Sample records for mining practical machine

  1. Use of IT platform in determination of efficiency of mining machines

    NASA Astrophysics Data System (ADS)

    Brodny, Jarosław; Tutak, Magdalena

    2018-01-01

    Determination of effective use of mining devices has very significant meaning for mining enterprises. High costs of their purchase and tenancy cause that these enterprises tend to the best use of possessed technical potential. However, specifics of mining production causes that this process not always proceeds without interferences. Practical experiences show that determination of objective measure of utilization of machine in mining enterprise is not simple. In the paper a proposition for solution of this problem is presented. For this purpose an IT platform and overall efficiency model OEE were used. This model enables to evaluate the machine in a range of its availability performance and quality of product, and constitutes a quantitative tool of TPM strategy. Adapted to the specificity of mining branch the OEE model together with acquired data from industrial automatic system enabled to determine the partial indicators and overall efficiency of tested machines. Studies were performed for a set of machines directly use in coal exploitation process. They were: longwall-shearer and armoured face conveyor, and beam stage loader. Obtained results clearly indicate that degree of use of machines by mining enterprises are unsatisfactory. Use of IT platforms will significantly facilitate the process of registration, archiving and analytical processing of the acquired data. In the paper there is presented methodology of determination of partial indices and total OEE together with a practical example of its application for investigated machines set. Also IT platform was characterized for its construction, function and application.

  2. 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.

  3. AstroML: Python-powered Machine Learning for Astronomy

    NASA Astrophysics Data System (ADS)

    Vander Plas, Jake; Connolly, A. J.; Ivezic, Z.

    2014-01-01

    As astronomical data sets grow in size and complexity, automated machine learning and data mining methods are becoming an increasingly fundamental component of research in the field. The astroML project (http://astroML.org) provides a common repository for practical examples of the data mining and machine learning tools used and developed by astronomical researchers, written in Python. The astroML module contains a host of general-purpose data analysis and machine learning routines, loaders for openly-available astronomical datasets, and fast implementations of specific computational methods often used in astronomy and astrophysics. The associated website features hundreds of examples of these routines being used for analysis of real astronomical datasets, while the associated textbook provides a curriculum resource for graduate-level courses focusing on practical statistics, machine learning, and data mining approaches within Astronomical research. This poster will highlight several of the more powerful and unique examples of analysis performed with astroML, all of which can be reproduced in their entirety on any computer with the proper packages installed.

  4. Machine-related injuries in the US mining industry and priorities for safety research.

    PubMed

    Ruff, Todd; Coleman, Patrick; Martini, Laura

    2011-03-01

    Researchers at the National Institute for Occupational Safety and Health studied mining accidents that involved a worker entangled in, struck by, or in contact with machinery or equipment in motion. The motivation for this study came from the large number of severe accidents, i.e. accidents resulting in a fatality or permanent disability, that are occurring despite available interventions. Accident descriptions were taken from an accident database maintained by the United States Department of Labor, Mine Safety and Health Administration, and 562 accidents that occurred during 2000-2007 fit the search criteria. Machine-related accidents accounted for 41% of all severe accidents in the mining industry during this period. Machinery most often involved in these accidents included conveyors, rock bolting machines, milling machines and haulage equipment such as trucks and loaders. The most common activities associated with these accidents were operation of the machine and maintenance and repair. The current methods to safeguard workers near machinery include mechanical guarding around moving components, lockout/tagout of machine power during maintenance and backup alarms for mobile equipment. To decrease accidents further, researchers recommend additional efforts in the development of new control technologies, training materials and dissemination of information on best practices.

  5. Methodology of selecting dozers for lignite open pit mines in Serbia

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

    Stojanovic, D.; Ignjatovic, D.; Kovacevic, S.

    1996-12-31

    Apart from the main production processes (coal and overburden mining, rail conveyors transportation and storage of excavated masses) performed by great-capacity mechanization at open pit mines, numerous and different auxiliary works, that often have crucial influence on both the work efficiency of main equipment and the maintenance of optimum technical conditions of machines and plants covering technological system of open pit, are present. Successful realization of work indispensably requires a proper and adequate selection of auxiliary machines according to their type quantity, capacity, power etc. thus highly respecting specific conditions existing at each and every open pit mine. A dozermore » is certainly the most important and representative auxiliary machine at single open pit mine. It is widely used in numerous works that, in fact, are preconditions for successful work of the main mechanization and consequently the very selection of a dozer ranges among the most important operations when selecting mechanization. This paper presents the methodology of dozers selection when lignite open pit mines are concerned. A mathematical model defining the volume of work required for dozers to perform at open pit mines and consequently the number of necessary dozers was designed. The model underwent testing in practice at big open pit mines and can be used in design of future open pits mines.« less

  6. Mining machines effectiveness and OEE Indicator

    NASA Astrophysics Data System (ADS)

    Korski, Jacek; Tobór-Osadnik, Katarzyna; Wyganowska, Małgorzata

    2017-11-01

    The situation in the hard coal industry in Poland is forcing the identification of effectual and practical indicators of the effectiveness of machinery and equipment. In the article, the authors discuss the possible use of the OEE indicator for the evaluation of production processes in hard-coal mines. In summary, recommendations are made to enable efficiency assessment of mining machinery using the OEE.

  7. 76 FR 70075 - Proximity Detection Systems for Continuous Mining Machines in Underground Coal Mines

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-11-10

    ... Detection Systems for Continuous Mining Machines in Underground Coal Mines AGENCY: Mine Safety and Health... proposed rule addressing Proximity Detection Systems for Continuous Mining Machines in Underground Coal... Detection Systems for Continuous Mining Machines in Underground Coal Mines. MSHA conducted hearings on...

  8. 76 FR 63238 - Proximity Detection Systems for Continuous Mining Machines in Underground Coal Mines

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-10-12

    ... Detection Systems for Continuous Mining Machines in Underground Coal Mines AGENCY: Mine Safety and Health... Agency's proposed rule addressing Proximity Detection Systems for Continuous Mining Machines in... proposed rule for Proximity Detection Systems on Continuous Mining Machines in Underground Coal Mines. Due...

  9. 30 CFR 75.1719-4 - Mining machines, cap lamps; requirements.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Mining machines, cap lamps; requirements. 75... Mining machines, cap lamps; requirements. (a) Paint used on exterior surfaces of mining machines shall... frames or reflecting tape shall be installed on each end of mining machines, except that continuous...

  10. 30 CFR 75.1719-4 - Mining machines, cap lamps; requirements.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Mining machines, cap lamps; requirements. 75... Mining machines, cap lamps; requirements. (a) Paint used on exterior surfaces of mining machines shall... frames or reflecting tape shall be installed on each end of mining machines, except that continuous...

  11. Distributed communications and control network for robotic mining

    NASA Technical Reports Server (NTRS)

    Schiffbauer, William H.

    1989-01-01

    The application of robotics to coal mining machines is one approach pursued to increase productivity while providing enhanced safety for the coal miner. Toward that end, a network composed of microcontrollers, computers, expert systems, real time operating systems, and a variety of program languages are being integrated that will act as the backbone for intelligent machine operation. Actual mining machines, including a few customized ones, have been given telerobotic semiautonomous capabilities by applying the described network. Control devices, intelligent sensors and computers onboard these machines are showing promise of achieving improved mining productivity and safety benefits. Current research using these machines involves navigation, multiple machine interaction, machine diagnostics, mineral detection, and graphical machine representation. Guidance sensors and systems employed include: sonar, laser rangers, gyroscopes, magnetometers, clinometers, and accelerometers. Information on the network of hardware/software and its implementation on mining machines are presented. Anticipated coal production operations using the network are discussed. A parallelism is also drawn between the direction of present day underground coal mining research to how the lunar soil (regolith) may be mined. A conceptual lunar mining operation that employs a distributed communication and control network is detailed.

  12. Microcomputer network for control of a continuous mining machine. Information circular/1993

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

    Schiffbauer, W.H.

    1993-01-01

    The paper details a microcomputer-based control and monitoring network that was developed in-house by the U.S. Bureau of Mines, and installed on a Joy 14 continuous mining machine. The network consists of microcomputers that are connected together via a single twisted pair cable. Each microcomputer was developed to provide a particular function in the control process. Machine-mounted microcomputers in conjunction with the appropriate sensors provide closed-loop control of the machine, navigation, and environmental monitoring. Off-the-machine microcomputers provide remote control of the machine, sensor status, and a connection to the network so that external computers can access network data and controlmore » the continuous mining machine. Although the network was installed on a Joy 14 continuous mining machine, its use extends beyond it. Its generic structure lends itself to installation onto most mining machine types.« less

  13. Automation and robotics technology for intelligent mining systems

    NASA Technical Reports Server (NTRS)

    Welsh, Jeffrey H.

    1989-01-01

    The U.S. Bureau of Mines is approaching the problems of accidents and efficiency in the mining industry through the application of automation and robotics to mining systems. This technology can increase safety by removing workers from hazardous areas of the mines or from performing hazardous tasks. The short-term goal of the Automation and Robotics program is to develop technology that can be implemented in the form of an autonomous mining machine using current continuous mining machine equipment. In the longer term, the goal is to conduct research that will lead to new intelligent mining systems that capitalize on the capabilities of robotics. The Bureau of Mines Automation and Robotics program has been structured to produce the technology required for the short- and long-term goals. The short-term goal of application of automation and robotics to an existing mining machine, resulting in autonomous operation, is expected to be accomplished within five years. Key technology elements required for an autonomous continuous mining machine are well underway and include machine navigation systems, coal-rock interface detectors, machine condition monitoring, and intelligent computer systems. The Bureau of Mines program is described, including status of key technology elements for an autonomous continuous mining machine, the program schedule, and future work. Although the program is directed toward underground mining, much of the technology being developed may have applications for space systems or mining on the Moon or other planets.

  14. 30 CFR 75.205 - Installation of roof support using mining machines with integral roof bolters.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... machines with integral roof bolters. 75.205 Section 75.205 Mineral Resources MINE SAFETY AND HEALTH... Roof Support § 75.205 Installation of roof support using mining machines with integral roof bolters. When roof bolts are installed by a continuous mining machine with intregal roof bolting equipment: (a...

  15. Research on Classification of Chinese Text Data Based on SVM

    NASA Astrophysics Data System (ADS)

    Lin, Yuan; Yu, Hongzhi; Wan, Fucheng; Xu, Tao

    2017-09-01

    Data Mining has important application value in today’s industry and academia. Text classification is a very important technology in data mining. At present, there are many mature algorithms for text classification. KNN, NB, AB, SVM, decision tree and other classification methods all show good classification performance. Support Vector Machine’ (SVM) classification method is a good classifier in machine learning research. This paper will study the classification effect based on the SVM method in the Chinese text data, and use the support vector machine method in the chinese text to achieve the classify chinese text, and to able to combination of academia and practical application.

  16. 78 FR 20949 - Proposed Collection; Comment Request; High-Voltage Continuous Mining Machines Standards for...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-04-08

    ... Collection; Comment Request; High-Voltage Continuous Mining Machines Standards for Underground Coal Mines... Act of 1995. This program helps to assure that requested data can be provided in the desired format... maintains the safe use of high-voltage continuous mining machines in underground coal mines by requiring...

  17. Manual of good practices for sanitation in coal mining operations

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

    Not Available

    The purpose of the manual was to act as a guideline, setting reasonable recommendations relative to mine sanitation which will enable mines to install adequate facilities and make appropriate alterations conserving and improving the health and welfare of the mine worker. A systematic evaluation was undertaken of the sanitation facilities and maintenance at coal mines. Consideration was given to central facilities including building, floors, walls, partitions, ceilings, lockers, baskets and benches, showers, toilets, lavatories, lighting, ventilation and temperature control, and maintenance. Also discussed were food vending machines, water source, water quality, water treatment, water delivery systems for underground and surfacemore » mines, sanitary waste disposal, workplace toilets in underground and surface mines, refuse control and handling for underground and surface mines, and pest control.« less

  18. Analysis of Availability of Longwall-Shearer Based On Its Working Cycle

    NASA Astrophysics Data System (ADS)

    Brodny, Jaroslaw; Tutak, Magdalena

    2017-12-01

    Effective use of any type of devices, particularly machines has very significant meaning for mining enterprises. High costs of their purchase and tenancy cause that these enterprises tend to the best use of own technical potential. However, characteristics of mining production causes that this process not always proceeds without interferences. Practical experiences show that determination of objective measure of utilization of machine in mining company is not simple. In the paper methodology allowing to solve this problem is presented. Longwall-shearer, as the most important machine between longwall mechanical complex. Also it was assumed that the most significant meaning for determination of effectiveness of longwall-shearer has its availability, i.e. its effective time of work related to standard time. Such an approach is conforming to OEE model. However, specification of mining branch causes that determined availability do not give actual state of longwall-shearer’s operation. Therefore, this availability was related to the operation cycle of longwall-shearer. In presented example a longwall-shearer works in unidirectional cycle of mining. It causes that in one direction longwall-shearer mines, moving with operating velocity, and in other direction it does not mine and moves with manoeuvre velocity. Such defined working cycle became a base for determinate availability of longwall-shearer. Using indications of industrial automatic system for each of working shift there were determined number of cycles of longwall-shearer and availability of each one. Accepted of such way of determination of availability of longwall-shearer enabled to perform accurate analysis of losses of its availability. These losses result from non-planned shutdowns of longwall-shearer. Thanks to performed analysis based on the operating cycle of longwall-shearer time of its standstill for particular phase of cycle were determined. Presented methodology of determination of longwall-shearer’s availability enables to obtain information which may be used for optimization of mining process. Knowledge of particular phases of longwall-shearer’s operation, in which reduced availability occurs, allows to direct the repairing actions exactly to these regions. Developed methodology and obtained results create great opportunities for practical application and improvement of effectiveness of underground exploitation.

  19. A microcomputer network for control of a continuous mining machine

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

    Schiffbauer, W.H.

    1993-12-31

    This report details a microcomputer-based control and monitoring network that was developed in-house by the U.S. Bureau of Mines and installed on a continuous mining machine. The network consists of microcomputers that are connected together via a single twisted-pair cable. Each microcomputer was developed to provide a particular function in the control process. Machine-mounted microcomputers, in conjunction with the appropriate sensors, provide closed-loop control of the machine, navigation, and environmental monitoring. Off-the-machine microcomputers provide remote control of the machine, sensor status, and a connection to the network so that external computers can access network data and control the continuous miningmore » machine. Because of the network`s generic structure, it can be installed on most mining machines.« less

  20. 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.

  1. Ten quick tips for machine learning in computational biology.

    PubMed

    Chicco, Davide

    2017-01-01

    Machine learning has become a pivotal tool for many projects in computational biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical researchers often do not have enough experience to run a data mining project effectively, and therefore can follow incorrect practices, that may lead to common mistakes or over-optimistic results. With this review, we present ten quick tips to take advantage of machine learning in any computational biology context, by avoiding some common errors that we observed hundreds of times in multiple bioinformatics projects. We believe our ten suggestions can strongly help any machine learning practitioner to carry on a successful project in computational biology and related sciences.

  2. 30 CFR 18.54 - High-voltage continuous mining machines.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... and Design Requirements § 18.54 High-voltage continuous mining machines. (a) Separation of high... removed. (c) Circuit-interrupting devices. Circuit-interrupting devices must be designed and installed to... ground. (e) Onboard ungrounded, three-phase power circuit. A continuous mining machine designed with an...

  3. 30 CFR 18.54 - High-voltage continuous mining machines.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... and Design Requirements § 18.54 High-voltage continuous mining machines. (a) Separation of high... removed. (c) Circuit-interrupting devices. Circuit-interrupting devices must be designed and installed to... ground. (e) Onboard ungrounded, three-phase power circuit. A continuous mining machine designed with an...

  4. 30 CFR 18.54 - High-voltage continuous mining machines.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... and Design Requirements § 18.54 High-voltage continuous mining machines. (a) Separation of high... removed. (c) Circuit-interrupting devices. Circuit-interrupting devices must be designed and installed to... ground. (e) Onboard ungrounded, three-phase power circuit. A continuous mining machine designed with an...

  5. 30 CFR 18.54 - High-voltage continuous mining machines.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... and Design Requirements § 18.54 High-voltage continuous mining machines. (a) Separation of high... removed. (c) Circuit-interrupting devices. Circuit-interrupting devices must be designed and installed to... ground. (e) Onboard ungrounded, three-phase power circuit. A continuous mining machine designed with an...

  6. 30 CFR 18.54 - High-voltage continuous mining machines.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... and Design Requirements § 18.54 High-voltage continuous mining machines. (a) Separation of high... removed. (c) Circuit-interrupting devices. Circuit-interrupting devices must be designed and installed to... ground. (e) Onboard ungrounded, three-phase power circuit. A continuous mining machine designed with an...

  7. 78 FR 45972 - Agency Information Collection Activities; Submission for OMB Review; Comment Request; High...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-07-30

    ... Coal Mines ACTION: Notice. SUMMARY: The Department of Labor (DOL) is submitting the Mine Safety and... Continuous Mining Machines Standards for Underground Coal Mines,'' to the Office of Management and Budget... continuous mining machines (HVCMM) in underground coal mines by requiring records of testing, examination and...

  8. 30 CFR 18.96 - Preparation of machines for inspection; requirements.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Field Approval of Electrically Operated Mining Equipment § 18.96 Preparation of machines for inspection... place at which a field approval investigation will be conducted with respect to any machine, the...

  9. 30 CFR 18.96 - Preparation of machines for inspection; requirements.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Field Approval of Electrically Operated Mining Equipment § 18.96 Preparation of machines for inspection... place at which a field approval investigation will be conducted with respect to any machine, the...

  10. Water spray ventilator system for continuous mining machines

    DOEpatents

    Page, Steven J.; Mal, Thomas

    1995-01-01

    The invention relates to a water spray ventilator system mounted on a continuous mining machine to streamline airflow and provide effective face ventilation of both respirable dust and methane in underground coal mines. This system has two side spray nozzles mounted one on each side of the mining machine and six spray nozzles disposed on a manifold mounted to the underside of the machine boom. The six spray nozzles are angularly and laterally oriented on the manifold so as to provide non-overlapping spray patterns along the length of the cutter drum.

  11. A Comparison of different learning models used in Data Mining for Medical Data

    NASA Astrophysics Data System (ADS)

    Srimani, P. K.; Koti, Manjula Sanjay

    2011-12-01

    The present study aims at investigating the different Data mining learning models for different medical data sets and to give practical guidelines to select the most appropriate algorithm for a specific medical data set. In practical situations, it is absolutely necessary to take decisions with regard to the appropriate models and parameters for diagnosis and prediction problems. Learning models and algorithms are widely implemented for rule extraction and the prediction of system behavior. In this paper, some of the well-known Machine Learning(ML) systems are investigated for different methods and are tested on five medical data sets. The practical criteria for evaluating different learning models are presented and the potential benefits of the proposed methodology for diagnosis and learning are suggested.

  12. Web Mining: Machine Learning for Web Applications.

    ERIC Educational Resources Information Center

    Chen, Hsinchun; Chau, Michael

    2004-01-01

    Presents an overview of machine learning research and reviews methods used for evaluating machine learning systems. Ways that machine-learning algorithms were used in traditional information retrieval systems in the "pre-Web" era are described, and the field of Web mining and how machine learning has been used in different Web mining…

  13. 75 FR 20918 - High-Voltage Continuous Mining Machine Standard for Underground Coal Mines

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-04-22

    ... DEPARTMENT OF LABOR Mine Safety and Health Administration 30 CFR Parts 18 and 75 RIN 1219-AB34 High-Voltage Continuous Mining Machine Standard for Underground Coal Mines Correction In rule document 2010-7309 beginning on page 17529 in the issue of Tuesday, April 6, 2010, make the following correction...

  14. The accident analysis of mobile mine machinery in Indian opencast coal mines.

    PubMed

    Kumar, R; Ghosh, A K

    2014-01-01

    This paper presents the analysis of large mining machinery related accidents in Indian opencast coal mines. The trends of coal production, share of mining methods in production, machinery deployment in open cast mines, size and population of machinery, accidents due to machinery, types and causes of accidents have been analysed from the year 1995 to 2008. The scrutiny of accidents during this period reveals that most of the responsible factors are machine reversal, haul road design, human fault, operator's fault, machine fault, visibility and dump design. Considering the types of machines, namely, dumpers, excavators, dozers and loaders together the maximum number of fatal accidents has been caused by operator's faults and human faults jointly during the period from 1995 to 2008. The novel finding of this analysis is that large machines with state-of-the-art safety system did not reduce the fatal accidents in Indian opencast coal mines.

  15. High pressure water jet mining machine

    DOEpatents

    Barker, Clark R.

    1981-05-05

    A high pressure water jet mining machine for the longwall mining of coal is described. The machine is generally in the shape of a plowshare and is advanced in the direction in which the coal is cut. The machine has mounted thereon a plurality of nozzle modules each containing a high pressure water jet nozzle disposed to oscillate in a particular plane. The nozzle modules are oriented to cut in vertical and horizontal planes on the leading edge of the machine and the coal so cut is cleaved off by the wedge-shaped body.

  16. Human factors model concerning the man-machine interface of mining crewstations

    NASA Technical Reports Server (NTRS)

    Rider, James P.; Unger, Richard L.

    1989-01-01

    The U.S. Bureau of Mines is developing a computer model to analyze the human factors aspect of mining machine operator compartments. The model will be used as a research tool and as a design aid. It will have the capability to perform the following: simulated anthropometric or reach assessment, visibility analysis, illumination analysis, structural analysis of the protective canopy, operator fatigue analysis, and computation of an ingress-egress rating. The model will make extensive use of graphics to simplify data input and output. Two dimensional orthographic projections of the machine and its operator compartment are digitized and the data rebuilt into a three dimensional representation of the mining machine. Anthropometric data from either an individual or any size population may be used. The model is intended for use by equipment manufacturers and mining companies during initial design work on new machines. In addition to its use in machine design, the model should prove helpful as an accident investigation tool and for determining the effects of machine modifications made in the field on the critical areas of visibility and control reach ability.

  17. Improving machine operation management efficiency via improving the vehicle park structure and using the production operation information database

    NASA Astrophysics Data System (ADS)

    Koptev, V. Yu

    2017-02-01

    The work represents the results of studying basic interconnected criteria of separate equipment units of the transport network machines fleet, depending on production and mining factors to improve the transport systems management. Justifying the selection of a control system necessitates employing new methodologies and models, augmented with stability and transport flow criteria, accounting for mining work development dynamics on mining sites. A necessary condition is the accounting of technical and operating parameters related to vehicle operation. Modern open pit mining dispatching systems must include such kinds of the information database. An algorithm forming a machine fleet is presented based on multi-variation task solution in connection with defining reasonable operating features of a machine working as a part of a complex. Proposals cited in the work may apply to mining machines (drilling equipment, excavators) and construction equipment (bulldozers, cranes, pile-drivers), city transport and other types of production activities using machine fleet.

  18. 30 CFR 56.14107 - Moving machine parts.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Moving machine parts. 56.14107 Section 56.14107 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE... Safety Devices and Maintenance Requirements § 56.14107 Moving machine parts. (a) Moving machine parts...

  19. 30 CFR 57.14107 - Moving machine parts.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Moving machine parts. 57.14107 Section 57.14107 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE... Equipment Safety Devices and Maintenance Requirements § 57.14107 Moving machine parts. (a) Moving machine...

  20. Application of Quality Management Tools for Evaluating the Failure Frequency of Cutter-Loader and Plough Mining Systems

    NASA Astrophysics Data System (ADS)

    Biały, Witold

    2017-06-01

    Failure frequency in the mining process, with a focus on the mining machine, has been presented and illustrated by the example of two coal-mines. Two mining systems have been subjected to analysis: a cutter-loader and a plough system. In order to reduce costs generated by failures, maintenance teams should regularly make sure that the machines are used and operated in a rational and effective way. Such activities will allow downtimes to be reduced, and, in consequence, will increase the effectiveness of a mining plant. The evaluation of mining machines' failure frequency contained in this study has been based on one of the traditional quality management tools - the Pareto chart.

  1. Service Modules for Coal Extraction

    NASA Technical Reports Server (NTRS)

    Gangal, M. D.; Lewis, E. V.

    1985-01-01

    Service train follows group of mining machines, paying out utility lines as machines progress into coal face. Service train for four mining machines removes gases and coal and provides water and electricity. Flexible, coiling armored carriers protect cables and hoses. High coal production attained by arraying row of machines across face, working side by side.

  2. Automated Coal-Mining System

    NASA Technical Reports Server (NTRS)

    Gangal, M. D.; Isenberg, L.; Lewis, E. V.

    1985-01-01

    Proposed system offers safety and large return on investment. System, operating by year 2000, employs machines and processes based on proven principles. According to concept, line of parallel machines, connected in groups of four to service modules, attacks face of coal seam. High-pressure water jets and central auger on each machine break face. Jaws scoop up coal chunks, and auger grinds them and forces fragments into slurry-transport system. Slurry pumped through pipeline to point of use. Concept for highly automated coal-mining system increases productivity, makes mining safer, and protects health of mine workers.

  3. 30 CFR 18.97 - Inspection of machines; minimum requirements.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Field Approval of Electrically Operated Mining Equipment § 18.97 Inspection of machines; minimum... shall be conducted by an electrical representative and such inspection shall include: (1) Examination of...

  4. 30 CFR 18.97 - Inspection of machines; minimum requirements.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Field Approval of Electrically Operated Mining Equipment § 18.97 Inspection of machines; minimum... shall be conducted by an electrical representative and such inspection shall include: (1) Examination of...

  5. 4. CARPENTER AND MACHINE SHOP AT EAST GREY ROCK MINE, ...

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

    4. CARPENTER AND MACHINE SHOP AT EAST GREY ROCK MINE, LOOKING EAST. THIS IS SAID TO BE THE OLDEST MINE BUILDING LEFT ON BUTTE HILL. SHIV WHEELS FROM VARIOUS LOCATIONS AROUND THE HILL ARE ALSO VISIBLE - Butte Mineyards, Butte, Silver Bow County, MT

  6. 30 CFR 75.832 - Frequency of examinations; recordkeeping.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... machine examination. At least once every 7 days, a qualified person must examine each high-voltage continuous mining machine to verify that electrical protection, equipment grounding, permissibility, cable... least once every 7 days, and prior to tramming the high-voltage continuous mining machine, a qualified...

  7. 30 CFR 18.49 - Connection boxes on machines.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Connection boxes on machines. 18.49 Section 18.49 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Construction and...

  8. 30 CFR 18.61 - Final inspection of complete machine.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Final inspection of complete machine. 18.61 Section 18.61 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Inspections...

  9. Bidirectional, Automatic Coal-Mining Machine

    NASA Technical Reports Server (NTRS)

    Collins, Earl R., Jr.

    1986-01-01

    Proposed coal-mining machine operates in both forward and reverse directions along mine face. New design increases efficiency and productivity, because does not stop cutting as it retreats to starting position after completing pass along face. To further increase efficiency, automatic miner carries its own machinery for crushing coal and feeding it to slurry-transport tube. Dual-drum mining machine cuts coal in two layers, crushes, mixes with water, and feeds it as slurry to haulage tube. At end of pass, foward drum raised so it becomes rear drum, and rear drum lowered, becoming forward drum for return pass.

  10. 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)

  11. 30 CFR 18.21 - Machines equipped with powered dust collectors.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Machines equipped with powered dust collectors. 18.21 Section 18.21 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES...

  12. 30 CFR 70.207 - Bimonthly sampling; mechanized mining units.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... air will be used to determine the average concentration for that mechanized mining unit. (e) Unless... sampling device as follows: (1) Conventional section using cutting machine. On the cutting machine operator or on the cutting machine within 36 inches inby the normal working position; (2) Conventional section...

  13. Examination of redirected continuous miner scrubber discharge configurations for exhaust face ventilation systems

    PubMed Central

    Organiscak, J.A.; Beck, T.W.

    2015-01-01

    The U.S. National Institute for Occupational Safety and Health (NIOSH) Office of Mine Safety and Health Research (OMSHR) has recently studied several redirected scrubber discharge configurations in its full-scale continuous miner gallery for both dust and gas control when using an exhaust face ventilation system. Dust and gas measurements around the continuous mining machine in the laboratory showed that the conventional scrubber discharge directed outby the face with a 12.2-m (40-ft) exhaust curtain setback appeared to be one of the better configurations for controlling dust and gas. Redirecting all the air toward the face equally up both sides of the machine increased the dust and gas concentrations around the machine. When all of the air was redirected toward the face on the off-curtain side of the machine, gas accumulations tended to be reduced at the face, at the expense of increased dust levels in the return and on the curtain side of the mining machine. A 6.1-m (20-ft) exhaust curtain setback without the scrubber operating resulted in the lowest dust levels around the continuous mining machine, but this configuration resulted in some of the highest levels of dust in the return and gas on the off-curtain side of the mining face. Two field studies showed some similarities to the laboratory findings, with elevated dust levels at the rear corners of the continuous miner when all of the scrubber exhaust was redirected toward the face either up the off-tubing side or equally up both sides of the mining machine. PMID:26251566

  14. Review of fire test methods and incident data for portable electric cables in underground coal mines

    NASA Astrophysics Data System (ADS)

    Braun, E.

    1981-06-01

    Electrically powered underground coal mining machinery is connected to a load center or distribution box by electric cables. The connecting cables used on mobile machines are required to meet fire performance requirements defined in the Code of Federal Regulations. This report reviews Mine Safety and Health Administration's (MSHA) current test method and compares it to British practices. Incident data for fires caused by trailing cable failures and splice failures were also reviewed. It was found that the MSHA test method is more severe than the British but that neither evaluated grouped cable fire performance. The incident data indicated that the grouped configuration of cables on a reel accounted for a majority of the fires since 1970.

  15. 30 CFR 18.93 - Application for field approval; filing procedures.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... pursuant to individual written applications for each machine submitted in triplicate on MSHA Form No. 6-1481, by the owner-coal mine operator of the machine. (2) Except as provided in paragraph (b) of this... Mine Health and Safety District Manager for the District in which such machine will be employed. (b...

  16. 30 CFR 18.93 - Application for field approval; filing procedures.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... pursuant to individual written applications for each machine submitted in triplicate on MSHA Form No. 6-1481, by the owner-coal mine operator of the machine. (2) Except as provided in paragraph (b) of this... Mine Health and Safety District Manager for the District in which such machine will be employed. (b...

  17. 30 CFR 18.93 - Application for field approval; filing procedures.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... pursuant to individual written applications for each machine submitted in triplicate on MSHA Form No. 6-1481, by the owner-coal mine operator of the machine. (2) Except as provided in paragraph (b) of this... Mine Health and Safety District Manager for the District in which such machine will be employed. (b...

  18. Underground coal mine instrumentation and test

    NASA Technical Reports Server (NTRS)

    Burchill, R. F.; Waldron, W. D.

    1976-01-01

    The need to evaluate mechanical performance of mine tools and to obtain test performance data from candidate systems dictate that an engineering data recording system be built. Because of the wide range of test parameters which would be evaluated, a general purpose data gathering system was designed and assembled to permit maximum versatility. A primary objective of this program was to provide a specific operating evaluation of a longwall mining machine vibration response under normal operating conditions. A number of mines were visited and a candidate for test evaluation was selected, based upon management cooperation, machine suitability, and mine conditions. Actual mine testing took place in a West Virginia mine.

  19. Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution: A Position Paper.

    PubMed

    Luo, Gang

    2017-12-01

    For user-friendliness, many software systems offer progress indicators for long-duration tasks. A typical progress indicator continuously estimates the remaining task execution time as well as the portion of the task that has been finished. Building a machine learning model often takes a long time, but no existing machine learning software supplies a non-trivial progress indicator. Similarly, running a data mining algorithm often takes a long time, but no existing data mining software provides a nontrivial progress indicator. In this article, we consider the problem of offering progress indicators for machine learning model building and data mining algorithm execution. We discuss the goals and challenges intrinsic to this problem. Then we describe an initial framework for implementing such progress indicators and two advanced, potential uses of them, with the goal of inspiring future research on this topic.

  20. Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution: A Position Paper

    PubMed Central

    Luo, Gang

    2017-01-01

    For user-friendliness, many software systems offer progress indicators for long-duration tasks. A typical progress indicator continuously estimates the remaining task execution time as well as the portion of the task that has been finished. Building a machine learning model often takes a long time, but no existing machine learning software supplies a non-trivial progress indicator. Similarly, running a data mining algorithm often takes a long time, but no existing data mining software provides a nontrivial progress indicator. In this article, we consider the problem of offering progress indicators for machine learning model building and data mining algorithm execution. We discuss the goals and challenges intrinsic to this problem. Then we describe an initial framework for implementing such progress indicators and two advanced, potential uses of them, with the goal of inspiring future research on this topic. PMID:29177022

  1. Color machine vision in industrial process control: case limestone mine

    NASA Astrophysics Data System (ADS)

    Paernaenen, Pekka H. T.; Lemstrom, Guy F.; Koskinen, Seppo

    1994-11-01

    An optical sorter technology has been developed to improve profitability of a mine by using color line scan machine vision technology. The new technology adapted longers the expected life time of the limestone mine and improves its efficiency. Also the project has proved that color line scan technology of today can successfully be applied to industrial use in harsh environments.

  2. Design of intelligent proximity detection zones to prevent striking and pinning fatalities around continuous mining machines.

    PubMed

    Bissert, P T; Carr, J L; DuCarme, J P; Smith, A K

    2016-01-01

    The continuous mining machine is a key piece of equipment used in underground coal mining operations. Over the past several decades these machines have been involved in a number of mine worker fatalities. Proximity detection systems have been developed to avert hazards associated with operating continuous mining machines. Incorporating intelligent design into proximity detection systems allows workers greater freedom to position themselves to see visual cues or avoid other hazards such as haulage equipment or unsupported roof or ribs. However, intelligent systems must be as safe as conventional proximity detection systems. An evaluation of the 39 fatal accidents for which the Mine Safety and Health Administration has published fatality investigation reports was conducted to determine whether the accident may have been prevented by conventional or intelligent proximity. Multiple zone configurations for the intelligent systems were studied to determine how system performance might be affected by the zone configuration. Researchers found that 32 of the 39 fatalities, or 82 percent, may have been prevented by both conventional and intelligent proximity systems. These results indicate that, by properly configuring the zones of an intelligent proximity detection system, equivalent protection to a conventional system is possible.

  3. Data Mining and Machine Learning in Astronomy

    NASA Astrophysics Data System (ADS)

    Ball, Nicholas M.; Brunner, Robert J.

    We review the current state of data mining and machine learning in astronomy. Data Mining can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those in which data mining techniques directly contributed to improving science, and important current and future directions, including probability density functions, parallel algorithms, Peta-Scale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.

  4. Electromagnetic Signal Feedback Control for Proximity Detection Systems

    NASA Astrophysics Data System (ADS)

    Smith, Adam K.

    Coal is the most abundant fossil fuel in the United States and remains an essential source of energy. While more than half of coal production comes from surface mining, nearly twice as many workers are employed by underground operations. One of the key pieces of equipment used in underground coal mining is the continuous mining machine. These large and powerful machines are operated in confined spaces by remote control. Since 1984, 40 mine workers in the U. S. have been killed when struck or pinned by a continuous mining machine. It is estimated that a majority of these accidents could have been prevented with the application of proximity detection systems. While proximity detection systems can significantly increase safety around a continuous mining machine, there are some system limitations. Commercially available proximity warning systems for continuous mining machines use magnetic field generators to detect workers and establish safe work areas around the machines. Several environmental factors, however, can influence and distort the magnetic fields. To minimize these effects, a control system has been developed using electromagnetic field strength and generator current to stabilize and control field drift induced by internal and external environmental factors. A laboratory test set-up was built using a ferrite-core magnetic field generator to produce a stable magnetic field. Previous work based on a field-invariant magnetic flux density model, which generically describes the electromagnetic field, is expanded upon. The analytically established transferable shell-based flux density distribution model is used to experimentally validate the control system. By controlling the current input to the ferrite-core generator, a more reliable and consistent magnetic field is produced. Implementation of this technology will improve accuracy and performance of existing commercial proximity detection systems. These research results will help reduce the risk of traumatic injuries and improve overall safety in the mining workplace.

  5. New directions in biomedical text annotation: definitions, guidelines and corpus construction

    PubMed Central

    Wilbur, W John; Rzhetsky, Andrey; Shatkay, Hagit

    2006-01-01

    Background While biomedical text mining is emerging as an important research area, practical results have proven difficult to achieve. We believe that an important first step towards more accurate text-mining lies in the ability to identify and characterize text that satisfies various types of information needs. We report here the results of our inquiry into properties of scientific text that have sufficient generality to transcend the confines of a narrow subject area, while supporting practical mining of text for factual information. Our ultimate goal is to annotate a significant corpus of biomedical text and train machine learning methods to automatically categorize such text along certain dimensions that we have defined. Results We have identified five qualitative dimensions that we believe characterize a broad range of scientific sentences, and are therefore useful for supporting a general approach to text-mining: focus, polarity, certainty, evidence, and directionality. We define these dimensions and describe the guidelines we have developed for annotating text with regard to them. To examine the effectiveness of the guidelines, twelve annotators independently annotated the same set of 101 sentences that were randomly selected from current biomedical periodicals. Analysis of these annotations shows 70–80% inter-annotator agreement, suggesting that our guidelines indeed present a well-defined, executable and reproducible task. Conclusion We present our guidelines defining a text annotation task, along with annotation results from multiple independently produced annotations, demonstrating the feasibility of the task. The annotation of a very large corpus of documents along these guidelines is currently ongoing. These annotations form the basis for the categorization of text along multiple dimensions, to support viable text mining for experimental results, methodology statements, and other forms of information. We are currently developing machine learning methods, to be trained and tested on the annotated corpus, that would allow for the automatic categorization of biomedical text along the general dimensions that we have presented. The guidelines in full detail, along with annotated examples, are publicly available. PMID:16867190

  6. EXACT2: the semantics of biomedical protocols

    PubMed Central

    2014-01-01

    Background The reliability and reproducibility of experimental procedures is a cornerstone of scientific practice. There is a pressing technological need for the better representation of biomedical protocols to enable other agents (human or machine) to better reproduce results. A framework that ensures that all information required for the replication of experimental protocols is essential to achieve reproducibility. Methods We have developed the ontology EXACT2 (EXperimental ACTions) that is designed to capture the full semantics of biomedical protocols required for their reproducibility. To construct EXACT2 we manually inspected hundreds of published and commercial biomedical protocols from several areas of biomedicine. After establishing a clear pattern for extracting the required information we utilized text-mining tools to translate the protocols into a machine amenable format. We have verified the utility of EXACT2 through the successful processing of previously 'unseen' (not used for the construction of EXACT2) protocols. Results The paper reports on a fundamentally new version EXACT2 that supports the semantically-defined representation of biomedical protocols. The ability of EXACT2 to capture the semantics of biomedical procedures was verified through a text mining use case. In this EXACT2 is used as a reference model for text mining tools to identify terms pertinent to experimental actions, and their properties, in biomedical protocols expressed in natural language. An EXACT2-based framework for the translation of biomedical protocols to a machine amenable format is proposed. Conclusions The EXACT2 ontology is sufficient to record, in a machine processable form, the essential information about biomedical protocols. EXACT2 defines explicit semantics of experimental actions, and can be used by various computer applications. It can serve as a reference model for for the translation of biomedical protocols in natural language into a semantically-defined format. PMID:25472549

  7. Application of Elements of TPM Strategy for Operation Analysis of Mining Machine

    NASA Astrophysics Data System (ADS)

    Brodny, Jaroslaw; Tutak, Magdalena

    2017-12-01

    Total Productive Maintenance (TPM) strategy includes group of activities and actions in order to maintenance machines in failure-free state and without breakdowns thanks to tending limitation of failures, non-planned shutdowns, lacks and non-planned service of machines. These actions are ordered to increase effectiveness of utilization of possessed devices and machines in company. Very significant element of this strategy is connection of technical actions with changes in their perception by employees. Whereas fundamental aim of introduction this strategy is improvement of economic efficiency of enterprise. Increasing competition and necessity of reduction of production costs causes that also mining enterprises are forced to introduce this strategy. In the paper examples of use of OEE model for quantitative evaluation of selected mining devices were presented. OEE model is quantitative tool of TPM strategy and can be the base for further works connected with its introduction. OEE indicator is the product of three components which include availability and performance of the studied machine and the quality of the obtained product. The paper presents the results of the effectiveness analysis of the use of a set of mining machines included in the longwall system, which is the first and most important link in the technological line of coal production. The set of analyzed machines included the longwall shearer, armored face conveyor and cruscher. From a reliability point of view, the analyzed set of machines is a system that is characterized by the serial structure. The analysis was based on data recorded by the industrial automation system used in the mines. This method of data acquisition ensured their high credibility and a full time synchronization. Conclusions from the research and analyses should be used to reduce breakdowns, failures and unplanned downtime, increase performance and improve production quality.

  8. Text mining for traditional Chinese medical knowledge discovery: a survey.

    PubMed

    Zhou, Xuezhong; Peng, Yonghong; Liu, Baoyan

    2010-08-01

    Extracting meaningful information and knowledge from free text is the subject of considerable research interest in the machine learning and data mining fields. Text data mining (or text mining) has become one of the most active research sub-fields in data mining. Significant developments in the area of biomedical text mining during the past years have demonstrated its great promise for supporting scientists in developing novel hypotheses and new knowledge from the biomedical literature. Traditional Chinese medicine (TCM) provides a distinct methodology with which to view human life. It is one of the most complete and distinguished traditional medicines with a history of several thousand years of studying and practicing the diagnosis and treatment of human disease. It has been shown that the TCM knowledge obtained from clinical practice has become a significant complementary source of information for modern biomedical sciences. TCM literature obtained from the historical period and from modern clinical studies has recently been transformed into digital data in the form of relational databases or text documents, which provide an effective platform for information sharing and retrieval. This motivates and facilitates research and development into knowledge discovery approaches and to modernize TCM. In order to contribute to this still growing field, this paper presents (1) a comparative introduction to TCM and modern biomedicine, (2) a survey of the related information sources of TCM, (3) a review and discussion of the state of the art and the development of text mining techniques with applications to TCM, (4) a discussion of the research issues around TCM text mining and its future directions. Copyright 2010 Elsevier Inc. All rights reserved.

  9. A systematic review of data mining and machine learning for air pollution epidemiology.

    PubMed

    Bellinger, Colin; Mohomed Jabbar, Mohomed Shazan; Zaïane, Osmar; Osornio-Vargas, Alvaro

    2017-11-28

    Data measuring airborne pollutants, public health and environmental factors are increasingly being stored and merged. These big datasets offer great potential, but also challenge traditional epidemiological methods. This has motivated the exploration of alternative methods to make predictions, find patterns and extract information. To this end, data mining and machine learning algorithms are increasingly being applied to air pollution epidemiology. We conducted a systematic literature review on the application of data mining and machine learning methods in air pollution epidemiology. We carried out our search process in PubMed, the MEDLINE database and Google Scholar. Research articles applying data mining and machine learning methods to air pollution epidemiology were queried and reviewed. Our search queries resulted in 400 research articles. Our fine-grained analysis employed our inclusion/exclusion criteria to reduce the results to 47 articles, which we separate into three primary areas of interest: 1) source apportionment; 2) forecasting/prediction of air pollution/quality or exposure; and 3) generating hypotheses. Early applications had a preference for artificial neural networks. In more recent work, decision trees, support vector machines, k-means clustering and the APRIORI algorithm have been widely applied. Our survey shows that the majority of the research has been conducted in Europe, China and the USA, and that data mining is becoming an increasingly common tool in environmental health. For potential new directions, we have identified that deep learning and geo-spacial pattern mining are two burgeoning areas of data mining that have good potential for future applications in air pollution epidemiology. We carried out a systematic review identifying the current trends, challenges and new directions to explore in the application of data mining methods to air pollution epidemiology. This work shows that data mining is increasingly being applied in air pollution epidemiology. The potential to support air pollution epidemiology continues to grow with advancements in data mining related to temporal and geo-spacial mining, and deep learning. This is further supported by new sensors and storage mediums that enable larger, better quality data. This suggests that many more fruitful applications can be expected in the future.

  10. 75 FR 17511 - Coal Mine Dust Sampling Devices

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-04-06

    ... Part III Department of Labor Mine Safety and Health Adminisration 30 CFR Parts 18, 74, and 75 Coal Mine Dust Sampling Devices; High-Voltage Continuous Mining Machine Standard for Underground Coal Mines...-AB61 Coal Mine Dust Sampling Devices AGENCY: Mine Safety and Health Administration, Labor. ACTION...

  11. Data Mining at NASA: From Theory to Applications

    NASA Technical Reports Server (NTRS)

    Srivastava, Ashok N.

    2009-01-01

    This slide presentation demonstrates the data mining/machine learning capabilities of NASA Ames and Intelligent Data Understanding (IDU) group. This will encompass the work done recently in the group by various group members. The IDU group develops novel algorithms to detect, classify, and predict events in large data streams for scientific and engineering systems. This presentation for Knowledge Discovery and Data Mining 2009 is to demonstrate the data mining/machine learning capabilities of NASA Ames and IDU group. This will encompass the work done re cently in the group by various group members.

  12. Software tool for data mining and its applications

    NASA Astrophysics Data System (ADS)

    Yang, Jie; Ye, Chenzhou; Chen, Nianyi

    2002-03-01

    A software tool for data mining is introduced, which integrates pattern recognition (PCA, Fisher, clustering, hyperenvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, Hyper Envelop, support vector machine, visualization. The principle and knowledge representation of some function models of data mining are described. The software tool of data mining is realized by Visual C++ under Windows 2000. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining has satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

  13. 30 CFR 57.22308 - Methane monitors (III mines).

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Methane monitors (III mines). 57.22308 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22308 Methane monitors (III mines). (a) Methane monitors shall be installed on continuous mining machines and longwall mining systems. (b) The...

  14. 30 CFR 57.22308 - Methane monitors (III mines).

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Methane monitors (III mines). 57.22308 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22308 Methane monitors (III mines). (a) Methane monitors shall be installed on continuous mining machines and longwall mining systems. (b) The...

  15. Assimilating Text-Mining & Bio-Informatics Tools to Analyze Cellulase structures

    NASA Astrophysics Data System (ADS)

    Satyasree, K. P. N. V., Dr; Lalitha Kumari, B., Dr; Jyotsna Devi, K. S. N. V.; Choudri, S. M. Roy; Pratap Joshi, K.

    2017-08-01

    Text-mining is one of the best potential way of automatically extracting information from the huge biological literature. To exploit its prospective, the knowledge encrypted in the text should be converted to some semantic representation such as entities and relations, which could be analyzed by machines. But large-scale practical systems for this purpose are rare. But text mining could be helpful for generating or validating predictions. Cellulases have abundant applications in various industries. Cellulose degrading enzymes are cellulases and the same producing bacteria - Bacillus subtilis & fungus Pseudomonas putida were isolated from top soil of Guntur Dt. A.P. India. Absolute cultures were conserved on potato dextrose agar medium for molecular studies. In this paper, we presented how well the text mining concepts can be used to analyze cellulase producing bacteria and fungi, their comparative structures are also studied with the aid of well-establised, high quality standard bioinformatic tools such as Bioedit, Swissport, Protparam, EMBOSSwin with which a complete data on Cellulases like structure, constituents of the enzyme has been obtained.

  16. Multiple-Swarm Ensembles: Improving the Predictive Power and Robustness of Predictive Models and Its Use in Computational Biology.

    PubMed

    Alves, Pedro; Liu, Shuang; Wang, Daifeng; Gerstein, Mark

    2018-01-01

    Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work, we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness. Furthermore, we develop a methodology of ensembling, Multi-Swarm Ensemble (MSWE) by using multiple particle swarm optimizations and demonstrate its ability to further enhance the performance of ensembles.

  17. 30 CFR 57.22306 - Methane monitors (I-A mines).

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Methane monitors (I-A mines). 57.22306 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22306 Methane monitors (I-A mines). (a) Methane monitors shall be installed on continuous mining machines, longwall mining systems, and on loading...

  18. 30 CFR 57.22307 - Methane monitors (II-A mines).

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Methane monitors (II-A mines). 57.22307 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22307 Methane monitors (II-A mines). (a) Methane monitors shall be installed on continuous mining machines, longwall mining systems, bench and face...

  19. 30 CFR 57.22306 - Methane monitors (I-A mines).

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Methane monitors (I-A mines). 57.22306 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22306 Methane monitors (I-A mines). (a) Methane monitors shall be installed on continuous mining machines, longwall mining systems, and on loading...

  20. 30 CFR 57.22307 - Methane monitors (II-A mines).

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Methane monitors (II-A mines). 57.22307 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22307 Methane monitors (II-A mines). (a) Methane monitors shall be installed on continuous mining machines, longwall mining systems, bench and face...

  1. Mechanization for Optimal Landscape Reclamation

    NASA Astrophysics Data System (ADS)

    Vondráčková, Terezie; Voštová, Věra; Kraus, Michal

    2017-12-01

    Reclamation is a method of ultimate utilization of land adversely affected by mining or other industrial activity. The paper explains the types of reclamation and the term “optimal reclamation”. Technological options of the long-lasting process of mine dumps reclamation starting with the removal of overlying rocks, transport and backfilling up to the follow-up remodelling of the mine dumps terrain. Technological units and equipment for stripping flow division. Stripping flow solution with respect to optimal reclamation. We recommend that the application of logistic chains and mining simulation with follow-up reclamation to open-pit mines be used for the implementation of optimal reclamation. In addition to a database of local heterogeneities of the stripped soil and reclaimed land, the flow of earths should be resolved in a manner allowing the most suitable soil substrate to be created for the restoration of agricultural and forest land on mine dumps. The methodology under development for the solution of a number of problems, including the geological survey of overlying rocks, extraction of stripping, their transport and backfilling in specified locations with the follow-up deployment of goal-directed reclamation. It will make possible to reduce the financial resources needed for the complex process chain by utilizing GIS, GPS and DGPS technologies, logistic tools and synergistic effects. When selecting machines for transport, moving and spreading of earths, various points of view and aspects must be taken into account. Among such aspects are e.g. the kind of earth to be operated by the respective construction machine, the kind of work activities to be performed, the machine’s capacity, the option to control the machine’s implement and economic aspects and clients’ requirements. All these points of view must be considered in the decision-making process so that the selected machine is capable of executing the required activity and that the use of an unsuitable machine is eliminated as it would result in a delay and increase in the project costs. Therefore, reclamation always includes extensive earth-moving work activities restoring the required relief of the land being reclaimed. Using the earth-moving machine capacity, the kind of soil in mine dumps, the kind of the work activity performed and the machine design, a SW application has been developed that allows the most suitable machine for the respective work technology to be selected with a view to preparing the land intended for reclamation.

  2. Comparison of Automated and Manual Recording of Brief Episodes of Intracranial Hypertension and Cerebral Hypoperfusion and Their Association with Outcome After Severe Traumatic Brain Injury

    DTIC Science & Technology

    2017-03-01

    neuro ICP care beyond trauma care. 15. SUBJECT TERMS Advanced machine learning techniques, intracranial pressure, vital signs, monitoring...death and disability in combat casualties [1,2]. Approximately 2 million head injuries occur annually in the United States, resulting in more than...editor. Machine learning and data mining in pattern recognition. Proceedings of the 8th International Workshop on Machine Learning and Data Mining in

  3. Chapter 16: text mining for translational bioinformatics.

    PubMed

    Cohen, K Bretonnel; Hunter, Lawrence E

    2013-04-01

    Text mining for translational bioinformatics is a new field with tremendous research potential. It is a subfield of biomedical natural language processing that concerns itself directly with the problem of relating basic biomedical research to clinical practice, and vice versa. Applications of text mining fall both into the category of T1 translational research-translating basic science results into new interventions-and T2 translational research, or translational research for public health. Potential use cases include better phenotyping of research subjects, and pharmacogenomic research. A variety of methods for evaluating text mining applications exist, including corpora, structured test suites, and post hoc judging. Two basic principles of linguistic structure are relevant for building text mining applications. One is that linguistic structure consists of multiple levels. The other is that every level of linguistic structure is characterized by ambiguity. There are two basic approaches to text mining: rule-based, also known as knowledge-based; and machine-learning-based, also known as statistical. Many systems are hybrids of the two approaches. Shared tasks have had a strong effect on the direction of the field. Like all translational bioinformatics software, text mining software for translational bioinformatics can be considered health-critical and should be subject to the strictest standards of quality assurance and software testing.

  4. 30 CFR 57.22309 - Methane monitors (V-A mines).

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Methane monitors (V-A mines). 57.22309 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22309 Methane monitors (V-A mines). (a) Methane monitors shall be installed on continuous mining machines used in or beyond the last open crosscut...

  5. 30 CFR 57.22309 - Methane monitors (V-A mines).

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Methane monitors (V-A mines). 57.22309 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22309 Methane monitors (V-A mines). (a) Methane monitors shall be installed on continuous mining machines used in or beyond the last open crosscut...

  6. 30 CFR 70.207 - Bimonthly sampling; mechanized mining units.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... sampling device as follows: (1) Conventional section using cutting machine. On the cutting machine operator or on the cutting machine within 36 inches inby the normal working position; (2) Conventional section shooting off the solid. On the loading machine operator or on the loading machine within 36 inches inby the...

  7. 30 CFR 70.207 - Bimonthly sampling; mechanized mining units.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... sampling device as follows: (1) Conventional section using cutting machine. On the cutting machine operator or on the cutting machine within 36 inches inby the normal working position; (2) Conventional section shooting off the solid. On the loading machine operator or on the loading machine within 36 inches inby the...

  8. 30 CFR 70.207 - Bimonthly sampling; mechanized mining units.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... sampling device as follows: (1) Conventional section using cutting machine. On the cutting machine operator or on the cutting machine within 36 inches inby the normal working position; (2) Conventional section shooting off the solid. On the loading machine operator or on the loading machine within 36 inches inby the...

  9. Mass Estimation and Its Applications

    DTIC Science & Technology

    2012-02-23

    parameters); e.g., the rect- angular kernel function has fixed width or fixed per unit size. But the rectangular function used in mass has no parameter...MassTER is implemented in JAVA , and we use DBSCAN in WEKA [13] and a version of DENCLUE implemented in R (www.r-project.org) in our empirical evaluation...Proceedings of SIGKDD, 2010, 989-998. [13] I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations

  10. Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers

    PubMed Central

    García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

    2016-01-01

    The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine. PMID:28773653

  11. Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers.

    PubMed

    García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

    2016-06-29

    The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine.

  12. 30 CFR 77.401 - Stationary grinding machines; protective devices.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Stationary grinding machines; protective... OF UNDERGROUND COAL MINES Safeguards for Mechanical Equipment § 77.401 Stationary grinding machines; protective devices. (a) Stationary grinding machines other than special bit grinders shall be equipped with...

  13. 30 CFR 77.401 - Stationary grinding machines; protective devices.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Stationary grinding machines; protective... OF UNDERGROUND COAL MINES Safeguards for Mechanical Equipment § 77.401 Stationary grinding machines; protective devices. (a) Stationary grinding machines other than special bit grinders shall be equipped with...

  14. Kernel Methods for Mining Instance Data in Ontologies

    NASA Astrophysics Data System (ADS)

    Bloehdorn, Stephan; Sure, York

    The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.

  15. Assessment and evaluation of noise controls on roof bolting equipment and a method for predicting sound pressure levels in underground coal mining

    NASA Astrophysics Data System (ADS)

    Matetic, Rudy J.

    Over-exposure to noise remains a widespread and serious health hazard in the U.S. mining industries despite 25 years of regulation. Every day, 80% of the nation's miners go to work in an environment where the time weighted average (TWA) noise level exceeds 85 dBA and more than 25% of the miners are exposed to a TWA noise level that exceeds 90 dBA, the permissible exposure limit (PEL). Additionally, MSHA coal noise sample data collected from 2000 to 2002 show that 65% of the equipment whose operators exceeded 100% noise dosage comprise only seven different types of machines; auger miners, bulldozers, continuous miners, front end loaders, roof bolters, shuttle cars (electric), and trucks. In addition, the MSHA data indicate that the roof bolter is third among all the equipment and second among equipment in underground coal whose operators exceed 100% dosage. A research program was implemented to: (1) determine, characterize and to measure sound power levels radiated by a roof bolting machine during differing drilling configurations (thrust, rotational speed, penetration rate, etc.) and utilizing differing types of drilling methods in high compressive strength rock media (>20,000 psi). The research approach characterized the sound power level results from laboratory testing and provided the mining industry with empirical data relative to utilizing differing noise control technologies (drilling configurations and types of drilling methods) in reducing sound power level emissions on a roof bolting machine; (2) distinguish and correlate the empirical data into one, statistically valid, equation, in which, provided the mining industry with a tool to predict overall sound power levels of a roof bolting machine given any type of drilling configuration and drilling method utilized in industry; (3) provided the mining industry with several approaches to predict or determine sound pressure levels in an underground coal mine utilizing laboratory test results from a roof bolting machine and (4) described a method for determining an operators' noise dosage of a roof bolting machine utilizing predicted or determined sound pressure levels.

  16. 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

  17. Determining Underground Mining Work Postures Using Motion Capture and Digital Human Modeling

    PubMed Central

    Lutz, Timothy J.; DuCarme, Joseph P.; Smith, Adam K.; Ambrose, Dean

    2017-01-01

    According to Mine Safety and Health Administration (MSHA) data, during 2008–2012 in the U.S., there were, on average, 65 lost-time accidents per year during routine mining and maintenance activities involving remote-controlled continuous mining machines (CMMs). To address this problem, the National Institute for Occupational Safety and Health (NIOSH) is currently investigating the implementation and integration of existing and emerging technologies in underground mines to provide automated, intelligent proximity detection (iPD) devices on CMMs. One research goal of NIOSH is to enhance the proximity detection system by improving its capability to track and determine identity, position, and posture of multiple workers, and to selectively disable machine functions to keep workers and machine operators safe. Posture of the miner can determine the safe working distance from a CMM by way of the variation in the proximity detection magnetic field. NIOSH collected and analyzed motion capture data and calculated joint angles of the back, hips, and knees from various postures on 12 human subjects. The results of the analysis suggests that lower body postures can be identified by observing the changes in joint angles of the right hip, left hip, right knee, and left knee. PMID:28626796

  18. 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.

  19. 30 CFR 18.22 - Boring-type machines equipped for auxiliary face ventilation.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Boring-type machines equipped for auxiliary... AND ACCESSORIES Construction and Design Requirements § 18.22 Boring-type machines equipped for auxiliary face ventilation. Each boring-type continuous-mining machine that is submitted for approval shall...

  20. 30 CFR 18.22 - Boring-type machines equipped for auxiliary face ventilation.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Boring-type machines equipped for auxiliary... AND ACCESSORIES Construction and Design Requirements § 18.22 Boring-type machines equipped for auxiliary face ventilation. Each boring-type continuous-mining machine that is submitted for approval shall...

  1. 30 CFR 18.22 - Boring-type machines equipped for auxiliary face ventilation.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Boring-type machines equipped for auxiliary... AND ACCESSORIES Construction and Design Requirements § 18.22 Boring-type machines equipped for auxiliary face ventilation. Each boring-type continuous-mining machine that is submitted for approval shall...

  2. 30 CFR 18.22 - Boring-type machines equipped for auxiliary face ventilation.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Boring-type machines equipped for auxiliary... AND ACCESSORIES Construction and Design Requirements § 18.22 Boring-type machines equipped for auxiliary face ventilation. Each boring-type continuous-mining machine that is submitted for approval shall...

  3. 30 CFR 18.22 - Boring-type machines equipped for auxiliary face ventilation.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Boring-type machines equipped for auxiliary... AND ACCESSORIES Construction and Design Requirements § 18.22 Boring-type machines equipped for auxiliary face ventilation. Each boring-type continuous-mining machine that is submitted for approval shall...

  4. Imrovement of operation stability of crucial parts and constructions when repairing dredges and other mining machines exploited in conditions of North

    NASA Astrophysics Data System (ADS)

    Broido, V. L.; Krasnoshtanov, S. U.

    2018-03-01

    The problems of a choice of rational technoloqy and materials for restoring crucial parts and large-sized welded constructions of dredges and other mining machines with use of methods of welding and surfasing are considered. Welding and surfacing occupy a significant share in the overall labor intensity of performing repair work at mining enterprises. Both manual arc welding and surfacing as well as mechanized methods are used, which ensure a 24-fold increase in productivity. The work shows examples of using the technology of restoring parts and structures at gold mining enterprises in Irkutsk region. Some marks of welding and surfasing materials are shown, which production is mastered by Irkutsk Heavy Engineering Plant (IZTM)

  5. Machine Learning.

    ERIC Educational Resources Information Center

    Kirrane, Diane E.

    1990-01-01

    As scientists seek to develop machines that can "learn," that is, solve problems by imitating the human brain, a gold mine of information on the processes of human learning is being discovered, expert systems are being improved, and human-machine interactions are being enhanced. (SK)

  6. Collaborative mining and interpretation of large-scale data for biomedical research insights.

    PubMed

    Tsiliki, Georgia; Karacapilidis, Nikos; Christodoulou, Spyros; Tzagarakis, Manolis

    2014-01-01

    Biomedical research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to efficiently and effectively collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different sources. In line with related research directives revealing that, in spite of the recent advances in data mining and computational analysis, humans can easily detect patterns which computer algorithms may have difficulty in finding, this paper reports on the practical use of an innovative web-based collaboration support platform in a biomedical research context. Arguing that dealing with data-intensive and cognitively complex settings is not a technical problem alone, the proposed platform adopts a hybrid approach that builds on the synergy between machine and human intelligence to facilitate the underlying sense-making and decision making processes. User experience shows that the platform enables more informed and quicker decisions, by displaying the aggregated information according to their needs, while also exploiting the associated human intelligence.

  7. Collaborative Mining and Interpretation of Large-Scale Data for Biomedical Research Insights

    PubMed Central

    Tsiliki, Georgia; Karacapilidis, Nikos; Christodoulou, Spyros; Tzagarakis, Manolis

    2014-01-01

    Biomedical research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to efficiently and effectively collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different sources. In line with related research directives revealing that, in spite of the recent advances in data mining and computational analysis, humans can easily detect patterns which computer algorithms may have difficulty in finding, this paper reports on the practical use of an innovative web-based collaboration support platform in a biomedical research context. Arguing that dealing with data-intensive and cognitively complex settings is not a technical problem alone, the proposed platform adopts a hybrid approach that builds on the synergy between machine and human intelligence to facilitate the underlying sense-making and decision making processes. User experience shows that the platform enables more informed and quicker decisions, by displaying the aggregated information according to their needs, while also exploiting the associated human intelligence. PMID:25268270

  8. 30 CFR 75.703 - Grounding offtrack direct-current machines and the enclosures of related detached components.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Grounding offtrack direct-current machines and...-UNDERGROUND COAL MINES Grounding § 75.703 Grounding offtrack direct-current machines and the enclosures of related detached components. [Statutory Provisions] The frames of all offtrack direct-current machines and...

  9. 30 CFR 75.703 - Grounding offtrack direct-current machines and the enclosures of related detached components.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Grounding offtrack direct-current machines and...-UNDERGROUND COAL MINES Grounding § 75.703 Grounding offtrack direct-current machines and the enclosures of related detached components. [Statutory Provisions] The frames of all offtrack direct-current machines and...

  10. [Characterization of severe acute occupational poisoning accidents related to asphyxiating gases in China between 1989 and 2003].

    PubMed

    Zhang, Min; Li, Tao; Wang, Huan-Qiang; Wang, Hong-Fei; Chen, Shu-Yang; Du, Xie-Yi; Qin, Jian; Zhang, Shuang; Ji, Li-Ying

    2006-12-01

    To analyze severe acute occupational poisoning accidents related to asphyxiating gases reported in China between 1989 and 2003, and to study the characteristics of severe acute occupational poisoning accidents and provide scientific evidences for prevention and control strategies. The data from the national occupational poisoning case reporting system were analyzed with descriptive methods. (1) There were 273 severe acute occupational poisoning accidents related to asphyxiating gases for 15 years with 1638 workers poisoned and 600 workers died, which accounted for 53.95% in total accidents and 35.17% of workers poisoned and 78.64% of workers died of all severe acute occupational poisoning accidents. The average poisoning age was (33.8 +/- 9.7) years old and the average death age was (36.6 +/- 10.0) years old. (2) Most of the accidents were caused by hydrogen sulfide, carbon monoxide and carbon dioxide respectively, and mainly occurred in chemical industry, mining, water disposal industry, paper making industry and brewing industry. The risk was higher in some jobs than others, such as cleanout, machine maintenance and repair, production, mine and digging. The poisoning accidents occurred more frequently from April to September each year and occurred in the confined space, in the basement and the mine, and workers died of poisoning mostly were men. (1) The severe acute occupational poisoning accidents related to asphyxiating gases are more dangerous than others. (2) The control of poisoning accidents related to hydrogen sulfide, carbon monoxide and carbon dioxide, which occurred easily in the confined space, should be paid more attention to, and good work practice should be developed on some posts, such as digging, cleanout, dredge, machine maintenance and repair and mine.

  11. An Approach to Realizing Process Control for Underground Mining Operations of Mobile Machines

    PubMed Central

    Song, Zhen; Schunnesson, Håkan; Rinne, Mikael; Sturgul, John

    2015-01-01

    The excavation and production in underground mines are complicated processes which consist of many different operations. The process of underground mining is considerably constrained by the geometry and geology of the mine. The various mining operations are normally performed in series at each working face. The delay of a single operation will lead to a domino effect, thus delay the starting time for the next process and the completion time of the entire process. This paper presents a new approach to the process control for underground mining operations, e.g. drilling, bolting, mucking. This approach can estimate the working time and its probability for each operation more efficiently and objectively by improving the existing PERT (Program Evaluation and Review Technique) and CPM (Critical Path Method). If the delay of the critical operation (which is on a critical path) inevitably affects the productivity of mined ore, the approach can rapidly assign mucking machines new jobs to increase this amount at a maximum level by using a new mucking algorithm under external constraints. PMID:26062092

  12. A Review of Extra-Terrestrial Mining Robot Concepts

    NASA Technical Reports Server (NTRS)

    Mueller, Robert P.; Van Susante, Paul J.

    2011-01-01

    Outer space contains a vast amount of resources that offer virtually unlimited wealth to the humans that can access and use them for commercial purposes. One of the key technologies for harvesting these resources is robotic mining of regolith, minerals, ices and metals. The harsh environment and vast distances create challenges that are handled best by robotic machines working in collaboration with human explorers. Humans will benefit from the resources that will be mined by robots. They will visit outposts and mining camps as required for exploration, commerce and scientific research, but a continuous presence is most likely to be provided by robotic mining machines that are remotely controlled by humans. There have been a variety of extra-terrestrial robotic mining concepts proposed over the last 100 years and this paper will attempt to summarize and review concepts in the public domain (government, industry and academia) to serve as an informational resource for future mining robot developers and operators. The challenges associated with these concepts will be discussed and feasibility will be assessed. Future needs associated with commercial efforts will also be investigated.

  13. An Approach to Realizing Process Control for Underground Mining Operations of Mobile Machines.

    PubMed

    Song, Zhen; Schunnesson, Håkan; Rinne, Mikael; Sturgul, John

    2015-01-01

    The excavation and production in underground mines are complicated processes which consist of many different operations. The process of underground mining is considerably constrained by the geometry and geology of the mine. The various mining operations are normally performed in series at each working face. The delay of a single operation will lead to a domino effect, thus delay the starting time for the next process and the completion time of the entire process. This paper presents a new approach to the process control for underground mining operations, e.g. drilling, bolting, mucking. This approach can estimate the working time and its probability for each operation more efficiently and objectively by improving the existing PERT (Program Evaluation and Review Technique) and CPM (Critical Path Method). If the delay of the critical operation (which is on a critical path) inevitably affects the productivity of mined ore, the approach can rapidly assign mucking machines new jobs to increase this amount at a maximum level by using a new mucking algorithm under external constraints.

  14. A Review of Extra-Terrestrial Mining Concepts

    NASA Technical Reports Server (NTRS)

    Mueller, R. P.; van Susante, P. J.

    2012-01-01

    Outer space contains a vast amount of resources that offer virtually unlimited wealth to the humans that can access and use them for commercial purposes. One of the key technologies for harvesting these resources is robotic mining of regolith, minerals, ices and metals. The harsh environment and vast distances create challenges that are handled best by robotic machines working in collaboration with human explorers. Humans will benefit from the resources that will be mined by robots. They will visit outposts and mining camps as required for exploration, commerce and scientific research, but a continuous presence is most likely to be provided by robotic mining machines that are remotely controlled by humans. There have been a variety of extra-terrestrial robotic mining concepts proposed over the last 40 years and this paper will attempt to summarize and review concepts in the public domain (government, industry and academia) to serve as an informational resource for future mining robot developers and operators. The challenges associated with these concepts will be discussed and feasibility will be assessed. Future needs associated with commercial efforts will also be investigated.

  15. 30 CFR 18.95 - Approval of machines constructed of components approved, accepted or certified under Bureau of...

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... approved, accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. 18.95 Section 18.95..., accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. Machines for which field approval... 2D, 2E, 2F, or 2G, shall be approved following a determination by the electrical representative that...

  16. 30 CFR 18.95 - Approval of machines constructed of components approved, accepted or certified under Bureau of...

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... approved, accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. 18.95 Section 18.95..., accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. Machines for which field approval... 2D, 2E, 2F, or 2G, shall be approved following a determination by the electrical representative that...

  17. 30 CFR 18.95 - Approval of machines constructed of components approved, accepted or certified under Bureau of...

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... approved, accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. 18.95 Section 18.95..., accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. Machines for which field approval... 2D, 2E, 2F, or 2G, shall be approved following a determination by the electrical representative that...

  18. 30 CFR 18.95 - Approval of machines constructed of components approved, accepted or certified under Bureau of...

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... approved, accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. 18.95 Section 18.95..., accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. Machines for which field approval... 2D, 2E, 2F, or 2G, shall be approved following a determination by the electrical representative that...

  19. 30 CFR 18.95 - Approval of machines constructed of components approved, accepted or certified under Bureau of...

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... approved, accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. 18.95 Section 18.95..., accepted or certified under Bureau of Mines Schedule 2D, 2E, 2F, or 2G. Machines for which field approval... 2D, 2E, 2F, or 2G, shall be approved following a determination by the electrical representative that...

  20. Cutter-loader apparatus having overhung shearer drum

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

    Groger, H.; Harms, E.E.

    1984-05-01

    A longwall mining machine includes a drum cutter-loader and face conveyor wherein the drum cutter-loader is overhung and is supported by a support arm adjacent to the mine face. Nozzles direct high pressure liquid jets against the forward edge of the support arm to cut away the mining face and permit the face side support arm to advance as the mining machine advances. In one embodiment the nozzles are provided along an inclined cutting edge at the forward end of the support arm. Such nozzles may be fixed or oscillating. In an alternative embodiment the nozzles are provided in themore » cylindrical edge zone of the shearer drum and direct the high pressure fluid jets against the cutter edge at the forward end of the support arm.« less

  1. New Trends in E-Science: Machine Learning and Knowledge Discovery in Databases

    NASA Astrophysics Data System (ADS)

    Brescia, Massimo

    2012-11-01

    Data mining, or Knowledge Discovery in Databases (KDD), while being the main methodology to extract the scientific information contained in Massive Data Sets (MDS), needs to tackle crucial problems since it has to orchestrate complex challenges posed by transparent access to different computing environments, scalability of algorithms, reusability of resources. To achieve a leap forward for the progress of e-science in the data avalanche era, the community needs to implement an infrastructure capable of performing data access, processing and mining in a distributed but integrated context. The increasing complexity of modern technologies carried out a huge production of data, whose related warehouse management and the need to optimize analysis and mining procedures lead to a change in concept on modern science. Classical data exploration, based on local user own data storage and limited computing infrastructures, is no more efficient in the case of MDS, worldwide spread over inhomogeneous data centres and requiring teraflop processing power. In this context modern experimental and observational science requires a good understanding of computer science, network infrastructures, Data Mining, etc. i.e. of all those techniques which fall into the domain of the so called e-science (recently assessed also by the Fourth Paradigm of Science). Such understanding is almost completely absent in the older generations of scientists and this reflects in the inadequacy of most academic and research programs. A paradigm shift is needed: statistical pattern recognition, object oriented programming, distributed computing, parallel programming need to become an essential part of scientific background. A possible practical solution is to provide the research community with easy-to understand, easy-to-use tools, based on the Web 2.0 technologies and Machine Learning methodology. Tools where almost all the complexity is hidden to the final user, but which are still flexible and able to produce efficient and reliable scientific results. All these considerations will be described in the detail in the chapter. Moreover, examples of modern applications offering to a wide variety of e-science communities a large spectrum of computational facilities to exploit the wealth of available massive data sets and powerful machine learning and statistical algorithms will be also introduced.

  2. Influence of continuous mining arrangements on respirable dust exposures

    PubMed Central

    Beck, T. W.; Organiscak, J. A.; Pollock, D. E.; Potts, J. D.; Reed, W. R.

    2017-01-01

    In underground continuous mining operations, ventilation, water sprays and machine-mounted flooded-bed scrubbers are the primary means of controlling respirable dust exposures at the working face. Changes in mining arrangements — such as face ventilation configuration, orientation of crosscuts mined in relation to the section ventilation and equipment operator positioning — can have impacts on the ability of dust controls to reduce occupational respirable dust exposures. This study reports and analyzes dust concentrations measured by the Pittsburgh Mining Research Division for remote-controlled continuous mining machine operators as well as haulage operators at 10 U.S. underground mines. The results of these respirable dust surveys show that continuous miner exposures varied little with depth of cut but are significantly higher with exhaust ventilation. Haulage operators experienced elevated concentrations with blowing face ventilation. Elevated dust concentrations were observed for both continuous miner operators and haulage operators when working in crosscuts driven into or counter to the section airflow. Individual cuts are highlighted to demonstrate instances of minimal and excessive dust exposures attributable to particular mining configurations. These findings form the basis for recommendations for lowering face worker respirable dust exposures. PMID:28529441

  3. Data mining in bioinformatics using Weka.

    PubMed

    Frank, Eibe; Hall, Mark; Trigg, Len; Holmes, Geoffrey; Witten, Ian H

    2004-10-12

    The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data pre-processing methods complemented by graphical user interfaces for data exploration and the experimental comparison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table. Its main objectives are to (a) assist users in extracting useful information from data and (b) enable them to easily identify a suitable algorithm for generating an accurate predictive model from it. http://www.cs.waikato.ac.nz/ml/weka.

  4. Mining protein function from text using term-based support vector machines

    PubMed Central

    Rice, Simon B; Nenadic, Goran; Stapley, Benjamin J

    2005-01-01

    Background Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents. Results The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent. Conclusion A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2. PMID:15960835

  5. 30 CFR 18.8 - Date for conducting investigation and tests.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ..., EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES General... determine the order of precedence for investigation and testing. If an electrical machine component or...

  6. 30 CFR 18.10 - Notice of approval or disapproval.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ..., EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES General... assembly of an electrical machine or accessory, MSHA will issue to the applicant either a written notice of...

  7. Text mining approach to predict hospital admissions using early medical records from the emergency department.

    PubMed

    Lucini, Filipe R; S Fogliatto, Flavio; C da Silveira, Giovani J; L Neyeloff, Jeruza; Anzanello, Michel J; de S Kuchenbecker, Ricardo; D Schaan, Beatriz

    2017-04-01

    Emergency department (ED) overcrowding is a serious issue for hospitals. Early information on short-term inward bed demand from patients receiving care at the ED may reduce the overcrowding problem, and optimize the use of hospital resources. In this study, we use text mining methods to process data from early ED patient records using the SOAP framework, and predict future hospitalizations and discharges. We try different approaches for pre-processing of text records and to predict hospitalization. Sets-of-words are obtained via binary representation, term frequency, and term frequency-inverse document frequency. Unigrams, bigrams and trigrams are tested for feature formation. Feature selection is based on χ 2 and F-score metrics. In the prediction module, eight text mining methods are tested: Decision Tree, Random Forest, Extremely Randomized Tree, AdaBoost, Logistic Regression, Multinomial Naïve Bayes, Support Vector Machine (Kernel linear) and Nu-Support Vector Machine (Kernel linear). Prediction performance is evaluated by F1-scores. Precision and Recall values are also informed for all text mining methods tested. Nu-Support Vector Machine was the text mining method with the best overall performance. Its average F1-score in predicting hospitalization was 77.70%, with a standard deviation (SD) of 0.66%. The method could be used to manage daily routines in EDs such as capacity planning and resource allocation. Text mining could provide valuable information and facilitate decision-making by inward bed management teams. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

  8. An Evolutionary Machine Learning Framework for Big Data Sequence Mining

    ERIC Educational Resources Information Center

    Kamath, Uday Krishna

    2014-01-01

    Sequence classification is an important problem in many real-world applications. Unlike other machine learning data, there are no "explicit" features or signals in sequence data that can help traditional machine learning algorithms learn and predict from the data. Sequence data exhibits inter-relationships in the elements that are…

  9. 30 CFR 18.96 - Preparation of machines for inspection; requirements.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Preparation of machines for inspection... Field Approval of Electrically Operated Mining Equipment § 18.96 Preparation of machines for inspection; requirements. (a) Upon receipt of written notice from the Health and Safety District Manager of the time and...

  10. 30 CFR 18.96 - Preparation of machines for inspection; requirements.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Preparation of machines for inspection... Field Approval of Electrically Operated Mining Equipment § 18.96 Preparation of machines for inspection; requirements. (a) Upon receipt of written notice from the Health and Safety District Manager of the time and...

  11. 30 CFR 18.96 - Preparation of machines for inspection; requirements.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Preparation of machines for inspection... Field Approval of Electrically Operated Mining Equipment § 18.96 Preparation of machines for inspection; requirements. (a) Upon receipt of written notice from the Health and Safety District Manager of the time and...

  12. 30 CFR 18.99 - Notice of approval or disapproval; letters of approval and approval plates.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... approval or disapproval of the machine. (a) If the qualified electrical representative recommends field..., DEPARTMENT OF LABOR TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Field Approval of Electrically Operated Mining Equipment § 18.99 Notice of approval or...

  13. 30 CFR 18.99 - Notice of approval or disapproval; letters of approval and approval plates.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... approval or disapproval of the machine. (a) If the qualified electrical representative recommends field..., DEPARTMENT OF LABOR TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Field Approval of Electrically Operated Mining Equipment § 18.99 Notice of approval or...

  14. 2. GENERAL VIEW LOOKING NORTHEAST, SHOWING COKE MACHINE (CENTER), INTERMEDIATE ...

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

    2. GENERAL VIEW LOOKING NORTHEAST, SHOWING COKE MACHINE (CENTER), INTERMEDIATE TIPPLE (RIGHT), AND OVENS - Shoaf Mine & Coke Works, East side of Shoaf, off Township Route 472, Shoaf, Fayette County, PA

  15. On Interestingness Measures for Mining Statistically Significant and Novel Clinical Associations from EMRs

    PubMed Central

    Abar, Orhan; Charnigo, Richard J.; Rayapati, Abner

    2017-01-01

    Association rule mining has received significant attention from both the data mining and machine learning communities. While data mining researchers focus more on designing efficient algorithms to mine rules from large datasets, the learning community has explored applications of rule mining to classification. A major problem with rule mining algorithms is the explosion of rules even for moderate sized datasets making it very difficult for end users to identify both statistically significant and potentially novel rules that could lead to interesting new insights and hypotheses. Researchers have proposed many domain independent interestingness measures using which, one can rank the rules and potentially glean useful rules from the top ranked ones. However, these measures have not been fully explored for rule mining in clinical datasets owing to the relatively large sizes of the datasets often encountered in healthcare and also due to limited access to domain experts for review/analysis. In this paper, using an electronic medical record (EMR) dataset of diagnoses and medications from over three million patient visits to the University of Kentucky medical center and affiliated clinics, we conduct a thorough evaluation of dozens of interestingness measures proposed in data mining literature, including some new composite measures. Using cumulative relevance metrics from information retrieval, we compare these interestingness measures against human judgments obtained from a practicing psychiatrist for association rules involving the depressive disorders class as the consequent. Our results not only surface new interesting associations for depressive disorders but also indicate classes of interestingness measures that weight rule novelty and statistical strength in contrasting ways, offering new insights for end users in identifying interesting rules. PMID:28736771

  16. 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

  17. Continuous Rating for Diggability Assessment in Surface Mines

    NASA Astrophysics Data System (ADS)

    IPHAR, Melih

    2016-10-01

    The rocks can be loosened either by drilling-blasting or direct excavation using powerful machines in opencast mining operations. The economics of rock excavation is considered for each method to be applied. If blasting operation is not preferred and also the geological structures and rock mass properties in site are convenient (favourable ground conditions) for ripping or direct excavation method by mining machines, the next step is to determine which machine or excavator should be selected for the excavation purposes. Many researchers have proposed several diggability or excavatability assessment methods for deciding on excavator type to be used in the field. Most of these systems are generally based on assigning a rating for the parameters having importance in rock excavation process. However, the sharp transitions between the two adjacent classes for a given parameter can lead to some uncertainties. In this paper, it has been proposed that varying rating should be assigned for a given parameter called as “continuous rating” instead of giving constant rating for a given class.

  18. Automated Data Assimilation and Flight Planning for Multi-Platform Observation Missions

    NASA Technical Reports Server (NTRS)

    Oza, Nikunj; Morris, Robert A.; Strawa, Anthony; Kurklu, Elif; Keely, Leslie

    2008-01-01

    This is a progress report on an effort in which our goal is to demonstrate the effectiveness of automated data mining and planning for the daily management of Earth Science missions. Currently, data mining and machine learning technologies are being used by scientists at research labs for validating Earth science models. However, few if any of these advanced techniques are currently being integrated into daily mission operations. Consequently, there are significant gaps in the knowledge that can be derived from the models and data that are used each day for guiding mission activities. The result can be sub-optimal observation plans, lack of useful data, and wasteful use of resources. Recent advances in data mining, machine learning, and planning make it feasible to migrate these technologies into the daily mission planning cycle. We describe the design of a closed loop system for data acquisition, processing, and flight planning that integrates the results of machine learning into the flight planning process.

  19. Pulsed, Hydraulic Coal-Mining Machine

    NASA Technical Reports Server (NTRS)

    Collins, Earl R., Jr.

    1986-01-01

    In proposed coal-cutting machine, piston forces water through nozzle, expelling pulsed jet that cuts into coal face. Spring-loaded piston reciprocates at end of travel to refill water chamber. Machine a onecylinder, two-cycle, internal-combustion engine, fueled by gasoline, diesel fuel, or hydrogen. Fuel converted more directly into mechanical energy of water jet.

  20. 30 CFR 75.209 - Automated Temporary Roof Support (ATRS) systems.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... paragraphs (b) and (c) of this section, an ATRS system shall be used with roof bolting machines and continuous-mining machines with integral roof bolters operated in a working section. The requirements of this paragraph shall be met according to the following schedule: (1) All new machines ordered after March 28...

  1. 30 CFR 75.209 - Automated Temporary Roof Support (ATRS) systems.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... paragraphs (b) and (c) of this section, an ATRS system shall be used with roof bolting machines and continuous-mining machines with integral roof bolters operated in a working section. The requirements of this paragraph shall be met according to the following schedule: (1) All new machines ordered after March 28...

  2. 30 CFR 75.209 - Automated Temporary Roof Support (ATRS) systems.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... paragraphs (b) and (c) of this section, an ATRS system shall be used with roof bolting machines and continuous-mining machines with integral roof bolters operated in a working section. The requirements of this paragraph shall be met according to the following schedule: (1) All new machines ordered after March 28...

  3. A data mining approach to predict in situ chlorinated ethene detoxification potential

    NASA Astrophysics Data System (ADS)

    Lee, J.; Im, J.; Kim, U.; Loeffler, F. E.

    2015-12-01

    Despite major advances in physicochemical remediation technologies, in situ biostimulation and bioaugmentation treatment aimed at stimulating Dehalococcoides mccartyi (Dhc) reductive dechlorination activity remains a cornerstone approach to remedy sites impacted with chlorinated ethenes. In practice, selecting the best remedial strategy is challenging due to uncertainties associated with the microbiology (e.g., presence and activity of Dhc) and geochemical factors influencing Dhc activity. Extensive groundwater datasets collected over decades of monitoring exist, but have not been systematically analyzed. In the present study, geochemical and microbial data sets collected from 35 wells at 5 contaminated sites were used to develop a predictive empirical model using a machine learning algorithm (i) to rank the relative importance of parameters that affect in situ reductive dechlorination potential, and (ii) to provide recommendations for selecting the optimal remediation strategy at a specific site. Classification and regression tree (CART) analysis was applied, and a representative classification tree model was developed that allowed short-term prediction of dechlorination potential. Indirect indicators for low dissolved oxygen (e.g., low NO3-and NO2-, high Fe2+ and CH4) were the most influential factors for predicting dechlorination potential, followed by total organic carbon content (TOC) and Dhc cell abundance. These findings indicate that machine learning-based data mining techniques applied to groundwater monitoring data can lead to the development of predictive groundwater remediation models. A major need for improving the predictive capabilities of the data mining approach is a curated, up-to-date and comprehensive collection of groundwater monitoring data.

  4. Novel diesel exhaust filters for underground mining vehicles

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

    Bickel, K.L.; Taubert, T.R.

    1995-12-31

    The U.S. Bureau of Mines (USBM) pioneered the development of disposable filters for reducing diesel particulate emissions from permissible mining machines. The USBM is now evaluating filter media that can withstand the high exhaust temperatures on nonpermissible machines. The goal of the evaluation is to find an inexpensive medium that can be cleaned or disposed of after use, and will reduce particulate emissions by 50 % or more. This report summarizes the results from screening tests of a lava rock and woven fiberglass filter media. The lava rock media exhibited low collection efficiencies, but with very low increases in exhaustmore » back pressure. Preliminary results indicate a collection efficiency exceeding 80 % for the woven fiber media. Testing of both media is continuing.« less

  5. Whole-body Vibration Exposure of Drill Operators in Iron Ore Mines and Role of Machine-Related, Individual, and Rock-Related Factors.

    PubMed

    Chaudhary, Dhanjee Kumar; Bhattacherjee, Ashis; Patra, Aditya Kumar; Chau, Nearkasen

    2015-12-01

    This study aimed to assess the whole-body vibration (WBV) exposure among large blast hole drill machine operators with regard to the International Organization for Standardization (ISO) recommended threshold values and its association with machine- and rock-related factors and workers' individual characteristics. The study population included 28 drill machine operators who had worked in four opencast iron ore mines in eastern India. The study protocol comprised the following: measurements of WBV exposure [frequency weighted root mean square (RMS) acceleration (m/s(2))], machine-related data (manufacturer of machine, age of machine, seat height, thickness, and rest height) collected from mine management offices, measurements of rock hardness, uniaxial compressive strength and density, and workers' characteristics via face-to-face interviews. More than 90% of the operators were exposed to a higher level WBV than the ISO upper limit and only 3.6% between the lower and upper limits, mainly in the vertical axis. Bivariate correlations revealed that potential predictors of total WBV exposure were: machine manufacturer (r = 0.453, p = 0.015), age of drill (r = 0.533, p = 0.003), and hardness of rock (r = 0.561, p = 0.002). The stepwise multiple regression model revealed that the potential predictors are age of operator (regression coefficient β = -0.052, standard error SE = 0.023), manufacturer (β = 1.093, SE = 0.227), rock hardness (β = 0.045, SE = 0.018), uniaxial compressive strength (β = 0.027, SE = 0.009), and density (β = -1.135, SE = 0.235). Prevention should include using appropriate machines to handle rock hardness, rock uniaxial compressive strength and density, and seat improvement using ergonomic approaches such as including a suspension system.

  6. Whole-body Vibration Exposure of Drill Operators in Iron Ore Mines and Role of Machine-Related, Individual, and Rock-Related Factors

    PubMed Central

    Chaudhary, Dhanjee Kumar; Bhattacherjee, Ashis; Patra, Aditya Kumar; Chau, Nearkasen

    2015-01-01

    Background This study aimed to assess the whole-body vibration (WBV) exposure among large blast hole drill machine operators with regard to the International Organization for Standardization (ISO) recommended threshold values and its association with machine- and rock-related factors and workers' individual characteristics. Methods The study population included 28 drill machine operators who had worked in four opencast iron ore mines in eastern India. The study protocol comprised the following: measurements of WBV exposure [frequency weighted root mean square (RMS) acceleration (m/s2)], machine-related data (manufacturer of machine, age of machine, seat height, thickness, and rest height) collected from mine management offices, measurements of rock hardness, uniaxial compressive strength and density, and workers' characteristics via face-to-face interviews. Results More than 90% of the operators were exposed to a higher level WBV than the ISO upper limit and only 3.6% between the lower and upper limits, mainly in the vertical axis. Bivariate correlations revealed that potential predictors of total WBV exposure were: machine manufacturer (r = 0.453, p = 0.015), age of drill (r = 0.533, p = 0.003), and hardness of rock (r = 0.561, p = 0.002). The stepwise multiple regression model revealed that the potential predictors are age of operator (regression coefficient β = −0.052, standard error SE = 0.023), manufacturer (β = 1.093, SE = 0.227), rock hardness (β = 0.045, SE = 0.018), uniaxial compressive strength (β = 0.027, SE = 0.009), and density (β = –1.135, SE = 0.235). Conclusion Prevention should include using appropriate machines to handle rock hardness, rock uniaxial compressive strength and density, and seat improvement using ergonomic approaches such as including a suspension system. PMID:26929838

  7. Effects of MERV 16 filters and routine work practices on enclosed cabs for reducing respirable dust and DPM exposures in an underground limestone mine

    PubMed Central

    Noll, J.D.; Cecala, A.B.; J.A.Organiscak; Rider, J.P.

    2015-01-01

    An effective technique to minimize miners’ respirable dust and diesel exposure on mobile mining equipment is to place mine operators in enclosed cabs with designed filtration and pressurization systems. Many factors affect the performance of these enclosed cab systems, and one of the most significant factors is the effectiveness of the filtration system. High-efficiency particulate air (HEPA)-type filters are typically used because they are highly efficient at capturing all types and sizes of particles, including those in the submicron range such as diesel particulate matter (DPM). However, in laboratory tests, minimum efficiency reporting value (MERV) 16 filters have proven to be highly efficient for capturing DPM and respirable dust. Also, MERV 16 filters can be less restrictive to cab airflow and less expensive than HEPA filters. To verify their effectiveness in the field, MERV 16 filters were used in the enclosed cab filtration system on a face drill and roof bolting mining machine and tested at an underground limestone mine. Test results showed that DPM and respirable dust concentrations were reduced by more than 90% when the cabs were properly sealed. However, when the cab door was opened periodically throughout the shift, the reduction efficiency of the MERV 16 filters was reduced to 80% on average. PMID:26236044

  8. Gene prioritization and clustering by multi-view text mining

    PubMed Central

    2010-01-01

    Background Text mining has become a useful tool for biologists trying to understand the genetics of diseases. In particular, it can help identify the most interesting candidate genes for a disease for further experimental analysis. Many text mining approaches have been introduced, but the effect of disease-gene identification varies in different text mining models. Thus, the idea of incorporating more text mining models may be beneficial to obtain more refined and accurate knowledge. However, how to effectively combine these models still remains a challenging question in machine learning. In particular, it is a non-trivial issue to guarantee that the integrated model performs better than the best individual model. Results We present a multi-view approach to retrieve biomedical knowledge using different controlled vocabularies. These controlled vocabularies are selected on the basis of nine well-known bio-ontologies and are applied to index the vast amounts of gene-based free-text information available in the MEDLINE repository. The text mining result specified by a vocabulary is considered as a view and the obtained multiple views are integrated by multi-source learning algorithms. We investigate the effect of integration in two fundamental computational disease gene identification tasks: gene prioritization and gene clustering. The performance of the proposed approach is systematically evaluated and compared on real benchmark data sets. In both tasks, the multi-view approach demonstrates significantly better performance than other comparing methods. Conclusions In practical research, the relevance of specific vocabulary pertaining to the task is usually unknown. In such case, multi-view text mining is a superior and promising strategy for text-based disease gene identification. PMID:20074336

  9. 30 CFR 18.97 - Inspection of machines; minimum requirements.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... all electrical components for materials, workmanship, design, and construction; (2) Examination of all components of the machine which have been approved or certified under Bureau of Mines Schedule 2D, 2E, 2F, or...

  10. 30 CFR 18.97 - Inspection of machines; minimum requirements.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... all electrical components for materials, workmanship, design, and construction; (2) Examination of all components of the machine which have been approved or certified under Bureau of Mines Schedule 2D, 2E, 2F, or...

  11. 30 CFR 18.97 - Inspection of machines; minimum requirements.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... all electrical components for materials, workmanship, design, and construction; (2) Examination of all components of the machine which have been approved or certified under Bureau of Mines Schedule 2D, 2E, 2F, or...

  12. SparkText: Biomedical Text Mining on Big Data Framework.

    PubMed

    Ye, Zhan; Tafti, Ahmad P; He, Karen Y; Wang, Kai; He, Max M

    Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment. In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes. This study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research.

  13. SparkText: Biomedical Text Mining on Big Data Framework

    PubMed Central

    He, Karen Y.; Wang, Kai

    2016-01-01

    Background Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment. Results In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes. Conclusions This study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research. PMID:27685652

  14. Hardening parts by chrome plating in manufacture and repair

    NASA Astrophysics Data System (ADS)

    Astanin, V. K.; Pukhov, E. V.; Stekolnikov, Y. A.; Emtsev, V. V.; Golikova, O. A.

    2018-03-01

    In the engineering industry, galvanic coatings are widely used to prolong the service life of the machines, which contribute to the increase in the strength of the parts and their resistance to environmental influences, temperature and pressure drops, wear and fretting corrosion. Galvanic coatings have been widely applied in engineering, including agriculture, aircraft building, mining, construction, and electronics. The article focuses on the manufacturing methods of new agricultural machinery parts and the repair techniques of worn parts by chrome plating. The main attention is paid to the unstable methods of chromium deposition (in pulsed and reversing modes) in low-concentration electrolytes, which makes it possible to increase the reliability and durability of the hardened parts operation by changing the conditions of electrocrystallization, that is, directed formation of the structure and texture, thickness, roughness and microhardness of chromium plating. The practical recommendations are given on the current and temperature regimes of chromium deposition and composition of baths used for the restoration and hardening of the machine parts. Moreover, the basic methods of machining allowances removal are analysed.

  15. Coal Mining Machinery Development As An Ecological Factor Of Progressive Technologies Implementation

    NASA Astrophysics Data System (ADS)

    Efremenkov, A. B.; Khoreshok, A. A.; Zhironkin, S. A.; Myaskov, A. V.

    2017-01-01

    At present, a significant amount of energy spent for the work of mining machines and coal mining equipment on coal mines and open pits goes to the coal grinding in the process of its extraction in mining faces. Meanwhile, the increase of small fractions in mined coal does not only reduce the profitability of its production, but also causes a further negative impact on the environment and degrades labor conditions for miners. The countermeasure to the specified processes is possible with the help of coal mining equipment development. However, against the background of the technological decrease of coal mine equipment applied in Russia the negative impact on the environment is getting reinforced.

  16. Thutmose - Investigation of Machine Learning-Based Intrusion Detection Systems

    DTIC Science & Technology

    2016-06-01

    research is being done to incorporate the field of machine learning into intrusion detection. Machine learning is a branch of artificial intelligence (AI...adversarial drift." Proceedings of the 2013 ACM workshop on Artificial intelligence and security. ACM. (2013) Kantarcioglu, M., Xi, B., and Clifton, C. "A...34 Proceedings of the 4th ACM workshop on Security and artificial intelligence . ACM. (2011) Dua, S., and Du, X. Data Mining and Machine Learning in

  17. Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics

    PubMed Central

    Torii, Manabu; Tilak, Sameer S.; Doan, Son; Zisook, Daniel S.; Fan, Jung-wei

    2016-01-01

    In an era when most of our life activities are digitized and recorded, opportunities abound to gain insights about population health. Online product reviews present a unique data source that is currently underexplored. Health-related information, although scarce, can be systematically mined in online product reviews. Leveraging natural language processing and machine learning tools, we were able to mine 1.3 million grocery product reviews for health-related information. The objectives of the study were as follows: (1) conduct quantitative and qualitative analysis on the types of health issues found in consumer product reviews; (2) develop a machine learning classifier to detect reviews that contain health-related issues; and (3) gain insights about the task characteristics and challenges for text analytics to guide future research. PMID:27375358

  18. Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics.

    PubMed

    Torii, Manabu; Tilak, Sameer S; Doan, Son; Zisook, Daniel S; Fan, Jung-Wei

    2016-01-01

    In an era when most of our life activities are digitized and recorded, opportunities abound to gain insights about population health. Online product reviews present a unique data source that is currently underexplored. Health-related information, although scarce, can be systematically mined in online product reviews. Leveraging natural language processing and machine learning tools, we were able to mine 1.3 million grocery product reviews for health-related information. The objectives of the study were as follows: (1) conduct quantitative and qualitative analysis on the types of health issues found in consumer product reviews; (2) develop a machine learning classifier to detect reviews that contain health-related issues; and (3) gain insights about the task characteristics and challenges for text analytics to guide future research.

  19. Machine Learning and Data Mining Methods in Diabetes Research.

    PubMed

    Kavakiotis, Ioannis; Tsave, Olga; Salifoglou, Athanasios; Maglaveras, Nicos; Vlahavas, Ioannis; Chouvarda, Ioanna

    2017-01-01

    The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

  20. Intelligent excavator control system for lunar mining system

    NASA Astrophysics Data System (ADS)

    Lever, Paul J. A.; Wang, Fei-Yue

    1995-01-01

    A major benefit of utilizing local planetary resources is that it reduces the need and cost of lifting materials from the Earth's surface into Earth orbit. The location of the moon makes it an ideal site for harvesting the materials needed to assist space activities. Here, lunar excavation will take place in the dynamic unstructured lunar environment, in which conditions are highly variable and unpredictable. Autonomous mining (excavation) machines are necessary to remove human operators from this hazardous environment. This machine must use a control system structure that can identify, plan, sense, and control real-time dynamic machine movements in the lunar environment. The solution is a vision-based hierarchical control structure. However, excavation tasks require force/torque sensor feedback to control the excavation tool after it has penetrated the surface. A fuzzy logic controller (FLC) is used to interpret the forces and torques gathered from a bucket mounted force/torque sensor during excavation. Experimental results from several excavation tests using the FLC are presented here. These results represent the first step toward an integrated sensing and control system for a lunar mining system.

  1. Drilling side holes from a borehole

    NASA Technical Reports Server (NTRS)

    Collins, E. R., Jr.

    1980-01-01

    Machine takes long horizontal stratum samples from confines of 21 cm bore hole. Stacked interlocking half cylindrical shells mate to form rigid thrust tube. Drive shaft and core storage device is flexible and retractable. Entire machine fits in 10 meter length of steel tube. Machine could drill drainage or ventilation holes in coal mines, or provide important information for geological, oil, and geothermal surveys.

  2. 42 CFR 84.97 - Test for carbon dioxide in inspired gas; open- and closed-circuit apparatus; maximum allowable...

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... machine. An acceptable method for measuring the concentration of carbon dioxide is described in Bureau of Mines Report of Investigations 6865, A Machine-Test Method for Measuring Carbon Dioxide in the Inspired... of 10.5 liters. (3) A sedentary breathing machine cam will be used. (4) The apparatus will be tested...

  3. 42 CFR 84.97 - Test for carbon dioxide in inspired gas; open- and closed-circuit apparatus; maximum allowable...

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... machine. An acceptable method for measuring the concentration of carbon dioxide is described in Bureau of Mines Report of Investigations 6865, A Machine-Test Method for Measuring Carbon Dioxide in the Inspired... of 10.5 liters. (3) A sedentary breathing machine cam will be used. (4) The apparatus will be tested...

  4. 42 CFR 84.97 - Test for carbon dioxide in inspired gas; open- and closed-circuit apparatus; maximum allowable...

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... machine. An acceptable method for measuring the concentration of carbon dioxide is described in Bureau of Mines Report of Investigations 6865, A Machine-Test Method for Measuring Carbon Dioxide in the Inspired... of 10.5 liters. (3) A sedentary breathing machine cam will be used. (4) The apparatus will be tested...

  5. Control system of mutually coupled switched reluctance motor drive of mining machines in generator mode

    NASA Astrophysics Data System (ADS)

    Ivanov, A. S.; Kalanchin, I. Yu; Pugacheva, E. E.

    2017-09-01

    One of the first electric motors, based on the use of electromagnets, was a reluctance motor in the XIX century. Due to the complexities in the implementation of control system the development of switched reluctance electric machines was repeatedly initiated only in 1960 thanks to the development of computers and power electronic devices. The main feature of these machines is the capacity to work both in engine mode and in generator mode. Thanks to a simple and reliable design in which there is no winding of the rotor, commutator, permanent magnets, a reactive gate-inductor electric drive operating in the engine mode is actively being introduced into various areas such as car industry, production of household appliances, wind power engineering, as well as responsible production processes in the oil and mining industries. However, the existing shortcomings of switched reluctance electric machines, such as nonlinear pulsations of electromagnetic moment, the presence of three or four phase supply system and sensor of rotor position prevent wide distribution of this kind of electric machines.

  6. 30 CFR 18.45 - Cable reels.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... constitute an integral part of a circuit for transmitting electrical energy. (d) Cable reels for shuttle cars... MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Construction and Design Requirements § 18.45 Cable reels. (a) A self-propelled machine, that receives electrical energy through a portable...

  7. 30 CFR 18.45 - Cable reels.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... constitute an integral part of a circuit for transmitting electrical energy. (d) Cable reels for shuttle cars... MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Construction and Design Requirements § 18.45 Cable reels. (a) A self-propelled machine, that receives electrical energy through a portable...

  8. 30 CFR 18.45 - Cable reels.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... constitute an integral part of a circuit for transmitting electrical energy. (d) Cable reels for shuttle cars... MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Construction and Design Requirements § 18.45 Cable reels. (a) A self-propelled machine, that receives electrical energy through a portable...

  9. 30 CFR 18.45 - Cable reels.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... constitute an integral part of a circuit for transmitting electrical energy. (d) Cable reels for shuttle cars... MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Construction and Design Requirements § 18.45 Cable reels. (a) A self-propelled machine, that receives electrical energy through a portable...

  10. 30 CFR 18.45 - Cable reels.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... constitute an integral part of a circuit for transmitting electrical energy. (d) Cable reels for shuttle cars... MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Construction and Design Requirements § 18.45 Cable reels. (a) A self-propelled machine, that receives electrical energy through a portable...

  11. An IPSO-SVM algorithm for security state prediction of mine production logistics system

    NASA Astrophysics Data System (ADS)

    Zhang, Yanliang; Lei, Junhui; Ma, Qiuli; Chen, Xin; Bi, Runfang

    2017-06-01

    A theoretical basis for the regulation of corporate security warning and resources was provided in order to reveal the laws behind the security state in mine production logistics. Considering complex mine production logistics system and the variable is difficult to acquire, a superior security status predicting model of mine production logistics system based on the improved particle swarm optimization and support vector machine (IPSO-SVM) is proposed in this paper. Firstly, through the linear adjustments of inertia weight and learning weights, the convergence speed and search accuracy are enhanced with the aim to deal with situations associated with the changeable complexity and the data acquisition difficulty. The improved particle swarm optimization (IPSO) is then introduced to resolve the problem of parameter settings in traditional support vector machines (SVM). At the same time, security status index system is built to determine the classification standards of safety status. The feasibility and effectiveness of this method is finally verified using the experimental results.

  12. Development of sensitized pick coal interface detector system

    NASA Technical Reports Server (NTRS)

    Burchill, R. F.

    1979-01-01

    One approach for detection of the coal interface is measurement of the pick cutting hoads and shock through the use of pick strain gage load cells and accelerometers. The cutting drum of a long wall mining machine contains a number of cutting picks. In order to measure pick loads and shocks, one pick was instrumented and telementry used to transmit the signals from the drum to an instrument-type tape recorder. A data system using FM telemetry was designed to transfer cutting bit load and shock information from the drum of a longwall shearer coal mining machine to a chassis mounted data recorder.

  13. Calculation of parameters of technological equipment for deep-sea mining

    NASA Astrophysics Data System (ADS)

    Yungmeister, D. A.; Ivanov, S. E.; Isaev, A. I.

    2018-03-01

    The actual problem of extracting minerals from the bottom of the world ocean is considered. On the ocean floor, three types of minerals are of interest: iron-manganese concretions (IMC), cobalt-manganese crusts (CMC) and sulphides. The analysis of known designs of machines and complexes for the extraction of IMC is performed. These machines are based on the principle of excavating the bottom surface; however such methods do not always correspond to “gentle” methods of mining. The ecological purity of such mining methods does not meet the necessary requirements. Such machines require the transmission of high electric power through the water column, which in some cases is a significant challenge. The authors analyzed the options of transportation of the extracted mineral from the bottom. The paper describes the design of machines that collect IMC by the method of vacuum suction. In this method, the gripping plates or drums are provided with cavities in which a vacuum is created and individual IMC are attracted to the devices by a pressure drop. The work of such machines can be called “gentle” processing technology of the bottom areas. Their environmental impact is significantly lower than mechanical devices that carry out the raking of IMC. The parameters of the device for lifting the IMC collected on the bottom are calculated. With the use of Kevlar ropes of serial production up to 0.06 meters in diameter, with a cycle time of up to 2 hours and a lifting speed of up to 3 meters per second, a productivity of about 400,000 tons per year can be realized for IMC. The development of machines based on the calculated parameters and approbation of their designs will create a unique complex for the extraction of minerals at oceanic deposits.

  14. Empirical study of seven data mining algorithms on different characteristics of datasets for biomedical classification applications.

    PubMed

    Zhang, Yiyan; Xin, Yi; Li, Qin; Ma, Jianshe; Li, Shuai; Lv, Xiaodan; Lv, Weiqi

    2017-11-02

    Various kinds of data mining algorithms are continuously raised with the development of related disciplines. The applicable scopes and their performances of these algorithms are different. Hence, finding a suitable algorithm for a dataset is becoming an important emphasis for biomedical researchers to solve practical problems promptly. In this paper, seven kinds of sophisticated active algorithms, namely, C4.5, support vector machine, AdaBoost, k-nearest neighbor, naïve Bayes, random forest, and logistic regression, were selected as the research objects. The seven algorithms were applied to the 12 top-click UCI public datasets with the task of classification, and their performances were compared through induction and analysis. The sample size, number of attributes, number of missing values, and the sample size of each class, correlation coefficients between variables, class entropy of task variable, and the ratio of the sample size of the largest class to the least class were calculated to character the 12 research datasets. The two ensemble algorithms reach high accuracy of classification on most datasets. Moreover, random forest performs better than AdaBoost on the unbalanced dataset of the multi-class task. Simple algorithms, such as the naïve Bayes and logistic regression model are suitable for a small dataset with high correlation between the task and other non-task attribute variables. K-nearest neighbor and C4.5 decision tree algorithms perform well on binary- and multi-class task datasets. Support vector machine is more adept on the balanced small dataset of the binary-class task. No algorithm can maintain the best performance in all datasets. The applicability of the seven data mining algorithms on the datasets with different characteristics was summarized to provide a reference for biomedical researchers or beginners in different fields.

  15. Machine Learning methods for Quantitative Radiomic Biomarkers.

    PubMed

    Parmar, Chintan; Grossmann, Patrick; Bussink, Johan; Lambin, Philippe; Aerts, Hugo J W L

    2015-08-17

    Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications in clinical care. In this radiomic study, fourteen feature selection methods and twelve classification methods were examined in terms of their performance and stability for predicting overall survival. A total of 440 radiomic features were extracted from pre-treatment computed tomography (CT) images of 464 lung cancer patients. To ensure the unbiased evaluation of different machine-learning methods, publicly available implementations along with reported parameter configurations were used. Furthermore, we used two independent radiomic cohorts for training (n = 310 patients) and validation (n = 154 patients). We identified that Wilcoxon test based feature selection method WLCX (stability = 0.84 ± 0.05, AUC = 0.65 ± 0.02) and a classification method random forest RF (RSD = 3.52%, AUC = 0.66 ± 0.03) had highest prognostic performance with high stability against data perturbation. Our variability analysis indicated that the choice of classification method is the most dominant source of performance variation (34.21% of total variance). Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice.

  16. Machine learning approaches to analysing textual injury surveillance data: a systematic review.

    PubMed

    Vallmuur, Kirsten

    2015-06-01

    To synthesise recent research on the use of machine learning approaches to mining textual injury surveillance data. Systematic review. The electronic databases which were searched included PubMed, Cinahl, Medline, Google Scholar, and Proquest. The bibliography of all relevant articles was examined and associated articles were identified using a snowballing technique. For inclusion, articles were required to meet the following criteria: (a) used a health-related database, (b) focused on injury-related cases, AND used machine learning approaches to analyse textual data. The papers identified through the search were screened resulting in 16 papers selected for review. Articles were reviewed to describe the databases and methodology used, the strength and limitations of different techniques, and quality assurance approaches used. Due to heterogeneity between studies meta-analysis was not performed. Occupational injuries were the focus of half of the machine learning studies and the most common methods described were Bayesian probability or Bayesian network based methods to either predict injury categories or extract common injury scenarios. Models were evaluated through either comparison with gold standard data or content expert evaluation or statistical measures of quality. Machine learning was found to provide high precision and accuracy when predicting a small number of categories, was valuable for visualisation of injury patterns and prediction of future outcomes. However, difficulties related to generalizability, source data quality, complexity of models and integration of content and technical knowledge were discussed. The use of narrative text for injury surveillance has grown in popularity, complexity and quality over recent years. With advances in data mining techniques, increased capacity for analysis of large databases, and involvement of computer scientists in the injury prevention field, along with more comprehensive use and description of quality assurance methods in text mining approaches, it is likely that we will see a continued growth and advancement in knowledge of text mining in the injury field. Copyright © 2015 Elsevier Ltd. All rights reserved.

  17. 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.

  18. 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.

  19. In Praise of Robots

    ERIC Educational Resources Information Center

    Sagan, Carl

    1975-01-01

    The author of this article believes that human survival depends upon the ability to develop and work with machines of high artificial intelligence. He lists uses of such machines, including terrestrial mining, outer space exploration, and other tasks too dangerous, too expensive, or too boring for human beings. (MA)

  20. [Effect of vibration, noise, physical exertion and unfavorable microclimate on carbohydrate metabolism in workers engaged into mining industry and machine building].

    PubMed

    Lapko, I V; Kir'iakov, V A; Antoshina, L I; Pavlovskaia, N A; Kondratovich, S V

    2014-01-01

    The authors studied influence of vibration, noise, physical overexertion and microclimate on carbohydrates metabolism and insulin resistance in metal mining industry workers. Findings are that vibration disease appeared to have maximal effect on insulin resistance test results and insulin level. The authors suggested biomarkers for early diagnosis of insulin resistance disorders in metal mining industry workers.

  1. Data Stream Mining

    NASA Astrophysics Data System (ADS)

    Gaber, Mohamed Medhat; Zaslavsky, Arkady; Krishnaswamy, Shonali

    Data mining is concerned with the process of computationally extracting hidden knowledge structures represented in models and patterns from large data repositories. It is an interdisciplinary field of study that has its roots in databases, statistics, machine learning, and data visualization. Data mining has emerged as a direct outcome of the data explosion that resulted from the success in database and data warehousing technologies over the past two decades (Fayyad, 1997,Fayyad, 1998,Kantardzic, 2003).

  2. Current Developments in Machine Learning Techniques in Biological Data Mining.

    PubMed

    Dumancas, Gerard G; Adrianto, Indra; Bello, Ghalib; Dozmorov, Mikhail

    2017-01-01

    This supplement is intended to focus on the use of machine learning techniques to generate meaningful information on biological data. This supplement under Bioinformatics and Biology Insights aims to provide scientists and researchers working in this rapid and evolving field with online, open-access articles authored by leading international experts in this field. Advances in the field of biology have generated massive opportunities to allow the implementation of modern computational and statistical techniques. Machine learning methods in particular, a subfield of computer science, have evolved as an indispensable tool applied to a wide spectrum of bioinformatics applications. Thus, it is broadly used to investigate the underlying mechanisms leading to a specific disease, as well as the biomarker discovery process. With a growth in this specific area of science comes the need to access up-to-date, high-quality scholarly articles that will leverage the knowledge of scientists and researchers in the various applications of machine learning techniques in mining biological data.

  3. AstroML: "better, faster, cheaper" towards state-of-the-art data mining and machine learning

    NASA Astrophysics Data System (ADS)

    Ivezic, Zeljko; Connolly, Andrew J.; Vanderplas, Jacob

    2015-01-01

    We present AstroML, a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, matplotlib, and astropy, and distributed under an open license. AstroML contains a growing library of statistical and machine learning routines for analyzing astronomical data in Python, loaders for several open astronomical datasets (such as SDSS and other recent major surveys), and a large suite of examples of analyzing and visualizing astronomical datasets. AstroML is especially suitable for introducing undergraduate students to numerical research projects and for graduate students to rapidly undertake cutting-edge research. The long-term goal of astroML is to provide a community repository for fast Python implementations of common tools and routines used for statistical data analysis in astronomy and astrophysics (see http://www.astroml.org).

  4. Traffic Flow Management: Data Mining Update

    NASA Technical Reports Server (NTRS)

    Grabbe, Shon R.

    2012-01-01

    This presentation provides an update on recent data mining efforts that have been designed to (1) identify like/similar days in the national airspace system, (2) cluster/aggregate national-level rerouting data and (3) apply machine learning techniques to predict when Ground Delay Programs are required at a weather-impacted airport

  5. 75 FR 17529 - High-Voltage Continuous Mining Machine Standard for Underground Coal Mines

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-04-06

    ..., requires manufacturers to provide safeguards against corona on all 4,160-volt circuits in explosion-proof enclosures. Corona is a luminous discharge that occurs around electric conductors that are subject to high electric stresses. Corona can cause premature breakdown of insulating materials in explosion-proof...

  6. 30 CFR 18.48 - Circuit-interrupting devices.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ..., AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Construction and.... Such a switch shall be designed to prevent electrical connection to the machine frame when the cable is... motor in the event the belt is stopped, or abnormally slowed down. Note: Short transfer-type conveyors...

  7. Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose

    PubMed Central

    Men, Hong; Shi, Yan; Fu, Songlin; Jiao, Yanan; Qiao, Yu; Liu, Jingjing

    2017-01-01

    Multi-sensor data fusion can provide more comprehensive and more accurate analysis results. However, it also brings some redundant information, which is an important issue with respect to finding a feature-mining method for intuitive and efficient analysis. This paper demonstrates a feature-mining method based on variable accumulation to find the best expression form and variables’ behavior affecting beer flavor. First, e-tongue and e-nose were used to gather the taste and olfactory information of beer, respectively. Second, principal component analysis (PCA), genetic algorithm-partial least squares (GA-PLS), and variable importance of projection (VIP) scores were applied to select feature variables of the original fusion set. Finally, the classification models based on support vector machine (SVM), random forests (RF), and extreme learning machine (ELM) were established to evaluate the efficiency of the feature-mining method. The result shows that the feature-mining method based on variable accumulation obtains the main feature affecting beer flavor information, and the best classification performance for the SVM, RF, and ELM models with 96.67%, 94.44%, and 98.33% prediction accuracy, respectively. PMID:28753917

  8. 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.

  9. Translations on North Korea No. 622

    DTIC Science & Technology

    1978-10-13

    Pyongyang Power Station 5 July Electric Factory Hamhung Machine Tool Factory Kosan Plastic Pipe Factory Sog’wangea Plastic Pipe Factory 8...August Factory Double Chollima Hamhung Disabled Veterans’ Plastic Goods Factory Mangyongdae Machine Tool Factory Kangso Coal Mine Tongdaewon Garment...21 Jul 78 p 4) innovating in machine tool production (NC 21 Jul 78 p 2) in 40 days of the 蔴 days of combat" raised coal production 10 percent

  10. Using random forest for the risk assessment of coal-floor water inrush in Panjiayao Coal Mine, northern China

    NASA Astrophysics Data System (ADS)

    Zhao, Dekang; Wu, Qiang; Cui, Fangpeng; Xu, Hua; Zeng, Yifan; Cao, Yufei; Du, Yuanze

    2018-04-01

    Coal-floor water-inrush incidents account for a large proportion of coal mine disasters in northern China, and accurate risk assessment is crucial for safe coal production. A novel and promising assessment model for water inrush is proposed based on random forest (RF), which is a powerful intelligent machine-learning algorithm. RF has considerable advantages, including high classification accuracy and the capability to evaluate the importance of variables; in particularly, it is robust in dealing with the complicated and non-linear problems inherent in risk assessment. In this study, the proposed model is applied to Panjiayao Coal Mine, northern China. Eight factors were selected as evaluation indices according to systematic analysis of the geological conditions and a field survey of the study area. Risk assessment maps were generated based on RF, and the probabilistic neural network (PNN) model was also used for risk assessment as a comparison. The results demonstrate that the two methods are consistent in the risk assessment of water inrush at the mine, and RF shows a better performance compared to PNN with an overall accuracy higher by 6.67%. It is concluded that RF is more practicable to assess the water-inrush risk than PNN. The presented method will be helpful in avoiding water inrush and also can be extended to various engineering applications.

  11. 30 CFR 27.24 - Power-shutoff component.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS Construction and Design Requirements § 27.24 Power... the machine or equipment when actuated by the methane detector at a methane concentration of 2.0... actuated by the methane detector, cause a control circuit to shut down the machine or equipment on which it...

  12. What happened to the TSM

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

    Brezovec, D.

    1983-11-01

    A new coal mining machine that was going to pull some 40 million tons of coal from the Appalachian coalfields by 1986 has had more than its share of start-up problems. The machine, known as the Thin Seam Miner (TSM), is a $2.7-million auger-type mining machine that is designed to bore 220 ft into new or abandoned highwalls (CA 5/82 p. 106). Gamma-ray sensors located near the continuous drum miner-type cutter head monitor for rock and other sensors monitor for methane. The machines are designed to produce about 425 tons per shift from a 36-in.-thick coal seam. The machines weremore » introduced officially to the American coal industry at a luncheon Aug. 19, 1981, in a ballroom at the Lexington, Ky., Hyatt Regency Hotel. At the luncheon, some 200 coal industry executives and others sipped champagne and listened to glowing reports of how 24 of the machines would produce 2.2 million tons of coal by the end of 1981 and 64 of the machines would produce 6.6 million tons by the end of 1982. The machines would be built in Holland by RijnSchelde-Verolme (RSV), a major Dutch shipbuilder, and managed in the United States by Advanced Coal Management (ACM), a company formed for the purpose by James D. Stacy, a colorful, cigar-smoking stock car owner whose experience in the coal business dated from only the mid-1970s.« less

  13. Development and testing of a computer assisted remote-control system for the compact loader-trammer. Report of Investigations/1992

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

    Ruff, T.M.

    1992-01-01

    A prototype mucking machine designed to operate in narrow vein stopes was developed by Foster-Miller, Inc., Waltham, MA, under contract with the U.S. Bureau of Mines. The machine, called a compact loader/trammer, or minimucker, was designed to replace slusher muckers in narrow-vein underground mines. The minimucker is a six-wheel-drive, skid-steered, load-haul-dump machine that loads muck at the front with a novel slide-bucket system and ejects it out the rear so that the machine does not have to be turned around. To correct deficiencies of the tether remote control system, a computer-based, radio remote control was retrofitted to the minimucker. Initialmore » tests indicated a need to assist the operator in guiding the machine in narrow stopes and an automatic guidance system that used ultrasonic ranging sensors and a wall-following algorithm was installed. Additional tests in a simulated test stope showed that these changes improved the operation of the minimucker. The design and functions of the minimucker and its computer-based, remote control system are reviewed, and an ultrasonic, sensor-based guidance system is described.« less

  14. Review of smoothing methods for enhancement of noisy data from heavy-duty LHD mining machines

    NASA Astrophysics Data System (ADS)

    Wodecki, Jacek; Michalak, Anna; Stefaniak, Paweł

    2018-01-01

    Appropriate analysis of data measured on heavy-duty mining machines is essential for processes monitoring, management and optimization. Some particular classes of machines, for example LHD (load-haul-dump) machines, hauling trucks, drilling/bolting machines etc. are characterized with cyclicity of operations. In those cases, identification of cycles and their segments or in other words - simply data segmentation is a key to evaluate their performance, which may be very useful from the management point of view, for example leading to introducing optimization to the process. However, in many cases such raw signals are contaminated with various artifacts, and in general are expected to be very noisy, which makes the segmentation task very difficult or even impossible. To deal with that problem, there is a need for efficient smoothing methods that will allow to retain informative trends in the signals while disregarding noises and other undesired non-deterministic components. In this paper authors present a review of various approaches to diagnostic data smoothing. Described methods can be used in a fast and efficient way, effectively cleaning the signals while preserving informative deterministic behaviour, that is a crucial to precise segmentation and other approaches to industrial data analysis.

  15. Automated Assessment of Patients' Self-Narratives for Posttraumatic Stress Disorder Screening Using Natural Language Processing and Text Mining.

    PubMed

    He, Qiwei; Veldkamp, Bernard P; Glas, Cees A W; de Vries, Theo

    2017-03-01

    Patients' narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented an automated assessment system to screen patients for posttraumatic stress disorder via a natural language processing and text-mining approach. Four machine-learning algorithms-including decision tree, naive Bayes, support vector machine, and an alternative classification approach called the product score model-were used in combination with n-gram representation models to identify patterns between verbal features in self-narratives and psychiatric diagnoses. With our sample, the product score model with unigrams attained the highest prediction accuracy when compared with practitioners' diagnoses. The addition of multigrams contributed most to balancing the metrics of sensitivity and specificity. This article also demonstrates that text mining is a promising approach for analyzing patients' self-expression behavior, thus helping clinicians identify potential patients from an early stage.

  16. Semisupervised Support Vector Machines With Tangent Space Intrinsic Manifold Regularization.

    PubMed

    Sun, Shiliang; Xie, Xijiong

    2016-09-01

    Semisupervised learning has been an active research topic in machine learning and data mining. One main reason is that labeling examples is expensive and time-consuming, while there are large numbers of unlabeled examples available in many practical problems. So far, Laplacian regularization has been widely used in semisupervised learning. In this paper, we propose a new regularization method called tangent space intrinsic manifold regularization. It is intrinsic to data manifold and favors linear functions on the manifold. Fundamental elements involved in the formulation of the regularization are local tangent space representations, which are estimated by local principal component analysis, and the connections that relate adjacent tangent spaces. Simultaneously, we explore its application to semisupervised classification and propose two new learning algorithms called tangent space intrinsic manifold regularized support vector machines (TiSVMs) and tangent space intrinsic manifold regularized twin SVMs (TiTSVMs). They effectively integrate the tangent space intrinsic manifold regularization consideration. The optimization of TiSVMs can be solved by a standard quadratic programming, while the optimization of TiTSVMs can be solved by a pair of standard quadratic programmings. The experimental results of semisupervised classification problems show the effectiveness of the proposed semisupervised learning algorithms.

  17. An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning

    PubMed Central

    Jo, ByungWan

    2018-01-01

    The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML) Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH4, CO, SO2, and H2S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R2 and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality. PMID:29561777

  18. An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning.

    PubMed

    Jo, ByungWan; Khan, Rana Muhammad Asad

    2018-03-21

    The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML) Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH₄, CO, SO₂, and H₂S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R ² and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality.

  19. CANFAR+Skytree: A Cloud Computing and Data Mining System for Astronomy

    NASA Astrophysics Data System (ADS)

    Ball, N. M.

    2013-10-01

    This is a companion Focus Demonstration article to the CANFAR+Skytree poster (Ball 2013, this volume), demonstrating the usage of the Skytree machine learning software on the Canadian Advanced Network for Astronomical Research (CANFAR) cloud computing system. CANFAR+Skytree is the world's first cloud computing system for data mining in astronomy.

  20. Data Mining in Finance: Using Counterfactuals To Generate Knowledge from Organizational Information Systems.

    ERIC Educational Resources Information Center

    Dhar, Vasant

    1998-01-01

    Shows how counterfactuals and machine learning methods can be used to guide exploration of large databases that addresses some of the fundamental problems that organizations face in learning from data. Discusses data mining, particularly in the financial arena; generating useful knowledge from data; and the evaluation of counterfactuals. (LRW)

  1. Data Mining in Earth System Science (DMESS 2011)

    Treesearch

    Forrest M. Hoffman; J. Walter Larson; Richard Tran Mills; Bhorn-Gustaf Brooks; Auroop R. Ganguly; William Hargrove; et al

    2011-01-01

    From field-scale measurements to global climate simulations and remote sensing, the growing body of very large and long time series Earth science data are increasingly difficult to analyze, visualize, and interpret. Data mining, information theoretic, and machine learning techniques—such as cluster analysis, singular value decomposition, block entropy, Fourier and...

  2. Evolving optimised decision rules for intrusion detection using particle swarm paradigm

    NASA Astrophysics Data System (ADS)

    Sivatha Sindhu, Siva S.; Geetha, S.; Kannan, A.

    2012-12-01

    The aim of this article is to construct a practical intrusion detection system (IDS) that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machine-learning paradigm enhances the detection accuracy of IDS. In this article, a rule-based approach with a family of six decision tree classifiers, namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern is introduced. In particular, the proposed swarm optimisation-based approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining (KDD) data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machine-learning algorithm.

  3. Mining the Galaxy Zoo Database: Machine Learning Applications

    NASA Astrophysics Data System (ADS)

    Borne, Kirk D.; Wallin, J.; Vedachalam, A.; Baehr, S.; Lintott, C.; Darg, D.; Smith, A.; Fortson, L.

    2010-01-01

    The new Zooniverse initiative is addressing the data flood in the sciences through a transformative partnership between professional scientists, volunteer citizen scientists, and machines. As part of this project, we are exploring the application of machine learning techniques to data mining problems associated with the large and growing database of volunteer science results gathered by the Galaxy Zoo citizen science project. We will describe the basic challenge, some machine learning approaches, and early results. One of the motivators for this study is the acquisition (through the Galaxy Zoo results database) of approximately 100 million classification labels for roughly one million galaxies, yielding a tremendously large and rich set of training examples for improving automated galaxy morphological classification algorithms. In our first case study, the goal is to learn which morphological and photometric features in the Sloan Digital Sky Survey (SDSS) database correlate most strongly with user-selected galaxy morphological class. As a corollary to this study, we are also aiming to identify which galaxy parameters in the SDSS database correspond to galaxies that have been the most difficult to classify (based upon large dispersion in their volunter-provided classifications). Our second case study will focus on similar data mining analyses and machine leaning algorithms applied to the Galaxy Zoo catalog of merging and interacting galaxies. The outcomes of this project will have applications in future large sky surveys, such as the LSST (Large Synoptic Survey Telescope) project, which will generate a catalog of 20 billion galaxies and will produce an additional astronomical alert database of approximately 100 thousand events each night for 10 years -- the capabilities and algorithms that we are exploring will assist in the rapid characterization and classification of such massive data streams. This research has been supported in part through NSF award #0941610.

  4. Development of Test Rig for Robotization of Mining Technological Processes - Oversized Rock Breaking Process Case

    NASA Astrophysics Data System (ADS)

    Pawel, Stefaniak; Jacek, Wodecki; Jakubiak, Janusz; Zimroz, Radoslaw

    2017-12-01

    Production chain (PCh) in underground copper ore mine consists of several subprocesses. From our perspective implementation of so called ZEPA approach (Zero Entry Production Area) might be very interesting [16]. In practice, it leads to automation/robotization of subprocesses in production area. In this paper was investigated a specific part of PCh i.e. a place when cyclic transport by LHDs is replaced with continuous transport by conveying system. Such place is called dumping point. The objective of dumping points with screen is primary classification of the material (into coarse and fine material) and breaking oversized rocks with hydraulic hammer. Current challenges for the underground mining include e.g. safety improvement as well as production optimization related to bottlenecks, stoppages and operational efficiency of the machines. As a first step, remote control of the hydraulic hammer has been introduced, which not only transferred the operator to safe workplace, but also allowed for more comfortable work environment and control over multiple technical objects by a single person. Today literature analysis shows that current mining industry around the world is oriented to automation and robotization of mining processes and reveals technological readiness for 4th industrial revolution. The paper is focused on preliminary analysis of possibilities for the use of the robotic system to rock-breaking process. Prototype test rig has been proposed and experimental works have been carried out. Automatic algorithms for detection of oversized rocks, crushing them as well as sweeping and loosening of material have been formulated. Obviously many simplifications have been assumed. Some near future works have been proposed.

  5. Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management.

    PubMed

    Chang, Ni-Bin; Bai, Kaixu; Chen, Chi-Farn

    2017-10-01

    Monitoring water quality changes in lakes, reservoirs, estuaries, and coastal waters is critical in response to the needs for sustainable development. This study develops a remote sensing-based multiscale modeling system by integrating multi-sensor satellite data merging and image reconstruction algorithms in support of feature extraction with machine learning leading to automate continuous water quality monitoring in environmentally sensitive regions. This new Earth observation platform, termed "cross-mission data merging and image reconstruction with machine learning" (CDMIM), is capable of merging multiple satellite imageries to provide daily water quality monitoring through a series of image processing, enhancement, reconstruction, and data mining/machine learning techniques. Two existing key algorithms, including Spectral Information Adaptation and Synthesis Scheme (SIASS) and SMart Information Reconstruction (SMIR), are highlighted to support feature extraction and content-based mapping. Whereas SIASS can support various data merging efforts to merge images collected from cross-mission satellite sensors, SMIR can overcome data gaps by reconstructing the information of value-missing pixels due to impacts such as cloud obstruction. Practical implementation of CDMIM was assessed by predicting the water quality over seasons in terms of the concentrations of nutrients and chlorophyll-a, as well as water clarity in Lake Nicaragua, providing synergistic efforts to better monitor the aquatic environment and offer insightful lake watershed management strategies. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. 76 FR 54163 - Proximity Detection Systems for Continuous Mining Machines in Underground Coal Mines

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-08-31

    ... analysis of fatalities and non-fatal accidents during the 1984 through 2010 period indicates that many of... under 30 CFR 18.82 and issued an experimental permit on May 30, 2003. After several revisions, the... Geosteering Tramguard TM System, which MSHA tested in June 2005 under an experimental permit on a remote...

  7. Development of a Workbench to Address the Educational Data Mining Bottleneck

    ERIC Educational Resources Information Center

    Rodrigo, Ma. Mercedes T.; Baker, Ryan S. J. d.; McLaren, Bruce M.; Jayme, Alejandra; Dy, Thomas T.

    2012-01-01

    In recent years, machine-learning software packages have made it easier for educational data mining researchers to create real-time detectors of cognitive skill as well as of metacognitive and motivational behavior that can be used to improve student learning. However, there remain challenges to overcome for these methods to become available to…

  8. Management Perspectives Pertaining to Root Cause Analyses of Nunn-McCurdy Breaches. Volume 4

    DTIC Science & Technology

    2013-01-01

    the FY2012 NDAA, the Army revised its initial budget request, allocating money from the purchase of new M2 .50 caliber machine guns to the...Quick-change machine gun barrel Explosive reactive armor Linear demolition charge system Full width, surface mine ploughs On-board vehicle power...Quantity Oversight of ACAT II Programs 45 for a restart to the program citing a “critical shortage of serviceable machine guns for our Soldiers who

  9. Image processing and machine learning for fully automated probabilistic evaluation of medical images.

    PubMed

    Sajn, Luka; Kukar, Matjaž

    2011-12-01

    The paper presents results of our long-term study on using image processing and data mining methods in a medical imaging. Since evaluation of modern medical images is becoming increasingly complex, advanced analytical and decision support tools are involved in integration of partial diagnostic results. Such partial results, frequently obtained from tests with substantial imperfections, are integrated into ultimate diagnostic conclusion about the probability of disease for a given patient. We study various topics such as improving the predictive power of clinical tests by utilizing pre-test and post-test probabilities, texture representation, multi-resolution feature extraction, feature construction and data mining algorithms that significantly outperform medical practice. Our long-term study reveals three significant milestones. The first improvement was achieved by significantly increasing post-test diagnostic probabilities with respect to expert physicians. The second, even more significant improvement utilizes multi-resolution image parametrization. Machine learning methods in conjunction with the feature subset selection on these parameters significantly improve diagnostic performance. However, further feature construction with the principle component analysis on these features elevates results to an even higher accuracy level that represents the third milestone. With the proposed approach clinical results are significantly improved throughout the study. The most significant result of our study is improvement in the diagnostic power of the whole diagnostic process. Our compound approach aids, but does not replace, the physician's judgment and may assist in decisions on cost effectiveness of tests. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  10. 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.

  11. BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture.

    PubMed

    Morota, Gota; Ventura, Ricardo V; Silva, Fabyano F; Koyama, Masanori; Fernando, Samodha C

    2018-04-14

    Precision animal agriculture is poised to rise to prominence in the livestock enterprise in the domains of management, production, welfare, sustainability, health surveillance, and environmental footprint. Considerable progress has been made in the use of tools to routinely monitor and collect information from animals and farms in a less laborious manner than before. These efforts have enabled the animal sciences to embark on information technology-driven discoveries to improve animal agriculture. However, the growing amount and complexity of data generated by fully automated, high-throughput data recording or phenotyping platforms, including digital images, sensor and sound data, unmanned systems, and information obtained from real-time noninvasive computer vision, pose challenges to the successful implementation of precision animal agriculture. The emerging fields of machine learning and data mining are expected to be instrumental in helping meet the daunting challenges facing global agriculture. Yet, their impact and potential in "big data" analysis have not been adequately appreciated in the animal science community, where this recognition has remained only fragmentary. To address such knowledge gaps, this article outlines a framework for machine learning and data mining and offers a glimpse into how they can be applied to solve pressing problems in animal sciences.

  12. 76 FR 37838 - Petitions for Modification of Application of Existing Mandatory Safety Standards

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-06-28

    ... may include periodic tests of methane levels and limits on the minimum methane concentrations that may...) Methane monitor(s) will be calibrated on the longwall, continuous mining machine, or cutting machine and... petitioner will test for methane with a hand-held methane detector at least every 10 minutes from the time...

  13. Opinion versus practice regarding the use of rehabilitation services in home care: an investigation using machine learning algorithms.

    PubMed

    Cheng, Lu; Zhu, Mu; Poss, Jeffrey W; Hirdes, John P; Glenny, Christine; Stolee, Paul

    2015-10-09

    Resources for home care rehabilitation are limited, and many home care clients who could benefit do not receive rehabilitation therapy. The interRAI Contact Assessment (CA) is a new screening instrument comprised of a subset of interRAI Home Care (HC) items, designed to be used as a preliminary assessment to identify which potential home care clients should be referred for a full assessment, or for services such as rehabilitation. We investigated which client characteristics are most relevant in predicting rehabilitation use in the full interRAI HC assessment. We applied two algorithms from machine learning and data mining - the LASSO and the random forest - to frequency matched interRAI HC and service utilization data for home care clients in Ontario, Canada. Analyses confirmed the importance of functional decline and mobility variables in targeting rehabilitation services, but suggested that other items in use as potential predictors may be less relevant. Six of the most highly ranked items related to ambulation. Diagnosis of cancer was highly associated with decreased rehabilitation use; however, cognitive status was not. Inconsistencies between variables considered important for classifying clients who need rehabilitation and those identified in this study based on use may indicate a discrepancy in the client characteristics considered relevant in theory versus actual practice.

  14. Method and apparatus for monitoring the thickness of a coal rib during rib formation

    DOEpatents

    Mowrey, Gary L.; Ganoe, Carl W.; Monaghan, William D.

    1996-01-01

    Apparatus for monitoring the position of a mining machine cutting a new entry in a coal seam relative to an adjacent, previously cut entry to determine the distance between a near face of the adjacent previously cut entry and a new face adjacent thereto of a new entry being cut by the mining machine which together define the thickness of a coal rib being formed between the new entry and the adjacent previously cut entry during the new entry-cutting operation. The monitoring apparatus; includes a transmit antenna mounted on the mining machine and spaced inwardly from the new face of the coal rib for transmitting radio energy towards the coal rib so that one portion of the radio energy is reflected by the new face which is defined at an air-coal interface between the new entry and the coal rib and another portion of the radio energy is reflected by the near face of the coal rib which is defined at an air-coal interface between the coal rib and the adjacent previously cut entry. A receive antenna mounted on the mining machine and spaced inwardly of the new face of the coal rib receives the one portion of the radio energy reflected by the new face and also receives the another portion of the radio energy reflected by the near face. A processor determines a first elapsed time period equal to the time required for the one portion of the radio energy reflected by the new face to travel between the transmit antenna and the receive antenna and also determines a second elapsed time period equal to the time required for the another portion of the radio energy reflected by the near face to travel between the transmit antenna and the receive antenna and thereafter calculates the thickness of the coal rib being formed as a function of the difference between the first and second elapsed time periods.

  15. Alkahest NuclearBLAST : a user-friendly BLAST management and analysis system

    PubMed Central

    Diener, Stephen E; Houfek, Thomas D; Kalat, Sam E; Windham, DE; Burke, Mark; Opperman, Charles; Dean, Ralph A

    2005-01-01

    Background - Sequencing of EST and BAC end datasets is no longer limited to large research groups. Drops in per-base pricing have made high throughput sequencing accessible to individual investigators. However, there are few options available which provide a free and user-friendly solution to the BLAST result storage and data mining needs of biologists. Results - Here we describe NuclearBLAST, a batch BLAST analysis, storage and management system designed for the biologist. It is a wrapper for NCBI BLAST which provides a user-friendly web interface which includes a request wizard and the ability to view and mine the results. All BLAST results are stored in a MySQL database which allows for more advanced data-mining through supplied command-line utilities or direct database access. NuclearBLAST can be installed on a single machine or clustered amongst a number of machines to improve analysis throughput. NuclearBLAST provides a platform which eases data-mining of multiple BLAST results. With the supplied scripts, the program can export data into a spreadsheet-friendly format, automatically assign Gene Ontology terms to sequences and provide bi-directional best hits between two datasets. Users with SQL experience can use the database to ask even more complex questions and extract any subset of data they require. Conclusion - This tool provides a user-friendly interface for requesting, viewing and mining of BLAST results which makes the management and data-mining of large sets of BLAST analyses tractable to biologists. PMID:15958161

  16. Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification.

    PubMed

    d'Alessandro, Brian; O'Neil, Cathy; LaGatta, Tom

    2017-06-01

    Recent research has helped to cultivate growing awareness that machine-learning systems fueled by big data can create or exacerbate troubling disparities in society. Much of this research comes from outside of the practicing data science community, leaving its members with little concrete guidance to proactively address these concerns. This article introduces issues of discrimination to the data science community on its own terms. In it, we tour the familiar data-mining process while providing a taxonomy of common practices that have the potential to produce unintended discrimination. We also survey how discrimination is commonly measured, and suggest how familiar development processes can be augmented to mitigate systems' discriminatory potential. We advocate that data scientists should be intentional about modeling and reducing discriminatory outcomes. Without doing so, their efforts will result in perpetuating any systemic discrimination that may exist, but under a misleading veil of data-driven objectivity.

  17. 20131201-1231_Green Machine Florida Canyon Hourly Data

    DOE Data Explorer

    Thibedeau, Joe

    2014-01-08

    Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 Dec to 31 Dec 2013.

  18. 20131101-1130_Green Machine Florida Canyon Hourly Data

    DOE Data Explorer

    Thibedeau, Joe

    2013-12-02

    Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 Nov to 30 Nov 2013.

  19. 20130416_Green Machine Florida Canyon Hourly Data

    DOE Data Explorer

    Vanderhoff, Alex

    2013-04-24

    Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 4/16/13.

  20. 20131001-1031_Green Machine Florida Canyon Hourly Data

    DOE Data Explorer

    Thibedeau, Joe

    2013-11-05

    Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 1 Oct 2013 to 31 Oct 2013.

  1. 20140201-0228_Green Machine Florida Canyon Hourly Data

    DOE Data Explorer

    Thibedeau, Joe

    2014-03-03

    Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 Feb to 28 Feb 2014.

  2. 20130801-0831_Green Machine Florida Canyon Hourly Data

    DOE Data Explorer

    Vanderhoff, Alex

    2013-09-10

    Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 8/1/13 to 8/31/13.

  3. 20140101-0131_Green Machine Florida Canyon Hourly Data

    DOE Data Explorer

    Thibedeau, Joe

    2014-02-03

    Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 Jan to 31 Jan 2014.

  4. 20140430_Green Machine Florida Canyon Hourly Data

    DOE Data Explorer

    Thibedeau, Joe

    2014-05-05

    Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 April to 30 April 2014.

  5. 20140301-0331_Green Machine Florida Canyon Hourly Data

    DOE Data Explorer

    Thibedeau, Joe

    2014-04-07

    Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 Mar to 31 Mar 2014.

  6. 20140501-0531_Green Machine Florida Canyon Hourly Data

    DOE Data Explorer

    Thibedeau, Joe

    2014-06-02

    Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 May to 31 May 2014.

  7. 20140601-0630_Green Machine Florida Canyon Hourly Data

    DOE Data Explorer

    Thibedeau, Joe

    2014-06-30

    Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 June to 30 June 2014.

  8. 20140701-0731_Green Machine Florida Canyon Hourly Data

    DOE Data Explorer

    Thibedeau, Joe

    2014-07-31

    Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 01 July to 31 July 2014.

  9. 20130901-0930_Green Machine Florida Canyon Hourly Data

    DOE Data Explorer

    Thibedeau, Joe

    2013-10-25

    Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 1 September 2013 to 30 September 2013.

  10. Green Machine Florida Canyon Hourly Data 20130731

    DOE Data Explorer

    Vanderhoff, Alex

    2013-08-30

    Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 7/1/13 to 7/31/13.

  11. 20130501-20130531_Green Machine Florida Canyon Hourly Data

    DOE Data Explorer

    Vanderhoff, Alex

    2013-06-18

    Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from May 2013

  12. Green Machine Florida Canyon Hourly Data

    DOE Data Explorer

    Vanderhoff, Alex

    2013-07-15

    Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant with output capacity expected in the 30-70kWe range. The Green Machine is an Organic Rankine Cycle power plant. The Florida Canyon machine is powered by geothermal brine with air cooled condensing. The data provided is an hourly summary from 6/1/13 to 6/30/13

  13. KSC-2012-3081

    NASA Image and Video Library

    2012-05-25

    CAPE CANAVERAL, Fla. – A team of competitors works with its machine during NASA's Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. The competition challenges university students to build machines that can collect soil such as the material found on the moon. Working inside the Caterpillar LunArena, the robotic craft dig soil that simulates lunar material. The event is judged by a machine's abilities to collect the soil, its design and operation, size, dust tolerance and its level of autonomy. Photo credit: NASA/Glenn Benson

  14. KSC-2012-3076

    NASA Image and Video Library

    2012-05-25

    CAPE CANAVERAL, Fla. – A team of competitors works with its machine during NASA's Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. The competition challenges university students to build machines that can collect soil such as the material found on the moon. Working inside the Caterpillar LunArena, the robotic craft dig soil that simulates lunar material. The event is judged by a machine's abilities to collect the soil, its design and operation, size, dust tolerance and its level of autonomy. Photo credit: NASA/Glenn Benson

  15. KSC-2012-3080

    NASA Image and Video Library

    2012-05-25

    CAPE CANAVERAL, Fla. – A team of competitors works with its machine during NASA's Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. The competition challenges university students to build machines that can collect soil such as the material found on the moon. Working inside the Caterpillar LunArena, the robotic craft dig soil that simulates lunar material. The event is judged by a machine's abilities to collect the soil, its design and operation, size, dust tolerance and its level of autonomy. Photo credit: NASA/Glenn Benson

  16. KSC-2012-3079

    NASA Image and Video Library

    2012-05-25

    CAPE CANAVERAL, Fla. – A team of competitors works with its machine during NASA's Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. The competition challenges university students to build machines that can collect soil such as the material found on the moon. Working inside the Caterpillar LunArena, the robotic craft dig soil that simulates lunar material. The event is judged by a machine's abilities to collect the soil, its design and operation, size, dust tolerance and its level of autonomy. Photo credit: NASA/Glenn Benson

  17. 30 CFR 48.7 - Training of miners assigned to a task in which they have had no previous experience; minimum...

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... to new work tasks as mobile equipment operators, drilling machine operators, haulage and conveyor... protective measures a miner can take against these hazards, and the contents of the mine's HazCom program... operation in the mine, which require new or different operating procedures. (4) Such other courses as may be...

  18. 30 CFR 18.82 - Permit to use experimental electric face equipment in a gassy mine or tunnel.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Permit to use experimental electric face... Modifications of Approved Machines, and Permits To Use Experimental Equipment § 18.82 Permit to use experimental... to use experimental electric face equipment in a gassy mine or tunnel will be considered only when...

  19. 30 CFR 18.82 - Permit to use experimental electric face equipment in a gassy mine or tunnel.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Permit to use experimental electric face... Modifications of Approved Machines, and Permits To Use Experimental Equipment § 18.82 Permit to use experimental... to use experimental electric face equipment in a gassy mine or tunnel will be considered only when...

  20. 30 CFR 18.82 - Permit to use experimental electric face equipment in a gassy mine or tunnel.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Permit to use experimental electric face... Modifications of Approved Machines, and Permits To Use Experimental Equipment § 18.82 Permit to use experimental... to use experimental electric face equipment in a gassy mine or tunnel will be considered only when...

  1. 30 CFR 18.82 - Permit to use experimental electric face equipment in a gassy mine or tunnel.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Permit to use experimental electric face... Modifications of Approved Machines, and Permits To Use Experimental Equipment § 18.82 Permit to use experimental... to use experimental electric face equipment in a gassy mine or tunnel will be considered only when...

  2. 30 CFR 18.82 - Permit to use experimental electric face equipment in a gassy mine or tunnel.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Permit to use experimental electric face... Modifications of Approved Machines, and Permits To Use Experimental Equipment § 18.82 Permit to use experimental... to use experimental electric face equipment in a gassy mine or tunnel will be considered only when...

  3. 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.

  4. CFD analysis on gas distribution for different scrubber redirection configurations in sump cut.

    PubMed

    Zheng, Y; Organiscak, J A; Zhou, L; Beck, T W; Rider, J P

    2015-01-01

    The National Institute for Occupational Safety and Health's Office of Mine Safety and Health Research recently developed a series of models using computational fluid dynamics (CFD) to study the gas distribution around a continuous mining machine with various fan-powered flooded bed scrubber discharge configurations. CFD models using Species Transport Model without reactions in FLUENT were constructed to evaluate the redirection of scrubber discharge toward the mining face rather than behind the return curtain. The following scenarios are considered in this study: 100 percent of the discharge redirected back toward the face on the off-curtain side of the continuous miner; 100 percent of the discharge redirected back toward the face, but divided equally to both sides of the machine; and 15 percent of the discharge redirected toward the face on the off-curtain side of the machine, with 85 percent directed into the return. These models were compared against a model with a conventional scrubber discharge, where air is directed away from the face into the return. The CFD models were calibrated and validated based on experimental data and accurately predicted sulfur hexafluoride (SF 6 ) gas levels at four gas monitoring locations. One additional prediction model was simulated to consider a different scrubber discharge angle for the 100 percent redirected, equally divided case. These models identified relatively high gassy areas around the continuous miner, which may not warrant their use in coal mines with medium to high methane liberation rates. This paper describes the methodology used to develop the CFD models, and the validation of the models based on experimental data.

  5. 19 CFR 10.530 - Definitions.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... of the following processes: Welding, flame spraying, surface machining, knurling, plating, sleeving...” means a person who grows, raises, mines, harvests, fishes, traps, hunts, manufactures, processes...

  6. Raising quality of maintenance and control of metallic structures in large-load technological machines

    NASA Astrophysics Data System (ADS)

    Drygin, M. Yu; Kuryshkin, N. P.

    2018-01-01

    Active growth of coal extraction and underinvestment of coal mining in Russia lead to the fact that technical state of more than 86% of technological machines at opencast coal mines is unacceptable. One of the most significant problems is unacceptable state of supporting metallic structures of excavators and mine dump trucks. The analysis has shown that defects in these metallic structures had been accumulated for a long time. Their removal by the existing method of repair welding was not effective - the flaws reappeared in 2-6 months of technological machines’ service. The authors detected the prime causes that did not allow to make a good repair welding joint. A new technology of repair welding had been tested and endorsed, and this allowed to reduce the number of welded joints’ flaws by 85% without additional raising welders’ qualification. As a result the number of flaws in metallic structures of the equipment had been reduced by 35 % as early as in the first year of using the new technology.

  7. Machine Learning in Medical Imaging.

    PubMed

    Giger, Maryellen L

    2018-03-01

    Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks, such as risk assessment, detection, diagnosis, prognosis, and therapy response, as well as in multi-omics disease discovery. A brief overview of the field is given here, allowing the reader to recognize the terminology, the various subfields, and components of machine learning, as well as the clinical potential. Radiomics, an expansion of computer-aided diagnosis, has been defined as the conversion of images to minable data. The ultimate benefit of quantitative radiomics is to (1) yield predictive image-based phenotypes of disease for precision medicine or (2) yield quantitative image-based phenotypes for data mining with other -omics for discovery (ie, imaging genomics). For deep learning in radiology to succeed, note that well-annotated large data sets are needed since deep networks are complex, computer software and hardware are evolving constantly, and subtle differences in disease states are more difficult to perceive than differences in everyday objects. In the future, machine learning in radiology is expected to have a substantial clinical impact with imaging examinations being routinely obtained in clinical practice, providing an opportunity to improve decision support in medical image interpretation. The term of note is decision support, indicating that computers will augment human decision making, making it more effective and efficient. The clinical impact of having computers in the routine clinical practice may allow radiologists to further integrate their knowledge with their clinical colleagues in other medical specialties and allow for precision medicine. Copyright © 2018. Published by Elsevier Inc.

  8. Effect of Temporal Relationships in Associative Rule Mining for Web Log Data

    PubMed Central

    Mohd Khairudin, Nazli; Mustapha, Aida

    2014-01-01

    The advent of web-based applications and services has created such diverse and voluminous web log data stored in web servers, proxy servers, client machines, or organizational databases. This paper attempts to investigate the effect of temporal attribute in relational rule mining for web log data. We incorporated the characteristics of time in the rule mining process and analysed the effect of various temporal parameters. The rules generated from temporal relational rule mining are then compared against the rules generated from the classical rule mining approach such as the Apriori and FP-Growth algorithms. The results showed that by incorporating the temporal attribute via time, the number of rules generated is subsequently smaller but is comparable in terms of quality. PMID:24587757

  9. Knowledge Exchange between Poland and Vietnam in Mining and Geology - the Status Quo and Future Development

    NASA Astrophysics Data System (ADS)

    Nguyen, Nga; Pham, Nguyet

    2018-03-01

    From the beginning of the 21st century, knowledge exchange between Poland and Vietnam in mining and geology has been focusing in technology, education and training. Since years, Polish academic and commercial partners have been developing a close collaboration with Vietnam National Coal - Mineral Industries Holding Corporation Limited. Major outcomes of the collaboration are installations and operation of mining equipments and machines in Vietnamese mining companies, and excellent training programs for graduate and post graduate students and mining staff for both countries, etc. From aspects of knowledge management in globalization, the article highlights the outstanding outcomes of knowledge exchanges between the two countries, outlines cultural and economic challenges for the exchange and proposes some improvement in the future.

  10. Machine learning and medicine: book review and commentary.

    PubMed

    Koprowski, Robert; Foster, Kenneth R

    2018-02-01

    This article is a review of the book "Master machine learning algorithms, discover how they work and implement them from scratch" (ISBN: not available, 37 USD, 163 pages) edited by Jason Brownlee published by the Author, edition, v1.10 http://MachineLearningMastery.com . An accompanying commentary discusses some of the issues that are involved with use of machine learning and data mining techniques to develop predictive models for diagnosis or prognosis of disease, and to call attention to additional requirements for developing diagnostic and prognostic algorithms that are generally useful in medicine. Appendix provides examples that illustrate potential problems with machine learning that are not addressed in the reviewed book.

  11. Environmental Propagation of Noise in Mines and Nearby Villages: A Study Through Noise Mapping

    PubMed Central

    Manwar, Veena D.; Mandal, Bibhuti B.; Pal, Asim K.

    2016-01-01

    Background: Noise mapping being an established practice in Europe is hardly practiced for noise management in India although it is mandatory in Indian mines as per guidelines of the Directorate General of Mines Safety (DGMS). As a pilot study, noise mapping was conducted in an opencast mine with three different models; one based on the baseline operating conditions in two shifts (Situation A), and two other virtual situations where either production targets were enhanced by extending working hours to three shifts (Situation B) or only by increased mechanization and not changing the duration of work (Situation C). Methods: Noise sources were categorized as point, line, area, and moving sources. Considering measured power of the sources, specific meteorological and geographical parameters, noise maps were generated using Predictor LimA software. Results: In all three situations, Lden values were 95 dB(A) and 70–80 dB(A) near drill machine and haul roads, respectively. Noise contours were wider in Situation C due to increase in frequency of dumpers. Lden values near Shovel 1 and Shovel 2 under Situation B increased by 5 dB and 3 dB, respectively due to expansion of working hours. In Situation C, noise levels were >82 dB(A) around shovels. Noise levels on both sides of conveyor belts were in the range of 80–85 dB(A) in Situations A and C whereas it was 85–90 dB(A) in Situation B. Near crusher plants, it ranged from 80 to 90 dB(A) in Situations A and C and between 85 and 95 dB(A) in Situation B. In all situations, noise levels near residential areas exceeded the Central Pollution Control Board (CPCB) limits, i.e., 55 dB(A). Conclusions: For all situations, predicted noise levels exceeded CPCB limits within the mine and nearby residential area. Residential areas near the crusher plants are vulnerable to increased noise propagation. It is recommended to put an acoustic barrier near the crusher plant to attenuate the noise propagation. PMID:27569406

  12. Environmental propagation of noise in mines and nearby villages: A study through noise mapping.

    PubMed

    Manwar, Veena D; Mandal, Bibhuti B; Pal, Asim K

    2016-01-01

    Noise mapping being an established practice in Europe is hardly practiced for noise management in India although it is mandatory in Indian mines as per guidelines of the Directorate General of Mines Safety (DGMS). As a pilot study, noise mapping was conducted in an opencast mine with three different models; one based on the baseline operating conditions in two shifts (Situation A), and two other virtual situations where either production targets were enhanced by extending working hours to three shifts (Situation B) or only by increased mechanization and not changing the duration of work (Situation C). Noise sources were categorized as point, line, area, and moving sources. Considering measured power of the sources, specific meteorological and geographical parameters, noise maps were generated using Predictor LimA software. In all three situations, Lden values were 95 dB(A) and 70-80 dB(A) near drill machine and haul roads, respectively. Noise contours were wider in Situation C due to increase in frequency of dumpers. Lden values near Shovel 1 and Shovel 2 under Situation B increased by 5 dB and 3 dB, respectively due to expansion of working hours. In Situation C, noise levels were >82 dB(A) around shovels. Noise levels on both sides of conveyor belts were in the range of 80-85 dB(A) in Situations A and C whereas it was 85-90 dB(A) in Situation B. Near crusher plants, it ranged from 80 to 90 dB(A) in Situations A and C and between 85 and 95 dB(A) in Situation B. In all situations, noise levels near residential areas exceeded the Central Pollution Control Board (CPCB) limits, i.e., 55 dB(A). For all situations, predicted noise levels exceeded CPCB limits within the mine and nearby residential area. Residential areas near the crusher plants are vulnerable to increased noise propagation. It is recommended to put an acoustic barrier near the crusher plant to attenuate the noise propagation.

  13. Development Of Knowledge Systems For Trouble Shooting Complex Production Machinery

    NASA Astrophysics Data System (ADS)

    Sanford, Richard L.; Novak, Thomas; Meigs, James R.

    1987-05-01

    This paper discusses the use of knowledge base system software for microcomputers to aid repairmen in diagnosing electrical failures in complex mining machinery. The knowledge base is constructed to allow the user to input initial symptoms of the failed machine, and the most probable cause of failure is traced through the knowledge base, with the software requesting additional information such as voltage or resistance measurements as needed. Although the case study presented is for an underground mining machine, results have application to any industry using complex machinery. Two commercial expert-system development tools (M1 TM and Insight 2+TM) and an Al language (Turbo PrologTM) are discussed with emphasis on ease of application and suitability for this study.

  14. Thin seam miner/trench mining concepts for Illinois Basin surface coal mines

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

    Caudle, R.D.; Lall, V.

    1985-07-01

    A hybrid surface/underground mining concept, trench-auger mining is an attempt to increase the depth to which coal seams can be surface mined economically by reducing the amount of overburden which must be removed and reclaimed. In this concept the coal seam is first exposed by digging a series of parallel trenches 400 to 1200 ft apart with conventional surface mining equipment. After surface mining the coal from the bottom of the trench, the coal under the surface between the trenches would be extracted with extended-depth augers, operating from the bottoms of the trenches. The RSV Mining Equipment Co. of Hollandmore » has developed a Thin Seam Miner (TSM). The TSM is essentially a remotely controlled, continuous underground mining machine. The hydraulically driven drum cutter head and coal handling auger flights can be operated from a distance outside the underground mine workings. The purpose of this study is to develop and evaluate Thin Seam Miner/Trench Mining (TSM/TM) concepts for use under conditions existing in the Illinois Coal Basin.« less

  15. Where tunneling equipment is heading

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

    Singhal, R.K.

    1984-02-01

    A variety of equipment is being used for roadheading and tunneling in the mining industry. This includes hydraulic/rotary precussive drills for use in conventional drill and blast, drum-type continuous miners, roadheaders, mini-and midi-full facers for small size openings, soft rock shielded tunnel boring machines, and hard rock tunnel boring machines. The availability, performance, and specifications for tunneling equipment are discussed.

  16. Measuring mine roof bolt strains

    DOEpatents

    Steblay, Bernard J.

    1986-01-01

    A mine roof bolt and a method of measuring the strain in mine roof bolts of this type are disclosed. According to the method, a flat portion on the head of the mine roof bolt is first machined. Next, a hole is drilled radially through the bolt at a predetermined distance from the bolt head. After installation of the mine roof bolt and loading, the strain of the mine roof bolt is measured by generating an ultrasonic pulse at the flat portion. The time of travel of the ultrasonic pulse reflected from the hole is measured. This time of travel is a function of the distance from the flat portion to the hole and increases as the bolt is loaded. Consequently, the time measurement is correlated to the strain in the bolt. Compensation for various factors affecting the travel time are also provided.

  17. 30 CFR 785.13 - Experimental practices mining.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 3 2011-07-01 2011-07-01 false Experimental practices mining. 785.13 Section 785.13 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR SURFACE COAL MINING AND RECLAMATION OPERATIONS PERMITS AND COAL EXPLORATION SYSTEMS UNDER...

  18. Cutting sound enhancement system for mining machines

    DOEpatents

    Leigh, Michael C.; Kwitowski, August J.

    1992-01-01

    A cutting sound enhancement system (10) for transmitting an audible signal from the cutting head (101) of a piece of mine machinery (100) to an operator at a remote station (200), wherein, the operator using a headphone unit (14) can monitor the difference in sounds being made solely by the cutting head (101) to determine the location of the roof, floor, and walls of a coal seam (50).

  19. 30 CFR 75.829 - Tramming continuous mining machines in and out of the mine and from section to section.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... accordance with movement requirements of high-voltage power centers and portable transformers (§ 75.812) and... transformer. A step-up transformer is a transformer that steps up the low or medium voltage to high voltage... supplying low or medium voltage to the step-up transformer must meet the applicable requirements of 30 CFR...

  20. 30 CFR 75.829 - Tramming continuous mining machines in and out of the mine and from section to section.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... accordance with movement requirements of high-voltage power centers and portable transformers (§ 75.812) and... transformer. A step-up transformer is a transformer that steps up the low or medium voltage to high voltage... supplying low or medium voltage to the step-up transformer must meet the applicable requirements of 30 CFR...

  1. 30 CFR 75.829 - Tramming continuous mining machines in and out of the mine and from section to section.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... accordance with movement requirements of high-voltage power centers and portable transformers (§ 75.812) and... transformer. A step-up transformer is a transformer that steps up the low or medium voltage to high voltage... supplying low or medium voltage to the step-up transformer must meet the applicable requirements of 30 CFR...

  2. 30 CFR 75.829 - Tramming continuous mining machines in and out of the mine and from section to section.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... accordance with movement requirements of high-voltage power centers and portable transformers (§ 75.812) and... transformer. A step-up transformer is a transformer that steps up the low or medium voltage to high voltage... supplying low or medium voltage to the step-up transformer must meet the applicable requirements of 30 CFR...

  3. 30 CFR 75.829 - Tramming continuous mining machines in and out of the mine and from section to section.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... accordance with movement requirements of high-voltage power centers and portable transformers (§ 75.812) and... transformer. A step-up transformer is a transformer that steps up the low or medium voltage to high voltage... supplying low or medium voltage to the step-up transformer must meet the applicable requirements of 30 CFR...

  4. Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

    PubMed Central

    Dipnall, Joanna F.

    2016-01-01

    Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. Methods The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009–2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. Results After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). Conclusion The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin. PMID:26848571

  5. Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.

    PubMed

    Dipnall, Joanna F; Pasco, Julie A; Berk, Michael; Williams, Lana J; Dodd, Seetal; Jacka, Felice N; Meyer, Denny

    2016-01-01

    Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.

  6. Using methods from the data mining and machine learning literature for disease classification and prediction: A case study examining classification of heart failure sub-types

    PubMed Central

    Austin, Peter C.; Tu, Jack V.; Ho, Jennifer E.; Levy, Daniel; Lee, Douglas S.

    2014-01-01

    Objective Physicians classify patients into those with or without a specific disease. Furthermore, there is often interest in classifying patients according to disease etiology or subtype. Classification trees are frequently used to classify patients according to the presence or absence of a disease. However, classification trees can suffer from limited accuracy. In the data-mining and machine learning literature, alternate classification schemes have been developed. These include bootstrap aggregation (bagging), boosting, random forests, and support vector machines. Study design and Setting We compared the performance of these classification methods with those of conventional classification trees to classify patients with heart failure according to the following sub-types: heart failure with preserved ejection fraction (HFPEF) vs. heart failure with reduced ejection fraction (HFREF). We also compared the ability of these methods to predict the probability of the presence of HFPEF with that of conventional logistic regression. Results We found that modern, flexible tree-based methods from the data mining literature offer substantial improvement in prediction and classification of heart failure sub-type compared to conventional classification and regression trees. However, conventional logistic regression had superior performance for predicting the probability of the presence of HFPEF compared to the methods proposed in the data mining literature. Conclusion The use of tree-based methods offers superior performance over conventional classification and regression trees for predicting and classifying heart failure subtypes in a population-based sample of patients from Ontario. However, these methods do not offer substantial improvements over logistic regression for predicting the presence of HFPEF. PMID:23384592

  7. On-line Machine Learning and Event Detection in Petascale Data Streams

    NASA Astrophysics Data System (ADS)

    Thompson, David R.; Wagstaff, K. L.

    2012-01-01

    Traditional statistical data mining involves off-line analysis in which all data are available and equally accessible. However, petascale datasets have challenged this premise since it is often impossible to store, let alone analyze, the relevant observations. This has led the machine learning community to investigate adaptive processing chains where data mining is a continuous process. Here pattern recognition permits triage and followup decisions at multiple stages of a processing pipeline. Such techniques can also benefit new astronomical instruments such as the Large Synoptic Survey Telescope (LSST) and Square Kilometre Array (SKA) that will generate petascale data volumes. We summarize some machine learning perspectives on real time data mining, with representative cases of astronomical applications and event detection in high volume datastreams. The first is a "supervised classification" approach currently used for transient event detection at the Very Long Baseline Array (VLBA). It injects known signals of interest - faint single-pulse anomalies - and tunes system parameters to recover these events. This permits meaningful event detection for diverse instrument configurations and observing conditions whose noise cannot be well-characterized in advance. Second, "semi-supervised novelty detection" finds novel events based on statistical deviations from previous patterns. It detects outlier signals of interest while considering known examples of false alarm interference. Applied to data from the Parkes pulsar survey, the approach identifies anomalous "peryton" phenomena that do not match previous event models. Finally, we consider online light curve classification that can trigger adaptive followup measurements of candidate events. Classifier performance analyses suggest optimal survey strategies, and permit principled followup decisions from incomplete data. These examples trace a broad range of algorithm possibilities available for online astronomical data mining. This talk describes research performed at the Jet Propulsion Laboratory, California Institute of Technology. Copyright 2012, All Rights Reserved. U.S. Government support acknowledged.

  8. Evolutionary and Neural Computing Based Decision Support System for Disease Diagnosis from Clinical Data Sets in Medical Practice.

    PubMed

    Sudha, M

    2017-09-27

    As a recent trend, various computational intelligence and machine learning approaches have been used for mining inferences hidden in the large clinical databases to assist the clinician in strategic decision making. In any target data the irrelevant information may be detrimental, causing confusion for the mining algorithm and degrades the prediction outcome. To address this issue, this study attempts to identify an intelligent approach to assist disease diagnostic procedure using an optimal set of attributes instead of all attributes present in the clinical data set. In this proposed Application Specific Intelligent Computing (ASIC) decision support system, a rough set based genetic algorithm is employed in pre-processing phase and a back propagation neural network is applied in training and testing phase. ASIC has two phases, the first phase handles outliers, noisy data, and missing values to obtain a qualitative target data to generate appropriate attribute reduct sets from the input data using rough computing based genetic algorithm centred on a relative fitness function measure. The succeeding phase of this system involves both training and testing of back propagation neural network classifier on the selected reducts. The model performance is evaluated with widely adopted existing classifiers. The proposed ASIC system for clinical decision support has been tested with breast cancer, fertility diagnosis and heart disease data set from the University of California at Irvine (UCI) machine learning repository. The proposed system outperformed the existing approaches attaining the accuracy rate of 95.33%, 97.61%, and 93.04% for breast cancer, fertility issue and heart disease diagnosis.

  9. Extracting semantically enriched events from biomedical literature

    PubMed Central

    2012-01-01

    Background Research into event-based text mining from the biomedical literature has been growing in popularity to facilitate the development of advanced biomedical text mining systems. Such technology permits advanced search, which goes beyond document or sentence-based retrieval. However, existing event-based systems typically ignore additional information within the textual context of events that can determine, amongst other things, whether an event represents a fact, hypothesis, experimental result or analysis of results, whether it describes new or previously reported knowledge, and whether it is speculated or negated. We refer to such contextual information as meta-knowledge. The automatic recognition of such information can permit the training of systems allowing finer-grained searching of events according to the meta-knowledge that is associated with them. Results Based on a corpus of 1,000 MEDLINE abstracts, fully manually annotated with both events and associated meta-knowledge, we have constructed a machine learning-based system that automatically assigns meta-knowledge information to events. This system has been integrated into EventMine, a state-of-the-art event extraction system, in order to create a more advanced system (EventMine-MK) that not only extracts events from text automatically, but also assigns five different types of meta-knowledge to these events. The meta-knowledge assignment module of EventMine-MK performs with macro-averaged F-scores in the range of 57-87% on the BioNLP’09 Shared Task corpus. EventMine-MK has been evaluated on the BioNLP’09 Shared Task subtask of detecting negated and speculated events. Our results show that EventMine-MK can outperform other state-of-the-art systems that participated in this task. Conclusions We have constructed the first practical system that extracts both events and associated, detailed meta-knowledge information from biomedical literature. The automatically assigned meta-knowledge information can be used to refine search systems, in order to provide an extra search layer beyond entities and assertions, dealing with phenomena such as rhetorical intent, speculations, contradictions and negations. This finer grained search functionality can assist in several important tasks, e.g., database curation (by locating new experimental knowledge) and pathway enrichment (by providing information for inference). To allow easy integration into text mining systems, EventMine-MK is provided as a UIMA component that can be used in the interoperable text mining infrastructure, U-Compare. PMID:22621266

  10. Extracting semantically enriched events from biomedical literature.

    PubMed

    Miwa, Makoto; Thompson, Paul; McNaught, John; Kell, Douglas B; Ananiadou, Sophia

    2012-05-23

    Research into event-based text mining from the biomedical literature has been growing in popularity to facilitate the development of advanced biomedical text mining systems. Such technology permits advanced search, which goes beyond document or sentence-based retrieval. However, existing event-based systems typically ignore additional information within the textual context of events that can determine, amongst other things, whether an event represents a fact, hypothesis, experimental result or analysis of results, whether it describes new or previously reported knowledge, and whether it is speculated or negated. We refer to such contextual information as meta-knowledge. The automatic recognition of such information can permit the training of systems allowing finer-grained searching of events according to the meta-knowledge that is associated with them. Based on a corpus of 1,000 MEDLINE abstracts, fully manually annotated with both events and associated meta-knowledge, we have constructed a machine learning-based system that automatically assigns meta-knowledge information to events. This system has been integrated into EventMine, a state-of-the-art event extraction system, in order to create a more advanced system (EventMine-MK) that not only extracts events from text automatically, but also assigns five different types of meta-knowledge to these events. The meta-knowledge assignment module of EventMine-MK performs with macro-averaged F-scores in the range of 57-87% on the BioNLP'09 Shared Task corpus. EventMine-MK has been evaluated on the BioNLP'09 Shared Task subtask of detecting negated and speculated events. Our results show that EventMine-MK can outperform other state-of-the-art systems that participated in this task. We have constructed the first practical system that extracts both events and associated, detailed meta-knowledge information from biomedical literature. The automatically assigned meta-knowledge information can be used to refine search systems, in order to provide an extra search layer beyond entities and assertions, dealing with phenomena such as rhetorical intent, speculations, contradictions and negations. This finer grained search functionality can assist in several important tasks, e.g., database curation (by locating new experimental knowledge) and pathway enrichment (by providing information for inference). To allow easy integration into text mining systems, EventMine-MK is provided as a UIMA component that can be used in the interoperable text mining infrastructure, U-Compare.

  11. Evaluation of a disposable diesel exhaust filter for permissible mining machines

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

    Ambs, J.L.; Cantrell, B.K.; Watts, W.F.

    1994-01-01

    The US Bureau of Mines (USBM) Diesel Research Program emphasizes the development and evaluation of emission control devices to reduce exposure of miners to diesel exhaust pollutants. Studies by the USBM have shown that diesel exhaust aerosol (DEA) contributes a substantial portion of the respirable aerosol in underground coal mines using diesel equipment not equipped with emission controls. The USBM and the Donaldson Co., Inc., Minneapolis, MN, have developed a low-temperature, disposable diesel exhaust filter (DDEF) for use on permissible diesel haulage vehicles equipped with waterbath exhaust conditioners. These were evaluated in three underground mines to determine their effectiveness inmore » reducing DEA concentrations. The DDEF reduced DEA concentrations from 70 to 90% at these mines. The usable life of the filter ranged from 10 to 32 h, depending on factors that affect DEA output, such as mine altitude, engine type, and duty-cycle. Cost per filter is approximately $40.« less

  12. Smart material screening machines using smart materials and controls

    NASA Astrophysics Data System (ADS)

    Allaei, Daryoush; Corradi, Gary; Waigand, Al

    2002-07-01

    The objective of this product is to address the specific need for improvements in the efficiency and effectiveness in physical separation technologies in the screening areas. Currently, the mining industry uses approximately 33 billion kW-hr per year, costing 1.65 billion dollars at 0.05 cents per kW-hr, of electrical energy for physical separations. Even though screening and size separations are not the single most energy intensive process in the mining industry, they are often the major bottleneck in the whole process. Improvements to this area offer tremendous potential in both energy savings and production improvements. Additionally, the vibrating screens used in the mining processing plants are the most costly areas from maintenance and worker health and safety point of views. The goal of this product is to reduce energy use in the screening and total processing areas. This goal is accomplished by developing an innovative screening machine based on smart materials and smart actuators, namely smart screen that uses advanced sensory system to continuously monitor the screening process and make appropriate adjustments to improve production. The theory behind the development of Smart Screen technology is based on two key technologies, namely smart actuators and smart Energy Flow ControlT (EFCT) strategies, developed initially for military applications. Smart Screen technology controls the flow of vibration energy and confines it to the screen rather than shaking much of the mass that makes up the conventional vibratory screening machine. Consequently, Smart Screens eliminates and downsizes many of the structural components associated with conventional vibratory screening machines. As a result, the surface area of the screen increases for a given envelope. This increase in usable screening surface area extends the life of the screens, reduces required maintenance by reducing the frequency of screen change-outs and improves throughput or productivity.

  13. EUROGRAM, September-October 1994

    DTIC Science & Technology

    1994-10-01

    finished a comprehensive report on the characteristics of mining- at the Institute, induced seismicity. The Kolar Gold Fields were first mined by the...engineering applications. Spherics standard and jets, unsteady combustion, and fuel cells. A full listing of ball products are machined and finished from...can now be directly downloaded ELO§RJ3t #94-)5 tSep-Oct 94) It chemis.r, proc:essing. finishing protocol and applications for ceramic matrix

  14. Literature Mining of Pathogenesis-Related Proteins in Human Pathogens for Database Annotation

    DTIC Science & Technology

    2009-10-01

    person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control...submission and for literature mining result display with automatically tagged abstracts. I. Literature data sets for machine learning algorithm training...mass spectrometry) proteomics data from Burkholderia strains. • Task1 ( M13 -15): Preliminary analysis of the Burkholderia proteomic space

  15. Discriminative and informative features for biomolecular text mining with ensemble feature selection.

    PubMed

    Van Landeghem, Sofie; Abeel, Thomas; Saeys, Yvan; Van de Peer, Yves

    2010-09-15

    In the field of biomolecular text mining, black box behavior of machine learning systems currently limits understanding of the true nature of the predictions. However, feature selection (FS) is capable of identifying the most relevant features in any supervised learning setting, providing insight into the specific properties of the classification algorithm. This allows us to build more accurate classifiers while at the same time bridging the gap between the black box behavior and the end-user who has to interpret the results. We show that our FS methodology successfully discards a large fraction of machine-generated features, improving classification performance of state-of-the-art text mining algorithms. Furthermore, we illustrate how FS can be applied to gain understanding in the predictions of a framework for biomolecular event extraction from text. We include numerous examples of highly discriminative features that model either biological reality or common linguistic constructs. Finally, we discuss a number of insights from our FS analyses that will provide the opportunity to considerably improve upon current text mining tools. The FS algorithms and classifiers are available in Java-ML (http://java-ml.sf.net). The datasets are publicly available from the BioNLP'09 Shared Task web site (http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/SharedTask/).

  16. Trends of Occupational Fatalities Involving Machines, United States, 1992–2010

    PubMed Central

    Marsh, Suzanne M.; Fosbroke, David E.

    2016-01-01

    Background This paper describes trends of occupational machine-related fatalities from 1992–2010. We examine temporal patterns by worker demographics, machine types (e.g., stationary, mobile), and industries. Methods We analyzed fatalities from Census of Fatal Occupational Injuries data provided by the Bureau of Labor Statistics to the National Institute for Occupational Safety and Health. We used injury source to identify machine-related incidents and Poisson regression to assess trends over the 19-year period. Results There was an average annual decrease of 2.8% in overall machine-related fatality rates from 1992 through 2010. Mobile machine-related fatality rates decreased an average of 2.6% annually and stationary machine-related rates decreased an average of 3.5% annually. Groups that continued to be at high risk included older workers; self-employed; and workers in agriculture/forestry/fishing, construction, and mining. Conclusion Addressing dangers posed by tractors, excavators, and other mobile machines needs to continue. High-risk worker groups should receive targeted information on machine safety. PMID:26358658

  17. Information mining in remote sensing imagery

    NASA Astrophysics Data System (ADS)

    Li, Jiang

    The volume of remotely sensed imagery continues to grow at an enormous rate due to the advances in sensor technology, and our capability for collecting and storing images has greatly outpaced our ability to analyze and retrieve information from the images. This motivates us to develop image information mining techniques, which is very much an interdisciplinary endeavor drawing upon expertise in image processing, databases, information retrieval, machine learning, and software design. This dissertation proposes and implements an extensive remote sensing image information mining (ReSIM) system prototype for mining useful information implicitly stored in remote sensing imagery. The system consists of three modules: image processing subsystem, database subsystem, and visualization and graphical user interface (GUI) subsystem. Land cover and land use (LCLU) information corresponding to spectral characteristics is identified by supervised classification based on support vector machines (SVM) with automatic model selection, while textural features that characterize spatial information are extracted using Gabor wavelet coefficients. Within LCLU categories, textural features are clustered using an optimized k-means clustering approach to acquire search efficient space. The clusters are stored in an object-oriented database (OODB) with associated images indexed in an image database (IDB). A k-nearest neighbor search is performed using a query-by-example (QBE) approach. Furthermore, an automatic parametric contour tracing algorithm and an O(n) time piecewise linear polygonal approximation (PLPA) algorithm are developed for shape information mining of interesting objects within the image. A fuzzy object-oriented database based on the fuzzy object-oriented data (FOOD) model is developed to handle the fuzziness and uncertainty. Three specific applications are presented: integrated land cover and texture pattern mining, shape information mining for change detection of lakes, and fuzzy normalized difference vegetation index (NDVI) pattern mining. The study results show the effectiveness of the proposed system prototype and the potentials for other applications in remote sensing.

  18. Exploiting Recurring Structure in a Semantic Network

    NASA Technical Reports Server (NTRS)

    Wolfe, Shawn R.; Keller, Richard M.

    2004-01-01

    With the growing popularity of the Semantic Web, an increasing amount of information is becoming available in machine interpretable, semantically structured networks. Within these semantic networks are recurring structures that could be mined by existing or novel knowledge discovery methods. The mining of these semantic structures represents an interesting area that focuses on mining both for and from the Semantic Web, with surprising applicability to problems confronting the developers of Semantic Web applications. In this paper, we present representative examples of recurring structures and show how these structures could be used to increase the utility of a semantic repository deployed at NASA.

  19. Supporting Solar Physics Research via Data Mining

    NASA Astrophysics Data System (ADS)

    Angryk, Rafal; Banda, J.; Schuh, M.; Ganesan Pillai, K.; Tosun, H.; Martens, P.

    2012-05-01

    In this talk we will briefly introduce three pillars of data mining (i.e. frequent patterns discovery, classification, and clustering), and discuss some possible applications of known data mining techniques which can directly benefit solar physics research. In particular, we plan to demonstrate applicability of frequent patterns discovery methods for the verification of hypotheses about co-occurrence (in space and time) of filaments and sigmoids. We will also show how classification/machine learning algorithms can be utilized to verify human-created software modules to discover individual types of solar phenomena. Finally, we will discuss applicability of clustering techniques to image data processing.

  20. Simulation of switching overvoltages in the mine electric power supply system

    NASA Astrophysics Data System (ADS)

    Ivanchenko, D. I.; Novozhilov, N. G.

    2017-02-01

    Overvoltages occur in mine power supply systems during switching off consumers with high inductive load, such as transformers, reactors and electrical machines. Overvoltages lead to an increase of insulation degradation rate and may cause electric faults, power outage, fire and explosion of methane and coal dust. This paper is dedicated to simulation of vacuum circuit breaker switching overvoltages in a mine power supply system by means of Simulink MATLAB. The model of the vacuum circuit breaker implements simulation of transient recovery voltage, current chopping and an electric arc. Obtained results were compared to available experimental data.

  1. Differential Diagnosis of Erythmato-Squamous Diseases Using Classification and Regression Tree.

    PubMed

    Maghooli, Keivan; Langarizadeh, Mostafa; Shahmoradi, Leila; Habibi-Koolaee, Mahdi; Jebraeily, Mohamad; Bouraghi, Hamid

    2016-10-01

    Differential diagnosis of Erythmato-Squamous Diseases (ESD) is a major challenge in the field of dermatology. The ESD diseases are placed into six different classes. Data mining is the process for detection of hidden patterns. In the case of ESD, data mining help us to predict the diseases. Different algorithms were developed for this purpose. we aimed to use the Classification and Regression Tree (CART) to predict differential diagnosis of ESD. we used the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. For this purpose, the dermatology data set from machine learning repository, UCI was obtained. The Clementine 12.0 software from IBM Company was used for modelling. In order to evaluation of the model we calculate the accuracy, sensitivity and specificity of the model. The proposed model had an accuracy of 94.84% (. 24.42) in order to correct prediction of the ESD disease. Results indicated that using of this classifier could be useful. But, it would be strongly recommended that the combination of machine learning methods could be more useful in terms of prediction of ESD.

  2. 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.

  3. Data Mining Citizen Science Results

    NASA Astrophysics Data System (ADS)

    Borne, K. D.

    2012-12-01

    Scientific discovery from big data is enabled through multiple channels, including data mining (through the application of machine learning algorithms) and human computation (commonly implemented through citizen science tasks). We will describe the results of new data mining experiments on the results from citizen science activities. Discovering patterns, trends, and anomalies in data are among the powerful contributions of citizen science. Establishing scientific algorithms that can subsequently re-discover the same types of patterns, trends, and anomalies in automatic data processing pipelines will ultimately result from the transformation of those human algorithms into computer algorithms, which can then be applied to much larger data collections. Scientific discovery from big data is thus greatly amplified through the marriage of data mining with citizen science.

  4. Text Mining Genotype-Phenotype Relationships from Biomedical Literature for Database Curation and Precision Medicine.

    PubMed

    Singhal, Ayush; Simmons, Michael; Lu, Zhiyong

    2016-11-01

    The practice of precision medicine will ultimately require databases of genes and mutations for healthcare providers to reference in order to understand the clinical implications of each patient's genetic makeup. Although the highest quality databases require manual curation, text mining tools can facilitate the curation process, increasing accuracy, coverage, and productivity. However, to date there are no available text mining tools that offer high-accuracy performance for extracting such triplets from biomedical literature. In this paper we propose a high-performance machine learning approach to automate the extraction of disease-gene-variant triplets from biomedical literature. Our approach is unique because we identify the genes and protein products associated with each mutation from not just the local text content, but from a global context as well (from the Internet and from all literature in PubMed). Our approach also incorporates protein sequence validation and disease association using a novel text-mining-based machine learning approach. We extract disease-gene-variant triplets from all abstracts in PubMed related to a set of ten important diseases (breast cancer, prostate cancer, pancreatic cancer, lung cancer, acute myeloid leukemia, Alzheimer's disease, hemochromatosis, age-related macular degeneration (AMD), diabetes mellitus, and cystic fibrosis). We then evaluate our approach in two ways: (1) a direct comparison with the state of the art using benchmark datasets; (2) a validation study comparing the results of our approach with entries in a popular human-curated database (UniProt) for each of the previously mentioned diseases. In the benchmark comparison, our full approach achieves a 28% improvement in F1-measure (from 0.62 to 0.79) over the state-of-the-art results. For the validation study with UniProt Knowledgebase (KB), we present a thorough analysis of the results and errors. Across all diseases, our approach returned 272 triplets (disease-gene-variant) that overlapped with entries in UniProt and 5,384 triplets without overlap in UniProt. Analysis of the overlapping triplets and of a stratified sample of the non-overlapping triplets revealed accuracies of 93% and 80% for the respective categories (cumulative accuracy, 77%). We conclude that our process represents an important and broadly applicable improvement to the state of the art for curation of disease-gene-variant relationships.

  5. Intelligent Mining Engineering Systems in the Structure of Industry 4.0

    NASA Astrophysics Data System (ADS)

    Rylnikova, Marina; Radchenko, Dmitriy; Klebanov, Dmitriy

    2017-11-01

    The solution of the problem of improving the human environment and working conditions at mines is based on the provision of the rationale of parameters and conditions for the implementation of an environmentally balanced cycle of comprehensive development of mineral deposits on the basis of the design of mining engineering systems characterized by the minimization of the human factor effect in danger zones of mining operations. In this area, robotized technologies are being developed, machinery and mechanisms with the elements of artificial intelligence, and mining and transport system automatic controls are being put into service throughout the world. In the upcoming decades, mining machines and mechanisms will be virtually industrial robots. The article presents the results of zoning of open-pit and underground mine production areas, as well as mining engineering system of combined development depending on the fact and periodicity of human presence in zones of mining processes. As a surface geotechnology case study, the software structure based on a modular concept is described. The performance philosophy of mining and transport equipment with the elements of artificial intelligence is shown when it is put into service in an open pit.

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

    Not Available

    This paper is actually a composite of two papers dealing with automation and computerized control of underground mining equipment. The paper primarily discusses drills, haulage equipment, and tunneling machines. It compares performance and cost benefits of conventional equipment to the new automated methods. The company involved are iron ore mining companies in Scandinavia. The papers also discusses the different equipment using air power, water power, hydraulic power, and computer power. The different drill rigs are compared for performance and cost.

  7. Knowledge based word-concept model estimation and refinement for biomedical text mining.

    PubMed

    Jimeno Yepes, Antonio; Berlanga, Rafael

    2015-02-01

    Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community. In the biomedical domain, the existence of a large number of terminological resources and knowledge bases (KB) has enabled a myriad of machine learning methods for different text mining related tasks. Unfortunately, KBs have not been devised for text mining tasks but for human interpretation, thus performance of KB-based methods is usually lower when compared to supervised machine learning methods. The disadvantage of supervised methods though is they require labeled training data and therefore not useful for large scale biomedical text mining systems. KB-based methods do not have this limitation. In this paper, we describe a novel method to generate word-concept probabilities from a KB, which can serve as a basis for several text mining tasks. This method not only takes into account the underlying patterns within the descriptions contained in the KB but also those in texts available from large unlabeled corpora such as MEDLINE. The parameters of the model have been estimated without training data. Patterns from MEDLINE have been built using MetaMap for entity recognition and related using co-occurrences. The word-concept probabilities were evaluated on the task of word sense disambiguation (WSD). The results showed that our method obtained a higher degree of accuracy than other state-of-the-art approaches when evaluated on the MSH WSD data set. We also evaluated our method on the task of document ranking using MEDLINE citations. These results also showed an increase in performance over existing baseline retrieval approaches. Copyright © 2014 Elsevier Inc. All rights reserved.

  8. Fusion of multiple quadratic penalty function support vector machines (QPFSVM) for automated sea mine detection and classification

    NASA Astrophysics Data System (ADS)

    Dobeck, Gerald J.; Cobb, J. Tory

    2002-08-01

    The high-resolution sonar is one of the principal sensors used by the Navy to detect and classify sea mines in minehunting operations. For such sonar systems, substantial effort has been devoted to the development of automated detection and classification (D/C) algorithms. These have been spurred by several factors including (1) aids for operators to reduce work overload, (2) more optimal use of all available data, and (3) the introduction of unmanned minehunting systems. The environments where sea mines are typically laid (harbor areas, shipping lanes, and the littorals) give rise to many false alarms caused by natural, biologic, and man-made clutter. The objective of the automated D/C algorithms is to eliminate most of these false alarms while still maintaining a very high probability of mine detection and classification (PdPc). In recent years, the benefits of fusing the outputs of multiple D/C algorithms have been studied. We refer to this as Algorithm Fusion. The results have been remarkable, including reliable robustness to new environments. The Quadratic Penalty Function Support Vector Machine (QPFSVM) algorithm to aid in the automated detection and classification of sea mines is introduced in this paper. The QPFSVM algorithm is easy to train, simple to implement, and robust to feature space dimension. Outputs of successive SVM algorithms are cascaded in stages (fused) to improve the Probability of Classification (Pc) and reduce the number of false alarms. Even though our experience has been gained in the area of sea mine detection and classification, the principles described herein are general and can be applied to fusion of any D/C problem (e.g., automated medical diagnosis or automatic target recognition for ballistic missile defense).

  9. 9. VIEW UPRIVER SHOWING BARGE AND CONSOLIDATED COAL'S DILWORTH MINELOOKING ...

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

    9. VIEW UPRIVER SHOWING BARGE AND CONSOLIDATED COAL'S DILWORTH MINE-LOOKING NORTHEAST. - W. A. Young & Sons Foundry & Machine Shop, On Water Street along Monongahela River, Rices Landing, Greene County, PA

  10. 30 CFR 46.8 - Annual refresher training.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... measures a miner can take against these hazards, and the contents of the mine's HazCom program... protection; and working around moving objects (machine guarding). [64 FR 53130, Sept. 30, 1999, as amended at...

  11. 30 CFR 75.823 - Scope.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    .... Sections 75.823 through 75.834 of this part are electrical safety standards applicable to 2,400 volt continuous mining machines and circuits. A “qualified person” as used in these sections means a person...

  12. Development of sensitized pick coal interface detector system

    NASA Technical Reports Server (NTRS)

    Burchill, R. F.

    1982-01-01

    One approach for detection of the coal interface is measurement of pick cutting loads and shock through the use of pick strain gage load cells and accelerometers. The cutting drum of a long wall mining machine contains a number of cutting picks. In order to measure pick loads and shocks, one pick was instrumented and telemetry used to transmit the signals from the drum to an instrument-type tape recorder. A data system using FM telemetry was designed to transfer cutting bit load and shock information from the drum of a longwall shearer coal mining machine to a chassis mounted data recorder. The design of components in the test data system were finalized, the required instruments were assembled, the instrument system was evaluated in an above-ground simulation test, and an underground test series to obtain tape recorded sensor data was conducted.

  13. Transfer Learning beyond Text Classification

    NASA Astrophysics Data System (ADS)

    Yang, Qiang

    Transfer learning is a new machine learning and data mining framework that allows the training and test data to come from different distributions or feature spaces. We can find many novel applications of machine learning and data mining where transfer learning is necessary. While much has been done in transfer learning in text classification and reinforcement learning, there has been a lack of documented success stories of novel applications of transfer learning in other areas. In this invited article, I will argue that transfer learning is in fact quite ubiquitous in many real world applications. In this article, I will illustrate this point through an overview of a broad spectrum of applications of transfer learning that range from collaborative filtering to sensor based location estimation and logical action model learning for AI planning. I will also discuss some potential future directions of transfer learning.

  14. 33 CFR 334.20 - Gulf of Maine off Cape Small, Maine; naval aircraft practice mining range area.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 33 Navigation and Navigable Waters 3 2013-07-01 2013-07-01 false Gulf of Maine off Cape Small, Maine; naval aircraft practice mining range area. 334.20 Section 334.20 Navigation and Navigable Waters... REGULATIONS § 334.20 Gulf of Maine off Cape Small, Maine; naval aircraft practice mining range area. (a) The...

  15. 33 CFR 334.20 - Gulf of Maine off Cape Small, Maine; naval aircraft practice mining range area.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 33 Navigation and Navigable Waters 3 2011-07-01 2011-07-01 false Gulf of Maine off Cape Small, Maine; naval aircraft practice mining range area. 334.20 Section 334.20 Navigation and Navigable Waters... REGULATIONS § 334.20 Gulf of Maine off Cape Small, Maine; naval aircraft practice mining range area. (a) The...

  16. 33 CFR 334.20 - Gulf of Maine off Cape Small, Maine; naval aircraft practice mining range area.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 33 Navigation and Navigable Waters 3 2014-07-01 2014-07-01 false Gulf of Maine off Cape Small, Maine; naval aircraft practice mining range area. 334.20 Section 334.20 Navigation and Navigable Waters... REGULATIONS § 334.20 Gulf of Maine off Cape Small, Maine; naval aircraft practice mining range area. (a) The...

  17. 33 CFR 334.20 - Gulf of Maine off Cape Small, Maine; naval aircraft practice mining range area.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 33 Navigation and Navigable Waters 3 2010-07-01 2010-07-01 false Gulf of Maine off Cape Small, Maine; naval aircraft practice mining range area. 334.20 Section 334.20 Navigation and Navigable Waters... REGULATIONS § 334.20 Gulf of Maine off Cape Small, Maine; naval aircraft practice mining range area. (a) The...

  18. 33 CFR 334.20 - Gulf of Maine off Cape Small, Maine; naval aircraft practice mining range area.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 33 Navigation and Navigable Waters 3 2012-07-01 2012-07-01 false Gulf of Maine off Cape Small, Maine; naval aircraft practice mining range area. 334.20 Section 334.20 Navigation and Navigable Waters... REGULATIONS § 334.20 Gulf of Maine off Cape Small, Maine; naval aircraft practice mining range area. (a) The...

  19. Restoring Forests and Associated Ecosystem Services on Appalachian Coal Surface Mines

    NASA Astrophysics Data System (ADS)

    Zipper, Carl E.; Burger, James A.; Skousen, Jeffrey G.; Angel, Patrick N.; Barton, Christopher D.; Davis, Victor; Franklin, Jennifer A.

    2011-05-01

    Surface coal mining in Appalachia has caused extensive replacement of forest with non-forested land cover, much of which is unmanaged and unproductive. Although forested ecosystems are valued by society for both marketable products and ecosystem services, forests have not been restored on most Appalachian mined lands because traditional reclamation practices, encouraged by regulatory policies, created conditions poorly suited for reforestation. Reclamation scientists have studied productive forests growing on older mine sites, established forest vegetation experimentally on recent mines, and identified mine reclamation practices that encourage forest vegetation re-establishment. Based on these findings, they developed a Forestry Reclamation Approach (FRA) that can be employed by coal mining firms to restore forest vegetation. Scientists and mine regulators, working collaboratively, have communicated the FRA to the coal industry and to regulatory enforcement personnel. Today, the FRA is used routinely by many coal mining firms, and thousands of mined hectares have been reclaimed to restore productive mine soils and planted with native forest trees. Reclamation of coal mines using the FRA is expected to restore these lands' capabilities to provide forest-based ecosystem services, such as wood production, atmospheric carbon sequestration, wildlife habitat, watershed protection, and water quality protection to a greater extent than conventional reclamation practices.

  20. Biomedical Informatics on the Cloud: A Treasure Hunt for Advancing Cardiovascular Medicine.

    PubMed

    Ping, Peipei; Hermjakob, Henning; Polson, Jennifer S; Benos, Panagiotis V; Wang, Wei

    2018-04-27

    In the digital age of cardiovascular medicine, the rate of biomedical discovery can be greatly accelerated by the guidance and resources required to unearth potential collections of knowledge. A unified computational platform leverages metadata to not only provide direction but also empower researchers to mine a wealth of biomedical information and forge novel mechanistic insights. This review takes the opportunity to present an overview of the cloud-based computational environment, including the functional roles of metadata, the architecture schema of indexing and search, and the practical scenarios of machine learning-supported molecular signature extraction. By introducing several established resources and state-of-the-art workflows, we share with our readers a broadly defined informatics framework to phenotype cardiovascular health and disease. © 2018 American Heart Association, Inc.

  1. Practical skills of the future innovator

    NASA Astrophysics Data System (ADS)

    Kaurov, Vitaliy

    2015-03-01

    Physics graduates face and often are disoriented by the complex and turbulent world of startups, incubators, emergent technologies, big data, social network engineering, and so on. In order to build the curricula that foster the skills necessary to navigate this world, we will look at the experiences at the Wolfram Science Summer School that gathers annually international students for already more than a decade. We will look at the examples of projects and see the development of such skills as innovative thinking, data mining, machine learning, cloud technologies, device connectivity and the Internet of things, network analytics, geo-information systems, formalized computable knowledge, and the adjacent applied research skills from graph theory to image processing and beyond. This should give solid ideas to educators who will build standard curricula adapted for innovation and entrepreneurship education.

  2. Diamond Eye: a distributed architecture for image data mining

    NASA Astrophysics Data System (ADS)

    Burl, Michael C.; Fowlkes, Charless; Roden, Joe; Stechert, Andre; Mukhtar, Saleem

    1999-02-01

    Diamond Eye is a distributed software architecture, which enables users (scientists) to analyze large image collections by interacting with one or more custom data mining servers via a Java applet interface. Each server is coupled with an object-oriented database and a computational engine, such as a network of high-performance workstations. The database provides persistent storage and supports querying of the 'mined' information. The computational engine provides parallel execution of expensive image processing, object recognition, and query-by-content operations. Key benefits of the Diamond Eye architecture are: (1) the design promotes trial evaluation of advanced data mining and machine learning techniques by potential new users (all that is required is to point a web browser to the appropriate URL), (2) software infrastructure that is common across a range of science mining applications is factored out and reused, and (3) the system facilitates closer collaborations between algorithm developers and domain experts.

  3. Adaption of Machine Fluid Analysis for Manufacturing - Final Report

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

    Pardini, Allan F.

    2005-08-16

    Pacific Northwest National Laboratory (PNNL: Operated by Battelle Memorial Institute for the Department of Energy) is working with the Department of Energy (DOE) to develop technology for the US mining industry. Filtration and lubricant suppliers to the pulp and paper industry had noted the recent accomplishments by PNNL and its industrial partners in the DOE OIT Mining Industry of the Future Program, and asked for assistance in adapting this DOE-funded technology to the pulp and paper industry.

  4. 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.

  5. Evaluation of a disposable diesel exhaust filter for permissible mining machines. Report of investigations/1994

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

    Ambs, J.L.; Cantrell, B.K.; Watts, W.F.

    1994-01-01

    The U.S. Bureau of Mines (USBM) Diesel Research Program emphasizes the development and evaluation of emission control devices to reduce exposure of miners to diesel exhaust pollutants. Studies by the USBM have shown that diesel exhaust aerosol (DEA) contributes a substantial portion of the respirable aerosol in underground coal mining using diesel equipment not equipped with emission controls. The USBM and the Donaldson Co., Inc., Minneapolis, MN, have developed a low-temperature, disposable diesel exhaust filter (DDEF) for use on permissible diesel haulage vehicles equipped with waterban exhaust conditioners. These were evaluated in three underground mines to determine their effectiveness inmore » reducing DEA concentrations. The DDEF reduced DEA concentrations from 70 to 90 pct at these mines. The usable life of the filter ranged from 10 to 32 h, depending on factors that affect DEA output, such as mine altitude, engine type, and duty-cycle. Cost per filter is approximately $40.« less

  6. Scalable Machine Learning for Massive Astronomical Datasets

    NASA Astrophysics Data System (ADS)

    Ball, Nicholas M.; Gray, A.

    2014-04-01

    We present the ability to perform data mining and machine learning operations on a catalog of half a billion astronomical objects. This is the result of the combination of robust, highly accurate machine learning algorithms with linear scalability that renders the applications of these algorithms to massive astronomical data tractable. We demonstrate the core algorithms kernel density estimation, K-means clustering, linear regression, nearest neighbors, random forest and gradient-boosted decision tree, singular value decomposition, support vector machine, and two-point correlation function. Each of these is relevant for astronomical applications such as finding novel astrophysical objects, characterizing artifacts in data, object classification (including for rare objects), object distances, finding the important features describing objects, density estimation of distributions, probabilistic quantities, and exploring the unknown structure of new data. The software, Skytree Server, runs on any UNIX-based machine, a virtual machine, or cloud-based and distributed systems including Hadoop. We have integrated it on the cloud computing system of the Canadian Astronomical Data Centre, the Canadian Advanced Network for Astronomical Research (CANFAR), creating the world's first cloud computing data mining system for astronomy. We demonstrate results showing the scaling of each of our major algorithms on large astronomical datasets, including the full 470,992,970 objects of the 2 Micron All-Sky Survey (2MASS) Point Source Catalog. We demonstrate the ability to find outliers in the full 2MASS dataset utilizing multiple methods, e.g., nearest neighbors. This is likely of particular interest to the radio astronomy community given, for example, that survey projects contain groups dedicated to this topic. 2MASS is used as a proof-of-concept dataset due to its convenience and availability. These results are of interest to any astronomical project with large and/or complex datasets that wishes to extract the full scientific value from its data.

  7. Scalable Machine Learning for Massive Astronomical Datasets

    NASA Astrophysics Data System (ADS)

    Ball, Nicholas M.; Astronomy Data Centre, Canadian

    2014-01-01

    We present the ability to perform data mining and machine learning operations on a catalog of half a billion astronomical objects. This is the result of the combination of robust, highly accurate machine learning algorithms with linear scalability that renders the applications of these algorithms to massive astronomical data tractable. We demonstrate the core algorithms kernel density estimation, K-means clustering, linear regression, nearest neighbors, random forest and gradient-boosted decision tree, singular value decomposition, support vector machine, and two-point correlation function. Each of these is relevant for astronomical applications such as finding novel astrophysical objects, characterizing artifacts in data, object classification (including for rare objects), object distances, finding the important features describing objects, density estimation of distributions, probabilistic quantities, and exploring the unknown structure of new data. The software, Skytree Server, runs on any UNIX-based machine, a virtual machine, or cloud-based and distributed systems including Hadoop. We have integrated it on the cloud computing system of the Canadian Astronomical Data Centre, the Canadian Advanced Network for Astronomical Research (CANFAR), creating the world's first cloud computing data mining system for astronomy. We demonstrate results showing the scaling of each of our major algorithms on large astronomical datasets, including the full 470,992,970 objects of the 2 Micron All-Sky Survey (2MASS) Point Source Catalog. We demonstrate the ability to find outliers in the full 2MASS dataset utilizing multiple methods, e.g., nearest neighbors, and the local outlier factor. 2MASS is used as a proof-of-concept dataset due to its convenience and availability. These results are of interest to any astronomical project with large and/or complex datasets that wishes to extract the full scientific value from its data.

  8. Toward Usable Interactive Analytics: Coupling Cognition and Computation

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

    Endert, Alexander; North, Chris; Chang, Remco

    Interactive analytics provide users a myriad of computational means to aid in extracting meaningful information from large and complex datasets. Much prior work focuses either on advancing the capabilities of machine-centric approaches by the data mining and machine learning communities, or human-driven methods by the visualization and CHI communities. However, these methods do not yet support a true human-machine symbiotic relationship where users and machines work together collaboratively and adapt to each other to advance an interactive analytic process. In this paper we discuss some of the inherent issues, outlining what we believe are the steps toward usable interactive analyticsmore » that will ultimately increase the effectiveness for both humans and computers to produce insights.« less

  9. Spiced-up ANFO mixture leads to super blasts for casting

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

    Chironis, N.P.

    1984-05-01

    There is one problem common to many coal operators in the mountainous regions of western Pennsylvania. As coal seams nearer the crop lines of their mine sites are removed, the overburden heights and stripping ratios increase to about 20-to-1, the range where coal becomes uneconomical to mine. Faced with this situation, a mine operator usually pursues one of four options: 1. Drive a drift mine, which means switching to underground operations with all the complexity and costs involved; 2. Purchase a larger dragline, which involves huge capital expenditures; 3. Bring in an augering machine to auger the exposed seams, amore » technique effective only for a very limited distance into the highwalls; 4. Discontinue operations, the route most operators take.« less

  10. Linking descriptive geology and quantitative machine learning through an ontology of lithological concepts

    NASA Astrophysics Data System (ADS)

    Klump, J. F.; Huber, R.; Robertson, J.; Cox, S. J. D.; Woodcock, R.

    2014-12-01

    Despite the recent explosion of quantitative geological data, geology remains a fundamentally qualitative science. Numerical data only constitute a certain part of data collection in the geosciences. In many cases, geological observations are compiled as text into reports and annotations on drill cores, thin sections or drawings of outcrops. The observations are classified into concepts such as lithology, stratigraphy, geological structure, etc. These descriptions are semantically rich and are generally supported by more quantitative observations using geochemical analyses, XRD, hyperspectral scanning, etc, but the goal is geological semantics. In practice it has been difficult to bring the different observations together due to differing perception or granularity of classification in human observation, or the partial observation of only some characteristics using quantitative sensors. In the past years many geological classification schemas have been transferred into ontologies and vocabularies, formalized using RDF and OWL, and published through SPARQL endpoints. Several lithological ontologies were compiled by stratigraphy.net and published through a SPARQL endpoint. This work is complemented by the development of a Python API to integrate this vocabulary into Python-based text mining applications. The applications for the lithological vocabulary and Python API are automated semantic tagging of geochemical data and descriptions of drill cores, machine learning of geochemical compositions that are diagnostic for lithological classifications, and text mining for lithological concepts in reports and geological literature. This combination of applications can be used to identify anomalies in databases, where composition and lithological classification do not match. It can also be used to identify lithological concepts in the literature and infer quantitative values. The resulting semantic tagging opens new possibilities for linking these diverse sources of data.

  11. 30 CFR 875.15 - Reclamation priorities for noncoal program.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... from the extreme danger of adverse effects of mineral mining and processing practices; (2) The protection of public health, safety, and general welfare from the adverse effects of mineral mining and... degraded by the adverse effects of mineral mining and processing practices. (c) Enhancement of facilities...

  12. 30 CFR 875.15 - Reclamation priorities for noncoal program.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... from the extreme danger of adverse effects of mineral mining and processing practices; (2) The protection of public health, safety, and general welfare from the adverse effects of mineral mining and... degraded by the adverse effects of mineral mining and processing practices. (c) Enhancement of facilities...

  13. 30 CFR 875.15 - Reclamation priorities for noncoal program.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... from the extreme danger of adverse effects of mineral mining and processing practices; (2) The protection of public health, safety, and general welfare from the adverse effects of mineral mining and... degraded by the adverse effects of mineral mining and processing practices. (c) Enhancement of facilities...

  14. 30 CFR 875.15 - Reclamation priorities for noncoal program.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... from the extreme danger of adverse effects of mineral mining and processing practices; (2) The protection of public health, safety, and general welfare from the adverse effects of mineral mining and... degraded by the adverse effects of mineral mining and processing practices. (c) Enhancement of facilities...

  15. Development and testing of a text-mining approach to analyse patients' comments on their experiences of colorectal cancer care.

    PubMed

    Wagland, Richard; Recio-Saucedo, Alejandra; Simon, Michael; Bracher, Michael; Hunt, Katherine; Foster, Claire; Downing, Amy; Glaser, Adam; Corner, Jessica

    2016-08-01

    Quality of cancer care may greatly impact on patients' health-related quality of life (HRQoL). Free-text responses to patient-reported outcome measures (PROMs) provide rich data but analysis is time and resource-intensive. This study developed and tested a learning-based text-mining approach to facilitate analysis of patients' experiences of care and develop an explanatory model illustrating impact on HRQoL. Respondents to a population-based survey of colorectal cancer survivors provided free-text comments regarding their experience of living with and beyond cancer. An existing coding framework was tested and adapted, which informed learning-based text mining of the data. Machine-learning algorithms were trained to identify comments relating to patients' specific experiences of service quality, which were verified by manual qualitative analysis. Comparisons between coded retrieved comments and a HRQoL measure (EQ5D) were explored. The survey response rate was 63.3% (21 802/34 467), of which 25.8% (n=5634) participants provided free-text comments. Of retrieved comments on experiences of care (n=1688), over half (n=1045, 62%) described positive care experiences. Most negative experiences concerned a lack of post-treatment care (n=191, 11% of retrieved comments) and insufficient information concerning self-management strategies (n=135, 8%) or treatment side effects (n=160, 9%). Associations existed between HRQoL scores and coded algorithm-retrieved comments. Analysis indicated that the mechanism by which service quality impacted on HRQoL was the extent to which services prevented or alleviated challenges associated with disease and treatment burdens. Learning-based text mining techniques were found useful and practical tools to identify specific free-text comments within a large dataset, facilitating resource-efficient qualitative analysis. This method should be considered for future PROM analysis to inform policy and practice. Study findings indicated that perceived care quality directly impacts on HRQoL. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

  16. 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.

  17. Teleoperated control system for underground room and pillar mining

    DOEpatents

    Mayercheck, William D.; Kwitowski, August J.; Brautigam, Albert L.; Mueller, Brian K.

    1992-01-01

    A teleoperated mining system is provided for remotely controlling the various machines involved with thin seam mining. A thin seam continuous miner located at a mining face includes a camera mounted thereon and a slave computer for controlling the miner and the camera. A plurality of sensors for relaying information about the miner and the face to the slave computer. A slave computer controlled ventilation sub-system which removes combustible material from the mining face. A haulage sub-system removes material mined by the continuous miner from the mining face to a collection site and is also controlled by the slave computer. A base station, which controls the supply of power and water to the continuous miner, haulage system, and ventilation systems, includes cable/hose handling module for winding or unwinding cables/hoses connected to the miner, an operator control module, and a hydraulic power and air compressor module for supplying air to the miner. An operator controlled host computer housed in the operator control module is connected to the slave computer via a two wire communications line.

  18. KSC-2012-3092

    NASA Image and Video Library

    2012-05-26

    CAPE CANAVERAL, Fla. – Teams taking part in NASA's Lunabotics Mining Competition were eligible for unique trophies such as this. The competition challenged university students to build machines that could collect soil such as the material found on the moon. Working inside the Caterpillar LunArena, the robotic craft dug soil that simulated lunar material. The event was judged by a machine's abilities to collect the soil, its design and operation, size, dust tolerance and its level of autonomy. Photo credit: NASA/Jim Grossmann

  19. KSC-2012-3072

    NASA Image and Video Library

    2012-05-22

    CAPE CANAVERAL, Fla. – Participants watch NASA's Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. The competition challenges university students to build machines that can collect soil such as the material found on the moon. Working inside the Caterpillar LunArena, the robotic craft dig soil that simulates lunar material. The event is judged by a machine's abilities to collect the soil, its design and operation, size, dust tolerance and its level of autonomy. Photo credit: NASA/Glenn Benson

  20. Open Research Challenges with Big Data - A Data-Scientist s Perspective

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

    Sukumar, Sreenivas R

    In this paper, we discuss data-driven discovery challenges of the Big Data era. We observe that recent innovations in being able to collect, access, organize, integrate, and query massive amounts of data from a wide variety of data sources have brought statistical data mining and machine learning under more scrutiny and evaluation for gleaning insights from the data than ever before. In that context, we pose and debate the question - Are data mining algorithms scaling with the ability to store and compute? If yes, how? If not, why not? We survey recent developments in the state-of-the-art to discuss emergingmore » and outstanding challenges in the design and implementation of machine learning algorithms at scale. We leverage experience from real-world Big Data knowledge discovery projects across domains of national security, healthcare and manufacturing to suggest our efforts be focused along the following axes: (i) the data science challenge - designing scalable and flexible computational architectures for machine learning (beyond just data-retrieval); (ii) the science of data challenge the ability to understand characteristics of data before applying machine learning algorithms and tools; and (iii) the scalable predictive functions challenge the ability to construct, learn and infer with increasing sample size, dimensionality, and categories of labels. We conclude with a discussion of opportunities and directions for future research.« less

  1. 30 CFR 874.13 - Reclamation objectives and priorities.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... Section 874.13 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE..., safety, and property from extreme danger of adverse effects of coal mining practices, including the... effects of coal mining practices; and (ii) Are adjacent to a site that has been or will be addressed to...

  2. Research on the factors influencing the price of commercial housing based on support vector machine (SVM)

    NASA Astrophysics Data System (ADS)

    Xiaoyang, Zhong; Hong, Ren; Jingxin, Gao

    2018-03-01

    With the gradual maturity of the real estate market in China, urban housing prices are also better able to reflect changes in market demand and the commodity property of commercial housing has become more and more obvious. Many scholars in our country have made a lot of research on the factors that affect the price of commercial housing in the city and the number of related research papers increased rapidly. These scholars’ research results provide valuable wealth to solve the problem of urban housing price changes in our country. However, due to the huge amount of literature, the vast amount of information is submerged in the library and cannot be fully utilized. Text mining technology has been widely concerned and developed in the field of Humanities and Social Sciences in recent years. But through the text mining technology to obtain the influence factors on the price of urban commercial housing is still relatively rare. In this paper, the research results of the existing scholars were excavated by text mining algorithm based on support vector machine in order to further make full use of the current research results and to provide a reference for stabilizing housing prices.

  3. Differential Diagnosis of Erythmato-Squamous Diseases Using Classification and Regression Tree

    PubMed Central

    Maghooli, Keivan; Langarizadeh, Mostafa; Shahmoradi, Leila; Habibi-koolaee, Mahdi; Jebraeily, Mohamad; Bouraghi, Hamid

    2016-01-01

    Introduction: Differential diagnosis of Erythmato-Squamous Diseases (ESD) is a major challenge in the field of dermatology. The ESD diseases are placed into six different classes. Data mining is the process for detection of hidden patterns. In the case of ESD, data mining help us to predict the diseases. Different algorithms were developed for this purpose. Objective: we aimed to use the Classification and Regression Tree (CART) to predict differential diagnosis of ESD. Methods: we used the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. For this purpose, the dermatology data set from machine learning repository, UCI was obtained. The Clementine 12.0 software from IBM Company was used for modelling. In order to evaluation of the model we calculate the accuracy, sensitivity and specificity of the model. Results: The proposed model had an accuracy of 94.84% ( Standard Deviation: 24.42) in order to correct prediction of the ESD disease. Conclusions: Results indicated that using of this classifier could be useful. But, it would be strongly recommended that the combination of machine learning methods could be more useful in terms of prediction of ESD. PMID:28077889

  4. Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets.

    PubMed

    Nariya, Maulik K; Kim, Jae Hyun; Xiong, Jian; Kleindl, Peter A; Hewarathna, Asha; Fisher, Adam C; Joshi, Sangeeta B; Schöneich, Christian; Forrest, M Laird; Middaugh, C Russell; Volkin, David B; Deeds, Eric J

    2017-11-01

    There is growing interest in generating physicochemical and biological analytical data sets to compare complex mixture drugs, for example, products from different manufacturers. In this work, we compare various crofelemer samples prepared from a single lot by filtration with varying molecular weight cutoffs combined with incubation for different times at different temperatures. The 2 preceding articles describe experimental data sets generated from analytical characterization of fractionated and degraded crofelemer samples. In this work, we use data mining techniques such as principal component analysis and mutual information scores to help visualize the data and determine discriminatory regions within these large data sets. The mutual information score identifies chemical signatures that differentiate crofelemer samples. These signatures, in many cases, would likely be missed by traditional data analysis tools. We also found that supervised learning classifiers robustly discriminate samples with around 99% classification accuracy, indicating that mathematical models of these physicochemical data sets are capable of identifying even subtle differences in crofelemer samples. Data mining and machine learning techniques can thus identify fingerprint-type attributes of complex mixture drugs that may be used for comparative characterization of products. Copyright © 2017 American Pharmacists Association®. All rights reserved.

  5. 30 CFR 75.830 - Splicing and repair of trailing cables.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... high-voltage trailing cable within 35 feet of the continuous mining machine is prohibited. (2) Only four (4) splices will be allowed at any one time for the portion of the trailing cable that extends...

  6. 30 CFR 75.830 - Splicing and repair of trailing cables.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... high-voltage trailing cable within 35 feet of the continuous mining machine is prohibited. (2) Only four (4) splices will be allowed at any one time for the portion of the trailing cable that extends...

  7. Mining machine with adjustable jib

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

    Hart, D.

    1987-05-26

    A mining machine is described having a pair of crawler tracks, a means for individually driving each of the crawler tracks, a frame mounted on the crawler tracks, an elongated jib carrying a sprocket at each end, an endless cutting chain supported on the sprockets, cutters and loading flights mounted on the endless cutting chain, and means on the frame supporting the elongated jib. The means support the elongated jib consisting of a bridge on the frame, at least one scissors linkage pivotally mounted on the bridge, and arm having a first end attached to the scissors linkage, a frontmore » plate mounted on the second end of the arm and means adjustably mounting the elongated jib on the front plate. The means adjustably mount the elongated jib on the front plate including a first means for rotating the elongated jib between a vertical position and a horizontal position.« less

  8. In-line drivetrain and four wheel drive work machine using same

    DOEpatents

    Hoff, Brian

    2008-08-05

    A four wheel drive articulated mine loader is powered by a fuel cell and propelled by a single electric motor. The drivetrain has the first axle, second axle, and motor arranged in series on the work machine chassis. Torque is carried from the electric motor to the back differential via a pinion meshed with the ring gear of the back differential. A second pinion oriented in an opposite direction away from the ring gear is coupled to a drive shaft to transfer torque from the ring gear to the differential of the front axle. Thus, the ring gear of the back differential acts both to receive torque from the motor and to transfer torque to the forward axle. The in-line drive configuration includes a single electric motor and a single reduction gear to power the four wheel drive mine loader.

  9. Algorithm of probabilistic assessment of fully-mechanized longwall downtime

    NASA Astrophysics Data System (ADS)

    Domrachev, A. N.; Rib, S. V.; Govorukhin, Yu M.; Krivopalov, V. G.

    2017-09-01

    The problem of increasing the load on a long fully-mechanized longwall has several aspects, one of which is the improvement of efficiency in using available stoping equipment due to the increase in coefficient of the machine operating time of a shearer and other mining machines that form an integral part of the longwall set of equipment. The task of predicting the reliability indicators of stoping equipment is solved by the statistical evaluation of parameters of downtime exponential distribution and failure recovery. It is more difficult to solve the problems of downtime accounting in case of accidents in the face workings and, despite the statistical data on accidents in mine workings, no solution has been found to date. The authors have proposed a variant of probability assessment of workings caving using Poisson distribution and the duration of their restoration using normal distribution. The above results confirm the possibility of implementing the approach proposed by the authors.

  10. Examining the relation between rock mass cuttability index and rock drilling properties

    NASA Astrophysics Data System (ADS)

    Yetkin, Mustafa E.; Özfırat, M. Kemal; Yenice, Hayati; Şimşir, Ferhan; Kahraman, Bayram

    2016-12-01

    Drilling rate is a substantial index value in drilling and excavation operations at mining. It is not only a help in determining physical and mechanical features of rocks, but also delivers strong estimations about instantaneous cutting rates. By this way, work durations to be finished on time, proper machine/equipment selection and efficient excavation works can be achieved. In this study, physical and mechanical properties of surrounding rocks and ore zones are determined by investigations carried out on specimens taken from an underground ore mine. Later, relationships among rock mass classifications, drillability rates, cuttability, and abrasivity have been investigated using multi regression analysis. As a result, equations having high regression rates have been found out among instantaneous cutting rates and geomechanical properties of rocks. Moreover, excavation machine selection for the study area has been made at the best possible interval.

  11. Coal face measurement system for underground use

    NASA Technical Reports Server (NTRS)

    1981-01-01

    A measurement system was developed for the Eickhoff longwall shearer to determine the contour of the coal face as it mines coal. Contour data are obtained by an indirect measurement technique based on evaluating the motion of the shearer during mining. Starting from a known location, points along the coal face are established through a knowledge of the machines' positions and yaw movements as it moves past the coal face. The hardware and system operation procedures are described. The tests of system performance and their results are reported.

  12. Development and implementation of the Good Neighbor Agreement (GNA) practice in the USA sustainable mining development.

    NASA Astrophysics Data System (ADS)

    Masaitis, Alexandra

    2014-05-01

    New economic, environmental and social challenges for the mining industry in the USA show the need to implement "responsible" mining practices that include improved community involvement. Conflicts which occur in the US territory and with US mining companies around the world are now common between the mining proponents, NGO's and communities. These conflicts can sometimes be alleviated by early development of modes of communication, and a formal discussion format that allows airing of concerns and potential resolution of problems. One of the methods that can formalize this process is to establish a Good Neighbor Agreement (GNA), which deals specifically with challenges in relationships between mining operations and the local communities. It is a new practice related to mining operations that are oriented toward social needs and concerns of local communities that arise during the normal life of a mine, which can achieve sustainable mining practices. The GNA project being currently developed at the University of Nevada, USA in cooperation with the Newmont Mining Corporation has a goal of creating an open company/community dialog that will help identify and address sociological and environmental concerns associated with mining. Discussion: The Good Neighbor Agreement currently evolving will address the following: 1. Identify spheres of possible cooperation between mining companies, government organizations, and NGO's. 2. Provide an economically viable mechanism for developing a partnership between mining operations and the local communities that will increase mining industry's accountability and provide higher levels of confidence for the community that a mine is operated in a safe and sustainable manner. Implementation of the GNA can help identify and evaluate conflict criteria in mining/community relationships; determine the status of concerns; determine the role and responsibilities of stakeholders; analyze problem resolution feasibility; maintain the community involvement and support through economic benefits and environmental safeguards; develop options for the concerns resolution. Difficulties in establishing the GNA standards include lack of insurance/bonding policies, and by the lack of audit and monitoring that could determine the level of exposure of the local community and the environment to the contaminants released at the mine sites. Since many problems of mines can occur during closure and post-closure, GNA's should address those issues also. The goal of the GNA is to have open access for the public to the safety, health, and environmental information pertaining to the mining operation, as well as to educate the local communities about mining practices that promote mutual acknowledgment of the need to build a relationship amenable to each other's needs. Frequent conflicts between mining companies and surrounding communities lead to work disruptions or even mine closures and show the necessity of a less confrontational approach to environmental and social justice. The Good Neighbor Agreement is a unique way to provide the benefits for the both mining operations and local community to provide a mechanism for risk redaction and communication that offer the potential to protect both mining and community interests.

  13. Literature classification for semi-automated updating of biological knowledgebases

    PubMed Central

    2013-01-01

    Background As the output of biological assays increase in resolution and volume, the body of specialized biological data, such as functional annotations of gene and protein sequences, enables extraction of higher-level knowledge needed for practical application in bioinformatics. Whereas common types of biological data, such as sequence data, are extensively stored in biological databases, functional annotations, such as immunological epitopes, are found primarily in semi-structured formats or free text embedded in primary scientific literature. Results We defined and applied a machine learning approach for literature classification to support updating of TANTIGEN, a knowledgebase of tumor T-cell antigens. Abstracts from PubMed were downloaded and classified as either "relevant" or "irrelevant" for database update. Training and five-fold cross-validation of a k-NN classifier on 310 abstracts yielded classification accuracy of 0.95, thus showing significant value in support of data extraction from the literature. Conclusion We here propose a conceptual framework for semi-automated extraction of epitope data embedded in scientific literature using principles from text mining and machine learning. The addition of such data will aid in the transition of biological databases to knowledgebases. PMID:24564403

  14. Ringo: Interactive Graph Analytics on Big-Memory Machines

    PubMed Central

    Perez, Yonathan; Sosič, Rok; Banerjee, Arijit; Puttagunta, Rohan; Raison, Martin; Shah, Pararth; Leskovec, Jure

    2016-01-01

    We present Ringo, a system for analysis of large graphs. Graphs provide a way to represent and analyze systems of interacting objects (people, proteins, webpages) with edges between the objects denoting interactions (friendships, physical interactions, links). Mining graphs provides valuable insights about individual objects as well as the relationships among them. In building Ringo, we take advantage of the fact that machines with large memory and many cores are widely available and also relatively affordable. This allows us to build an easy-to-use interactive high-performance graph analytics system. Graphs also need to be built from input data, which often resides in the form of relational tables. Thus, Ringo provides rich functionality for manipulating raw input data tables into various kinds of graphs. Furthermore, Ringo also provides over 200 graph analytics functions that can then be applied to constructed graphs. We show that a single big-memory machine provides a very attractive platform for performing analytics on all but the largest graphs as it offers excellent performance and ease of use as compared to alternative approaches. With Ringo, we also demonstrate how to integrate graph analytics with an iterative process of trial-and-error data exploration and rapid experimentation, common in data mining workloads. PMID:27081215

  15. Ringo: Interactive Graph Analytics on Big-Memory Machines.

    PubMed

    Perez, Yonathan; Sosič, Rok; Banerjee, Arijit; Puttagunta, Rohan; Raison, Martin; Shah, Pararth; Leskovec, Jure

    2015-01-01

    We present Ringo, a system for analysis of large graphs. Graphs provide a way to represent and analyze systems of interacting objects (people, proteins, webpages) with edges between the objects denoting interactions (friendships, physical interactions, links). Mining graphs provides valuable insights about individual objects as well as the relationships among them. In building Ringo, we take advantage of the fact that machines with large memory and many cores are widely available and also relatively affordable. This allows us to build an easy-to-use interactive high-performance graph analytics system. Graphs also need to be built from input data, which often resides in the form of relational tables. Thus, Ringo provides rich functionality for manipulating raw input data tables into various kinds of graphs. Furthermore, Ringo also provides over 200 graph analytics functions that can then be applied to constructed graphs. We show that a single big-memory machine provides a very attractive platform for performing analytics on all but the largest graphs as it offers excellent performance and ease of use as compared to alternative approaches. With Ringo, we also demonstrate how to integrate graph analytics with an iterative process of trial-and-error data exploration and rapid experimentation, common in data mining workloads.

  16. Recent advances in environmental data mining

    NASA Astrophysics Data System (ADS)

    Leuenberger, Michael; Kanevski, Mikhail

    2016-04-01

    Due to the large amount and complexity of data available nowadays in geo- and environmental sciences, we face the need to develop and incorporate more robust and efficient methods for their analysis, modelling and visualization. An important part of these developments deals with an elaboration and application of a contemporary and coherent methodology following the process from data collection to the justification and communication of the results. Recent fundamental progress in machine learning (ML) can considerably contribute to the development of the emerging field - environmental data science. The present research highlights and investigates the different issues that can occur when dealing with environmental data mining using cutting-edge machine learning algorithms. In particular, the main attention is paid to the description of the self-consistent methodology and two efficient algorithms - Random Forest (RF, Breiman, 2001) and Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. Despite the fact that they are based on two different concepts, i.e. decision trees vs artificial neural networks, they both propose promising results for complex, high dimensional and non-linear data modelling. In addition, the study discusses several important issues of data driven modelling, including feature selection and uncertainties. The approach considered is accompanied by simulated and real data case studies from renewable resources assessment and natural hazards tasks. In conclusion, the current challenges and future developments in statistical environmental data learning are discussed. References - Breiman, L., 2001. Random Forests. Machine Learning 45 (1), 5-32. - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392. - Leuenberger, M., Kanevski, M., 2015. Extreme Learning Machines for spatial environmental data. Computers and Geosciences 85, 64-73.

  17. Seminal quality prediction using data mining methods.

    PubMed

    Sahoo, Anoop J; Kumar, Yugal

    2014-01-01

    Now-a-days, some new classes of diseases have come into existences which are known as lifestyle diseases. The main reasons behind these diseases are changes in the lifestyle of people such as alcohol drinking, smoking, food habits etc. After going through the various lifestyle diseases, it has been found that the fertility rates (sperm quantity) in men has considerably been decreasing in last two decades. Lifestyle factors as well as environmental factors are mainly responsible for the change in the semen quality. The objective of this paper is to identify the lifestyle and environmental features that affects the seminal quality and also fertility rate in man using data mining methods. The five artificial intelligence techniques such as Multilayer perceptron (MLP), Decision Tree (DT), Navie Bayes (Kernel), Support vector machine+Particle swarm optimization (SVM+PSO) and Support vector machine (SVM) have been applied on fertility dataset to evaluate the seminal quality and also to predict the person is either normal or having altered fertility rate. While the eight feature selection techniques such as support vector machine (SVM), neural network (NN), evolutionary logistic regression (LR), support vector machine plus particle swarm optimization (SVM+PSO), principle component analysis (PCA), chi-square test, correlation and T-test methods have been used to identify more relevant features which affect the seminal quality. These techniques are applied on fertility dataset which contains 100 instances with nine attribute with two classes. The experimental result shows that SVM+PSO provides higher accuracy and area under curve (AUC) rate (94% & 0.932) among multi-layer perceptron (MLP) (92% & 0.728), Support Vector Machines (91% & 0.758), Navie Bayes (Kernel) (89% & 0.850) and Decision Tree (89% & 0.735) for some of the seminal parameters. This paper also focuses on the feature selection process i.e. how to select the features which are more important for prediction of fertility rate. In this paper, eight feature selection methods are applied on fertility dataset to find out a set of good features. The investigational results shows that childish diseases (0.079) and high fever features (0.057) has less impact on fertility rate while age (0.8685), season (0.843), surgical intervention (0.7683), alcohol consumption (0.5992), smoking habit (0.575), number of hours spent on setting (0.4366) and accident (0.5973) features have more impact. It is also observed that feature selection methods increase the accuracy of above mentioned techniques (multilayer perceptron 92%, support vector machine 91%, SVM+PSO 94%, Navie Bayes (Kernel) 89% and decision tree 89%) as compared to without feature selection methods (multilayer perceptron 86%, support vector machine 86%, SVM+PSO 85%, Navie Bayes (Kernel) 83% and decision tree 84%) which shows the applicability of feature selection methods in prediction. This paper lightens the application of artificial techniques in medical domain. From this paper, it can be concluded that data mining methods can be used to predict a person with or without disease based on environmental and lifestyle parameters/features rather than undergoing various medical test. In this paper, five data mining techniques are used to predict the fertility rate and among which SVM+PSO provide more accurate results than support vector machine and decision tree.

  18. Applications of machine learning and data mining methods to detect associations of rare and common variants with complex traits.

    PubMed

    Lu, Ake Tzu-Hui; Austin, Erin; Bonner, Ashley; Huang, Hsin-Hsiung; Cantor, Rita M

    2014-09-01

    Machine learning methods (MLMs), designed to develop models using high-dimensional predictors, have been used to analyze genome-wide genetic and genomic data to predict risks for complex traits. We summarize the results from six contributions to our Genetic Analysis Workshop 18 working group; these investigators applied MLMs and data mining to analyses of rare and common genetic variants measured in pedigrees. To develop risk profiles, group members analyzed blood pressure traits along with single-nucleotide polymorphisms and rare variant genotypes derived from sequence and imputation analyses in large Mexican American pedigrees. Supervised MLMs included penalized regression with varying penalties, support vector machines, and permanental classification. Unsupervised MLMs included sparse principal components analysis and sparse graphical models. Entropy-based components analyses were also used to mine these data. None of the investigators fully capitalized on the genetic information provided by the complete pedigrees. Their approaches either corrected for the nonindependence of the individuals within the pedigrees or analyzed only those who were independent. Some methods allowed for covariate adjustment, whereas others did not. We evaluated these methods using a variety of metrics. Four contributors conducted primary analyses on the real data, and the other two research groups used the simulated data with and without knowledge of the underlying simulation model. One group used the answers to the simulated data to assess power and type I errors. Although the MLMs applied were substantially different, each research group concluded that MLMs have advantages over standard statistical approaches with these high-dimensional data. © 2014 WILEY PERIODICALS, INC.

  19. 30 CFR 877.13 - Entry and consent to reclaim.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... effects of past coal mining if consent from the owner is obtained. (b) If consent is not obtained, then... mining practices; (2) The adverse effects are at a state where, in the interest of the public health..., control, or prevent the adverse effects of past coal mining practices is not known or readily available...

  20. 30 CFR 56.6102 - Explosive material storage practices.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Explosive material storage practices. 56.6102 Section 56.6102 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-SURFACE METAL AND NONMETAL MINES Explosives Storage § 56.6102 Explosive material storag...

  1. 30 CFR 57.6102 - Explosive material storage practices.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Explosive material storage practices. 57.6102 Section 57.6102 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-UNDERGROUND METAL AND NONMETAL MINES Explosives Storage-Surface and Underground §...

  2. 30 CFR 57.6102 - Explosive material storage practices.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Explosive material storage practices. 57.6102 Section 57.6102 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-UNDERGROUND METAL AND NONMETAL MINES Explosives Storage-Surface and Underground §...

  3. 30 CFR 56.6102 - Explosive material storage practices.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Explosive material storage practices. 56.6102 Section 56.6102 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-SURFACE METAL AND NONMETAL MINES Explosives Storage § 56.6102 Explosive material storag...

  4. 30 CFR 57.6102 - Explosive material storage practices.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Explosive material storage practices. 57.6102 Section 57.6102 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-UNDERGROUND METAL AND NONMETAL MINES Explosives Storage-Surface and Underground §...

  5. 30 CFR 56.6102 - Explosive material storage practices.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Explosive material storage practices. 56.6102 Section 56.6102 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-SURFACE METAL AND NONMETAL MINES Explosives Storage § 56.6102 Explosive material storag...

  6. Gravimetric surveys for assessing rock mass condition around a mine shaft

    NASA Astrophysics Data System (ADS)

    Madej, Janusz

    2017-06-01

    The fundamentals of use of vertical gravimetric surveying method in mine shafts are presented in the paper. The methods of gravimetric measurements and calculation of interval and complex density are discussed in detail. The density calculations are based on an original method accounting for the gravity influence of the mine shaft thus guaranteeing closeness of calculated and real values of density of rocks beyond the shaft lining. The results of many gravimetric surveys performed in shafts are presented and interpreted. As a result, information about the location of heterogeneous zones of work beyond the shaft lining is obtained. In many cases, these zones used to threaten the safe operation of machines and utilities in the shaft.

  7. 30 CFR 75.1908 - Nonpermissible diesel-powered equipment; categories.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... the mine fire fighting and evacuation plan under § 75.1502. [61 FR 55527, Oct. 25, 1996; 70 FR 36347... such machine or device, which job site is occupied by a miner. (d) Diesel-powered ambulances and fire...

  8. CESAR research in intelligent machines

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

    Weisbin, C.R.

    1986-01-01

    The Center for Engineering Systems Advanced Research (CESAR) was established in 1983 as a national center for multidisciplinary, long-range research and development in machine intelligence and advanced control theory for energy-related applications. Intelligent machines of interest here are artificially created operational systems that are capable of autonomous decision making and action. The initial emphasis for research is remote operations, with specific application to dexterous manipulation in unstructured dangerous environments where explosives, toxic chemicals, or radioactivity may be present, or in other environments with significant risk such as coal mining or oceanographic missions. Potential benefits include reduced risk to man inmore » hazardous situations, machine replication of scarce expertise, minimization of human error due to fear or fatigue, and enhanced capability using high resolution sensors and powerful computers. A CESAR goal is to explore the interface between the advanced teleoperation capability of today, and the autonomous machines of the future.« less

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

    Ken L. Stratton

    The objective of this project is to investigate the applicability of a combined Global Positioning System and Inertial Measurement Unit (GPS/IMU) for information based displays on earthmoving machines and for automated earthmoving machines in the future. This technology has the potential of allowing an information-based product like Caterpillar's Computer Aided Earthmoving System (CAES) to operate in areas with satellite shading. Satellite shading is an issue in open pit mining because machines are routinely required to operate close to high walls, which reduces significantly the amount of the visible sky to the GPS antenna mounted on the machine. An inertial measurementmore » unit is a product, which provides data for the calculation of position based on sensing accelerations and rotation rates of the machine's rigid body. When this information is coupled with GPS it results in a positioning system that can maintain positioning capability during time periods of shading.« less

  10. KSC-2012-3069

    NASA Image and Video Library

    2012-05-22

    CAPE CANAVERAL, Fla. – A robotic vehicle takes part in NASA's Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. The competition challenges university students to build machines that can collect soil such as the material found on the moon. Working inside the Caterpillar LunArena, the robotic craft dig soil that simulates lunar material. The event is judged by a machine's abilities to collect the soil, its design and operation, size, dust tolerance and its level of autonomy. Photo credit: NASA/Glenn Benson

  11. KSC-2012-3075

    NASA Image and Video Library

    2012-05-25

    CAPE CANAVERAL, Fla. – A team of competitors prepares for a turn in NASA's Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. The competition challenges university students to build machines that can collect soil such as the material found on the moon. Working inside the Caterpillar LunArena, the robotic craft dig soil that simulates lunar material. The event is judged by a machine's abilities to collect the soil, its design and operation, size, dust tolerance and its level of autonomy. Photo credit: NASA/Glenn Benson

  12. KSC-2012-3077

    NASA Image and Video Library

    2012-05-25

    CAPE CANAVERAL, Fla. – A team of competitors prepares for a turn in NASA's Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. The competition challenges university students to build machines that can collect soil such as the material found on the moon. Working inside the Caterpillar LunArena, the robotic craft dig soil that simulates lunar material. The event is judged by a machine's abilities to collect the soil, its design and operation, size, dust tolerance and its level of autonomy. Photo credit: NASA/Glenn Benson

  13. KSC-2012-3074

    NASA Image and Video Library

    2012-05-25

    CAPE CANAVERAL, Fla. – A team of competitors waits for a turn in NASA's Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. The competition challenges university students to build machines that can collect soil such as the material found on the moon. Working inside the Caterpillar LunArena, the robotic craft dig soil that simulates lunar material. The event is judged by a machine's abilities to collect the soil, its design and operation, size, dust tolerance and its level of autonomy. Photo credit: NASA/Glenn Benson

  14. KSC-2012-3078

    NASA Image and Video Library

    2012-05-25

    CAPE CANAVERAL, Fla. – A videogame simulates driving excavators during NASA's Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. The competition challenges university students to build machines that can collect soil such as the material found on the moon. Working inside the Caterpillar LunArena, the robotic craft dig soil that simulates lunar material. The event is judged by a machine's abilities to collect the soil, its design and operation, size, dust tolerance and its level of autonomy. Photo credit: NASA/Glenn Benson

  15. KSC-2012-3071

    NASA Image and Video Library

    2012-05-22

    CAPE CANAVERAL, Fla. – A robotic mascot moves among participants during NASA's Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. The competition challenges university students to build machines that can collect soil such as the material found on the moon. Working inside the Caterpillar LunArena, the robotic craft dig soil that simulates lunar material. The event is judged by a machine's abilities to collect the soil, its design and operation, size, dust tolerance and its level of autonomy. Photo credit: NASA/Glenn Benson

  16. Principles of adoption of the successful environmental practices used in developed countries into mining industry in developing countries.

    NASA Astrophysics Data System (ADS)

    Masaitis, Alexandra

    2013-04-01

    The successful implementation of the environmental practices in the mining industry is of a paramount importance, as it not only prevents both local and trans-border pollution but also guarantees clean and healthy environment for the people regardless of their place of habitation. It is especially important to encourage the progress of the environmental practices implementation in developing countries because such countries have resource-oriented economy based on exploitation of nonrenewable resources. Poor environmental practices in developing countries will lead to local environmental crises that could eventually spill into surrounding countries including the most economically advanced. This abstract is a summary of a two-year research project attempted (1) to determine deficiencies of the mining sector ecological practices in developing countries and (2) to suggest substitute practices from developed countries that could be adapted to the developing countries reality. The following research methods were used: 1. The method of the system analysis, where the system is an interaction of the sets of environmental practices with the global mining sector; 2. The comparative method of inquiry, where the comparison was made between environmental protection practices as implemented in the US (developed country) and the developing countries such as RF, Mongolia mining sectors; 3. Quantitative date analysis, where date was collected from "The collection of statistic data", Russian Geographic Society Annual reports, the US EPA open reports, and the USGS Reports; The following results were obtained: Identified the systemic crisis of the ecological environmental policies and practices in the mining sector in developing countries based on the exploitation of nonrenewable resources, absence of the ecological interest by the mining companies that lack mechanisms of environmental and public health protection, the lack of insurance policy, the lack of risk assistance, and in the presence of the audit and monitoring that do not address the local conditions of the mining operations. Based on the above the following concepts were thought of to improve the environmental conditions in mining sector: 1. Was developed the Regional Environmental Management principle based on the local conditions such as physiographic region, local population, and socioeconomic conditions of the area; 2. Devised were criteria for the risk assessment for developing countries. Where the fundamental principals were public health, both near and far from the operation, environmental and biodiversity impact, waste management, long- and short- term rehabilitation plans, compliance with international standards and norms. 3. Developed was the mechanism of the economic motivation to make mining operations "environmentally friendly" that includes defrayal of expenses from both direct and indirect damages. 4. Identified were spheres of possible cooperation between mining companies, government organizations, and the NGOs. These include development of international standards for Good Neighbor Agreement, exchange of environmental information, exchange of successful environmental, health, and safety practices between mining operations from developed and developing countries. The study showed the dire necessity for the mining industry that operates in developing countries to adopt the successful environmental practices used in developed countries. To achieve this goal the Regional Environmental Management principle, the risk assessment criteria, the mechanism of the economic motivation and the principles for international cooperation can play an extremely important role.

  17. Large-Scale Linear Optimization through Machine Learning: From Theory to Practical System Design and Implementation

    DTIC Science & Technology

    2016-08-10

    AFRL-AFOSR-JP-TR-2016-0073 Large-scale Linear Optimization through Machine Learning: From Theory to Practical System Design and Implementation ...2016 4.  TITLE AND SUBTITLE Large-scale Linear Optimization through Machine Learning: From Theory to Practical System Design and Implementation 5a...performances on various machine learning tasks and it naturally lends itself to fast parallel implementations . Despite this, very little work has been

  18. 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.

  19. Big data challenges for large radio arrays

    NASA Astrophysics Data System (ADS)

    Jones, D. L.; Wagstaff, K.; Thompson, D. R.; D'Addario, L.; Navarro, R.; Mattmann, C.; Majid, W.; Lazio, J.; Preston, J.; Rebbapragada, U.

    2012-03-01

    Future large radio astronomy arrays, particularly the Square Kilometre Array (SKA), will be able to generate data at rates far higher than can be analyzed or stored affordably with current practices. This is, by definition, a "big data" problem, and requires an end-to-end solution if future radio arrays are to reach their full scientific potential. Similar data processing, transport, storage, and management challenges face next-generation facilities in many other fields. The Jet Propulsion Laboratory is developing technologies to address big data issues, with an emphasis in three areas: 1) Lower-power digital processing architectures to make highvolume data generation operationally affordable, 2) Date-adaptive machine learning algorithms for real-time analysis (or "data triage") of large data volumes, and 3) Scalable data archive systems that allow efficient data mining and remote user code to run locally where the data are stored.

  20. 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

  1. 30 CFR 75.1719-4 - Mining machines, cap lamps; requirements.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... or reflecting tape shall have an area of not less than 10 square inches. (c) Each person who goes... of 6 square inches of reflecting tape or equivalent paint or material on each side and back. [41 FR...

  2. 30 CFR 75.1719-4 - Mining machines, cap lamps; requirements.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... or reflecting tape shall have an area of not less than 10 square inches. (c) Each person who goes... of 6 square inches of reflecting tape or equivalent paint or material on each side and back. [41 FR...

  3. 30 CFR 75.1719-4 - Mining machines, cap lamps; requirements.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... or reflecting tape shall have an area of not less than 10 square inches. (c) Each person who goes... of 6 square inches of reflecting tape or equivalent paint or material on each side and back. [41 FR...

  4. 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

  5. Application of data mining approaches to drug delivery.

    PubMed

    Ekins, Sean; Shimada, Jun; Chang, Cheng

    2006-11-30

    Computational approaches play a key role in all areas of the pharmaceutical industry from data mining, experimental and clinical data capture to pharmacoeconomics and adverse events monitoring. They will likely continue to be indispensable assets along with a growing library of software applications. This is primarily due to the increasingly massive amount of biology, chemistry and clinical data, which is now entering the public domain mainly as a result of NIH and commercially funded projects. We are therefore in need of new methods for mining this mountain of data in order to enable new hypothesis generation. The computational approaches include, but are not limited to, database compilation, quantitative structure activity relationships (QSAR), pharmacophores, network visualization models, decision trees, machine learning algorithms and multidimensional data visualization software that could be used to improve drug delivery after mining public and/or proprietary data. We will discuss some areas of unmet needs in the area of data mining for drug delivery that can be addressed with new software tools or databases of relevance to future pharmaceutical projects.

  6. 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.

  7. jCompoundMapper: An open source Java library and command-line tool for chemical fingerprints

    PubMed Central

    2011-01-01

    Background The decomposition of a chemical graph is a convenient approach to encode information of the corresponding organic compound. While several commercial toolkits exist to encode molecules as so-called fingerprints, only a few open source implementations are available. The aim of this work is to introduce a library for exactly defined molecular decompositions, with a strong focus on the application of these features in machine learning and data mining. It provides several options such as search depth, distance cut-offs, atom- and pharmacophore typing. Furthermore, it provides the functionality to combine, to compare, or to export the fingerprints into several formats. Results We provide a Java 1.6 library for the decomposition of chemical graphs based on the open source Chemistry Development Kit toolkit. We reimplemented popular fingerprinting algorithms such as depth-first search fingerprints, extended connectivity fingerprints, autocorrelation fingerprints (e.g. CATS2D), radial fingerprints (e.g. Molprint2D), geometrical Molprint, atom pairs, and pharmacophore fingerprints. We also implemented custom fingerprints such as the all-shortest path fingerprint that only includes the subset of shortest paths from the full set of paths of the depth-first search fingerprint. As an application of jCompoundMapper, we provide a command-line executable binary. We measured the conversion speed and number of features for each encoding and described the composition of the features in detail. The quality of the encodings was tested using the default parametrizations in combination with a support vector machine on the Sutherland QSAR data sets. Additionally, we benchmarked the fingerprint encodings on the large-scale Ames toxicity benchmark using a large-scale linear support vector machine. The results were promising and could often compete with literature results. On the large Ames benchmark, for example, we obtained an AUC ROC performance of 0.87 with a reimplementation of the extended connectivity fingerprint. This result is comparable to the performance achieved by a non-linear support vector machine using state-of-the-art descriptors. On the Sutherland QSAR data set, the best fingerprint encodings showed a comparable or better performance on 5 of the 8 benchmarks when compared against the results of the best descriptors published in the paper of Sutherland et al. Conclusions jCompoundMapper is a library for chemical graph fingerprints with several tweaking possibilities and exporting options for open source data mining toolkits. The quality of the data mining results, the conversion speed, the LPGL software license, the command-line interface, and the exporters should be useful for many applications in cheminformatics like benchmarks against literature methods, comparison of data mining algorithms, similarity searching, and similarity-based data mining. PMID:21219648

  8. Survey of Machine Learning Methods for Database Security

    NASA Astrophysics Data System (ADS)

    Kamra, Ashish; Ber, Elisa

    Application of machine learning techniques to database security is an emerging area of research. In this chapter, we present a survey of various approaches that use machine learning/data mining techniques to enhance the traditional security mechanisms of databases. There are two key database security areas in which these techniques have found applications, namely, detection of SQL Injection attacks and anomaly detection for defending against insider threats. Apart from the research prototypes and tools, various third-party commercial products are also available that provide database activity monitoring solutions by profiling database users and applications. We present a survey of such products. We end the chapter with a primer on mechanisms for responding to database anomalies.

  9. Paradox in AI - AI 2.0: The Way to Machine Consciousness

    NASA Astrophysics Data System (ADS)

    Palensky, Peter; Bruckner, Dietmar; Tmej, Anna; Deutsch, Tobias

    Artificial Intelligence, the big promise of the last millennium, has apparently made its way into our daily lives. Cell phones with speech control, evolutionary computing in data mining or power grids, optimized via neural network, show its applicability in industrial environments. The original expectation of true intelligence and thinking machines lies still ahead of us. Researchers are, however, optimistic as never before. This paper tries to compare the views, challenges and approaches of several disciplines: engineering, psychology, neuroscience, philosophy. It gives a short introduction to Psychoanalysis, discusses the term consciousness, social implications of intelligent machines, related theories, and expectations and shall serve as a starting point for first attempts of combining these diverse thoughts.

  10. Analysis of the current rib support practices and techniques in U.S. coal mines

    PubMed Central

    Mohamed, Khaled M.; Murphy, Michael M.; Lawson, Heather E.; Klemetti, Ted

    2016-01-01

    Design of rib support systems in U.S. coal mines is based primarily on local practices and experience. A better understanding of current rib support practices in U.S. coal mines is crucial for developing a sound engineering rib support design tool. The objective of this paper is to analyze the current practices of rib control in U.S. coal mines. Twenty underground coal mines were studied representing various coal basins, coal seams, geology, loading conditions, and rib control strategies. The key findings are: (1) any rib design guideline or tool should take into account external rib support as well as internal bolting; (2) rib bolts on their own cannot contain rib spall, especially in soft ribs subjected to significant load—external rib control devices such as mesh are required in such cases to contain rib sloughing; (3) the majority of the studied mines follow the overburden depth and entry height thresholds recommended by the Program Information Bulletin 11-29 issued by the Mine Safety and Health Administration; (4) potential rib instability occurred when certain geological features prevailed—these include draw slate and/or bone coal near the rib/roof line, claystone partings, and soft coal bench overlain by rock strata; (5) 47% of the studied rib spall was classified as blocky—this could indicate a high potential of rib hazards; and (6) rib injury rates of the studied mines for the last three years emphasize the need for more rib control management for mines operating at overburden depths between 152.4 m and 304.8 m. PMID:27648341

  11. Human Systems Integration (HSI) Associated Development Activities in Japan

    DTIC Science & Technology

    2008-06-12

    machine learning and data mining methods. The continuous effort ( KAIZEN ) to improve the analysis phases are illustrated in Figure 14. Although there...model Extraction of a workflow Extraction of a control rule Variation analysis and improvement Plant operation KAIZEN Fig. 14

  12. Support Vector Machines for Multitemporal and Multisensor Change Detection in a Mining Area

    NASA Astrophysics Data System (ADS)

    Hecheltjen, Antje; Waske, Bjorn; Thonfeld, Frank; Braun, Matthias; Menz, Gunter

    2010-12-01

    Long-term change detection often implies the challenge of incorporating multitemporal data from different sensors. Most of the conventional change detection algorithms are designed for bi-temporal datasets from the same sensors detecting only the existence of changes. The labeling of change areas remains a difficult task. To overcome such drawbacks, much attention has been given lately to algorithms arising from machine learning, such as Support Vector Machines (SVMs). While SVMs have been applied successfully for land cover classifications, the exploitation of this approach for change detection is still in its infancy. Few studies have already proven the applicability of SVMs for bi- and multitemporal change detection using data from one sensor only. In this paper we demonstrate the application of SVM for multitemporal and -sensor change detection. Our study site covers lignite open pit mining areas in the German state North Rhine-Westphalia. The dataset consists of bi-temporal Landsat data and multi-temporal ERS SAR data covering two time slots (2001 and 2009). The SVM is conducted using the IDL program imageSVM. Change is deduced from one time slot to the next resulting in two change maps. In contrast to change detection, which is based on post-classification comparison, change detection is seen here as a specific classification problem. Thus, changes are directly classified from a layer-stack of the two years. To reduce the number of change classes, we created a change mask using the magnitude of Change Vector Analysis (CVA). Training data were selected for different change classes (e.g. forest to mining or mining to agriculture) as well as for the no-change classes (e.g. agriculture). Subsequently, they were divided in two independent sets for training the SVMs and accuracy assessment, respectively. Our study shows the applicability of SVMs to classify changes via SVMs. The proposed method yielded a change map of reclaimed and active mines. The use of ERS SAR data, however, did not add to the accuracy compared to Landsat data only. A great advantage compared to other change detection approaches are the labeled change maps, which are a direct output of the methodology. Our approach also overcomes the drawback of post-classification comparison, namely the propagation of classification inaccuracies.

  13. Digital Family History Data Mining with Neural Networks: A Pilot Study.

    PubMed

    Hoyt, Robert; Linnville, Steven; Thaler, Stephen; Moore, Jeffrey

    2016-01-01

    Following the passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, electronic health records were widely adopted by eligible physicians and hospitals in the United States. Stage 2 meaningful use menu objectives include a digital family history but no stipulation as to how that information should be used. A variety of data mining techniques now exist for these data, which include artificial neural networks (ANNs) for supervised or unsupervised machine learning. In this pilot study, we applied an ANN-based simulation to a previously reported digital family history to mine the database for trends. A graphical user interface was created to display the input of multiple conditions in the parents and output as the likelihood of diabetes, hypertension, and coronary artery disease in male and female offspring. The results of this pilot study show promise in using ANNs to data mine digital family histories for clinical and research purposes.

  14. 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.

  15. REMOTE SENSING AND MOUNTAINTOP MINING

    EPA Science Inventory

    Coal mining is Appalachia has undergone dramatic changes in the past decade. Modem mining practices know as Mountaintop Mining (MTM) and Valley Fills (VF) are at the center of an environmental and legal controversy that has spawned lawsuits and major environmental investigations....

  16. A practical guide to big data research in psychology.

    PubMed

    Chen, Eric Evan; Wojcik, Sean P

    2016-12-01

    The massive volume of data that now covers a wide variety of human behaviors offers researchers in psychology an unprecedented opportunity to conduct innovative theory- and data-driven field research. This article is a practical guide to conducting big data research, covering data management, acquisition, processing, and analytics (including key supervised and unsupervised learning data mining methods). It is accompanied by walkthrough tutorials on data acquisition, text analysis with latent Dirichlet allocation topic modeling, and classification with support vector machines. Big data practitioners in academia, industry, and the community have built a comprehensive base of tools and knowledge that makes big data research accessible to researchers in a broad range of fields. However, big data research does require knowledge of software programming and a different analytical mindset. For those willing to acquire the requisite skills, innovative analyses of unexpected or previously untapped data sources can offer fresh ways to develop, test, and extend theories. When conducted with care and respect, big data research can become an essential complement to traditional research. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  17. Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

    PubMed Central

    2011-01-01

    Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing. PMID:21849043

  18. Matisse: A Visual Analytics System for Exploring Emotion Trends in Social Media Text Streams

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

    Steed, Chad A; Drouhard, Margaret MEG G; Beaver, Justin M

    Dynamically mining textual information streams to gain real-time situational awareness is especially challenging with social media systems where throughput and velocity properties push the limits of a static analytical approach. In this paper, we describe an interactive visual analytics system, called Matisse, that aids with the discovery and investigation of trends in streaming text. Matisse addresses the challenges inherent to text stream mining through the following technical contributions: (1) robust stream data management, (2) automated sentiment/emotion analytics, (3) interactive coordinated visualizations, and (4) a flexible drill-down interaction scheme that accesses multiple levels of detail. In addition to positive/negative sentiment prediction,more » Matisse provides fine-grained emotion classification based on Valence, Arousal, and Dominance dimensions and a novel machine learning process. Information from the sentiment/emotion analytics are fused with raw data and summary information to feed temporal, geospatial, term frequency, and scatterplot visualizations using a multi-scale, coordinated interaction model. After describing these techniques, we conclude with a practical case study focused on analyzing the Twitter sample stream during the week of the 2013 Boston Marathon bombings. The case study demonstrates the effectiveness of Matisse at providing guided situational awareness of significant trends in social media streams by orchestrating computational power and human cognition.« less

  19. Smart Point Cloud: Definition and Remaining Challenges

    NASA Astrophysics Data System (ADS)

    Poux, F.; Hallot, P.; Neuville, R.; Billen, R.

    2016-10-01

    Dealing with coloured point cloud acquired from terrestrial laser scanner, this paper identifies remaining challenges for a new data structure: the smart point cloud. This concept arises with the statement that massive and discretized spatial information from active remote sensing technology is often underused due to data mining limitations. The generalisation of point cloud data associated with the heterogeneity and temporality of such datasets is the main issue regarding structure, segmentation, classification, and interaction for an immediate understanding. We propose to use both point cloud properties and human knowledge through machine learning to rapidly extract pertinent information, using user-centered information (smart data) rather than raw data. A review of feature detection, machine learning frameworks and database systems indexed both for mining queries and data visualisation is studied. Based on existing approaches, we propose a new 3-block flexible framework around device expertise, analytic expertise and domain base reflexion. This contribution serves as the first step for the realisation of a comprehensive smart point cloud data structure.

  20. Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method

    NASA Astrophysics Data System (ADS)

    Khandelwal, Manoj; Monjezi, M.

    2013-03-01

    Backbreak is an undesirable phenomenon in blasting operations. It can cause instability of mine walls, falling down of machinery, improper fragmentation, reduced efficiency of drilling, etc. The existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, the application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict backbreak in blasting operations of Soungun iron mine, Iran, incorporating rock properties and blast design parameters using the support vector machine (SVM) method. To investigate the suitability of this approach, the predictions by SVM have been compared with multivariate regression analysis (MVRA). The coefficient of determination (CoD) and the mean absolute error (MAE) were taken as performance measures. It was found that the CoD between measured and predicted backbreak was 0.987 and 0.89 by SVM and MVRA, respectively, whereas the MAE was 0.29 and 1.07 by SVM and MVRA, respectively.

  1. Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT.

    PubMed

    Shouval, R; Bondi, O; Mishan, H; Shimoni, A; Unger, R; Nagler, A

    2014-03-01

    Data collected from hematopoietic SCT (HSCT) centers are becoming more abundant and complex owing to the formation of organized registries and incorporation of biological data. Typically, conventional statistical methods are used for the development of outcome prediction models and risk scores. However, these analyses carry inherent properties limiting their ability to cope with large data sets with multiple variables and samples. Machine learning (ML), a field stemming from artificial intelligence, is part of a wider approach for data analysis termed data mining (DM). It enables prediction in complex data scenarios, familiar to practitioners and researchers. Technological and commercial applications are all around us, gradually entering clinical research. In the following review, we would like to expose hematologists and stem cell transplanters to the concepts, clinical applications, strengths and limitations of such methods and discuss current research in HSCT. The aim of this review is to encourage utilization of the ML and DM techniques in the field of HSCT, including prediction of transplantation outcome and donor selection.

  2. Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology.

    PubMed

    VoPham, Trang; Hart, Jaime E; Laden, Francine; Chiang, Yao-Yi

    2018-04-17

    Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning (e.g., deep learning), data mining, and high-performance computing to extract knowledge from spatial big data. In environmental epidemiology, exposure modeling is a commonly used approach to conduct exposure assessment to determine the distribution of exposures in study populations. geoAI technologies provide important advantages for exposure modeling in environmental epidemiology, including the ability to incorporate large amounts of big spatial and temporal data in a variety of formats; computational efficiency; flexibility in algorithms and workflows to accommodate relevant characteristics of spatial (environmental) processes including spatial nonstationarity; and scalability to model other environmental exposures across different geographic areas. The objectives of this commentary are to provide an overview of key concepts surrounding the evolving and interdisciplinary field of geoAI including spatial data science, machine learning, deep learning, and data mining; recent geoAI applications in research; and potential future directions for geoAI in environmental epidemiology.

  3. KSC-2012-3070

    NASA Image and Video Library

    2012-05-22

    CAPE CANAVERAL, Fla. – A team of competitors works with a robotic vehicle taking part in NASA's Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. The competition challenges university students to build machines that can collect soil such as the material found on the moon. Working inside the Caterpillar LunArena, the robotic craft dig soil that simulates lunar material. The event is judged by a machine's abilities to collect the soil, its design and operation, size, dust tolerance and its level of autonomy. Photo credit: NASA/Glenn Benson

  4. KSC-2012-3093

    NASA Image and Video Library

    2012-05-26

    CAPE CANAVERAL, Fla. – The team from the University of Alabama team took home the Joe Kosmo Award for Excellence for designing and operating the winning robotic vehicle during NASA's Lunabotics Mining Competition. The competition challenged university students to build machines that could collect soil such as the material found on the moon. Working inside the Caterpillar LunArena, the robotic craft dug soil that simulated lunar material. The event was judged by a machine's abilities to collect the soil, its design and operation, size, dust tolerance and its level of autonomy. Photo credit: NASA/Jim Grossmann

  5. 40 CFR 434.82 - Effluent limitations attainable by the application of the best practicable control technology...

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... application of the best practicable control technology currently available (BPT). 434.82 Section 434.82... practicable control technology currently available (BPT). Except as provided in 40 CFR 125.30 through 125.32, the following effluent limitations apply to mine drainage from applicable areas of western coal mining...

  6. 40 CFR 434.82 - Effluent limitations attainable by the application of the best practicable control technology...

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... application of the best practicable control technology currently available (BPT). 434.82 Section 434.82... practicable control technology currently available (BPT). Except as provided in 40 CFR 125.30 through 125.32, the following effluent limitations apply to mine drainage from applicable areas of western coal mining...

  7. 40 CFR 434.82 - Effluent limitations attainable by the application of the best practicable control technology...

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... application of the best practicable control technology currently available (BPT). 434.82 Section 434.82... practicable control technology currently available (BPT). Except as provided in 40 CFR 125.30 through 125.32, the following effluent limitations apply to mine drainage from applicable areas of western coal mining...

  8. The relationship between reinforcement and gaming machine choice.

    PubMed

    Haw, John

    2008-03-01

    The present study assessed whether prior reinforcement experiences were related to gaming machine choice and the decision to change gaming machines during a session of gambling. Seventy undergraduate students (48 women, 22 men; mean age = 22.05 years) were presented with two visually identical simulated gaming machines in a practice phase. These simulated machines differed only in the rate of reinforcement. After the practice phase, participants were asked to choose a machine to play in the test phase and were allowed to change machines at will. Two measures of reinforcement were employed; frequency of wins and payback rate. Results indicated that neither measure of reinforcement was related to machine choice, but both were predictors of when participants changed machines. A post-hoc analysis of the 33 participants who changed machines during the test phase found a significant relationship between machine choice and prior reinforcement. For these participants, payback rate was significantly related to machine choice, unlike frequency of wins.

  9. MANAGEMENT AND TREATMENT OF WATER FROM HARD-ROCK MINES {ENGINEERING ISSUE}

    EPA Science Inventory

    This Engineering Issue document on treatment of mining waters is a practical guide to understanding and selecting technologies for the environmental management of waste materials and effluents at hard-rock mines. For the purposes of this discussion, hard-rock mining primarily ref...

  10. Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy

    PubMed Central

    Knijnenburg, Theo A.; Klau, Gunnar W.; Iorio, Francesco; Garnett, Mathew J.; McDermott, Ultan; Shmulevich, Ilya; Wessels, Lodewyk F. A.

    2016-01-01

    Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. We present ‘Logic Optimization for Binary Input to Continuous Output’ (LOBICO), a computational approach that infers small and easily interpretable logic models of binary input features that explain a continuous output variable. Applying LOBICO to a large cancer cell line panel, we find that logic combinations of multiple mutations are more predictive of drug response than single gene predictors. Importantly, we show that the use of the continuous information leads to robust and more accurate logic models. LOBICO implements the ability to uncover logic models around predefined operating points in terms of sensitivity and specificity. As such, it represents an important step towards practical application of interpretable logic models. PMID:27876821

  11. Learning classification with auxiliary probabilistic information

    PubMed Central

    Nguyen, Quang; Valizadegan, Hamed; Hauskrecht, Milos

    2012-01-01

    Finding ways of incorporating auxiliary information or auxiliary data into the learning process has been the topic of active data mining and machine learning research in recent years. In this work we study and develop a new framework for classification learning problem in which, in addition to class labels, the learner is provided with an auxiliary (probabilistic) information that reflects how strong the expert feels about the class label. This approach can be extremely useful for many practical classification tasks that rely on subjective label assessment and where the cost of acquiring additional auxiliary information is negligible when compared to the cost of the example analysis and labelling. We develop classification algorithms capable of using the auxiliary information to make the learning process more efficient in terms of the sample complexity. We demonstrate the benefit of the approach on a number of synthetic and real world data sets by comparing it to the learning with class labels only. PMID:25309141

  12. Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy.

    PubMed

    Knijnenburg, Theo A; Klau, Gunnar W; Iorio, Francesco; Garnett, Mathew J; McDermott, Ultan; Shmulevich, Ilya; Wessels, Lodewyk F A

    2016-11-23

    Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. We present 'Logic Optimization for Binary Input to Continuous Output' (LOBICO), a computational approach that infers small and easily interpretable logic models of binary input features that explain a continuous output variable. Applying LOBICO to a large cancer cell line panel, we find that logic combinations of multiple mutations are more predictive of drug response than single gene predictors. Importantly, we show that the use of the continuous information leads to robust and more accurate logic models. LOBICO implements the ability to uncover logic models around predefined operating points in terms of sensitivity and specificity. As such, it represents an important step towards practical application of interpretable logic models.

  13. KSC-2012-2886

    NASA Image and Video Library

    2012-05-21

    CAPE CANAVERAL, Fla. – Practice sessions get under way for the third annual Lunabotics Mining Competition at the Rocket Garden at NASA’s Kennedy Space Center Visitor Complex in Florida. More than 50 teams of undergraduate and graduate students from eight countries are participating. The teams have designed and built remote-controlled or autonomous robots that can excavate simulated lunar soil. During the competition, the teams' designs, known as lunabots, will go head-to-head to determine whose machine can collect and deposit the most simulated moon dust within a specified amount of time. The competition is a NASA Human Exploration and Operations Mission Directorate project designed to engage and retain students in the science, technology, engineering and mathematics, or STEM, fields of study. The project provides a competitive environment that may result in innovative ideas and solutions that potentially could be applied to future NASA missions. For more information, visit http://www.nasa.gov/lunabotics. Photo credit: NASA/Frankie Martin

  14. Learning by doing at the Colorado School of Mines

    NASA Astrophysics Data System (ADS)

    Furtak, Thomas E.; Ruskell, Todd G.

    2013-03-01

    With over 260 majors, the undergraduate physics program at CSM is among the largest in the country. An underlying theme in this success is experiential learning, starting with a studio teaching method in the introductory calculus-based physics courses. After their second year students complete a 6-week full-time summer course devoted to hands-on practical knowledge and skills, including machine shop techniques, high-vacuum technology, applied optics, electronic control systems, and computational tools. This precedes a two-semester laboratory sequence that can be taught at an advanced level because of the students' experience. The required capstone senior course is a year-long open-ended challenge in which students partner with members of the faculty to work on authentic research projects, teaming with grad students or post-docs as contributing members to the department's externally funded scholarship. All of these features are important components of our B.S. degree, Engineering Physics, which is officially accredited by ABET.

  15. Peaceful Uses of Fusion

    DOE R&D Accomplishments Database

    Teller, E.

    1958-07-03

    Applications of thermonuclear energy for peaceful and constructive purposes are surveyed. Developments and problems in the release and control of fusion energy are reviewed. It is pointed out that the future of thermonuclear power reactors will depend upon the construction of a machine that produces more electric energy than it consumes. The fuel for thermonuclear reactors is cheap and practically inexhaustible. Thermonuclear reactors produce less dangerous radioactive materials than fission reactors and, when once brought under control, are not as likely to be subject to dangerous excursions. The interaction of the hot plasma with magnetic fields opens the way for the direct production of electricity. It is possible that explosive fusion energy released underground may be harnessed for the production of electricity before the same feat is accomplished in controlled fusion processes. Applications of underground detonations of fission devices in mining and for the enhancement of oil flow in large low-specific-yield formations are also suggested.

  16. 30 CFR 75.342 - Methane monitors.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Methane monitors. 75.342 Section 75.342 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Ventilation § 75.342 Methane monitors. (a)(1) MSHA approved methane monitors shall be installed on all face cutting machines, continuous miners, longwall face...

  17. 30 CFR 75.825 - Power centers.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ....825 Power centers. (a) Main disconnecting switch. The power center supplying high voltage power to the continuous mining machine must be equipped with a main disconnecting switch that, when in the open position... the main disconnecting switch required in paragraph (a) of this section, the power center must be...

  18. 30 CFR 75.825 - Power centers.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ....825 Power centers. (a) Main disconnecting switch. The power center supplying high voltage power to the continuous mining machine must be equipped with a main disconnecting switch that, when in the open position... the main disconnecting switch required in paragraph (a) of this section, the power center must be...

  19. 30 CFR 75.825 - Power centers.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ....825 Power centers. (a) Main disconnecting switch. The power center supplying high voltage power to the continuous mining machine must be equipped with a main disconnecting switch that, when in the open position... the main disconnecting switch required in paragraph (a) of this section, the power center must be...

  20. 30 CFR 56.6405 - Firing devices.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... HEALTH SAFETY AND HEALTH STANDARDS-SURFACE METAL AND NONMETAL MINES Explosives Electric Blasting § 56... all electric detonators to be fired with the type of circuits used. Storage or dry cell batteries are not permitted as power sources. (b) Blasting machines shall be tested, repaired, and maintained in...

  1. Author Detection on a Mobile Phone

    DTIC Science & Technology

    2011-03-01

    handwriting , and to mine sales data for profitable trends. Two broad categories of machine learning are supervised learn- ing and unsupervised learning...evaluation,” AI 2006: Advances in Artificial Intelligence, p. 1015–1021, 2006. [23] “Gartner says worldwide mobile phone sales grew 17 per cent in first

  2. 30 CFR 75.342 - Methane monitors.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Methane monitors. 75.342 Section 75.342 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Ventilation § 75.342 Methane monitors. (a)(1) MSHA approved methane monitors shall be installed on all face cutting machines, continuous miners, longwall face...

  3. 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.

  4. Exploring virtual mental practice in maintenance task training.

    PubMed

    Bauerle, Tim; Brnich, Michael J; Navoyski, Jason

    - This paper aims to contribute to a general understanding of mental practice by investigating the utility of and participant reaction to a virtual reality maintenance training among underground coal mine first responders. - Researchers at the National Institute for Occupational Safety and Health's Office of Mine Safety and Health Research (OMSHR) developed software to provide opportunities for mine rescue team members to learn to inspect, assemble and test their closed-circuit breathing apparatus and to practice those skills. In total, 31 mine rescue team members utilized OMSHR's BG 4 Benching Trainer software and provided feedback to the development team. After training, participants completed a brief post-training questionnaire, which included demographics, perceived training climate and general training evaluation items. - The results overall indicate a generally positive reaction to and high perceived utility of the BG 4 benching software. In addition, the perceived training climate appears to have an effect on the perceived utility of the mental practice virtual reality game, with benchmen from mines with more positive training climates reporting greater perceived efficacy in the training's ability to prepare trainees for real emergencies. - This paper helps to broaden current applications of mental practice and is one of the few empirical investigations into a non-rehabilitation virtual reality extension of mental practice. This paper also contributes to the growing literature advocating for greater usage of accurate and well-informed mental practice techniques, tools and methodologies, especially for occupational populations with limitations on exposure to hands-on training.

  5. CANFAR + Skytree: Mining Massive Datasets as an Essential Part of the Future of Astronomy

    NASA Astrophysics Data System (ADS)

    Ball, Nicholas M.

    2013-01-01

    The future study of large astronomical datasets, consisting of hundreds of millions to billions of objects, will be dominated by large computing resources, and by analysis tools of the necessary scalability and sophistication to extract useful information. Significant effort will be required to fulfil their potential as a provider of the next generation of science results. To-date, computing systems have allowed either sophisticated analysis of small datasets, e.g., most astronomy software, or simple analysis of large datasets, e.g., database queries. At the Canadian Astronomy Data Centre, we have combined our cloud computing system, the Canadian Advanced Network for Astronomical Research (CANFAR), with the world's most advanced machine learning software, Skytree, to create the world's first cloud computing system for data mining in astronomy. This allows the full sophistication of the huge fields of data mining and machine learning to be applied to the hundreds of millions of objects that make up current large datasets. CANFAR works by utilizing virtual machines, which appear to the user as equivalent to a desktop. Each machine is replicated as desired to perform large-scale parallel processing. Such an arrangement carries far more flexibility than other cloud systems, because it enables the user to immediately install and run the same code that they already utilize for science on their desktop. We demonstrate the utility of the CANFAR + Skytree system by showing science results obtained, including assigning photometric redshifts with full probability density functions (PDFs) to a catalog of approximately 133 million galaxies from the MegaPipe reductions of the Canada-France-Hawaii Telescope Legacy Wide and Deep surveys. Each PDF is produced nonparametrically from 100 instances of the photometric parameters for each galaxy, generated by perturbing within the errors on the measurements. Hence, we produce, store, and assign redshifts to, a catalog of over 13 billion object instances. This catalog is comparable in size to those expected from next-generation surveys, such as Large Synoptic Survey Telescope. The CANFAR+Skytree system is open for use by any interested member of the astronomical community.

  6. Super short term forecasting of photovoltaic power generation output in micro grid

    NASA Astrophysics Data System (ADS)

    Gong, Cheng; Ma, Longfei; Chi, Zhongjun; Zhang, Baoqun; Jiao, Ran; Yang, Bing; Chen, Jianshu; Zeng, Shuang

    2017-01-01

    The prediction model combining data mining and support vector machine (SVM) was built. Which provide information of photovoltaic (PV) power generation output for economic operation and optimal control of micro gird, and which reduce influence of power system from PV fluctuation. Because of the characteristic which output of PV rely on radiation intensity, ambient temperature, cloudiness, etc., so data mining was brought in. This technology can deal with large amounts of historical data and eliminate superfluous data, by using fuzzy classifier of daily type and grey related degree. The model of SVM was built, which can dock with information from data mining. Based on measured data from a small PV station, the prediction model was tested. The numerical example shows that the prediction model is fast and accurate.

  7. Screening Electronic Health Record-Related Patient Safety Reports Using Machine Learning.

    PubMed

    Marella, William M; Sparnon, Erin; Finley, Edward

    2017-03-01

    The objective of this study was to develop a semiautomated approach to screening cases that describe hazards associated with the electronic health record (EHR) from a mandatory, population-based patient safety reporting system. Potentially relevant cases were identified through a query of the Pennsylvania Patient Safety Reporting System. A random sample of cases were manually screened for relevance and divided into training, testing, and validation data sets to develop a machine learning model. This model was used to automate screening of remaining potentially relevant cases. Of the 4 algorithms tested, a naive Bayes kernel performed best, with an area under the receiver operating characteristic curve of 0.927 ± 0.023, accuracy of 0.855 ± 0.033, and F score of 0.877 ± 0.027. The machine learning model and text mining approach described here are useful tools for identifying and analyzing adverse event and near-miss reports. Although reporting systems are beginning to incorporate structured fields on health information technology and the EHR, these methods can identify related events that reporters classify in other ways. These methods can facilitate analysis of legacy safety reports by retrieving health information technology-related and EHR-related events from databases without fields and controlled values focused on this subject and distinguishing them from reports in which the EHR is mentioned only in passing. Machine learning and text mining are useful additions to the patient safety toolkit and can be used to semiautomate screening and analysis of unstructured text in safety reports from frontline staff.

  8. Issues of Exploitation of Induction Motors in the Course of Underground Mining Operations

    NASA Astrophysics Data System (ADS)

    Gumula, Stanisław; Hudy, Wiktor; Piaskowska-Silarska, Malgorzata; Pytel, Krzysztof

    2017-09-01

    Mining industry is one of the most important customers of electric motors. The most commonly used in the contemporary mining industry is alternating current machines used for processing electrical energy into mechanical energy. The operating problems and the influence of qualitative interference acting on the inputs of individual regulators to field-oriented system in the course of underground mining operations has been presented in the publication. The object of controlling the speed is a slip-ring induction motor. Settings of regulators were calculated using an evolutionary algorithm. Examination of system dynamics was performed by a computer with the use of the MATLAB / Simulink software. According to analyzes, large distortion of input signals of regulators adversely affects the rotational speed that pursued by the control system, which may cause a large vibration of the whole system and, consequently, its much faster destruction. Designed system is characterized by a significantly better resistance to interference. The system is stable with the properly selected settings of regulators, which is particularly important during the operation of machinery used in underground mining.

  9. Hybrid EEG-EOG brain-computer interface system for practical machine control.

    PubMed

    Punsawad, Yunyong; Wongsawat, Yodchanan; Parnichkun, Manukid

    2010-01-01

    Practical issues such as accuracy with various subjects, number of sensors, and time for training are important problems of existing brain-computer interface (BCI) systems. In this paper, we propose a hybrid framework for the BCI system that can make machine control more practical. The electrooculogram (EOG) is employed to control the machine in the left and right directions while the electroencephalogram (EEG) is employed to control the forword, no action, and complete stop motions of the machine. By using only 2-channel biosignals, the average classification accuracy of more than 95% can be achieved.

  10. Impact of surface coal mining on soil hydraulic properties

    Treesearch

    X. Liu; J. Q. Wu; P. W. Conrad; S. Dun; C. S. Todd; R. L. McNearny; William Elliot; H. Rhee; P. Clark

    2016-01-01

    Soil erosion is strongly related to soil hydraulic properties. Understanding how surface coal mining affects these properties is therefore important in developing effective management practices to control erosion during reclamation. To determine the impact of mining activities on soil hydraulic properties, soils from undisturbed areas, areas of roughly graded mine...

  11. Semi-Supervised Clustering for High-Dimensional and Sparse Features

    ERIC Educational Resources Information Center

    Yan, Su

    2010-01-01

    Clustering is one of the most common data mining tasks, used frequently for data organization and analysis in various application domains. Traditional machine learning approaches to clustering are fully automated and unsupervised where class labels are unknown a priori. In real application domains, however, some "weak" form of side…

  12. 30 CFR 27.21 - Methane-monitoring system.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Methane-monitoring system. 27.21 Section 27.21... APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS Construction and Design Requirements § 27.21 Methane-monitoring system. (a) A methane-monitoring system shall be so designed that any machine or equipment, which...

  13. Generating a Spanish Affective Dictionary with Supervised Learning Techniques

    ERIC Educational Resources Information Center

    Bermudez-Gonzalez, Daniel; Miranda-Jiménez, Sabino; García-Moreno, Raúl-Ulises; Calderón-Nepamuceno, Dora

    2016-01-01

    Nowadays, machine learning techniques are being used in several Natural Language Processing (NLP) tasks such as Opinion Mining (OM). OM is used to analyse and determine the affective orientation of texts. Usually, OM approaches use affective dictionaries in order to conduct sentiment analysis. These lexicons are labeled manually with affective…

  14. The Technology Review 10: Emerging Technologies that Will Change the World.

    ERIC Educational Resources Information Center

    Technology Review, 2001

    2001-01-01

    Identifies 10 emerging areas of technology that will soon have a profound impact on the economy and on how people live and work: brain-machine interfaces; flexible transistors; data mining; digital rights management; biometrics; natural language processing; microphotonics; untangling code; robot design; and microfluidics. In each area, one…

  15. 30 CFR 27.21 - Methane-monitoring system.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Methane-monitoring system. 27.21 Section 27.21... APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS Construction and Design Requirements § 27.21 Methane-monitoring system. (a) A methane-monitoring system shall be so designed that any machine or equipment, which...

  16. 30 CFR 27.21 - Methane-monitoring system.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Methane-monitoring system. 27.21 Section 27.21... APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS Construction and Design Requirements § 27.21 Methane-monitoring system. (a) A methane-monitoring system shall be so designed that any machine or equipment, which...

  17. 30 CFR 27.21 - Methane-monitoring system.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Methane-monitoring system. 27.21 Section 27.21... APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS Construction and Design Requirements § 27.21 Methane-monitoring system. (a) A methane-monitoring system shall be so designed that any machine or equipment, which...

  18. 30 CFR 27.21 - Methane-monitoring system.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Methane-monitoring system. 27.21 Section 27.21... APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS Construction and Design Requirements § 27.21 Methane-monitoring system. (a) A methane-monitoring system shall be so designed that any machine or equipment, which...

  19. Obtaining Accurate Probabilities Using Classifier Calibration

    ERIC Educational Resources Information Center

    Pakdaman Naeini, Mahdi

    2016-01-01

    Learning probabilistic classification and prediction models that generate accurate probabilities is essential in many prediction and decision-making tasks in machine learning and data mining. One way to achieve this goal is to post-process the output of classification models to obtain more accurate probabilities. These post-processing methods are…

  20. 30 CFR 75.205 - Installation of roof support using mining machines with integral roof bolters.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... feet or more apart shall be installed with a wooden crossbar at least 3 inches thick and 8 inches wide... less than 9 feet apart shall be installed with a wooden plank at least 2 inches thick and 8 inches wide...

  1. Topic Models for Link Prediction in Document Networks

    ERIC Educational Resources Information Center

    Kataria, Saurabh

    2012-01-01

    Recent explosive growth of interconnected document collections such as citation networks, network of web pages, content generated by crowd-sourcing in collaborative environments, etc., has posed several challenging problems for data mining and machine learning community. One central problem in the domain of document networks is that of "link…

  2. Using MSD prevention for cultural change in mining: Queensland Government/Anglo Coal Industry partnership.

    PubMed

    Tilbury, Trudy; Sanderson, Liz

    2012-01-01

    Queensland Mining has a strong focus on safety performance, but risk management of health, including Musculoskeletal Disorders (MSDs) continues to have a lower priority. The reliance on individual screening of workers and lower level approaches such as manual handling training is part of the coal mining 'culture'. Initiatives such as the New South Wales and Queensland Mining joint project to develop good practice guidance for mining has allowed for a more consistent message on participatory ergonomics and prevention of MSD. An evidence based practice approach, including the introduction of participatory ergonomics and safe design principles, was proposed to Anglo American Coal operations in Queensland. The project consisted of a skills analysis of current health personnel, design of a facilitated participatory ergonomics training program, site visits to identify good practice and champions, and a graduated mentoring program for health personnel. Early results demonstrate a number of sites are benefiting from site taskforces with a focus on positive performance outcomes.

  3. Characterization of airborne float coal dust emitted during continuous mining, longwall mining and belt transport.

    PubMed

    Shahan, M R; Seaman, C E; Beck, T W; Colinet, J F; Mischler, S E

    2017-09-01

    Float coal dust is produced by various mining methods, carried by ventilating air and deposited on the floor, roof and ribs of mine airways. If deposited, float dust is re-entrained during a methane explosion. Without sufficient inert rock dust quantities, this float coal dust can propagate an explosion throughout mining entries. Consequently, controlling float coal dust is of critical interest to mining operations. Rock dusting, which is the adding of inert material to airway surfaces, is the main control technique currently used by the coal mining industry to reduce the float coal dust explosion hazard. To assist the industry in reducing this hazard, the Pittsburgh Mining Research Division of the U.S. National Institute for Occupational Safety and Health initiated a project to investigate methods and technologies to reduce float coal dust in underground coal mines through prevention, capture and suppression prior to deposition. Field characterization studies were performed to determine quantitatively the sources, types and amounts of dust produced during various coal mining processes. The operations chosen for study were a continuous miner section, a longwall section and a coal-handling facility. For each of these operations, the primary dust sources were confirmed to be the continuous mining machine, longwall shearer and conveyor belt transfer points, respectively. Respirable and total airborne float dust samples were collected and analyzed for each operation, and the ratio of total airborne float coal dust to respirable dust was calculated. During the continuous mining process, the ratio of total airborne float coal dust to respirable dust ranged from 10.3 to 13.8. The ratios measured on the longwall face were between 18.5 and 21.5. The total airborne float coal dust to respirable dust ratio observed during belt transport ranged between 7.5 and 21.8.

  4. Identification of Shearer Cutting Patterns Using Vibration Signals Based on a Least Squares Support Vector Machine with an Improved Fruit Fly Optimization Algorithm

    PubMed Central

    Si, Lei; Wang, Zhongbin; Liu, Xinhua; Tan, Chao; Liu, Ze; Xu, Jing

    2016-01-01

    Shearers play an important role in fully mechanized coal mining face and accurately identifying their cutting pattern is very helpful for improving the automation level of shearers and ensuring the safety of coal mining. The least squares support vector machine (LSSVM) has been proven to offer strong potential in prediction and classification issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. In this paper, an improved fly optimization algorithm (IFOA) to optimize the parameters of LSSVM was presented and the LSSVM coupled with IFOA (IFOA-LSSVM) was used to identify the shearer cutting pattern. The vibration acceleration signals of five cutting patterns were collected and the special state features were extracted based on the ensemble empirical mode decomposition (EEMD) and the kernel function. Some examples on the IFOA-LSSVM model were further presented and the results were compared with LSSVM, PSO-LSSVM, GA-LSSVM and FOA-LSSVM models in detail. The comparison results indicate that the proposed approach was feasible, efficient and outperformed the others. Finally, an industrial application example at the coal mining face was demonstrated to specify the effect of the proposed system. PMID:26771615

  5. A Review of Lunar Regolith Excavation Robotic Device Prototypes

    NASA Technical Reports Server (NTRS)

    Mueller, Robert P.; Van Susante, Paul J.

    2011-01-01

    The excavation of lunar regolith is desirable for use as a feedstock for oxygen production processes as well as civil engineering purposes and for the fabrication of parts and structures. This is known as In-Situ Resource Utilization (ISRU). More recently, there has been mounting evidence that water ice exists at the poles of the Moon, buried in the regolith where thermally stable conditions exist. This means that regolith excavation will be required to mine the water ice which is believed to be. mixed in with the regolith, or bonded to it. The mined water ice can then be electrolyzed to produce hydrogen and oxygen propellants which could form the basis of a cis-lunar transportation system using in-situ derived propellants. In 2007, the National Aeronautics & Space Administration (NASA) sponsored a Lunar Regolith Excavation Competition as part of its Centennial Challenges program, The competition was not won and it was held again in 2008 and 2009, when it was won by a university team. A $500,000 prize was awarded to the winning team by NASA. In 2010, NASA continued the competition as a spinoff of the Centennial Challenges, which is restricted to university participation only. This competition is known as the "Lunabotics Mining Competition" and is hosted by NASA at Kennedy Space Center. Twenty three American university teams competed in the 2010 Lunabotics Mining Competition. The competition was held again in May 2011 with over 60 teams registered, including international participation. The competition will be held again in May 2012 at Kennedy Space Center in Florida. . This paper contains a thorough review of the various regolith eX,cavation robotic device prototypes that competed in these NASA competitions, and will. classify the machines and their methods of excavation to document the variety of ideas that were spawned and built to compete at these events. It is hoped that documentation of these robots will serve to help future robotic excavation designers and provide a historical reference for future lunar mining machine endeavors.

  6. Production Machine Shop Employment Competencies. Part One: Practices and Principles.

    ERIC Educational Resources Information Center

    Bishart, Gus; Werner, Claire

    Competencies for production machine shop are provided for the first of four topic areas: principles and practice of machine shop. Each competency appears in a one-page format. It is presented as a goal statement followed by one or more "indicator" statements, which are performance objectives describing an ability that, upon attainment,…

  7. 75 FR 17410 - Agency Forms Undergoing Paperwork Reduction Act Review

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-04-06

    ... the practical barriers that may prevent wider adoption of NIOSH recommendations and practices designed to safeguard mine workers. In the Spring of 2007, NIOSH conducted a pretest of the survey questionnaire with nine underground coal mine operators. The pretest instrument contained 81 questions...

  8. 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...

  9. Mining Company Involved in Environmental Disaster Now Advises Sustainability Institute at University of Michigan

    ERIC Educational Resources Information Center

    Blumenstyk, Goldie

    2007-01-01

    In the 1990s, the giant mining company now known as BHP Billiton drew worldwide condemnation for the environmental damage caused by its copper and gold mine in Papua, New Guinea. Its mining practices destroyed the way of life of thousands of farming and fishing families who lived along and subsisted on the rivers polluted by the mine, and it was…

  10. A New Data Mining Scheme Using Artificial Neural Networks

    PubMed Central

    Kamruzzaman, S. M.; Jehad Sarkar, A. M.

    2011-01-01

    Classification is one of the data mining problems receiving enormous attention in the database community. Although artificial neural networks (ANNs) have been successfully applied in a wide range of machine learning applications, they are however often regarded as black boxes, i.e., their predictions cannot be explained. To enhance the explanation of ANNs, a novel algorithm to extract symbolic rules from ANNs has been proposed in this paper. ANN methods have not been effectively utilized for data mining tasks because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by human experts. With the proposed approach, concise symbolic rules with high accuracy, that are easily explainable, can be extracted from the trained ANNs. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the accuracy. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of benchmark data mining classification problems. PMID:22163866

  11. A new genome-mining tool redefines the lasso peptide biosynthetic landscape

    PubMed Central

    Tietz, Jonathan I.; Schwalen, Christopher J.; Patel, Parth S.; Maxson, Tucker; Blair, Patricia M.; Tai, Hua-Chia; Zakai, Uzma I.; Mitchell, Douglas A.

    2016-01-01

    Ribosomally synthesized and post-translationally modified peptide (RiPP) natural products are attractive for genome-driven discovery and re-engineering, but limitations in bioinformatic methods and exponentially increasing genomic data make large-scale mining difficult. We report RODEO (Rapid ORF Description and Evaluation Online), which combines hidden Markov model-based analysis, heuristic scoring, and machine learning to identify biosynthetic gene clusters and predict RiPP precursor peptides. We initially focused on lasso peptides, which display intriguing physiochemical properties and bioactivities, but their hypervariability renders them challenging prospects for automated mining. Our approach yielded the most comprehensive mapping of lasso peptide space, revealing >1,300 compounds. We characterized the structures and bioactivities of six lasso peptides, prioritized based on predicted structural novelty, including an unprecedented handcuff-like topology and another with a citrulline modification exceptionally rare among bacteria. These combined insights significantly expand the knowledge of lasso peptides, and more broadly, provide a framework for future genome-mining efforts. PMID:28244986

  12. Effects of retrofit emission controls and work practices on perchloroethylene exposures in small dry-cleaning shops.

    PubMed

    Ewers, Lynda M; Ruder, Avima M; Petersen, Martin R; Earnest, G Scott; Goldenhar, Linda M

    2002-02-01

    The effectiveness of commercially available interventions for reducing workers' perchloroethylene exposures in three small dry-cleaning shops was evaluated. Depending upon machine configuration, the intervention consisted of the addition of either a refrigerated condenser or a closed-loop carbon adsorber to the existing dry-cleaning machine. These relatively inexpensive (less than $5000) engineering controls were designed to reduce perchloroethylene emissions when dry-cleaning machine doors were opened for loading or unloading. Effectiveness of the interventions was judged by comparing pre- and postintervention perchloroethylene exposures using three types of measurements in each shop: (1) full-shift, personal breathing zone, air monitoring, (2) next-morning, end-exhaled worker breath concentrations of perchloroethylene, and (3) differences in the end-exhaled breath perchloroethylene concentrations before and after opening the dry-cleaning machine door. In general, measurements supported the hypothesis that machine operators' exposures to perchloroethylene can be reduced. However, work practices, especially maintenance practices, influenced exposures more than was originally anticipated. Only owners of dry-cleaning machines in good repair, with few leaks, should consider retrofitting them, and only after consultation with their machine's manufacturer. If machines are in poor condition, a new machine or alternative technology should be considered. Shop owners and employees should never circumvent safety features on dry-cleaning machines.

  13. Privacy-preserving restricted boltzmann machine.

    PubMed

    Li, Yu; Zhang, Yuan; Ji, Yue

    2014-01-01

    With the arrival of the big data era, it is predicted that distributed data mining will lead to an information technology revolution. To motivate different institutes to collaborate with each other, the crucial issue is to eliminate their concerns regarding data privacy. In this paper, we propose a privacy-preserving method for training a restricted boltzmann machine (RBM). The RBM can be got without revealing their private data to each other when using our privacy-preserving method. We provide a correctness and efficiency analysis of our algorithms. The comparative experiment shows that the accuracy is very close to the original RBM model.

  14. Privacy-Preserving Restricted Boltzmann Machine

    PubMed Central

    Li, Yu

    2014-01-01

    With the arrival of the big data era, it is predicted that distributed data mining will lead to an information technology revolution. To motivate different institutes to collaborate with each other, the crucial issue is to eliminate their concerns regarding data privacy. In this paper, we propose a privacy-preserving method for training a restricted boltzmann machine (RBM). The RBM can be got without revealing their private data to each other when using our privacy-preserving method. We provide a correctness and efficiency analysis of our algorithms. The comparative experiment shows that the accuracy is very close to the original RBM model. PMID:25101139

  15. Abandoned Mine Lands Program - Division of Mining, Land, and Water

    Science.gov Websites

    , safety, general welfare and property from extreme danger resulting from the adverse effects of past coal mining practices. 2. Protection of public health, safety and general welfare from adverse effects of past lands and waters and the environment previously degraded by adverse effects of past coal mining

  16. Using Data Mining to Teach Applied Statistics and Correlation

    ERIC Educational Resources Information Center

    Hartnett, Jessica L.

    2016-01-01

    This article describes two class activities that introduce the concept of data mining and very basic data mining analyses. Assessment data suggest that students learned some of the conceptual basics of data mining, understood some of the ethical concerns related to the practice, and were able to perform correlations via the Statistical Package for…

  17. 43 CFR 3809.5 - How does BLM define certain terms used in this subpart?

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... (Continued) BUREAU OF LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINING CLAIMS... may determine that it is practical to avoid or eliminate particular impacts. Mining claim means any unpatented mining claim, millsite, or tunnel site located under the mining laws. The term also applies to...

  18. 43 CFR 3809.5 - How does BLM define certain terms used in this subpart?

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... (Continued) BUREAU OF LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINING CLAIMS... may determine that it is practical to avoid or eliminate particular impacts. Mining claim means any unpatented mining claim, millsite, or tunnel site located under the mining laws. The term also applies to...

  19. 43 CFR 3809.5 - How does BLM define certain terms used in this subpart?

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... (Continued) BUREAU OF LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINING CLAIMS... may determine that it is practical to avoid or eliminate particular impacts. Mining claim means any unpatented mining claim, millsite, or tunnel site located under the mining laws. The term also applies to...

  20. 43 CFR 3809.5 - How does BLM define certain terms used in this subpart?

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... (Continued) BUREAU OF LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) MINING CLAIMS... may determine that it is practical to avoid or eliminate particular impacts. Mining claim means any unpatented mining claim, millsite, or tunnel site located under the mining laws. The term also applies to...

  1. Exploring virtual mental practice in maintenance task training

    PubMed Central

    Bauerle, Tim; Brnich, Michael J.; Navoyski, Jason

    2016-01-01

    Purpose – This paper aims to contribute to a general understanding of mental practice by investigating the utility of and participant reaction to a virtual reality maintenance training among underground coal mine first responders. Design/methodology/approach – Researchers at the National Institute for Occupational Safety and Health's Office of Mine Safety and Health Research (OMSHR) developed software to provide opportunities for mine rescue team members to learn to inspect, assemble and test their closed-circuit breathing apparatus and to practice those skills. In total, 31 mine rescue team members utilized OMSHR's BG 4 Benching Trainer software and provided feedback to the development team. After training, participants completed a brief post-training questionnaire, which included demographics, perceived training climate and general training evaluation items. Findings – The results overall indicate a generally positive reaction to and high perceived utility of the BG 4 benching software. In addition, the perceived training climate appears to have an effect on the perceived utility of the mental practice virtual reality game, with benchmen from mines with more positive training climates reporting greater perceived efficacy in the training's ability to prepare trainees for real emergencies. Originality/value – This paper helps to broaden current applications of mental practice and is one of the few empirical investigations into a non-rehabilitation virtual reality extension of mental practice. This paper also contributes to the growing literature advocating for greater usage of accurate and well-informed mental practice techniques, tools and methodologies, especially for occupational populations with limitations on exposure to hands-on training. PMID:27594801

  2. Pre-operative prediction of surgical morbidity in children: comparison of five statistical models.

    PubMed

    Cooper, Jennifer N; Wei, Lai; Fernandez, Soledad A; Minneci, Peter C; Deans, Katherine J

    2015-02-01

    The accurate prediction of surgical risk is important to patients and physicians. Logistic regression (LR) models are typically used to estimate these risks. However, in the fields of data mining and machine-learning, many alternative classification and prediction algorithms have been developed. This study aimed to compare the performance of LR to several data mining algorithms for predicting 30-day surgical morbidity in children. We used the 2012 National Surgical Quality Improvement Program-Pediatric dataset to compare the performance of (1) a LR model that assumed linearity and additivity (simple LR model) (2) a LR model incorporating restricted cubic splines and interactions (flexible LR model) (3) a support vector machine, (4) a random forest and (5) boosted classification trees for predicting surgical morbidity. The ensemble-based methods showed significantly higher accuracy, sensitivity, specificity, PPV, and NPV than the simple LR model. However, none of the models performed better than the flexible LR model in terms of the aforementioned measures or in model calibration or discrimination. Support vector machines, random forests, and boosted classification trees do not show better performance than LR for predicting pediatric surgical morbidity. After further validation, the flexible LR model derived in this study could be used to assist with clinical decision-making based on patient-specific surgical risks. Copyright © 2014 Elsevier Ltd. All rights reserved.

  3. Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas.

    PubMed

    Karan, Shivesh Kishore; Samadder, Sukha Ranjan

    2016-08-01

    One objective of the present study was to evaluate the performance of support vector machine (SVM)-based image classification technique with the maximum likelihood classification (MLC) technique for a rapidly changing landscape of an open-cast mine. The other objective was to assess the change in land use pattern due to coal mining from 2006 to 2016. Assessing the change in land use pattern accurately is important for the development and monitoring of coalfields in conjunction with sustainable development. For the present study, Landsat 5 Thematic Mapper (TM) data of 2006 and Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data of 2016 of a part of Jharia Coalfield, Dhanbad, India, were used. The SVM classification technique provided greater overall classification accuracy when compared to the MLC technique in classifying heterogeneous landscape with limited training dataset. SVM exceeded MLC in handling a difficult challenge of classifying features having near similar reflectance on the mean signature plot, an improvement of over 11 % was observed in classification of built-up area, and an improvement of 24 % was observed in classification of surface water using SVM; similarly, the SVM technique improved the overall land use classification accuracy by almost 6 and 3 % for Landsat 5 and Landsat 8 images, respectively. Results indicated that land degradation increased significantly from 2006 to 2016 in the study area. This study will help in quantifying the changes and can also serve as a basis for further decision support system studies aiding a variety of purposes such as planning and management of mines and environmental impact assessment.

  4. Multi-dimensional classification of biomedical text: Toward automated, practical provision of high-utility text to diverse users

    PubMed Central

    Shatkay, Hagit; Pan, Fengxia; Rzhetsky, Andrey; Wilbur, W. John

    2008-01-01

    Motivation: Much current research in biomedical text mining is concerned with serving biologists by extracting certain information from scientific text. We note that there is no ‘average biologist’ client; different users have distinct needs. For instance, as noted in past evaluation efforts (BioCreative, TREC, KDD) database curators are often interested in sentences showing experimental evidence and methods. Conversely, lab scientists searching for known information about a protein may seek facts, typically stated with high confidence. Text-mining systems can target specific end-users and become more effective, if the system can first identify text regions rich in the type of scientific content that is of interest to the user, retrieve documents that have many such regions, and focus on fact extraction from these regions. Here, we study the ability to characterize and classify such text automatically. We have recently introduced a multi-dimensional categorization and annotation scheme, developed to be applicable to a wide variety of biomedical documents and scientific statements, while intended to support specific biomedical retrieval and extraction tasks. Results: The annotation scheme was applied to a large corpus in a controlled effort by eight independent annotators, where three individual annotators independently tagged each sentence. We then trained and tested machine learning classifiers to automatically categorize sentence fragments based on the annotation. We discuss here the issues involved in this task, and present an overview of the results. The latter strongly suggest that automatic annotation along most of the dimensions is highly feasible, and that this new framework for scientific sentence categorization is applicable in practice. Contact: shatkay@cs.queensu.ca PMID:18718948

  5. 278. Photocopied July 1978. NOTICE TO MINE EMPLOYEES, CITIZENS AND ...

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

    278. Photocopied July 1978. NOTICE TO MINE EMPLOYEES, CITIZENS AND NON-CITIZENS, ADVISING THEM OF ACCEPTABLE BEHAVIOR AND PROPER SHOP PRACTICES DURING WARTIME. WRITTEN IN SIX LANGUAGES. C. 1918. - Quincy Mining Company, Hancock, Houghton County, MI

  6. Environmental considerations related to mining of nonfuel minerals

    USGS Publications Warehouse

    Seal, Robert R.; Piatak, Nadine M.; Kimball, Bryn E.; Hammarstrom, Jane M.; Schulz, Klaus J.; DeYoung,, John H.; Seal, Robert R.; Bradley, Dwight C.

    2017-12-19

    Throughout most of human history, environmental stewardship during mining has not been a priority partly because of the lack of applicable laws and regulations and partly because of ignorance about the effects that mining can have on the environment. In the United States, the National Environmental Policy Act of 1969, in conjunction with related laws, codified a more modern approach to mining, including the responsibility for environmental stewardship, and provided a framework for incorporating environmental protection into mine planning. Today, similar frameworks are in place in the other developed countries of the world, and international mining companies generally follow similar procedures wherever they work in the world. The regulatory guidance has fostered an international effort among all stakeholders to identify best practices for environmental stewardship.The modern approach to mining using best practices involves the following: (a) establishment of a pre-mining baseline from which to monitor environmental effects during mining and help establish geologically reasonable closure goals; (b) identification of environmental risks related to mining through standardized approaches; and (c) formulation of an environmental closure plan before the start of mining. A key aspect of identifying the environmental risks and mitigating those risks is understanding how the risks vary from one deposit type to another—a concept that forms the basis for geoenvironmental mineral-deposit models.Accompanying the quest for best practices is the goal of making mining sustainable into the future. Sustainable mine development is generally considered to be development that meets the needs of the present generation without compromising the ability of future generations to meet their own needs. The concept extends beyond the availability of nonrenewable mineral commodities and includes the environmental and social effects of mine development.Global population growth, meanwhile, has decreased the percentage of inhabitable land available to support society’s material needs. Presently, the land area available to supply the mineral resources, energy resources, water, food, shelter, and waste disposal needs of all Earth’s inhabitants is estimated to be 135 square meters per person. Continued global population growth will only increase the challenges of sustainable mining.Current trends in mining are also expected to lead to new environmental challenges in the future, among which are mine-waste management issues related to mining larger deposits for lower ore grade; water-management issues related to both the mining of larger deposits and the changes in precipitation brought about by climate change; and greenhouse gas issues related to reducing the carbon footprint of larger, more energy-intensive mining operations.

  7. Seminar for High School Students “Practice on Manufacturing Technology by Advanced Machine Tools”

    NASA Astrophysics Data System (ADS)

    Marui, Etsuo; Yamawaki, Masao; Taga, Yuken; Omoto, Ken'ichi; Miyaji, Reiji; Ogura, Takahiro; Tsubata, Yoko; Sakai, Toshimasa

    The seminar ‘Practice on Manufacturing Technology by Advanced Machine Tools’ for high school students was held at the supporting center for technology education of Gifu University, under the sponsorship of the Japan Society of Mechanical Engineers. This seminar was held, hoping that many students become interested in manufacturing through the experience of the seminar. Operating CNC milling machine and CNC wire-cut electric discharge machine, they made original nameplates. Participants made the program to control CNC machine tools themselves. In this report, some valuable results obtained through such experience are explained.

  8. One method for life time estimation of a bucket wheel machine for coal moving

    NASA Astrophysics Data System (ADS)

    Vîlceanu, Fl; Iancu, C.

    2016-08-01

    Rehabilitation of outdated equipment with lifetime expired, or in the ultimate life period, together with high cost investments for their replacement, makes rational the efforts made to extend their life. Rehabilitation involves checking operational safety based on relevant expertise of metal structures supporting effective resistance and assessing the residual lifetime. The bucket wheel machine for coal constitute basic machine within deposits of coal of power plants. The estimate of remaining life can be done by checking the loading on the most stressed subassembly by Finite Element Analysis on a welding detail. The paper presents step-by-step the method of calculus applied in order to establishing the residual lifetime of a bucket wheel machine for coal moving using non-destructive methods of study (fatigue cracking analysis + FEA). In order to establish the actual state of machine and areas subject to study, was done FEA of this mining equipment, performed on the geometric model of mechanical analyzed structures, with powerful CAD/FEA programs. By applying the method it can be calculated residual lifetime, by extending the results from the most stressed area of the equipment to the entire machine, and thus saving time and money from expensive replacements.

  9. Data Mining and Machine Learning Models for Predicting Drug Likeness and their Disease or Organ Category

    NASA Astrophysics Data System (ADS)

    Yosipof, Abraham; Guedes, Rita C.; García-Sosa, Alfonso T.

    2018-05-01

    Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neuronal network (ANN), k nearest neighbors (kNN), and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule.

  10. Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category.

    PubMed

    Yosipof, Abraham; Guedes, Rita C; García-Sosa, Alfonso T

    2018-01-01

    Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features or in case of visualization methods uncover underlying patterns in the feature space. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neural network (ANN), k nearest neighbors (kNN), and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule.

  11. Facilitating knowledge discovery and visualization through mining contextual data from published studies: lessons from JournalMap

    USDA-ARS?s Scientific Manuscript database

    Valuable information on the location and context of ecological studies are locked up in publications in myriad formats that are not easily machine readable. This presents significant challenges to building geographic-based tools to search for and visualize sources of ecological knowledge. JournalMap...

  12. Random Forest as a Predictive Analytics Alternative to Regression in Institutional Research

    ERIC Educational Resources Information Center

    He, Lingjun; Levine, Richard A.; Fan, Juanjuan; Beemer, Joshua; Stronach, Jeanne

    2018-01-01

    In institutional research, modern data mining approaches are seldom considered to address predictive analytics problems. The goal of this paper is to highlight the advantages of tree-based machine learning algorithms over classic (logistic) regression methods for data-informed decision making in higher education problems, and stress the success of…

  13. 30 CFR 56.14130 - Roll-over protective structures (ROPS) and seat belts.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... Mining Machines,”, 1986; or (2) SAE J1194, “Roll-Over Protective Structures (ROPS) for Wheeled... when operating graders from a standing position, the grader operator shall wear safety lines and a... meet the requirement of SAE J386, “Operator Restraint System for Off-Road Work Machines” (1985, 1993...

  14. 30 CFR 56.14130 - Roll-over protective structures (ROPS) and seat belts.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... Mining Machines,”, 1986; or (2) SAE J1194, “Roll-Over Protective Structures (ROPS) for Wheeled... when operating graders from a standing position, the grader operator shall wear safety lines and a... meet the requirement of SAE J386, “Operator Restraint System for Off-Road Work Machines” (1985, 1993...

  15. 30 CFR 56.14130 - Roll-over protective structures (ROPS) and seat belts.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... Mining Machines,”, 1986; or (2) SAE J1194, “Roll-Over Protective Structures (ROPS) for Wheeled... when operating graders from a standing position, the grader operator shall wear safety lines and a... meet the requirement of SAE J386, “Operator Restraint System for Off-Road Work Machines” (1985, 1993...

  16. 30 CFR 56.14130 - Roll-over protective structures (ROPS) and seat belts.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... Mining Machines,”, 1986; or (2) SAE J1194, “Roll-Over Protective Structures (ROPS) for Wheeled... when operating graders from a standing position, the grader operator shall wear safety lines and a... meet the requirement of SAE J386, “Operator Restraint System for Off-Road Work Machines” (1985, 1993...

  17. 30 CFR 56.14130 - Roll-over protective structures (ROPS) and seat belts.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... Mining Machines,”, 1986; or (2) SAE J1194, “Roll-Over Protective Structures (ROPS) for Wheeled... when operating graders from a standing position, the grader operator shall wear safety lines and a... meet the requirement of SAE J386, “Operator Restraint System for Off-Road Work Machines” (1985, 1993...

  18. Multimodal Learning Analytics and Education Data Mining: Using Computational Technologies to Measure Complex Learning Tasks

    ERIC Educational Resources Information Center

    Blikstein, Paulo; Worsley, Marcelo

    2016-01-01

    New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics…

  19. Ground penetrating radar coal measurements demonstration at the U.S. Bureau of Mines Research Center, Pittsburgh, Pennsylvania. Final report

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

    Gardner, D.; Guerrier, J.; Martinez, M.

    1994-01-04

    In situ and near real-time measurements of coal seam thickness have been identified by industry as a highly desirable component of robotic mining systems. With it, a continuous mining machine can be guided close to the varying boundary of the seam while the cutting operation is underway. This provides the mining operation the ability to leave behind the high-sulfur, high-particulate coal which is concentrated near the seam boundary. The result is near total recovery of high quality coal resources, an increase in mining efficiency, and opportunities for improved safety through reduction in personnel in the most hazardous coal cutting areas.more » In situ, real-time coal seam measurements using the Special Technologies Laboratory (STL) ground penetrating radar (GPR) technology were shown feasible by a demonstration in a Utah coal mine on April 21, 1994. This report describes the October 18, 1994 in situ GPR measurements of coal seam thickness at the US Bureau of Mines (USBM) robotic mining testing laboratory. In this report, an overview of the measurements at the USBM Laboratory is given. It is followed by a description of the technical aspects of the STL frequency modulated-continuous wave (FM-CW) GPR system. Section 4 provides a detailed description of the USBM Laboratory measurements and the conditions under which they were taken. Section 5 offers conclusions and possibilities for future communications.« less

  20. Designing Green Stormwater Infrastructure for Hydrologic and Human Benefits: An Image Based Machine Learning Approach

    NASA Astrophysics Data System (ADS)

    Rai, A.; Minsker, B. S.

    2014-12-01

    Urbanization over the last century has degraded our natural water resources by increasing storm-water runoff, reducing nutrient retention, and creating poor ecosystem health downstream. The loss of tree canopy and expansion of impervious area and storm sewer systems have significantly decreased infiltration and evapotranspiration, increased stream-flow velocities, and increased flood risk. These problems have brought increasing attention to catchment-wide implementation of green infrastructure (e.g., decentralized green storm water management practices such as bioswales, rain gardens, permeable pavements, tree box filters, cisterns, urban wetlands, urban forests, stream buffers, and green roofs) to replace or supplement conventional storm water management practices and create more sustainable urban water systems. Current green infrastructure (GI) practice aims at mitigating the negative effects of urbanization by restoring pre-development hydrology and ultimately addressing water quality issues at an urban catchment scale. The benefits of green infrastructure extend well beyond local storm water management, as urban green spaces are also major contributors to human health. Considerable research in the psychological sciences have shown significant human health benefits from appropriately designed green spaces, yet impacts on human wellbeing have not yet been formally considered in GI design frameworks. This research is developing a novel computational green infrastructure (GI) design framework that integrates hydrologic requirements with criteria for human wellbeing. A supervised machine learning model is created to identify specific patterns in urban green spaces that promote human wellbeing; the model is linked to RHESSYS model to evaluate GI designs in terms of both hydrologic and human health benefits. An application of the models to Dead Run Watershed in Baltimore showed that image mining methods were able to capture key elements of human preferences that could improve tree-based GI design. Hydrologic benefits associated with these features were substantial, indicating that increased urban tree coverage and a more integrated GI design approach can significantly increase both human and hydrologic benefits.

  1. A review on data mining and continuous optimization applications in computational biology and medicine.

    PubMed

    Weber, Gerhard-Wilhelm; Ozöğür-Akyüz, Süreyya; Kropat, Erik

    2009-06-01

    An emerging research area in computational biology and biotechnology is devoted to mathematical modeling and prediction of gene-expression patterns; it nowadays requests mathematics to deeply understand its foundations. This article surveys data mining and machine learning methods for an analysis of complex systems in computational biology. It mathematically deepens recent advances in modeling and prediction by rigorously introducing the environment and aspects of errors and uncertainty into the genetic context within the framework of matrix and interval arithmetics. Given the data from DNA microarray experiments and environmental measurements, we extract nonlinear ordinary differential equations which contain parameters that are to be determined. This is done by a generalized Chebychev approximation and generalized semi-infinite optimization. Then, time-discretized dynamical systems are studied. By a combinatorial algorithm which constructs and follows polyhedra sequences, the region of parametric stability is detected. In addition, we analyze the topological landscape of gene-environment networks in terms of structural stability. As a second strategy, we will review recent model selection and kernel learning methods for binary classification which can be used to classify microarray data for cancerous cells or for discrimination of other kind of diseases. This review is practically motivated and theoretically elaborated; it is devoted to a contribution to better health care, progress in medicine, a better education, and more healthy living conditions.

  2. ChemBrowser: a flexible framework for mining chemical documents.

    PubMed

    Wu, Xian; Zhang, Li; Chen, Ying; Rhodes, James; Griffin, Thomas D; Boyer, Stephen K; Alba, Alfredo; Cai, Keke

    2010-01-01

    The ability to extract chemical and biological entities and relations from text documents automatically has great value to biochemical research and development activities. The growing maturity of text mining and artificial intelligence technologies shows promise in enabling such automatic chemical entity extraction capabilities (called "Chemical Annotation" in this paper). Many techniques have been reported in the literature, ranging from dictionary and rule-based techniques to machine learning approaches. In practice, we found that no single technique works well in all cases. A combinatorial approach that allows one to quickly compose different annotation techniques together for a given situation is most effective. In this paper, we describe the key challenges we face in real-world chemical annotation scenarios. We then present a solution called ChemBrowser which has a flexible framework for chemical annotation. ChemBrowser includes a suite of customizable processing units that might be utilized in a chemical annotator, a high-level language that describes the composition of various processing units that would form a chemical annotator, and an execution engine that translates the composition language to an actual annotator that can generate annotation results for a given set of documents. We demonstrate the impact of this approach by tailoring an annotator for extracting chemical names from patent documents and show how this annotator can be easily modified with simple configuration alone.

  3. Implementation of Paste Backfill Mining Technology in Chinese Coal Mines

    PubMed Central

    Chang, Qingliang; Zhou, Huaqiang; Bai, Jianbiao

    2014-01-01

    Implementation of clean mining technology at coal mines is crucial to protect the environment and maintain balance among energy resources, consumption, and ecology. After reviewing present coal clean mining technology, we introduce the technology principles and technological process of paste backfill mining in coal mines and discuss the components and features of backfill materials, the constitution of the backfill system, and the backfill process. Specific implementation of this technology and its application are analyzed for paste backfill mining in Daizhuang Coal Mine; a practical implementation shows that paste backfill mining can improve the safety and excavation rate of coal mining, which can effectively resolve surface subsidence problems caused by underground mining activities, by utilizing solid waste such as coal gangues as a resource. Therefore, paste backfill mining is an effective clean coal mining technology, which has widespread application. PMID:25258737

  4. Implementation of paste backfill mining technology in Chinese coal mines.

    PubMed

    Chang, Qingliang; Chen, Jianhang; Zhou, Huaqiang; Bai, Jianbiao

    2014-01-01

    Implementation of clean mining technology at coal mines is crucial to protect the environment and maintain balance among energy resources, consumption, and ecology. After reviewing present coal clean mining technology, we introduce the technology principles and technological process of paste backfill mining in coal mines and discuss the components and features of backfill materials, the constitution of the backfill system, and the backfill process. Specific implementation of this technology and its application are analyzed for paste backfill mining in Daizhuang Coal Mine; a practical implementation shows that paste backfill mining can improve the safety and excavation rate of coal mining, which can effectively resolve surface subsidence problems caused by underground mining activities, by utilizing solid waste such as coal gangues as a resource. Therefore, paste backfill mining is an effective clean coal mining technology, which has widespread application.

  5. Research on target tracking in coal mine based on optical flow method

    NASA Astrophysics Data System (ADS)

    Xue, Hongye; Xiao, Qingwei

    2015-03-01

    To recognize, track and count the bolting machine in coal mine video images, a real-time target tracking method based on the Lucas-Kanade sparse optical flow is proposed in this paper. In the method, we judge whether the moving target deviate from its trajectory, predicate and correct the position of the moving target. The method solves the problem of failure to track the target or lose the target because of the weak light, uneven illumination and blocking. Using the VC++ platform and Opencv lib we complete the recognition and tracking. The validity of the method is verified by the result of the experiment.

  6. Quantification of histone modification ChIP-seq enrichment for data mining and machine learning applications

    PubMed Central

    2011-01-01

    Background The advent of ChIP-seq technology has made the investigation of epigenetic regulatory networks a computationally tractable problem. Several groups have applied statistical computing methods to ChIP-seq datasets to gain insight into the epigenetic regulation of transcription. However, methods for estimating enrichment levels in ChIP-seq data for these computational studies are understudied and variable. Since the conclusions drawn from these data mining and machine learning applications strongly depend on the enrichment level inputs, a comparison of estimation methods with respect to the performance of statistical models should be made. Results Various methods were used to estimate the gene-wise ChIP-seq enrichment levels for 20 histone methylations and the histone variant H2A.Z. The Multivariate Adaptive Regression Splines (MARS) algorithm was applied for each estimation method using the estimation of enrichment levels as predictors and gene expression levels as responses. The methods used to estimate enrichment levels included tag counting and model-based methods that were applied to whole genes and specific gene regions. These methods were also applied to various sizes of estimation windows. The MARS model performance was assessed with the Generalized Cross-Validation Score (GCV). We determined that model-based methods of enrichment estimation that spatially weight enrichment based on average patterns provided an improvement over tag counting methods. Also, methods that included information across the entire gene body provided improvement over methods that focus on a specific sub-region of the gene (e.g., the 5' or 3' region). Conclusion The performance of data mining and machine learning methods when applied to histone modification ChIP-seq data can be improved by using data across the entire gene body, and incorporating the spatial distribution of enrichment. Refinement of enrichment estimation ultimately improved accuracy of model predictions. PMID:21834981

  7. Identification of Work-Related Musculoskeletal Disorders in Mining

    PubMed Central

    Weston, Eric; Pollard, Jonisha P.

    2016-01-01

    Work-related musculoskeletal disorder (WMSD) prevention measures have been studied in great depth throughout various industries. While the nature and causes of these disorders have been characterized in many industries, WMSDs occurring in the U.S. mining sector have not been characterized for several years. In this report, MSHA accident/injury/illness data from 2009 to 2013 were characterized to determine the most frequently reported WMSDs in the U.S. mining sector. WMSDs were most frequently reported in workers with less than 5 years or more than 20 years of mining experience. The number of days lost from work was the highest for shoulder and knee injuries and was found to increase with worker age. Underground and surface coal, surface stone and stone processing plants experienced the greatest number of WMSDs over the period studied. WMSDs were most commonly caused by an employee suffering from an overexertion, falls or being struck by an object while performing materials handling, maintenance and repair tasks, getting on or off equipment or machines, and walking or running. The injury trends presented should be used to help determine the focus of future WMSD prevention research in mining. PMID:27294012

  8. Prediction and innovative control strategies for oxygen and hazardous gases from diesel emission in underground mines.

    PubMed

    Kurnia, Jundika C; Sasmito, Agus P; Wong, Wai Yap; Mujumdar, Arun S

    2014-05-15

    Diesel engine is widely used in underground mining machines due to its efficiency, ease of maintenance, reliability and durability. However, it possesses significant danger to the miners and mining operations as it releases hazardous gases (CO, NO, CO2) and fine particles which can be easily inhaled by the miners. Moreover, the diesel engine consumes significant amount of oxygen which can lead to insufficient oxygen supply for miners. It is therefore critical to maintain sufficient oxygen supply while keeping hazardous gas concentrations from diesel emission below the maximum allowable level. The objective of this study is to propose and to examine various innovative ventilation strategies to control oxygen and hazardous gas concentrations in underground mine to ensure safety, productivity and cost related to energy consumption. Airflow distribution, oxygen and hazardous gas dispersion as well as ambient temperature within the mining area are evaluated by utilizing the well-established computational fluid dynamics (CFD) approach. The results suggest that our newly proposed ventilation design performs better as compared to the conventional design to handle hazardous gases from diesel emission. Copyright © 2014 Elsevier B.V. All rights reserved.

  9. Toward Accountable Discrimination-Aware Data Mining: The Importance of Keeping the Human in the Loop-and Under the Looking Glass.

    PubMed

    Berendt, Bettina; Preibusch, Sören

    2017-06-01

    "Big Data" and data-mined inferences are affecting more and more of our lives, and concerns about their possible discriminatory effects are growing. Methods for discrimination-aware data mining and fairness-aware data mining aim at keeping decision processes supported by information technology free from unjust grounds. However, these formal approaches alone are not sufficient to solve the problem. In the present article, we describe reasons why discrimination with data can and typically does arise through the combined effects of human and machine-based reasoning, and argue that this requires a deeper understanding of the human side of decision-making with data mining. We describe results from a large-scale human-subjects experiment that investigated such decision-making, analyzing the reasoning that participants reported during their task to assess whether a loan request should or would be granted. We derive data protection by design strategies for making decision-making discrimination-aware in an accountable way, grounding these requirements in the accountability principle of the European Union General Data Protection Regulation, and outline how their implementations can integrate algorithmic, behavioral, and user interface factors.

  10. Theoretical and experimental analysis of an equivalent circuit model for the investigation of shallow landmines with acoustic methods

    NASA Astrophysics Data System (ADS)

    Borgioli, G.; Bulletti, A.; Calzolai, M.; Capineri, L.; Falorni, P.; Masotti, L.; Valentini, S.; Windsor, C.

    2007-10-01

    Acoustic methods have been recently investigated for the detection of shallow landmines. Some plastic landmines have a flexible case which can made to vibrate by an airborne excitation like a loudspeaker. The soil-mine system shows a resonant behavior which is used as a signature to discriminate from other rigid objects. The mechanical resonance can be detected at the soil surface by a remote sensing systems like a laser interferometer. An equivalent physical model of the mine-soil system has been investigated having the known physical characteristics of mine simulants. The authors designed and built a test-object with known mechanical characteristics (mass, elasticity, damping factor). The model has been characterized in laboratory and the results compared with the classic mass-spring loss oscillator described by Voigt. The vibrations at the soil surface have been measured in various positions with a micro machined accelerometer. The results of the simulations for the acceleration of the soil-mine system agree well with the experiment. The calibrated mine model is useful to investigate the variation of the resonance frequency for various buried depths and to compare the results for different soils in different environmental conditions.

  11. Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.

    PubMed

    Piccinini, Filippo; Balassa, Tamas; Szkalisity, Abel; Molnar, Csaba; Paavolainen, Lassi; Kujala, Kaisa; Buzas, Krisztina; Sarazova, Marie; Pietiainen, Vilja; Kutay, Ulrike; Smith, Kevin; Horvath, Peter

    2017-06-28

    High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org. Copyright © 2017 Elsevier Inc. All rights reserved.

  12. ABC for AIDS prevention in Guinea: migrant gold mining communities address their risks.

    PubMed

    Kis, Adam Daniel

    2010-04-01

    Contrary to expectation when compared with other migrant mining zones of sub-Saharan Africa, the nation of Guinea has a comparatively low and stable HIV rate. In addition, the regions with the largest gold, diamond, and bauxite mining operations report the lowest HIV rates within the country. This research set out to explain practices and beliefs within gold mining communities near Siguiri, Guinea--the highest-producing gold mining zone in the country--that may contribute to this phenomenon, particularly as they relate to the Abstinence, Be faithful, use a Condom approach to AIDS prevention. Structured interviews on a randomly selected sample of 460 adults and regular visitation to 16 pharmacies and health clinics within the mining zone yielded data showing that abstinence and condom use are minimally practiced for AIDS prevention. Instead, faithfulness to partners was overwhelmingly reported as the method of choice for AIDS avoidance. In addition, this research explored ways in which local conceptions of fidelity differed from those generally understood in other contexts, including engagement in short-term marriages at the gold mining sites.

  13. Survey of nine surface mines in North America. [Nine different mines in USA and Canada

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

    Hayes, L.G.; Brackett, R.D.; Floyd, F.D.

    This report presents the information gathered by three mining engineers in a 1980 survey of nine surface mines in the United States and Canada. The mines visited included seven coal mines, one copper mine, and one tar sands mine selected as representative of present state of the art in open pit, strip, and terrace pit mining. The purpose of the survey was to investigate mining methods, equipment requirements, operating costs, reclamation procedures and costs, and other aspects of current surface mining practices in order to acquire basic data for a study comparing conventional and terrace pit mining methods, particularly inmore » deeper overburdens. The survey was conducted as part of a project under DOE Contract No. DE-AC01-79ET10023 titled The Development of Optimal Terrace Pit Coal Mining Systems.« less

  14. 40 CFR 434.82 - Effluent limitations attainable by the application of the best practicable control technology...

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... application of the best practicable control technology currently available (BPT). 434.82 Section 434.82... control technology currently available (BPT). Except as provided in 40 CFR 125.30 through 125.32, the following effluent limitations apply to mine drainage from applicable areas of western coal mining...

  15. 40 CFR 440.142 - Effluent limitations representing the degree of effluent reduction attainable by the application...

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... degree of effluent reduction attainable by the application of the best practicable control technology... (CONTINUED) EFFLUENT GUIDELINES AND STANDARDS ORE MINING AND DRESSING POINT SOURCE CATEGORY Gold Placer Mine... application of the best practicable control technology currently available (BPT). Except as provided in 40 CFR...

  16. 40 CFR 434.82 - Effluent limitations attainable by the application of the best practicable control technology...

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... application of the best practicable control technology currently available (BPT). 434.82 Section 434.82... control technology currently available (BPT). Except as provided in 40 CFR 125.30 through 125.32, the following effluent limitations apply to mine drainage from applicable areas of western coal mining...

  17. Building a Bridge or Digging a Pipeline? Clinical Data Mining in Evidence-Informed Knowledge Building

    ERIC Educational Resources Information Center

    Epstein, Irwin

    2015-01-01

    Challenging the "bridge metaphor" theme of this conference, this article contends that current practice-research integration strategies are more like research-to-practice "pipelines." The purpose of this article is to demonstrate the potential of clinical data-mining studies conducted by practitioners, practitioner-oriented PhD…

  18. The evolution and practical application of machine translation system (1)

    NASA Astrophysics Data System (ADS)

    Tominaga, Isao; Sato, Masayuki

    This paper describes a development, practical applicatioin, problem of a system, evaluation of practical system, and development trend of machine translation. Most recent system contains next four problems. 1) the vagueness of a text, 2) a difference of the definition of the terminology between different language, 3) the preparing of a large-scale translation dictionary, 4) the development of a software for the logical inference. Machine translation system is already used practically in many industry fields. However, many problems are not solved. The implementation of an ideal system will be after 15 years. Also, this paper described seven evaluation items detailedly. This English abstract was made by Mu system.

  19. MERCURY CONTRIBUTIONS TO THE ENVIRONMENT FROM HISTORIC MINING PRACTICES

    EPA Science Inventory

    Significant quantities of mercury have been released to the environment as a result of historic precious metal mining. Many gold and silver deposits are enriched in mercury, which is released during mining and processing activities. Historically in the U.S., although a modern ...

  20. Information Theory, Inference and Learning Algorithms

    NASA Astrophysics Data System (ADS)

    Mackay, David J. C.

    2003-10-01

    Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

  1. Various Recrystallizations of CL-20 (HNIW hexanitrohexaazaisowurtzitane).

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

    Phillips, Jason Joe

    Impact sensitivity testing was performed using a modified Bureau of Mines (MBOM) impactor manufactured by Safety Management Services, Inc., shown in Figure 1. Type-12 tooling was utilized on this machine with a 2.5kg impactor and matching intermediate mass. This particular machine is capable of a maximum drop height of 115cm with 0.1cm increments, though 1cm increments are typically used. Sample material was placed (35 ± 2mg) onto 1 inch squares of Norton brand 180A Garnet sandpaper. Positive results were detected visually or audibly by the operator as smoke, flash, report, charring/tearing of the sandpaper, etc.

  2. The metrological assurance of quality petroleum products. Methods for determining octane and sulfur numbers in the petroleum products

    NASA Astrophysics Data System (ADS)

    Shalkov, A. V.; Fadeev, Y. A.

    2018-01-01

    At present, solving environmental problems in industrially developed regions with a large concentration of mining and machine building enterprises is one of the main socially important tasks. Taking into account the increase in the volume of mining, there is an increase in the environmental burden, which affects the internal migration of the population. This is particularly evident in the examples of single-industry towns, in which a gradual decrease in the young workable population occurs. The article presents an analysis of the sources of maximum pollution of the environment by coal mining enterprises. Modern methods of controlling automobile fuel were analyzed. The analysis of fuel quality and the environmental assessment of combustion products was carried out. The equipment used in the article makes it possible to exclude substandard fuel and to reduce harmful emissions of vehicles to the atmosphere.

  3. Critical parameters for coarse coal underground slurry haulage systems

    NASA Technical Reports Server (NTRS)

    Maynard, D. P.

    1981-01-01

    Factors are identified which must be considered in meeting the requirements of a transportation system for conveying, in a pipeline, the coal mined by a continuous mining machine to a storage location neat the mine entrance or to a coal preparation plant located near the surface. For successful operation, the slurry haulage the system should be designed to operated in the turbulent flow regime at a flow rate at least 30% greater than the deposition velocity (slurry flow rate at which the solid particles tend to settle in the pipe). The capacity of the haulage system should be compatible with the projected coal output. Partical size, solid concentration, density, and viscosity of the suspension are if importance as well as the selection of the pumps, pipes, and valves. The parameters with the greatest effect on system performance ar flow velocity, pressure coal particle size, and solids concentration.

  4. Community Economic Identity: The Coal Industry and Ideology Construction in West Virginia

    ERIC Educational Resources Information Center

    Bell, Shannon Elizabeth; York, Richard

    2010-01-01

    Economic changes and the machinations of the treadmill of production have dramatically reduced the number of jobs provided by extractive industries, such as mining and timber, in the United States and other affluent nations in the post-World War II era. As the importance of these industries to national, regional, and local economies wanes,…

  5. The ACODEA Framework: Developing Segmentation and Classification Schemes for Fully Automatic Analysis of Online Discussions

    ERIC Educational Resources Information Center

    Mu, Jin; Stegmann, Karsten; Mayfield, Elijah; Rose, Carolyn; Fischer, Frank

    2012-01-01

    Research related to online discussions frequently faces the problem of analyzing huge corpora. Natural Language Processing (NLP) technologies may allow automating this analysis. However, the state-of-the-art in machine learning and text mining approaches yields models that do not transfer well between corpora related to different topics. Also,…

  6. Combining Natural Language Processing and Statistical Text Mining: A Study of Specialized versus Common Languages

    ERIC Educational Resources Information Center

    Jarman, Jay

    2011-01-01

    This dissertation focuses on developing and evaluating hybrid approaches for analyzing free-form text in the medical domain. This research draws on natural language processing (NLP) techniques that are used to parse and extract concepts based on a controlled vocabulary. Once important concepts are extracted, additional machine learning algorithms,…

  7. Developing an Intelligent Diagnosis and Assessment E-Learning Tool for Introductory Programming

    ERIC Educational Resources Information Center

    Huang, Chenn-Jung; Chen, Chun-Hua; Luo, Yun-Cheng; Chen, Hong-Xin; Chuang, Yi-Ta

    2008-01-01

    Recently, a lot of open source e-learning platforms have been offered for free in the Internet. We thus incorporate the intelligent diagnosis and assessment tool into an open software e-learning platform developed for programming language courses, wherein the proposed learning diagnosis assessment tools based on text mining and machine learning…

  8. 30 CFR 57.14130 - Roll-over protective structures (ROPS) and seat belts for surface equipment.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... Protective Structures (ROPS) for Construction, Earthmoving, Forestry, and Mining Machines,”, 1986; or (2) SAE... operating graders from a standing position, the grader operator shall wear safety lines and a harness in... requirement of SAE J386, “Operator Restraint System for Off-Road Work Machines” (1985, 1993, or 1997), or SAE...

  9. 30 CFR 57.14130 - Roll-over protective structures (ROPS) and seat belts for surface equipment.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... Protective Structures (ROPS) for Construction, Earthmoving, Forestry, and Mining Machines,”, 1986; or (2) SAE... operating graders from a standing position, the grader operator shall wear safety lines and a harness in... requirement of SAE J386, “Operator Restraint System for Off-Road Work Machines” (1985, 1993, or 1997), or SAE...

  10. 30 CFR 57.14130 - Roll-over protective structures (ROPS) and seat belts for surface equipment.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... Protective Structures (ROPS) for Construction, Earthmoving, Forestry, and Mining Machines,”, 1986; or (2) SAE... operating graders from a standing position, the grader operator shall wear safety lines and a harness in... requirement of SAE J386, “Operator Restraint System for Off-Road Work Machines” (1985, 1993, or 1997), or SAE...

  11. 30 CFR 57.14130 - Roll-over protective structures (ROPS) and seat belts for surface equipment.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... Protective Structures (ROPS) for Construction, Earthmoving, Forestry, and Mining Machines,”, 1986; or (2) SAE... operating graders from a standing position, the grader operator shall wear safety lines and a harness in... requirement of SAE J386, “Operator Restraint System for Off-Road Work Machines” (1985, 1993, or 1997), or SAE...

  12. 30 CFR 57.14130 - Roll-over protective structures (ROPS) and seat belts for surface equipment.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... Protective Structures (ROPS) for Construction, Earthmoving, Forestry, and Mining Machines,”, 1986; or (2) SAE... operating graders from a standing position, the grader operator shall wear safety lines and a harness in... requirement of SAE J386, “Operator Restraint System for Off-Road Work Machines” (1985, 1993, or 1997), or SAE...

  13. 78 FR 49774 - Petitions for Modification of Application of Existing Mandatory Safety Standards

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-08-15

    ... the well. (7) Calibrate the methane monitors on the longwall, continuous mining machine, or cutting..., test methane levels with a hand- held methane detector at least every 10 minutes from the time that... methane levels are less than 1.0 percent in all areas that will be exposed to flames and sparks from the...

  14. Physically absorbable reagents-collectors in elementary flotation

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

    S.A. Kondrat'ev; I.G. Bochkarev

    2007-09-15

    Based on the reviewed researches held at the Institute of Mining, Siberian Branch, Russian Academy of Sciences, the effect of physically absorbable reagents-collectors on formation of a flotation complex and its stability in turbulent pulp flows in flotation machines of basic types is considered. The basic requirements for physically absorbable reagents-collectors at different flotation stages are established.

  15. Development and Validation of a Learning Analytics Framework: Two Case Studies Using Support Vector Machines

    ERIC Educational Resources Information Center

    Ifenthaler, Dirk; Widanapathirana, Chathuranga

    2014-01-01

    Interest in collecting and mining large sets of educational data on student background and performance to conduct research on learning and instruction has developed as an area generally referred to as learning analytics. Higher education leaders are recognizing the value of learning analytics for improving not only learning and teaching but also…

  16. 25 CFR 213.40 - Cancellations.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... FIVE CIVILIZED TRIBES, OKLAHOMA, FOR MINING Operations § 213.40 Cancellations. (a) When, in the opinion... or of the applicable regulations, or if mining operations are conducted wastefully and without regard to good mining practice, the Secretary of the Interior shall have the right at any time after 30 days...

  17. Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges

    PubMed Central

    Singhal, Ayush; Leaman, Robert; Catlett, Natalie; Lemberger, Thomas; McEntyre, Johanna; Polson, Shawn; Xenarios, Ioannis; Arighi, Cecilia; Lu, Zhiyong

    2016-01-01

    Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system ‘accuracy’ remains a challenge and identify several additional common difficulties and potential research directions including (i) the ‘scalability’ issue due to the increasing need of mining information from millions of full-text articles, (ii) the ‘interoperability’ issue of integrating various text-mining systems into existing curation workflows and (iii) the ‘reusability’ issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. Finally, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators. PMID:28025348

  18. Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges

    DOE PAGES

    Singhal, Ayush; Leaman, Robert; Catlett, Natalie; ...

    2016-12-26

    Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system ‘accuracy’ remains a challenge and identify several additional common difficulties and potential research directions including (i) the ‘scalability’ issue due to themore » increasing need of mining information from millions of full-text articles, (ii) the ‘interoperability’ issue of integrating various text-mining systems into existing curation workflows and (iii) the ‘reusability’ issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. In conclusion, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators.« less

  19. Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges

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

    Singhal, Ayush; Leaman, Robert; Catlett, Natalie

    Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system ‘accuracy’ remains a challenge and identify several additional common difficulties and potential research directions including (i) the ‘scalability’ issue due to themore » increasing need of mining information from millions of full-text articles, (ii) the ‘interoperability’ issue of integrating various text-mining systems into existing curation workflows and (iii) the ‘reusability’ issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. In conclusion, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators.« less

  20. Characterization of airborne float coal dust emitted during continuous mining, longwall mining and belt transport

    PubMed Central

    Shahan, M.R.; Seaman, C.E.; Beck, T.W.; Colinet, J.F.; Mischler, S.E.

    2017-01-01

    Float coal dust is produced by various mining methods, carried by ventilating air and deposited on the floor, roof and ribs of mine airways. If deposited, float dust is re-entrained during a methane explosion. Without sufficient inert rock dust quantities, this float coal dust can propagate an explosion throughout mining entries. Consequently, controlling float coal dust is of critical interest to mining operations. Rock dusting, which is the adding of inert material to airway surfaces, is the main control technique currently used by the coal mining industry to reduce the float coal dust explosion hazard. To assist the industry in reducing this hazard, the Pittsburgh Mining Research Division of the U.S. National Institute for Occupational Safety and Health initiated a project to investigate methods and technologies to reduce float coal dust in underground coal mines through prevention, capture and suppression prior to deposition. Field characterization studies were performed to determine quantitatively the sources, types and amounts of dust produced during various coal mining processes. The operations chosen for study were a continuous miner section, a longwall section and a coal-handling facility. For each of these operations, the primary dust sources were confirmed to be the continuous mining machine, longwall shearer and conveyor belt transfer points, respectively. Respirable and total airborne float dust samples were collected and analyzed for each operation, and the ratio of total airborne float coal dust to respirable dust was calculated. During the continuous mining process, the ratio of total airborne float coal dust to respirable dust ranged from 10.3 to 13.8. The ratios measured on the longwall face were between 18.5 and 21.5. The total airborne float coal dust to respirable dust ratio observed during belt transport ranged between 7.5 and 21.8. PMID:28936001

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