports. The correlation between mammographic imaging features and breast cancer subtype was analyzed using one-way analysis of variance and the Fisher exact test. The NLP algorithm was able to obtain key characteristics for 543 patients who met the inclusion criteria. Patients with estrogen receptor-positive tumors were more likely to have spiculated margins (P = .0008), and those with tumors that overexpressed human epidermal growth factor receptor 2 (HER2) were more likely to have heterogeneous and pleomorphic calcifications (P = .0078 and P = .0002, respectively). Mammographic imaging characteristics, obtained from an automated text search and the extraction of mammogram reports using NLP techniques, correlated with pathologic breast cancer subtype. The results of the current study validate previously reported trends assessed by manual data collection. Furthermore, NLP provides an automated means with which to scale up data extraction and analysis for clinical decision support. Cancer 2017;114-121. © 2016 American Cancer Society. © 2016 American Cancer Society.

Evaluating bump control techniques through convergence monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Campoli, A.A.

1987-07-01

A coal mine bump is the violent failure of a pillar or pillars due to overstress. Retreat coal mining concentrates stresses on the pillars directly outby gob areas, and the situation becomes critical when mining a coalbed encased in rigid associated strata. Bump control techniques employed by the Olga Mine, McDowell County, WV, were evaluated through convergence monitoring in a Bureau of Mines study. Olga uses a novel pillar splitting mining method to extract 55-ft by 70-ft chain pillars, under 1,100 to 1,550 ft of overburden. Three rows of pillars are mined simultaneously to soften the pillar line and reducemore » strain energy storage capacity. Localized stress reduction (destressing) techniques, auger drilling and shot firing, induced approximately 0.1 in. of roof-to-floor convergence in ''high'' -stress pillars near the gob line. Auger drilling of a ''low''-stress pillar located between two barrier pillars produced no convergence effects.« less

Diagnosis of the three-phase induction motor using thermal imaging

NASA Astrophysics Data System (ADS)

Glowacz, Adam; Glowacz, Zygfryd

2017-03-01

Three-phase induction motors are used in the industry commonly for example woodworking machines, blowers, pumps, conveyors, elevators, compressors, mining industry, automotive industry, chemical industry and railway applications. Diagnosis of faults is essential for proper maintenance. Faults may damage a motor and damaged motors generate economic losses caused by breakdowns in production lines. In this paper the authors develop fault diagnostic techniques of the three-phase induction motor. The described techniques are based on the analysis of thermal images of three-phase induction motor. The authors analyse thermal images of 3 states of the three-phase induction motor: healthy three-phase induction motor, three-phase induction motor with 2 broken bars, three-phase induction motor with faulty ring of squirrel-cage. In this paper the authors develop an original method of the feature extraction of thermal images MoASoID (Method of Areas Selection of Image Differences). This method compares many training sets together and it selects the areas with the biggest changes for the recognition process. Feature vectors are obtained with the use of mentioned MoASoID and image histogram. Next 3 methods of classification are used: NN (the Nearest Neighbour classifier), K-means, BNN (the back-propagation neural network). The described fault diagnostic techniques are useful for protection of three-phase induction motor and other types of rotating electrical motors such as: DC motors, generators, synchronous motors.

Application of LANDSAT-2 data to the implementation and enforcement of the Pennsylvania Surface Mining Conservation and Reclamation Act

NASA Technical Reports Server (NTRS)

Russell, O. R. (Principal Investigator); Nichols, D. A.; Anderson, R.

1977-01-01

The author has identified the following significant results. Evaluation of LANDSAT imagery indicates severe limitations in its utility for surface mine land studies. Image stripping resulting from unequal detector response on satellite degrades the image quality to the extent that images of scales larger than 1:125,000 are of limited value for manual interpretation. Computer processing of LANDSAT data to improve image quality is essential; the removal of scanline stripping and enhancement of mine land reflectance data combined with color composite printing permits useful photographic enlargements to approximately 1:60,000.

Big, Deep, and Smart Data in Scanning Probe Microscopy

DOE PAGES

Kalinin, Sergei V.; Strelcov, Evgheni; Belianinov, Alex; ...

2016-09-27

Scanning probe microscopy techniques open the door to nanoscience and nanotechnology by enabling imaging and manipulation of structure and functionality of matter on nanometer and atomic scales. We analyze the discovery process by SPM in terms of information flow from tip-surface junction to the knowledge adoption by scientific community. Furthermore, we discuss the challenges and opportunities offered by merging of SPM and advanced data mining, visual analytics, and knowledge discovery technologies.

An evaluation of processing InSAR Sentinel-1A/B data for correlation of mining subsidence with mining induced tremors in the Upper Silesian Coal Basin (Poland)

NASA Astrophysics Data System (ADS)

Krawczyk, Artur; Grzybek, Radosław

2018-01-01

The Satellite Radar Interferometry is one of the common methods that allow to measure the land subsidence caused by the underground black coal excavation. The interferometry images processed from the repeat-pass Synthetic Aperture Radar (SAR) systems give the spatial image of the terrain subjected to the surface subsidence over mining areas. Until now, the InSAR methods using data from the SAR Systems like ERS-1/ERS-2 and Envisat-1 were limited to a repeat-pass cycle of 35-day only. Recently, the ESA launched Sentinel-1A and 1B, and together they can provide the InSAR coverage in a 6-day repeat cycle. The studied area was the Upper Silesian Coal Basin in Poland, where the underground coal mining causes continuous subsidence of terrain surface and mining tremors (mine-induced seismicity). The main problem was with overlapping the subsidence caused by the mining exploitation with the epicentre tremors. Based on the Sentinel SAR images, research was done in regard to the correlation between the short term ground subsidence range border and the mine-induced seismicity epicentres localisation.

Recent development of feature extraction and classification multispectral/hyperspectral images: a systematic literature review

NASA Astrophysics Data System (ADS)

Setiyoko, A.; Dharma, I. G. W. S.; Haryanto, T.

2017-01-01

Multispectral data and hyperspectral data acquired from satellite sensor have the ability in detecting various objects on the earth ranging from low scale to high scale modeling. These data are increasingly being used to produce geospatial information for rapid analysis by running feature extraction or classification process. Applying the most suited model for this data mining is still challenging because there are issues regarding accuracy and computational cost. This research aim is to develop a better understanding regarding object feature extraction and classification applied for satellite image by systematically reviewing related recent research projects. A method used in this research is based on PRISMA statement. After deriving important points from trusted sources, pixel based and texture-based feature extraction techniques are promising technique to be analyzed more in recent development of feature extraction and classification.

Mining hidden data to predict patient prognosis: texture feature extraction and machine learning in mammography

NASA Astrophysics Data System (ADS)

Leighs, J. A.; Halling-Brown, M. D.; Patel, M. N.

2018-03-01

The UK currently has a national breast cancer-screening program and images are routinely collected from a number of screening sites, representing a wealth of invaluable data that is currently under-used. Radiologists evaluate screening images manually and recall suspicious cases for further analysis such as biopsy. Histological testing of biopsy samples confirms the malignancy of the tumour, along with other diagnostic and prognostic characteristics such as disease grade. Machine learning is becoming increasingly popular for clinical image classification problems, as it is capable of discovering patterns in data otherwise invisible. This is particularly true when applied to medical imaging features; however clinical datasets are often relatively small. A texture feature extraction toolkit has been developed to mine a wide range of features from medical images such as mammograms. This study analysed a dataset of 1,366 radiologist-marked, biopsy-proven malignant lesions obtained from the OPTIMAM Medical Image Database (OMI-DB). Exploratory data analysis methods were employed to better understand extracted features. Machine learning techniques including Classification and Regression Trees (CART), ensemble methods (e.g. random forests), and logistic regression were applied to the data to predict the disease grade of the analysed lesions. Prediction scores of up to 83% were achieved; sensitivity and specificity of the models trained have been discussed to put the results into a clinical context. The results show promise in the ability to predict prognostic indicators from the texture features extracted and thus enable prioritisation of care for patients at greatest risk.

MSL: Facilitating automatic and physical analysis of published scientific literature in PDF format

PubMed Central

Ahmed, Zeeshan; Dandekar, Thomas

2018-01-01

Published scientific literature contains millions of figures, including information about the results obtained from different scientific experiments e.g. PCR-ELISA data, microarray analysis, gel electrophoresis, mass spectrometry data, DNA/RNA sequencing, diagnostic imaging (CT/MRI and ultrasound scans), and medicinal imaging like electroencephalography (EEG), magnetoencephalography (MEG), echocardiography  (ECG), positron-emission tomography (PET) images. The importance of biomedical figures has been widely recognized in scientific and medicine communities, as they play a vital role in providing major original data, experimental and computational results in concise form. One major challenge for implementing a system for scientific literature analysis is extracting and analyzing text and figures from published PDF files by physical and logical document analysis. Here we present a product line architecture based bioinformatics tool ‘Mining Scientific Literature (MSL)’, which supports the extraction of text and images by interpreting all kinds of published PDF files using advanced data mining and image processing techniques. It provides modules for the marginalization of extracted text based on different coordinates and keywords, visualization of extracted figures and extraction of embedded text from all kinds of biological and biomedical figures using applied Optimal Character Recognition (OCR). Moreover, for further analysis and usage, it generates the system’s output in different formats including text, PDF, XML and images files. Hence, MSL is an easy to install and use analysis tool to interpret published scientific literature in PDF format. PMID:29721305

Using Local Event Tomography to Image Changes in the Rock Mass in the Kiirunavaara Iron Ore Mine, Northern Sweden

NASA Astrophysics Data System (ADS)

Lund, B.; Berglund, K.; Tryggvason, A.; Dineva, S.; Jonsson, L.

2017-12-01

Although induced seismic events in a mining environment are a potential hazard, they can be used to gain information about the rock mass in the mine which otherwise would be very difficult to obtain. In this study we use approximately 1.2 million mining induced seismic events in the Kiirunavaara iron ore mine in northernmost Sweden to image the rock mass using local event travel-time tomography. The Kiirunavaara mine is the largest underground iron ore mine in the world. The ore body is a magnetite sheet of 4 km length, with an average thickness of 80 m, which dips approximately 55° to the east. The events are of various origins such as shear slip on fractures, non-shear events and blasts, with magnitudes of up to 2.5. We use manually picked P- and S-wave arrival times from the routine processing in the tomography and we require that both phases are present at at least five geophones. For the tomography we use the 3D local earthquake tomography code PStomo_eq (Tryggvason et al., 2002), which we adjusted to the mining scale. The tomographic images show clearly defined regions of high and low velocities. Prominent low S-velocity zones are associated with mapped clay zones. Regions of ore where mining is ongoing and the near-ore tunnel infrastructure in the foot-wall also show generally low P- and S-velocities. The ore at depths below the current mining levels is imaged both as a low S-velocity zone but even more pronounced as a high Vp/Vs ratio zone. The tomography shows higher P- and S-velocities in the foot-wall away from the areas of mine infrastructure. We relocate all 1.2 million events in the new 3D velocity model. The relocation significantly enhances the clarity of the event distribution in space and we can much more easily identify seismically active structures, such as e.g. the deformation of the ore passes. The large number of events makes it possible to do detailed studies of the temporal evolution of stability in the mine. We present preliminary results of time-lapse tomography in an area where a few events of magnitude 2+ occurred in September 2015. We also image temporal velocity changes around the mining front at depth.

Multispectral image fusion for detecting land mines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clark, G.A.; Sengupta, S.K.; Aimonetti, W.D.

1995-04-01

This report details a system which fuses information contained in registered images from multiple sensors to reduce the effects of clutter and improve the ability to detect surface and buried land mines. The sensor suite currently consists of a camera that acquires images in six bands (400nm, 500nm, 600nm, 700nm, 800nm and 900nm). Past research has shown that it is extremely difficult to distinguish land mines from background clutter in images obtained from a single sensor. It is hypothesized, however, that information fused from a suite of various sensors is likely to provide better detection reliability, because the suite ofmore » sensors detects a variety of physical properties that are more separable in feature space. The materials surrounding the mines can include natural materials (soil, rocks, foliage, water, etc.) and some artifacts.« less

Enhancement of the visibility of objects located below the surface of a scattering medium

DOEpatents

Demos, Stavros

2013-11-19

Techniques are provided for enhancing the visibility of objects located below the surface of a scattering medium such as tissue, water and smoke. Examples of such an object include a vein located below the skin, a mine located below the surface of the sea and a human in a location covered by smoke. The enhancement of the image contrast of a subsurface structure is based on the utilization of structured illumination. In the specific application of this invention to image the veins in the arm or other part of the body, the issue of how to control the intensity of the image of a metal object (such as a needle) that must be inserted into the vein is also addressed.

Clinical applications of textural analysis in non-small cell lung cancer.

PubMed

Phillips, Iain; Ajaz, Mazhar; Ezhil, Veni; Prakash, Vineet; Alobaidli, Sheaka; McQuaid, Sarah J; South, Christopher; Scuffham, James; Nisbet, Andrew; Evans, Philip

2018-01-01

Lung cancer is the leading cause of cancer mortality worldwide. Treatment pathways include regular cross-sectional imaging, generating large data sets which present intriguing possibilities for exploitation beyond standard visual interpretation. This additional data mining has been termed "radiomics" and includes semantic and agnostic approaches. Textural analysis (TA) is an example of the latter, and uses a range of mathematically derived features to describe an image or region of an image. Often TA is used to describe a suspected or known tumour. TA is an attractive tool as large existing image sets can be submitted to diverse techniques for data processing, presentation, interpretation and hypothesis testing with annotated clinical outcomes. There is a growing anthology of published data using different TA techniques to differentiate between benign and malignant lung nodules, differentiate tissue subtypes of lung cancer, prognosticate and predict outcome and treatment response, as well as predict treatment side effects and potentially aid radiotherapy planning. The aim of this systematic review is to summarize the current published data and understand the potential future role of TA in managing lung cancer.

A strategy for selecting data mining techniques in metabolomics.

PubMed

Banimustafa, Ahmed Hmaidan; Hardy, Nigel W

2012-01-01

There is a general agreement that the development of metabolomics depends not only on advances in chemical analysis techniques but also on advances in computing and data analysis methods. Metabolomics data usually requires intensive pre-processing, analysis, and mining procedures. Selecting and applying such procedures requires attention to issues including justification, traceability, and reproducibility. We describe a strategy for selecting data mining techniques which takes into consideration the goals of data mining techniques on the one hand, and the goals of metabolomics investigations and the nature of the data on the other. The strategy aims to ensure the validity and soundness of results and promote the achievement of the investigation goals.

Quantifying Forest and Coastal Disturbance from Industrial Mining Using Satellite Time Series Analysis Under Very Cloudy Conditions

NASA Astrophysics Data System (ADS)

Alonzo, M.; Van Den Hoek, J.; Ahmed, N.

2015-12-01

The open-pit Grasberg mine, located in the highlands of Western Papua, Indonesia, and operated by PT Freeport Indonesia (PT-FI), is among the world's largest in terms of copper and gold production. Over the last 27 years, PT-FI has used the Ajkwa River to transport an estimated 1.3 billion tons of tailings from the mine into the so-called Ajkwa Deposition Area (ADA). The ADA is the product of aggradation and lateral expansion of the Ajkwa River into the surrounding lowland rainforest and mangroves, which include species important to the livelihoods of indigenous Papuans. Mine tailings that do not settle in the ADA disperse into the Arafura Sea where they increase levels of suspended particulate matter (SPM) and associated concentrations of dissolved copper. Despite the mine's large-scale operations, ecological impact of mine tailings deposition on the forest and estuarial ecosystems have received minimal formal study. While ground-based inquiries are nearly impossible due to access restrictions, assessment via satellite remote sensing is promising but hindered by extreme cloud cover. In this study, we characterize ridgeline-to-coast environmental impacts along the Ajkwa River, from the Grasberg mine to the Arafura Sea between 1987 and 2014. We use "all available" Landsat TM and ETM+ images collected over this time period to both track pixel-level vegetation disturbance and monitor changes in coastal SPM levels. Existing temporal segmentation algorithms are unable to assess both acute and protracted trajectories of vegetation change due to pervasive cloud cover. In response, we employ robust, piecewise linear regression on noisy vegetation index (NDVI) data in a manner that is relatively insensitive to atmospheric contamination. Using this disturbance detection technique we constructed land cover histories for every pixel, based on 199 image dates, to differentiate processes of vegetation decline, disturbance, and regrowth. Using annual reports from PT-FI, we show that the changing extent and spatial patterns of riparian vegetation disturbance directly correlate with yearly tailings production rates. While the rate of vegetation disturbance decreased after 1998, SPM levels along the Arafura coast increased, suggesting the failure of PT-FI to fully confine tailings to the ADA.

Data mining techniques for assisting the diagnosis of pressure ulcer development in surgical patients.

PubMed

Su, Chao-Ton; Wang, Pa-Chun; Chen, Yan-Cheng; Chen, Li-Fei

2012-08-01

Pressure ulcer is a serious problem during patient care processes. The high risk factors in the development of pressure ulcer remain unclear during long surgery. Moreover, past preventive policies are hard to implement in a busy operation room. The objective of this study is to use data mining techniques to construct the prediction model for pressure ulcers. Four data mining techniques, namely, Mahalanobis Taguchi System (MTS), Support Vector Machines (SVMs), decision tree (DT), and logistic regression (LR), are used to select the important attributes from the data to predict the incidence of pressure ulcers. Measurements of sensitivity, specificity, F(1), and g-means were used to compare the performance of four classifiers on the pressure ulcer data set. The results show that data mining techniques obtain good results in predicting the incidence of pressure ulcer. We can conclude that data mining techniques can help identify the important factors and provide a feasible model to predict pressure ulcer development.

Visual information mining in remote sensing image archives

NASA Astrophysics Data System (ADS)

Pelizzari, Andrea; Descargues, Vincent; Datcu, Mihai P.

2002-01-01

The present article focuses on the development of interactive exploratory tools for visually mining the image content in large remote sensing archives. Two aspects are treated: the iconic visualization of the global information in the archive and the progressive visualization of the image details. The proposed methods are integrated in the Image Information Mining (I2M) system. The images and image structure in the I2M system are indexed based on a probabilistic approach. The resulting links are managed by a relational data base. Both the intrinsic complexity of the observed images and the diversity of user requests result in a great number of associations in the data base. Thus new tools have been designed to visualize, in iconic representation the relationships created during a query or information mining operation: the visualization of the query results positioned on the geographical map, quick-looks gallery, visualization of the measure of goodness of the query, visualization of the image space for statistical evaluation purposes. Additionally the I2M system is enhanced with progressive detail visualization in order to allow better access for operator inspection. I2M is a three-tier Java architecture and is optimized for the Internet.

Mechanisms of Arsenic Sequestration by Prosopis juliflora during the Phytostabilization of Metalliferous Mine Tailings.

PubMed

Hammond, Corin M; Root, Robert A; Maier, Raina M; Chorover, Jon

2018-02-06

Phytostabilization is a cost-effective long-term bioremediation technique for the immobilization of metalliferous mine tailings. However, the biogeochemical processes affecting metal(loid) molecular stabilization and mobility in the root zone remain poorly resolved. The roots of Prosopis juliflora grown for up to 36 months in compost-amended pyritic mine tailings from a federal Superfund site were investigated by microscale and bulk synchrotron X-ray absorption spectroscopy (XAS) and multiple energy micro-X-ray fluorescence imaging to determine iron, arsenic, and sulfur speciation, abundance, and spatial distribution. Whereas ferrihydrite-bound As(V) species predominated in the initial bulk mine tailings, the rhizosphere speciation of arsenic was distinctly different. Root-associated As(V) was immobilized on the root epidermis bound to ferric sulfate precipitates and within root vacuoles as trivalent As(III)-(SR) 3  tris-thiolate complexes. Molar Fe-to-As ratios of root epidermis tissue were two times higher than the 15% compost-amended bulk tailings growth medium. Rhizoplane-associated ferric sulfate phases that showed a high capacity to scavenge As(V) were dissimilar from the bulk-tailings mineralogy as shown by XAS and X-ray diffraction, indicating a root-surface mechanism for their formation or accumulation.

Non-Invasive Imaging of Reactor Cores Using Cosmic Ray Muons

NASA Astrophysics Data System (ADS)

Milner, Edward

2011-10-01

Cosmic ray muons penetrate deeply in material, with some passing completely through very thick objects. This penetrating quality is the basis of two distinct, but related imaging techniques. The first measures the number of cosmic ray muons transmitted through parts of an object. Relatively fewer muons are absorbed along paths in which they encounter less material, compared to higher density paths, so the relative density of material is measured. This technique is called muon transmission imaging, and has been used to infer the density and structure of a variety of large masses, including mine overburden, volcanoes, pyramids, and buildings. In a second, more recently developed technique, the angular deflection of muons is measured by trajectory-tracking detectors placed on two opposing sides of an object. Muons are deflected more strongly by heavy nuclei, since multiple Coulomb scattering angle is approximately proportional to the nuclear charge. Therefore, a map showing regions of large deflection will identify the location of uranium in contrast to lighter nuclei. This technique is termed muon scattering tomography (MST) and has been developed to screen shipping containers for the presence of concealed nuclear material. Both techniques are a good way of non-invasively inspecting objects. A previously unexplored topic was applying MST to imaging large objects. Here we demonstrate extending the MST technique to the task of identifying relatively thick objects inside very thick shielding. We measured cosmic ray muons passing through a physical arrangement of material similar to a nuclear reactor, with thick concrete shielding and a heavy metal core. Newly developed algorithms were used to reconstruct an image of the ``mock reactor core,'' with resolution of approximately 30 cm.

Processing of multispectral thermal IR data for geologic applications

NASA Technical Reports Server (NTRS)

Kahle, A. B.; Madura, D. P.; Soha, J. M.

1979-01-01

Multispectral thermal IR data were acquired with a 24-channel scanner flown in an aircraft over the E. Tintic Utah mining district. These digital image data required extensive computer processing in order to put the information into a format useful for a geologic photointerpreter. Simple enhancement procedures were not sufficient to reveal the total information content because the data were highly correlated in all channels. The data were shown to be dominated by temperature variations across the scene, while the much more subtle spectral variations between the different rock types were of interest. The image processing techniques employed to analyze these data are described.

Diavik Mine, Canada

NASA Image and Video Library

2017-04-13

This image from NASA Terra spacecraft shows the Diavik Mine in northern Canada.The largest diamond found in North America came from the Diavik Mine. The Foxfire diamond weighs an impressive 187 carats, and was discovered in August 2015; it has been displayed in several museums throughout North America. The Diavik mine is located on an island in Lac de Gras, within the Lac de Gras kimberlite field, among other diamond mines. The image was acquired September 23, 2016, covers an area of 13.8 by 19.4 km, and is located at 64.5 degrees north, 110.2 degrees west. https://photojournal.jpl.nasa.gov/catalog/PIA21536

Goldstrike Mine, Nevada

NASA Image and Video Library

2017-05-15

This image from NASA Terra spacecraft shows Goldstrike in northeast Nevada, the largest gold mine in North America. The mine complex, (including the Betze-Post-Screamer open-pit, and Meikle and Rodeo underground mines) is owned and operated by the world's largest gold mining company, Barrick Gold. Gold occurs as microscopically fine grains, with an average grade of 0.1 ounces per ton of ore. Estimates of reserves are as high as 35 million ounces of gold. The image was acquired September 25, 2010, covers an area of 15 by 15 km, and is located at 41 degrees north, 116.4 degrees west. https://photojournal.jpl.nasa.gov/catalog/PIA21665

Breast Imaging in the Era of Big Data: Structured Reporting and Data Mining.

PubMed

Margolies, Laurie R; Pandey, Gaurav; Horowitz, Eliot R; Mendelson, David S

2016-02-01

The purpose of this article is to describe structured reporting and the development of large databases for use in data mining in breast imaging. The results of millions of breast imaging examinations are reported with structured tools based on the BI-RADS lexicon. Much of these data are stored in accessible media. Robust computing power creates great opportunity for data scientists and breast imagers to collaborate to improve breast cancer detection and optimize screening algorithms. Data mining can create knowledge, but the questions asked and their complexity require extremely powerful and agile databases. New data technologies can facilitate outcomes research and precision medicine.

Introduction to the JASIST Special Topic Issue on Web Retrieval and Mining: A Machine Learning Perspective.

ERIC Educational Resources Information Center

Chen, Hsinchun

2003-01-01

Discusses information retrieval techniques used on the World Wide Web. Topics include machine learning in information extraction; relevance feedback; information filtering and recommendation; text classification and text clustering; Web mining, based on data mining techniques; hyperlink structure; and Web size. (LRW)

Site Characterization for Remote Minefield Detection Scanner (REMIDS) system Data Acquisition

DTIC Science & Technology

1991-04-01

pattern - Standard A ) (US Army Engineer School 1988 ). This pattern dictates two straight rows of mines at each end of the area located 100 m apart...Westpoint, NY. Cespedes, E. R., Goodson, R. A ., and Ginsberg, I. W. 1988 (April). "Multi- sensor Image Processing Techniques for Real-Time Standoff...Monterey, CA. Gleason, H. A ., and Cronquist , A . 1963. Manual of Vascular Plants, D. Van Nostrand Co., New York. Goodson, R. A ., Cress, D. H., and

Big, Deep, and Smart Data in Scanning Probe Microscopy.

PubMed

Kalinin, Sergei V; Strelcov, Evgheni; Belianinov, Alex; Somnath, Suhas; Vasudevan, Rama K; Lingerfelt, Eric J; Archibald, Richard K; Chen, Chaomei; Proksch, Roger; Laanait, Nouamane; Jesse, Stephen

2016-09-27

Scanning probe microscopy (SPM) techniques have opened the door to nanoscience and nanotechnology by enabling imaging and manipulation of the structure and functionality of matter at nanometer and atomic scales. Here, we analyze the scientific discovery process in SPM by following the information flow from the tip-surface junction, to knowledge adoption by the wider scientific community. We further discuss the challenges and opportunities offered by merging SPM with advanced data mining, visual analytics, and knowledge discovery technologies.

Multi-objects recognition for distributed intelligent sensor networks

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Cao, Yuan; Desai, Sachi; Hohil, Myron E.

2008-04-01

This paper proposes an innovative approach for multi-objects recognition for homeland security and defense based intelligent sensor networks. Unlike the conventional way of information analysis, data mining in such networks is typically characterized with high information ambiguity/uncertainty, data redundancy, high dimensionality and real-time constrains. Furthermore, since a typical military based network normally includes multiple mobile sensor platforms, ground forces, fortified tanks, combat flights, and other resources, it is critical to develop intelligent data mining approaches to fuse different information resources to understand dynamic environments, to support decision making processes, and finally to achieve the goals. This paper aims to address these issues with a focus on multi-objects recognition. Instead of classifying a single object as in the traditional image classification problems, the proposed method can automatically learn multiple objectives simultaneously. Image segmentation techniques are used to identify the interesting regions in the field, which correspond to multiple objects such as soldiers or tanks. Since different objects will come with different feature sizes, we propose a feature scaling method to represent each object in the same number of dimensions. This is achieved by linear/nonlinear scaling and sampling techniques. Finally, support vector machine (SVM) based learning algorithms are developed to learn and build the associations for different objects, and such knowledge will be adaptively accumulated for objects recognition in the testing stage. We test the effectiveness of proposed method in different simulated military environments.

User Oriented Platform for Data Analytics in Medical Imaging Repositories.

PubMed

Valerio, Miguel; Godinho, Tiago Marques; Costa, Carlos

2016-01-01

The production of medical imaging studies and associated data has been growing in the last decades. Their primary use is to support medical diagnosis and treatment processes. However, the secondary use of the tremendous amount of stored data is generally more limited. Nowadays, medical imaging repositories have turned into rich databanks holding not only the images themselves, but also a wide range of metadata related to the medical practice. Exploring these repositories through data analysis and business intelligence techniques has the potential of increasing the efficiency and quality of the medical practice. Nevertheless, the continuous production of tremendous amounts of data makes their analysis difficult by conventional approaches. This article proposes a novel automated methodology to derive knowledge from medical imaging repositories that does not disrupt the regular medical practice. Our method is able to apply statistical analysis and business intelligence techniques directly on top of live institutional repositories. It is a Web-based solution that provides extensive dashboard capabilities, including complete charting and reporting options, combined with data mining components. Moreover, it enables the operator to set a wide multitude of query parameters and operators through the use of an intuitive graphical interface.

Automated localization and segmentation techniques for B-mode ultrasound images: A review.

PubMed

Meiburger, Kristen M; Acharya, U Rajendra; Molinari, Filippo

2018-01-01

B-mode ultrasound imaging is used extensively in medicine. Hence, there is a need to have efficient segmentation tools to aid in computer-aided diagnosis, image-guided interventions, and therapy. This paper presents a comprehensive review on automated localization and segmentation techniques for B-mode ultrasound images. The paper first describes the general characteristics of B-mode ultrasound images. Then insight on the localization and segmentation of tissues is provided, both in the case in which the organ/tissue localization provides the final segmentation and in the case in which a two-step segmentation process is needed, due to the desired boundaries being too fine to locate from within the entire ultrasound frame. Subsequenly, examples of some main techniques found in literature are shown, including but not limited to shape priors, superpixel and classification, local pixel statistics, active contours, edge-tracking, dynamic programming, and data mining. Ten selected applications (abdomen/kidney, breast, cardiology, thyroid, liver, vascular, musculoskeletal, obstetrics, gynecology, prostate) are then investigated in depth, and the performances of a few specific applications are compared. In conclusion, future perspectives for B-mode based segmentation, such as the integration of RF information, the employment of higher frequency probes when possible, the focus on completely automatic algorithms, and the increase in available data are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Challenges and Opportunities for Extracting Cardiovascular Risk Biomarkers from Imaging Data

NASA Astrophysics Data System (ADS)

Kakadiaris, I. A.; Mendizabal-Ruiz, E. G.; Kurkure, U.; Naghavi, M.

Complications attributed to cardiovascular diseases (CDV) are the leading cause of death worldwide. In the United States, sudden heart attack remains the number one cause of death and accounts for the majority of the 280 billion burden of cardiovascular diseases. In spite of the advancements in cardiovascular imaging techniques, the rate of deaths due to unpredicted heart attack remains high. Thus, novel computational tools are of critical need, in order to mine quantitative parameters from the imaging data for early detection of persons with a high likelihood of developing a heart attack in the near future (vulnerable patients). In this paper, we present our progress in the research of computational methods for the extraction of cardiovascular risk biomarkers from cardiovascular imaging data. In particular, we focus on the methods developed for the analysis of intravascular ultrasound (IVUS) data.

Zebra Crossing Spotter: Automatic Population of Spatial Databases for Increased Safety of Blind Travelers

PubMed Central

Ahmetovic, Dragan; Manduchi, Roberto; Coughlan, James M.; Mascetti, Sergio

2016-01-01

In this paper we propose a computer vision-based technique that mines existing spatial image databases for discovery of zebra crosswalks in urban settings. Knowing the location of crosswalks is critical for a blind person planning a trip that includes street crossing. By augmenting existing spatial databases (such as Google Maps or OpenStreetMap) with this information, a blind traveler may make more informed routing decisions, resulting in greater safety during independent travel. Our algorithm first searches for zebra crosswalks in satellite images; all candidates thus found are validated against spatially registered Google Street View images. This cascaded approach enables fast and reliable discovery and localization of zebra crosswalks in large image datasets. While fully automatic, our algorithm could also be complemented by a final crowdsourcing validation stage for increased accuracy. PMID:26824080

Histological Image Feature Mining Reveals Emergent Diagnostic Properties for Renal Cancer

PubMed Central

Kothari, Sonal; Phan, John H.; Young, Andrew N.; Wang, May D.

2016-01-01

Computer-aided histological image classification systems are important for making objective and timely cancer diagnostic decisions. These systems use combinations of image features that quantify a variety of image properties. Because researchers tend to validate their diagnostic systems on specific cancer endpoints, it is difficult to predict which image features will perform well given a new cancer endpoint. In this paper, we define a comprehensive set of common image features (consisting of 12 distinct feature subsets) that quantify a variety of image properties. We use a data-mining approach to determine which feature subsets and image properties emerge as part of an “optimal” diagnostic model when applied to specific cancer endpoints. Our goal is to assess the performance of such comprehensive image feature sets for application to a wide variety of diagnostic problems. We perform this study on 12 endpoints including 6 renal tumor subtype endpoints and 6 renal cancer grade endpoints. Keywords-histology, image mining, computer-aided diagnosis PMID:28163980

Efficient mining of association rules for the early diagnosis of Alzheimer's disease

NASA Astrophysics Data System (ADS)

Chaves, R.; Górriz, J. M.; Ramírez, J.; Illán, I. A.; Salas-Gonzalez, D.; Gómez-Río, M.

2011-09-01

In this paper, a novel technique based on association rules (ARs) is presented in order to find relations among activated brain areas in single photon emission computed tomography (SPECT) imaging. In this sense, the aim of this work is to discover associations among attributes which characterize the perfusion patterns of normal subjects and to make use of them for the early diagnosis of Alzheimer's disease (AD). Firstly, voxel-as-feature-based activation estimation methods are used to find the tridimensional activated brain regions of interest (ROIs) for each patient. These ROIs serve as input to secondly mine ARs with a minimum support and confidence among activation blocks by using a set of controls. In this context, support and confidence measures are related to the proportion of functional areas which are singularly and mutually activated across the brain. Finally, we perform image classification by comparing the number of ARs verified by each subject under test to a given threshold that depends on the number of previously mined rules. Several classification experiments were carried out in order to evaluate the proposed methods using a SPECT database that consists of 41 controls (NOR) and 56 AD patients labeled by trained physicians. The proposed methods were validated by means of the leave-one-out cross validation strategy, yielding up to 94.87% classification accuracy, thus outperforming recent developed methods for computer aided diagnosis of AD.

Passive IR polarization sensors: a new technology for mine detection

NASA Astrophysics Data System (ADS)

Barbour, Blair A.; Jones, Michael W.; Barnes, Howard B.; Lewis, Charles P.

1998-09-01

The problem of mine and minefield detection continues to provide a significant challenge to sensor systems. Although the various sensor technologies (infrared, ground penetrating radar, etc.) may excel in certain situations there does not exist a single sensor technology that can adequately detect mines in all conditions such as time of day, weather, buried or surface laid, etc. A truly robust mine detection system will likely require the fusion of data from multiple sensor technologies. The performance of these systems, however, will ultimately depend on the performance of the individual sensors. Infrared (IR) polarimetry is a new and innovative sensor technology that adds substantial capabilities to the detection of mines. IR polarimetry improves on basic IR imaging by providing improved spatial resolution of the target, an inherent ability to suppress clutter, and the capability for zero (Delta) T imaging. Nichols Research Corporation (Nichols) is currently evaluating the effectiveness of IR polarization for mine detection. This study is partially funded by the U.S. Army Night Vision & Electronic Sensors Directorate (NVESD). The goal of the study is to demonstrate, through phenomenology studies and limited field trials, that IR polarizaton outperforms conventional IR imaging in the mine detection arena.

76 FR 51274 - Supplemental Nutrition Assistance Program: Major System Failures

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-18

... data mining as necessary to determine if losses are occurring in the process of issuing benefits. It is... further by using data mining techniques on States' data or analyzing QC data for error patterns that may... conjunction with an additional sample of cases. Data mining techniques may be employed when QC data cannot...

Assessment of practicality of remote sensing techniques for a study of the effects of strip mining in Alabama

NASA Technical Reports Server (NTRS)

Hughes, T. H.; Dillion, A. C., III; White, J. R., Jr.; Drummond, S. E., Jr.; Hooks, W. G.

1975-01-01

Because of the volume of coal produced by strip mining, the proximity of mining operations, and the diversity of mining methods (e.g. contour stripping, area stripping, multiple seam stripping, and augering, as well as underground mining), the Warrior Coal Basin seemed best suited for initial studies on the physical impact of strip mining in Alabama. Two test sites, (Cordova and Searles) representative of the various strip mining techniques and environmental problems, were chosen for intensive studies of the correlation between remote sensing and ground truth data. Efforts were eventually concentrated in the Searles Area, since it is more accessible and offers a better opportunity for study of erosional and depositional processes than the Cordova Area.

Environmental characterisation of coal mine waste rock in the field: an example from New Zealand

NASA Astrophysics Data System (ADS)

Hughes, J.; Craw, D.; Peake, B.; Lindsay, P.; Weber, P.

2007-08-01

Characterisation of mine waste rock with respect to acid generation potential is a necessary part of routine mine operations, so that environmentally benign waste rock stacks can be constructed for permanent storage. Standard static characterisation techniques, such as acid neutralisation capacity (ANC), maximum potential acidity, and associated acid-base accounting, require laboratory tests that can be difficult to obtain rapidly at remote mine sites. We show that a combination of paste pH and a simple portable carbonate dissolution test, both techniques that can be done in the field in a 15 min time-frame, is useful for distinguishing rocks that are potentially acid-forming from those that are acid-neutralising. Use of these techniques could allow characterisation of mine wastes at the metre scale during mine excavation operations. Our application of these techniques to pyrite-bearing (total S = 1-4 wt%) but variably calcareous coal mine overburden shows that there is a strong correlation between the portable carbonate dissolution technique and laboratory-determined ANC measurements (range of 0-10 wt% calcite equivalent). Paste pH measurements on the same rocks are bimodal, with high-sulphur, low-calcite rocks yielding pH near 3 after 10 min, whereas high-ANC rocks yield paste pH of 7-8. In our coal mine example, the field tests were most effective when used in conjunction with stratigraphy. However, the same field tests have potential for routine use in any mine in which distinction of acid-generating rocks from acid-neutralising rocks is required. Calibration of field-based acid-base accounting characteristics of the rocks with laboratory-based static and/or kinetic tests is still necessary.

Semi-automated based ground-truthing GUI for airborne imagery

NASA Astrophysics Data System (ADS)

Phan, Chung; Lydic, Rich; Moore, Tim; Trang, Anh; Agarwal, Sanjeev; Tiwari, Spandan

2005-06-01

Over the past several years, an enormous amount of airborne imagery consisting of various formats has been collected and will continue into the future to support airborne mine/minefield detection processes, improve algorithm development, and aid in imaging sensor development. The ground-truthing of imagery is a very essential part of the algorithm development process to help validate the detection performance of the sensor and improving algorithm techniques. The GUI (Graphical User Interface) called SemiTruth was developed using Matlab software incorporating signal processing, image processing, and statistics toolboxes to aid in ground-truthing imagery. The semi-automated ground-truthing GUI is made possible with the current data collection method, that is including UTM/GPS (Universal Transverse Mercator/Global Positioning System) coordinate measurements for the mine target and fiducial locations on the given minefield layout to support in identification of the targets on the raw imagery. This semi-automated ground-truthing effort has developed by the US Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD), Countermine Division, Airborne Application Branch with some support by the University of Missouri-Rolla.

Application of EREP imagery to fracture-related mine safety hazards in coal mining and mining-environmental problems in Indiana. [Indiana and Illinois

NASA Technical Reports Server (NTRS)

Wier, C. E. (Principal Investigator); Powell, R. L.; Amato, R. V.; Russell, O. R.; Martin, K. R.

1975-01-01

The author has identified the following significant results. This investigation evaluated the applicability of a variety of sensor types, formats, and resolution capabilities to the study of both fuel and nonfuel mined lands. The image reinforcement provided by stereo viewing of the EREP images proved useful for identifying lineaments and for mined lands mapping. Skylab S190B color and color infrared transparencies were the most useful EREP imagery. New information on lineament and fracture patterns in the bedrock of Indiana and Illinois extracted from analysis of the Skylab imagery has contributed to furthering the geological understanding of this portion of the Illinois basin.

The Analysis of Object-Based Change Detection in Mining Area: a Case Study with Pingshuo Coal Mine

NASA Astrophysics Data System (ADS)

Zhang, M.; Zhou, W.; Li, Y.

2017-09-01

Accurate information on mining land use and land cover change are crucial for monitoring and environmental change studies. In this paper, RapidEye Remote Sensing Image (Map 2012) and SPOT7 Remote Sensing Image (Map 2015) in Pingshuo Mining Area are selected to monitor changes combined with object-based classification and change vector analysis method, we also used R in highresolution remote sensing image for mining land classification, and found the feasibility and the flexibility of open source software. The results show that (1) the classification of reclaimed mining land has higher precision, the overall accuracy and kappa coefficient of the classification of the change region map were 86.67 % and 89.44 %. It's obvious that object-based classification and change vector analysis which has a great significance to improve the monitoring accuracy can be used to monitor mining land, especially reclaiming mining land; (2) the vegetation area changed from 46 % to 40 % accounted for the proportion of the total area from 2012 to 2015, and most of them were transformed into the arable land. The sum of arable land and vegetation area increased from 51 % to 70 %; meanwhile, build-up land has a certain degree of increase, part of the water area was transformed into arable land, but the extent of the two changes is not obvious. The result illustrated the transformation of reclaimed mining area, at the same time, there is still some land convert to mining land, and it shows the mine is still operating, mining land use and land cover are the dynamic procedure.

Using imaging spectroscopy to map acidic mine waste

USGS Publications Warehouse

Swayze, G.A.; Smith, K.S.; Clark, R.N.; Sutley, S.J.; Pearson, R.M.; Vance, J.S.; Hageman, P.L.; Briggs, P.H.; Meier, A.L.; Singleton, M.J.; Roth, S.

2000-01-01

The process of pyrite oxidation at the surface of mine waste may produce acidic water that is gradually neutralized as it drains away from the waste, depositing different Fe-bearing secondary minerals in roughly concentric zones that emanate from mine-waste piles. These Fe-bearing minerals are indicators of the geochemical conditions under which they form. Airborne and orbital imaging spectrometers can be used to map these mineral zones because each of these Fe-bearing secondary minerals is spectrally unique. In this way, imaging spectroscopy can be used to rapidly screen entire mining districts for potential sources of surface acid drainage and to detect acid producing minerals in mine waste or unmined rock outcrops. Spectral data from the AVIRIS instrument were used to evaluate mine waste at the California Gulch Superfund Site near Leadville, CO. Laboratory leach tests of surface samples show that leachate pH is most acidic and metals most mobile in samples from the inner jarosite zone and that leachate pH is near-neutral and metals least mobile in samples from the outer goethite zone.

Research on preventive technologies for bed-separation water hazard in China coal mines

NASA Astrophysics Data System (ADS)

Gui, Herong; Tong, Shijie; Qiu, Weizhong; Lin, Manli

2018-03-01

Bed-separation water is one of the major water hazards in coal mines. Targeted researches on the preventive technologies are of paramount importance to safe mining. This article studied the restrictive effect of geological and mining factors, such as lithological properties of roof strata, coal seam inclination, water source to bed separations, roof management method, dimensions of mining working face, and mining progress, on the formation of bed-separation water hazard. The key techniques to prevent bed-separation water-related accidents include interception, diversion, destructing the buffer layer, grouting and backfilling, etc. The operation and efficiency of each technique are corroborated in field engineering cases. The results of this study will offer reference to countries with similar mining conditions in the researches on bed-separation water burst and hazard control in coal mines.

Text Mining in Biomedical Domain with Emphasis on Document Clustering.

PubMed

Renganathan, Vinaitheerthan

2017-07-01

With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail. Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise.

Survey of Natural Language Processing Techniques in Bioinformatics.

PubMed

Zeng, Zhiqiang; Shi, Hua; Wu, Yun; Hong, Zhiling

2015-01-01

Informatics methods, such as text mining and natural language processing, are always involved in bioinformatics research. In this study, we discuss text mining and natural language processing methods in bioinformatics from two perspectives. First, we aim to search for knowledge on biology, retrieve references using text mining methods, and reconstruct databases. For example, protein-protein interactions and gene-disease relationship can be mined from PubMed. Then, we analyze the applications of text mining and natural language processing techniques in bioinformatics, including predicting protein structure and function, detecting noncoding RNA. Finally, numerous methods and applications, as well as their contributions to bioinformatics, are discussed for future use by text mining and natural language processing researchers.

Discovery and first models of the quadruply lensed quasar SDSS J1433+6007

NASA Astrophysics Data System (ADS)

Agnello, Adriano; Grillo, Claudio; Jones, Tucker; Treu, Tommaso; Bonamigo, Mario; Suyu, Sherry H.

2018-03-01

We report the discovery of the quadruply lensed quasar SDSS J1433+6007 (RA = 14:33:22.8, Dec. = +60:07:13.44), mined in the SDSS DR12 photometric catalogues using a novel outlier-selection technique, without prior spectroscopic or ultraviolet excess information. Discovery data obtained at the Nordic Optical Telescope (La Palma) show nearly identical quasar spectra at zs = 2.737 ± 0.003 and four quasar images in a fold configuration, one of which sits on a blue arc, with maximum separation 3.6 arcsec. The deflector redshift is zl = 0.407 ± 0.002, from Keck-ESI spectra. We describe the selection procedure, discovery and follow-up, image positions and BVRi magnitudes, and first results and forecasts from lens model fit to the relative image positions.

Phaedra, a protocol-driven system for analysis and validation of high-content imaging and flow cytometry.

PubMed

Cornelissen, Frans; Cik, Miroslav; Gustin, Emmanuel

2012-04-01

High-content screening has brought new dimensions to cellular assays by generating rich data sets that characterize cell populations in great detail and detect subtle phenotypes. To derive relevant, reliable conclusions from these complex data, it is crucial to have informatics tools supporting quality control, data reduction, and data mining. These tools must reconcile the complexity of advanced analysis methods with the user-friendliness demanded by the user community. After review of existing applications, we realized the possibility of adding innovative new analysis options. Phaedra was developed to support workflows for drug screening and target discovery, interact with several laboratory information management systems, and process data generated by a range of techniques including high-content imaging, multicolor flow cytometry, and traditional high-throughput screening assays. The application is modular and flexible, with an interface that can be tuned to specific user roles. It offers user-friendly data visualization and reduction tools for HCS but also integrates Matlab for custom image analysis and the Konstanz Information Miner (KNIME) framework for data mining. Phaedra features efficient JPEG2000 compression and full drill-down functionality from dose-response curves down to individual cells, with exclusion and annotation options, cell classification, statistical quality controls, and reporting.

Comparing digital data processing techniques for surface mine and reclamation monitoring

NASA Technical Reports Server (NTRS)

Witt, R. G.; Bly, B. G.; Campbell, W. J.; Bloemer, H. H. L.; Brumfield, J. O.

1982-01-01

The results of three techniques used for processing Landsat digital data are compared for their utility in delineating areas of surface mining and subsequent reclamation. An unsupervised clustering algorithm (ISOCLS), a maximum-likelihood classifier (CLASFY), and a hybrid approach utilizing canonical analysis (ISOCLS/KLTRANS/ISOCLS) were compared by means of a detailed accuracy assessment with aerial photography at NASA's Goddard Space Flight Center. Results show that the hybrid approach was superior to the traditional techniques in distinguishing strip mined and reclaimed areas.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chironis, N.P.

This book contains a wealth of valuable information carefully selected and compiled from recent issues of Coal Age magazine. Much of the source material has been gathered by Coal Age Editors during their visits to coal mines, research establishments, universities and technical symposiums. Equally important are the articles and data contributed by over 50 top experts, many of whom are well known to the mining industry. Specifically, this easy-to-use handbook is divided into eleven key areas of underground mining. Here you will find the latest information on continuous mining techniques, longwall and shortwall methods and equipment, specialized mining and boringmore » systems, continuous haulage techniques, improved roof control and ventilation methods, mine communications and instrumentation, power systems, fire control methods, and new mining regulations. There is also a section on engineering and management considerations, including the modern use of computer terminals, practical techniques for picking leaders and for encouraging more safety consciousness in employees, factors affecting absenteeism, and some highly important financial considerations. All of this valuable information has been thoroughly indexed to provide immediate access to the specific data needed by the reader.« less

A REAL-TIME COAL CONTENT/ORE GRADE (C2OC) SENSOR

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rand Swanson

2005-04-01

This is the final report of a three year DOE funded project titled ''A real-time coal content/ore grade (C{sub 2}OG) sensor''. The sensor, which is based on hyperspectral imaging technology, was designed to give a machine vision assay of ore or coal. Sensors were designed and built at Resonon, Inc., and then deployed at the Stillwater Mining Company core room in southcentral Montana for analyzing platinum/palladium ore and at the Montana Tech Spectroscopy Lab for analyzing coal and other materials. The Stillwater sensor imaged 91' of core and analyzed this data for surface sulfides which are considered to be pathfindermore » minerals for platinum/palladium at this mine. Our results indicate that the sensor could deliver a relative ore grade provided tool markings and iron oxidation were kept to a minimum. Coal, talc, and titanium sponge samples were also imaged and analyzed for content and grade with promising results. This research has led directly to a DOE SBIR Phase II award for Resonon to develop a down-hole imaging spectrometer based on the same imaging technology used in the Stillwater core room C{sub 2}OG sensor. The Stillwater Mining Company has estimated that this type of imaging system could lead to a 10% reduction in waste rock from their mine and provide a $650,000 benefit per year. The proposed system may also lead to an additional 10% of ore tonnage, which would provide a total economic benefit of more than $3.1 million per year. If this benefit could be realized on other metal ores for which the proposed technology is suitable, the possible economic benefits to U.S. mines is over $70 million per year. In addition to these currently lost economic benefits, there are also major energy losses from mining waste rock and environmental impacts from mining, processing, and disposing of waste rock.« less

Data mining in pharma sector: benefits.

PubMed

Ranjan, Jayanthi

2009-01-01

The amount of data getting generated in any sector at present is enormous. The information flow in the pharma industry is huge. Pharma firms are progressing into increased technology-enabled products and services. Data mining, which is knowledge discovery from large sets of data, helps pharma firms to discover patterns in improving the quality of drug discovery and delivery methods. The paper aims to present how data mining is useful in the pharma industry, how its techniques can yield good results in pharma sector, and to show how data mining can really enhance in making decisions using pharmaceutical data. This conceptual paper is written based on secondary study, research and observations from magazines, reports and notes. The author has listed the types of patterns that can be discovered using data mining in pharma data. The paper shows how data mining is useful in the pharma industry and how its techniques can yield good results in pharma sector. Although much work can be produced for discovering knowledge in pharma data using data mining, the paper is limited to conceptualizing the ideas and view points at this stage; future work may include applying data mining techniques to pharma data based on primary research using the available, famous significant data mining tools. Research papers and conceptual papers related to data mining in Pharma industry are rare; this is the motivation for the paper.

Data Mining in Child Welfare.

ERIC Educational Resources Information Center

Schoech, Dick; Quinn, Andrew; Rycraft, Joan R.

2000-01-01

Examines the historical and larger context of data mining and describes data mining processes, techniques, and tools. Illustrates these using a child welfare dataset concerning the employee turnover that is mined, using logistic regression and a Bayesian neural network. Discusses the data mining process, the resulting models, their predictive…

Text Mining in Biomedical Domain with Emphasis on Document Clustering

PubMed Central

2017-01-01

Objectives With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. Methods This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Results Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail. Conclusions Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise. PMID:28875048

The Weather Forecast Using Data Mining Research Based on Cloud Computing.

NASA Astrophysics Data System (ADS)

Wang, ZhanJie; Mazharul Mujib, A. B. M.

2017-10-01

Weather forecasting has been an important application in meteorology and one of the most scientifically and technologically challenging problem around the world. In my study, we have analyzed the use of data mining techniques in forecasting weather. This paper proposes a modern method to develop a service oriented architecture for the weather information systems which forecast weather using these data mining techniques. This can be carried out by using Artificial Neural Network and Decision tree Algorithms and meteorological data collected in Specific time. Algorithm has presented the best results to generate classification rules for the mean weather variables. The results showed that these data mining techniques can be enough for weather forecasting.

Utilizing Skylab data in on-going resources management programs in the state of Ohio

NASA Technical Reports Server (NTRS)

Baldridge, P. E. (Principal Investigator); Goesling, P. H.; Martin, T. A.; Wukelic, G. E.; Stephan, J. G.; Smail, H. E.; Ebbert, T. F.

1975-01-01

The author has identified the following significant results. The use of Skylab imagery for total area woodland surveys was found to be more accurate and cheaper than conventional surveys using aerial photo-plot techniques. Machine-aided (primarily density slicing) analyses of Skylab 190A and 190B color and infrared color photography demonstrated the feasibility of using such data for differentiating major timber classes including pines, hardwoods, mixed, cut, and brushland providing such analyses are made at scales of 1:24,000 and larger. Manual and machine-assisted image analysis indicated that spectral and spatial capabilities of Skylab EREP photography are adequate to distinguish most parameters of current, coal surface mining concern associated with: (1) active mining, (2) orphan lands, (3) reclaimed lands, and (4) active reclamation. Excellent results were achieved when comparing Skylab and aerial photographic interpretations of detailed surface mining features. Skylab photographs when combined with other data bases (e.g., census, agricultural land productivity, and transportation networks), provide a comprehensive, meaningful, and integrated view of major elements involved in the urbanization/encroachment process.

Mapping alteration minerals at prospect, outcrop and drill core scales using imaging spectrometry

PubMed Central

Kruse, Fred A.; L. Bedell, Richard; Taranik, James V.; Peppin, William A.; Weatherbee, Oliver; Calvin, Wendy M.

2011-01-01

Imaging spectrometer data (also known as ‘hyperspectral imagery’ or HSI) are well established for detailed mineral mapping from airborne and satellite systems. Overhead data, however, have substantial additional potential when used together with ground-based measurements. An imaging spectrometer system was used to acquire airborne measurements and to image in-place outcrops (mine walls) and boxed drill core and rock chips using modified sensor-mounting configurations. Data were acquired at 5 nm nominal spectral resolution in 360 channels from 0.4 to 2.45 μm. Analysis results using standardized hyperspectral methodologies demonstrate rapid extraction of representative mineral spectra and mapping of mineral distributions and abundances in map-plan, with core depth, and on the mine walls. The examples shown highlight the capabilities of these data for mineral mapping. Integration of these approaches promotes improved understanding of relations between geology, alteration and spectral signatures in three dimensions and should lead to improved efficiency of mine development, operations and ultimately effective mine closure. PMID:25937681

Mining biomedical images towards valuable information retrieval in biomedical and life sciences

PubMed Central

Ahmed, Zeeshan; Zeeshan, Saman; Dandekar, Thomas

2016-01-01

Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries. PMID:27538578

Association mining of dependency between time series

NASA Astrophysics Data System (ADS)

Hafez, Alaaeldin

2001-03-01

Time series analysis is considered as a crucial component of strategic control over a broad variety of disciplines in business, science and engineering. Time series data is a sequence of observations collected over intervals of time. Each time series describes a phenomenon as a function of time. Analysis on time series data includes discovering trends (or patterns) in a time series sequence. In the last few years, data mining has emerged and been recognized as a new technology for data analysis. Data Mining is the process of discovering potentially valuable patterns, associations, trends, sequences and dependencies in data. Data mining techniques can discover information that many traditional business analysis and statistical techniques fail to deliver. In this paper, we adapt and innovate data mining techniques to analyze time series data. By using data mining techniques, maximal frequent patterns are discovered and used in predicting future sequences or trends, where trends describe the behavior of a sequence. In order to include different types of time series (e.g. irregular and non- systematic), we consider past frequent patterns of the same time sequences (local patterns) and of other dependent time sequences (global patterns). We use the word 'dependent' instead of the word 'similar' for emphasis on real life time series where two time series sequences could be completely different (in values, shapes, etc.), but they still react to the same conditions in a dependent way. In this paper, we propose the Dependence Mining Technique that could be used in predicting time series sequences. The proposed technique consists of three phases: (a) for all time series sequences, generate their trend sequences, (b) discover maximal frequent trend patterns, generate pattern vectors (to keep information of frequent trend patterns), use trend pattern vectors to predict future time series sequences.

Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants

PubMed Central

Wahabzada, Mirwaes; Mahlein, Anne-Katrin; Bauckhage, Christian; Steiner, Ulrike; Oerke, Erich-Christian; Kersting, Kristian

2016-01-01

Modern phenotyping and plant disease detection methods, based on optical sensors and information technology, provide promising approaches to plant research and precision farming. In particular, hyperspectral imaging have been found to reveal physiological and structural characteristics in plants and to allow for tracking physiological dynamics due to environmental effects. In this work, we present an approach to plant phenotyping that integrates non-invasive sensors, computer vision, as well as data mining techniques and allows for monitoring how plants respond to stress. To uncover latent hyperspectral characteristics of diseased plants reliably and in an easy-to-understand way, we “wordify” the hyperspectral images, i.e., we turn the images into a corpus of text documents. Then, we apply probabilistic topic models, a well-established natural language processing technique that identifies content and topics of documents. Based on recent regularized topic models, we demonstrate that one can track automatically the development of three foliar diseases of barley. We also present a visualization of the topics that provides plant scientists an intuitive tool for hyperspectral imaging. In short, our analysis and visualization of characteristic topics found during symptom development and disease progress reveal the hyperspectral language of plant diseases. PMID:26957018

The Hazards of Data Mining in Healthcare.

PubMed

Househ, Mowafa; Aldosari, Bakheet

2017-01-01

From the mid-1990s, data mining methods have been used to explore and find patterns and relationships in healthcare data. During the 1990s and early 2000's, data mining was a topic of great interest to healthcare researchers, as data mining showed some promise in the use of its predictive techniques to help model the healthcare system and improve the delivery of healthcare services. However, it was soon discovered that mining healthcare data had many challenges relating to the veracity of healthcare data and limitations around predictive modelling leading to failures of data mining projects. As the Big Data movement has gained momentum over the past few years, there has been a reemergence of interest in the use of data mining techniques and methods to analyze healthcare generated Big Data. Much has been written on the positive impacts of data mining on healthcare practice relating to issues of best practice, fraud detection, chronic disease management, and general healthcare decision making. Little has been written about the limitations and challenges of data mining use in healthcare. In this review paper, we explore some of the limitations and challenges in the use of data mining techniques in healthcare. Our results show that the limitations of data mining in healthcare include reliability of medical data, data sharing between healthcare organizations, inappropriate modelling leading to inaccurate predictions. We conclude that there are many pitfalls in the use of data mining in healthcare and more work is needed to show evidence of its utility in facilitating healthcare decision-making for healthcare providers, managers, and policy makers and more evidence is needed on data mining's overall impact on healthcare services and patient care.

A case-based reasoning tool for breast cancer knowledge management with data mining concepts and techniques

NASA Astrophysics Data System (ADS)

Demigha, Souâd.

2016-03-01

The paper presents a Case-Based Reasoning Tool for Breast Cancer Knowledge Management to improve breast cancer screening. To develop this tool, we combine both concepts and techniques of Case-Based Reasoning (CBR) and Data Mining (DM). Physicians and radiologists ground their diagnosis on their expertise (past experience) based on clinical cases. Case-Based Reasoning is the process of solving new problems based on the solutions of similar past problems and structured as cases. CBR is suitable for medical use. On the other hand, existing traditional hospital information systems (HIS), Radiological Information Systems (RIS) and Picture Archiving Information Systems (PACS) don't allow managing efficiently medical information because of its complexity and heterogeneity. Data Mining is the process of mining information from a data set and transform it into an understandable structure for further use. Combining CBR to Data Mining techniques will facilitate diagnosis and decision-making of medical experts.

Study on key techniques for camera-based hydrological record image digitization

NASA Astrophysics Data System (ADS)

Li, Shijin; Zhan, Di; Hu, Jinlong; Gao, Xiangtao; Bo, Ping

2015-10-01

With the development of information technology, the digitization of scientific or engineering drawings has received more and more attention. In hydrology, meteorology, medicine and mining industry, the grid drawing sheet is commonly used to record the observations from sensors. However, these paper drawings may be destroyed and contaminated due to improper preservation or overuse. Further, it will be a heavy workload and prone to error if these data are manually transcripted into the computer. Hence, in order to digitize these drawings, establishing the corresponding data base will ensure the integrity of data and provide invaluable information for further research. This paper presents an automatic system for hydrological record image digitization, which consists of three key techniques, i.e., image segmentation, intersection point localization and distortion rectification. First, a novel approach to the binarization of the curves and grids in the water level sheet image has been proposed, which is based on the fusion of gradient and color information adaptively. Second, a fast search strategy for cross point location is invented and point-by-point processing is thus avoided, with the help of grid distribution information. And finally, we put forward a local rectification method through analyzing the central portions of the image and utilizing the domain knowledge of hydrology. The processing speed is accelerated, while the accuracy is still satisfying. Experiments on several real water level records show that our proposed techniques are effective and capable of recovering the hydrological observations accurately.

CARIBIAM: constrained Association Rules using Interactive Biological IncrementAl Mining.

PubMed

Rahal, Imad; Rahhal, Riad; Wang, Baoying; Perrizo, William

2008-01-01

This paper analyses annotated genome data by applying a very central data-mining technique known as Association Rule Mining (ARM) with the aim of discovering rules and hypotheses capable of yielding deeper insights into this type of data. In the literature, ARM has been noted for producing an overwhelming number of rules. This work proposes a new technique capable of using domain knowledge in the form of queries in order to efficiently mine only the subset of the associations that are of interest to investigators in an incremental and interactive manner.

Technique for predicting ground-water discharge to surface coal mines and resulting changes in head

USGS Publications Warehouse

Weiss, L.S.; Galloway, D.L.; Ishii, Audrey L.

1986-01-01

Changes in seepage flux and head (groundwater level) from groundwater drainage into a surface coal mine can be predicted by a technique that considers drainage from the unsaturated zone. The user applies site-specific data to precalculated head and seepage-flux profiles. Groundwater flow through hypothetical aquifer cross sections was simulated using the U.S. Geological Survey finite-difference model, VS2D, which considers variably saturated two-dimensional flow. Conceptual models considered were (1) drainage to a first cut, and (2) drainage to multiple cuts, which includes drainage effects of an area surface mine. Dimensionless head and seepage flux profiles from 246 simulations are presented. Step-by-step instructions and examples are presented. Users are required to know aquifer characteristics and to estimate size and timing of the mine operation at a proposed site. Calculated groundwater drainage to the mine is from one excavated face only. First cut considers confined and unconfined aquifers of a wide range of permeabilities; multiple cuts considers unconfined aquifers of higher permeabilities only. The technique, developed for Illinois coal-mining regions that use area surface mining and evaluated with an actual field example, will be useful in assessing potential hydrologic impacts of mining. Application is limited to hydrogeologic settings and mine operations similar to those considered. Fracture flow, recharge, and leakage are nor considered. (USGS)

Dynamic X-ray diffraction imaging of the ferroelectric response in bismuth ferrite

DOE PAGES

Laanait, Nouamane; Saenrang, Wittawat; Zhou, Hua; ...

2017-03-21

In this study, X-ray diffraction imaging is rapidly emerging as a powerful technique by which one can capture the local structure of crystalline materials at the nano- and meso-scale. Here, we present investigations of the dynamic structure of epitaxial monodomain BiFeO 3 thin-films using a novel full-field Bragg diffraction imaging modality. By taking advantage of the depth penetration of hard X-rays and their exquisite sensitivity to the atomic structure, we imaged in situ and in operando, the electric field-driven structural responses of buried BiFeO 3 epitaxial thin-films in micro-capacitor devices, with sub-100 nm lateral resolution. These imaging investigations were carriedmore » out at acquisition frame rates that reached up to 20 Hz and data transfer rates of 40 MB/s, while accessing diffraction contrast that is sensitive to the entire three-dimensional unit cell configuration. We mined these large datasets for material responses by employing matrix decomposition techniques, such as independent component analysis. We found that this statistical approach allows the extraction of the salient physical properties of the ferroelectric response of the material, such as coercive fields and transient spatiotemporal modulations in their piezoelectric response, and also facilitates their decoupling from extrinsic sources that are instrument specific.« less

Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques.

PubMed

Sanmiquel, Lluís; Bascompta, Marc; Rossell, Josep M; Anticoi, Hernán Francisco; Guash, Eduard

2018-03-07

An analysis of occupational accidents in the mining sector was conducted using the data from the Spanish Ministry of Employment and Social Safety between 2005 and 2015, and data-mining techniques were applied. Data was processed with the software Weka. Two scenarios were chosen from the accidents database: surface and underground mining. The most important variables involved in occupational accidents and their association rules were determined. These rules are composed of several predictor variables that cause accidents, defining its characteristics and context. This study exposes the 20 most important association rules in the sector-either surface or underground mining-based on the statistical confidence levels of each rule as obtained by Weka. The outcomes display the most typical immediate causes, along with the percentage of accidents with a basis in each association rule. The most important immediate cause is body movement with physical effort or overexertion, and the type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident are different between the two scenarios. Data-mining techniques were chosen as a useful tool to find out the root cause of the accidents.

Data Mining: Going beyond Traditional Statistics

ERIC Educational Resources Information Center

Zhao, Chun-Mei; Luan, Jing

2006-01-01

The authors provide an overview of data mining, giving special attention to the relationship between data mining and statistics to unravel some misunderstandings about the two techniques. (Contains 1 figure.)

Mine Water Treatment in Hongai Coal Mines

NASA Astrophysics Data System (ADS)

Dang, Phuong Thao; Dang, Vu Chi

2018-03-01

Acid mine drainage (AMD) is recognized as one of the most serious environmental problem associated with mining industry. Acid water, also known as acid mine drainage forms when iron sulfide minerals found in the rock of coal seams are exposed to oxidizing conditions in coal mining. Until 2009, mine drainage in Hongai coal mines was not treated, leading to harmful effects on humans, animals and aquatic ecosystem. This report has examined acid mine drainage problem and techniques for acid mine drainage treatment in Hongai coal mines. In addition, selection and criteria for the design of the treatment systems have been presented.

Mining biomedical images towards valuable information retrieval in biomedical and life sciences.

PubMed

Ahmed, Zeeshan; Zeeshan, Saman; Dandekar, Thomas

2016-01-01

Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries. © The Author(s) 2016. Published by Oxford University Press.

Mining Very High Resolution INSAR Data Based On Complex-GMRF Cues And Relevance Feedback

NASA Astrophysics Data System (ADS)

Singh, Jagmal; Popescu, Anca; Soccorsi, Matteo; Datcu, Mihai

2012-01-01

With the increase in number of remote sensing satellites, the number of image-data scenes in our repositories is also increasing and a large quantity of these scenes are never received and used. Thus automatic retrieval of de- sired image-data using query by image content to fully utilize the huge repository volume is becoming of great interest. Generally different users are interested in scenes containing different kind of objects and structures. So its important to analyze all the image information mining (IIM) methods so that its easier for user to select a method depending upon his/her requirement. We concentrate our study only on high-resolution SAR images and we propose to use InSAR observations instead of only one single look complex (SLC) images for mining scenes containing coherent objects such as high-rise buildings. However in case of objects with less coherence like areas with vegetation cover, SLC images exhibits better performance. We demonstrate IIM performance comparison using complex-Gauss Markov Random Fields as texture descriptor for image patches and SVM relevance- feedback.

In-situ identification of anti-personnel mines using acoustic resonant spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perry, R L; Roberts, R S

1999-02-01

A new technique for identifying buried Anti-Personnel Mines is described, and a set of preliminary experiments designed to assess the feasibility of this technique is presented. Analysis of the experimental results indicates that the technique has potential, but additional work is required to bring the technique to fruition. In addition to the experimental results presented here, a technique used to characterize the sensor employed in the experiments is detailed.

Responses of Terrestrial Herpetofauna to Persistent, Novel Ecosystems Resulting from Mountaintop Removal Mining

Treesearch

Jennifer M. Williams; Donald J. Brown; Petra B. Wood

2017-01-01

Mountaintop removal mining is a large-scale surface mining technique that removes entire floral and faunal communities, along with soil horizons located above coal seams. In West Virginia, the majority of this mining occurs on forested mountaintops. However, after mining ceases the land is typically reclaimed to grasslands and shrublands, resulting in novel ecosystems...

Using Open Web APIs in Teaching Web Mining

ERIC Educational Resources Information Center

Chen, Hsinchun; Li, Xin; Chau, M.; Ho, Yi-Jen; Tseng, Chunju

2009-01-01

With the advent of the World Wide Web, many business applications that utilize data mining and text mining techniques to extract useful business information on the Web have evolved from Web searching to Web mining. It is important for students to acquire knowledge and hands-on experience in Web mining during their education in information systems…

Land mine detection using multispectral image fusion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clark, G.A.; Sengupta, S.K.; Aimonetti, W.D.

1995-03-29

Our system fuses information contained in registered images from multiple sensors to reduce the effects of clutter and improve the ability to detect surface and buried land mines. The sensor suite currently consists of a camera that acquires images in six bands (400nm, 500nm, 600nm, 700nm, 800nm and 900nm). Past research has shown that it is extremely difficult to distinguish land mines from background clutter in images obtained from a single sensor. It is hypothesized, however, that information fused from a suite of various sensors is likely to provide better detection reliability, because the suite of sensors detects a varietymore » of physical properties that are more separable in feature space. The materials surrounding the mines can include natural materials (soil, rocks, foliage, water, etc.) and some artifacts. We use a supervised learning pattern recognition approach to detecting the metal and plastic land mines. The overall process consists of four main parts: Preprocessing, feature extraction, feature selection, and classification. These parts are used in a two step process to classify a subimage. We extract features from the images, and use feature selection algorithms to select only the most important features according to their contribution to correct detections. This allows us to save computational complexity and determine which of the spectral bands add value to the detection system. The most important features from the various sensors are fused using a supervised learning pattern classifier (the probabilistic neural network). We present results of experiments to detect land mines from real data collected from an airborne platform, and evaluate the usefulness of fusing feature information from multiple spectral bands.« less

Reduction of capsule endoscopy reading times by unsupervised image mining.

PubMed

Iakovidis, D K; Tsevas, S; Polydorou, A

2010-09-01

The screening of the small intestine has become painless and easy with wireless capsule endoscopy (WCE) that is a revolutionary, relatively non-invasive imaging technique performed by a wireless swallowable endoscopic capsule transmitting thousands of video frames per examination. The average time required for the visual inspection of a full 8-h WCE video ranges from 45 to 120min, depending on the experience of the examiner. In this paper, we propose a novel approach to WCE reading time reduction by unsupervised mining of video frames. The proposed methodology is based on a data reduction algorithm which is applied according to a novel scheme for the extraction of representative video frames from a full length WCE video. It can be used either as a video summarization or as a video bookmarking tool, providing the comparative advantage of being general, unbounded by the finiteness of a training set. The number of frames extracted is controlled by a parameter that can be tuned automatically. Comprehensive experiments on real WCE videos indicate that a significant reduction in the reading times is feasible. In the case of the WCE videos used this reduction reached 85% without any loss of abnormalities.

Data-Mining Technologies for Diabetes: A Systematic Review

PubMed Central

Marinov, Miroslav; Mosa, Abu Saleh Mohammad; Yoo, Illhoi; Boren, Suzanne Austin

2011-01-01

Background The objective of this study is to conduct a systematic review of applications of data-mining techniques in the field of diabetes research. Method We searched the MEDLINE database through PubMed. We initially identified 31 articles by the search, and selected 17 articles representing various data-mining methods used for diabetes research. Our main interest was to identify research goals, diabetes types, data sets, data-mining methods, data-mining software and technologies, and outcomes. Results The applications of data-mining techniques in the selected articles were useful for extracting valuable knowledge and generating new hypothesis for further scientific research/experimentation and improving health care for diabetes patients. The results could be used for both scientific research and real-life practice to improve the quality of health care diabetes patients. Conclusions Data mining has played an important role in diabetes research. Data mining would be a valuable asset for diabetes researchers because it can unearth hidden knowledge from a huge amount of diabetes-related data. We believe that data mining can significantly help diabetes research and ultimately improve the quality of health care for diabetes patients. PMID:22226277

Surface mining

Treesearch

Robert Leopold; Bruce Rowland; Reed Stalder

1979-01-01

The surface mining process consists of four phases: (1) exploration; (2) development; (3) production; and (4) reclamation. A variety of surface mining methods has been developed, including strip mining, auger, area strip, open pit, dredging, and hydraulic. Sound planning and design techniques are essential to implement alternatives to meet the myriad of laws,...

A Survey of Educational Data-Mining Research

ERIC Educational Resources Information Center

Huebner, Richard A.

2013-01-01

Educational data mining (EDM) is an emerging discipline that focuses on applying data mining tools and techniques to educationally related data. The discipline focuses on analyzing educational data to develop models for improving learning experiences and improving institutional effectiveness. A literature review on educational data mining topics…

A Comparative Study of Data Mining Techniques on Football Match Prediction

NASA Astrophysics Data System (ADS)

Rosli, Che Mohamad Firdaus Che Mohd; Zainuri Saringat, Mohd; Razali, Nazim; Mustapha, Aida

2018-05-01

Data prediction have become a trend in today’s business or organization. This paper is set to predict match outcomes for association football from the perspective of football club managers and coaches. This paper explored different data mining techniques used for predicting the match outcomes where the target class is win, draw and lose. The main objective of this research is to find the most accurate data mining technique that fits the nature of football data. The techniques tested are Decision Trees, Neural Networks, Bayesian Network, and k-Nearest Neighbors. The results from the comparative experiments showed that Decision Trees produced the highest average prediction accuracy in the domain of football match prediction by 99.56%.

Mineral Mapping with Imaging Spectroscopy: The Ray Mine, AZ

NASA Technical Reports Server (NTRS)

Clark, Roger N.; Vance, J. Sam; Livo, K. Eric; Green, Robert O.

1998-01-01

Mineral maps generated for the Ray Mine, Arizona were analyzed to determine if imaging spectroscopy can provide accurate information for environmental management of active and abandoned mine regions. The Ray Mine, owned by the ASARCO Corporation, covers an area of 5700 acres and is situated in Pinal County, Arizona about 70 miles north of Tucson near Hayden, Arizona. This open-pit mine has been a major source of copper since 1911, producing an estimated 4.5 million tons of copper since its inception. Until 1955 mining was accomplished by underground block caving and shrinkage stope methods. (excavation by working in stepped series usually employed in a vertical or steeply inclined orebody) In 1955, the mine was completely converted to open pit method mining with the bulk of the production from sulfide ore using recovery by concentrating and smelting. Beginning in 1969 a significant production contribution has been from the leaching and solvent extraction-electrowinnowing method of silicate and oxide ores. Published reserves in the deposit as of 1992 are 1.1 billion tons at 0.6 percent copper. The Environmental Protection Agency, in conjunction with ASARCO, and NASA/JPL obtained AVIRIS data over the mine in 1997 as part of the EPA Advanced Measurement Initiative (AMI) (Tom Mace, Principal Investigator). This AVIRIS data set is being used to compare and contrast the accuracy and environmental monitoring capabilities of remote sensing technologies: visible-near-IR imaging spectroscopy, multispectral visible and, near-IR sensors, thermal instruments, and radar platforms. The goal of this effort is to determine if these various technologies provide useful information for envirorunental management of active and abandoned mine sites in the arid western United States. This paper focuses on the analysis of AVIRIS data for assessing the impact of the Ray Mine on Mineral Creek. Mineral Creek flows to the Gila River. This paper discusses our preliminary AVIRIS mineral mapping and environmental findings.

Using text-mining techniques in electronic patient records to identify ADRs from medicine use.

PubMed

Warrer, Pernille; Hansen, Ebba Holme; Juhl-Jensen, Lars; Aagaard, Lise

2012-05-01

This literature review included studies that use text-mining techniques in narrative documents stored in electronic patient records (EPRs) to investigate ADRs. We searched PubMed, Embase, Web of Science and International Pharmaceutical Abstracts without restrictions from origin until July 2011. We included empirically based studies on text mining of electronic patient records (EPRs) that focused on detecting ADRs, excluding those that investigated adverse events not related to medicine use. We extracted information on study populations, EPR data sources, frequencies and types of the identified ADRs, medicines associated with ADRs, text-mining algorithms used and their performance. Seven studies, all from the United States, were eligible for inclusion in the review. Studies were published from 2001, the majority between 2009 and 2010. Text-mining techniques varied over time from simple free text searching of outpatient visit notes and inpatient discharge summaries to more advanced techniques involving natural language processing (NLP) of inpatient discharge summaries. Performance appeared to increase with the use of NLP, although many ADRs were still missed. Due to differences in study design and populations, various types of ADRs were identified and thus we could not make comparisons across studies. The review underscores the feasibility and potential of text mining to investigate narrative documents in EPRs for ADRs. However, more empirical studies are needed to evaluate whether text mining of EPRs can be used systematically to collect new information about ADRs. © 2011 The Authors. British Journal of Clinical Pharmacology © 2011 The British Pharmacological Society.

Using text-mining techniques in electronic patient records to identify ADRs from medicine use

PubMed Central

Warrer, Pernille; Hansen, Ebba Holme; Juhl-Jensen, Lars; Aagaard, Lise

2012-01-01

This literature review included studies that use text-mining techniques in narrative documents stored in electronic patient records (EPRs) to investigate ADRs. We searched PubMed, Embase, Web of Science and International Pharmaceutical Abstracts without restrictions from origin until July 2011. We included empirically based studies on text mining of electronic patient records (EPRs) that focused on detecting ADRs, excluding those that investigated adverse events not related to medicine use. We extracted information on study populations, EPR data sources, frequencies and types of the identified ADRs, medicines associated with ADRs, text-mining algorithms used and their performance. Seven studies, all from the United States, were eligible for inclusion in the review. Studies were published from 2001, the majority between 2009 and 2010. Text-mining techniques varied over time from simple free text searching of outpatient visit notes and inpatient discharge summaries to more advanced techniques involving natural language processing (NLP) of inpatient discharge summaries. Performance appeared to increase with the use of NLP, although many ADRs were still missed. Due to differences in study design and populations, various types of ADRs were identified and thus we could not make comparisons across studies. The review underscores the feasibility and potential of text mining to investigate narrative documents in EPRs for ADRs. However, more empirical studies are needed to evaluate whether text mining of EPRs can be used systematically to collect new information about ADRs. PMID:22122057

Post-acquisition data mining techniques for LC-MS/MS-acquired data in drug metabolite identification.

PubMed

Dhurjad, Pooja Sukhdev; Marothu, Vamsi Krishna; Rathod, Rajeshwari

2017-08-01

Metabolite identification is a crucial part of the drug discovery process. LC-MS/MS-based metabolite identification has gained widespread use, but the data acquired by the LC-MS/MS instrument is complex, and thus the interpretation of data becomes troublesome. Fortunately, advancements in data mining techniques have simplified the process of data interpretation with improved mass accuracy and provide a potentially selective, sensitive, accurate and comprehensive way for metabolite identification. In this review, we have discussed the targeted (extracted ion chromatogram, mass defect filter, product ion filter, neutral loss filter and isotope pattern filter) and untargeted (control sample comparison, background subtraction and metabolomic approaches) post-acquisition data mining techniques, which facilitate the drug metabolite identification. We have also discussed the importance of integrated data mining strategy.

Knowledge Discovery and Data Mining: An Overview

NASA Technical Reports Server (NTRS)

Fayyad, U.

1995-01-01

The process of knowledge discovery and data mining is the process of information extraction from very large databases. Its importance is described along with several techniques and considerations for selecting the most appropriate technique for extracting information from a particular data set.

Application of Information-Theoretic Data Mining Techniques in a National Ambulatory Practice Outcomes Research Network

PubMed Central

Wright, Adam; Ricciardi, Thomas N.; Zwick, Martin

2005-01-01

The Medical Quality Improvement Consortium data warehouse contains de-identified data on more than 3.6 million patients including their problem lists, test results, procedures and medication lists. This study uses reconstructability analysis, an information-theoretic data mining technique, on the MQIC data warehouse to empirically identify risk factors for various complications of diabetes including myocardial infarction and microalbuminuria. The risk factors identified match those risk factors identified in the literature, demonstrating the utility of the MQIC data warehouse for outcomes research, and RA as a technique for mining clinical data warehouses. PMID:16779156

Prediction of pork quality parameters by applying fractals and data mining on MRI.

PubMed

Caballero, Daniel; Pérez-Palacios, Trinidad; Caro, Andrés; Amigo, José Manuel; Dahl, Anders B; ErsbØll, Bjarne K; Antequera, Teresa

2017-09-01

This work firstly investigates the use of MRI, fractal algorithms and data mining techniques to determine pork quality parameters non-destructively. The main objective was to evaluate the capability of fractal algorithms (Classical Fractal algorithm, CFA; Fractal Texture Algorithm, FTA and One Point Fractal Texture Algorithm, OPFTA) to analyse MRI in order to predict quality parameters of loin. In addition, the effect of the sequence acquisition of MRI (Gradient echo, GE; Spin echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple linear regression, MLR) were analysed. Both fractal algorithm, FTA and OPFTA are appropriate to analyse MRI of loins. The sequence acquisition, the fractal algorithm and the data mining technique seems to influence on the prediction results. For most physico-chemical parameters, prediction equations with moderate to excellent correlation coefficients were achieved by using the following combinations of acquisition sequences of MRI, fractal algorithms and data mining techniques: SE-FTA-MLR, SE-OPFTA-IR, GE-OPFTA-MLR, SE-OPFTA-MLR, with the last one offering the best prediction results. Thus, SE-OPFTA-MLR could be proposed as an alternative technique to determine physico-chemical traits of fresh and dry-cured loins in a non-destructive way with high accuracy. Copyright © 2017. Published by Elsevier Ltd.

Ground-truthing AVIRIS mineral mapping at Cuprite, Nevada

NASA Technical Reports Server (NTRS)

Swayze, Gregg; Clark, Roger N.; Kruse, Fred; Sutley, Steve; Gallagher, Andrea

1992-01-01

Mineral abundance maps of 18 minerals were made of the Cuprite Mining District using 1990 AVIRIS data and the Multiple Spectral Feature Mapping Algorithm (MSFMA) as discussed in Clark et al. This technique uses least-squares fitting between a scaled laboratory reference spectrum and ground calibrated AVIRIS data for each pixel. Multiple spectral features can be fitted for each mineral and an unlimited number of minerals can be mapped simultaneously. Quality of fit and depth from continuum numbers for each mineral are calculated for each pixel and the results displayed as a multicolor image.

Detection and mapping of volcanic rock assemblages and associated hydrothermal alteration with Thermal Infrared Multiband Scanner (TIMS) data Comstock Lode Mining District, Virginia City, Nevada

NASA Technical Reports Server (NTRS)

Taranik, James V.; Hutsinpiller, Amy; Borengasser, Marcus

1986-01-01

Thermal Infrared Multispectral Scanner (TIMS) data were acquired over the Virginia City area on September 12, 1984. The data were acquired at approximately 1130 hours local time (1723 IRIG). The TIMS data were analyzed using both photointerpretation and digital processing techniques. Karhuen-Loeve transformations were utilized to display variations in radiant spectral emittance. The TIMS image data were compared with color infrared metric camera photography, LANDSAT Thematic Mapper (TM) data, and key areas were photographed in the field.

Data Mining and Knowledge Management in Higher Education -Potential Applications.

ERIC Educational Resources Information Center

Luan, Jing

This paper introduces a new decision support tool, data mining, in the context of knowledge management. The most striking features of data mining techniques are clustering and prediction. The clustering aspect of data mining offers comprehensive characteristics analysis of students, while the predicting function estimates the likelihood for a…

Mineralogic variations in fluvial sediments contaminated by mine tailings as determined from AVIRIS data, Coeur D'Alene River Valley, Idaho

NASA Technical Reports Server (NTRS)

Farrand, W. H.; Harsanyi, Joseph C.

1995-01-01

The success of imaging spectrometry in mineralogic mapping of natural terrains indicates that the technology can also be used to assess the environmental impact of human activities in certain instances. Specifically, this paper describes an investigation into the use of data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for mapping the spread of, and assessing changes in, the mineralogic character of tailings from a major silver and base metal mining district. The area under investigation is the Coeur d'Alene River Valley in northern Idaho. Mining has been going on in and around the towns of Kellogg and Wallace, Idaho since the 1880's. In the Kellogg-Smelterville Flats area, west of Kellogg, mine tailings were piled alongside the South Fork of the Coeur d'Alene River. Until the construction of tailings ponds in 1968 much of these waste materials were washed directly into the South Fork. The Kellogg-Smelterville area was declared an Environmental Protection Agency (EPA) Superfund site in 1983 and remediation efforts are currently underway. Recent studies have demonstrated that sediments in the Coeur d'Alene River and in the northern part of Lake Coeur d'Alene, into which the river flows, are highly enriched in Ag, Cu, Pb, Zn, Cd, Hg, As, and Sb. These trace metals have become aggregated in iron oxide and oxyhydroxide minerals and/or mineraloids. Reflectance spectra of iron-rich tailing materials are shown. Also shown are spectra of hematite and goethite. The broad bandwidth and long band center (near 1 micron) of the Fe(3+) crystal-field band of the iron-rich sediment samples combined with the lack of features on the Fe(3+) -O(2-) charge transfer absorption edge indicates that the ferric oxide and/or oxyhydroxide in these sediments is poorly crystalline to amorphous in character. Similar features are seen in poorly crystalline basaltic weathering products (e.g., palagonites). The problem of mapping and analyzing the downriver occurrences of iron rich tailings in the Coeur d'Alene (CDA) River Valley using remotely sensed data is complicated by the full vegetation cover present in the area. Because exposures of rock and soil were sparse, the data processing techniques used in this study were sensitive to detecting materials at subpixel scales. The methods used included spectral mixture analysis and a constrained energy minimization technique.

Facilitating the exploitation of ERTS imagery using snow enhancement techniques

NASA Technical Reports Server (NTRS)

Wobber, F. J.; Martin, K. (Principal Investigator); Amato, R. V.; Leshendok, T.

1973-01-01

The author has identified the following significant results. Comparative analysis of snow-free and snow-covered imagery of the New England Test Area has resulted in a larger number of lineaments mapped from snow-covered imagery in three out of four sets of comparative imagery. Analysts unfamiliar with the New England Test Area were utilized; the quality of imagery was independently judged to be uniform. In all image sets, a greater total length of lineaments was mapped with the snow-covered imagery. The value of this technique for fracture mapping in areas with thick soil cover is suggested. A number of potentially useful environmental applications of snow enhancement related to such areas as mining, land use, and hydrology have been identified.

Multi-frequency subspace migration for imaging of perfectly conducting, arc-like cracks in full- and limited-view inverse scattering problems

NASA Astrophysics Data System (ADS)

Park, Won-Kwang

2015-02-01

Multi-frequency subspace migration imaging techniques are usually adopted for the non-iterative imaging of unknown electromagnetic targets, such as cracks in concrete walls or bridges and anti-personnel mines in the ground, in the inverse scattering problems. It is confirmed that this technique is very fast, effective, robust, and can not only be applied to full- but also to limited-view inverse problems if a suitable number of incidents and corresponding scattered fields are applied and collected. However, in many works, the application of such techniques is heuristic. With the motivation of such heuristic application, this study analyzes the structure of the imaging functional employed in the subspace migration imaging technique in two-dimensional full- and limited-view inverse scattering problems when the unknown targets are arbitrary-shaped, arc-like perfectly conducting cracks located in the two-dimensional homogeneous space. In contrast to the statistical approach based on statistical hypothesis testing, our approach is based on the fact that the subspace migration imaging functional can be expressed by a linear combination of the Bessel functions of integer order of the first kind. This is based on the structure of the Multi-Static Response (MSR) matrix collected in the far-field at nonzero frequency in either Transverse Magnetic (TM) mode (Dirichlet boundary condition) or Transverse Electric (TE) mode (Neumann boundary condition). The investigation of the expression of imaging functionals gives us certain properties of subspace migration and explains why multi-frequency enhances imaging resolution. In particular, we carefully analyze the subspace migration and confirm some properties of imaging when a small number of incident fields are applied. Consequently, we introduce a weighted multi-frequency imaging functional and confirm that it is an improved version of subspace migration in TM mode. Various results of numerical simulations performed on the far-field data affected by large amounts of random noise are similar to the analytical results derived in this study, and they provide a direction for future studies.

Mineral Mapping Using AVIRIS Data at Ray Mine, AZ

NASA Technical Reports Server (NTRS)

McCubbin, Ian; Lang, Harold; Green, Robert O.; Roberts, Dar

1998-01-01

Imaging Spectroscopy enables the identification and mapping of surface mineralogy over large areas. This study focused on assessing the utility of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data for environmental impact analysis over the Environmental Protection Agency's (EPA) high priority Superfund site Ray Mine, AZ. Using the Spectral Angle Mapper (SAM) algorithm to analyze AVIRIS data makes it possible to map surface materials that are indicative of acid generating minerals. The improved performance of the AVIRIS sensor since 1996 provides data with sufficient signal to noise ratio to characterize up to 8 image endmembers. Specifically we employed SAM to map minerals associated with mine generated acid waste, namely jarositc, goethite, and hematite, in the presence of a complex mineralogical background.

Effective and efficient analysis of spatio-temporal data

NASA Astrophysics Data System (ADS)

Zhang, Zhongnan

Spatio-temporal data mining, i.e., mining knowledge from large amount of spatio-temporal data, is a highly demanding field because huge amounts of spatio-temporal data have been collected in various applications, ranging from remote sensing, to geographical information systems (GIS), computer cartography, environmental assessment and planning, etc. The collection data far exceeded human's ability to analyze which make it crucial to develop analysis tools. Recent studies on data mining have extended to the scope of data mining from relational and transactional datasets to spatial and temporal datasets. Among the various forms of spatio-temporal data, remote sensing images play an important role, due to the growing wide-spreading of outer space satellites. In this dissertation, we proposed two approaches to analyze the remote sensing data. The first one is about applying association rules mining onto images processing. Each image was divided into a number of image blocks. We built a spatial relationship for these blocks during the dividing process. This made a large number of images into a spatio-temporal dataset since each image was shot in time-series. The second one implemented co-occurrence patterns discovery from these images. The generated patterns represent subsets of spatial features that are located together in space and time. A weather analysis is composed of individual analysis of several meteorological variables. These variables include temperature, pressure, dew point, wind, clouds, visibility and so on. Local-scale models provide detailed analysis and forecasts of meteorological phenomena ranging from a few kilometers to about 100 kilometers in size. When some of above meteorological variables have some special change tendency, some kind of severe weather will happen in most cases. Using the discovery of association rules, we found that some special meteorological variables' changing has tight relation with some severe weather situation that will happen very soon. This dissertation is composed of three parts: an introduction, some basic knowledges and relative works, and my own three contributions to the development of approaches for spatio-temporal data mining: DYSTAL algorithm, STARSI algorithm, and COSTCOP+ algorithm.

Text Mining in Organizational Research

PubMed Central

Kobayashi, Vladimer B.; Berkers, Hannah A.; Kismihók, Gábor; Den Hartog, Deanne N.

2017-01-01

Despite the ubiquity of textual data, so far few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically increasing the amount data that can be analyzed. This article aims to acquaint organizational researchers with the fundamental logic underpinning text mining, the analytical stages involved, and contemporary techniques that may be used to achieve different types of objectives. The specific analytical techniques reviewed are (a) dimensionality reduction, (b) distance and similarity computing, (c) clustering, (d) topic modeling, and (e) classification. We describe how text mining may extend contemporary organizational research by allowing the testing of existing or new research questions with data that are likely to be rich, contextualized, and ecologically valid. After an exploration of how evidence for the validity of text mining output may be generated, we conclude the article by illustrating the text mining process in a job analysis setting using a dataset composed of job vacancies. PMID:29881248

Text Mining in Organizational Research.

PubMed

Kobayashi, Vladimer B; Mol, Stefan T; Berkers, Hannah A; Kismihók, Gábor; Den Hartog, Deanne N

2018-07-01

Despite the ubiquity of textual data, so far few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically increasing the amount data that can be analyzed. This article aims to acquaint organizational researchers with the fundamental logic underpinning text mining, the analytical stages involved, and contemporary techniques that may be used to achieve different types of objectives. The specific analytical techniques reviewed are (a) dimensionality reduction, (b) distance and similarity computing, (c) clustering, (d) topic modeling, and (e) classification. We describe how text mining may extend contemporary organizational research by allowing the testing of existing or new research questions with data that are likely to be rich, contextualized, and ecologically valid. After an exploration of how evidence for the validity of text mining output may be generated, we conclude the article by illustrating the text mining process in a job analysis setting using a dataset composed of job vacancies.

A Proposed Data Fusion Architecture for Micro-Zone Analysis and Data Mining

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kevin McCarthy; Milos Manic

Data Fusion requires the ability to combine or “fuse” date from multiple data sources. Time Series Analysis is a data mining technique used to predict future values from a data set based upon past values. Unlike other data mining techniques, however, Time Series places special emphasis on periodicity and how seasonal and other time-based factors tend to affect trends over time. One of the difficulties encountered in developing generic time series techniques is the wide variability of the data sets available for analysis. This presents challenges all the way from the data gathering stage to results presentation. This paper presentsmore » an architecture designed and used to facilitate the collection of disparate data sets well suited to Time Series analysis as well as other predictive data mining techniques. Results show this architecture provides a flexible, dynamic framework for the capture and storage of a myriad of dissimilar data sets and can serve as a foundation from which to build a complete data fusion architecture.« less

Application of Three Existing Stope Boundary Optimisation Methods in an Operating Underground Mine

NASA Astrophysics Data System (ADS)

Erdogan, Gamze; Yavuz, Mahmut

2017-12-01

The underground mine planning and design optimisation process have received little attention because of complexity and variability of problems in underground mines. Although a number of optimisation studies and software tools are available and some of them, in special, have been implemented effectively to determine the ultimate-pit limits in an open pit mine, there is still a lack of studies for optimisation of ultimate stope boundaries in underground mines. The proposed approaches for this purpose aim at maximizing the economic profit by selecting the best possible layout under operational, technical and physical constraints. In this paper, the existing three heuristic techniques including Floating Stope Algorithm, Maximum Value Algorithm and Mineable Shape Optimiser (MSO) are examined for optimisation of stope layout in a case study. Each technique is assessed in terms of applicability, algorithm capabilities and limitations considering the underground mine planning challenges. Finally, the results are evaluated and compared.

Evaluation of the environmental contamination at an abandoned mining site using multivariate statistical techniques--the Rodalquilar (Southern Spain) mining district.

PubMed

Bagur, M G; Morales, S; López-Chicano, M

2009-11-15

Unsupervised and supervised pattern recognition techniques such as hierarchical cluster analysis, principal component analysis, factor analysis and linear discriminant analysis have been applied to water samples recollected in Rodalquilar mining district (Southern Spain) in order to identify different sources of environmental pollution caused by the abandoned mining industry. The effect of the mining activity on waters was monitored determining the concentration of eleven elements (Mn, Ba, Co, Cu, Zn, As, Cd, Sb, Hg, Au and Pb) by inductively coupled plasma mass spectrometry (ICP-MS). The Box-Cox transformation has been used to transform the data set in normal form in order to minimize the non-normal distribution of the geochemical data. The environmental impact is affected mainly by the mining activity developed in the zone, the acid drainage and finally by the chemical treatment used for the benefit of gold.

Large-Scale medical image analytics: Recent methodologies, applications and Future directions.

PubMed

Zhang, Shaoting; Metaxas, Dimitris

2016-10-01

Despite the ever-increasing amount and complexity of annotated medical image data, the development of large-scale medical image analysis algorithms has not kept pace with the need for methods that bridge the semantic gap between images and diagnoses. The goal of this position paper is to discuss and explore innovative and large-scale data science techniques in medical image analytics, which will benefit clinical decision-making and facilitate efficient medical data management. Particularly, we advocate that the scale of image retrieval systems should be significantly increased at which interactive systems can be effective for knowledge discovery in potentially large databases of medical images. For clinical relevance, such systems should return results in real-time, incorporate expert feedback, and be able to cope with the size, quality, and variety of the medical images and their associated metadata for a particular domain. The design, development, and testing of the such framework can significantly impact interactive mining in medical image databases that are growing rapidly in size and complexity and enable novel methods of analysis at much larger scales in an efficient, integrated fashion. Copyright © 2016. Published by Elsevier B.V.

Using Helicopter Electromagnetic Surveys to Identify Potential Hazards at Mine Waste Impoundments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hammack, R.W.

2008-01-01

In July 2003, helicopter electromagnetic surveys were conducted at 14 coal waste impoundments in southern West Virginia. The purpose of the surveys was to detect conditions that could lead to impoundment failure either by structural failure of the embankment or by the flooding of adjacent or underlying mine works. Specifically, the surveys attempted to: 1) identify saturated zones within the mine waste, 2) delineate filtrate flow paths through the embankment or into adjacent strata and receiving streams, and 3) identify flooded mine workings underlying or adjacent to the waste impoundment. Data from the helicopter surveys were processed to generate conductivity/depthmore » images. Conductivity/depth images were then spatially linked to georeferenced air photos or topographic maps for interpretation. Conductivity/depth images were found to provide a snapshot of the hydrologic conditions that exist within the impoundment. This information can be used to predict potential areas of failure within the embankment because of its ability to image the phreatic zone. Also, the electromagnetic survey can identify areas of unconsolidated slurry in the decant basin and beneath the embankment. Although shallow, flooded mineworks beneath the impoundment were identified by this survey, it cannot be assumed that electromagnetic surveys can detect all underlying mines. A preliminary evaluation of the data implies that helicopter electromagnetic surveys can provide a better understanding of the phreatic zone than the piezometer arrays that are typically used.« less

Secure and Efficient Transmission of Hyperspectral Images for Geosciences Applications

NASA Astrophysics Data System (ADS)

Carpentieri, Bruno; Pizzolante, Raffaele

2017-12-01

Hyperspectral images are acquired through air-borne or space-borne special cameras (sensors) that collect information coming from the electromagnetic spectrum of the observed terrains. Hyperspectral remote sensing and hyperspectral images are used for a wide range of purposes: originally, they were developed for mining applications and for geology because of the capability of this kind of images to correctly identify various types of underground minerals by analysing the reflected spectrums, but their usage has spread in other application fields, such as ecology, military and surveillance, historical research and even archaeology. The large amount of data obtained by the hyperspectral sensors, the fact that these images are acquired at a high cost by air-borne sensors and that they are generally transmitted to a base, makes it necessary to provide an efficient and secure transmission protocol. In this paper, we propose a novel framework that allows secure and efficient transmission of hyperspectral images, by combining a reversible invisible watermarking scheme, used in conjunction with digital signature techniques, and a state-of-art predictive-based lossless compression algorithm.

Hyperspectral analysis for qualitative and quantitative features related to acid mine drainage at a remediated open-pit mine

NASA Astrophysics Data System (ADS)

Davies, G.; Calvin, W. M.

2015-12-01

The exposure of pyrite to oxygen and water in mine waste environments is known to generate acidity and the accumulation of secondary iron minerals. Sulfates and secondary iron minerals associated with acid mine drainage (AMD) exhibit diverse spectral properties in the ultraviolet, visible and near-infrared regions of the electromagnetic spectrum. The use of hyperspectral imagery for identification of AMD mineralogy and contamination has been well studied. Fewer studies have examined the impacts of hydrologic variations on mapping AMD or the unique spectral signatures of mine waters. Open-pit mine lakes are an additional environmental hazard which have not been widely studied using imaging spectroscopy. A better understanding of AMD variation related to climate fluctuations and the spectral signatures of contaminated surface waters will aid future assessments of environmental contamination. This study examined the ability of multi-season airborne hyperspectral data to identify the geochemical evolution of substances and contaminant patterns at the Leviathan Mine Superfund site. The mine is located 24 miles southeast of Lake Tahoe and contains remnant tailings piles and several AMD collection ponds. The objectives were to 1) distinguish temporal changes in mineralogy at a the remediated open-pit sulfur mine, 2) identify the absorption features of mine affected waters, and 3) quantitatively link water spectra to known dissolved iron concentrations. Images from NASA's AVIRIS instrument were collected in the spring, summer, and fall seasons for two consecutive years at Leviathan (HyspIRI campaign). Images had a spatial resolution of 15 meters at nadir. Ground-based surveys using the ASD FieldSpecPro spectrometer and laboratory spectral and chemical analysis complemented the remote sensing data. Temporal changes in surface mineralogy were difficult to distinguish. However, seasonal changes in pond water quality were identified. Dissolved ferric iron and chlorophyll-a concentrations were determined to be the major influences on pond water spectral variation.

Application of text mining for customer evaluations in commercial banking

NASA Astrophysics Data System (ADS)

Tan, Jing; Du, Xiaojiang; Hao, Pengpeng; Wang, Yanbo J.

2015-07-01

Nowadays customer attrition is increasingly serious in commercial banks. To combat this problem roundly, mining customer evaluation texts is as important as mining customer structured data. In order to extract hidden information from customer evaluations, Textual Feature Selection, Classification and Association Rule Mining are necessary techniques. This paper presents all three techniques by using Chinese Word Segmentation, C5.0 and Apriori, and a set of experiments were run based on a collection of real textual data that includes 823 customer evaluations taken from a Chinese commercial bank. Results, consequent solutions, some advice for the commercial bank are given in this paper.

Introduction to the mining of clinical data.

PubMed

Harrison, James H

2008-03-01

The increasing volume of medical data online, including laboratory data, represents a substantial resource that can provide a foundation for improved understanding of disease presentation, response to therapy, and health care delivery processes. Data mining supports these goals by providing a set of techniques designed to discover similarities and relationships between data elements in large data sets. Currently, medical data have several characteristics that increase the difficulty of applying these techniques, although there have been notable medical data mining successes. Future developments in integrated medical data repositories, standardized data representation, and guidelines for the appropriate research use of medical data will decrease the barriers to mining projects.

Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques

PubMed Central

Sanmiquel, Lluís; Bascompta, Marc; Rossell, Josep M.; Anticoi, Hernán Francisco; Guash, Eduard

2018-01-01

An analysis of occupational accidents in the mining sector was conducted using the data from the Spanish Ministry of Employment and Social Safety between 2005 and 2015, and data-mining techniques were applied. Data was processed with the software Weka. Two scenarios were chosen from the accidents database: surface and underground mining. The most important variables involved in occupational accidents and their association rules were determined. These rules are composed of several predictor variables that cause accidents, defining its characteristics and context. This study exposes the 20 most important association rules in the sector—either surface or underground mining—based on the statistical confidence levels of each rule as obtained by Weka. The outcomes display the most typical immediate causes, along with the percentage of accidents with a basis in each association rule. The most important immediate cause is body movement with physical effort or overexertion, and the type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident are different between the two scenarios. Data-mining techniques were chosen as a useful tool to find out the root cause of the accidents. PMID:29518921

An Evaluation of Fractal Surface Measurement Methods for Characterizing Landscape Complexity from Remote-Sensing Imagery

NASA Technical Reports Server (NTRS)

Lam, Nina Siu-Ngan; Qiu, Hong-Lie; Quattrochi, Dale A.; Emerson, Charles W.; Arnold, James E. (Technical Monitor)

2001-01-01

The rapid increase in digital data volumes from new and existing sensors necessitates the need for efficient analytical tools for extracting information. We developed an integrated software package called ICAMS (Image Characterization and Modeling System) to provide specialized spatial analytical functions for interpreting remote sensing data. This paper evaluates the three fractal dimension measurement methods: isarithm, variogram, and triangular prism, along with the spatial autocorrelation measurement methods Moran's I and Geary's C, that have been implemented in ICAMS. A modified triangular prism method was proposed and implemented. Results from analyzing 25 simulated surfaces having known fractal dimensions show that both the isarithm and triangular prism methods can accurately measure a range of fractal surfaces. The triangular prism method is most accurate at estimating the fractal dimension of higher spatial complexity, but it is sensitive to contrast stretching. The variogram method is a comparatively poor estimator for all of the surfaces, particularly those with higher fractal dimensions. Similar to the fractal techniques, the spatial autocorrelation techniques are found to be useful to measure complex images but not images with low dimensionality. These fractal measurement methods can be applied directly to unclassified images and could serve as a tool for change detection and data mining.

A Test Study to Display Buried Anti-Tank Landmines with GPR and Research Soil Characteristics with CRS

NASA Astrophysics Data System (ADS)

Kadioglu, Selma; Kagan Kadioglu, Yusuf

2014-05-01

An anti-tank mine (AT mine) is a type of land mine designed to damage or destroy vehicles including tanks and armored fighting vehicles. Anti-tank mines typically have a much larger explosive charge, and a fuze designed only to be triggered by vehicles or, in some cases, tampering with the mine. There are a lot of AT mine types. In our test study, MK4 and MK5 AT mine types has been used. The Mk 5 was a cylindrical metal cased U.K. anti-tank blast mine that entered service in 1943, during the Second World War. General Specifications of them are 203 mm diameter, 127 mm height, 4.4-5.7 kg weight, 2.05-3.75 kg of TNT explosive content and 350 lbs operating pressure respectively. The aims of the test study were to image anti-tank landmine with GPR method and to analyse the soil characteristics before the mines made explode and after made be exploded and determine changing of the soil characteristics. We realized data measurement on the real 6 unexploded anti-tank landmine buried approximately 15 cm in depth. The mines spaced 3 m were buried in two lines. Space between lines was 1.5 m. We gathered data on the profiles, approximately 7 m, with a Ramac CUII system and 800 MHz shielded antenna. We collected soil samples on the mines, near and around the mines, on the area in village. We collected soil samples before exploding and after exploding mines. We imaged anti-tank landmines on the depth slices of the GPR data and in their interactive transparent 3D subsets successfully. We used polarized microscope and confocal Raman spectroscopy (CRS) to identify soil characteristic before and after exploitation. The results presented that GPR method and its 3D imaging were successful to determine AT mines, and there was no important changing on mineralogical and petrographical characterization of the soil before and after exploding processing. This project has been supported by Ankara University under grant no 11B6055002. The study is a contribution to the EU funded COST action TU1208, "Civil Engineering Applications of Ground penetrating Radar".

Deriving preference order of post-mining land-uses through MLSA framework: application of an outranking technique

NASA Astrophysics Data System (ADS)

Soltanmohammadi, Hossein; Osanloo, Morteza; Aghajani Bazzazi, Abbas

2009-08-01

This study intends to take advantage of a previously developed framework for mined land suitability analysis (MLSA) consisted of economical, social, technical and mine site factors to achieve a partial and also a complete pre-order of feasible post-mining land-uses. Analysis by an outranking multi-attribute decision-making (MADM) technique, called PROMETHEE (preference ranking organization method for enrichment evaluation), was taken into consideration because of its clear advantages on the field of MLSA as compared with MADM ranking techniques. Application of the proposed approach on a mined land can be completed through some successive steps. First, performance of the MLSA attributes is scored locally by each individual decision maker (DM). Then the assigned performance scores are normalized and the deviation amplitudes of non-dominated alternatives are calculated. Weights of the attributes are calculated by another MADM technique namely, analytical hierarchy process (AHP) in a separate procedure. Using the Gaussian preference function beside the weights, the preference indexes of the land-use alternatives are obtained. Calculation of the outgoing and entering flows of the alternatives and one by one comparison of these values will lead to partial pre-order of them and calculation of the net flows, will lead to a ranked preference for each land-use. At the final step, utilizing the PROMETHEE group decision support system which incorporates judgments of all the DMs, a consensual ranking can be derived. In this paper, preference order of post-mining land-uses for a hypothetical mined land has been derived according to judgments of one DM to reveal applicability of the proposed approach.

Underwater electro-optical system for mine identification

NASA Astrophysics Data System (ADS)

Strand, Michael P.

1995-06-01

The Electro-Optic Identification (EOID) Sensors project is developing a Laser Visual Iidentification Sensor (LVIS) for identification of proud, partially buried, and moored mines in shallow water/very shallow water. LVIS will be deployed in small diameter underwater vehicles, including unmanned underwater vehicles (UUVs). Since the mission is mine identification, LVIS must: a) deliver high quality images in turbid coastal waters, while b) being compatible with the size and power constraints imposed by the intended deployment platforms. This project is sponsored by the Office of Naval Research, as a part of the AOA Mine Reconnaissance/Hunter program. High quality images which retain target detail and contrast are required for mine identification. LVIS will be designed to produce images of minelike contacts (MLC) of sufficient quality to allow identification while operating in turbid coastal waters from a small diameter UUV. Technology goals for the first generation LVIS are a) identification range up to 40 feet for proud, partially buried, and moored MLCs under coastal water conditions; b) day/night operation from a UUV operating at speeds up to 4 knots; c) power consumption less than 500 watts, with 275 watts being typical; and d) packaged within a 32-inch long portion of a 21-inch diameter vehicle section.

Application of remote-sensing techniques to hydrologic studies in selected coal-mine areas of southeastern Kansas

USGS Publications Warehouse

Kenny, J.F.; McCauley, J.R.

1983-01-01

Disturbances resulting from intensive coal mining in the Cherry Creek basin of southeastern Kansas were investigated using color and color-infrared aerial photography in conjunction with water-quality data from simultaneously acquired samples. Imagery was used to identify the type and extent of vegetative cover on strip-mined lands and the extent and success of reclamation practices. Drainage patterns, point sources of acid mine drainage, and recharge areas for underground mines were located for onsite inspection. Comparison of these interpretations with water-quality data illustrated differences between the eastern and western parts of the Cherry Creek basin. Contamination in the eastern part is due largely to circulation of water from unreclaimed strip mines and collapse features through the network of underground mines and subsequent discharge of acidic drainage through seeps. Contamination in the western part is primarily caused by runoff and seepage from strip-mined lands in which surfaces have frequently been graded and limed but are generally devoid of mature stands of soil-anchoring vegetation. The successful use of aerial photography in the study of Cherry Creek basin indicates the potential of using remote-sensing techniques in studies of other coal-mined regions. (USGS)

30 CFR 282.28 - Environmental protection measures.

Code of Federal Regulations, 2010 CFR

2010-07-01

... recent research or improved monitoring techniques. (5) When prototype test mining is proposed, the lessee...) The sampling techniques and procedures to be used to acquire the needed data and information; (ii) The... evaluation of the approved Delineation, Testing, or Mining Plan. The Director's review of the air quality...

Analyzing Teaching Performance of Instructors Using Data Mining Techniques

ERIC Educational Resources Information Center

Mardikyan, Sona; Badur, Bertain

2011-01-01

Student evaluations to measure the teaching effectiveness of instructor's are very frequently applied in higher education for many years. This study investigates the factors associated with the assessment of instructors teaching performance using two different data mining techniques; stepwise regression and decision trees. The data collected…

Monitoring and inversion on land subsidence over mining area with InSAR technique

USGS Publications Warehouse

Wang, Y.; Zhang, Q.; Zhao, C.; Lu, Z.; Ding, X.

2011-01-01

The Wulanmulun town, located in Inner Mongolia, is one of the main mining areas of Shendong Company such as Shangwan coal mine and Bulianta coal mine, which has been suffering serious mine collapse with the underground mine withdrawal. We use ALOS/PALSAR data to extract land deformation under these regions, in which Small Baseline Subsets (SBAS) method was applied. Then we compared InSAR results with the underground mining activities, and found high correlations between them. Lastly we applied Distributed Dislocation (Okada) model to invert the mine collapse mechanism. ?? 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

15 CFR 970.600 - General.

Code of Federal Regulations, 2012 CFR

2012-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 970.600 - General.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 970.600 - General.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2012 CFR

2012-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2011 CFR

2011-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 970.600 - General.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 970.600 - General.

Code of Federal Regulations, 2011 CFR

2011-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

VRLane: a desktop virtual safety management program for underground coal mine

NASA Astrophysics Data System (ADS)

Li, Mei; Chen, Jingzhu; Xiong, Wei; Zhang, Pengpeng; Wu, Daozheng

2008-10-01

VR technologies, which generate immersive, interactive, and three-dimensional (3D) environments, are seldom applied to coal mine safety work management. In this paper, a new method that combined the VR technologies with underground mine safety management system was explored. A desktop virtual safety management program for underground coal mine, called VRLane, was developed. The paper mainly concerned about the current research advance in VR, system design, key techniques and system application. Two important techniques were introduced in the paper. Firstly, an algorithm was designed and implemented, with which the 3D laneway models and equipment models can be built on the basis of the latest mine 2D drawings automatically, whereas common VR programs established 3D environment by using 3DS Max or the other 3D modeling software packages with which laneway models were built manually and laboriously. Secondly, VRLane realized system integration with underground industrial automation. VRLane not only described a realistic 3D laneway environment, but also described the status of the coal mining, with functions of displaying the run states and related parameters of equipment, per-alarming the abnormal mining events, and animating mine cars, mine workers, or long-wall shearers. The system, with advantages of cheap, dynamic, easy to maintenance, provided a useful tool for safety production management in coal mine.

Text mining and its potential applications in systems biology.

PubMed

Ananiadou, Sophia; Kell, Douglas B; Tsujii, Jun-ichi

2006-12-01

With biomedical literature increasing at a rate of several thousand papers per week, it is impossible to keep abreast of all developments; therefore, automated means to manage the information overload are required. Text mining techniques, which involve the processes of information retrieval, information extraction and data mining, provide a means of solving this. By adding meaning to text, these techniques produce a more structured analysis of textual knowledge than simple word searches, and can provide powerful tools for the production and analysis of systems biology models.

Computerized Liquid Crystal Phase Identification by Neural Networks Analysis of Polarizing Microscopy Textures

NASA Astrophysics Data System (ADS)

Karaszi, Zoltan; Konya, Andrew; Dragan, Feodor; Jakli, Antal; CPIP/LCI; CS Dept. of Kent State University Collaboration

Polarizing optical microscopy (POM) is traditionally the best-established method of studying liquid crystals, and using POM started already with Otto Lehman in 1890. An expert, who is familiar with the science of optics of anisotropic materials and typical textures of liquid crystals, can identify phases with relatively large confidence. However, for unambiguous identification usually other expensive and time-consuming experiments are needed. Replacement of the subjective and qualitative human eye-based liquid crystal texture analysis with quantitative computerized image analysis technique started only recently and were used to enhance the detection of smooth phase transitions, determine order parameter and birefringence of specific liquid crystal phases. We investigate if the computer can recognize and name the phase where the texture was taken. To judge the potential of reliable image recognition based on this procedure, we used 871 images of liquid crystal textures belonging to five main categories: Nematic, Smectic A, Smectic C, Cholesteric and Crystal, and used a Neural Network Clustering Technique included in the data mining software package in Java ``WEKA''. A neural network trained on a set of 827 LC textures classified the remaining 44 textures with 80% accuracy.

Imaging via complete cantilever dynamic detection: General dynamic mode imaging and spectroscopy in scanning probe microscopy

DOE PAGES

Somnath, Suhas; Collins, Liam; Matheson, Michael A.; ...

2016-09-08

We develop and implement a multifrequency spectroscopy and spectroscopic imaging mode, referred to as general dynamic mode (GDM), that captures the complete spatially- and stimulus dependent information on nonlinear cantilever dynamics in scanning probe microscopy (SPM). GDM acquires the cantilever response including harmonics and mode mixing products across the entire broadband cantilever spectrum as a function of excitation frequency. GDM spectra substitute the classical measurements in SPM, e.g. amplitude and phase in lock-in detection. Here, GDM is used to investigate the response of a purely capacitively driven cantilever. We use information theory techniques to mine the data and verify themore » findings with governing equations and classical lock-in based approaches. We explore the dependence of the cantilever dynamics on the tip–sample distance, AC and DC driving bias. This approach can be applied to investigate the dynamic behavior of other systems within and beyond dynamic SPM. In conclusion, GDM is expected to be useful for separating the contribution of different physical phenomena in the cantilever response and understanding the role of cantilever dynamics in dynamic AFM techniques.« less

Applying Web Usage Mining for Personalizing Hyperlinks in Web-Based Adaptive Educational Systems

ERIC Educational Resources Information Center

Romero, Cristobal; Ventura, Sebastian; Zafra, Amelia; de Bra, Paul

2009-01-01

Nowadays, the application of Web mining techniques in e-learning and Web-based adaptive educational systems is increasing exponentially. In this paper, we propose an advanced architecture for a personalization system to facilitate Web mining. A specific Web mining tool is developed and a recommender engine is integrated into the AHA! system in…

COMPARISON OF DATA FROM SYNTHETIC LEACHATE AND DIRECT SAMPLING OF ACID DRAINAGE FROM MINE WASTES: IMPLICATIONS FOR MERCURY TRANSPORT AND WASTE MANAGEMENT

EPA Science Inventory

The Sulphur Bank Mercury Mine (SBMM) in Lake County, California operated from the 1860s through the 1950's. Mining for sulfur started with surface operations and progressed to shaft, then open pit techniques to obtain mercury. Mining has resulted in deposition of approximately ...

Application of EREP imagery to fracture-related mine safety hazards and environmental problems in mining. [Indiana

NASA Technical Reports Server (NTRS)

Wier, C. E.; Wobber, F. J.; Amato, R. V.; Russell, O. R. (Principal Investigator)

1974-01-01

The author has identified the following significant results. All Skylab 2 imagery received to date has been analyzed manually and data related to fracture analysis and mined land inventories has been summarized on map-overlays. A comparison of the relative utility of the Skylab image products for fracture detection, soil tone/vegetation contrast mapping, and mined land mapping has been completed. Numerous fracture traces were detected on both color and black and white transparencies. Unique fracture trace data which will contribute to the investigator's mining hazards analysis were noted on the EREP imagery; these data could not be detected on ERTS-1 imagery or high altitude aircraft color infrared photography. Stream segments controlled by fractures or joint systems could be identified in more detail than with ERTS-1 imagery of comparable scale. ERTS-1 mine hazards products will be modified to demonstrate the value of this additional data. Skylab images were used successfully to update a mined land map of Indiana made in 1972. Changes in mined area as small as two acres can be identified. As the Energy Crisis increases the demand for coal, such demonstrations of the application of Skylab data to coal resources will take on new importance.

Examining Online Learning Patterns with Data Mining Techniques in Peer-Moderated and Teacher-Moderated Courses

ERIC Educational Resources Information Center

Hung, Jui-Long; Crooks, Steven M.

2009-01-01

The student learning process is important in online learning environments. If instructors can "observe" online learning behaviors, they can provide adaptive feedback, adjust instructional strategies, and assist students in establishing patterns of successful learning activities. This study used data mining techniques to examine and…

Mechanical Energy Propagation and Backscattering in Nominally Dry Soil: Imaging Buried Land Mines

NASA Astrophysics Data System (ADS)

Sen, Surajit

2003-04-01

The imaging of shallow buried objects in a complex medium, e.g., nominally dry sand, is an outstanding challenge. Such imaging is of relevance in connection with the detection and subsequent imaging of buried non-metallic anti-personnel land mines and in other applications. It has been shown that gentle mechanical impulses and low frequency sound waves with frequencies roughly between 150-350 Hz or so can penetrate distances of up to a foot in sand. Hence, such signals can potentially be useful in the detection and perhaps in the imaging of shallow buried objects. It is presently unclear whether high frequency signals can be effectively used to image shallow buried objects. Impulses can typically penetrate larger distances into sand and soil. Both impulses and continuous sound waves can be used for imaging shallow buried objects. The talk shall briefly review the state-of-the-art in low frequency sound propagation in soil and shall discuss the current understanding of impulse propagation and backscattering in nominally dry sand beds. It will be argued that impulse based imaging may have the potential to be a simple and fast way to detect and image small non-metallic mines. Research supported by the National Science Foundation Grant No. NSF-CMS 0070055.

Application of ERTS-A imagery to fracture related mine safety hazards in the coal mining industry

NASA Technical Reports Server (NTRS)

Wier, C. E.; Wobber, F. J. (Principal Investigator)

1973-01-01

The author has identified the following significant results. The most important result to date is the demonstration of the special value of repetitive ERTS-1 multiband coverage for detecting previously unknown fracture lineaments despite the presence of a deep glacial overburden. The Illinois Basin is largely covered with glacial drift and few rock outcrops are present. A contribution to the geological understanding of Illinois and Indiana has been made. Analysis of ERTS-1 imagery has provided useful information to the State of Indiana concerning the surface mined lands. The contrast between healthy vegetation and bare ground as imaged by Band 7 is sharp and substantial detail can be obtained concerning the extent of disturbed lands, associated water bodies, large haul roads, and extent of mined lands revegetation. Preliminary results of analysis suggest a reasonable correlation between image-detected fractures and mine roof fall accidents for a few areas investigated. ERTS-1 applications to surface mining operations appear probable, but further investigations are required. The likelihood of applying ERTS-1 derived fracture data to improve coal mine safety in the entire Illinois Basin is suggested from studies conducted in Indiana.

Site investigation report mine research project GUE 70-14.10, Guernsey, Ohio.

DOT National Transportation Integrated Search

2003-06-01

Geophysical investigative techniques can be a valuable supplement to standard subsurface investigations for the : evaluation of abandoned underground coal mine workings and their potential impacts at the ground surface. The GUE : 70 - 14.10 Mine Rese...

Using 3D Reflection Seismics for Deep Platinum Mine Planning and Risk Mitigation: A Case Study from the Bushveld Complex, South Africa

NASA Astrophysics Data System (ADS)

Scheiber-Enslin, S. E.; Manzi, M. S.; Webb, S. J.

2017-12-01

Loss-of-ground in mining is a common problem. Using the integration of high resolution aeromagnetic and 3D reflection seismic data to delineate the causative geological features allows for more efficient mine planning and risk reduction. High resolution data from Impala Platinum mine in the western Bushveld Complex are used to image potholes, iron-rich ultramafic pegmatoids (IRUPs), faults, dykes and diapirs that may impact the economic horizons (UG2). Imaging of these structures was previously limited to outcrop, both on surface and underground, as well as 2D seismic data. These high resolution seismic data are able to resolve faults with throws as small as 10 m. A diapir is imaged in the southwest of the study area with a diameter of approximately 6 km. The diapir has a depth extend of around 4 km below the UG2 horizon and displaces the horizon by 350 m. It has been suggested that topographic highs in the Transvaal Supergroup basement initiate the formation of these diapirs as new magma is injected into the chamber. The origin of the diapir within the layered basement rocks, and disruption of layering within the complex is visible on the seismic section. In the north of the study area a large region of slumping or several merged potholes is identified that is up to 2.5 km in length, with up to 700 m of vertical displacement. Ductile deformation that formed the potholes is imaged on the seismic section, with the UG2 cutting down into the footwall. However, brittle deformation of the UG2 is also imaged with faulting at the edges of the regions of slumping. The edges of these slump regions are also characterised by the emplacement of iron-rich ultramafic pegmatoids (IRUPs), which show up as regions of diffuse reflectivity on the seismic data and magnetic highs. The proximity of these faults and IRUPs to the edges of the slump structure brings in to question whether they contribute to pothole formation. The diapir and slump structure displaces the economic UG2 horizon at the mining levels and cause faulting of the horizon. Imaging of these structures could be used for future mining planning and design to assess and mitigate the risks posed by these features during mining activities.

Analysis of Hospital Processes with Process Mining Techniques.

PubMed

Orellana García, Arturo; Pérez Alfonso, Damián; Larrea Armenteros, Osvaldo Ulises

2015-01-01

Process mining allows for discovery, monitoring, and improving processes identified in information systems from their event logs. In hospital environments, process analysis has been a crucial factor for cost reduction, control and proper use of resources, better patient care, and achieving service excellence. This paper presents a new component for event logs generation in the Hospital Information System or HIS, developed at University of Informatics Sciences. The event logs obtained are used for analysis of hospital processes with process mining techniques. The proposed solution intends to achieve the generation of event logs in the system with high quality. The performed analyses allowed for redefining functions in the system and proposed proper flow of information. The study exposed the need to incorporate process mining techniques in hospital systems to analyze the processes execution. Moreover, we illustrate its application for making clinical and administrative decisions for the management of hospital activities.

Knowledge Driven Image Mining with Mixture Density Mercer Kernels

NASA Technical Reports Server (NTRS)

Srivastava, Ashok N.; Oza, Nikunj

2004-01-01

This paper presents a new methodology for automatic knowledge driven image mining based on the theory of Mercer Kernels; which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. In that high dimensional feature space, linear clustering, prediction, and classification algorithms can be applied and the results can be mapped back down to the original image space. Thus, highly nonlinear structure in the image can be recovered through the use of well-known linear mathematics in the feature space. This process has a number of advantages over traditional methods in that it allows for nonlinear interactions to be modelled with only a marginal increase in computational costs. In this paper, we present the theory of Mercer Kernels, describe its use in image mining, discuss a new method to generate Mercer Kernels directly from data, and compare the results with existing algorithms on data from the MODIS (Moderate Resolution Spectral Radiometer) instrument taken over the Arctic region. We also discuss the potential application of these methods on the Intelligent Archive, a NASA initiative for developing a tagged image data warehouse for the Earth Sciences.

Knowledge Driven Image Mining with Mixture Density Mercer Kernals

NASA Technical Reports Server (NTRS)

Srivastava, Ashok N.; Oza, Nikunj

2004-01-01

This paper presents a new methodology for automatic knowledge driven image mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. In that high dimensional feature space, linear clustering, prediction, and classification algorithms can be applied and the results can be mapped back down to the original image space. Thus, highly nonlinear structure in the image can be recovered through the use of well-known linear mathematics in the feature space. This process has a number of advantages over traditional methods in that it allows for nonlinear interactions to be modelled with only a marginal increase in computational costs. In this paper we present the theory of Mercer Kernels; describe its use in image mining, discuss a new method to generate Mercer Kernels directly from data, and compare the results with existing algorithms on data from the MODIS (Moderate Resolution Spectral Radiometer) instrument taken over the Arctic region. We also discuss the potential application of these methods on the Intelligent Archive, a NASA initiative for developing a tagged image data warehouse for the Earth Sciences.

The future of European mining: What should our mining industry learn from the Skouries and Rosia Montana community gold conflicts

NASA Astrophysics Data System (ADS)

Hardy, Liam; Heller, Shaun; Faltyn, Rowan; Stefanaki, Anna; Economidou, Romina; Savin, Irina; Hood, Leo; Conway, Christopher

2017-04-01

The popular image of mining portrayed by media and by a majority of public opinion is a dominantly negative one. From worker's rights to environmental damages, disasters such as the Copiapó mine collapse (Chile), the acid mine drainage at Lousal (Portugal) and the Pb contamination of waters around the Tyndrum mines (Scotland) overshadow initiatives like the ICMM. Some companies receive little praise despite creating active community education and investment projects, while others simply build higher barbed wire fences and attempt to weather the protests, budgeting them into mine life assessments. This image problem, combined with the decentralised political segregation of Europe and the increased power of grass-roots protest initiatives (such as Antigold in Greece), has resulted in mining companies joining a long list of industries effected by the 'auto-protest' reaction in face of development, regardless of potential regional and national benefits, there is a pre-existing lack of trust in corporate and government powers to protect community interests. The poor management of existing licences is thus becoming a significant danger to future operations and the wider industry. Here we report on the Rosia Montana dispute (Romania) and the ongoing Skouries conflict (Greece). We then discuss how the European mining industry may need to significantly adapt its exploration and community engagement strategies to avoid future conflicts and, present a recent example of how effective suitably organised community engagement projects can be for local mining initiatives from Southern Portugal.

Advanced geophysical underground coal gasification monitoring

DOE PAGES

Mellors, Robert; Yang, X.; White, J. A.; ...

2014-07-01

Underground Coal Gasification (UCG) produces less surface impact, atmospheric pollutants and greenhouse gas than traditional surface mining and combustion. Therefore, it may be useful in mitigating global change caused by anthropogenic activities. Careful monitoring of the UCG process is essential in minimizing environmental impact. Here we first summarize monitoring methods that have been used in previous UCG field trials. We then discuss in more detail a number of promising advanced geophysical techniques. These methods – seismic, electromagnetic, and remote sensing techniques – may provide improved and cost-effective ways to image both the subsurface cavity growth and surface subsidence effects. Activemore » and passive seismic data have the promise to monitor the burn front, cavity growth, and observe cavity collapse events. Electrical resistance tomography (ERT) produces near real time tomographic images autonomously, monitors the burn front and images the cavity using low-cost sensors, typically running within boreholes. Interferometric synthetic aperture radar (InSAR) is a remote sensing technique that has the capability to monitor surface subsidence over the wide area of a commercial-scale UCG operation at a low cost. It may be possible to infer cavity geometry from InSAR (or other surface topography) data using geomechanical modeling. The expected signals from these monitoring methods are described along with interpretive modeling for typical UCG cavities. They are illustrated using field results from UCG trials and other relevant subsurface operations.« less

Imaging near surface mineral targets with ambient seismic noise

NASA Astrophysics Data System (ADS)

Dales, P.; Audet, P.; Olivier, G.

2017-12-01

To keep up with global metal and mineral demand, new ore-deposits have to be discovered on a regular basis. This task is becoming increasingly difficult, since easily accessible deposits have been exhausted to a large degree. The typical procedure for mineral exploration begins with geophysical surveys followed by a drilling program to investigate potential targets. Since the retrieved drill core samples are one-dimensional observations, the many holes needed to interpolate and interpret potential deposits can lead to very high costs. To reduce the amount of drilling, active seismic imaging is sometimes used as an intermediary, however the active sources (e.g. large vibrating trucks or explosive shots) are expensive and unsuitable for operation in remote or environmentally sensitive areas. In recent years, passive seismic imaging using ambient noise has emerged as a novel, low-cost and environmentally sensitive approach for exploring the sub-surface. This technique dispels with active seismic sources and instead uses ambient seismic noise such as ocean waves, traffic or minor earthquakes. Unfortunately at this point, passive surveys are not capable of reaching the required resolution to image the vast majority of the ore-bodies that are being explored. In this presentation, we will show the results of an experiment where ambient seismic noise recorded on 60 seismic stations was used to image a near-mine target. The target consists of a known ore-body that has been partially exhausted by mining efforts roughly 100 years ago. The experiment examined whether ambient seismic noise interferometry can be used to image the intact and exhausted ore deposit. A drilling campaign was also conducted near the target which offers the opportunity to compare the two methods. If the accuracy and resolution of passive seismic imaging can be improved to that of active surveys (and beyond), this method could become an inexpensive intermediary step in the exploration process and result in a large decrease in the amount of drilling required to investigate and identify high-grade ore deposits.

Data Mining Techniques for Customer Relationship Management

NASA Astrophysics Data System (ADS)

Guo, Feng; Qin, Huilin

2017-10-01

Data mining have made customer relationship management (CRM) a new area where firms can gain a competitive advantage, and play a key role in the firms’ management decision. In this paper, we first analyze the value and application fields of data mining techniques for CRM, and further explore how data mining applied to Customer churn analysis. A new business culture is developing today. The conventional production centered and sales purposed market strategy is gradually shifting to customer centered and service purposed. Customers’ value orientation is increasingly affecting the firms’. And customer resource has become one of the most important strategic resources. Therefore, understanding customers’ needs and discriminating the most contributed customers has become the driving force of most modern business.

30 CFR 282.23 - Testing Plan.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Resources BUREAU OF OCEAN ENERGY MANAGEMENT, REGULATION, AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR... lessee needs more information to develop a detailed Mining Plan than is obtainable under an approved... techniques or technology or mining equipment, or to determine environmental effects by a pilot test mining...

Utilization of volume correlation filters for underwater mine identification in LIDAR imagery

NASA Astrophysics Data System (ADS)

Walls, Bradley

2008-04-01

Underwater mine identification persists as a critical technology pursued aggressively by the Navy for fleet protection. As such, new and improved techniques must continue to be developed in order to provide measurable increases in mine identification performance and noticeable reductions in false alarm rates. In this paper we show how recent advances in the Volume Correlation Filter (VCF) developed for ground based LIDAR systems can be adapted to identify targets in underwater LIDAR imagery. Current automated target recognition (ATR) algorithms for underwater mine identification employ spatial based three-dimensional (3D) shape fitting of models to LIDAR data to identify common mine shapes consisting of the box, cylinder, hemisphere, truncated cone, wedge, and annulus. VCFs provide a promising alternative to these spatial techniques by correlating 3D models against the 3D rendered LIDAR data.

Improve Data Mining and Knowledge Discovery Through the Use of MatLab

NASA Technical Reports Server (NTRS)

Shaykhian, Gholam Ali; Martin, Dawn (Elliott); Beil, Robert

2011-01-01

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Improve Data Mining and Knowledge Discovery through the use of MatLab

NASA Technical Reports Server (NTRS)

Shaykahian, Gholan Ali; Martin, Dawn Elliott; Beil, Robert

2011-01-01

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(TradeMark)(MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Intelligent and integrated techniques for coalbed methane (CBM) recovery and reduction of greenhouse gas emission.

PubMed

Qianting, Hu; Yunpei, Liang; Han, Wang; Quanle, Zou; Haitao, Sun

2017-07-01

Coalbed methane (CBM) recovery is a crucial approach to realize the exploitation of a clean energy and the reduction of the greenhouse gas emission. In the past 10 years, remarkable achievements on CBM recovery have been obtained in China. However, some key difficulties still exist such as long borehole drilling in complicated geological condition, and poor gas drainage effect due to low permeability. In this study, intelligent and integrated techniques for CBM recovery are introduced. These integrated techniques mainly include underground CBM recovery techniques and ground well CBM recovery techniques. The underground CBM recovery techniques consist of the borehole formation technique, gas concentration improvement technique, and permeability enhancement technique. According to the division of mining-induced disturbance area, the ground well arrangement area and well structure type in mining-induced disturbance developing area and mining-induced disturbance stable area are optimized to significantly improve the ground well CBM recovery. Besides, automatic devices such as drilling pipe installation device are also developed to achieve remote control of data recording, which makes the integrated techniques intelligent. These techniques can provide key solutions to some long-term difficulties in CBM recovery.

Assessment of hospital processes using a process mining technique: Outpatient process analysis at a tertiary hospital.

PubMed

Yoo, Sooyoung; Cho, Minsu; Kim, Eunhye; Kim, Seok; Sim, Yerim; Yoo, Donghyun; Hwang, Hee; Song, Minseok

2016-04-01

Many hospitals are increasing their efforts to improve processes because processes play an important role in enhancing work efficiency and reducing costs. However, to date, a quantitative tool has not been available to examine the before and after effects of processes and environmental changes, other than the use of indirect indicators, such as mortality rate and readmission rate. This study used process mining technology to analyze process changes based on changes in the hospital environment, such as the construction of a new building, and to measure the effects of environmental changes in terms of consultation wait time, time spent per task, and outpatient care processes. Using process mining technology, electronic health record (EHR) log data of outpatient care before and after constructing a new building were analyzed, and the effectiveness of the technology in terms of the process was evaluated. Using the process mining technique, we found that the total time spent in outpatient care did not increase significantly compared to that before the construction of a new building, considering that the number of outpatients increased, and the consultation wait time decreased. These results suggest that the operation of the outpatient clinic was effective after changes were implemented in the hospital environment. We further identified improvements in processes using the process mining technique, thereby demonstrating the usefulness of this technique for analyzing complex hospital processes at a low cost. This study confirmed the effectiveness of process mining technology at an actual hospital site. In future studies, the use of process mining technology will be expanded by applying this approach to a larger variety of process change situations. Copyright © 2016. Published by Elsevier Ireland Ltd.

SEASONAL VARIATIONS OF DISSOLVED MERCURY CONCENTRATIONS AT THE SULPHUR BANK MERCURY MINE, CLEAR LAKE, CALIFORNIA: IMPLICATIONS FOR MINE DRAINAGE MONITORING

EPA Science Inventory

The Sulphur Bank Mercury Mine in Lake County, California (SBMM) was operated from the 1860s through the 1950s. Mining for sulfur started with surface operations and then progressed to shaft and later open pit techniques to obtain mercury. SBMM is located adjacent to the shore o...

Data Mining in Social Media

NASA Astrophysics Data System (ADS)

Barbier, Geoffrey; Liu, Huan

The rise of online social media is providing a wealth of social network data. Data mining techniques provide researchers and practitioners the tools needed to analyze large, complex, and frequently changing social media data. This chapter introduces the basics of data mining, reviews social media, discusses how to mine social media data, and highlights some illustrative examples with an emphasis on social networking sites and blogs.

Coastal Benthic Optical Properties Fluorescence Imaging Laser Line Scan Sensor

DTIC Science & Technology

2001-09-30

Acquisition of Electro - Optic Identification (EOID) sensors for MLC identification is currently underway to support both Air Mine Counter-Measures (AMCM) and Surface Mine Counter-Measures (SMCM) operations.

A way toward analyzing high-content bioimage data by means of semantic annotation and visual data mining

NASA Astrophysics Data System (ADS)

Herold, Julia; Abouna, Sylvie; Zhou, Luxian; Pelengaris, Stella; Epstein, David B. A.; Khan, Michael; Nattkemper, Tim W.

2009-02-01

In the last years, bioimaging has turned from qualitative measurements towards a high-throughput and highcontent modality, providing multiple variables for each biological sample analyzed. We present a system which combines machine learning based semantic image annotation and visual data mining to analyze such new multivariate bioimage data. Machine learning is employed for automatic semantic annotation of regions of interest. The annotation is the prerequisite for a biological object-oriented exploration of the feature space derived from the image variables. With the aid of visual data mining, the obtained data can be explored simultaneously in the image as well as in the feature domain. Especially when little is known of the underlying data, for example in the case of exploring the effects of a drug treatment, visual data mining can greatly aid the process of data evaluation. We demonstrate how our system is used for image evaluation to obtain information relevant to diabetes study and screening of new anti-diabetes treatments. Cells of the Islet of Langerhans and whole pancreas in pancreas tissue samples are annotated and object specific molecular features are extracted from aligned multichannel fluorescence images. These are interactively evaluated for cell type classification in order to determine the cell number and mass. Only few parameters need to be specified which makes it usable also for non computer experts and allows for high-throughput analysis.

Recommendation in Higher Education Using Data Mining Techniques

ERIC Educational Resources Information Center

Vialardi, Cesar; Bravo, Javier; Shafti, Leila; Ortigosa, Alvaro

2009-01-01

One of the main problems faced by university students is to take the right decision in relation to their academic itinerary based on available information (for example courses, schedules, sections, classrooms and professors). In this context, this work proposes the use of a recommendation system based on data mining techniques to help students to…

Applications of Deep Learning and Reinforcement Learning to Biological Data.

PubMed

Mahmud, Mufti; Kaiser, Mohammed Shamim; Hussain, Amir; Vassanelli, Stefano

2018-06-01

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.

Preliminary geological investigation of AIS data at Mary Kathleen, Queensland, Australia

NASA Technical Reports Server (NTRS)

Huntington, J. F.; Green, A. A.; Craig, M. D.; Cocks, T. D.

1986-01-01

The Airborne Imaging Spectrometer (AIS) was flown over granitic, volcanic, and calc-silicate terrain around the Mary Kathleen Uranium Mine in Queensland, in a test of its mineralocial mapping capabilities. An analysis strategy and restoration and enhancement techniques were developed to process the 128 band AIS data. A preliminary analysis of one of three AIS flight lines shows that the data contains considerable spectral variation but that it is also contaminated by second-order leakage of radiation from the near-infrared region. This makes the recognition of expected spectral absorption shapes very difficult. The effect appears worst in terrains containing considerable vegetation. Techniques that try to predict this supplementary radiation coupled with the log residual analytical technique show that expected mineral absorption spectra can be derived. The techniques suggest that with additional refinement correction procedures, the Australian AIS data may be revised. Application of the log residual analysis method has proved very successful on the cuprite, Nevada data set, and for highlighting the alunite, linite, and SiOH mineralogy.

Tiny videos: a large data set for nonparametric video retrieval and frame classification.

PubMed

Karpenko, Alexandre; Aarabi, Parham

2011-03-01

In this paper, we present a large database of over 50,000 user-labeled videos collected from YouTube. We develop a compact representation called "tiny videos" that achieves high video compression rates while retaining the overall visual appearance of the video as it varies over time. We show that frame sampling using affinity propagation-an exemplar-based clustering algorithm-achieves the best trade-off between compression and video recall. We use this large collection of user-labeled videos in conjunction with simple data mining techniques to perform related video retrieval, as well as classification of images and video frames. The classification results achieved by tiny videos are compared with the tiny images framework [24] for a variety of recognition tasks. The tiny images data set consists of 80 million images collected from the Internet. These are the largest labeled research data sets of videos and images available to date. We show that tiny videos are better suited for classifying scenery and sports activities, while tiny images perform better at recognizing objects. Furthermore, we demonstrate that combining the tiny images and tiny videos data sets improves classification precision in a wider range of categories.

Physics Mining of Multi-Source Data Sets

NASA Technical Reports Server (NTRS)

Helly, John; Karimabadi, Homa; Sipes, Tamara

2012-01-01

Powerful new parallel data mining algorithms can produce diagnostic and prognostic numerical models and analyses from observational data. These techniques yield higher-resolution measures than ever before of environmental parameters by fusing synoptic imagery and time-series measurements. These techniques are general and relevant to observational data, including raster, vector, and scalar, and can be applied in all Earth- and environmental science domains. Because they can be highly automated and are parallel, they scale to large spatial domains and are well suited to change and gap detection. This makes it possible to analyze spatial and temporal gaps in information, and facilitates within-mission replanning to optimize the allocation of observational resources. The basis of the innovation is the extension of a recently developed set of algorithms packaged into MineTool to multi-variate time-series data. MineTool is unique in that it automates the various steps of the data mining process, thus making it amenable to autonomous analysis of large data sets. Unlike techniques such as Artificial Neural Nets, which yield a blackbox solution, MineTool's outcome is always an analytical model in parametric form that expresses the output in terms of the input variables. This has the advantage that the derived equation can then be used to gain insight into the physical relevance and relative importance of the parameters and coefficients in the model. This is referred to as physics-mining of data. The capabilities of MineTool are extended to include both supervised and unsupervised algorithms, handle multi-type data sets, and parallelize it.

Comparative performance between compressed and uncompressed airborne imagery

NASA Astrophysics Data System (ADS)

Phan, Chung; Rupp, Ronald; Agarwal, Sanjeev; Trang, Anh; Nair, Sumesh

2008-04-01

The US Army's RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD), Countermine Division is evaluating the compressibility of airborne multi-spectral imagery for mine and minefield detection application. Of particular interest is to assess the highest image data compression rate that can be afforded without the loss of image quality for war fighters in the loop and performance of near real time mine detection algorithm. The JPEG-2000 compression standard is used to perform data compression. Both lossless and lossy compressions are considered. A multi-spectral anomaly detector such as RX (Reed & Xiaoli), which is widely used as a core algorithm baseline in airborne mine and minefield detection on different mine types, minefields, and terrains to identify potential individual targets, is used to compare the mine detection performance. This paper presents the compression scheme and compares detection performance results between compressed and uncompressed imagery for various level of compressions. The compression efficiency is evaluated and its dependence upon different backgrounds and other factors are documented and presented using multi-spectral data.

Naica Mine, Chihuahua, Mexico

NASA Image and Video Library

2007-10-02

The Naica mine in Chihuahua, Mexico, with its enormous gypsum crystals, may well be called the "Queen of the Giant Crystals localities." Though the Naica mine is no show mine, but still a working lead-zinc mine hosted in layered limestones, the first of several crystal caves was discovered in 1910. This "Cave of the Swords" contained extraordinary large sword-like selenite (gypsum) crystals up to 2 m long. In 2000 another crystal cave system was discovered at 300 m depth, even more spectacular than the original cave. Inside were free growing gypsum crystals up to 12 m long and 2 m in diameter. The ASTER image uses SWIR bands 4, 6, and 8 in RGB. Limestone is displayed in yellow-green colors, vegetation is red. The image was acquired February 16, 2004, covers an area of 26 x 23.5 km, and is located near 27.8 degrees north latitude, 105.5 degrees west longitude. The photo of crystals was taken from: http://www.thatcrystalsite.com/. http://photojournal.jpl.nasa.gov/catalog/PIA10615

Earth Observations taken by the Expedition 13 crew

NASA Image and Video Library

2006-05-06

ISS013-E-14843 (6 May 2006) --- Calcite Quarry, Michigan is featured in this image photographed by an Expedition 13 crewmember on the International Space Station. While the Great Lakes region of North America is well known for its importance to shipping between the United States, Canada, and the Atlantic Ocean, it is also the location of an impressive structure in the continent's bedrock -- the Michigan Basin, NASA scientists point out. The Basin looks much like a large bull's-eye defined by the arrangement of exposed rock layers, which all tilt inwards towards the center forming a huge bowl-shaped structure. While this "bowl" is not readily apparent while on the ground, detailed mapping of the rock units on a regional scale revealed the structure to geologists. The outer layers of the Basin include thick deposits of carbonates (limestone and dolomite). These carbonate rocks are mined throughout the Great Lakes region using large open-pit mines. The largest carbonate mine in the world, Calcite Quarry, is depicted in this image. The mine has been active for over 85 years; the worked area (grey region in image center) measures approximately 7 kilometers long by 4 kilometers wide, and is crossed by several access roads (white) into various areas of the mine.

Uranium aqueous speciation in the vicinity of the former uranium mining sites using the diffusive gradients in thin films and ultrafiltration techniques.

PubMed

Drozdzak, Jagoda; Leermakers, Martine; Gao, Yue; Elskens, Marc; Phrommavanh, Vannapha; Descostes, Michael

2016-03-24

The performance of the Diffusive Gradients in Thin films (DGT) technique with Chelex(®)-100, Metsorb™ and Diphonix(®) as binding phases was evaluated in the vicinity of the former uranium mining sites of Chardon and L'Ecarpière (Loire-Atlantique department in western France). This is the first time that the DGT technique with three different binding agents was employed for the aqueous U determination in the context of uranium mining environments. The fractionation and speciation of uranium were investigated using a multi-methodological approach using filtration (0.45 μm, 0.2 μm), ultrafiltration (500 kDa, 100 kDa and 10 kDa) coupled to geochemical speciation modelling (PhreeQC) and the DGT technique. The ultrafiltration data showed that at each sampling point uranium was present mostly in the 10 kDa truly dissolved fraction and the geochemical modelling speciation calculations indicated that U speciation was markedly predominated by CaUO2(CO3)3(2-). In natural waters, no significant difference was observed in terms of U uptake between Chelex(®)-100 and Metsorb™, while similar or inferior U uptake was observed on Diphonix(®) resin. In turn, at mining influenced sampling spots, the U accumulation on DGT-Diphonix(®) was higher than on DGT-Chelex(®)-100 and DGT-Metsorb™, probably because their performance was disturbed by the extreme composition of the mining waters. The use of Diphonix(®) resin leads to a significant advance in the application and development of the DGT technique for determination of U in mining influenced environments. This investigation demonstrated that such multi-technique approach provides a better picture of U speciation and enables to assess more accurately the potentially bioavailable U pool. Copyright © 2016 Elsevier B.V. All rights reserved.

Lithium Mining, Nevada

NASA Image and Video Library

2014-08-05

This image from NASA Terra spacecraft shows the once-abandoned mining town of Silver Peak, Nevada, which began to thrive again when Foote Mineral Company began extracting lithium from brine below the floor of Clayton Valley in 1966.

Process Mining Online Assessment Data

ERIC Educational Resources Information Center

Pechenizkiy, Mykola; Trcka, Nikola; Vasilyeva, Ekaterina; van der Aalst, Wil; De Bra, Paul

2009-01-01

Traditional data mining techniques have been extensively applied to find interesting patterns, build descriptive and predictive models from large volumes of data accumulated through the use of different information systems. The results of data mining can be used for getting a better understanding of the underlying educational processes, for…

Market applications of Resistivity, Induced Polarisation, Magnetic Resonance and Electromagnetic methods for Groundwater Investigations, Mining Exploration, Environmental and Engineering Surveys

NASA Astrophysics Data System (ADS)

Bernard, J.

2012-12-01

The Manufacturers of geophysical instruments have been facing these past decades the fast evolution of the electronics and of the computer sciences. More automatisms have been introduced into the equipment and into the processing and interpretation software which may let believe that conducting geophysical surveys requires less understanding of the method and less experience than in the past. Hence some misunderstandings in the skills that are needed to make the geophysical results well integrated among the global information which the applied geologist needs to acquire to be successful in his applications. Globally, the demand in geophysical investigation goes towards more penetration depth, requiring more powerful transmitters, and towards a better resolution, requiring more data such as in 3D analysis. Budgets aspects strongly suggest a high efficiency in the field associated to high speed data processing. The innovation is required in all aspects of geophysics to fit with the market needs, including new technological (instruments, software) and methodological (methods, procedures, arrays) developments. The structures in charge of the geophysical work can be public organisations (institutes, ministries, geological surveys,…) or can come from the private sector (large companies, sub-contractors, consultants, …), each one of them getting their own constraints in the field work and in the processing and interpretation phases. In the applications concerning Groundwater investigations, Mining Exploration, Environmental and Engineering surveys, examples of data and their interpretation presently carried out all around the world will be presented for DC Resistivity (Vertical Electrical Sounding, 2D, 3D Resistivity Imaging, Resistivity Monitoring), Induced Polarisation (Time Domain 2D, 3D arrays for mining and environmental), Magnetic Resonance Sounding (direct detection and characterisation of groundwater) and Electromagnetic (multi-component and multi-spacing Frequency Domain Sounding and Profiling technique). The place that Geophysics takes in the market among the other investigation techniques is, and will remain, dependant on the quality of the results obtained, despite the uncertainties linked to the field (noise aspects) and to the interpretation (equivalence aspects), under the control of budget decisions.Resistivity Imaging measurements for groundwater investigations

A group filter algorithm for sea mine detection

NASA Astrophysics Data System (ADS)

Cobb, J. Tory; An, Myoung; Tolimieri, Richard

2005-06-01

Automatic detection of sea mines in coastal regions is a difficult task due to the highly variable sea bottom conditions present in the underwater environment. Detection systems must be able to discriminate objects which vary in size, shape, and orientation from naturally occurring and man-made clutter. Additionally, these automated systems must be computationally efficient to be incorporated into unmanned underwater vehicle (UUV) sensor systems characterized by high sensor data rates and limited processing abilities. Using noncommutative group harmonic analysis, a fast, robust sea mine detection system is created. A family of unitary image transforms associated to noncommutative groups is generated and applied to side scan sonar image files supplied by Naval Surface Warfare Center Panama City (NSWC PC). These transforms project key image features, geometrically defined structures with orientations, and localized spectral information into distinct orthogonal components or feature subspaces of the image. The performance of the detection system is compared against the performance of an independent detection system in terms of probability of detection (Pd) and probability of false alarm (Pfa).

InSAR Monitoring OF Landslides In Britain: BGS' Feasibility Map And First ISBAS Studies Over The South Wales Coalfield

NASA Astrophysics Data System (ADS)

Cigna, F.; Bateson, L.; Dashwood, C.; Jordan, C. J.; Sowter, A.; Boon, D.

2013-12-01

InSAR is an accepted method for monitoring ground motion, however its applicability in non-urban areas is generally limited except for rocky terrains. This paper investigates a new method for deriving improved results outside the urban environment. Topographic distortions to the ERS-1/2 and ENVISAT SAR acquisition modes are simulated based on high resolution DTMs of the landmass of Britain. Persistent Scatterers (PS) densities are predicted by calibrating the CORINE Land Cover 2006 dataset using PS data available via the ESA Terrafirma and EC FP7 PanGeo projects. The InSAR feasibility to monitor land motions is discussed for the South Wales Coalfield, and the Intermittent Small Baseline Subset (ISBAS) technique is tested over the Coalfield using 55 ERS-1/2 images (1992-1999). With unprecedented target coverage, ISBAS reveals up to 1cm/yr uplift in areas of former coal mining, likely associated with groundwater rebound following cessation of mine water pumping.

Study on online community user motif using web usage mining

NASA Astrophysics Data System (ADS)

Alphy, Meera; Sharma, Ajay

2016-04-01

The Web usage mining is the application of data mining, which is used to extract useful information from the online community. The World Wide Web contains at least 4.73 billion pages according to Indexed Web and it contains at least 228.52 million pages according Dutch Indexed web on 6th august 2015, Thursday. It’s difficult to get needed data from these billions of web pages in World Wide Web. Here is the importance of web usage mining. Personalizing the search engine helps the web user to identify the most used data in an easy way. It reduces the time consumption; automatic site search and automatic restore the useful sites. This study represents the old techniques to latest techniques used in pattern discovery and analysis in web usage mining from 1996 to 2015. Analyzing user motif helps in the improvement of business, e-commerce, personalisation and improvement of websites.

Contribution to understanding the post-mining landscape - Application of airborn LiDAR and historical maps at the example from Silesian Upland (Poland)

NASA Astrophysics Data System (ADS)

Gawior, D.; Rutkiewicz, P.; Malik, I.; Wistuba, M.

2017-11-01

LiDAR data provide new insights into the historical development of mining industry recorded in the topography and landscape. In the study on the lead ore mining in the 13th-17th century we identified remnants of mining activity in relief that are normally obscured by dense vegetation. The industry in Tarnowice Plateau was based on exploitation of galena from the bedrock. New technologies, including DEM from airborne LiDAR provide show that present landscape and relief of post-mining area under study developed during several, subsequent phases of exploitation when different techniques of exploitation were used and probably different types of ores were exploited. Study conducted on the Tarnowice Plateau proved that combining GIS visualization techniques with historical maps, among all geological maps, is a promising approach in reconstructing development of anthropogenic relief and landscape..

Exploring patterns of epigenetic information with data mining techniques.

PubMed

Aguiar-Pulido, Vanessa; Seoane, José A; Gestal, Marcos; Dorado, Julián

2013-01-01

Data mining, a part of the Knowledge Discovery in Databases process (KDD), is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management. Analyses of epigenetic data have evolved towards genome-wide and high-throughput approaches, thus generating great amounts of data for which data mining is essential. Part of these data may contain patterns of epigenetic information which are mitotically and/or meiotically heritable determining gene expression and cellular differentiation, as well as cellular fate. Epigenetic lesions and genetic mutations are acquired by individuals during their life and accumulate with ageing. Both defects, either together or individually, can result in losing control over cell growth and, thus, causing cancer development. Data mining techniques could be then used to extract the previous patterns. This work reviews some of the most important applications of data mining to epigenetics.

Visual Systems for Interactive Exploration and Mining of Large-Scale Neuroimaging Data Archives

PubMed Central

Bowman, Ian; Joshi, Shantanu H.; Van Horn, John D.

2012-01-01

While technological advancements in neuroimaging scanner engineering have improved the efficiency of data acquisition, electronic data capture methods will likewise significantly expedite the populating of large-scale neuroimaging databases. As they do and these archives grow in size, a particular challenge lies in examining and interacting with the information that these resources contain through the development of compelling, user-driven approaches for data exploration and mining. In this article, we introduce the informatics visualization for neuroimaging (INVIZIAN) framework for the graphical rendering of, and dynamic interaction with the contents of large-scale neuroimaging data sets. We describe the rationale behind INVIZIAN, detail its development, and demonstrate its usage in examining a collection of over 900 T1-anatomical magnetic resonance imaging (MRI) image volumes from across a diverse set of clinical neuroimaging studies drawn from a leading neuroimaging database. Using a collection of cortical surface metrics and means for examining brain similarity, INVIZIAN graphically displays brain surfaces as points in a coordinate space and enables classification of clusters of neuroanatomically similar MRI images and data mining. As an initial step toward addressing the need for such user-friendly tools, INVIZIAN provides a highly unique means to interact with large quantities of electronic brain imaging archives in ways suitable for hypothesis generation and data mining. PMID:22536181

MineScan: non-image data monitoring and mining from imaging modalities

NASA Astrophysics Data System (ADS)

Zaidi, Shayan M.; Huff, Dov; Bhalodia, Pankit; Mongkolwat, Pattanasak; Channin, David S.

2003-05-01

This project is intended to capture and interactively display non-image information routinely generated by imaging modalities. This information relates to the device's performance of the individual procedures and is not necessarily available in other information streams such as DICOM headers. While originally intended for use in servicing the modalities, this information can also be presented to radiologists and administrators within the department for both micro- and macro-management purposes. This data can help hospital administrators and radiologists manage available resources and discover clues to indicate what modifications in hospital operations might significantly improve its ability to provide efficient patient care. Data is collected from a departmental CT scanner. The data consists of a running record of exams followed by a list of processing records logged over a 24-hour period. MineScan extracts information from these records and stores it into a database. A statistical program is run once a day to collect relevant metrics. MineScan can be accessed via a Web browser or through an advanced prototype PACS workstation. This information, if provided in real-time, can be used to manage operations in a busy department. Even when provided historically, the data can be used to assess current activity, analyze trends and plan future operations.

Reduced-order model for underwater target identification using proper orthogonal decomposition

NASA Astrophysics Data System (ADS)

Ramesh, Sai Sudha; Lim, Kian Meng

2017-03-01

Research on underwater acoustics has seen major development over the past decade due to its widespread applications in domains such as underwater communication/navigation (SONAR), seismic exploration and oceanography. In particular, acoustic signatures from partially or fully buried targets can be used in the identification of buried mines for mine counter measures (MCM). Although there exist several techniques to identify target properties based on SONAR images and acoustic signatures, these methods first employ a feature extraction method to represent the dominant characteristics of a data set, followed by the use of an appropriate classifier based on neural networks or the relevance vector machine. The aim of the present study is to demonstrate the applications of proper orthogonal decomposition (POD) technique in capturing dominant features of a set of scattered pressure signals, and subsequent use of the POD modes and coefficients in the identification of partially buried underwater target parameters such as its location, size and material density. Several numerical examples are presented to demonstrate the performance of the system identification method based on POD. Although the present study is based on 2D acoustic model, the method can be easily extended to 3D models and thereby enables cost-effective representations of large-scale data.

Data Mining for Financial Applications

NASA Astrophysics Data System (ADS)

Kovalerchuk, Boris; Vityaev, Evgenii

This chapter describes Data Mining in finance by discussing financial tasks, specifics of methodologies and techniques in this Data Mining area. It includes time dependence, data selection, forecast horizon, measures of success, quality of patterns, hypothesis evaluation, problem ID, method profile, attribute-based and relational methodologies. The second part of the chapter discusses Data Mining models and practice in finance. It covers use of neural networks in portfolio management, design of interpretable trading rules and discovering money laundering schemes using decision rules and relational Data Mining methodology.

Examining Mobile Learning Trends 2003-2008: A Categorical Meta-Trend Analysis Using Text Mining Techniques

ERIC Educational Resources Information Center

Hung, Jui-Long; Zhang, Ke

2012-01-01

This study investigated the longitudinal trends of academic articles in Mobile Learning (ML) using text mining techniques. One hundred and nineteen (119) refereed journal articles and proceedings papers from the SCI/SSCI database were retrieved and analyzed. The taxonomies of ML publications were grouped into twelve clusters (topics) and four…

Restoration of tropical moist forest on bauxite mined lands in the Brazilian Amazon

Treesearch

John A Parrotta; Oliver H. Knowles

1999-01-01

We evaluated forest structure and composition in 9- to 13-year-old stands established on a bauxite-mined site at Trombetas (Pará), Brazil, using four different reforestation techniques following initial site preparation and topsoil replacement. These techniques included reliance on natural forest regeneration, mixed commercial species plantings of mostly exotic timber...

Data-Mining Techniques in Detecting Factors Linked to Academic Achievement

ERIC Educational Resources Information Center

Martínez Abad, Fernando; Chaparro Caso López, Alicia A.

2017-01-01

In light of the emergence of statistical analysis techniques based on data mining in education sciences, and the potential they offer to detect non-trivial information in large databases, this paper presents a procedure used to detect factors linked to academic achievement in large-scale assessments. The study is based on a non-experimental,…

An Evaluation of Text Mining Tools as Applied to Selected Scientific and Engineering Literature.

ERIC Educational Resources Information Center

Trybula, Walter J.; Wyllys, Ronald E.

2000-01-01

Addresses an approach to the discovery of scientific knowledge through an examination of data mining and text mining techniques. Presents the results of experiments that investigated knowledge acquisition from a selected set of technical documents by domain experts. (Contains 15 references.) (Author/LRW)

Illustrated surface mining methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1979-01-01

This manual provides a visual synopsis of surface coal mining methods in the United States. The manual presents various surface mining methods and techniques through artist renderings and appropriate descriptions. The productive coal fields of the United States were divided into four regions according to geology and physiography. A glossay of terminology is included. (DP)

Analyzing Student Inquiry Data Using Process Discovery and Sequence Classification

ERIC Educational Resources Information Center

Emond, Bruno; Buffett, Scott

2015-01-01

This paper reports on results of applying process discovery mining and sequence classification mining techniques to a data set of semi-structured learning activities. The main research objective is to advance educational data mining to model and support self-regulated learning in heterogeneous environments of learning content, activities, and…

A Contextualized, Differential Sequence Mining Method to Derive Students' Learning Behavior Patterns

ERIC Educational Resources Information Center

Kinnebrew, John S.; Loretz, Kirk M.; Biswas, Gautam

2013-01-01

Computer-based learning environments can produce a wealth of data on student learning interactions. This paper presents an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs a novel combination of sequence mining techniques to identify deferentially…

Weipa, Queensland, Australia

NASA Image and Video Library

2014-08-21

This image from NASA Terra spacecraft shows the world largest bauxite mine found near Weipa, Queensland, Australia. The rich aluminum deposits were first recognized on the end of the Cape York Peninsula in 1955, and mining began in 1960.

Escondida Mine, Chile

NASA Technical Reports Server (NTRS)

2002-01-01

Full resolution visible and near-infrared image (1.4 MB) Full resolution shortwave infrared image (1.6 MB) This Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image covers 30 by 23 km (full images 30 x 37 km) in the Atacama Desert, Chile, and was acquired on April 23, 2000. The Escondida copper, gold, and silver open-pit mine is at an elevation of 3050 m, and began operations in 1990. Current capacity is 127,000 tons/day of ore; in 1999 production totaled 827,000 tons of copper, 150,000 ounces of gold, and 3.53 million ounces of silver. Primary concentrate of the ore is done on-site; the concentrate is then sent to the coast for further processing through a 170 km long, 9-inch pipe. Escondida is related geologically to three porphyry bodies intruded along the Chilean West Fissure Fault System. A high grade supergene cap overlies primary sulfide ore. The top image is a conventional 3-2-1 (near infrared, red, green) RGB composite. The bottom image displays shortwave infrared bands 4-6-8 (1.65um, 2.205um, 2.33um) in RGB, and highlights the different rock types present on the surface, as well as the changes caused by mining. Image courtesy NASA/GSFC/MITI/ERSDAC/JAROS, and U.S./Japan ASTER Science Team

Discovering significant evolution patterns from satellite image time series.

PubMed

Petitjean, François; Masseglia, Florent; Gançarski, Pierre; Forestier, Germain

2011-12-01

Satellite Image Time Series (SITS) provide us with precious information on land cover evolution. By studying these series of images we can both understand the changes of specific areas and discover global phenomena that spread over larger areas. Changes that can occur throughout the sensing time can spread over very long periods and may have different start time and end time depending on the location, which complicates the mining and the analysis of series of images. This work focuses on frequent sequential pattern mining (FSPM) methods, since this family of methods fits the above-mentioned issues. This family of methods consists of finding the most frequent evolution behaviors, and is actually able to extract long-term changes as well as short term ones, whenever the change may start and end. However, applying FSPM methods to SITS implies confronting two main challenges, related to the characteristics of SITS and the domain's constraints. First, satellite images associate multiple measures with a single pixel (the radiometric levels of different wavelengths corresponding to infra-red, red, etc.), which makes the search space multi-dimensional and thus requires specific mining algorithms. Furthermore, the non evolving regions, which are the vast majority and overwhelm the evolving ones, challenge the discovery of these patterns. We propose a SITS mining framework that enables discovery of these patterns despite these constraints and characteristics. Our proposal is inspired from FSPM and provides a relevant visualization principle. Experiments carried out on 35 images sensed over 20 years show the proposed approach makes it possible to extract relevant evolution behaviors.

Identification of hydrothermal alteration minerals for exploring of porphyry copper deposit using ASTER data, SE Iran

NASA Astrophysics Data System (ADS)

Pour, Amin Beiranvnd; Hashim, Mazlan

2011-11-01

The NW-SE trending Central Iranian Volcanic Belt hosts many well-known porphyry copper deposits in Iran. It becomes an interesting area for remote sensing investigations to explore the new prospects of porphyry copper and vein type epithermal gold mineralization. Two copper mining districts in southeastern segment of the volcanic belt, including Meiduk and Sarcheshmeh have been selected in the present study. The performance of Principal Component Analysis, band ratio and Minimum Noise Fraction transformation has been evaluated for the visible and near infrared (VNIR) and, shortwave infrared (SWIR) subsystems of ASTER data. The image processing techniques indicated the distribution of iron oxides and vegetation in the VNIR subsystem. Hydrothermal alteration mineral zones associated with porphyry copper mineralization identified and discriminated based on distinctive shortwave infrared (SWIR) properties of the ASTER data in a regional scale. These techniques identified new prospects of porphyry copper mineralization in the study areas. The spatial distribution of hydrothermal alteration zones has been verified by in situ inspection, X-ray diffraction (XRD) analysis, and spectral reflectance measurements. Results indicated that the integration of the image processing techniques has a great ability to obtain significant and comprehensive information for the reconnaissance stages of porphyry copper exploration in a regional scale. The results of this research can assist exploration geologists to find new prospects of porphyry copper and gold deposits in the other virgin regions before costly detailed ground investigations. Consequently, the introduced image processing techniques can create an optimum idea about possible location of the new prospects.

Characterization of Urban Landscape Using Super-Resolution UAS Data, Multiple Textural Scales and Data-Mining Techniques

NASA Astrophysics Data System (ADS)

Voss, M.; Blundell, B.

2015-12-01

Characterization of urban environments is a high priority for the U.S. Army as battlespaces have transitioned from the predominantly open spaces of the 20th century to urban areas where soldiers have reduced situational awareness due to the diversity and density of their surroundings. Creating high-resolution urban terrain geospatial information will improve mission planning and soldier effectiveness. In this effort, super-resolution true-color imagery was collected with an Altivan NOVA unmanned aerial system over the Muscatatuck Urban Training Center near Butlerville, Indiana on September 16, 2014. Multispectral texture analysis using different algorithms was conducted for urban surface characterization at a variety of scales. Training samples extracted from the true-color and texture images. These data were processed using a variety of meta-algorithms with a decision tree classifier to create a high-resolution urban features map. In addition to improving accuracy over traditional image classification methods, this technique allowed the determination of the most significant textural scales in creating urban terrain maps for tactical exploitation.

Video mining using combinations of unsupervised and supervised learning techniques

NASA Astrophysics Data System (ADS)

Divakaran, Ajay; Miyahara, Koji; Peker, Kadir A.; Radhakrishnan, Regunathan; Xiong, Ziyou

2003-12-01

We discuss the meaning and significance of the video mining problem, and present our work on some aspects of video mining. A simple definition of video mining is unsupervised discovery of patterns in audio-visual content. Such purely unsupervised discovery is readily applicable to video surveillance as well as to consumer video browsing applications. We interpret video mining as content-adaptive or "blind" content processing, in which the first stage is content characterization and the second stage is event discovery based on the characterization obtained in stage 1. We discuss the target applications and find that using a purely unsupervised approach are too computationally complex to be implemented on our product platform. We then describe various combinations of unsupervised and supervised learning techniques that help discover patterns that are useful to the end-user of the application. We target consumer video browsing applications such as commercial message detection, sports highlights extraction etc. We employ both audio and video features. We find that supervised audio classification combined with unsupervised unusual event discovery enables accurate supervised detection of desired events. Our techniques are computationally simple and robust to common variations in production styles etc.

Mining MaNGA for Merging Galaxies: A New Imaging and Kinematic Technique from Hydrodynamical Simulations

NASA Astrophysics Data System (ADS)

Nevin, Becky; Comerford, Julia M.; Blecha, Laura

2018-06-01

Merging galaxies play a key role in galaxy evolution, and progress in our understanding of galaxy evolution is slowed by the difficulty of making accurate galaxy merger identifications. Mergers are typically identified using imaging alone, which has its limitations and biases. With the growing popularity of integral field spectroscopy (IFS), it is now possible to use kinematic signatures to improve galaxy merger identifications. I use GADGET-3 hydrodynamical simulations of merging galaxies with the radiative transfer code SUNRISE, the later of which enables me to apply the same analysis to simulations and observations. From the simulated galaxies, I have developed the first merging galaxy classification scheme that is based on kinematics and imaging. Utilizing a Linear Discriminant Analysis tool, I have determined which kinematic and imaging predictors are most useful for identifying mergers of various merger parameters (such as orientation, mass ratio, gas fraction, and merger stage). I will discuss the strengths and limitations of the classification technique and then my initial results for applying the classification to the >10,000 observed galaxies in the MaNGA (Mapping Nearby Galaxies at Apache Point) IFS survey. Through accurate identification of merging galaxies in the MaNGA survey, I will advance our understanding of supermassive black hole growth in galaxy mergers and other open questions related to galaxy evolution.

The LSST Data Mining Research Agenda

NASA Astrophysics Data System (ADS)

Borne, K.; Becla, J.; Davidson, I.; Szalay, A.; Tyson, J. A.

2008-12-01

We describe features of the LSST science database that are amenable to scientific data mining, object classification, outlier identification, anomaly detection, image quality assurance, and survey science validation. The data mining research agenda includes: scalability (at petabytes scales) of existing machine learning and data mining algorithms; development of grid-enabled parallel data mining algorithms; designing a robust system for brokering classifications from the LSST event pipeline (which may produce 10,000 or more event alerts per night) multi-resolution methods for exploration of petascale databases; indexing of multi-attribute multi-dimensional astronomical databases (beyond spatial indexing) for rapid querying of petabyte databases; and more.

Decreased gray matter volume in the left hippocampus and bilateral calcarine cortex in coal mine flood disaster survivors with recent onset PTSD.

PubMed

Zhang, Jian; Tan, Qingrong; Yin, Hong; Zhang, Xiaoliang; Huan, Yi; Tang, Lihua; Wang, Huaihai; Xu, Junqing; Li, Lingjiang

2011-05-31

Although limbic structure changes have been found in chronic and recent onset post-traumatic stress disorder (PTSD) patients, there are few studies about brain structure changes in recent onset PTSD patients after a single extreme and prolonged trauma. In the current study, 20 coal mine flood disaster survivors underwent magnetic resonance imaging (MRI). Voxel-based morphometry (VBM) and region of interest (ROI) techniques were used to detect the gray matter and white matter volume changes in 10 survivors with recent onset PTSD and 10 survivors without PTSD. The correlation between the Clinician-Administered PTSD Scale (CAPS) and gray matter density in the ROI was also studied. Compared with survivors without PTSD, survivors with PTSD had significantly decreased gray matter volume and density in left anterior hippocampus, left parahippocampal gyrus, and bilateral calcarine cortex. The CAPS score correlated negatively with the gray matter density in bilateral calcarine cortex and left hippocampus in coal mine disaster survivors. Our study suggests that the gray matter volume and density of limbic structure decreased in recent onset PTSD patients who were exposed to extreme trauma. PTSD symptom severity was associated with gray matter density in calcarine cortex and hippocampus. 2010 Elsevier Ireland Ltd. All rights reserved.

Haneş and Valea Vinului (Romania) closed mines Acid Mine Drainages (AMDs)--actual condition and passive treatment remediation proposal.

PubMed

Măicăneanu, Andrada; Bedelean, Horea; Ardelean, Marius; Burcă, Silvia; Stanca, Maria

2013-10-01

Acid Mine Drainages (AMDs) from Haneş and Valea Vinului (Romania) closed mines were considered for characterization and treatment using a local zeolitic volcanic tuff, ZVT, (Măcicaş, Cluj County, Romania). Water samples were collected from two locations, before and after discharging point in case of Haneş mine, and on three horizons in case of Valea Vinului mine. Physico-chemical (pH, total solid, heavy metal ions concentration) analyses showed that the environment is strongly affected by these AMD discharges even if the mines were closed years ago. Iron, manganese and zinc were the main pollutants identified in Haneş mine AMD, while zinc is the one mainly present in case of Valea Vinului AMD. A batch technique (no stirring) in which the ZVT was put in contact with the AMD sample was proposed as a passive remediation technique. ZVT successfully remove heavy metal ion from AMD. According to heavy metal ion concentrations, removal efficiencies are reaching 100%, varying as follows, Fe(2+)>Zn(2+)>Mn(2+). When the ZVT was compared with two cationic resins (strong, SAR and weak acid, WAR) the following series was depicted, SAR>ZVT>WAR. Copyright © 2013 Elsevier Ltd. All rights reserved.

Semi-automation of Doppler Spectrum Image Analysis for Grading Aortic Valve Stenosis Severity.

PubMed

Niakšu, O; Balčiunaitė, G; Kizlaitis, R J; Treigys, P

2016-01-01

Doppler echocardiography analysis has become a golden standard in the modern diagnosis of heart diseases. In this paper, we propose a set of techniques for semi-automated parameter extraction for aortic valve stenosis severity grading. The main objectives of the study is to create echocardiography image processing techniques, which minimize manual image processing work of clinicians and leads to reduced human error rates. Aortic valve and left ventricle output tract spectrogram images have been processed and analyzed. A novel method was developed to trace systoles and to extract diagnostic relevant features. The results of the introduced method have been compared to the findings of the participating cardiologists. The experimental results showed the accuracy of the proposed method is comparable to the manual measurement performed by medical professionals. Linear regression analysis of the calculated parameters and the measurements manually obtained by the cardiologists resulted in the strongly correlated values: peak systolic velocity's and mean pressure gradient's R2 both equal to 0.99, their means' differences equal to 0.02 m/s and 4.09 mmHg, respectively, and aortic valve area's R2 of 0.89 with the two methods means' difference of 0.19 mm. The introduced Doppler echocardiography images processing method can be used as a computer-aided assistance in the aortic valve stenosis diagnostics. In our future work, we intend to improve precision of left ventricular outflow tract spectrogram measurements and apply data mining methods to propose a clinical decision support system for diagnosing aortic valve stenosis.

Using NASA Earth Observing Satellites and Statistical Model Analysis to Monitor Vegetation and Habitat Rehabilitation in Southwest Virginia's Reclaimed Mine Lands

NASA Astrophysics Data System (ADS)

Tate, Z.; Dusenge, D.; Elliot, T. S.; Hafashimana, P.; Medley, S.; Porter, R. P.; Rajappan, R.; Rodriguez, P.; Spangler, J.; Swaminathan, R. S.; VanGundy, R. D.

2014-12-01

The majority of the population in southwest Virginia depends economically on coal mining. In 2011, coal mining generated $2,000,000 in tax revenue to Wise County alone. However, surface mining completely removes land cover and leaves the land exposed to erosion. The destruction of the forest cover directly impacts local species, as some are displaced and others perish in the mining process. Even though surface mining has a negative impact on the environment, land reclamation efforts are in place to either restore mined areas to their natural vegetated state or to transform these areas for economic purposes. This project aimed to monitor the progress of land reclamation and the effect on the return of local species. By incorporating NASA Earth observations, such as Landsat 8 Operational Land Imager (OLI) and Landsat 5 Thematic Mapper (TM), re-vegetation process in reclamation sites was estimated through a Time series analysis using the Normalized Difference Vegetation Index (NDVI). A continuous source of cloud free images was accomplished by utilizing the Spatial and Temporal Adaptive Reflectance Fusion Model (STAR-FM). This model developed synthetic Landsat imagery by integrating the high-frequency temporal information from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and high-resolution spatial information from Landsat sensors In addition, the Maximum Entropy Modeling (MaxENT), an eco-niche model was used to estimate the adaptation of animal species to the newly formed habitats. By combining factors such as land type, precipitation from Tropical Rainfall Measuring Mission (TRMM), and slope from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the MaxENT model produced a statistical analysis on the probability of species habitat. Altogether, the project compiled the ecological information which can be used to identify suitable habitats for local species in reclaimed mined areas.

Biogeochemical behaviour and bioremediation of uranium in waters of abandoned mines.

PubMed

Mkandawire, Martin

2013-11-01

The discharges of uranium and associated radionuclides as well as heavy metals and metalloids from waste and tailing dumps in abandoned uranium mining and processing sites pose contamination risks to surface and groundwater. Although many more are being planned for nuclear energy purposes, most of the abandoned uranium mines are a legacy of uranium production that fuelled arms race during the cold war of the last century. Since the end of cold war, there have been efforts to rehabilitate the mining sites, initially, using classical remediation techniques based on high chemical and civil engineering. Recently, bioremediation technology has been sought as alternatives to the classical approach due to reasons, which include: (a) high demand of sites requiring remediation; (b) the economic implication of running and maintaining the facilities due to high energy and work force demand; and (c) the pattern and characteristics of contaminant discharges in most of the former uranium mining and processing sites prevents the use of classical methods. This review discusses risks of uranium contamination from abandoned uranium mines from the biogeochemical point of view and the potential and limitation of uranium bioremediation technique as alternative to classical approach in abandoned uranium mining and processing sites.

Advances in Machine Learning and Data Mining for Astronomy

NASA Astrophysics Data System (ADS)

Way, Michael J.; Scargle, Jeffrey D.; Ali, Kamal M.; Srivastava, Ashok N.

2012-03-01

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

WHIDE—a web tool for visual data mining colocation patterns in multivariate bioimages

PubMed Central

Kölling, Jan; Langenkämper, Daniel; Abouna, Sylvie; Khan, Michael; Nattkemper, Tim W.

2012-01-01

Motivation: Bioimaging techniques rapidly develop toward higher resolution and dimension. The increase in dimension is achieved by different techniques such as multitag fluorescence imaging, Matrix Assisted Laser Desorption / Ionization (MALDI) imaging or Raman imaging, which record for each pixel an N-dimensional intensity array, representing local abundances of molecules, residues or interaction patterns. The analysis of such multivariate bioimages (MBIs) calls for new approaches to support users in the analysis of both feature domains: space (i.e. sample morphology) and molecular colocation or interaction. In this article, we present our approach WHIDE (Web-based Hyperbolic Image Data Explorer) that combines principles from computational learning, dimension reduction and visualization in a free web application. Results: We applied WHIDE to a set of MBI recorded using the multitag fluorescence imaging Toponome Imaging System. The MBI show field of view in tissue sections from a colon cancer study and we compare tissue from normal/healthy colon with tissue classified as tumor. Our results show, that WHIDE efficiently reduces the complexity of the data by mapping each of the pixels to a cluster, referred to as Molecular Co-Expression Phenotypes and provides a structural basis for a sophisticated multimodal visualization, which combines topology preserving pseudocoloring with information visualization. The wide range of WHIDE's applicability is demonstrated with examples from toponome imaging, high content screens and MALDI imaging (shown in the Supplementary Material). Availability and implementation: The WHIDE tool can be accessed via the BioIMAX website http://ani.cebitec.uni-bielefeld.de/BioIMAX/; Login: whidetestuser; Password: whidetest. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: tim.nattkemper@uni-bielefeld.de PMID:22390938

Millennium Open Pit Mine, Alberta, Canada

NASA Image and Video Library

2007-11-26

Near Fort McMurray, Alberta, Canada, on the east bank of the Athabasca River, are found the Steepbank and Millennium open pit mines. These images were acquired by NASA Terra satellite on September 22, 2000 and July 31, 2007.

Privacy Preserving Technique for Euclidean Distance Based Mining Algorithms Using a Wavelet Related Transform

NASA Astrophysics Data System (ADS)

Kadampur, Mohammad Ali; D. v. L. N., Somayajulu

Privacy preserving data mining is an art of knowledge discovery without revealing the sensitive data of the data set. In this paper a data transformation technique using wavelets is presented for privacy preserving data mining. Wavelets use well known energy compaction approach during data transformation and only the high energy coefficients are published to the public domain instead of the actual data proper. It is found that the transformed data preserves the Eucleadian distances and the method can be used in privacy preserving clustering. Wavelets offer the inherent improved time complexity.

Survey of Analysis of Crime Detection Techniques Using Data Mining and Machine Learning

NASA Astrophysics Data System (ADS)

Prabakaran, S.; Mitra, Shilpa

2018-04-01

Data mining is the field containing procedures for finding designs or patterns in a huge dataset, it includes strategies at the convergence of machine learning and database framework. It can be applied to various fields like future healthcare, market basket analysis, education, manufacturing engineering, crime investigation etc. Among these, crime investigation is an interesting application to process crime characteristics to help the society for a better living. This paper survey various data mining techniques used in this domain. This study may be helpful in designing new strategies for crime prediction and analysis.

Remote mineral mapping using AVIRIS data at Summitville, Colorado and the adjacent San Juan Mountains

NASA Technical Reports Server (NTRS)

King, Trude V. V.; Clark, Roger N.; Ager, Cathy; Swayze, Gregg A.

1995-01-01

We have demonstrated the unique utility of imaging spectroscopy in mapping mineral distribution. In the Summitville mining region we have shown that the mine site does not contribute clay minerals to the Alamosa River, but does contribute Fe-bearing minerals. Such minerals have the potential to carry heavy metals. This application illustrates only one specific environmental application of imaging spectroscopy data. For instance, the types of minerals we can map with confidence are those frequently associated with environmental problems related to active and abandoned mine lands. Thus, the potential utility of this technology to the field of environmental science has yet to be fully explored.

Development of computerized stocktaking system in mine surveying for ore mineral volume calculation in covered storehouses

NASA Astrophysics Data System (ADS)

Valdman, V. V.; Gridnev, S. O.

2017-10-01

The article examines into the vital issues of measuring and calculating the raw stock volumes in covered storehouses at mining and processing plants. The authors bring out two state-of-the-art high-technology solutions: 1 - to use the ground-based laser scanning system (the method is reasonably accurate and dependable, but costly and time consuming; it also requires the stoppage of works in the storehouse); 2 - to use the fundamentally new computerized stocktaking system in mine surveying for the ore mineral volume calculation, based on the profile digital images. These images are obtained via vertical projection of the laser plane onto the surface of the stored raw materials.

Study of the crater deformation of the CODELCO/Andina mine using the satellite and ground data

NASA Astrophysics Data System (ADS)

Caverlotti-Silva, M. A.; Arellano-Baeza, A. A.

2011-12-01

The correct monitoring of the subsidence of the craters related to the underground mine exploitation is one of the most important endeavors of the satellite remote sensing. The ASTER and LANDSAT satellite images have been used to study the deformation of the crater of the CODELCO/Andina mine, Valparaiso Region, Chile. The high-resolution satellite images were used to detect changes in the lineament patterns related to the subsidence. These results were compared with the ground deformation extracted from the GPS and topography station networks. It was found that sudden changes in the lineament patterns appear when the ground deformation overcomes a definite threshold.

76 FR 7868 - Center for Scientific Review; Notice of Closed Meetings

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-11

... Special Emphasis Panel, Small Business: Computational Biology, Image Processing and Data Mining. Date... for Scientific Review Special Emphasis Panel, Quick Trial on Imaging and Image-Guided Intervention...

Data mining and visualization techniques

DOEpatents

Wong, Pak Chung [Richland, WA; Whitney, Paul [Richland, WA; Thomas, Jim [Richland, WA

2004-03-23

Disclosed are association rule identification and visualization methods, systems, and apparatus. An association rule in data mining is an implication of the form X.fwdarw.Y where X is a set of antecedent items and Y is the consequent item. A unique visualization technique that provides multiple antecedent, consequent, confidence, and support information is disclosed to facilitate better presentation of large quantities of complex association rules.

[Analysis of syndrome discipline of generalized anxiety disorder using data mining techniques].

PubMed

Tang, Qi-sheng; Sun, Wen-jun; Qu, Miao; Guo, Dong-fang

2012-09-01

To study the use of data mining techniques in analyzing the syndrome discipline of generalized anxiety disorder (GAD). From August 1, 2009 to July 31, 2010, 705 patients with GAD in 10 hospitals of Beijing were investigated over one year. Data mining techniques, such as Bayes net and cluster analysis, were used to analyze the syndrome discipline of GAD. A total of 61 symptoms of GAD were screened out. By using Bayes net, nine syndromes of GAD were abstracted based on the symptoms. Eight syndromes were abstracted by cluster analysis. After screening for duplicate syndromes and combining the experts' experience and traditional Chinese medicine theory, six syndromes of GAD were defined. These included depressed liver qi transforming into fire, phlegm-heat harassing the heart, liver depression and spleen deficiency, heart-kidney non-interaction, dual deficiency of the heart and spleen, and kidney deficiency and liver yang hyperactivity. Based on the results, the draft of Syndrome Diagnostic Criteria for Generalized Anxiety Disorder was developed. Data mining techniques such as Bayes net and cluster analysis have certain future potential for establishing syndrome models and analyzing syndrome discipline, thus they are suitable for the research of syndrome differentiation.

Evaluation of the mining techniques in constructing a traditional Chinese-language nursing recording system.

PubMed

Liao, Pei-Hung; Chu, William; Chu, Woei-Chyn

2014-05-01

In 2009, the Department of Health, part of Taiwan's Executive Yuan, announced the advent of electronic medical records to reduce medical expenses and facilitate the international exchange of medical record information. An information technology platform for nursing records in medical institutions was then quickly established, which improved nursing information systems and electronic databases. The purpose of the present study was to explore the usability of the data mining techniques to enhance completeness and ensure consistency of nursing records in the database system.First, the study used a Chinese word-segmenting system on common and special terms often used by the nursing staff. We also used text-mining techniques to collect keywords and create a keyword lexicon. We then used an association rule and artificial neural network to measure the correlation and forecasting capability for keywords. Finally, nursing staff members were provided with an on-screen pop-up menu to use when establishing nursing records. Our study found that by using mining techniques we were able to create a powerful keyword lexicon and establish a forecasting model for nursing diagnoses, ensuring the consistency of nursing terminology and improving the nursing staff's work efficiency and productivity.

Data mining in child welfare.

PubMed

Schoech, D; Quinn, A; Rycraft, J R

2000-01-01

Data mining is the sifting through of voluminous data to extract knowledge for decision making. This article illustrates the context, concepts, processes, techniques, and tools of data mining, using statistical and neural network analyses on a dataset concerning employee turnover. The resulting models and their predictive capability, advantages and disadvantages, and implications for decision support are highlighted.

Redundancy and Novelty Mining in the Business Blogosphere

ERIC Educational Resources Information Center

Tsai, Flora S.; Chan, Kap Luk

2010-01-01

Purpose: The paper aims to explore the performance of redundancy and novelty mining in the business blogosphere, which has not been studied before. Design/methodology/approach: Novelty mining techniques are implemented to single out novel information out of a massive set of text documents. This paper adopted the mixed metric approach which…

Reforestation of mined land in the northeastern and north-central U.S.

Treesearch

Walter H. Davidson; Russell J. Hutnik; Delbert E. Parr

1984-01-01

This paper reviews the state of the art of surface mine reclamation for forestry in Pennsylvania, Maryland, West Virginia, Ohio, Indiana, and Illinois. Legislative constraints, socioeconomic issues, factors limiting the success of reforestation efforts, post-mining land-use trends, species options, and establishment techniques are discussed. Sources of assistance to...

Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution

ERIC Educational Resources Information Center

Kinnebrew, John S.; Biswas, Gautam

2012-01-01

Our learning-by-teaching environment, Betty's Brain, captures a wealth of data on students' learning interactions as they teach a virtual agent. This paper extends an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs sequence mining techniques to…

Increasing the Reliability of the Work of Artificial Filtering Arrays for the Purification of Quarry Waste Water

NASA Astrophysics Data System (ADS)

Tyulenev, Maxim; Lesin, Yury; Litvin, Oleg; Maliukhina, Elena; Abay, Asmelash

2017-11-01

Features of geological structure of the Kuznetsk coal basin stipulate the application of a low-cost open technique of coal mining, which is more advantageous both from the economic standpoint, and by safety criteria of mining. However, open mining affects significantly the water resources of region. Intensive pollution of reservoirs and water courses, exhaustion of the underground water-bearing layers, violation of a hydrographic network, etc. be-long to the main disadvantages of an open technique of coal mining. Besides, the volume of the water coming into the mining producers exceeds signi-ficantly the needed quantity. According to the data of annual reports of ecology and natural resources department, 348.277 million m3 of water were ta-ken away during production of soft coal, brown coal and lignum fossil from waters of Kemerovo region in 2013 (mostly from underground water objects (96,5%) when draining of mine openings). At the same time, only 87.018 million m3 of water (25%) has been used within a year.

Methods and costs of thin-seam mining. Final report, 25 September 1977-24 January 1979. [Thin seam in association with a thick seam

DOE Office of Scientific and Technical Information (OSTI.GOV)

Finch, T.E.; Fidler, E.L.

1981-02-01

This report defines the state of the art (circa 1978) in removing thin coal seams associated with vastly thicker seams found in the surface coal mines of the western United States. New techniques are evaluated and an innovative method and machine is proposed. Western states resource recovery regulations are addressed and representative mining operations are examined. Thin seam recovery is investigated through its effect on (1) overburden removal, (2) conventional seam extraction methods, and (3) innovative techniques. Equations and graphs are used to accommodate the variable stratigraphic positions in the mining sequence on which thin seams occur. Industrial concern andmore » agency regulations provided the impetus for this study of total resource recovery. The results are a compendium of thin seam removal methods and costs. The work explains how the mining industry recovers thin coal seams in western surface mines where extremely thick seams naturally hold the most attention. It explains what new developments imply and where to look for new improvements and their probable adaptability.« less

Field test of an alternative longwall gate road design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cox, R.M.; Vandergrift, T.L.; McDonnell, J.P.

1994-01-01

The US Bureau of Mines (USBM) MULSIM/ML modeling technique has been used to analyze anticipated stress distributions for a proposed alternative longwall gate road design for a western Colorado coal mine. The model analyses indicated that the alternative gate road design would reduce stresses in the headgate entry. To test the validity of the alternative gate road design under actual mining conditions, a test section of the alternative system was incorporated into a subsequent set of gate roads developed at the mine. The alternative gate road test section was instrumented with borehole pressure cells, as part of an ongoing USBMmore » research project to monitor ground pressure changes as longwall mining progressed. During the excavation of the adjacent longwall panels, the behavior of the alternative gate road system was monitored continuously using the USBM computer-assisted Ground Control Management System. During these field tests, the alternative gate road system was first monitored and evaluated as a headgate, and later monitored and evaluated as a tailgate. The results of the field tests confirmed the validity of using the MULSIM/NL modeling technique to evaluate mine designs.« less

All-Optical Fibre Networks For Coal Mines

NASA Astrophysics Data System (ADS)

Zientkiewicz, Jacek K.

1987-09-01

A topic of the paper is fiber-optic integrated network (FOIN) suited to the most hostile environments existing in coal mines. The use of optical fibres for transmission of mine instrumentation data offers the prospects of improved safety and immunity to electromagnetic interference (EMI). The feasibility of optically powered sensors has opened up new opportunities for research into optical signal processing architectures. This article discusses a new fibre-optic sensor network involving a time domain multiplexing(TDM)scheme and optical signal processing techniques. The pros and cons of different FOIN topologies with respect to coal mine applications are considered. The emphasis has been placed on a recently developed all-optical fibre network using spread spectrum code division multiple access (COMA) techniques. The all-optical networks have applications in explosive environments where electrical isolation is required.

Issues of data governance associated with data mining in medical research: experiences from an empirical study.

PubMed

Nahar, Jesmin; Imam, Tasadduq; Tickle, Kevin S; Garcia-Alonso, Debora

2013-01-01

This chapter is a review of data mining techniques used in medical research. It will cover the existing applications of these techniques in the identification of diseases, and also present the authors' research experiences in medical disease diagnosis and analysis. A computational diagnosis approach can have a significant impact on accurate diagnosis and result in time and cost effective solutions. The chapter will begin with an overview of computational intelligence concepts, followed by details on different classification algorithms. Use of association learning, a well recognised data mining procedure, will also be discussed. Many of the datasets considered in existing medical data mining research are imbalanced, and the chapter focuses on this issue as well. Lastly, the chapter outlines the need of data governance in this research domain.

Data Mining Research with the LSST

NASA Astrophysics Data System (ADS)

Borne, Kirk D.; Strauss, M. A.; Tyson, J. A.

2007-12-01

The LSST catalog database will exceed 10 petabytes, comprising several hundred attributes for 5 billion galaxies, 10 billion stars, and over 1 billion variable sources (optical variables, transients, or moving objects), extracted from over 20,000 square degrees of deep imaging in 5 passbands with thorough time domain coverage: 1000 visits over the 10-year LSST survey lifetime. The opportunities are enormous for novel scientific discoveries within this rich time-domain ultra-deep multi-band survey database. Data Mining, Machine Learning, and Knowledge Discovery research opportunities with the LSST are now under study, with a potential for new collaborations to develop to contribute to these investigations. We will describe features of the LSST science database that are amenable to scientific data mining, object classification, outlier identification, anomaly detection, image quality assurance, and survey science validation. We also give some illustrative examples of current scientific data mining research in astronomy, and point out where new research is needed. In particular, the data mining research community will need to address several issues in the coming years as we prepare for the LSST data deluge. The data mining research agenda includes: scalability (at petabytes scales) of existing machine learning and data mining algorithms; development of grid-enabled parallel data mining algorithms; designing a robust system for brokering classifications from the LSST event pipeline (which may produce 10,000 or more event alerts per night); multi-resolution methods for exploration of petascale databases; visual data mining algorithms for visual exploration of the data; indexing of multi-attribute multi-dimensional astronomical databases (beyond RA-Dec spatial indexing) for rapid querying of petabyte databases; and more. Finally, we will identify opportunities for synergistic collaboration between the data mining research group and the LSST Data Management and Science Collaboration teams.

A Visual mining based framework for classification accuracy estimation

NASA Astrophysics Data System (ADS)

Arun, Pattathal Vijayakumar

2013-12-01

Classification techniques have been widely used in different remote sensing applications and correct classification of mixed pixels is a tedious task. Traditional approaches adopt various statistical parameters, however does not facilitate effective visualisation. Data mining tools are proving very helpful in the classification process. We propose a visual mining based frame work for accuracy assessment of classification techniques using open source tools such as WEKA and PREFUSE. These tools in integration can provide an efficient approach for getting information about improvements in the classification accuracy and helps in refining training data set. We have illustrated framework for investigating the effects of various resampling methods on classification accuracy and found that bilinear (BL) is best suited for preserving radiometric characteristics. We have also investigated the optimal number of folds required for effective analysis of LISS-IV images. Techniki klasyfikacji są szeroko wykorzystywane w różnych aplikacjach teledetekcyjnych, w których poprawna klasyfikacja pikseli stanowi poważne wyzwanie. Podejście tradycyjne wykorzystujące różnego rodzaju parametry statystyczne nie zapewnia efektywnej wizualizacji. Wielce obiecujące wydaje się zastosowanie do klasyfikacji narzędzi do eksploracji danych. W artykule zaproponowano podejście bazujące na wizualnej analizie eksploracyjnej, wykorzystujące takie narzędzia typu open source jak WEKA i PREFUSE. Wymienione narzędzia ułatwiają korektę pół treningowych i efektywnie wspomagają poprawę dokładności klasyfikacji. Działanie metody sprawdzono wykorzystując wpływ różnych metod resampling na zachowanie dokładności radiometrycznej i uzyskując najlepsze wyniki dla metody bilinearnej (BL).

GENERAL EXTERIOR VIEW, LOOKING NORTHEAST, OF THE SURFACE PLANT WITH ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

GENERAL EXTERIOR VIEW, LOOKING NORTHEAST, OF THE SURFACE PLANT WITH CONVEYORS. JIM WALTER RESOURCES INC. MINING DIVISION OPERATES FOUR UNDERGROUND COAL MINES IN THE BLUE CREEK COAL FIELD OF BIRMINGHAM DISTRICT, THREE IN TUSCALOOSA COUNTY AND ONE IN JEFFERSON COUNTY. TOTAL ANNUAL PRODUCTION IS 8,000,000 TONS. AT 2,300 DEEP, JIM WALTER'S BROOKWOOD MINES ARE THE DEEPEST UNDERGROUND COAL MINES IN NORTH AMERICA. THEY PRODUCE A HIGH-GRADE MEDIUM VOLATILE LOW SULPHUR METALLURGICAL COAL. THE BROOKWOOD NO. 5 MINE (PICTURED IN THIS PHOTOGRAPH) EMPLOYS THE LONGWALL MINING TECHNIQUES WITH BELTS CONVEYING COAL FROM UNDERGROUND OPERATIONS TO THE SURFACE. - JIm Walter Resources, Incorporated, Brookwood No. 5 Mine, 12972 Lock 17 Road, Brookwood, Tuscaloosa County, AL

Use of an automatic earth resistivity system for detection of abandoned mine workings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peters, W.R.; Burdick, R.

1982-04-01

Under the sponsorship of the US Bureau of Mines, a surface-operated automatic high resolution earth resistivity system and associated computer data processing techniques have been designed and constructed for use as a potential means of detecting abandoned coal mine workings. The hardware and software aspects of the new system are described together with applications of the method to the survey and mapping of abandoned mine workings.

Earth observation taken by the Expedition 11 crew

NASA Image and Video Library

2005-06-25

ISS011-E-09620 (26 June 2005) --- Grasberg Mine, Indonesia is featured in this image photographed by an Expedition 11 crewmember on the International Space Station. Located in the Sudirman Mountains of the Irian Jaya province of Indonesia, the Grasberg complex (also known as the Freeport Mine) is one of the largest gold and copper mining operations in the world. The Sudirman Mountains form the western portion of the Maoke Range that extend across Irian Jaya from west to the east-southeast. According to scientists, these ranges were formed by ongoing collision of the northward-moving Australian and westward-moving Pacific tectonic plates. Intrusion of hot magma into sedimentary rock layers during uplift of the mountains resulted in the formation of copper- and gold-bearing ore bodies. Rich copper ore bodies were discovered in the area in 1936, and the Grasberg gold-bearing ore bodies were discovered in 1988. This image illustrates the approximately 4 kilometers-wide open-pit portion of the mine complex; there are also extensive underground mine workings. Access roads for trucks hauling ore and waste rock are visible along the sides of the pit.

Enhanced Approximate Nearest Neighbor via Local Area Focused Search.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gonzales, Antonio; Blazier, Nicholas Paul

Approximate Nearest Neighbor (ANN) algorithms are increasingly important in machine learning, data mining, and image processing applications. There is a large family of space- partitioning ANN algorithms, such as randomized KD-Trees, that work well in practice but are limited by an exponential increase in similarity comparisons required to optimize recall. Additionally, they only support a small set of similarity metrics. We present Local Area Fo- cused Search (LAFS), a method that enhances the way queries are performed using an existing ANN index. Instead of a single query, LAFS performs a number of smaller (fewer similarity comparisons) queries and focuses onmore » a local neighborhood which is refined as candidates are identified. We show that our technique improves performance on several well known datasets and is easily extended to general similarity metrics using kernel projection techniques.« less

Imaging mass spectrometry and genome mining reveal highly antifungal virulence factor of mushroom soft rot pathogen.

PubMed

Graupner, Katharina; Scherlach, Kirstin; Bretschneider, Tom; Lackner, Gerald; Roth, Martin; Gross, Harald; Hertweck, Christian

2012-12-21

Caught in the act: imaging mass spectrometry of a button mushroom infected with the soft rot pathogen Janthinobacterium agaricidamnosum in conjunction with genome mining revealed jagaricin as a highly antifungal virulence factor that is not produced under standard cultivation conditions. The structure of jagaricin was rigorously elucidated by a combination of physicochemical analyses, chemical derivatization, and bioinformatics. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Characterization of particulate emissions from Australian open-cut coal mines: Toward improved emission estimates.

PubMed

Richardson, Claire; Rutherford, Shannon; Agranovski, Igor

2018-06-01

Given the significance of mining as a source of particulates, accurate characterization of emissions is important for the development of appropriate emission estimation techniques for use in modeling predictions and to inform regulatory decisions. The currently available emission estimation methods for Australian open-cut coal mines relate primarily to total suspended particulates and PM 10 (particulate matter with an aerodynamic diameter <10 μm), and limited data are available relating to the PM 2.5 (<2.5 μm) size fraction. To provide an initial analysis of the appropriateness of the currently available emission estimation techniques, this paper presents results of sampling completed at three open-cut coal mines in Australia. The monitoring data demonstrate that the particulate size fraction varies for different mining activities, and that the region in which the mine is located influences the characteristics of the particulates emitted to the atmosphere. The proportion of fine particulates in the sample increased with distance from the source, with the coarse fraction being a more significant proportion of total suspended particulates close to the source of emissions. In terms of particulate composition, the results demonstrate that the particulate emissions are predominantly sourced from naturally occurring geological material, and coal comprises less than 13% of the overall emissions. The size fractionation exhibited by the sampling data sets is similar to that adopted in current Australian emission estimation methods but differs from the size fractionation presented in the U.S. Environmental Protection Agency methodology. Development of region-specific emission estimation techniques for PM 10 and PM 2.5 from open-cut coal mines is necessary to allow accurate prediction of particulate emissions to inform regulatory decisions and for use in modeling predictions. Development of region-specific emission estimation techniques for PM 10 and PM 2.5 from open-cut coal mines is necessary to allow accurate prediction of particulate emissions to inform regulatory decisions and for use in modeling predictions. Comprehensive air quality monitoring was undertaken, and corresponding recommendations were provided.

Categorizing biomedicine images using novel image features and sparse coding representation

PubMed Central

2013-01-01

Background Images embedded in biomedical publications carry rich information that often concisely summarize key hypotheses adopted, methods employed, or results obtained in a published study. Therefore, they offer valuable clues for understanding main content in a biomedical publication. Prior studies have pointed out the potential of mining images embedded in biomedical publications for automatically understanding and retrieving such images' associated source documents. Within the broad area of biomedical image processing, categorizing biomedical images is a fundamental step for building many advanced image analysis, retrieval, and mining applications. Similar to any automatic categorization effort, discriminative image features can provide the most crucial aid in the process. Method We observe that many images embedded in biomedical publications carry versatile annotation text. Based on the locations of and the spatial relationships between these text elements in an image, we thus propose some novel image features for image categorization purpose, which quantitatively characterize the spatial positions and distributions of text elements inside a biomedical image. We further adopt a sparse coding representation (SCR) based technique to categorize images embedded in biomedical publications by leveraging our newly proposed image features. Results we randomly selected 990 images of the JPG format for use in our experiments where 310 images were used as training samples and the rest were used as the testing cases. We first segmented 310 sample images following the our proposed procedure. This step produced a total of 1035 sub-images. We then manually labeled all these sub-images according to the two-level hierarchical image taxonomy proposed by [1]. Among our annotation results, 316 are microscopy images, 126 are gel electrophoresis images, 135 are line charts, 156 are bar charts, 52 are spot charts, 25 are tables, 70 are flow charts, and the remaining 155 images are of the type "others". A serial of experimental results are obtained. Firstly, each image categorizing results is presented, and next image categorizing performance indexes such as precision, recall, F-score, are all listed. Different features which include conventional image features and our proposed novel features indicate different categorizing performance, and the results are demonstrated. Thirdly, we conduct an accuracy comparison between support vector machine classification method and our proposed sparse representation classification method. At last, our proposed approach is compared with three peer classification method and experimental results verify our impressively improved performance. Conclusions Compared with conventional image features that do not exploit characteristics regarding text positions and distributions inside images embedded in biomedical publications, our proposed image features coupled with the SR based representation model exhibit superior performance for classifying biomedical images as demonstrated in our comparative benchmark study. PMID:24565470

Distributed Information Search and Retrieval for Astronomical Resource Discovery and Data Mining

NASA Astrophysics Data System (ADS)

Murtagh, Fionn; Guillaume, Damien

Information search and retrieval has become by nature a distributed task. We look at tools and techniques which are of importance in this area. Current technological evolution can be summarized as the growing stability and cohesiveness of distributed architectures of searchable objects. The objects themselves are more often than not multimedia, including published articles or grey literature reports, yellow page services, image data, catalogs, presentation and online display materials, and ``operations'' information such as scheduling and publicly accessible proposal information. The evolution towards distributed architectures, protocols and formats, and the direction of our own work, are focussed on in this paper.

Mapping informal small-scale mining features in a data-sparse tropical environment with a small UAS

USGS Publications Warehouse

Chirico, Peter G.; Dewitt, Jessica D.

2017-01-01

This study evaluates the use of a small unmanned aerial system (UAS) to collect imagery over artisanal mining sites in West Africa. The purpose of this study is to consider how very high-resolution imagery and digital surface models (DSMs) derived from structure-from-motion (SfM) photogrammetric techniques from a small UAS can fill the gap in geospatial data collection between satellite imagery and data gathered during field work to map and monitor informal mining sites in tropical environments. The study compares both wide-angle and narrow field of view camera systems in the collection and analysis of high-resolution orthoimages and DSMs of artisanal mining pits. The results of the study indicate that UAS imagery and SfM photogrammetric techniques permit DSMs to be produced with a high degree of precision and relative accuracy, but highlight the challenges of mapping small artisanal mining pits in remote and data sparse terrain.

a Novel Image Acquisition and Processing Procedure for Fast Tunnel Dsm Production

NASA Astrophysics Data System (ADS)

Roncella, R.; Umili, G.; Forlani, G.

2012-07-01

In mining operations the evaluation of the stability condition of the excavated front are critic to ensure a safe and correct planning of the subsequent activities. The procedure currently used to this aim has some shortcomings: safety for the geologist, completeness of data collection and objective documentation of the results. In the last decade it has been shown that the geostructural parameters necessary to the stability analysis can be derived from high resolution digital surface models (DSM) of rock faces. With the objective to overcome the limitation of the traditional survey and to minimize data capture times, so reducing delays on mining site operations, a photogrammetric system to generate high resolution DSM of tunnels has been realized. A fast, effective and complete data capture method has been developed and the orientation and restitution phases have been largely automated. The survey operations take no more than required to the traditional ones; no additional topographic measurements other than those available are required. To make the data processing fast and economic our Structure from Motion procedure has been slightly modified to adapt to the peculiar block geometry while, the DSM of the tunnel is created using automatic image correlation techniques. The geomechanical data are sampled on the DSM, by using the acquired images in a GUI and a segmentation procedure to select discontinuity planes. To allow an easier and faster identification of relevant features of the surface of the tunnel, using again an automatic procedure, an orthophoto of the tunnel is produced. A case study where a tunnel section of ca. 130 m has been surveyed is presented.

Multimodality Data Integration in Epilepsy

PubMed Central

Muzik, Otto; Chugani, Diane C.; Zou, Guangyu; Hua, Jing; Lu, Yi; Lu, Shiyong; Asano, Eishi; Chugani, Harry T.

2007-01-01

An important goal of software development in the medical field is the design of methods which are able to integrate information obtained from various imaging and nonimaging modalities into a cohesive framework in order to understand the results of qualitatively different measurements in a larger context. Moreover, it is essential to assess the various features of the data quantitatively so that relationships in anatomical and functional domains between complementing modalities can be expressed mathematically. This paper presents a clinically feasible software environment for the quantitative assessment of the relationship among biochemical functions as assessed by PET imaging and electrophysiological parameters derived from intracranial EEG. Based on the developed software tools, quantitative results obtained from individual modalities can be merged into a data structure allowing a consistent framework for advanced data mining techniques and 3D visualization. Moreover, an effort was made to derive quantitative variables (such as the spatial proximity index, SPI) characterizing the relationship between complementing modalities on a more generic level as a prerequisite for efficient data mining strategies. We describe the implementation of this software environment in twelve children (mean age 5.2 ± 4.3 years) with medically intractable partial epilepsy who underwent both high-resolution structural MR and functional PET imaging. Our experiments demonstrate that our approach will lead to a better understanding of the mechanisms of epileptogenesis and might ultimately have an impact on treatment. Moreover, our software environment holds promise to be useful in many other neurological disorders, where integration of multimodality data is crucial for a better understanding of the underlying disease mechanisms. PMID:17710251

Impacts of surface gold mining on land use systems in Western Ghana.

PubMed

Schueler, Vivian; Kuemmerle, Tobias; Schröder, Hilmar

2011-07-01

Land use conflicts are becoming increasingly apparent from local to global scales. Surface gold mining is an extreme source of such a conflict, but mining impacts on local livelihoods often remain unclear. Our goal here was to assess land cover change due to gold surface mining in Western Ghana, one of the world's leading gold mining regions, and to study how these changes affected land use systems. We used Landsat satellite images from 1986-2002 to map land cover change and field interviews with farmers to understand the livelihood implications of mining-related land cover change. Our results showed that surface mining resulted in deforestation (58%), a substantial loss of farmland (45%) within mining concessions, and widespread spill-over effects as relocated farmers expand farmland into forests. This points to rapidly eroding livelihood foundations, suggesting that the environmental and social costs of Ghana's gold boom may be much higher than previously thought.

Computer-aided visual assessment in mine planning and design

Treesearch

Michael Hatfield; A. J. LeRoy Balzer; Roger E. Nelson

1979-01-01

A computer modeling technique is described for evaluating the visual impact of a proposed surface mine located within the viewshed of a national park. A computer algorithm analyzes digitized USGS baseline topography and identifies areas subject to surface disturbance visible from the park. Preliminary mine and reclamation plan information is used to describe how the...

Learner Typologies Development Using OIndex and Data Mining Based Clustering Techniques

ERIC Educational Resources Information Center

Luan, Jing

2004-01-01

This explorative data mining project used distance based clustering algorithm to study 3 indicators, called OIndex, of student behavioral data and stabilized at a 6-cluster scenario following an exhaustive explorative study of 4, 5, and 6 cluster scenarios produced by K-Means and TwoStep algorithms. Using principles in data mining, the study…

A Quantitative Analysis of Organizational Factors That Relate to Data Mining Success

ERIC Educational Resources Information Center

Huebner, Richard A.

2017-01-01

The ubiquity of data in various forms has fueled the need for advanced data-mining techniques within organizations. The advent of data mining methods used to uncover hidden nuggets of information buried within large data sets has also fueled the need for determining how these unique projects can be successful. There are many challenges associated…

Educational Data Mining Applications and Tasks: A Survey of the Last 10 Years

ERIC Educational Resources Information Center

Bakhshinategh, Behdad; Zaiane, Osmar R.; ElAtia, Samira; Ipperciel, Donald

2018-01-01

Educational Data Mining (EDM) is the field of using data mining techniques in educational environments. There exist various methods and applications in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as pure research objectives, which tend to improve our understanding of the learning…

Drakelands Mine, England

NASA Image and Video Library

2015-08-21

The Drakelands Mine (previously known as the Hemerdon Mine) is a historic tungsten and tin mine located northeast of Plymouth, England. Tin and tungsten deposits were discovered in 1867, and the mine operated until 1944. Last year work started to re-open the mine, as it hosts the fourth-largest tungsten and tin deposits in the world. Tungsten has innumerable uses due to its incredible density and high melting temperature. Yet more than 80% of world supply is controlled by China, who has imposed restriction on export of the metal. The image covers an area of 17 by 18.9 km, was acquired June 5, 2013, and is located at 50.4 degrees north, 4 degrees west. http://photojournal.jpl.nasa.gov/catalog/PIA19757

Rule-based statistical data mining agents for an e-commerce application

NASA Astrophysics Data System (ADS)

Qin, Yi; Zhang, Yan-Qing; King, K. N.; Sunderraman, Rajshekhar

2003-03-01

Intelligent data mining techniques have useful e-Business applications. Because an e-Commerce application is related to multiple domains such as statistical analysis, market competition, price comparison, profit improvement and personal preferences, this paper presents a hybrid knowledge-based e-Commerce system fusing intelligent techniques, statistical data mining, and personal information to enhance QoS (Quality of Service) of e-Commerce. A Web-based e-Commerce application software system, eDVD Web Shopping Center, is successfully implemented uisng Java servlets and an Oracle81 database server. Simulation results have shown that the hybrid intelligent e-Commerce system is able to make smart decisions for different customers.

Application of Modern Tools and Techniques for Mine Safety & Disaster Management

NASA Astrophysics Data System (ADS)

Kumar, Dheeraj

2016-04-01

The implementation of novel systems and adoption of improvised equipment in mines help mining companies in two important ways: enhanced mine productivity and improved worker safety. There is a substantial need for adoption of state-of-the-art automation technologies in the mines to ensure the safety and to protect health of mine workers. With the advent of new autonomous equipment used in the mine, the inefficiencies are reduced by limiting human inconsistencies and error. The desired increase in productivity at a mine can sometimes be achieved by changing only a few simple variables. Significant developments have been made in the areas of surface and underground communication, robotics, smart sensors, tracking systems, mine gas monitoring systems and ground movements etc. Advancement in information technology in the form of internet, GIS, remote sensing, satellite communication, etc. have proved to be important tools for hazard reduction and disaster management. This paper is mainly focused on issues pertaining to mine safety and disaster management and some of the recent innovations in the mine automations that could be deployed in mines for safe mining operations and for avoiding any unforeseen mine disaster.

Imaging the state of the rock mass in the Kiirunavaara iron ore mine, Sweden, using local event tomography

NASA Astrophysics Data System (ADS)

Lund, Björn; Berglund, Karin; Tryggvason, Ari; Dineva, Savka; Jonsson, Linda

2017-04-01

Induced seismic events in a mining environment are a potential hazard, but they can be used to gain information about the rock mass in the mine which otherwise would be very difficult to obtain. In this study we use approximately 1.2 million mining induced seismic events in the Kiirunavaara iron ore mine in northernmost Sweden to image the rock mass using local event travel-time tomography. In addition, relocation of the events significantly improves the possibility to infer structural information and rock damage. The Kiirunavaara mine is one of the largest underground iron ore mines in the world. The ore body is a magnetite sheet of 4 km length, with an average thickness of 80 m, which dips approximately 55° to the east. Mining production is now at a depth of 785 - 855 m. During 2015 the seismic system in the mine recorded on average approximately 1,000 local seismic events per day. The events are of various origins such as shear slip on fractures, non-shear events and blasts, with magnitudes of up to 2.5. We use manually picked P- and S-waves in the tomography and we require that both phases are present as we found that events from the routine processing need screening for anomalous P- versus S-travel times, indicating occasional erroneous phase associations. For the tomography we use the 3D local earthquake tomography code PStomo_eq (Tryggvason et al., 2002), which we adjusted to the mining scale. The study volume is 1.2 x 1.8 x 1.8 km and the velocity model grid size is 10x10x10 meter. The tomographic images show clearly defined regions of high and low velocities. Low velocity zones are associated with mapped clay zones and areas of mined out ore, and also with the near-ore tunnel infrastructure in the foot-wall. We also see how the low S-velocity anomaly continues to depth below the current mining levels, following the inferred direction of the ore. The tomography shows higher P- and S-velocities in the foot-wall away from the areas of mine infrastructure. We relocate all 1.2 million events in the new 3D velocity model. The relocation significantly enhances the clarity of the event distribution in space and we can much more easily identify seismically active structures. One example of this is the clarity with which deformation of the ore-passes is correlated with the intensity and distribution of relocated seismic events. The relocations also show more structures in areas of the mine where rock stability is a significant problem. The large number of events makes it possible to do detailed studies of the temporal evolution of stability in the mine. We present preliminary results of time-lapse tomography in an area where a few events of magnitude 2+ occurred in September 2015.

Distributed Storage Algorithm for Geospatial Image Data Based on Data Access Patterns.

PubMed

Pan, Shaoming; Li, Yongkai; Xu, Zhengquan; Chong, Yanwen

2015-01-01

Declustering techniques are widely used in distributed environments to reduce query response time through parallel I/O by splitting large files into several small blocks and then distributing those blocks among multiple storage nodes. Unfortunately, however, many small geospatial image data files cannot be further split for distributed storage. In this paper, we propose a complete theoretical system for the distributed storage of small geospatial image data files based on mining the access patterns of geospatial image data using their historical access log information. First, an algorithm is developed to construct an access correlation matrix based on the analysis of the log information, which reveals the patterns of access to the geospatial image data. Then, a practical heuristic algorithm is developed to determine a reasonable solution based on the access correlation matrix. Finally, a number of comparative experiments are presented, demonstrating that our algorithm displays a higher total parallel access probability than those of other algorithms by approximately 10-15% and that the performance can be further improved by more than 20% by simultaneously applying a copy storage strategy. These experiments show that the algorithm can be applied in distributed environments to help realize parallel I/O and thereby improve system performance.

Integrative image segmentation optimization and machine learning approach for high quality land-use and land-cover mapping using multisource remote sensing data

NASA Astrophysics Data System (ADS)

Gibril, Mohamed Barakat A.; Idrees, Mohammed Oludare; Yao, Kouame; Shafri, Helmi Zulhaidi Mohd

2018-01-01

The growing use of optimization for geographic object-based image analysis and the possibility to derive a wide range of information about the image in textual form makes machine learning (data mining) a versatile tool for information extraction from multiple data sources. This paper presents application of data mining for land-cover classification by fusing SPOT-6, RADARSAT-2, and derived dataset. First, the images and other derived indices (normalized difference vegetation index, normalized difference water index, and soil adjusted vegetation index) were combined and subjected to segmentation process with optimal segmentation parameters obtained using combination of spatial and Taguchi statistical optimization. The image objects, which carry all the attributes of the input datasets, were extracted and related to the target land-cover classes through data mining algorithms (decision tree) for classification. To evaluate the performance, the result was compared with two nonparametric classifiers: support vector machine (SVM) and random forest (RF). Furthermore, the decision tree classification result was evaluated against six unoptimized trials segmented using arbitrary parameter combinations. The result shows that the optimized process produces better land-use land-cover classification with overall classification accuracy of 91.79%, 87.25%, and 88.69% for SVM and RF, respectively, while the results of the six unoptimized classifications yield overall accuracy between 84.44% and 88.08%. Higher accuracy of the optimized data mining classification approach compared to the unoptimized results indicates that the optimization process has significant impact on the classification quality.

Challenges in recovering resources from acid mine drainage

USGS Publications Warehouse

Nordstrom, D. Kirk; Bowell, Robert J.; Campbell, Kate M.; Alpers, Charles N.

2017-01-01

Metal recovery from mine waters and effluents is not a new approach but one that has occurred largely opportunistically over the last four millennia. Due to the need for low-cost resources and increasingly stringent environmental conditions, mine waters are being considered in a fresh light with a designed, deliberate approach to resource recovery often as part of a larger water treatment evaluation. Mine water chemistry is highly dependent on many factors including geology, ore deposit composition and mineralogy, mining methods, climate, site hydrology, and others. Mine waters are typically Ca-Mg-SO4±Al±Fe with a broad range in pH and metal content. The main issue in recovering components of these waters having potential economic value, such as base metals or rare earth elements, is the separation of these from more reactive metals such as Fe and Al. Broad categories of methods for separating and extracting substances from acidic mine drainage are chemical and biological. Chemical methods include solution, physicochemical, and electrochemical technologies. Advances in membrane techniques such as reverse osmosis have been substantial and the technique is both physical and chemical. Biological methods may be further divided into microbiological and macrobiological, but only the former is considered here as a recovery method, as the latter is typically used as a passive form of water treatment.

Stability Analysis of Railway Subgrade in Mining Area Based on Dinsar

NASA Astrophysics Data System (ADS)

Xu, J.; Hu, J.; Ding, J.

2018-04-01

DInSAR technology have been applied to monitor the mining subsidence and the stability of the railway subgrade. A total of 10 Sentinel-1A images acquired from 2015/9/26 to 2016/2/23 were used in DInSAR analysis. The study mining area is about 13.4 km2. Mining have induced serious land subsidence involve a large area that causing different levels of damages to infrastructures on the land. There is an important railway near the mining area, the DInSAR technology is applied to analyse the subsidence near the railway, which can warn early the possible deformation that may occur during underground mining. The DInSAR results was verified by the field measurement. The results show that the mining did not cause subsidence of railway subgrade and did not affect the stability of railway subgrade.

Data mining and medical world: breast cancers' diagnosis, treatment, prognosis and challenges.

PubMed

Oskouei, Rozita Jamili; Kor, Nasroallah Moradi; Maleki, Saeid Abbasi

2017-01-01

The amount of data in electronic and real world is constantly on the rise. Therefore, extracting useful knowledge from the total available data is very important and time consuming task. Data mining has various techniques for extracting valuable information or knowledge from data. These techniques are applicable for all data that are collected inall fields of science. Several research investigations are published about applications of data mining in various fields of sciences such as defense, banking, insurances, education, telecommunications, medicine and etc. This investigation attempts to provide a comprehensive survey about applications of data mining techniques in breast cancer diagnosis, treatment & prognosis till now. Further, the main challenges in these area is presented in this investigation. Since several research studies currently are going on in this issues, therefore, it is necessary to have a complete survey about all researches which are completed up to now, along with the results of those studies and important challenges which are currently exist in this area for helping young researchers and presenting to them the main problems that are still exist in this area.

Data mining and medical world: breast cancers’ diagnosis, treatment, prognosis and challenges

PubMed Central

Oskouei, Rozita Jamili; Kor, Nasroallah Moradi; Maleki, Saeid Abbasi

2017-01-01

The amount of data in electronic and real world is constantly on the rise. Therefore, extracting useful knowledge from the total available data is very important and time consuming task. Data mining has various techniques for extracting valuable information or knowledge from data. These techniques are applicable for all data that are collected inall fields of science. Several research investigations are published about applications of data mining in various fields of sciences such as defense, banking, insurances, education, telecommunications, medicine and etc. This investigation attempts to provide a comprehensive survey about applications of data mining techniques in breast cancer diagnosis, treatment & prognosis till now. Further, the main challenges in these area is presented in this investigation. Since several research studies currently are going on in this issues, therefore, it is necessary to have a complete survey about all researches which are completed up to now, along with the results of those studies and important challenges which are currently exist in this area for helping young researchers and presenting to them the main problems that are still exist in this area. PMID:28401016

Autumn olive (Elaeagnus umbellata) presence and proliferation on former surface coal mines in Eastern USA

USGS Publications Warehouse

Oliphant, Adam J.; Wynne, R.H.; Zipper, Carl E.; Ford, W. Mark; Donovan, P. F.; Li, Jing

2017-01-01

Invasive plants threaten native plant communities. Surface coal mines in the Appalachian Mountains are among the most disturbed landscapes in North America, but information about land cover characteristics of Appalachian mined lands is lacking. The invasive shrub autumn olive (Elaeagnus umbellata) occurs on these sites and interferes with ecosystem recovery by outcompeting native trees, thus inhibiting re-establishment of the native woody-plant community. We analyzed Landsat 8 satellite imagery to describe autumn olive’s distribution on post-mined lands in southwestern Virginia within the Appalachian coalfield. Eight images from April 2013 through January 2015 served as input data. Calibration and validation data obtained from high-resolution aerial imagery were used to develop a land cover classification model that identified areas where autumn olive was a primary component of land cover. Results indicate that autumn olive cover was sufficiently dense to enable detection on approximately 12.6 % of post-mined lands within the study area. The classified map had user’s and producer’s accuracies of 85.3 and 78.6 %, respectively, for the autumn olive coverage class. Overall accuracy was assessed in reference to an independent validation dataset at 96.8 %. Autumn olive was detected more frequently on mines disturbed prior to 2003, the last year of known plantings, than on lands disturbed by more recent mining. These results indicate that autumn olive growing on reclaimed coal mines in Virginia and elsewhere in eastern USA can be mapped using Landsat 8 Operational Land Imager imagery; and that autumn olive occurrence is a significant landscape vegetation feature on former surface coal mines in the southwestern Virginia segment of the Appalachian coalfield.

Earth Observations taken by the Expedition 16 Crew

NASA Image and Video Library

2008-03-05

ISS016-E-031056 (3 March 2008) --- Cananea Copper Mine, Sonora, Mexico is featured in this image photographed by an Expedition 16 crewmember on the International Space Station. One of the largest open-pit copper mines in the world, the Cananea mine produced over 164,000 tons of copper in 2006. The mine is located approximately 40 kilometers south of the border between the USA (Arizona) and Mexico (Sonora). Copper and gold ores at Cananea are found in a porphyry copper deposit, a geological structure formed by crystal-rich magma moving upwards through pre-existing rock layers. A porphyry - an igneous rock with large crystals in a fine-grained matrix -- is formed as the magma cools and crystallizes. While crystallization is occurring, hot fluids can circulate through the magma and surrounding rocks via fractures. This hydrothermal alteration of the rocks typically forms copper-bearing and other minerals. Much of the Cananea mine's ore is concentrated in breccia pipes -- mineralized rod or chimney-shaped bodies that contain broken rock fragments. The active, two-kilometers-in-diameter Colorada Pit (top right) is recognizable in this image by the concentric steps or benches cut around its perimeter. These benches allow for access into the pit for extraction of ore and waste materials. Water (black) is visible filling the bottom of the pit, and several other basins in the surrounding area. The city of Cananea -- marked by its street grid -- is located to the northeast of the mine workings. A leachate reservoir is located to the east of the mine (lower left) for removal and evaporation of water pumped from the mine workings -- the bluish-white coloration of deposits near the reservoir suggests the high mineral content of the leachate. A worker strike halted mine operations in 2007.

Spatio-Temporal Mining of PolSAR Satellite Image Time Series

NASA Astrophysics Data System (ADS)

Julea, A.; Meger, N.; Trouve, E.; Bolon, Ph.; Rigotti, C.; Fallourd, R.; Nicolas, J.-M.; Vasile, G.; Gay, M.; Harant, O.; Ferro-Famil, L.

2010-12-01

This paper presents an original data mining approach for describing Satellite Image Time Series (SITS) spatially and temporally. It relies on pixel-based evolution and sub-evolution extraction. These evolutions, namely the frequent grouped sequential patterns, are required to cover a minimum surface and to affect pixels that are sufficiently connected. These spatial constraints are actively used to face large data volumes and to select evolutions making sense for end-users. In this paper, a specific application to fully polarimetric SAR image time series is presented. Preliminary experiments performed on a RADARSAT-2 SITS covering the Chamonix Mont-Blanc test-site are used to illustrate the proposed approach.

Application of the Deformation Information System for automated analysis and mapping of mining terrain deformations - case study from SW Poland

NASA Astrophysics Data System (ADS)

Blachowski, Jan; Grzempowski, Piotr; Milczarek, Wojciech; Nowacka, Anna

2015-04-01

Monitoring, mapping and modelling of mining induced terrain deformations are important tasks for quantifying and minimising threats that arise from underground extraction of useful minerals and affect surface infrastructure, human safety, the environment and security of the mining operation itself. The number of methods and techniques used for monitoring and analysis of mining terrain deformations is wide and expanding with the progress in geographical information technologies. These include for example: terrestrial geodetic measurements, Global Navigation Satellite Systems, remote sensing, GIS based modelling and spatial statistics, finite element method modelling, geological modelling, empirical modelling using e.g. the Knothe theory, artificial neural networks, fuzzy logic calculations and other. The presentation shows the results of numerical modelling and mapping of mining terrain deformations for two cases of underground mining sites in SW Poland, hard coal one (abandoned) and copper ore (active) using the functionalities of the Deformation Information System (DIS) (Blachowski et al, 2014 @ http://meetingorganizer.copernicus.org/EGU2014/EGU2014-7949.pdf). The functionalities of the spatial data modelling module of DIS have been presented and its applications in modelling, mapping and visualising mining terrain deformations based on processing of measurement data (geodetic and GNSS) for these two cases have been characterised and compared. These include, self-developed and implemented in DIS, automation procedures for calculating mining terrain subsidence with different interpolation techniques, calculation of other mining deformation parameters (i.e. tilt, horizontal displacement, horizontal strain and curvature), as well as mapping mining terrain categories based on classification of the values of these parameters as used in Poland. Acknowledgments. This work has been financed from the National Science Centre Project "Development of a numerical method of mining ground deformation modelling in complex geological and mining conditions" UMO-2012/07/B/ST10/04297 executed at the Faculty of Geoengineering, Mining and Geology of the Wroclaw University of Technology (Poland).

Estimating natural background groundwater chemistry, Questa molybdenum mine, New Mexico

USGS Publications Warehouse

Verplanck, Phillip L.; Nordstrom, D. Kirk; Plumlee, Geoffrey S.; Walker, Bruce M.; Morgan, Lisa A.; Quane, Steven L.

2010-01-01

This 2 1/2 day field trip will present an overview of a U.S. Geological Survey (USGS) project whose objective was to estimate pre-mining groundwater chemistry at the Questa molybdenum mine, New Mexico. Because of intense debate among stakeholders regarding pre-mining groundwater chemistry standards, the New Mexico Environment Department and Chevron Mining Inc. (formerly Molycorp) agreed that the USGS should determine pre-mining groundwater quality at the site. In 2001, the USGS began a 5-year, multidisciplinary investigation to estimate pre-mining groundwater chemistry utilizing a detailed assessment of a proximal natural analog site and applied an interdisciplinary approach to infer pre-mining conditions. The trip will include a surface tour of the Questa mine and key locations in the erosion scar areas and along the Red River. The trip will provide participants with a detailed understanding of geochemical processes that influence pre-mining environmental baselines in mineralized areas and estimation techniques for determining pre-mining baseline conditions.

Unmanned Mine of the 21st Centuries

NASA Astrophysics Data System (ADS)

Semykina, Irina; Grigoryev, Aleksandr; Gargayev, Andrey; Zavyalov, Valeriy

2017-11-01

The article is analytical. It considers the construction principles of the automation system structure which realize the concept of «unmanned mine». All of these principles intend to deal with problems caused by a continuous complication of mining-and-geological conditions at coalmine such as the labor safety and health protection, the weak integration of different mining automation subsystems and the deficiency of optimal balance between a quantity of resource and energy consumed by mining machines and their throughput. The authors describe the main problems and neck stage of mining machines autonomation and automation subsystem. The article makes a general survey of the applied «unmanned technology» in the field of mining such as the remotely operated autonomous complexes, the underground positioning systems of mining machines using infrared radiation in mine workings etc. The concept of «unmanned mine» is considered with an example of the robotic road heading machine. In the final, the authors analyze the techniques and methods that could solve the task of underground mining without human labor.

Geomechanical analysis applied to geological carbon dioxide sequestration, induced seismicity in deep mines, and detection of stress-induced velocity anisotropy in sub-salt environments

NASA Astrophysics Data System (ADS)

Lucier, Amie Marie

The role of geomechanical analysis in characterizing the feasibility of CO2 sequestration in deep saline aquifers is addressed in two investigations. The first investigation was completed as part of the Ohio River Valley CO2 Storage Project. We completed a geomechanical analysis of the Rose Run Sandstone, a potential injection zone, and its adjacent formations at the American Electric Power's 1.3 GW Mountaineer Power Plant in New Haven, West Virginia. The results of this analysis were then used to evaluate the feasibility of anthropogenic CO2 sequestration in the potential injection zone. First, we incorporated the results of the geomechanical analysis with a geostatistical aquifer model in CO2 injection flow simulations to test the effects of introducing a hydraulic fracture to increase injectivity. Then, we determined that horizontal injection wells at the Mountaineer site are feasible because the high rock strength ensures that such wells would be stable in the local stress state. Finally, we evaluated the potential for injection-induced seismicity. The second investigation concerning CO2 sequestration was motivated by the modeling and fluid flow simulation results from the first study. The geomechanics-based assessment workflow follows a bottom-up approach for evaluating regional deep saline aquifer CO2 injection and storage feasibility. The CO2 storage capacity of an aquifer is a function of its porous volume as well as its CO2 injectivity. For a saline aquifer to be considered feasible in this assessment it must be able to store a specified amount of CO2 at a reasonable cost per ton of CO 2. The proposed assessment workflow has seven steps. The workflow was applied to a case study of the Rose Run sandstone in the eastern Ohio River Valley. We found that it is feasible in this region to inject and store 113 Mt CO2/yr for 30 years at an associated well cost of less than 1.31 US$/t CO2, but only if injectivity enhancement techniques such as hydraulic fracturing and injection induced micro-seismicity are implemented. The second issue to which we apply geomechanical analysis in this thesis is mining-induced stress perturbations and induced seismicity in the TauTona gold mine, which is located in the Witwatersrand Basin of South Africa and is one of the deepest underground mines in the world. In the first investigation, we developed and tested a new technique for determining the virgin stress state near the TauTona gold mine. This technique follows an iterative forward modeling approach that combines observations of drilling induced borehole failures in borehole images, boundary element modeling of the mining-induced stress perturbations, and forward modeling of borehole failures based on the results of the boundary element modeling. The final result was a well constrained range of principal stress orientations and magnitudes that are consistent with all the observed failures and other stress indicators. In the second investigation, we used this constrained stress state to examine the likelihood of faulting to occur both on pre-existing fault planes that are optimally oriented to the virgin stress state and on faults affected by the mining-perturbed stress field, the latter of which is calculated with boundary element modeling. We made several recommendations that could potentially increase safety in deep South African mines as development continues. Finally, the third issue addressed in this thesis is the detection of stress-induced shear wave velocity anisotropy in a sub-salt environment. In this study, we tested a technique proposed by Boness and Zoback (2006) to identify structure-induced velocity anisotropy and isolate possible stress-induced velocity anisotropy. The investigation used cross-dipole sonic data from three deep water sub-salt wells in the Gulf of Mexico. First, we determined the parameters necessary to ensure the quality of the fast azimuth data used in our analysis. We then characterized the quality controlled measured fast directions as either structure-induced or stress-induced based on the results of the Boness and Zoback (2006) technique. We found that this technique supplements the use of dispersion curve analysis for characterizing anisotropy mechanisms. We also find that this technique has the potential to provide information on the stresses that can be used to validate numerical models of salt-related stress perturbations. (Abstract shortened by UMI.)

From spoken narratives to domain knowledge: mining linguistic data for medical image understanding.

PubMed

Guo, Xuan; Yu, Qi; Alm, Cecilia Ovesdotter; Calvelli, Cara; Pelz, Jeff B; Shi, Pengcheng; Haake, Anne R

2014-10-01

Extracting useful visual clues from medical images allowing accurate diagnoses requires physicians' domain knowledge acquired through years of systematic study and clinical training. This is especially true in the dermatology domain, a medical specialty that requires physicians to have image inspection experience. Automating or at least aiding such efforts requires understanding physicians' reasoning processes and their use of domain knowledge. Mining physicians' references to medical concepts in narratives during image-based diagnosis of a disease is an interesting research topic that can help reveal experts' reasoning processes. It can also be a useful resource to assist with design of information technologies for image use and for image case-based medical education systems. We collected data for analyzing physicians' diagnostic reasoning processes by conducting an experiment that recorded their spoken descriptions during inspection of dermatology images. In this paper we focus on the benefit of physicians' spoken descriptions and provide a general workflow for mining medical domain knowledge based on linguistic data from these narratives. The challenge of a medical image case can influence the accuracy of the diagnosis as well as how physicians pursue the diagnostic process. Accordingly, we define two lexical metrics for physicians' narratives--lexical consensus score and top N relatedness score--and evaluate their usefulness by assessing the diagnostic challenge levels of corresponding medical images. We also report on clustering medical images based on anchor concepts obtained from physicians' medical term usage. These analyses are based on physicians' spoken narratives that have been preprocessed by incorporating the Unified Medical Language System for detecting medical concepts. The image rankings based on lexical consensus score and on top 1 relatedness score are well correlated with those based on challenge levels (Spearman correlation>0.5 and Kendall correlation>0.4). Clustering results are largely improved based on our anchor concept method (accuracy>70% and mutual information>80%). Physicians' spoken narratives are valuable for the purpose of mining the domain knowledge that physicians use in medical image inspections. We also show that the semantic metrics introduced in the paper can be successfully applied to medical image understanding and allow discussion of additional uses of these metrics. Copyright © 2014 Elsevier B.V. All rights reserved.

Geophysical experiments for the pre-reclamation assessment of industrial and municipal waste landfills

NASA Astrophysics Data System (ADS)

Balia, R.; Littarru, B.

2010-03-01

Two examples of combined application of geophysical techniques for the pre-reclamation study of old waste landfills in Sardinia, Italy, are illustrated. The first one concerned a mine tailings basin and the second one a municipal solid waste landfill; both disposal sites date back to the 1970-80s. The gravity, shallow reflection, resistivity and induced polarization methods were employed in different combinations at the two sites, and in both cases useful information on the landfill's geometry has been obtained. The gravity method proved effective for locating the boundaries of the landfill and the shallow reflection seismic technique proved effective for the precise imaging of the landfill's bottom; conversely the electrical techniques, though widely employed for studying waste landfills, provided mainly qualitative and debatable results. The overall effectiveness of the surveys has been highly improved through the combined use of different techniques, whose individual responses, being strongly dependent on their specific basic physical characteristic and the complexity of the situation to be studied, did not show the same effectiveness at the two places.

Data mining and visualization of average images in a digital hand atlas

NASA Astrophysics Data System (ADS)

Zhang, Aifeng; Gertych, Arkadiusz; Liu, Brent J.; Huang, H. K.

2005-04-01

We have collected a digital hand atlas containing digitized left hand radiographs of normally developed children grouped accordingly by age, sex, and race. A set of features stored in a database reflecting patient's stage of skeletal development has been calculated by automatic image processing procedures. This paper addresses a new concept, "average" image in the digital hand atlas. The "average" reference image in the digital atlas is selected for each of the groups of normal developed children with the best representative skeletal maturity based on bony features. A data mining procedure was designed and applied to find the average image through average feature vector matching. It also provides a temporary solution for the missing feature problem through polynomial regression. As more cases are added to the digital hand atlas, it can grow to provide clinicians accurate reference images to aid the bone age assessment process.

Data Mining Methods for Recommender Systems

NASA Astrophysics Data System (ADS)

Amatriain, Xavier; Jaimes*, Alejandro; Oliver, Nuria; Pujol, Josep M.

In this chapter, we give an overview of the main Data Mining techniques used in the context of Recommender Systems. We first describe common preprocessing methods such as sampling or dimensionality reduction. Next, we review the most important classification techniques, including Bayesian Networks and Support Vector Machines. We describe the k-means clustering algorithm and discuss several alternatives. We also present association rules and related algorithms for an efficient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied.

Integrated mined-area reclamation and land-use planning. Volume 3C. A case study of surface mining and reclamation planning: Georgia Kaolin Company Clay Mines, Washington County, Georgia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guernsey, J L; Brown, L A; Perry, A O

1978-02-01

This case study examines the reclamation practices of the Georgia Kaolin's American Industrial Clay Company Division, a kaolin producer centered in Twiggs, Washington, and Wilkinson Counties, Georgia. The State of Georgia accounts for more than one-fourth of the world's kaolin production and about three-fourths of U.S. kaolin output. The mining of kaolin in Georgia illustrates the effects of mining and reclaiming lands disturbed by area surface mining. The disturbed areas are reclaimed under the rules and regulations of the Georgia Surface Mining Act of 1968. The natural conditions influencing the reclamation methodologies and techniques are markedly unique from those ofmore » other mining operations. The environmental disturbances and procedures used in reclaiming the kaolin mined lands are reviewed and implications for planners are noted.« less

Void detection beneath reinforced concrete sections: The practical application of ground-penetrating radar and ultrasonic techniques

NASA Astrophysics Data System (ADS)

Cassidy, Nigel J.; Eddies, Rod; Dods, Sam

2011-08-01

Ground-penetrating radar (GPR) and ultrasonic 'pulse echo' techniques are well-established methods for the imaging, investigation and analysis of steel reinforced concrete structures and are important civil engineering survey tools. GPR is, arguably, the more widely-used technique as it is suitable for a greater range of problem scenarios (i.e., from rebar mapping to moisture content determination). Ultrasonic techniques are traditionally associated with the engineering-based, non-destructive testing of concrete structures and their integrity analyses (e.g., flaw detection, shear/longitudinal velocity determination, etc). However, when used in an appropriate manner, both techniques can be considered complementary and provide a unique way of imaging the sub-surface that is suited to a range of geotechnical problems. In this paper, we present a comparative study between mid-to-high frequency GPR (450 MHz and 900 MHz) and array-based, shear wave, pulse-echo ultrasonic surveys using proprietary instruments and conventional GPR data processing and visualisation techniques. Our focus is the practical detection of sub-metre scale voids located under steel reinforced concrete sections in realistic survey conditions (e.g., a capped, relict mine shaft or vent). Representative two-dimensional (2D) sections are presented for both methods illustrating the similarities/differences in signal response and the temporal-spatial target resolutions achieved with each technique. The use of three-dimensional data volumes and time slices (or 'C-scans') for advanced interpretation is also demonstrated, which although common in GPR applications is under-utilised as a technique in general ultrasonic surveys. The results show that ultrasonic methods can perform as well as GPR for this specific investigation scenario and that they have the potential of overcoming some of the inherent limitations of GPR investigations (i.e., the need for careful antenna frequency selection and survey design in order to image through the rebar meshes). More importantly, we show that standard GPR data collection, processing and visualisation techniques can be used with both types of data without users needing to change existing operational protocols or survey criteria.

Use of an automatic resistivity system for detecting abandoned mine workings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peters, W.R.; Burdick, R.G.

1983-01-01

A high-resolution earth resistivity system has been designed and constructed for use as a means of detecting abandoned coal mine workings. The automatic pole-dipole earth resistivity technique has already been applied to the detection of subsurface voids for military applications. The hardware and software of the system are described, together with applications for surveying and mapping abandoned coal mine workings. Field tests are presented to illustrate the detection of both air-filled and water-filled mine workings.

Preventing spontaneous combustion after mine closing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lewicki, G.

1987-11-01

The author explains how the Northern Coal Company and a Houston-based firefighting firm developed an innovative technique to reduce the risk of spontaneous combustion after mine closing in its Rienau number2 Mine. The ''Light Water TM'' ATC series of firefighting foam concentrates were designed for extinguishing flammable liquid fires. By slightly altering the chemicals, the concentrates could be used to seal the coal ribs, floor, and roof, reducing the risk of combustion. Subsequent monitoring of the mine has identified no signs of heating.

Artisanal and Small-Scale Gold Mining Without Mercury

EPA Pesticide Factsheets

Mercury-free techniques are safer for miners, their families and local communities. They can also help miners qualify for certification under fair-mined standards, potentially allowing them to market their gold at higher prices.

The Pollution Detectives: Part II. Lead and Zinc Mining.

ERIC Educational Resources Information Center

Sanderson, P. L.

1988-01-01

Describes a field trip taken to an old mining area to study water pollution. Discussed are methods for silt analysis, reagent preparation, color charts, techniques, fieldwork, field results, and a laboratory study. (CW)

Live-cell imaging.

PubMed

Cole, Richard

2014-01-01

It would be hard to argue that live-cell imaging has not changed our view of biology. The past 10 years have seen an explosion of interest in imaging cellular processes, down to the molecular level. There are now many advanced techniques being applied to live cell imaging. However, cellular health is often under appreciated. For many researchers, if the cell at the end of the experiment has not gone into apoptosis or is blebbed beyond recognition, than all is well. This is simply incorrect. There are many factors that need to be considered when performing live-cell imaging in order to maintain cellular health such as: imaging modality, media, temperature, humidity, PH, osmolality, and photon dose. The wavelength of illuminating light, and the total photon dose that the cells are exposed to, comprise two of the most important and controllable parameters of live-cell imaging. The lowest photon dose that achieves a measureable metric for the experimental question should be used, not the dose that produces cover photo quality images. This is paramount to ensure that the cellular processes being investigated are in their in vitro state and not shifted to an alternate pathway due to environmental stress. The timing of the mitosis is an ideal canary in the gold mine, in that any stress induced from the imaging will result in the increased length of mitosis, thus providing a control model for the current imagining conditions.

Live-cell imaging

PubMed Central

Cole, Richard

2014-01-01

It would be hard to argue that live-cell imaging has not changed our view of biology. The past 10 years have seen an explosion of interest in imaging cellular processes, down to the molecular level. There are now many advanced techniques being applied to live cell imaging. However, cellular health is often under appreciated. For many researchers, if the cell at the end of the experiment has not gone into apoptosis or is blebbed beyond recognition, than all is well. This is simply incorrect. There are many factors that need to be considered when performing live-cell imaging in order to maintain cellular health such as: imaging modality, media, temperature, humidity, PH, osmolality, and photon dose. The wavelength of illuminating light, and the total photon dose that the cells are exposed to, comprise two of the most important and controllable parameters of live-cell imaging. The lowest photon dose that achieves a measureable metric for the experimental question should be used, not the dose that produces cover photo quality images. This is paramount to ensure that the cellular processes being investigated are in their in vitro state and not shifted to an alternate pathway due to environmental stress. The timing of the mitosis is an ideal canary in the gold mine, in that any stress induced from the imaging will result in the increased length of mitosis, thus providing a control model for the current imagining conditions. PMID:25482523

Split Bregman's optimization method for image construction in compressive sensing

NASA Astrophysics Data System (ADS)

Skinner, D.; Foo, S.; Meyer-Bäse, A.

2014-05-01

The theory of compressive sampling (CS) was reintroduced by Candes, Romberg and Tao, and D. Donoho in 2006. Using a priori knowledge that a signal is sparse, it has been mathematically proven that CS can defY Nyquist sampling theorem. Theoretically, reconstruction of a CS image relies on the minimization and optimization techniques to solve this complex almost NP-complete problem. There are many paths to consider when compressing and reconstructing an image but these methods have remained untested and unclear on natural images, such as underwater sonar images. The goal of this research is to perfectly reconstruct the original sonar image from a sparse signal while maintaining pertinent information, such as mine-like object, in Side-scan sonar (SSS) images. Goldstein and Osher have shown how to use an iterative method to reconstruct the original image through a method called Split Bregman's iteration. This method "decouples" the energies using portions of the energy from both the !1 and !2 norm. Once the energies are split, Bregman iteration is used to solve the unconstrained optimization problem by recursively solving the problems simultaneously. The faster these two steps or energies can be solved then the faster the overall method becomes. While the majority of CS research is still focused on the medical field, this paper will demonstrate the effectiveness of the Split Bregman's methods on sonar images.

Instances selection algorithm by ensemble margin

NASA Astrophysics Data System (ADS)

Saidi, Meryem; Bechar, Mohammed El Amine; Settouti, Nesma; Chikh, Mohamed Amine

2018-05-01

The main limit of data mining algorithms is their inability to deal with the huge amount of available data in a reasonable processing time. A solution of producing fast and accurate results is instances and features selection. This process eliminates noisy or redundant data in order to reduce the storage and computational cost without performances degradation. In this paper, a new instance selection approach called Ensemble Margin Instance Selection (EMIS) algorithm is proposed. This approach is based on the ensemble margin. To evaluate our approach, we have conducted several experiments on different real-world classification problems from UCI Machine learning repository. The pixel-based image segmentation is a field where the storage requirement and computational cost of applied model become higher. To solve these limitations we conduct a study based on the application of EMIS and other instance selection techniques for the segmentation and automatic recognition of white blood cells WBC (nucleus and cytoplasm) in cytological images.

Introduction to machine learning for brain imaging.

PubMed

Lemm, Steven; Blankertz, Benjamin; Dickhaus, Thorsten; Müller, Klaus-Robert

2011-05-15

Machine learning and pattern recognition algorithms have in the past years developed to become a working horse in brain imaging and the computational neurosciences, as they are instrumental for mining vast amounts of neural data of ever increasing measurement precision and detecting minuscule signals from an overwhelming noise floor. They provide the means to decode and characterize task relevant brain states and to distinguish them from non-informative brain signals. While undoubtedly this machinery has helped to gain novel biological insights, it also holds the danger of potential unintentional abuse. Ideally machine learning techniques should be usable for any non-expert, however, unfortunately they are typically not. Overfitting and other pitfalls may occur and lead to spurious and nonsensical interpretation. The goal of this review is therefore to provide an accessible and clear introduction to the strengths and also the inherent dangers of machine learning usage in the neurosciences. Copyright © 2010 Elsevier Inc. All rights reserved.

Ontology-guided organ detection to retrieve web images of disease manifestation: towards the construction of a consumer-based health image library.

PubMed

Chen, Yang; Ren, Xiaofeng; Zhang, Guo-Qiang; Xu, Rong

2013-01-01

Visual information is a crucial aspect of medical knowledge. Building a comprehensive medical image base, in the spirit of the Unified Medical Language System (UMLS), would greatly benefit patient education and self-care. However, collection and annotation of such a large-scale image base is challenging. To combine visual object detection techniques with medical ontology to automatically mine web photos and retrieve a large number of disease manifestation images with minimal manual labeling effort. As a proof of concept, we first learnt five organ detectors on three detection scales for eyes, ears, lips, hands, and feet. Given a disease, we used information from the UMLS to select affected body parts, ran the pretrained organ detectors on web images, and combined the detection outputs to retrieve disease images. Compared with a supervised image retrieval approach that requires training images for every disease, our ontology-guided approach exploits shared visual information of body parts across diseases. In retrieving 2220 web images of 32 diseases, we reduced manual labeling effort to 15.6% while improving the average precision by 3.9% from 77.7% to 81.6%. For 40.6% of the diseases, we improved the precision by 10%. The results confirm the concept that the web is a feasible source for automatic disease image retrieval for health image database construction. Our approach requires a small amount of manual effort to collect complex disease images, and to annotate them by standard medical ontology terms.

A demonstration of ERTS-1 analog and digital techniques applied to strip mining in Maryland and West Virginia

NASA Technical Reports Server (NTRS)

Anderson, A. T.; Schubert, J.

1974-01-01

The largest contour strip mining operations in western Maryland and West Virginia are located within the Georges Creek and the Upper Potomac Basins. These two coal basins lie within the Georges Creek (Wellersburg) syncline. The disturbed strip mine areas were delineated with the surrounding geological and vegetation features using ERTS-1 data in both analog (imagery) and digital form. The two digital systems used were: (1) the ERTS-Analysis system, a point-by-point digital analysis of spectral signatures based on known spectral values, and (2) the LARS Automatic Data Processing System. The digital techniques being developed will later be incorporated into a data base for land use planning. These two systems aided in efforts to determine the extent and state of strip mining in this region. Aircraft data, ground verification information, and geological field studies also aided in the application of ERTS-1 imagery to perform an integrated analysis that assessed the adverse effects of strip mining. The results indicated that ERTS can both monitor and map the extent of strip mining to determine immediately the acreage affected and indicate where future reclamation and revegetation may be necessary.

An integrated environment monitoring system for underground coal mines--Wireless Sensor Network subsystem with multi-parameter monitoring.

PubMed

Zhang, Yu; Yang, Wei; Han, Dongsheng; Kim, Young-Il

2014-07-21

Environment monitoring is important for the safety of underground coal mine production, and it is also an important application of Wireless Sensor Networks (WSNs). We put forward an integrated environment monitoring system for underground coal mine, which uses the existing Cable Monitoring System (CMS) as the main body and the WSN with multi-parameter monitoring as the supplementary technique. As CMS techniques are mature, this paper mainly focuses on the WSN and the interconnection between the WSN and the CMS. In order to implement the WSN for underground coal mines, two work modes are designed: periodic inspection and interrupt service; the relevant supporting technologies, such as routing mechanism, collision avoidance, data aggregation, interconnection with the CMS, etc., are proposed and analyzed. As WSN nodes are limited in energy supply, calculation and processing power, an integrated network management scheme is designed in four aspects, i.e., topology management, location management, energy management and fault management. Experiments were carried out both in a laboratory and in a real underground coal mine. The test results indicate that the proposed integrated environment monitoring system for underground coal mines is feasible and all designs performed well as expected.

Quakefinder: A scalable data mining system for detecting earthquakes from space

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stolorz, P.; Dean, C.

1996-12-31

We present an application of novel massively parallel datamining techniques to highly precise inference of important physical processes from remote sensing imagery. Specifically, we have developed and applied a system, Quakefinder, that automatically detects and measures tectonic activity in the earth`s crust by examination of satellite data. We have used Quakefinder to automatically map the direction and magnitude of ground displacements due to the 1992 Landers earthquake in Southern California, over a spatial region of several hundred square kilometers, at a resolution of 10 meters, to a (sub-pixel) precision of 1 meter. This is the first calculation that has evermore » been able to extract area-mapped information about 2D tectonic processes at this level of detail. We outline the architecture of the Quakefinder system, based upon a combination of techniques drawn from the fields of statistical inference, massively parallel computing and global optimization. We confirm the overall correctness of the procedure by comparison of our results with known locations of targeted faults obtained by careful and time-consuming field measurements. The system also performs knowledge discovery by indicating novel unexplained tectonic activity away from the primary faults that has never before been observed. We conclude by discussing the future potential of this data mining system in the broad context of studying subtle spatio-temporal processes within massive image streams.« less

Data Mining and Knowledge Discovery tools for exploiting big Earth-Observation data

NASA Astrophysics Data System (ADS)

Espinoza Molina, D.; Datcu, M.

2015-04-01

The continuous increase in the size of the archives and in the variety and complexity of Earth-Observation (EO) sensors require new methodologies and tools that allow the end-user to access a large image repository, to extract and to infer knowledge about the patterns hidden in the images, to retrieve dynamically a collection of relevant images, and to support the creation of emerging applications (e.g.: change detection, global monitoring, disaster and risk management, image time series, etc.). In this context, we are concerned with providing a platform for data mining and knowledge discovery content from EO archives. The platform's goal is to implement a communication channel between Payload Ground Segments and the end-user who receives the content of the data coded in an understandable format associated with semantics that is ready for immediate exploitation. It will provide the user with automated tools to explore and understand the content of highly complex images archives. The challenge lies in the extraction of meaningful information and understanding observations of large extended areas, over long periods of time, with a broad variety of EO imaging sensors in synergy with other related measurements and data. The platform is composed of several components such as 1.) ingestion of EO images and related data providing basic features for image analysis, 2.) query engine based on metadata, semantics and image content, 3.) data mining and knowledge discovery tools for supporting the interpretation and understanding of image content, 4.) semantic definition of the image content via machine learning methods. All these components are integrated and supported by a relational database management system, ensuring the integrity and consistency of Terabytes of Earth Observation data.

Coal Mining, Germany

NASA Image and Video Library

2001-08-01

This simulated natural color ASTER image in the German state of North Rhine Westphalia covers an area of 30 by 36 km, and was acquired on August 26, 2000. On the right side of the image are 3 enormous opencast coalmines. The Hambach opencast coal mine has recently been brought to full output capacity through the addition of the No. 293 giant bucket wheel excavator. This is the largest machine in the world; it is twice as long as a soccer field and as tall as a building with 30 floors. To uncover the 2.4 billion tons of brown coal (lignite) found at Hambach, five years were required to remove a 200-m-thick layer of waste sand and to redeposit it off site. The mine currently yields 30 million tons of lignite annually, with annual capacity scheduled to increase to 40 million tons in coming years. The image is centered at 51 degrees north latitude, 6.4 degrees east longitude. http://photojournal.jpl.nasa.gov/catalog/PIA02676

South Africa, Namibia Diamond Deposits (close-up)

NASA Technical Reports Server (NTRS)

1998-01-01

This radar image shows a close up view of a portion of the Richtersveld National Park and Orange River (top of image) in the Northern Cape Province of the Republic of South Africa. The Orange River marks the boundary between South Africa to the south and Namibia to the north. This is an area of active mining for diamonds, which were washed downstream from the famous Kimberley Diamond Area, millions of years ago when the river was much larger. The mining is focused on ancient drainages of the Orange River which are currently buried by think layers of sand and gravel. Scientists are investigating whether these ancient drainages can be seen with the radar's ability to penetrate sand cover in extremely dry regions. A mine, shown in yellow, is on the southern bank of the river in an abandoned bend which is known as an 'oxbow.' The small bright circular areas (left edge of image) west of the mine circles are fields of a large ostrich farm that are being watered with pivot irrigation. The large dark area in the center of the image is the Kubus Pluton, a body of granite rock that broke through the surrounding rocks about 550 million years ago. North is toward the upper right. The area shown is about 35 by 25 kilometers (21.8 by 15.5 miles) centered at 28.4 degrees south latitude, 16.8 degrees east longitude. Colors are assigned to different radar frequencies and polarizations as follows: red is L-band horizontally transmitted and horizontally received; green is L-band horizontally transmitted and vertically received; blue is C-band horizontally transmitted and vertically received. The image was acquired on April 18, 1994 by the Spaceborne Imaging Radar-C/X-band Synthetic Aperture (SIR-C/X-SAR) imaging radar when it flew aboard the space shuttle Endeavour. SIR-C/X-SAR is a joint mission of the U.S./German and Italian space agencies.

South Africa, Namibia Diamond Deposits

NASA Technical Reports Server (NTRS)

1998-01-01

This radar image covers a portion of the Richtersveld National Park and Orange River (top of image) in the Northern Cape Province of the Republic of South Africa. The Orange River marks the boundary between South Africa to the south and Namibia to the north. This is an area of active mining for diamonds, which were washed downstream from the famous Kimberley Diamond Area, millions of years ago when the river was much larger. The mining is focused on ancient drainages of the Orange River which are currently buried by think layers of sand and gravel. Scientists are investigating whether these ancient drainages can be seen with the radar's ability to penetrate sand cover in extremely dry regions. A mine, shown in yellow, is on the southern bank of the river in an abandoned bend which is known as an 'oxbow.' The small bright circular areas (left edge of image) west of the mine circles are fields of a large ostrich farm that are being watered with pivot irrigation. The large dark area in the center of the image is the Kubus Pluton, a body of granite rock that broke through the surrounding rocks about 550 million years ago. North is toward the upper right. The area shown is about 55 by 60 kilometers (34 by 37 miles) centered at 28.4 degrees south latitude, 16.8 degrees east longitude. Colors are assigned to different radar frequencies and polarizations as follows: red is L-band horizontally transmitted and horizontally received; green is L-band horizontally transmitted and vertically received; blue is C-band horizontally transmitted and vertically received. The image was acquired on April 18, 1994 by the Spaceborne Imaging Radar-C/X-band Synthetic Aperture (SIR-C/X-SAR) imaging radar when it flew aboard the space shuttle Endeavour. SIR-C/X-SAR is a joint mission of the U.S./German and Italian space agencies.

Forecasting the ocean optical environment in support of Navy mine warfare operations

NASA Astrophysics Data System (ADS)

Ladner, S. D.; Arnone, R.; Jolliff, J.; Casey, B.; Matulewski, K.

2012-06-01

A 3D ocean optical forecast system called TODS (Tactical Ocean Data System) has been developed to determine the performance of underwater LIDAR detection/identification systems. TODS fuses optical measurements from gliders, surface satellite optical properties, and 3D ocean forecast circulation models to extend the 2-dimensional surface satellite optics into a 3-dimensional optical volume including subsurface optical layers of beam attenuation coefficient (c) and diver visibility. Optical 3D nowcast and forecasts are combined with electro-optical identification (EOID) models to determine the underwater LIDAR imaging performance field used to identify subsurface mine threats in rapidly changing coastal regions. TODS was validated during a recent mine warfare exercise with Helicopter Mine Countermeasures Squadron (HM-14). Results include the uncertainties in the optical forecast and lidar performance and sensor tow height predictions that are based on visual detection and identification metrics using actual mine target images from the EOID system. TODS is a new capability of coupling the 3D optical environment and EOID system performance and is proving important for the MIW community as both a tactical decision aid and for use in operational planning, improving timeliness and efficiency in clearance operations.

A semantic model for multimodal data mining in healthcare information systems.

PubMed

Iakovidis, Dimitris; Smailis, Christos

2012-01-01

Electronic health records (EHRs) are representative examples of multimodal/multisource data collections; including measurements, images and free texts. The diversity of such information sources and the increasing amounts of medical data produced by healthcare institutes annually, pose significant challenges in data mining. In this paper we present a novel semantic model that describes knowledge extracted from the lowest-level of a data mining process, where information is represented by multiple features i.e. measurements or numerical descriptors extracted from measurements, images, texts or other medical data, forming multidimensional feature spaces. Knowledge collected by manual annotation or extracted by unsupervised data mining from one or more feature spaces is modeled through generalized qualitative spatial semantics. This model enables a unified representation of knowledge across multimodal data repositories. It contributes to bridging the semantic gap, by enabling direct links between low-level features and higher-level concepts e.g. describing body parts, anatomies and pathological findings. The proposed model has been developed in web ontology language based on description logics (OWL-DL) and can be applied to a variety of data mining tasks in medical informatics. It utility is demonstrated for automatic annotation of medical data.

Large-Scale Overlays and Trends: Visually Mining, Panning and Zooming the Observable Universe.

PubMed

Luciani, Timothy Basil; Cherinka, Brian; Oliphant, Daniel; Myers, Sean; Wood-Vasey, W Michael; Labrinidis, Alexandros; Marai, G Elisabeta

2014-07-01

We introduce a web-based computing infrastructure to assist the visual integration, mining and interactive navigation of large-scale astronomy observations. Following an analysis of the application domain, we design a client-server architecture to fetch distributed image data and to partition local data into a spatial index structure that allows prefix-matching of spatial objects. In conjunction with hardware-accelerated pixel-based overlays and an online cross-registration pipeline, this approach allows the fetching, displaying, panning and zooming of gigabit panoramas of the sky in real time. To further facilitate the integration and mining of spatial and non-spatial data, we introduce interactive trend images-compact visual representations for identifying outlier objects and for studying trends within large collections of spatial objects of a given class. In a demonstration, images from three sky surveys (SDSS, FIRST and simulated LSST results) are cross-registered and integrated as overlays, allowing cross-spectrum analysis of astronomy observations. Trend images are interactively generated from catalog data and used to visually mine astronomy observations of similar type. The front-end of the infrastructure uses the web technologies WebGL and HTML5 to enable cross-platform, web-based functionality. Our approach attains interactive rendering framerates; its power and flexibility enables it to serve the needs of the astronomy community. Evaluation on three case studies, as well as feedback from domain experts emphasize the benefits of this visual approach to the observational astronomy field; and its potential benefits to large scale geospatial visualization in general.

The design and implementation of web mining in web sites security

NASA Astrophysics Data System (ADS)

Li, Jian; Zhang, Guo-Yin; Gu, Guo-Chang; Li, Jian-Li

2003-06-01

The backdoor or information leak of Web servers can be detected by using Web Mining techniques on some abnormal Web log and Web application log data. The security of Web servers can be enhanced and the damage of illegal access can be avoided. Firstly, the system for discovering the patterns of information leakages in CGI scripts from Web log data was proposed. Secondly, those patterns for system administrators to modify their codes and enhance their Web site security were provided. The following aspects were described: one is to combine web application log with web log to extract more information, so web data mining could be used to mine web log for discovering the information that firewall and Information Detection System cannot find. Another approach is to propose an operation module of web site to enhance Web site security. In cluster server session, Density-Based Clustering technique is used to reduce resource cost and obtain better efficiency.

High-resolution topography along surface rupture of the 16 October 1999 Hector Mine, California (Mw 7.1) from airborne laser swath mapping

USGS Publications Warehouse

Hudnutt, K.W.; Borsa, A.; Glennie, C.; Minster, J.-B.

2002-01-01

In order to document surface rupture associated with the Hector Mine earthquake, in particular, the area of maximum slip and the deformed surface of Lavic Lake playa, we acquired high-resolution data using relatively new topographic-mapping methods. We performed a raster-laser scan of the main surface breaks along the entire rupture zone, as well as along an unruptured portion of the Bullion fault. The image of the ground surface produced by this method is highly detailed, comparable to that obtained when geologists make particularly detailed site maps for geomorphic or paleoseismic studies. In this case, however, for the first time after a surface-rupturing earthquake, the detailed mapping is along the entire fault zone rather than being confined to selected sites. These data are geodetically referenced, using the Global Positioning System, thus enabling more accurate mapping of the rupture traces. In addition, digital photographs taken along the same flight lines can be overlaid onto the precise topographic data, improving terrain visualization. We demonstrate the potential of these techniques for measuring fault-slip vectors.

Growth and Heavy Metal Accumulation of Koelreuteria Paniculata Seedlings and Their Potential for Restoring Manganese Mine Wastelands in Hunan, China

PubMed Central

Huang, Zhihong; Xiang, Wenhua; Ma, Yu’e; Lei, Pifeng; Tian, Dalun; Deng, Xiangwen; Yan, Wende; Fang, Xi

2015-01-01

The planting of trees on mine wastelands is an effective, long-term technique for phytoremediation of heavy metal-contaminated wastes. In this study, a pot experiment with seedlings of Koelreuteria paniculata under six treatments of local mine wastes was designed to determine the major constraints on tree establishment and to evaluate the feasibility of planting K. paniculata on manganese mine wastelands. Results showed that K. paniculata grew well in mine tailings, and also under a regime of equal amounts of mine tailings and soil provided in adjacent halves of pots. In contrast, mine sludge did not favor survival and growth because its clay texture limited fine root development. The bio-concentration factor and the translocation factor were mostly less than 1, indicating a low phytoextraction potential for K. paniculata. K. paniculata is suited to restore manganese mine sludge by mixing the mine sludge with local mine tailings or soil. PMID:25654773

Machine processing of remotely sensed data; Proceedings of the Fifth Annual Symposium, Purdue University, West Lafayette, Ind., June 27-29, 1979

NASA Technical Reports Server (NTRS)

Tendam, I. M. (Editor); Morrison, D. B.

1979-01-01

Papers are presented on techniques and applications for the machine processing of remotely sensed data. Specific topics include the Landsat-D mission and thematic mapper, data preprocessing to account for atmospheric and solar illumination effects, sampling in crop area estimation, the LACIE program, the assessment of revegetation on surface mine land using color infrared aerial photography, the identification of surface-disturbed features through a nonparametric analysis of Landsat MSS data, the extraction of soil data in vegetated areas, and the transfer of remote sensing computer technology to developing nations. Attention is also given to the classification of multispectral remote sensing data using context, the use of guided clustering techniques for Landsat data analysis in forest land cover mapping, crop classification using an interactive color display, and future trends in image processing software and hardware.

Monitoring of the mercury mining site Almadén implementing remote sensing technologies.

PubMed

Schmid, Thomas; Rico, Celia; Rodríguez-Rastrero, Manuel; José Sierra, María; Javier Díaz-Puente, Fco; Pelayo, Marta; Millán, Rocio

2013-08-01

The Almadén area in Spain has a long history of mercury mining with prolonged human-induced activities that are related to mineral extraction and metallurgical processes before the closure of the mines and a more recent post period dominated by projects that reclaim the mine dumps and tailings and recuperating the entire mining area. Furthermore, socio-economic alternatives such as crop cultivation, livestock breeding and tourism are increasing in the area. Up till now, only scattered information on these activities is available from specific studies. However, improved acquisition systems using satellite borne data in the last decades opens up new possibilities to periodically study an area of interest. Therefore, comparing the influence of these activities on the environment and monitoring their impact on the ecosystem vastly improves decision making for the public policy makers to implement appropriate land management measures and control environmental degradation. The objective of this work is to monitor environmental changes affected by human-induced activities within the Almadén area occurring before, during and after the mine closure over a period of nearly three decades. To achieve this, data from numerous sources at different spatial scales and time periods are implemented into a methodology based on advanced remote sensing techniques. This includes field spectroradiometry measurements, laboratory analyses and satellite borne data of different surface covers to detect land cover and use changes throughout the mining area. Finally, monitoring results show that the distribution of areas affected by mercury mining is rapidly diminishing since activities ceased and that rehabilitated mining areas form a new landscape. This refers to mine tailings that have been sealed and revegetated as well as an open pit mine that has been converted to an "artificial" lake surface. Implementing a methodology based on remote sensing techniques that integrate data from several sources at different scales greatly improves the regional characterization and monitoring of an area dominated by mercury mining activities. Copyright © 2013 Elsevier Inc. All rights reserved.

Noise-based body-wave seismic tomography in an active underground mine.

NASA Astrophysics Data System (ADS)

Olivier, G.; Brenguier, F.; Campillo, M.; Lynch, R.; Roux, P.

2014-12-01

Over the last decade, ambient noise tomography has become increasingly popular to image the earth's upper crust. The seismic noise recorded in the earth's crust is dominated by surface waves emanating from the interaction of the ocean with the solid earth. These surface waves are low frequency in nature ( < 1 Hz) and not usable for imaging smaller structures associated with mining or oil and gas applications. The seismic noise recorded at higher frequencies are typically from anthropogenic sources, which are short lived, spatially unstable and not well suited for constructing seismic Green's functions between sensors with conventional cross-correlation methods. To examine the use of ambient noise tomography for smaller scale applications, continuous data were recorded for 5 months in an active underground mine in Sweden located more than 1km below surface with 18 high frequency seismic sensors. A wide variety of broadband (10 - 3000 Hz) seismic noise sources are present in an active underground mine ranging from drilling, scraping, trucks, ore crushers and ventilation fans. Some of these sources generate favorable seismic noise, while others are peaked in frequency and not usable. In this presentation, I will show that the noise generated by mining activity can be useful if periods of seismic noise are carefully selected. Although noise sources are not temporally stable and not evenly distributed around the sensor array, good estimates of the seismic Green's functions between sensors can be retrieved for a broad frequency range (20 - 400 Hz) when a selective stacking scheme is used. For frequencies below 100 Hz, the reconstructed Green's functions show clear body-wave arrivals for almost all of the 153 sensor pairs. The arrival times of these body-waves are picked and used to image the local velocity structure. The resulting 3-dimensional image shows a high velocity structure that overlaps with a known ore-body. The material properties of the ore-body differ from the host rock and is likely the cause of the observed high velocity structure. For frequencies above 200 Hz, the seismic waves are multiply scattered by the tunnels and excavations and used to determine the scattering properties of the medium. The results of this study should be useful for future imaging and exploration projects in mining and oil and gas industries.

An Expertise Recommender using Web Mining

NASA Technical Reports Server (NTRS)

Joshi, Anupam; Chandrasekaran, Purnima; ShuYang, Michelle; Ramakrishnan, Ramya

2001-01-01

This report explored techniques to mine web pages of scientists to extract information regarding their expertise, build expertise chains and referral webs, and semi automatically combine this information with directory information services to create a recommender system that permits query by expertise. The approach included experimenting with existing techniques that have been reported in research literature in recent past , and adapted them as needed. In addition, software tools were developed to capture and use this information.

Assessing the Ability of Hyperspectral Data to Detect Lyngbya SPP.: A Potential Biological Indicator for Presence of Metal Objects in the Littoral Environment

DTIC Science & Technology

2006-12-01

environment. This concept would have potential benefits and applications in mine detection and countermeasure techniques. Using a USB2000 field...be distinguished by a different phycocyanin absorption, at 615-632 nm. 15. NUMBER OF PAGES 261 14. SUBJECT TERMS Hyperspectral... applications in mine detection and countermeasure techniques. Using a USB2000 field spectroradiometer, a spectral library was developed for the

The effects of different representations on static structure analysis of computer malware signatures.

PubMed

Narayanan, Ajit; Chen, Yi; Pang, Shaoning; Tao, Ban

2013-01-01

The continuous growth of malware presents a problem for internet computing due to increasingly sophisticated techniques for disguising malicious code through mutation and the time required to identify signatures for use by antiviral software systems (AVS). Malware modelling has focused primarily on semantics due to the intended actions and behaviours of viral and worm code. The aim of this paper is to evaluate a static structure approach to malware modelling using the growing malware signature databases now available. We show that, if malware signatures are represented as artificial protein sequences, it is possible to apply standard sequence alignment techniques in bioinformatics to improve accuracy of distinguishing between worm and virus signatures. Moreover, aligned signature sequences can be mined through traditional data mining techniques to extract metasignatures that help to distinguish between viral and worm signatures. All bioinformatics and data mining analysis were performed on publicly available tools and Weka.

The Effects of Different Representations on Static Structure Analysis of Computer Malware Signatures

PubMed Central

Narayanan, Ajit; Chen, Yi; Pang, Shaoning; Tao, Ban

2013-01-01

The continuous growth of malware presents a problem for internet computing due to increasingly sophisticated techniques for disguising malicious code through mutation and the time required to identify signatures for use by antiviral software systems (AVS). Malware modelling has focused primarily on semantics due to the intended actions and behaviours of viral and worm code. The aim of this paper is to evaluate a static structure approach to malware modelling using the growing malware signature databases now available. We show that, if malware signatures are represented as artificial protein sequences, it is possible to apply standard sequence alignment techniques in bioinformatics to improve accuracy of distinguishing between worm and virus signatures. Moreover, aligned signature sequences can be mined through traditional data mining techniques to extract metasignatures that help to distinguish between viral and worm signatures. All bioinformatics and data mining analysis were performed on publicly available tools and Weka. PMID:23983644

Earth Observations taken by the Expedition 13 crew

NASA Image and Video Library

2006-08-02

ISS013-E-63766 (2 Aug. 2006) --- Berkeley Pit and Butte, Montana are featured in this image photographed by an Expedition 13 crewmember on the International Space Station. The city of Butte, Montana has long been a center of mining activity. Underground mining of copper began in Butte in the 1870s, and by 1901 underground workings had extended to the groundwater table. Thus began the creation of an intricate complex of underground drains and pumps to lower the groundwater level and continue the extraction of copper. Water extracted from the mines was so rich in dissolved copper sulfate that it was also "mined" (by chemical precipitation) for the copper it contained. In 1955, the Anaconda Copper Mining Company began open-pit mining for copper in what is now know as the Berkeley Pit (dark oblong area in center). The mine took advantage of the existing subterranean drainage and pump network to lower groundwater until 1982, when the new owner ARCO suspended operations at the mine. The groundwater level swiftly rose, and today water in the Pit is more than 900 feet deep. Many features of the mine workings are visible in this image such as the many terraced levels and access roadways of the open mine pits (gray and tan sculptured surfaces). A large gray tailings pile of waste rock and an adjacent tailings pond are visible to the north of the Berkeley Pit. Color changes in the tailings pond are due primarily to changing water depth. The Berkeley Pit is listed as a federal Superfund site due to its highly acidic water, which contains high concentrations of metals such as copper and zinc. The Berkeley Pit receives groundwater flowing through the surrounding bedrock and acts as a "terminal pit" or sink for these heavy metal-laden waters. Ongoing efforts include regulation of water flow into the pit to reduce filling of the Pit and potential release of contaminated water into local aquifers or surface streams.

On the classification techniques in data mining for microarray data classification

NASA Astrophysics Data System (ADS)

Aydadenta, Husna; Adiwijaya

2018-03-01

Cancer is one of the deadly diseases, according to data from WHO by 2015 there are 8.8 million more deaths caused by cancer, and this will increase every year if not resolved earlier. Microarray data has become one of the most popular cancer-identification studies in the field of health, since microarray data can be used to look at levels of gene expression in certain cell samples that serve to analyze thousands of genes simultaneously. By using data mining technique, we can classify the sample of microarray data thus it can be identified with cancer or not. In this paper we will discuss some research using some data mining techniques using microarray data, such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, k-Nearest Neighbor (kNN), and C4.5, and simulation of Random Forest algorithm with technique of reduction dimension using Relief. The result of this paper show performance measure (accuracy) from classification algorithm (SVM, ANN, Naive Bayes, kNN, C4.5, and Random Forets).The results in this paper show the accuracy of Random Forest algorithm higher than other classification algorithms (Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, k-Nearest Neighbor (kNN), and C4.5). It is hoped that this paper can provide some information about the speed, accuracy, performance and computational cost generated from each Data Mining Classification Technique based on microarray data.

Parallel object-oriented data mining system

DOEpatents

Kamath, Chandrika; Cantu-Paz, Erick

2004-01-06

A data mining system uncovers patterns, associations, anomalies and other statistically significant structures in data. Data files are read and displayed. Objects in the data files are identified. Relevant features for the objects are extracted. Patterns among the objects are recognized based upon the features. Data from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) sky survey was used to search for bent doubles. This test was conducted on data from the Very Large Array in New Mexico which seeks to locate a special type of quasar (radio-emitting stellar object) called bent doubles. The FIRST survey has generated more than 32,000 images of the sky to date. Each image is 7.1 megabytes, yielding more than 100 gigabytes of image data in the entire data set.

Data mining for the identification of metabolic syndrome status

PubMed Central

Worachartcheewan, Apilak; Schaduangrat, Nalini; Prachayasittikul, Virapong; Nantasenamat, Chanin

2018-01-01

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS. PMID:29383020

Data mining for the identification of metabolic syndrome status.

PubMed

Worachartcheewan, Apilak; Schaduangrat, Nalini; Prachayasittikul, Virapong; Nantasenamat, Chanin

2018-01-01

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS.

Cooperative organic mine avoidance path planning

NASA Astrophysics Data System (ADS)

McCubbin, Christopher B.; Piatko, Christine D.; Peterson, Adam V.; Donnald, Creighton R.; Cohen, David

2005-06-01

The JHU/APL Path Planning team has developed path planning techniques to look for paths that balance the utility and risk associated with different routes through a minefield. Extending on previous years' efforts, we investigated real-world Naval mine avoidance requirements and developed a tactical decision aid (TDA) that satisfies those requirements. APL has developed new mine path planning techniques using graph based and genetic algorithms which quickly produce near-minimum risk paths for complicated fitness functions incorporating risk, path length, ship kinematics, and naval doctrine. The TDA user interface, a Java Swing application that obtains data via Corba interfaces to path planning databases, allows the operator to explore a fusion of historic and in situ mine field data, control the path planner, and display the planning results. To provide a context for the minefield data, the user interface also renders data from the Digital Nautical Chart database, a database created by the National Geospatial-Intelligence Agency containing charts of the world's ports and coastal regions. This TDA has been developed in conjunction with the COMID (Cooperative Organic Mine Defense) system. This paper presents a description of the algorithms, architecture, and application produced.

Slope stability radar for monitoring mine walls

NASA Astrophysics Data System (ADS)

Reeves, Bryan; Noon, David A.; Stickley, Glen F.; Longstaff, Dennis

2001-11-01

Determining slope stability in a mining operation is an important task. This is especially true when the mine workings are close to a potentially unstable slope. A common technique to determine slope stability is to monitor the small precursory movements, which occur prior to collapse. The slope stability radar has been developed to remotely scan a rock slope to continuously monitor the spatial deformation of the face. Using differential radar interferometry, the system can detect deformation movements of a rough wall with sub-millimeter accuracy, and with high spatial and temporal resolution. The effects of atmospheric variations and spurious signals can be reduced via signal processing means. The advantage of radar over other monitoring techniques is that it provides full area coverage without the need for mounted reflectors or equipment on the wall. In addition, the radar waves adequately penetrate through rain, dust and smoke to give reliable measurements, twenty-four hours a day. The system has been trialed at three open-cut coal mines in Australia, which demonstrated the potential for real-time monitoring of slope stability during active mining operations.

Fluid placement of fixated scrubber sludge to reduce surface subsidence and to abate acid mine drainage in abandoned underground coal mines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meiers, R.J.; Golden, D.; Gray, R.

1995-12-31

Indianapolis Power and Light Company (IPL) began researching the use of fluid placement techniques of the fixated scrubber sludge (FSS) to reduce surface subsidence from underground coal mines to develop an economic alternative to low strength concrete grout. Abandoned underground coal mines surround property adjacent to IPL`s coal combustion by-product (CCBP) landfill at the Petersburg Generating Station. Landfill expansion into these areas is in question because of the high potential for sinkhole subsidence to develop. Sinkholes manifesting at the surface would put the integrity of a liner or runoff pond containment structure for a CCBP disposal facility at risk. Themore » fluid placement techniques of the FSS as a subsidence abatement technology was demonstrated during an eight week period in September, October, and November 1994 at the Petersburg Generating Station. The success of this technology will be determined by the percentage of the mine void filled, strength of the FSS placed, and the overall effects on the hydrogeologic environment. The complete report for this project will be finalized in early 1996.« less

Sensor feature fusion for detecting buried objects

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clark, G.A.; Sengupta, S.K.; Sherwood, R.J.

1993-04-01

Given multiple registered images of the earth`s surface from dual-band sensors, our system fuses information from the sensors to reduce the effects of clutter and improve the ability to detect buried or surface target sites. The sensor suite currently includes two sensors (5 micron and 10 micron wavelengths) and one ground penetrating radar (GPR) of the wide-band pulsed synthetic aperture type. We use a supervised teaming pattern recognition approach to detect metal and plastic land mines buried in soil. The overall process consists of four main parts: Preprocessing, feature extraction, feature selection, and classification. These parts are used in amore » two step process to classify a subimage. Thee first step, referred to as feature selection, determines the features of sub-images which result in the greatest separability among the classes. The second step, image labeling, uses the selected features and the decisions from a pattern classifier to label the regions in the image which are likely to correspond to buried mines. We extract features from the images, and use feature selection algorithms to select only the most important features according to their contribution to correct detections. This allows us to save computational complexity and determine which of the sensors add value to the detection system. The most important features from the various sensors are fused using supervised teaming pattern classifiers (including neural networks). We present results of experiments to detect buried land mines from real data, and evaluate the usefulness of fusing feature information from multiple sensor types, including dual-band infrared and ground penetrating radar. The novelty of the work lies mostly in the combination of the algorithms and their application to the very important and currently unsolved operational problem of detecting buried land mines from an airborne standoff platform.« less

Industrial application of semantic process mining

NASA Astrophysics Data System (ADS)

Espen Ingvaldsen, Jon; Atle Gulla, Jon

2012-05-01

Process mining relates to the extraction of non-trivial and useful information from information system event logs. It is a new research discipline that has evolved significantly since the early work on idealistic process logs. Over the last years, process mining prototypes have incorporated elements from semantics and data mining and targeted visualisation techniques that are more user-friendly to business experts and process owners. In this article, we present a framework for evaluating different aspects of enterprise process flows and address practical challenges of state-of-the-art industrial process mining. We also explore the inherent strengths of the technology for more efficient process optimisation.

LANDSAT inventory of surface-mined areas using extendible digital techniques

NASA Technical Reports Server (NTRS)

Anderson, A. T.; Schultz, D. T.; Buchman, N.

1975-01-01

Multispectral LANDSAT imagery was analyzed to provide a rapid and accurate means of identification, classification, and measurement of strip-mined surfaces in Western Maryland. Four band analysis allows distinction of a variety of strip-mine associated classes, but has limited extendibility. A method for surface area measurements of strip mines, which is both geographically and temporally extendible, has been developed using band-ratioed LANDSAT reflectance data. The accuracy of area measurement by this method, averaged over three LANDSAT scenes taken between September 1972 and July 1974, is greater than 93%. Total affected acreage of large (50 hectare/124 acre) mines can be measured to within 1.0%.

Open-source tools for data mining.

PubMed

Zupan, Blaz; Demsar, Janez

2008-03-01

With a growing volume of biomedical databases and repositories, the need to develop a set of tools to address their analysis and support knowledge discovery is becoming acute. The data mining community has developed a substantial set of techniques for computational treatment of these data. In this article, we discuss the evolution of open-source toolboxes that data mining researchers and enthusiasts have developed over the span of a few decades and review several currently available open-source data mining suites. The approaches we review are diverse in data mining methods and user interfaces and also demonstrate that the field and its tools are ready to be fully exploited in biomedical research.

Solar Data Mining at Georgia State University

NASA Astrophysics Data System (ADS)

Angryk, R.; Martens, P. C.; Schuh, M.; Aydin, B.; Kempton, D.; Banda, J.; Ma, R.; Naduvil-Vadukootu, S.; Akkineni, V.; Küçük, A.; Filali Boubrahimi, S.; Hamdi, S. M.

2016-12-01

In this talk we give an overview of research projects related to solar data analysis that are conducted at Georgia State University. We will provide update on multiple advances made by our research team on the analysis of image parameters, spatio-temporal patterns mining, temporal data analysis and our experiences with big, heterogeneous solar data visualization, analysis, processing and storage. We will talk about up-to-date data mining methodologies, and their importance for big data-driven solar physics research.

Spectral methods to detect surface mines

NASA Astrophysics Data System (ADS)

Winter, Edwin M.; Schatten Silvious, Miranda

2008-04-01

Over the past five years, advances have been made in the spectral detection of surface mines under minefield detection programs at the U. S. Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD). The problem of detecting surface land mines ranges from the relatively simple, the detection of large anti-vehicle mines on bare soil, to the very difficult, the detection of anti-personnel mines in thick vegetation. While spatial and spectral approaches can be applied to the detection of surface mines, spatial-only detection requires many pixels-on-target such that the mine is actually imaged and shape-based features can be exploited. This method is unreliable in vegetated areas because only part of the mine may be exposed, while spectral detection is possible without the mine being resolved. At NVESD, hyperspectral and multi-spectral sensors throughout the reflection and thermal spectral regimes have been applied to the mine detection problem. Data has been collected on mines in forest and desert regions and algorithms have been developed both to detect the mines as anomalies and to detect the mines based on their spectral signature. In addition to the detection of individual mines, algorithms have been developed to exploit the similarities of mines in a minefield to improve their detection probability. In this paper, the types of spectral data collected over the past five years will be summarized along with the advances in algorithm development.

Knowledge Discovery and Data Mining in Iran's Climatic Researches

NASA Astrophysics Data System (ADS)

Karimi, Mostafa

2013-04-01

Advances in measurement technology and data collection is the database gets larger. Large databases require powerful tools for analysis data. Iterative process of acquiring knowledge from information obtained from data processing is done in various forms in all scientific fields. However, when the data volume large, and many of the problems the Traditional methods cannot respond. in the recent years, use of databases in various scientific fields, especially atmospheric databases in climatology expanded. in addition, increases in the amount of data generated by the climate models is a challenge for analysis of it for extraction of hidden pattern and knowledge. The approach to this problem has been made in recent years uses the process of knowledge discovery and data mining techniques with the use of the concepts of machine learning, artificial intelligence and expert (professional) systems is overall performance. Data manning is analytically process for manning in massive volume data. The ultimate goal of data mining is access to information and finally knowledge. climatology is a part of science that uses variety and massive volume data. Goal of the climate data manning is Achieve to information from variety and massive atmospheric and non-atmospheric data. in fact, Knowledge Discovery performs these activities in a logical and predetermined and almost automatic process. The goal of this research is study of uses knowledge Discovery and data mining technique in Iranian climate research. For Achieve This goal, study content (descriptive) analysis and classify base method and issue. The result shown that in climatic research of Iran most clustering, k-means and wards applied and in terms of issues precipitation and atmospheric circulation patterns most introduced. Although several studies in geography and climate issues with statistical techniques such as clustering and pattern extraction is done, Due to the nature of statistics and data mining, but cannot say for internal climate studies in data mining and knowledge discovery techniques are used. However, it is necessary to use the KDD Approach and DM techniques in the climatic studies, specific interpreter of climate modeling result.

76 FR 45300 - Notice of Issuance of Materials License SUA-1597 and Record of Decision for Uranerz Energy...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-28

... considered but eliminated from detailed analysis include conventional uranium mining and milling, conventional mining and heap leach processing, alternative site location, alternate lixiviants, and alternate...'s Agencywide Document Access and Management System (ADAMS), which provides text and image files of...

Comparative Data Mining Analysis for Information Retrieval of MODIS Images: Monitoring Lake Turbidity Changes at Lake Okeechobee, Florida

EPA Science Inventory

In the remote sensing field, a frequently recurring question is: Which computational intelligence or data mining algorithms are most suitable for the retrieval of essential information given that most natural systems exhibit very high non-linearity. Among potential candidates mig...

The use of the DInSAR method in the monitoring of road damage caused by mining activities

NASA Astrophysics Data System (ADS)

Murdzek, Radosław; Malik, Hubert; Leśniak, Andrzej

2018-04-01

This paper reviews existing remote sensing methods of road damage detection and demonstrates the possibility of using DInSAR (Differential Interferometry SAR) method to identify endangered road sections. In this study two radar images collected by Sentinel-1 satellite have been used. Images were acquired with 24 days interval in 2015. The analysis allowed to estimate the scale of the post-mining deformation that occurred in Upper Silesia and to indicate areas where road infrastructure is particularly vulnerable to damage.

A review of EO image information mining

NASA Astrophysics Data System (ADS)

Quartulli, Marco; Olaizola, Igor G.

2013-01-01

We analyze the state of the art of content-based retrieval in Earth observation image archives focusing on complete systems showing promise for operational implementation. The different paradigms at the basis of the main system families are introduced. The approaches taken are considered, focusing in particular on the phases after primitive feature extraction. The solutions envisaged for the issues related to feature simplification and synthesis, indexing, semantic labeling are reviewed. The methodologies for query specification and execution are evaluated. Conclusions are drawn on the state of published research in Earth observation (EO) mining.

Blasting Rocks and Blasting Cars Applied Engineering

ScienceCinema

LBNL

2017-12-09

June 30, 2004 Berkeley Lab lecture: Deb Hopkins works with industries like automobile, mining and paper to improve their evaluation and measuring techniques. For several years, she has coordinated ... June 30, 2004 Berkeley Lab lecture: Deb Hopkins works with industries like automobile, mining and paper to improve their evaluation and measuring techniques. For several years, she has coordinated a program at Berkeley Lab funded under the Partnership for a New Generation of Vehicles, a collaboration between the federal government and the U.S. Council for Automotive Research. Nondestructive evaluation techniques to test a car's structural integrity are being developed for auto assembly lines.

Multiple comparisons permutation test for image based data mining in radiotherapy.

PubMed

Chen, Chun; Witte, Marnix; Heemsbergen, Wilma; van Herk, Marcel

2013-12-23

: Comparing incidental dose distributions (i.e. images) of patients with different outcomes is a straightforward way to explore dose-response hypotheses in radiotherapy. In this paper, we introduced a permutation test that compares images, such as dose distributions from radiotherapy, while tackling the multiple comparisons problem. A test statistic Tmax was proposed that summarizes the differences between the images into a single value and a permutation procedure was employed to compute the adjusted p-value. We demonstrated the method in two retrospective studies: a prostate study that relates 3D dose distributions to failure, and an esophagus study that relates 2D surface dose distributions of the esophagus to acute esophagus toxicity. As a result, we were able to identify suspicious regions that are significantly associated with failure (prostate study) or toxicity (esophagus study). Permutation testing allows direct comparison of images from different patient categories and is a useful tool for data mining in radiotherapy.

Realising the knowledge spiral in healthcare: the role of data mining and knowledge management.

PubMed

Wickramasinghe, Nilmini; Bali, Rajeev K; Gibbons, M Chris; Schaffer, Jonathan

2008-01-01

Knowledge Management (KM) is an emerging business approach aimed at solving current problems such as competitiveness and the need to innovate which are faced by businesses today. The premise for the need for KM is based on a paradigm shift in the business environment where knowledge is central to organizational performance . Organizations trying to embrace KM have many tools, techniques and strategies at their disposal. A vital technique in KM is data mining which enables critical knowledge to be gained from the analysis of large amounts of data and information. The healthcare industry is a very information rich industry. The collecting of data and information permeate most, if not all areas of this industry; however, the healthcare industry has yet to fully embrace KM, let alone the new evolving techniques of data mining. In this paper, we demonstrate the ubiquitous benefits of data mining and KM to healthcare by highlighting their potential to enable and facilitate superior clinical practice and administrative management to ensue. Specifically, we show how data mining can realize the knowledge spiral by effecting the four key transformations identified by Nonaka of turning: (1) existing explicit knowledge to new explicit knowledge, (2) existing explicit knowledge to new tacit knowledge, (3) existing tacit knowledge to new explicit knowledge and (4) existing tacit knowledge to new tacit knowledge. This is done through the establishment of theoretical models that respectively identify the function of the knowledge spiral and the powers of data mining, both exploratory and predictive, in the knowledge discovery process. Our models are then applied to a healthcare data set to demonstrate the potential of this approach as well as the implications of such an approach to the clinical and administrative aspects of healthcare. Further, we demonstrate how these techniques can facilitate hospitals to address the six healthcare quality dimensions identified by the Committee for Quality Healthcare.

Mining High-Dimensional Data

NASA Astrophysics Data System (ADS)

Wang, Wei; Yang, Jiong

With the rapid growth of computational biology and e-commerce applications, high-dimensional data becomes very common. Thus, mining high-dimensional data is an urgent problem of great practical importance. However, there are some unique challenges for mining data of high dimensions, including (1) the curse of dimensionality and more crucial (2) the meaningfulness of the similarity measure in the high dimension space. In this chapter, we present several state-of-art techniques for analyzing high-dimensional data, e.g., frequent pattern mining, clustering, and classification. We will discuss how these methods deal with the challenges of high dimensionality.

The application of satellite data in monitoring strip mines

NASA Technical Reports Server (NTRS)

Sharber, L. A.; Shahrokhi, F.

1977-01-01

Strip mines in the New River Drainage Basin of Tennessee were studied through use of Landsat-1 imagery and aircraft photography. A multilevel analysis, involving conventional photo interpretation techniques, densitometric methods, multispectral analysis and statistical testing was applied to the data. The Landsat imagery proved adequate for monitoring large-scale change resulting from active mining and land-reclamation projects. However, the spatial resolution of the satellite imagery rendered it inadequate for assessment of many smaller strip mines, in the region which may be as small as a few hectares.

The Use of Ground Penetrating Radar to Exploring Sedimentary Ore In North-Central Saudi Arabia

NASA Astrophysics Data System (ADS)

Almutairi, Yasir; Almutair, Muteb

2015-04-01

Ground Penetrating Radar (GPR) is a non-destructive geophysical method that provides a continuous subsurface profile, without drilling. This geophysical technique has great potential in delineating the extension of bauxites ore in north-central Saudi Arabia. Bauxite is from types sedimentary ores. This study aim to evaluate the effectiveness of Ground Penetrating Radar (GPR) to illustrate the subsurface feature of the Bauxite deposits at some selected mining areas north-central Saudi Arabia. Bauxite is a heterogeneous material that consists of complex metals such as alumina and aluminum. An efficient and cost-effect exploration method for bauxite mine in Saudi Arabia is required. Ground penetrating radar (GPR) measurements have been carrying out along outcrop in order to assess the potential of GPR data for imaging and characterising different lithological facies. To do so, we have tested different antenna frequencies to acquire the electromagnetic signals along a 90 m profile using the IDS system. This system equipped with a 25 MHz antenna that allows investigating the Bauxite layer at shallow depths where the clay layers may existed. Therefore, the 25 MHz frequency antenna has been used in this study insure better resolution of the subsurface and to get more penetration to image the Bauxite layer. After the GPR data acquisition, this data must be processed in order to be more easily visualized and interpreted. Data processing was done using Reflex 6.0 software. A series of tests were carried out in frequency filtering on a sample of radar sections, which was considered to better represent the entire set of data. Our results indicated that the GPR profiling has a very good agreement for mapping the bauxite layer depth at range of 7 m to 11 m. This study has emphasized that the high-resolution GPR method is the robust and cost-effect technique to map the Bauxite layer. The exploration of Bauxite resource using the GPR technique could reduce the number of holes to be strategically placed in the most promising zones.

Gaining Insights on Nasopharyngeal Carcinoma Treatment Outcome Using Clinical Data Mining Techniques.

PubMed

Ghaibeh, A Ammar; Kasem, Asem; Ng, Xun Jin; Nair, Hema Latha Krishna; Hirose, Jun; Thiruchelvam, Vinesh

2018-01-01

The analysis of Electronic Health Records (EHRs) is attracting a lot of research attention in the medical informatics domain. Hospitals and medical institutes started to use data mining techniques to gain new insights from the massive amounts of data that can be made available through EHRs. Researchers in the medical field have often used descriptive statistics and classical statistical methods to prove assumed medical hypotheses. However, discovering new insights from large amounts of data solely based on experts' observations is difficult. Using data mining techniques and visualizations, practitioners can find hidden knowledge, identify interesting patterns, or formulate new hypotheses to be further investigated. This paper describes a work in progress on using data mining methods to analyze clinical data of Nasopharyngeal Carcinoma (NPC) cancer patients. NPC is the fifth most common cancer among Malaysians, and the data analyzed in this study was collected from three states in Malaysia (Kuala Lumpur, Sabah and Sarawak), and is considered to be the largest up-to-date dataset of its kind. This research is addressing the issue of cancer recurrence after the completion of radiotherapy and chemotherapy treatment. We describe the procedure, problems, and insights gained during the process.

Data mining of air traffic control operational errors

DOT National Transportation Integrated Search

2006-01-01

In this paper we present the results of : applying data mining techniques to identify patterns and : anomalies in air traffic control operational errors (OEs). : Reducing the OE rate is of high importance and remains a : challenge in the aviation saf...

Application of a Novel Liquid Nitrogen Control Technique for Heat Stress and Fire Prevention in Underground Mines.

PubMed

Shi, Bobo; Ma, Lingjun; Dong, Wei; Zhou, Fubao

2015-01-01

With the continually increasing mining depths, heat stress and spontaneous combustion hazards in high-temperature mines are becoming increasingly severe. Mining production risks from natural hazards and exposures to hot and humid environments can cause occupational diseases and other work-related injuries. Liquid nitrogen injection, an engineering control developed to reduce heat stress and spontaneous combustion hazards in mines, was successfully utilized for environmental cooling and combustion prevention in an underground mining site named "Y120205 Working Face" (Y120205 mine) of Yangchangwan colliery. Both localized humidities and temperatures within the Y120205 mine decreased significantly with liquid nitrogen injection. The maximum percentage drop in temperature and humidity of the Y120205 mine were 21.9% and 10.8%, respectively. The liquid nitrogen injection system has the advantages of economical price, process simplicity, energy savings and emission reduction. The optimized heat exchanger used in the liquid nitrogen injection process achieved superior air-cooling results, resulting in considerable economic benefits.

Application of ASTER and Landsat 8 imagery data and mathematical evaluation method in detecting iron minerals contamination in the Chadormalu iron mine area, central Iran

NASA Astrophysics Data System (ADS)

Moghtaderi, Arsia; Moore, Farid; Ranjbar, Hojjatollah

2017-01-01

Satellite images are widely used to map geological and environmental features at different map scales. The ability of visible to near-infrared (VNIR) scanner systems to map gossans, rich in iron and associated with weathered sulfide occurrences, as well as to characterize regoliths, is perhaps one of the most important current applications of this technology. Initial results of this study show that advanced space-borne thermal emission and reflection (ASTER), VNIR, and short-wave infrared radiometer scanner systems can be used successfully to map iron ores. By applying internal average relative reflectance, false color composite, minimum noise fraction transform, and mathematical evaluation method (MEM) techniques, iron contaminations were successfully detected in the Chadormalu iron mine area of central Iran. An attempt was also made to discriminate between the geogenic and anthropogenic iron contaminations in the vicinity of the Chadormalu iron deposit. This research compares ASTER and Landsat 8 data images and the MEM with the band ratio method in a full scope view scale and demonstrates ASTER image data capability in detecting iron contaminations in the Chadormalu area. This indicates that ASTER bands 3, 2, and 1 have a higher spatial (15 m) resolution compared with sensors used in previous works. In addition, the capability of the MEM in detecting Fe-contaminants, unlike the color judgments of the band ratio method, can discriminate between iron pollution in an alluvial plain and the Fe-contents of the host and country rocks in the study area. This study proved that Landsat 8 data illustrate exaggeration both in the MEM and band ratio final results (outputs) and cannot display iron contamination in detail.

[Hygienic and ergonomic analysis of the technology for sinking main and subsidiary mine shafts].

PubMed

Meniaĭlo, N I; Tyshlek, E G; Gritsenko, V S; Shemiakin, G M

1989-01-01

The labour conditions in mine shafts do not correspond to the existing ergonomic and hygienic norms. Drilling and blasting techniques are most hazardous as to the gravity and duration of the factors involved. Working conditions normalization should be based on the elaboration of specifically innovative technologies which should envisage the workers' periodic staying in the mine shaft area during the work shift.

Chiquicamata Mine, Chile

NASA Image and Video Library

2016-08-24

Chuquicamata, in Chile's Atacama Desert, is the largest open pit copper mine in the world, by excavated volume. The copper deposits were first exploited in pre-Hispanic times. Open pit mining began in the early 20th century when a method was developed to work low grade oxidized copper ores. The image was acquired September 2, 2007, covers an area of 19.5 by 29.3 km, and is located at 22.1 degrees south, 68.9 degrees west. http://photojournal.jpl.nasa.gov/catalog/PIA20973

Clustering and Dimensionality Reduction to Discover Interesting Patterns in Binary Data

NASA Astrophysics Data System (ADS)

Palumbo, Francesco; D'Enza, Alfonso Iodice

The attention towards binary data coding increased consistently in the last decade due to several reasons. The analysis of binary data characterizes several fields of application, such as market basket analysis, DNA microarray data, image mining, text mining and web-clickstream mining. The paper illustrates two different approaches exploiting a profitable combination of clustering and dimensionality reduction for the identification of non-trivial association structures in binary data. An application in the Association Rules framework supports the theory with the empirical evidence.

Hydraulic hoisting and backfilling

NASA Astrophysics Data System (ADS)

Sauermann, H. B.

In a country such as South Africa, with its large deep level mining industry, improvements in mining and hoisting techniques could result in substantial savings. Hoisting techniques, for example, may be improved by the introduction of hydraulic hoisting. The following are some of the advantages of hydraulic hoisting as against conventional skip hoisting: (1) smaller shafts are required because the pipes to hoist the same quantity of ore hydraulically require less space in the shaft than does skip hoisting equipment; (2) the hoisting capacity of a mine can easily be increased without the necessity of sinking new shafts. Large savings in capital costs can thus be made; (3) fully automatic control is possible with hydraulic hoisting and therefore less manpower is required; and (4) health and safety conditions will be improved.

Using data mining techniques to characterize participation in observational studies.

PubMed

Linden, Ariel; Yarnold, Paul R

2016-12-01

Data mining techniques are gaining in popularity among health researchers for an array of purposes, such as improving diagnostic accuracy, identifying high-risk patients and extracting concepts from unstructured data. In this paper, we describe how these techniques can be applied to another area in the health research domain: identifying characteristics of individuals who do and do not choose to participate in observational studies. In contrast to randomized studies where individuals have no control over their treatment assignment, participants in observational studies self-select into the treatment arm and therefore have the potential to differ in their characteristics from those who elect not to participate. These differences may explain part, or all, of the difference in the observed outcome, making it crucial to assess whether there is differential participation based on observed characteristics. As compared to traditional approaches to this assessment, data mining offers a more precise understanding of these differences. To describe and illustrate the application of data mining in this domain, we use data from a primary care-based medical home pilot programme and compare the performance of commonly used classification approaches - logistic regression, support vector machines, random forests and classification tree analysis (CTA) - in correctly classifying participants and non-participants. We find that CTA is substantially more accurate than the other models. Moreover, unlike the other models, CTA offers transparency in its computational approach, ease of interpretation via the decision rules produced and provides statistical results familiar to health researchers. Beyond their application to research, data mining techniques could help administrators to identify new candidates for participation who may most benefit from the intervention. © 2016 John Wiley & Sons, Ltd.

Comparison of data mining techniques applied to fetal heart rate parameters for the early identification of IUGR fetuses.

PubMed

Magenes, G; Bellazzi, R; Malovini, A; Signorini, M G

2016-08-01

The onset of fetal pathologies can be screened during pregnancy by means of Fetal Heart Rate (FHR) monitoring and analysis. Noticeable advances in understanding FHR variations were obtained in the last twenty years, thanks to the introduction of quantitative indices extracted from the FHR signal. This study searches for discriminating Normal and Intra Uterine Growth Restricted (IUGR) fetuses by applying data mining techniques to FHR parameters, obtained from recordings in a population of 122 fetuses (61 healthy and 61 IUGRs), through standard CTG non-stress test. We computed N=12 indices (N=4 related to time domain FHR analysis, N=4 to frequency domain and N=4 to non-linear analysis) and normalized them with respect to the gestational week. We compared, through a 10-fold crossvalidation procedure, 15 data mining techniques in order to select the more reliable approach for identifying IUGR fetuses. The results of this comparison highlight that two techniques (Random Forest and Logistic Regression) show the best classification accuracy and that both outperform the best single parameter in terms of mean AUROC on the test sets.

Geologic applications of thermal-inertia mapping from satellite. [Powder River, Wyoming; Cubeza Prieta, Arizona, and Yellowstone National Park

NASA Technical Reports Server (NTRS)

Offield, T. W. (Principal Investigator); Watson, K.; Hummer-Miller, S.

1981-01-01

In the Powder River Basin, Wyo., narrow geologic units having thermal inertias which contrast with their surroundings can be discriminated in optimal images. A few subtle thermal inertia anomalies coincide with areas of helium leakage believed to be associated with deep oil and gas concentrations. The most important results involved delineation of tectonic framework elements some of which were not previously recognized. Thermal and thermal inertia images also permit mapping of geomorphic textural domains. A thermal lineament appears to reveal a basement discontinuity which involves the Homestake Mine in the Black Hill, a zone of Tertiary igneous activity and facies control in oil producing horizons. Applications of these data to the Cabeza Prieta, Ariz., area illustrate their potential for igneous rock type discrimination. Extension to Yellowstone National Park resulted in the detection of additional structural information but surface hydrothermal features could not be distinguished with any confidence. A thermal inertia mapping algorithm, a fast and accurate image registration technique, and an efficient topographic slope and elevation correction method were developed.

Galaxy Classifications with Deep Learning

NASA Astrophysics Data System (ADS)

Lukic, Vesna; Brüggen, Marcus

2017-06-01

Machine learning techniques have proven to be increasingly useful in astronomical applications over the last few years, for example in object classification, estimating redshifts and data mining. One example of object classification is classifying galaxy morphology. This is a tedious task to do manually, especially as the datasets become larger with surveys that have a broader and deeper search-space. The Kaggle Galaxy Zoo competition presented the challenge of writing an algorithm to find the probability that a galaxy belongs in a particular class, based on SDSS optical spectroscopy data. The use of convolutional neural networks (convnets), proved to be a popular solution to the problem, as they have also produced unprecedented classification accuracies in other image databases such as the database of handwritten digits (MNIST †) and large database of images (CIFAR ‡). We experiment with the convnets that comprised the winning solution, but using broad classifications. The effect of changing the number of layers is explored, as well as using a different activation function, to help in developing an intuition of how the networks function and to see how they can be applied to radio galaxy images.

Sustainable rehabilitation of mining waste and acid mine drainage using geochemistry, mine type, mineralogy, texture, ore extraction and climate knowledge.

PubMed

Anawar, Hossain Md

2015-08-01

The oxidative dissolution of sulfidic minerals releases the extremely acidic leachate, sulfate and potentially toxic elements e.g., As, Ag, Cd, Cr, Cu, Hg, Ni, Pb, Sb, Th, U, Zn, etc. from different mine tailings and waste dumps. For the sustainable rehabilitation and disposal of mining waste, the sources and mechanisms of contaminant generation, fate and transport of contaminants should be clearly understood. Therefore, this study has provided a critical review on (1) recent insights in mechanisms of oxidation of sulfidic minerals, (2) environmental contamination by mining waste, and (3) remediation and rehabilitation techniques, and (4) then developed the GEMTEC conceptual model/guide [(bio)-geochemistry-mine type-mineralogy- geological texture-ore extraction process-climatic knowledge)] to provide the new scientific approach and knowledge for remediation of mining wastes and acid mine drainage. This study has suggested the pre-mining geological, geochemical, mineralogical and microtextural characterization of different mineral deposits, and post-mining studies of ore extraction processes, physical, geochemical, mineralogical and microbial reactions, natural attenuation and effect of climate change for sustainable rehabilitation of mining waste. All components of this model should be considered for effective and integrated management of mining waste and acid mine drainage. Copyright © 2015 Elsevier Ltd. All rights reserved.

75 FR 48366 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-10

...: OMB Desk Officer for the Department of Labor--Mine Safety and Health Administration (MSHA), Office of..., electronic, mechanical, or other technological collection techniques or other forms of information technology, e.g., permitting electronic submission of responses. Agency: Mine Safety and Health Administration...

Water induced geohazards measured with spaceborne interferometry techniques

NASA Astrophysics Data System (ADS)

Poncos, V.; Serban, F.; Teleaga, D.; Ciocan, V.; Sorin, M.; Caranda, D.; Zamfirescu, F.; Andrei, M.; Copaescu, S.; Radu, M.; Raduca, V.

2012-04-01

Natural and anthropogenic occurrence of groundwater is inducing surficial crustal deformation processes that can be accurately measured with high spatial density from space, regardless of the ground access conditions. The detection of the surface deformation allows uncovering spatial and temporal patterns of subsurface processes such as land subsidence, cave-ins and differential ground settlement related to water content. InSAR measurements combined with ground truth data permit estimation of the mechanical properties of the rocks and the development of models and scenarios to predict disaster events such as cave-ins, landslides and soil liquefaction in the case of an Earthquake. A number of three sites in Romania that suffer of ground instability because of the water component will be presented. The DInSAR, Interferograms Stacking and Persistent Scatterers Interferometry techniques were applied to retrieve as accurate as possible the displacement information. The first studied site is the city of Bucharest; using 7 years of ERS data ground instability was detected on a large area that represents the historical watershed of the Dambovita river. A network of water wells shows that the ground instability is directly proportional to the groundwater depth. The second site is the Ocnele Mari brine extraction area. The exploitation of the Ocnele Mari salt deposit started from the Roman Empire time using the mining technology and from 1954 the salt dissolution technology which involves injecting water into the ground using a well and extracting the brine (water and salt) through another well. The extraction of salt through dissolution led to slow ground subsidence but the flooding and dissolution of the Roman caves led to catastrophic cave-ins and the relocation of an entire village. The water injection technique is still applied and the Roman cave system is an unknown, therefore further catastrophic events are expected. The existing theoretical simulations of the subsidence process are performed using a Finite Element Method (FEM), which calculates the distribution of the state of strains and stresses in the rock masses, in an elasto-plastic behavior. The ground deformation is presently measured with leveling instrumentation and an effort is being made to adopt the InSAR results for a better spatial and temporal coverage that should refine the existing model. The third site is a number of 4 tailing retention ponds at different stages of their life. The tailing ponds are hydrotechnical structures of permeable type designed for the safe storage of mining detritus byproducts and disposal of the water contained in these byproducts. Starting in 1998 approximately 550 mines have been closed and introduced in a conservation process. In order to prevent ecological and human damage, all these mines and storage ponds for mining tailings are required to be under continuous monitoring. Using 15 high-resolution Spotlight TerraSAR-X images, the stability of the storage pond was monitored over a period of 5 months during 2011. Interferometric stacking techniques and PSI analysis were applied in order to generate deformation maps and deformation profiles. In the same time, GPS measurements and Electrical Tomography for water content were used as independent measurements.

Study of application of ERTS-A imagery to fracture-related mine safety hazards in the coal mining industry

NASA Technical Reports Server (NTRS)

Wier, C. E.; Wobber, F. J. (Principal Investigator); Russell, O. R.; Amato, R. V.; Leshendok, T.

1973-01-01

The author has identified the following significant results. The Kings Station Mine in Gibson County, Indiana has experienced considerable roof fall problems. Detailed fracture mapping of the mine area was done with ERTS-1 and aircraft imagery, and a prediction map of roof problem areas was produced in advance of a visit. The visit to the mine and discussions with the operator indicated that of four zones mapped as potential problem areas, three coincided with areas of excessive roof fall. This positive correlation of 75% lends confidence to the validity of the technique being applied in the investigation. The mine officials expressed an interest in the project and are anxious to see the final product maps which are forthcoming.

Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response

PubMed Central

Sun, Chongjing; Fu, Yan; Zhou, Junlin; Gao, Hui

2014-01-01

Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need different privacy levels protection. To solve this problem, we give a personalized privacy-preserving method by using the randomized response technique. By providing different privacy levels for different attributes, this method can get a higher accuracy on frequent itemset mining than the traditional method providing the same privacy level. Finally, our experimental results show that our method can have better results on the frequent itemset mining while preserving personalized privacy. PMID:25143989

Personalized privacy-preserving frequent itemset mining using randomized response.

PubMed

Sun, Chongjing; Fu, Yan; Zhou, Junlin; Gao, Hui

2014-01-01

Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need different privacy levels protection. To solve this problem, we give a personalized privacy-preserving method by using the randomized response technique. By providing different privacy levels for different attributes, this method can get a higher accuracy on frequent itemset mining than the traditional method providing the same privacy level. Finally, our experimental results show that our method can have better results on the frequent itemset mining while preserving personalized privacy.

Data Mining and Complex Problems: Case Study in Composite Materials

NASA Technical Reports Server (NTRS)

Rabelo, Luis; Marin, Mario

2009-01-01

Data mining is defined as the discovery of useful, possibly unexpected, patterns and relationships in data using statistical and non-statistical techniques in order to develop schemes for decision and policy making. Data mining can be used to discover the sources and causes of problems in complex systems. In addition, data mining can support simulation strategies by finding the different constants and parameters to be used in the development of simulation models. This paper introduces a framework for data mining and its application to complex problems. To further explain some of the concepts outlined in this paper, the potential application to the NASA Shuttle Reinforced Carbon-Carbon structures and genetic programming is used as an illustration.

Application of geostatistics to coal-resource characterization and mine planning. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kauffman, P.W.; Walton, D.R.; Martuneac, L.

1981-12-01

Geostatistics is a proven method of ore reserve estimation in many non-coal mining areas but little has been published concerning its application to coal resources. This report presents the case for using geostatistics for coal mining applications and describes how a coal mining concern can best utilize geostatistical techniques for coal resource characterization and mine planning. An overview of the theory of geostatistics is also presented. Many of the applications discussed are documented in case studies that are a part of the report. The results of an exhaustive literature search are presented and recommendations are made for needed future researchmore » and demonstration projects.« less

76 FR 5216 - Notice of Availability of Final Supplemental Environmental Impact Statement for the Nichols Ranch...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-28

... Uranium Recovery Project, located in the Pumpkin Buttes Uranium Mining District within the Powder River.... Alternatives that were considered, but were eliminated from detailed analysis, include conventional mining and... an Agencywide Documents and Management System (ADAMS), which provides text and image files of the NRC...

76 FR 53500 - Notice of the Nuclear Regulatory Commission Issuance of Materials License SUA-1598 and Record of...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-26

... (ADAMS), which provides text and image files of the NRC's public documents in the NRC Library at http... considered, but eliminated from detailed analysis, include conventional uranium mining and milling, conventional mining and heap leach processing, alternate lixiviants, and alternative wastewater disposal...

Content-Based Indexing and Teaching Focus Mining for Lecture Videos

ERIC Educational Resources Information Center

Lin, Yu-Tzu; Yen, Bai-Jang; Chang, Chia-Hu; Lee, Greg C.; Lin, Yu-Chih

2010-01-01

Purpose: The purpose of this paper is to propose an indexing and teaching focus mining system for lecture videos recorded in an unconstrained environment. Design/methodology/approach: By applying the proposed algorithms in this paper, the slide structure can be reconstructed by extracting slide images from the video. Instead of applying…

Using optical flow for the detection of floating mines in IR image sequences

NASA Astrophysics Data System (ADS)

Borghgraef, Alexander; Acheroy, Marc

2006-09-01

In the first Gulf War, unmoored floating mines proved to be a real hazard for shipping traffic. An automated system capable of detecting these and other free-floating small objects, using readily available sensors such as infra-red cameras, would prove to be a valuable mine-warfare asset, and could double as a collision avoidance mechanism, and a search-and-rescue aid. The noisy background provided by the sea surface, and occlusion by waves make it difficult to detect small floating objects using only algorithms based upon the intensity, size or shape of the target. This leads us to look at the sequence of images for temporal detection characteristics. The target's apparent motion is such a determinant, given the contrast between the bobbing motion of the floating object and the strong horizontal component present in the propagation of the wavefronts. We have applied the Proesmans optical flow algorithm to IR video footage of practice mines, in order to extract the motion characteristic and a threshold on the vertical motion characteristic is then imposed to detect the floating targets.

Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques

PubMed Central

Mande, Sharmila S.

2016-01-01

The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only 'pair-wise interactions'. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoint. This study explores the applicability of one of the earliest association rule mining algorithm i.e. the 'Apriori algorithm' for deriving 'microbial association rules' from the taxonomic profile of given microbial community. The classical Apriori approach derives association rules by analysing patterns of co-occurrence/co-exclusion between various '(subsets of) features/items' across various samples. Using real-world microbiome data, the efficiency/utility of this rule mining approach in deciphering multiple (biologically meaningful) association patterns between 'subsets/subgroups' of microbes (constituting microbiome samples) is demonstrated. As an example, association rules derived from publicly available gut microbiome datasets indicate an association between a group of microbes (Faecalibacterium, Dorea, and Blautia) that are known to have mutualistic metabolic associations among themselves. Application of the rule mining approach on gut microbiomes (sourced from the Human Microbiome Project) further indicated similar microbial association patterns in gut microbiomes irrespective of the gender of the subjects. A Linux implementation of the Association Rule Mining (ARM) software (customised for deriving 'microbial association rules' from microbiome data) is freely available for download from the following link: http://metagenomics.atc.tcs.com/arm. PMID:27124399

Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques.

PubMed

Tandon, Disha; Haque, Mohammed Monzoorul; Mande, Sharmila S

2016-01-01

The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only 'pair-wise interactions'. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoint. This study explores the applicability of one of the earliest association rule mining algorithm i.e. the 'Apriori algorithm' for deriving 'microbial association rules' from the taxonomic profile of given microbial community. The classical Apriori approach derives association rules by analysing patterns of co-occurrence/co-exclusion between various '(subsets of) features/items' across various samples. Using real-world microbiome data, the efficiency/utility of this rule mining approach in deciphering multiple (biologically meaningful) association patterns between 'subsets/subgroups' of microbes (constituting microbiome samples) is demonstrated. As an example, association rules derived from publicly available gut microbiome datasets indicate an association between a group of microbes (Faecalibacterium, Dorea, and Blautia) that are known to have mutualistic metabolic associations among themselves. Application of the rule mining approach on gut microbiomes (sourced from the Human Microbiome Project) further indicated similar microbial association patterns in gut microbiomes irrespective of the gender of the subjects. A Linux implementation of the Association Rule Mining (ARM) software (customised for deriving 'microbial association rules' from microbiome data) is freely available for download from the following link: http://metagenomics.atc.tcs.com/arm.

Applications of multi-season hyperspectral remote sensing for acid mine water characterization and mapping of secondary iron minerals associated with acid mine drainage

NASA Astrophysics Data System (ADS)

Davies, Gwendolyn E.

Acid mine drainage (AMD) resulting from the oxidation of sulfides in mine waste is a major environmental issue facing the mining industry today. Open pit mines, tailings ponds, ore stockpiles, and waste rock dumps can all be significant sources of pollution, primarily heavy metals. These large mining-induced footprints are often located across vast geographic expanses and are difficult to access. With the continuing advancement of imaging satellites, remote sensing may provide a useful monitoring tool for pit lake water quality and the rapid assessment of abandoned mine sites. This study explored the applications of laboratory spectroscopy and multi-season hyperspectral remote sensing for environmental monitoring of mine waste environments. Laboratory spectral experiments were first performed on acid mine waters and synthetic ferric iron solutions to identify and isolate the unique spectral properties of mine waters. These spectral characterizations were then applied to airborne hyperspectral imagery for identification of poor water quality in AMD ponds at the Leviathan Mine Superfund site, CA. Finally, imagery varying in temporal and spatial resolutions were used to identify changes in mineralogy over weathering overburden piles and on dry AMD pond liner surfaces at the Leviathan Mine. Results show the utility of hyperspectral remote sensing for monitoring a diverse range of surfaces associated with AMD.

Knowledge-Based Reinforcement Learning for Data Mining

NASA Astrophysics Data System (ADS)

Kudenko, Daniel; Grzes, Marek

Data Mining is the process of extracting patterns from data. Two general avenues of research in the intersecting areas of agents and data mining can be distinguished. The first approach is concerned with mining an agent’s observation data in order to extract patterns, categorize environment states, and/or make predictions of future states. In this setting, data is normally available as a batch, and the agent’s actions and goals are often independent of the data mining task. The data collection is mainly considered as a side effect of the agent’s activities. Machine learning techniques applied in such situations fall into the class of supervised learning. In contrast, the second scenario occurs where an agent is actively performing the data mining, and is responsible for the data collection itself. For example, a mobile network agent is acquiring and processing data (where the acquisition may incur a certain cost), or a mobile sensor agent is moving in a (perhaps hostile) environment, collecting and processing sensor readings. In these settings, the tasks of the agent and the data mining are highly intertwined and interdependent (or even identical). Supervised learning is not a suitable technique for these cases. Reinforcement Learning (RL) enables an agent to learn from experience (in form of reward and punishment for explorative actions) and adapt to new situations, without a teacher. RL is an ideal learning technique for these data mining scenarios, because it fits the agent paradigm of continuous sensing and acting, and the RL agent is able to learn to make decisions on the sampling of the environment which provides the data. Nevertheless, RL still suffers from scalability problems, which have prevented its successful use in many complex real-world domains. The more complex the tasks, the longer it takes a reinforcement learning algorithm to converge to a good solution. For many real-world tasks, human expert knowledge is available. For example, human experts have developed heuristics that help them in planning and scheduling resources in their work place. However, this domain knowledge is often rough and incomplete. When the domain knowledge is used directly by an automated expert system, the solutions are often sub-optimal, due to the incompleteness of the knowledge, the uncertainty of environments, and the possibility to encounter unexpected situations. RL, on the other hand, can overcome the weaknesses of the heuristic domain knowledge and produce optimal solutions. In the talk we propose two techniques, which represent first steps in the area of knowledge-based RL (KBRL). The first technique [1] uses high-level STRIPS operator knowledge in reward shaping to focus the search for the optimal policy. Empirical results show that the plan-based reward shaping approach outperforms other RL techniques, including alternative manual and MDP-based reward shaping when it is used in its basic form. We showed that MDP-based reward shaping may fail and successful experiments with STRIPS-based shaping suggest modifications which can overcome encountered problems. The STRIPSbased method we propose allows expressing the same domain knowledge in a different way and the domain expert can choose whether to define an MDP or STRIPS planning task. We also evaluated the robustness of the proposed STRIPS-based technique to errors in the plan knowledge. In case that STRIPS knowledge is not available, we propose a second technique [2] that shapes the reward with hierarchical tile coding. Where the Q-function is represented with low-level tile coding, a V-function with coarser tile coding can be learned in parallel and used to approximate the potential for ground states. In the context of data mining, our KBRL approaches can also be used for any data collection task where the acquisition of data may incur considerable cost. In addition, observing the data collection agent in specific scenarios may lead to new insights into optimal data collection behaviour in the respective domains. In future work, we intend to demonstrate and evaluate our techniques on concrete real-world data mining applications.

Airborne Thermal Infrared Multispectral Scanner (TIMS) images over disseminated gold deposits, Osgood Mountains, Humboldt County, Nevada

NASA Technical Reports Server (NTRS)

Krohn, M. Dennis

1986-01-01

The U.S. Geological Survey (USGS) acquired airborne Thermal Infrared Multispectral Scanner (TIMS) images over several disseminated gold deposits in northern Nevada in 1983. The aerial surveys were flown to determine whether TIMS data could depict jasperoids (siliceous replacement bodies) associated with the gold deposits. The TIMS data were collected over the Pinson and Getchell Mines in the Osgood Mountains, the Carlin, Maggie Creek, Bootstrap, and other mines in the Tuscarora Mountains, and the Jerritt Canyon Mine in the Independence Mountains. The TIMS data seem to be a useful supplement to conventional geochemical exploration for disseminated gold deposits in the western United States. Siliceous outcrops are readily separable in the TIMS image from other types of host rocks. Different forms of silicification are not readily separable, yet, due to limitations of spatial resolution and spectral dynamic range. Features associated with the disseminated gold deposits, such as the large intrusive bodies and fault structures, are also resolvable on TIMS data. Inclusion of high-resolution thermal inertia data would be a useful supplement to the TIMS data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Greenwalt, R J; Magnoli, D

The purpose of this study is to determine battlefield effectiveness of the self-healing minefield (''Frogs'') concept system compared to basecases of the standard AP/AT (anti-personnel/anti-tank) mixed minefield, the AT (anti-tank) pure minefield, and no minefields. This involves tactical modeling where a basecase with and without mines is compared to the concept system. However, it is first necessary to establish system characteristics and behavior of the Frog mine and minefield in order to do the tactical modeling. This initial report provides emerging insights into various minefield parameters in order to allow better program definition early in the conceptual development. In themore » following sections of this report, we investigate the self-healing minefield's ground pattern and several concepts for movement (''jump'') of a mine. Basic enemy breaching techniques are compared for the different mine movement concepts. These results are then used in the (Joint Conflict and Tactical Simulation) JCATS tactical model to evaluate minefield effects in a combat situation. The three basecases and the Frogs concept are used against a North Korean mechanized rifle battalion and outcomes are compared. Preliminary results indicate: (1) Possible breaching techniques for the self-healing minefield were proposed and compared through simulation modeling. Of these, the best breaching counter to the self-healing minefield is the ''wide-lane'' breach technique. (2) Several methods for mine movement are tested and the optimal method from this group was selected for use in the modeling. However, continued work is needed on jump criteria; a more sophisticated model may reduce the advantage of the breach counter. (3) The battle scenario used in this study is a very difficult defense for Blue. In the three baseline cases (no mines, AT mines only, and mixed AT/AP minefield), Blue loses. Only in the Frog case does Blue win, and it is a high casualty win.« less

The Propagation of Seismic Waves in the Presence of Strong Elastic Property Contrasts

NASA Astrophysics Data System (ADS)

Saleh, R.; Jeyaraj, R.; Milkereit, B.; Liu, Q.; Valley, B.

2012-12-01

In an active underground mine there are many seismic activities taking place, such as seismic noises, blasts, tremors and microseismic events. In between the activities, the microseismic events are mainly used for monitoring purposes. The frequency content of microseismic events can be up to few KHz, which can result in wavelengths on the order of a few meters in hard rock environment. In an underground mine, considering the presence of both small wavelength and strong elastic contrasts, the simulation of seismic wave propagation is a challenge. With the recent availability of detailed 3D rock property models of mines, in addition to the development of efficient numerical techniques (such as Spectral Element Method (SEM)), and parallel computation facilities, a solution for such a problem is achievable. Most seismic wave scattering studies focus on large scales (>1 km) and weak elastic contrasts (velocity perturbations less than 10%). However, scattering in the presence of small-scale heterogeneities and large elastic contrasts is an area of ongoing research. In a mine environment, the presence of strong contrast discontinuities such as massive ore bodies, tunnels and infrastructure lead to discontinuities of displacement and/or stress tensor components, and have significant impact on the propagation of seismic waves. In order to obtain an accurate image of wave propagation in such a complex media, it is necessary to consider the presence of these discontinuities in numerical models. In this study, the effects of such a contrast are illustrated with 2D/3D modeling and compared with real broadband 3-component seismic data. The real broadband 3-component seismic data will be obtained in one of the Canadian underground mines in Ontario. One of the possible scenarios investigated in this study that may explain the observed complexity in seismic wavefield pattern in hard rock environments is the effect of near field displacements rather than far field. Considering the distribution of seismic sensors in a mine and the presence of seismic events within a mine, the recorded wavefield may represent a near-field displacement, which is not the case for most of seismic studies. The role of receiver characterization on the recorded event near the surface or around fault zones is also investigated. Using 2D/3D modeling, the effects of Vp/Vs variation on vertical and horizontal components of recorded amplitude has been shown.

30 CFR 582.23 - Testing Plan.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Resources BUREAU OF OCEAN ENERGY MANAGEMENT, DEPARTMENT OF THE INTERIOR OFFSHORE OPERATIONS IN THE OUTER... detailed Mining Plan than is obtainable under an approved Delineation Plan, to prepare feasibility studies, to carry out a pilot program to evaluate processing techniques or technology or mining equipment, or...

30 CFR 582.23 - Testing Plan.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Resources BUREAU OF OCEAN ENERGY MANAGEMENT, DEPARTMENT OF THE INTERIOR OFFSHORE OPERATIONS IN THE OUTER... detailed Mining Plan than is obtainable under an approved Delineation Plan, to prepare feasibility studies, to carry out a pilot program to evaluate processing techniques or technology or mining equipment, or...

30 CFR 582.23 - Testing Plan.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Resources BUREAU OF OCEAN ENERGY MANAGEMENT, DEPARTMENT OF THE INTERIOR OFFSHORE OPERATIONS IN THE OUTER... detailed Mining Plan than is obtainable under an approved Delineation Plan, to prepare feasibility studies, to carry out a pilot program to evaluate processing techniques or technology or mining equipment, or...

75 FR 53345 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-31

... the Department of Labor--Mine Safety and Health Administration (MSHA), Office of Management and Budget..., mechanical, or other technological collection techniques or other forms of information technology, e.g., permitting electronic submission of responses. Agency: Mine Safety and Health Administration. Type of Review...

ASSESSING AND MANAGING MERCURY FROM HISTORIC AND CURRENT MINING ACTIVITIES

EPA Science Inventory

In order for ORD to address uncertainties resulting from past or historical mining practices a technology transfer workshop was conducted in November, 2000 in San Francisco, CA. Two primary objectives for this workshop were: 1) identify state-of-the-science practices and techniqu...

Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

PubMed

Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

2013-11-01

The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

Localisation of an Unknown Number of Land Mines Using a Network of Vapour Detectors

PubMed Central

Chhadé, Hiba Haj; Abdallah, Fahed; Mougharbel, Imad; Gning, Amadou; Julier, Simon; Mihaylova, Lyudmila

2014-01-01

We consider the problem of localising an unknown number of land mines using concentration information provided by a wireless sensor network. A number of vapour sensors/detectors, deployed in the region of interest, are able to detect the concentration of the explosive vapours, emanating from buried land mines. The collected data is communicated to a fusion centre. Using a model for the transport of the explosive chemicals in the air, we determine the unknown number of sources using a Principal Component Analysis (PCA)-based technique. We also formulate the inverse problem of determining the positions and emission rates of the land mines using concentration measurements provided by the wireless sensor network. We present a solution for this problem based on a probabilistic Bayesian technique using a Markov chain Monte Carlo sampling scheme, and we compare it to the least squares optimisation approach. Experiments conducted on simulated data show the effectiveness of the proposed approach. PMID:25384008

Application of the pessimistic pruning to increase the accuracy of C4.5 algorithm in diagnosing chronic kidney disease

NASA Astrophysics Data System (ADS)

Muslim, M. A.; Herowati, A. J.; Sugiharti, E.; Prasetiyo, B.

2018-03-01

A technique to dig valuable information buried or hidden in data collection which is so big to be found an interesting patterns that was previously unknown is called data mining. Data mining has been applied in the healthcare industry. One technique used data mining is classification. The decision tree included in the classification of data mining and algorithm developed by decision tree is C4.5 algorithm. A classifier is designed using applying pessimistic pruning in C4.5 algorithm in diagnosing chronic kidney disease. Pessimistic pruning use to identify and remove branches that are not needed, this is done to avoid overfitting the decision tree generated by the C4.5 algorithm. In this paper, the result obtained using these classifiers are presented and discussed. Using pessimistic pruning shows increase accuracy of C4.5 algorithm of 1.5% from 95% to 96.5% in diagnosing of chronic kidney disease.

Using Decision Trees for Estimating Mode Choice of Trips in Buca-Izmir

NASA Astrophysics Data System (ADS)

Oral, L. O.; Tecim, V.

2013-05-01

Decision makers develop transportation plans and models for providing sustainable transport systems in urban areas. Mode Choice is one of the stages in transportation modelling. Data mining techniques can discover factors affecting the mode choice. These techniques can be applied with knowledge process approach. In this study a data mining process model is applied to determine the factors affecting the mode choice with decision trees techniques by considering individual trip behaviours from household survey data collected within Izmir Transportation Master Plan. From this perspective transport mode choice problem is solved on a case in district of Buca-Izmir, Turkey with CRISP-DM knowledge process model.

Blasting Rocks and Blasting Cars Applied Engineering

DOE Office of Scientific and Technical Information (OSTI.GOV)

LBNL

2008-07-02

June 30, 2004 Berkeley Lab lecture: Deb Hopkins works with industries like automobile, mining and paper to improve their evaluation and measuring techniques. For several years, she has coordinated ... June 30, 2004 Berkeley Lab lecture: Deb Hopkins works with industries like automobile, mining and paper to improve their evaluation and measuring techniques. For several years, she has coordinated a program at Berkeley Lab funded under the Partnership for a New Generation of Vehicles, a collaboration between the federal government and the U.S. Council for Automotive Research. Nondestructive evaluation techniques to test a car's structural integrity are being developed formore » auto assembly lines.« less

Development of a tree classifier for discrimination of surface mine activity from Landsat digital data

NASA Technical Reports Server (NTRS)

Solomon, J. L.; Miller, W. F.; Quattrochi, D. A.

1979-01-01

In a cooperative project with the Geological Survey of Alabama, the Mississippi State Remote Sensing Applications Program has developed a single purpose, decision-tree classifier using band-ratioing techniques to discriminate various stages of surface mining activity. The tree classifier has four levels and employs only two channels in classification at each level. An accurate computation of the amount of disturbed land resulting from the mining activity can be made as a product of the classification output. The utilization of Landsat data provides a cost-efficient, rapid, and accurate means of monitoring surface mining activities.

Big data mining: In-database Oracle data mining over hadoop

NASA Astrophysics Data System (ADS)

Kovacheva, Zlatinka; Naydenova, Ina; Kaloyanova, Kalinka; Markov, Krasimir

2017-07-01

Big data challenges different aspects of storing, processing and managing data, as well as analyzing and using data for business purposes. Applying Data Mining methods over Big Data is another challenge because of huge data volumes, variety of information, and the dynamic of the sources. Different applications are made in this area, but their successful usage depends on understanding many specific parameters. In this paper we present several opportunities for using Data Mining techniques provided by the analytical engine of RDBMS Oracle over data stored in Hadoop Distributed File System (HDFS). Some experimental results are given and they are discussed.

Handling Dynamic Weights in Weighted Frequent Pattern Mining

NASA Astrophysics Data System (ADS)

Ahmed, Chowdhury Farhan; Tanbeer, Syed Khairuzzaman; Jeong, Byeong-Soo; Lee, Young-Koo

Even though weighted frequent pattern (WFP) mining is more effective than traditional frequent pattern mining because it can consider different semantic significances (weights) of items, existing WFP algorithms assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of an item can vary with time. Reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. In this paper, we introduce the concept of a dynamic weight for each item, and propose an algorithm, DWFPM (dynamic weighted frequent pattern mining), that makes use of this concept. Our algorithm can address situations where the weight (price or significance) of an item varies dynamically. It exploits a pattern growth mining technique to avoid the level-wise candidate set generation-and-test methodology. Furthermore, it requires only one database scan, so it is eligible for use in stream data mining. An extensive performance analysis shows that our algorithm is efficient and scalable for WFP mining using dynamic weights.

Monitoring of environmental effects of coal strip mining from satellite imagery

NASA Technical Reports Server (NTRS)

Brooks, R. L.; Parra, C. G.

1976-01-01

This paper evaluates satellite imagery as a means of monitoring coal strip mines and their environmental effects. The satellite imagery employed is Skylab EREP S-190A and S-190B from SL-2, SL-3 and SL-4 missions; a large variety of camera/film/filter combinations has been reviewed. The investigation includes determining the applicability of satellite imagery for detection of disturbed acreage in areas of coal surface mining as well as the much more detailed monitoring of specific surface-mining operations, including: active mines, inactive mines, highwalls, ramp roads, pits, water impoundments and their associated acidity, graded areas and types of grading, and reclamed areas. Techniques have been developed to enable mining personnel to utilize this imagery in a practical and economic manner, requiring no previous photo-interpretation background and no purchases of expensive viewing or data-analysis equipment. To corroborate the photo-interpretation results, on-site observations were made in the very active mining area near Madisonville, Kentucky.

An Integrated Environment Monitoring System for Underground Coal Mines—Wireless Sensor Network Subsystem with Multi-Parameter Monitoring

PubMed Central

Zhang, Yu; Yang, Wei; Han, Dongsheng; Kim, Young-Il

2014-01-01

Environment monitoring is important for the safety of underground coal mine production, and it is also an important application of Wireless Sensor Networks (WSNs). We put forward an integrated environment monitoring system for underground coal mine, which uses the existing Cable Monitoring System (CMS) as the main body and the WSN with multi-parameter monitoring as the supplementary technique. As CMS techniques are mature, this paper mainly focuses on the WSN and the interconnection between the WSN and the CMS. In order to implement the WSN for underground coal mines, two work modes are designed: periodic inspection and interrupt service; the relevant supporting technologies, such as routing mechanism, collision avoidance, data aggregation, interconnection with the CMS, etc., are proposed and analyzed. As WSN nodes are limited in energy supply, calculation and processing power, an integrated network management scheme is designed in four aspects, i.e., topology management, location management, energy management and fault management. Experiments were carried out both in a laboratory and in a real underground coal mine. The test results indicate that the proposed integrated environment monitoring system for underground coal mines is feasible and all designs performed well as expected. PMID:25051037

An assessment of AVIRIS data for hydrothermal alteration mapping in the Goldfield Mining District, Nevada

NASA Technical Reports Server (NTRS)

Carrere, Veronique; Abrams, Michael J.

1988-01-01

Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data were acquired over the Goldfield Mining District, Nevada, in September 1987. Goldfield is one of the group of large epithermal precious metal deposits in Tertiary volcanic rocks, associated with silicic volcanism and caldera formation. Hydrothermal alteration consists of silicification along fractures, advanced agrillic and argillic zones further away from veins and more widespread propylitic zones. An evaluation of AVIRIS data quality was performed. Faults in the data, related to engineering problems and a different behavior of the instrument while on-board the U2, were encountered. Consequently, a decision was made to use raw data and correct them only for dark current variations and detector read-out-delays. New software was written to that effect. Atmospheric correction was performed using the flat field correction technique. Analysis of the data was then performed to extract spectral information, mainly concentrating on the 2 to 2.45 micron window, as the alteration minerals of interest have their distinctive spectral reflectance features in this region. Principally kaolinite and alunite spectra were clearly obtained. Mapping of the different minerals and alteration zones was attempted using ratios and clustering techniques. Poor signal-to-noise performance of the instrument and the lack of appropriate software prevented the production of an alteration map of the area. Spectra extracted locally from the AVIRIS data were checked in the field by collecting representative samples of the outcrops.

A Multiagent-based Intrusion Detection System with the Support of Multi-Class Supervised Classification

NASA Astrophysics Data System (ADS)

Shyu, Mei-Ling; Sainani, Varsha

The increasing number of network security related incidents have made it necessary for the organizations to actively protect their sensitive data with network intrusion detection systems (IDSs). IDSs are expected to analyze a large volume of data while not placing a significantly added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel data mining assisted multiagent-based intrusion detection system (DMAS-IDS) is proposed, particularly with the support of multiclass supervised classification. These agents can detect and take predefined actions against malicious activities, and data mining techniques can help detect them. Our proposed DMAS-IDS shows superior performance compared to central sniffing IDS techniques, and saves network resources compared to other distributed IDS with mobile agents that activate too many sniffers causing bottlenecks in the network. This is one of the major motivations to use a distributed model based on multiagent platform along with a supervised classification technique.

EU-FP7-iMars: Analysis of Mars Multi-Resolution Images using Auto-Coregistration, Data Mining and Crowd Source Techniques

NASA Astrophysics Data System (ADS)

Ivanov, Anton; Oberst, Jürgen; Yershov, Vladimir; Muller, Jan-Peter; Kim, Jung-Rack; Gwinner, Klaus; Van Gasselt, Stephan; Morley, Jeremy; Houghton, Robert; Bamford, Steven; Sidiropoulos, Panagiotis

Understanding the role of different planetary surface formation processes within our Solar System is one of the fundamental goals of planetary science research. There has been a revolution in planetary surface observations over the last 15 years, especially in 3D imaging of surface shape. This has led to the ability to be able to overlay different epochs back to the mid-1970s, examine time-varying changes (such as the recent discovery of boulder movement, tracking inter-year seasonal changes and looking for occurrences of fresh craters. Consequently we are seeing a dramatic improvement in our understanding of surface formation processes. Since January 2004, the ESA Mars Express has been acquiring global data, especially HRSC stereo (12.5-25 m nadir images) with 87% coverage with more than 65% useful for stereo mapping. NASA began imaging the surface of Mars, initially from flybys in the 1960s and then from the first orbiter with image resolution less than 100 m in the late 1970s from Viking Orbiter. The most recent orbiter, NASA MRO, has acquired surface imagery of around 1% of the Martian surface from HiRISE (at ≈20 cm) and ≈5% from CTX (≈6 m) in stereo. Within the iMars project (http://i-Mars.eu), a fully automated large-scale processing (“Big Data”) solution is being developed to generate the best possible multi-resolution DTM of Mars. In addition, HRSC OrthoRectified Images (ORI) will be used as a georeference basis so that all higher resolution ORIs will be co-registered to the HRSC DTMs (50-100m grid) products generated at DLR and, from CTX (6-20 m grid) and HiRISE (1-3 m grids) on a large-scale Linux cluster based at MSSL. The HRSC products will be employed to provide a geographic reference for all current, future and historical NASA products using automated co-registration based on feature points and initial results will be shown here. In 2015, many of the entire NASA and ESA orbital images will be co-registered and the updated georeferencing information employed to generate a time series of terrain relief with corrected ORIs back to 1977. Web-GIS using OGC protocols will be employed to allow visual exploration of changes to the surface. Data mining processing chains are being developed to search for changes in the Martian surface from 1971-2015 and the output of this data mining will be compared against the results from citizen scientists’ measurements in a specialized Zooniverse implementation. The final co-registered data sets will be distributed through both European and US channels in a manner to be decided towards the end of the project. Acknowledgements: The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under iMars grant agreement no. 607379.

Molecular- and nm-scale Investigation of the Structure and Compositional Heterogeneity of Naturally Occurring Ferrihydrite

NASA Astrophysics Data System (ADS)

Cismasu, C.; Michel, F. M.; Stebbins, J. F.; Tcaciuc, A. P.; Brown, G. E.

2008-12-01

Ferrihydrite is a hydrated Fe(III) nano-oxide that forms in vast quantities in contaminated acid mine drainage environments. As a result of its high surface area, ferrihydrite is an important environmental sorbent, and plays an essential role in the geochemical cycling of pollutant metal(loid)s in these settings. Despite its environmental relevance, this nanomineral remains one of the least understood environmental solids in terms of its structure (bulk and surface), compositional variations, and the factors affecting its reactivity. Under natural aqueous conditions, ferrihydrite often precipitates in the presence of several inorganic compounds such as aluminum, silica, arsenic, etc., or in the presence of organic matter. These impurities can affect the molecular-level structure of naturally occurring ferrihydrite, thus modifying fundamental properties that are directly correlated with solid-phase stability and surface reactivity. Currently there exists a significant gap in our understanding of the structure of synthetic vs. natural ferrihydrites, due to the inherent difficulties associated to the investigation of these poorly crystalline nanophases. In this study, we combined synchrotron- and laboratory-based techniques to characterize naturally occurring ferrihydrite from an acid mine drainage system situated at the New Idria mercury mine in California. We used high-energy X-ray total scattering and pair distribution function analysis to elucidate quantitative structural details of these samples. We have additionally used scanning transmission X-ray microscopy high resolution imaging (30 nm) to evaluate the spatial relationship of major elements Si, Al, and C within ferrihydrite. Al, Si and C K-edge near- edge X-ray absorption fine structure spectroscopy and 27Al nuclear magnetic resonance spectroscopy were used to obtain short-range structural information. By combining these techniques we attain the highest level of resolution permitted by current analytical methods to study such naturally occurring nanomaterials, both at the molecular- and nm-scale. This work provides structural information at the short-, medium- and long- range, as well as evidence of compositional heterogeneity, and mineral/organic matter associations.

Applications of Subsurface Radar for Mine Detection

DTIC Science & Technology

1990-12-31

sofware routines for signal/image processing and image display, which are included in the Appendix along with examples of recent images obtained of the... maxima and minima. The case of the M19 shown a main backscattering lobe only 5* wide. These results demonstrate the realiability and consistency of

Applications of imaging spectroscopy data: A case study at Summitville, Colorado

USGS Publications Warehouse

King, Trude V.V.; Clark, Roger N.; Swayze, Gregg A.

2000-01-01

From 1985 through 1992, the Summitville open-pit mine produced gold from lowgrade ore using cyanide heap-leach techniques, a method to extract gold whereby the ore pile is sprayed with water containing cyanide, which dissolves the minute gold grains. Environmental problems due to mining activity at Summitville include significant increases in acidic and metal-rich drainage from the site, leakage of cyanide-bearing solutions from the heap-leach pad into an underdrain system, and several surface leaks of cyanide-bearing solutions into the Wightman Fork of the Alamosa River. In general, drainage from the Summitville mine moves downslope into the Wightman Fork, a small tributary of the Alamosa River, which in turn flows east into the Terrace Reservoir before entering the agricultural lands of the San Luis Valley. The increase in the trace-metal burden of the Alamosa River watershed due to the mining activities at Summitville is of concern to farmers and fisherman, as well as Federal and State of Colorado agencies having responsibility for land stewardship. The environment of the Summitville area is a result of 1) its geologic evolution, that culminated in the formation of precious-metal mineral deposits; and 2) previous metal mining activity. Mining accentuates, accelerates, and pertubates natural geochemical processes. The development of underground workings, open pits, mill tailings, and spoil heaps and the extractive processing of ore enhances the likelihood of releasing chemicals and elements to the surrounding areas and at increased rates relative to unmined areas. Both mined and unmined mineralized areas can produce acid drainage from the formation and movement of highly acidic water rich in heavy metals. This acidic water forms principally through the chemical reaction of oxygenated surface water and shallow subsurface water with rocks that contain sulfide minerals, producing sulphuric acid. Heavy metals can be leached by the acid solution that comes in contact with mineralized rocks, a process that may be enhanced by bacterial action. The resulting fluids may be highly toxic and, when mixed with groundwater, surface water, and soil, may have harmful effects on humans, animals, and plants. Thus, understanding the geologic and hydrologic history of this area is a critical piece of the environmental puzzle in the Summitville area. The Summitville mine operators had ceased active mining and begun environmental remediation, including treatment of the heap-leach pile and installation of a water-treatment facility, when it declared bankruptcy in December 1992 and abandoned the mine site. The U.S. Environmental Protection Agency (EPA) immediately took over the Summitville site under EPA Superfund Emergency Response authority. Summitville has focused public attention on the environmental effects of modern mineral-resource development. Soon after the mine was abandoned, Federal, State, and local agencies, along with Alamosa River water users and private companies, began extensive studies at the mine site and surrounding areas. These studies included analysis of water, soil, livestock and vegetation. The role of the U.S. Geological Survey (USGS) was to provide geologic, hydrologic and agricultural information about the mine and surrounding area and to describe and evaluate the environmental condition of the Summitville mine and the downstream effects of the mine on the San Luis Valley (King 1995).

Establishment of trees and shrubs on lands disturbed by mining in the West

Treesearch

Ardell J. Bjugstad

1984-01-01

Increased research and development of cultural practices and species has assured success of establishment of trees and shrubs on lands disturbed by surface mining. Trickle irrigation and water harvesting techniques have increased survival of planted stock by 250 percent for some species.

Using Text Mining to Characterize Online Discussion Facilitation

ERIC Educational Resources Information Center

Ming, Norma; Baumer, Eric

2011-01-01

Facilitating class discussions effectively is a critical yet challenging component of instruction, particularly in online environments where student and faculty interaction is limited. Our goals in this research were to identify facilitation strategies that encourage productive discussion, and to explore text mining techniques that can help…