Data Mining Practical Machine Learning Tools and Techniques

CERN Document Server

Witten, Ian H; Hall, Mark A

2011-01-01

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place

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

Science.gov (United States)

2011-08-31

... Detection Systems for Continuous Mining Machines in Underground Coal Mines AGENCY: Mine Safety and Health... (except full-face continuous mining machines) with proximity detection systems. Miners working near..., each underground coal mine operator would be required to install proximity detection systems on...

High pressure water jet mining machine

Science.gov (United States)

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.

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

Science.gov (United States)

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 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...

30 CFR 18.54 - High-voltage continuous mining machines.

Science.gov (United States)

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false High-voltage continuous mining machines. 18.54... and Design Requirements § 18.54 High-voltage continuous mining machines. (a) Separation of high... ground. (e) Onboard ungrounded, three-phase power circuit. A continuous mining machine designed with an...

Comparisons of Hydraulic Performance in Permanent Maglev Pump for Water-Jet Propulsion

Directory of Open Access Journals (Sweden)

Puyu Cao

2014-08-01

Full Text Available The operation of water-jet propulsion can generate nonuniform inflow that may be detrimental to the performance of the water-jets. To reduce disadvantages of the nonuniform inflow, a rim-driven water-jet propulsion was designed depending on the technology of passive magnetic levitation. Insufficient understanding of large performance deviations between the normal water-jets (shaft and permanent maglev water-jets (shaftless is a major problem in this paper. CFD was directly adopted in the feasibility and superiority of permanent maglev water-jets. Comparison and discussion of the hydraulic performance were carried out. The shaftless duct firstly has a drop in hydraulic losses (K1, since it effectively avoids the formation and evolution of the instability secondary vortex by the normalized helicity analysis. Then, the shaftless intake duct improves the inflow field of the water-jet pump, with consequencing the drop in the backflow and blocking on the blade shroud. So that the shaftless water-jet pump delivers higher flow rate and head to the propulsion than the shaft. Eventually, not only can the shaftless model increase the thrust and efficiency, but it has the ability to extend the working range and broaden the high efficiency region as well.

Proposition of a Solution for the Setting of the Abrasive Waterjet Cutting Technology

Czech Academy of Sciences Publication Activity Database

Valíček, Jan; Harničárová, M.; Kušnerová, M.; Grznárik, R.; Zavadil, J.

2013-01-01

Roč. 13, č. 5 (2013), s. 279-285 ISSN 1335-8871 R&D Projects: GA MŠk ED2.1.00/03.0082 Institutional support: RVO:68145535 Keywords : abrasive waterjet cutting of materials * surface topography function * correlation relations * surface roughness * optimization of technology Subject RIV: JQ - Machines ; Tools Impact factor: 1.162, year: 2013 http://www.degruyter.com/view/j/msr.2013.13.issue-5/msr-2013-0041/msr-2013-0041. xml

Water spray ventilator system for continuous mining machines

Science.gov (United States)

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.

Comparative study on the performance of Pod type waterjet by experiment and computation

Directory of Open Access Journals (Sweden)

Moon-Chan Kim

2010-03-01

Full Text Available A comparative study between a computation and an experiment has been conducted to predict the performance of a Pod type waterjet for an amphibious wheeled vehicle. The Pod type waterjet has been chosen on the basis of the required specific speed of more than 2500. As the Pod type waterjet is an extreme type of axial flow type waterjet, theoretical as well as experimental works about Pod type waterjets are very rare. The main purpose of the present study is to validate and compare to the experimental results of the Pod type waterjet with the developed CFD in-house code based on the RANS equations. The developed code has been validated by comparing with the experimental results of the well-known turbine problem. The validation also extended to the flush type waterjet where the pressures along the duct surface and also velocities at nozzle area have been compared with experimental results. The Pod type waterjet has been designed and the performance of the designed waterjet system including duct, impeller and stator was analyzed by the previously mentioned in-house CFD Code. The pressure distributions and limiting streamlines on the blade surfaces were computed to confirm the performance of the designed waterjets. In addition, the torque and momentum were computed to find the entire efficiency and these were compared with the model test results. Measurements were taken of the flow rate at the nozzle exit, static pressure at the various sections along the duct and also the nozzle, revolution of the impeller, torque, thrust and towing forces at various advance speeds for the prediction of performance as well as for comparison with the computations. Based on these measurements, the performance was analyzed according to the ITTC96 standard analysis method. The full-scale effective and the delivered power of the wheeled vehicle were estimated for the prediction of the service speed. This paper emphasizes the confirmation of the ITTC96 analysis method and

Surface mining machines problems of maintenance and modernization

CERN Document Server

Rusiński, Eugeniusz; Moczko, Przemysław; Pietrusiak, Damian

2017-01-01

This unique volume imparts practical information on the operation, maintenance, and modernization of heavy performance machines such as lignite mine machines, bucket wheel excavators, and spreaders. Problems of large scale machines (mega machines) are highly specific and not well recognized in the common mechanical engineering environment. Prof. Rusiński and his co-authors identify solutions that increase the durability of these machines as well as discuss methods of failure analysis and technical condition assessment procedures. "Surface Mining Machines: Problems in Maintenance and Modernization" stands as a much-needed guidebook for engineers facing the particular challenges of heavy performance machines and offers a distinct and interesting demonstration of scale-up issues for researchers and scientists from across the fields of machine design and mechanical engineering.

Data Mining and Machine Learning in Astronomy

Science.gov (United States)

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.

Advances in Machine Learning and Data Mining for Astronomy

Science.gov (United States)

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.

Mixed-Flow Waterjet (MxWJ) Model 5662-1: Initial Study of Yaw Effects on Waterjet Powering and Transom Depth Effects on Waterjet Priming

National Research Council Canada - National Science Library

Cusanelli, Dominic S

2007-01-01

...: (1) The effects of model yaw angles on waterjet powering. Model-scale rotor force measurements of thrust and torque at angles of yaw up to 3 degrees showed little variation compared to the equivalent forces measured at zero yaw angle...

Experimental Study of the Ultrasonic Vibration-Assisted Abrasive Waterjet Micromachining the Quartz Glass

Directory of Open Access Journals (Sweden)

Rongguo Hou

2018-01-01

Full Text Available The ultrasonic vibration is used to enhance the capability of the abrasive water micromachining glass. And, the ultrasonic vibration is activated on the abrasive waterjet nozzle. The quality of the flow is improved, and the velocity of the abrasive is increased because of the addition of the ultrasonic energy. The relevant experimental results indicate that the erosion depth and the material volume removal of the glass are obviously increased when ultrasonic vibration is working. As for the influence of process parameters on the material removal of the glass such as vibration amplitude, system pressure, distance of the standoff, and abrasive size, the experimental results indicate that the system pressure and vibration contribute greatly to the glass material removal. Also, the erosion depth and the volume of material removal are increased with the increase in the vibration amplitude and system pressure. There are some uplifts found at the edge of erosion pit. Then, it can be inferred that the plastic method is an important material removal method during the machining process of ultrasonic vibration-assisted abrasive waterjet.

Experimental Research on the Specific Energy Consumption of Rock Breakage Using Different Waterjet-Assisted Cutting Heads

Directory of Open Access Journals (Sweden)

Hongxiang Jiang

2018-01-01

Full Text Available To investigate the specific energy consumption (SE of rock breakage by cutting heads assisted by different types of waterjet and to identify optimal waterjet parameters and assistance types, rock cutting with and without waterjets was carried on a rock fragmentation test bed. SE is a comprehensive evaluation index and was developed according to the applied load on cutting head, and the SE under different cutting conditions was compared and analyzed. The results show that the SE of rock breakage without waterjet assistance increased with the increasing of rock compressive strength (RCS but that the limited drilling depth decreased. The effect of the waterjet pressure on the SE of rock breakage by the cutting head I was marked, and SE decreased by 30∼40% when the ratio between RCS and waterjet pressure was less than 1. However, the function of the waterjet assistance was poor; therefore, a ratio of 1 could be used to distinguish the rock breakage effect of cutting head I. For cutting head II, the rock damage from the waterjet impact was limited due to the large waterjet standoff distance; thus the rock breakage performance of cutting head II was also limited. The waterjet impacting at the tip of the conical pick using cutting head III could enter into the cracks caused by the mechanical pick and fracture the rock. Therefore, the rock breakage performance of cutting head III was better than that of cutting head II.

Waterjet cutting of periprosthetic interface tissue in loosened hip prostheses: an in vitro feasibility study

NARCIS (Netherlands)

Kraaij, Gert; Tuijthof, Gabrielle J. M.; Dankelman, Jenny; Nelissen, Rob G. H. H.; Valstar, Edward R.

2015-01-01

Waterjet cutting technology is considered a promising technology to be used for minimally invasive removal of interface tissue surrounding aseptically loose hip prostheses. The goal of this study was to investigate the feasibility of waterjet cutting of interface tissue membrane. Waterjets with 0.2

Waterjet cutting of periprosthetic interface tissue in loosened hip prostheses: an in vitro feasibility study.

Science.gov (United States)

Kraaij, Gert; Tuijthof, Gabrielle J M; Dankelman, Jenny; Nelissen, Rob G H H; Valstar, Edward R

2015-02-01

Waterjet cutting technology is considered a promising technology to be used for minimally invasive removal of interface tissue surrounding aseptically loose hip prostheses. The goal of this study was to investigate the feasibility of waterjet cutting of interface tissue membrane. Waterjets with 0.2 mm and 0.6 mm diameter, a stand-off distance of 5 mm, and a traverse speed of 0.5 mm/s were used to cut interface tissue samples in half. The water flow through the nozzle was controlled by means of a valve. By changing the flow, the resulting waterjet pressure was regulated. Tissue sample thickness and the required waterjet pressures were measured. Mean thickness of the samples tested within the 0.2 mm nozzle group was 2.3 mm (SD 0.7 mm) and within the 0.6 mm nozzle group 2.6 mm (SD 0.9 mm). The required waterjet pressure to cut samples was between 10 and 12 MPa for the 0.2 mm nozzle and between 5 and 10 MPa for the 0.6 mm nozzle. Cutting bone or bone cement requires about 3 times higher waterjet pressure (30-50 MPa, depending on used nozzle diameter) and therefore we consider waterjet cutting as a safe technique to be used for minimally invasive interface tissue removal. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

Experimental Investigation on the Influence of a Double-Walled Confined Width on the Velocity Field of a Submerged Waterjet

Directory of Open Access Journals (Sweden)

Xiaolong Ding

2017-12-01

Full Text Available The current research on confined submerged waterjets mainly focuses on the flow field of the impinging jet and wall jet. The double-sided wall vertically confined waterjet, which is widely used in many fields such as mining, cleaning and surface strengthening, has rarely been studied so far. In order to explore the influence of a double-sided wall confined width on the velocity field of submerged waterjet, an experiment was conducted with the application of 2D particle image velocimetry (PIV technology. The distribution of mean velocity and turbulent velocity in both horizontal and vertical planes was used to characterize the flow field under various confined widths. The results show that the vertical confinement has an obvious effect on the decay rate of the mean centerline velocity. When the confined width changes from 15 to 5, the velocity is reduced by 20%. In addition, with the decrease of the confined width, the jet has a tendency to spread horizontally. The vertically confined region induces a space hysteresis effect which changes the location of the transition region moving downstream. There are local negative pressure zones separating the fluid and the wall. This study of a double-walled confined jet provides some valuable information with respect to its mechanism and industrial application.

Rapid prototyping of robotic platforms

CSIR Research Space (South Africa)

De Ronde, Willis

2016-11-01

Full Text Available of thickness up to 200mm can be cut to create prototype chassis/ bodies or even the final product. One of the few limitations is the cutting of certain laminated materials, as this tends to produce delaminated cutting edges or even fractures in the case... mine inspection robot (Shongololo). Shongololo’s frame is made from engineering plastics while the chassis of Dassie was made from aluminium and cut using abrasive waterjet machining. The advantage of using abrasive waterjet machining is the speed...

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

Science.gov (United States)

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...

Abrasive-waterjet cutting of thick concrete and waterjet cleaning for nuclear facility decommissioning and decontamination

International Nuclear Information System (INIS)

Echert, D.C.; Hashish, M.; Marvin, M.H.

1987-01-01

Two tools have been developed for use by the nuclear industry: the Deep Kerf tool and the Cleaner/Scarifier tool. The Deep Kerf tool is designed to cut through thick, reinforced concrete structures to facilitate their decommissioning. It employs the abrasive-waterjet (AWJ) cutting technology. The basis of the system is a rotary nozzle that makes a slot in the concrete wide enough to accommodate the cutting tool as it advances. In this program, concrete as thick as 1.5 m was cut through from one side. A shroud and vacuum system covers the opening of the slot during cutting to contain the spoils with greater than 99% efficiency. The Cleaner/Scarifier tool was designed for removing the surface layers of contaminated concrete and decontaminating metal surfaces. It uses ultrahigh-pressure waterjets mounted on a rotating arm to remove or clean the target surface. Spoils recovery with a shroud and vacuum system is over 99% complete for both horizontal and vertical surfaces

Use of IT platform in determination of efficiency of mining machines

Science.gov (United States)

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.

Hydrochromic molecular switches for water-jet rewritable paper

Science.gov (United States)

Sheng, Lan; Li, Minjie; Zhu, Shaoyin; Li, Hao; Xi, Guan; Li, Yong-Gang; Wang, Yi; Li, Quanshun; Liang, Shaojun; Zhong, Ke; Zhang, Sean Xiao-An

2014-01-01

The days of rewritable paper are coming, printers of the future will use water-jet paper. Although several kinds of rewritable paper have been reported, practical usage of them is rare. Herein, a new rewritable paper for ink-free printing is proposed and demonstrated successfully by using water as the sole trigger to switch hydrochromic dyes on solid media. Water-jet prints with various colours are achieved with a commercial desktop printer based on these hydrochromic rewritable papers. The prints can be erased and rewritten dozens of times with no significant loss in colour quality. This rewritable paper is promising in that it can serve an eco-friendly information display to meet the increasing global needs for environmental protection.

Surface integrity in tangential turning of hybrid MMC A359/B4C/Al2O3by abrasive waterjet

Czech Academy of Sciences Publication Activity Database

Srivastava, A. K.; Naga, A.; Dixita, A. R.; Tiwaric, S.; Ščučka, Jiří; Zeleňák, Michal; Hloch, Sergej; Hlaváček, Petr

2017-01-01

Roč. 28, č. 28 (2017), s. 11-20 ISSN 1526-6125 R&D Projects: GA MŠk(CZ) LO1406; GA MŠk ED2.1.00/03.0082 Institutional support: RVO:68145535 Keywords : metal matrix composite * abrasive waterjet turning * surface topography * surface roughness * residual stresses Subject RIV: JQ - Machines ; Tools OBOR OECD: Mechanical engineering Impact factor: 2.322, year: 2016 http://www.sciencedirect.com/science/article/pii/S1526612517301287

Economic lifetime of a drilling machine:a case study on mining industry

OpenAIRE

Hamodi, Hussan; Lundberg, Jan; Jonsson, Adam

2013-01-01

Underground mines use many different types of machinery duringthe drift mining processes of drilling, charging, blasting, loading, scaling andbolting. Drilling machines play a critical role in the mineral extraction processand thus are important economically. However, as the machines age, theirefficiency and effectiveness decrease, negatively affecting productivity andprofitability and increasing total cost. Hence, the economic replacementlifetime of the machine is a key performance indicator...

Advances in machine learning and data mining for astronomy

CERN Document Server

Way, Michael J

2012-01-01

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

Numerical simulation of internal flow in mixed-flow waterjet propulsion

International Nuclear Information System (INIS)

Wu, T T; Pan, Z Y; Zhang, D Q; Jia, Y Y

2012-01-01

In order to reveal the internal flow characteristic of a mixed-flow waterjet propulsion, a mixed-flow waterjet propulsion under different conditions was simulated based on multi-reference frame(MRF), the standard k − ε turbulent model and SIMPLEC algorithm. The relationship between pump performance instability and internal flow was obtained. The numerical results showed that characteristic instability occurred at 0.65-0.67Q BEP , the reason is that the backflow on the vaned diffuser hub-side blocks the downstream flow from the impeller. Therefore, the flow separates on the pressure surface of the impeller outlet and a strong vortex is generated, then the characteristic instability appeared due to the instability of internal flow. Backflow was found in diffuser passage at 0.65 Q BEP and 0.85 Q BEP , as flow rate decreases, the backflow region and velocity increases. Pressure fluctuation at diffuser inlet and diffuser passages was severe at at 0.65 Q BEP . According to the numerical simulation, the mixed-flow waterjet propulsion has characteristic instability at partial flow rate condition.

Statistical and Machine-Learning Data Mining Techniques for Better Predictive Modeling and Analysis of Big Data

CERN Document Server

Ratner, Bruce

2011-01-01

The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has

Machining of {gamma}-TiAl

Energy Technology Data Exchange (ETDEWEB)

Aust, E.; Niemann, H.-R. [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Werkstofforschung

1999-09-01

Knowledge of the machining parameters for titanium aluminides of the type {gamma}-TiAl is essential for the acceptance and application of this new heat-resistant light-weight material for high performance components in automobile and aircraft engines. This work evaluates drilling, turning, sawing, milling, electroerosion, grinding, and high-pressure water-jetting of primary castings. The results indicate that there is a potential for each machining process, but a high quality of surface finish can only be achieved by some of the processes. (orig.)

CFD Based Erosion Modelling of Abrasive Waterjet Nozzle using Discrete Phase Method

International Nuclear Information System (INIS)

Kamarudin, Naqib Hakim; Prasada Rao, A K; Azhari, Azmir

2016-01-01

In Abrasive Waterjet (AWJ) machining, the nozzle is the most critical component that influences the performance, precision and economy. Exposure to a high speed jet and abrasives makes it susceptible to wear erosion which requires for frequent replacement. The present works attempts to simulate the erosion of the nozzle wall using computational fluid dynamics. The erosion rate of the nozzle was simulated under different operating conditions. The simulation was carried out in several steps which is flow modelling, particle tracking and erosion rate calculation. Discrete Phase Method (DPM) and K-ε turbulence model was used for the simulation. Result shows that different operating conditions affect the erosion rate as well as the flow interaction of water, air and abrasives. The simulation results correlates well with past work. (paper)

Opinion Mining in Latvian Text Using Semantic Polarity Analysis and Machine Learning Approach

Directory of Open Access Journals (Sweden)

Gatis Špats

2016-07-01

Full Text Available In this paper we demonstrate approaches for opinion mining in Latvian text. Authors have applied, combined and extended results of several previous studies and public resources to perform opinion mining in Latvian text using two approaches, namely, semantic polarity analysis and machine learning. One of the most significant constraints that make application of opinion mining for written content classification in Latvian text challenging is the limited publicly available text corpora for classifier training. We have joined several sources and created a publically available extended lexicon. Our results are comparable to or outperform current achievements in opinion mining in Latvian. Experiments show that lexicon-based methods provide more accurate opinion mining than the application of Naive Bayes machine learning classifier on Latvian tweets. Methods used during this study could be further extended using human annotators, unsupervised machine learning and bootstrapping to create larger corpora of classified text.

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

Science.gov (United States)

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Installation of roof support using mining... 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...

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

Science.gov (United States)

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.

Archetypal analysis for machine learning and data mining

DEFF Research Database (Denmark)

Mørup, Morten; Hansen, Lars Kai

2012-01-01

of the observed data. We further demonstrate that the aa model is relevant for feature extraction and dimensionality reduction for a large variety of machine learning problems taken from computer vision, neuroimaging, chemistry, text mining and collaborative filtering leading to highly interpretable...

Mining the Kepler Data using Machine Learning

Science.gov (United States)

Walkowicz, Lucianne; Howe, A. R.; Nayar, R.; Turner, E. L.; Scargle, J.; Meadows, V.; Zee, A.

2014-01-01

Kepler's high cadence and incredible precision has provided an unprecedented view into stars and their planetary companions, revealing both expected and novel phenomena and systems. Due to the large number of Kepler lightcurves, the discovery of novel phenomena in particular has often been serendipitous in the course of searching for known forms of variability (for example, the discovery of the doubly pulsating elliptical binary KOI-54, originally identified by the transiting planet search pipeline). In this talk, we discuss progress on mining the Kepler data through both supervised and unsupervised machine learning, intended to both systematically search the Kepler lightcurves for rare or anomalous variability, and to create a variability catalog for community use. Mining the dataset in this way also allows for a quantitative identification of anomalous variability, and so may also be used as a signal-agnostic form of optical SETI. As the Kepler data are exceptionally rich, they provide an interesting counterpoint to machine learning efforts typically performed on sparser and/or noisier survey data, and will inform similar characterization carried out on future survey datasets.

Refueling machine with relative positioning capability

International Nuclear Information System (INIS)

Challberg, R.C.; Jones, C.R.

1998-01-01

A refueling machine is disclosed having relative positioning capability for refueling a nuclear reactor. The refueling machine includes a pair of articulated arms mounted on a refueling bridge. Each arm supports a respective telescoping mast. Each telescoping mast is designed to flex laterally in response to application of a lateral thrust on the end of the mast. A pendant mounted on the end of the mast carries an air-actuated grapple, television cameras, ultrasonic transducers and waterjet thrusters. The ultrasonic transducers are used to detect the gross position of the grapple relative to the bail of a nuclear fuel assembly in the fuel core. The television cameras acquire an image of the bail which is compared to a pre-stored image in computer memory. The pendant can be rotated until the television image and the pre-stored image match within a predetermined tolerance. Similarly, the waterjet thrusters can be used to apply lateral thrust to the end of the flexible mast to place the grapple in a fine position relative to the bail as a function of the discrepancy between the television and pre-stored images. 11 figs

Refueling machine with relative positioning capability

Science.gov (United States)

Challberg, R.C.; Jones, C.R.

1998-12-15

A refueling machine is disclosed having relative positioning capability for refueling a nuclear reactor. The refueling machine includes a pair of articulated arms mounted on a refueling bridge. Each arm supports a respective telescoping mast. Each telescoping mast is designed to flex laterally in response to application of a lateral thrust on the end of the mast. A pendant mounted on the end of the mast carries an air-actuated grapple, television cameras, ultrasonic transducers and waterjet thrusters. The ultrasonic transducers are used to detect the gross position of the grapple relative to the bail of a nuclear fuel assembly in the fuel core. The television cameras acquire an image of the bail which is compared to a pre-stored image in computer memory. The pendant can be rotated until the television image and the pre-stored image match within a predetermined tolerance. Similarly, the waterjet thrusters can be used to apply lateral thrust to the end of the flexible mast to place the grapple in a fine position relative to the bail as a function of the discrepancy between the television and pre-stored images. 11 figs.

Waterjet drilling in porcine bone: the effect of the nozzle diameter and bone architecture on the hole dimensions

NARCIS (Netherlands)

den Dunnen, Steven; Mulder, Lars; Kerkhoffs, Gino M. M. J.; Dankelman, Jenny; Tuijthof, Gabrielle J. M.

2013-01-01

Using waterjets instead of rigid drill bits for bone drilling can be beneficial due to the absence of thermal damage and a consequent sharp cut. Additionally, waterjet technology allows the development of flexible instruments that facilitate maneuvering through complex joint spaces. Controlling the

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

Science.gov (United States)

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.

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

Science.gov (United States)

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

Multimedia Superabrasive, Laser Cladding, and Waterjet Technology Performance Support System

International Nuclear Information System (INIS)

Bohley, M.C.; Ciccateri, T.J.

1998-01-01

incorporated into the electronic information retrieval portion of the PSS. On-line reference manuals covering Operations, Maintenance, Mechanical, Electrical, and Peripherals provide text and illustrations to the machine operator in a traditional structure, but additionally offer the capability to search voluminous amounts of technical data and retrieve specific information on request. This project provided the project team with a detailed understanding of the knowledge and information required to produce and support advanced machine tools. In addition it resulted in the design and construction of a prototype Grinders PSS that contains all the logic and interfaces necessary to integrate product information from the Huffman Waterjets and Lasers product lines

Research on axial thrust of the waterjet pump based on CFD under cavitation conditions

International Nuclear Information System (INIS)

Shen, Z H; Pan, Z Y

2015-01-01

Based on RANS equations, performance of a contra-rotating axial-flow waterjet pump without hydrodynamic cavitation state had been obtained combined with shear stress transport turbulence model. Its cavitation hydrodynamic performance was calculated and analysed with mixture homogeneous flow cavitation model based on Rayleigh-Plesset equations. The results shows that the cavitation causes axial thrust of waterjet pump to drop. Furthermore, axial thrust and head cavitation characteristic curve is similar. However, the drop point of the axial thrust is postponed by 5.1% comparing with one of head, and the critical point of the axial thrust is postponed by 2.6%

Research on axial thrust of the waterjet pump based on CFD under cavitation conditions

Science.gov (United States)

Shen, Z. H.; Pan, Z. Y.

2015-01-01

Based on RANS equations, performance of a contra-rotating axial-flow waterjet pump without hydrodynamic cavitation state had been obtained combined with shear stress transport turbulence model. Its cavitation hydrodynamic performance was calculated and analysed with mixture homogeneous flow cavitation model based on Rayleigh-Plesset equations. The results shows that the cavitation causes axial thrust of waterjet pump to drop. Furthermore, axial thrust and head cavitation characteristic curve is similar. However, the drop point of the axial thrust is postponed by 5.1% comparing with one of head, and the critical point of the axial thrust is postponed by 2.6%.

Mineral mining machines

Energy Technology Data Exchange (ETDEWEB)

Mc Gaw, B H

1984-01-01

A machine for mining minerals is patented. It is a cutter loader with a drum actuating element of the worm type equipped with a multitude of cutting teeth reinforced with tungsten carbide. A feature of the patented machine is that all of the cutting teeth and holders on the drum have the identical design. This is achieved through selecting a slant angle for the cutting teeth which is the mean between the slant angle of the conventional radial teeth and the slant angle of the advance teeth. This, in turn, is provided thanks to the corresponding slant of the holders relative to the drum and (or) the slant of the cutting part of the teeth relative to their stems. Thus, the advance teeth projecting beyond the surface of the drum on the face side and providing upper and lateral clearances have the same angle of attack as the radial teeth, that is, from 20 to 35 degrees. A series of modifications of the cutting teeth is patented. One of the designs allows the cutting tooth to occupy a varying position relative to the drum, from the conventional vertical to an inverted, axially projecting position. In the last case the tooth in the extraction process provides the upper and lateral clearances for the drum on the face side. Among the different modifications of the cutting teeth, a design is proposed which provides for the presence of a stem which is shaped like a truncated cone. This particular stem is designed for use jointly with a wedge which unfastens the teeth and is placed in a holder. The latter is completed in a transverse slot thanks to which the rear end of the stem is compressed, which simplifies replacement of a tooth. Channels are provided in the patented machine for feeding water to the worm spiral, the holders and the cutting teeth themselves in order to deal with dust.

Applicability of water-jet cutting technology to nuclear facility decommissioning

International Nuclear Information System (INIS)

Abe, Tadashi; Nisizaki, Tadashi; Matumura, Hiroyuki; Ikemoto, Yosikazu; Simizu, Hideki

1991-01-01

In nuclear facilities there exist, besides relatively simple components, such as vessels and piping, numerous complex components including the multilayered plate with water layer in between, a bunch of thin tubes and composite lamination of dissimilar materials like metal/non-metal. In conventional development of reactor dismantling technology, the technology development has been made mainly for remote cutting of thick-walled structures like the reactor pressure vessel and the reactor internals. These techniques, however, are not always suitable in cutting the above-mentioned structures. As means of cutting such structures efficiently, these is available the abrasion water-jet cutting technology. This technology is now drawing attention for cutting or shaping new materials like composite material and ceramics in high precision and high efficiency. In the present report by way of its feasibility in nuclear facilities decommissioning the following are described. Principle and features of the water-jet cutting technology, system con-figuration, cutting or shaping performance, and some examples of the cutting and shaping. (author)

Machine Learning and Data Mining Methods in Diabetes Research.

Science.gov (United States)

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.

Mechanical characteristics of a double-fed machine in asynchronous mode and prospects of its application in the electric drive of mining machines

Science.gov (United States)

Ostrovlyanchik, V. Yu; Popolzin, I. Yu; Kubarev, V. A.; Marshev, D. A.

2017-09-01

The concept of a double-fed machine as an asynchronous motor with a phase rotor and a source of additional voltage is defined. Based on the analysis of a circuit replacing the double-fed machine, an expression is derived relating the moment, slip, amplitude and phase of additional voltage across the rotor. The conditions maximizing the moment with respect to amplitude and phase of additional voltage in the rotor circuit are also obtained, the phase surface of function of machine electromagnetic moment is constructed. The analysis of basic equation of electric drive motion in relation to electric drive of mine hoisting installations and the conclusion about the necessity of work in all four quadrants of coordinate plane “moment-slip” are made. Family of mechanical characteristics is constructed for a double-fed machine and its achievable speed control range in asynchronous mode is determined. Based on the type of mechanical characteristics and the calculated range of speed control, the conclusion is made about the suitability of using a dual-fed asynchronous machine for driving mine mechanisms with a small required speed control range and the need for organizing a combined operating mode for driving mine hoisting installations and other mechanisms with a large speed control range.

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

Science.gov (United States)

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

Methods and means for the in-house training of mining machine operators

Directory of Open Access Journals (Sweden)

Velikanov Vladimir

2017-01-01

Full Text Available This study investigates the quality issue of the in-house training process for mining machine operators. The authors prove the urgency of the designated problem. The changes in modern society, as well as the development of science and technology have a direct impact on the vocational education system. This paper describes the main aspects of the in-house training process of mining machine operators; define the essence, structure, contents, and main directions of its revitalization. The following solutions are proposed in order to improve the quality of the in-house training process: to use the original method based on a rating system of the operator knowledge evaluation, active and interactive forms of using modern training technologies. The authors conducted testing techniques in mining enterprises with the aim of confirming the adequacy of the suggested approaches. The results are given in the work. It was proposed that the methods and tools integration has a positive impact on professional training system.

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

Science.gov (United States)

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.

Tomographic particle image velocimetry of a water-jet for low volume harvesting of fat tissue for regenerative medicine

Directory of Open Access Journals (Sweden)

Drobek Christoph

2015-09-01

Full Text Available Particle Image Velocimetry (PIV measurements of a water-jet for water-assisted liposuction (WAL are carried out to investigate the distribution of velocity and therefore momentum and acting force on the human sub-cutaneous fat tissue. These results shall validate CFD simulations and force sensor measurements of the water-jet and support the development of a new WAL device that is able to harvest low volumes of fat tissue for regenerative medicine even gentler than regular WAL devices.

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

Science.gov (United States)

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

Mine drivage in hydraulic mines

Energy Technology Data Exchange (ETDEWEB)

Ehkber, B Ya

1983-09-01

From 20 to 25% of labor cost in hydraulic coal mines falls on mine drivage. Range of mine drivage is high due to the large number of shortwalls mined by hydraulic monitors. Reducing mining cost in hydraulic mines depends on lowering drivage cost by use of new drivage systems or by increasing efficiency of drivage systems used at present. The following drivage methods used in hydraulic mines are compared: heading machines with hydraulic haulage of cut rocks and coal, hydraulic monitors with hydraulic haulage, drilling and blasting with hydraulic haulage of blasted rocks. Mining and geologic conditions which influence selection of the optimum mine drivage system are analyzed. Standardized cross sections of mine roadways driven by the 3 methods are shown in schemes. Support systems used in mine roadways are compared: timber supports, roof bolts, roof bolts with steel elements, and roadways driven in rocks without a support system. Heading machines (K-56MG, GPKG, 4PU, PK-3M) and hydraulic monitors (GMDTs-3M, 12GD-2) used for mine drivage are described. Data on mine drivage in hydraulic coal mines in the Kuzbass are discussed. From 40 to 46% of roadways are driven by heading machines with hydraulic haulage and from 12 to 15% by hydraulic monitors with hydraulic haulage.

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

Science.gov (United States)

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)

Time cycle calculation procedure for the special crew during the mining mobile machine complex operation

International Nuclear Information System (INIS)

Shmurygin, V; Lukyanov, V; Maslovsky, A

2015-01-01

The relevance of the research is specified by the necessity to optimize the delft mobile tunneling equipment operation. Target of the research is tunneling time cycle justification for the special crew during the mining mobile machine complex operation. Methods of the research included the consideration of operation organization schemes in the drifting face and effective use of the mobile equipment during mine exploratory working operations. Time cycle calculation procedures for major processes have been considered. This has been done for the special crew during the mobile machine complex operations for several working faces and various organization schemes

Trans-umbilical endoscopic cholecystectomy with a water-jet hybrid-knife: a pilot animal study.

Science.gov (United States)

Jiang, Sheng-Jun; Shi, Hong; Swar, Gyanendra; Wang, Hai-Xia; Liu, Xiao-Jing; Wang, Yong-Guang

2013-10-28

To investigate the feasibility and safety of Natural orifice trans-umbilical endoscopic cholecystectomy with a water-jet hybrid-knife in a non-survival porcine model. Pure natural orifice transluminal endoscopic surgery (NOTES) cholecystectomy was performed on three non-survival pigs, by transumbilical approach, using a water-jet hybrid-knife. Under general anesthesia, the following steps detailed the procedure: (1) incision of the umbilicus followed by the passage of a double-channel flexible endoscope through an overtube into the peritoneal cavity; (2) establishment of pneumoperitoneum; (3) abdominal exploration; (4) endoscopic cholecystectomy: dissection of the gallbladder performed using water jet equipment, ligation of the cystic artery and duct conducted using nylon loops; and (5) necropsy with macroscopic evaluation. Transumbilical endoscopic cholecystectomy was successfully completed in the first and third pig, with minor bleedings. The dissection times were 137 and 42 min, respectively. The total operation times were 167 and 69 min, respectively. And the lengths of resected specimen were 6.5 and 6.1 cm, respectively. Instillation of the fluid into the gallbladder bed produced edematous, distended tissue making separation safe and easy. Reliable ligation using double nylon loops insured the safety of cutting between the loops. There were no intraoperative complications or hemodynamic instability. Uncontrolled introperative bleeding occurred in the second case, leading to the operation failure. Pure NOTES trans-umbilical cholecystectomy with a water-jet hybrid-knife appears to be feasible and safe. Further investigation of this technique with long-term follow-up in animals is needed to confirm the preliminary observation.

Data mining practical machine learning tools and techniques

CERN Document Server

Witten, Ian H

2005-01-01

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same

Automated system of monitoring and positioning of functional units of mining technological machines for coal-mining enterprises

Directory of Open Access Journals (Sweden)

Meshcheryakov Yaroslav

2018-01-01

Full Text Available This article is show to the development of an automated monitoring and positioning system for functional nodes of mining technological machines. It describes the structure, element base, algorithms for identifying the operating states of a walking excavator; various types of errors in the functioning of microelectromechanical gyroscopes and accelerometers, as well as methods for their correction based on the Madgwick fusion filter. The results of industrial tests of an automated monitoring and positioning system for functional units on one of the opencast coal mines of Kuzbass are presented. This work is addressed to specialists working in the fields of the development of embedded systems and control systems, radio electronics, mechatronics, and robotics.

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

OpenAIRE

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

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 reg...

CONCEPTUAL DESIGN FOR A RADICALLY SMALLER, HIGHLY ADAPTIVE AND APPLICATION-FLEXIBLE MINING MACHINE FOR UTILITY AND DEVELOPMENT WORK

Energy Technology Data Exchange (ETDEWEB)

Andrew H. Stern

2004-12-20

The aim of this research project was to develop a preliminary ''conceptual design'' for a radically smaller, highly adaptive and application-flexible underground coal mining machine, for performing non-production utility work and/or also undertake limited production mining for the recovery of reserves that would otherwise be lost. Whereas historically, mining philosophies have reflected a shift to increasing larger mechanized systems [such as the continuous miner (CM)], specific mining operations that do not benefit from the economy of the large mining equipment are often ignored or addressed with significant inefficiencies. Developing this prototype concept will create a new class of equipment that can provide opportunities to re-think the very structure of the mining system across a broad range of possibilities, not able to be met by existing machinery. The approach involved pooling the collective input from mining professionals, using a structured listing of desired inputs in the form of a questionnaire, which was used to define the range of desired design specifications. From these inputs, a conceptual specification was blended, by the author, to embody the general concurrence of mission concepts for this machine.

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

Science.gov (United States)

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.

Data Mining and Machine Learning Methods for Dementia Research.

Science.gov (United States)

Li, Rui

2018-01-01

Patient data in clinical research often includes large amounts of structured information, such as neuroimaging data, neuropsychological test results, and demographic variables. Given the various sources of information, we can develop computerized methods that can be a great help to clinicians to discover hidden patterns in the data. The computerized methods often employ data mining and machine learning algorithms, lending themselves as the computer-aided diagnosis (CAD) tool that assists clinicians in making diagnostic decisions. In this chapter, we review state-of-the-art methods used in dementia research, and briefly introduce some recently proposed algorithms subsequently.

DIAGNOSIS OF THE WINDING MACHINE IN THE OLD SHAFT WITH SKIP IN LONEA MINING PLANT

Directory of Open Access Journals (Sweden)

Răzvan Bogdan ITU

2017-05-01

Full Text Available To study the operation of the winding machine in the Old Shaft with Skip in Lonea Mining Plant, the dynamic analysis of the driving wheel (Koepe wheel was performed, by resistive electric tensometry methods, acceleration measurements, and vibromechanical analysis on the bearings of Koepe driving wheels, on functioning cycles and vibromechanical analysis of the reduction gear. The paper presents aspects regarding vibromechanical measurements and resistive electric tensometry methods in the winding machine..

Energy transfer during the hydroentanglement of fibres

CSIR Research Space (South Africa)

Moyo, D

2012-10-01

Full Text Available .kashan.co.za] ABSTRACT The hydroentanglement of fibres is achieved by the energy of the high-velocity waterjets. This method is highly energy intensive and costly, hence the attempt to study the energy transfer during the process. Generally, the amount of energy used... in the nonwoven fabric strength were studied. In the study, the energies of the waterjets transferred to every fabric sample as a function of the waterjet pressure, machine speed, machine efficiency and the web area weight were quantified, and the resultant...

A Review of Machine Learning and Data Mining Approaches for Business Applications in Social Networks

OpenAIRE

Evis Trandafili; Marenglen Biba

2013-01-01

Social networks have an outstanding marketing value and developing data mining methods for viral marketing is a hot topic in the research community. However, most social networks remain impossible to be fully analyzed and understood due to prohibiting sizes and the incapability of traditional machine learning and data mining approaches to deal with the new dimension in the learning process related to the large-scale environment where the data are produced. On one hand, the birth and evolution...

Diagnostics of the Technical State of Bearings of Mining Machines Base Assemblies

Science.gov (United States)

Gerike, Boris L.; Mokrushev, Andrey A.

2017-10-01

The article reviews the methods of technical diagnostics of equipment used during maintenance of mining machines in accordance with their actual technical state, and considers the basics of vibration parameters measuring. The classification of existing methods for diagnosing the technical condition of rolling bearings is given. The advantages and disadvantages of these methods are considered. The main defects of rolling bearings arising during manufacturing, transportation, storage, and operation are considered.

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

Science.gov (United States)

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.

Diagnostic measurements on the great machines conditions of lignite surface mines

Energy Technology Data Exchange (ETDEWEB)

Helebrant, F.; Jurman, J.; Fries, J. [Technical University of Ostrava, Ostrava-Poruba (Czech Republic)

2005-07-01

An analysis of the diagnosis of loading and service dependability of a rail-mounted excavator used in surface lignite mining is described. Wheel power vibrations in electric motor bearings and electric motor input bearings to the gearbox were measured in situ, in horizontal, vertical, and axial directions. The data were analyzed using a mathematical relationship. The results are presented in a loading diagram that shows the deterioration and the acceptable lower bound of machine conditions over time. Work is continuing. 5 refs., 1 fig.

Automation and robotics technology for intelligent mining systems

Science.gov (United States)

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.

Extending mine life

International Nuclear Information System (INIS)

Anon.

1984-01-01

Mine layouts, new machines and techniques, research into problem areas of ground control and so on, are highlighted in this report on extending mine life. The main resources taken into account are coal mining, uranium mining, molybdenum and gold mining

Foreword for the special issue of selected papers from the 1st ECML/PKDD Workshop on Privacy and Security issues in Data Mining and Machine Learning

OpenAIRE

Aris Gkoulalas-Divanis; Yucel Saygin; Vassilios S. Verykios

2011-01-01

The first Workshop on Privacy and Security issues in Data Mining and Machine Learning (PSDML 2010) was organized on September 24, 2010 at Barcelona, Spain, in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). Privacy and security-related aspects of data mining and machine learning have been the topic of active research during the last decade due to the existence of numerous applications with privacy and/or...

Unmanned Mine of the 21st Centuries

Science.gov (United States)

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.

Introducing Machine Learning Concepts with WEKA.

Science.gov (United States)

Smith, Tony C; Frank, Eibe

2016-01-01

This chapter presents an introduction to data mining with machine learning. It gives an overview of various types of machine learning, along with some examples. It explains how to download, install, and run the WEKA data mining toolkit on a simple data set, then proceeds to explain how one might approach a bioinformatics problem. Finally, it includes a brief summary of machine learning algorithms for other types of data mining problems, and provides suggestions about where to find additional information.

TENDENCY OF APPLYING LHD MEHANIZATION IN MINING WORKINGS

Directory of Open Access Journals (Sweden)

Vladimir Rendulić

1995-12-01

Full Text Available Changed working conditions in deep mining workings (underground rooms of a mine havc lead, by application of diesel driven mechanization to the tendency of introducing the eleetric LHD machines in mines. However, although the flexibilily of electric mining machines has been improved due to the efforts of factories producing mining machines, the diesel units are still more flexible in application, although their maintenance in pit drives is more exspensive (the paper is published in Croatian.

An experimental investigation on the pressure characteristics of high speed self-resonating pulsed waterjets influenced by feeding pipe diameter

Energy Technology Data Exchange (ETDEWEB)

Li, Dong; Kang, Dong; Ding, Xiao Long; Wang, Xiao Huan; Fang, Zhen Long [School of Power and Mechanical Engineering, Wuhan University, Hubei Province (China)

2016-11-15

The destructive power of a continuous waterjet issuing from a nozzle can be greatly enhanced by generating self-resonance in the nozzle assembly to produce a Self-resonating pulsed waterjet (SRPW). To further improve the performance of SRPW, effects of feeding pipe diameter on the pressure characteristics were experimentally investigated by measuring and analyzing the axial pressure oscillation peaks and amplitudes. Four organ-pipe nozzles of different chamber lengths and three feeding pipes of different diameters were employed. Results show that feeding pipe diameter cannot change the feature of SRPW of having an optimum standoff distance, but it slightly changes the oscillating frequency of the jet. It is also found that feeding pipe diameter significantly affects the magnitudes of pressure oscillation peak and amplitude, largely depending on the pump pressure and standoff distance. The enhancement or attenuation of the pressure oscillation peak and amplitude can be differently affected by the same feeding pipe diameter.

Ergonomics in drivers' cabs on open-cast mining machines; Ergonomie bei Fuehrerstaenden auf Tagebaugeraeten

Energy Technology Data Exchange (ETDEWEB)

Vater, L. [Ergonomie/Gefahrstoffe, Vattenfall Europe Mining AG, Senftenberg (Germany)

2004-08-12

Ergonomically designed driver's cabs also contribute directly to the increase in safety at work. In the course of the electrical re-design of the open-cast mining machines new drivers' cabs, which eliminate ergonomic deficits, were used. Other important aspects in addition to the improvements in the environmental factors noise, vibration and dust, are in particular the visibility conditions, visualisation of process data and monitoring as well as operating concepts. Taking into account the different types of machine drivers' cabs with a modified basic design and bearing design are used. Optimisation of the installation of the monitors and the basic structuring of the control panels was carried out. In addition to the increase in the effectiveness of control another aim is to minimise faulty operation by the driver when changing machines frequently. (orig.)

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

Science.gov (United States)

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.

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

Science.gov (United States)

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

Evaluation of abrasive waterjet produced titan surfaces topography by spectral analysis techniques

Directory of Open Access Journals (Sweden)

D. Kozak

2012-01-01

Full Text Available Experimental study of a titan grade 2 surface topography prepared by abrasive waterjet cutting is performed using methods of the spectral analysis. Topographic data are acquired by means of the optical profilometr MicroProf®FRT. Estimation of the areal power spectral density of the studied surface is carried out using the periodogram method combined with the Welch´s method. Attention is paid to a structure of the areal power spectral density, which is characterized by means of the angular power spectral density. This structure of the areal spectral density is linked to the fine texture of the surface studied.

Edu-mining: A Machine Learning Approach

Science.gov (United States)

Srimani, P. K.; Patil, Malini M.

2011-12-01

Mining Educational data is an emerging interdisciplinary research area that mainly deals with the development of methods to explore the data stored in educational institutions. The educational data is referred as Edu-DATA. Queries related to Edu-DATA are of practical interest as SQL approach is insufficient and needs to be focused in a different way. The paper aims at developing a technique called Edu-MINING which converts raw data coming from educational institutions using data mining techniques into useful information. The discovered knowledge will have a great impact on the educational research and practices. Edu-MINING explores Edu-DATA, discovers new knowledge and suggests useful methods to improve the quality of education with regard to teaching-learning process. This is illustrated through a case study.

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

Science.gov (United States)

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

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

Directory of Open Access Journals (Sweden)

Joanna F Dipnall

Full Text Available 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

Machine Learning.

Science.gov (United States)

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)

DIAGNOSIS OF THE WINDING MACHINE IN THE OLD SHAFT WITH SKIP IN LONEA MINING PLANT

OpenAIRE

Răzvan Bogdan ITU; Vilhelm ITU

2017-01-01

To study the operation of the winding machine in the Old Shaft with Skip in Lonea Mining Plant, the dynamic analysis of the driving wheel (Koepe wheel) was performed, by resistive electric tensometry methods, acceleration measurements, and vibromechanical analysis on the bearings of Koepe driving wheels, on functioning cycles and vibromechanical analysis of the reduction gear. The paper presents aspects regarding vibromechanical measurements and resistive electric tensometry me...

Web Mining

Science.gov (United States)

Fürnkranz, Johannes

The World-Wide Web provides every internet citizen with access to an abundance of information, but it becomes increasingly difficult to identify the relevant pieces of information. Research in web mining tries to address this problem by applying techniques from data mining and machine learning to Web data and documents. This chapter provides a brief overview of web mining techniques and research areas, most notably hypertext classification, wrapper induction, recommender systems and web usage mining.

Effect of Abrasive Waterjet Peening Surface Treatment of Steel Plates on the Strength of Single-Lap Adhesive Joints

Directory of Open Access Journals (Sweden)

Kamil Anasiewicz

2017-09-01

Full Text Available The paper presents results of comparative study of shear strength of single–lap adhesive joints, depending on the method of surface preparation of steel plates with increased corrosion resistance. The method of preparing adherend surfaces is often one of the most important factors determining the strength of adhesive joints. Appropriate geometric surface development and cleaning of the surface enhances adhesion forces between adherend material and adhesive. One of the methods of shaping engineering materials is waterjet cutting, which in the AWJP – abrasive waterjet peening variant, serves to shape flat surfaces of the material by changing the roughness and introducing stresses into the surface layer. These changes are valuable when preparing adhesive joints. In the study, surface roughness parameters obtained with AWJP treatment, were analyzed in direct relation to the strength of the adhesive joint. As a consequence of the experimental results analysis, the increase in the strength of the adhesive joints was observed in a certain range of parameters used for AWJP treatment. A decrease in shear strength of adhesive joint with the most modified topography of overlap surface was observed.

30 CFR 18.97 - Inspection of machines; minimum requirements.

Science.gov (United States)

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Inspection of machines; minimum requirements... 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...

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

Science.gov (United States)

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.

Text Mining Applications and Theory

CERN Document Server

Berry, Michael W

2010-01-01

Text Mining: Applications and Theory presents the state-of-the-art algorithms for text mining from both the academic and industrial perspectives.  The contributors span several countries and scientific domains: universities, industrial corporations, and government laboratories, and demonstrate the use of techniques from machine learning, knowledge discovery, natural language processing and information retrieval to design computational models for automated text analysis and mining. This volume demonstrates how advancements in the fields of applied mathematics, computer science, machine learning

Analytical study on U/G coal mine CPT and inferences

Energy Technology Data Exchange (ETDEWEB)

Dey, N.C.; Mukhopadhyay, S. [Bengal Engineering College, Howrath (India). Dept. of Mining and Geology

1999-08-01

The analytical aspects of underground CPT (coal mine cost per tonne), which varies from mine to mine due to the different weightages of various contributing factors, are described. The CPT is not only dictated by the increasing wages but also by the availability of man-hour and accountability of machine utilization. An optimal blend of labour-intensive and machine-intensive methods involving least investment and operating cost, is a challenge for the coal industry. Technology upgradation and implementation, higher skill and morale, excellence in planning and monitoring, optimization in capacity utilization, and better consumer acceptability of coal will consistently improve the financial health of the coal mining sector. Other factors which will help improve the financial health of coal mining industries are (1) cost propaganda like safety week celebration; (2) cost consciousness at all levels; (3) noticeboard comprising the cost of man-hour and machine- hour; (4) no idle time for men as well as machine; (5) care to increase the life of machines; (6) scope of target amendment in a year; (7) prior to introducing costly machines, due weightage to be given on coal grade, mine life, geo-mining conditions; and (8) award to most economic mine and punishment to others rated below the BEP (break- even point). 2 refs., 3 figs.

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

Science.gov (United States)

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).

Availability analysis of selected mining machinery

Directory of Open Access Journals (Sweden)

Brodny Jarosław

2017-06-01

Full Text Available Underground extraction of coal is characterized by high variability of mining and geological conditions in which it is conducted. Despite ever more effective methods and tools, used to identify the factors influencing this process, mining machinery, used in mining underground, work in difficult and not always foreseeable conditions, which means that these machines should be very universal and reliable. Additionally, a big competition, occurring on the coal market, causes that it is necessary to take action in order to reduce the cost of its production, e.g. by increasing the efficiency of utilization machines. To meet this objective it should be pro-ceed with analysis presented in this paper. The analysis concerns to availability of utilization selected mining machinery, conducted using the model of OEE, which is a tool for quantitative estimate strategy TPM. In this article we considered the machines being part of the mechanized longwall complex and the basis of analysis was the data recording by the industrial automation system. Using this data set we evaluated the availability of studied machines and the structure of registered breaks in their work. The results should be an important source of information for maintenance staff and management of mining plants, needed to improve the economic efficiency of underground mining.

Machine technology: a survey

International Nuclear Information System (INIS)

Barbier, M.M.

1981-01-01

An attempt was made to find existing machines that have been upgraded and that could be used for large-scale decontamination operations outdoors. Such machines are in the building industry, the mining industry, and the road construction industry. The road construction industry has yielded the machines in this presentation. A review is given of operations that can be done with the machines available

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

Science.gov (United States)

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Preparation of machines for inspection... 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...

The analysis of human error as causes in the maintenance of machines: a case study in mining companies

Directory of Open Access Journals (Sweden)

Kovacevic, Srdja

2016-12-01

Full Text Available This paper describes the two-step method used to analyse the factors and aspects influencing human error during the maintenance of mining machines. The first step is the cause-effect analysis, supported by brainstorming, where five factors and 21 aspects are identified. During the second step, the group fuzzy analytic hierarchy process is used to rank the identified factors and aspects. A case study is done on mining companies in Serbia. The key aspects are ranked according to an analysis that included experts who assess risks in mining companies (a maintenance engineer, a technologist, an ergonomist, a psychologist, and an organisational scientist. Failure to follow technical maintenance instructions, poor organisation of the training process, inadequate diagnostic equipment, and a lack of understanding of the work process are identified as the most important causes of human error.

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

ByungWan Jo

2018-03-01

Full Text Available 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.

Technological and mining analysis of mechanized systems used in roadways in Polish mines

Energy Technology Data Exchange (ETDEWEB)

Sikora, W; Giza, T; Siwiec, J [Politechnika Slaska, Gliwice (Poland). Instytut Mechanizacji Gornictwa

1987-01-01

Analyzes methods of mine drivage in Poland and materials handling systems. Of 1,620 km of roadways driven in 1982, 12% fell on roadways driven in coal and 88% on roadways driven in stone or stone and coal. Roadways driven in coal in most cases were situated at depths from 500 to 700 m. Roadway cross-section ranged from 12 to 18 m{sup 2}. Roadways in stone or stone and coal were driven by drilling and blasting. Loaders were used for stone handling. Roadways in coal were driven by heading machines. Advance rates of mine drivage by heading machines were 2 to 3 times higher than those by drilling and blasting with loaders for stone handling. Basic statistical data characterizing roadways and drivage methods are evaluated: roadway dimensions and depth advance rate depending on drivage methods and mining condition, types of heading machines and loaders.

30 CFR 18.49 - Connection boxes on machines.

Science.gov (United States)

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Connection boxes on machines. 18.49 Section 18..., AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Construction and Design Requirements § 18.49 Connection boxes on machines. Connection boxes used to facilitate replacement...

Data mining mobile devices

CERN Document Server

Mena, Jesus

2013-01-01

With today's consumers spending more time on their mobiles than on their PCs, new methods of empirical stochastic modeling have emerged that can provide marketers with detailed information about the products, content, and services their customers desire.Data Mining Mobile Devices defines the collection of machine-sensed environmental data pertaining to human social behavior. It explains how the integration of data mining and machine learning can enable the modeling of conversation context, proximity sensing, and geospatial location throughout large communities of mobile users

Method of Automatic Ontology Mapping through Machine Learning and Logic Mining

Institute of Scientific and Technical Information of China (English)

王英林

2004-01-01

Ontology mapping is the bottleneck of handling conflicts among heterogeneous ontologies and of implementing reconfiguration or interoperability of legacy systems. We proposed an ontology mapping method by using machine learning, type constraints and logic mining techniques. This method is able to find concept correspondences through instances and the result is optimized by using an error function; it is able to find attribute correspondence between two equivalent concepts and the mapping accuracy is enhanced by combining together instances learning, type constraints and the logic relations that are imbedded in instances; moreover, it solves the most common kind of categorization conflicts. We then proposed a merging algorithm to generate the shared ontology and proposed a reconfigurable architecture for interoperation based on multi agents. The legacy systems are encapsulated as information agents to participate in the integration system. Finally we give a simplified case study.

Proceedings. Fourth international symposium on mine mechanisation and automation

Energy Technology Data Exchange (ETDEWEB)

Gurgenci, H.; Hood, M. [eds.

1997-12-31

Papers in the first volume are presented under the following session headings: drilling; mining robotics; machine monitoring; mine automation systems; reliability and maintenance; mine automation - communications mechanical excavation of medium-strength rock; and new mining equipment technologies. The second volume covers: mechanical excavation of hard rock; autonomous vehicles; mechanical excavation industry experience; machine guidance; applications of rock mechanics, mine planning management and scheduling; orebody delineation; and safety. Selected papers have been abstracted separately for the IEA Coal Research databases available on CD-ROM and the worldwide web.

Technological highwall mining

Energy Technology Data Exchange (ETDEWEB)

Davison, I. [Highwall Systems (United States)

2006-09-15

The paper explores the issues facing highwall mining. Based in Chilhowie, Virginia, American Highwall Systems has developed a highwall mining system that will allow the mining of coal seams from 26 in to 10 ft in thickness. The first production model, AH51, began mining in August 2006. Technologies incorporated into the company's mining machines to improve the performance, enhance the efficiency, and improve the reliability of the highwall mining equipment incorporate technologies from many disciplines. Technology as applied to design engineering, manufacturing and fabrication engineering, control and monitoring computer hardware and software has played an important role in the evolution of the American Highwall Systems design concept. 5 photos.

Data mining theories, algorithms, and examples

CERN Document Server

Ye, Nong

2013-01-01

AN OVERVIEW OF DATA MINING METHODOLOGIESIntroduction to data mining methodologiesMETHODOLOGIES FOR MINING CLASSIFICATION AND PREDICTION PATTERNSRegression modelsBayes classifiersDecision treesMulti-layer feedforward artificial neural networksSupport vector machinesSupervised clusteringMETHODOLOGIES FOR MINING CLUSTERING AND ASSOCIATION PATTERNSHierarchical clusteringPartitional clusteringSelf-organized mapProbability distribution estimationAssociation rulesBayesian networksMETHODOLOGIES FOR MINING DATA REDUCTION PATTERNSPrincipal components analysisMulti-dimensional scalingLatent variable anal

30 CFR 18.21 - Machines equipped with powered dust collectors.

Science.gov (United States)

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Machines equipped with powered dust collectors... TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Construction and Design Requirements § 18.21 Machines equipped with powered dust collectors. Powered dust...

Software tool for data mining and its applications

Science.gov (United States)

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.

Pneumatic automation systems in coal mines

Energy Technology Data Exchange (ETDEWEB)

Shmatkov, N.A.; Kiklevich, Yu.N.

1981-04-01

Giprougleavtomatizatsiya, Avtomatgormash, Dongiprouglemash, VNIIGD and other plants develop 30 new pneumatic systems for mine machines and equipment control each year. The plants produce about 200 types of pneumatic systems. Major pneumatic systems for face systems, machines and equipment are reviewed: Sirena system for remote control of ANShch and AShchM face systems for steep coal seams, UPS control systems for pump stations, PAUZA control system for stowing machines, remote control system of B100-200 drilling machines, PUSK control system for coal cutter loaders with pneumatic drive (A-70, Temp), PUVSh control system for ventilation barriers activated from moving electric locomotives, PAZ control system for skip hoist loading. Specifications of the systems are given. Economic benefit produced by the pneumatic control systems are evaluated (from 1,500 to 40,000 rubles/year). Using the systems increases productivity of face machines and other machines used in black coal mines by 5 to 30%.

Mining software specifications methodologies and applications

CERN Document Server

Lo, David

2011-01-01

An emerging topic in software engineering and data mining, specification mining tackles software maintenance and reliability issues that cost economies billions of dollars each year. The first unified reference on the subject, Mining Software Specifications: Methodologies and Applications describes recent approaches for mining specifications of software systems. Experts in the field illustrate how to apply state-of-the-art data mining and machine learning techniques to address software engineering concerns. In the first set of chapters, the book introduces a number of studies on mining finite

Data Mining at NASA: From Theory to Applications

Science.gov (United States)

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.

Experimental Study on Abrasive Waterjet Polishing of Hydraulic Turbine Blades

International Nuclear Information System (INIS)

Khakpour, H; Birglenl, L; Tahan, A; Paquet, F

2014-01-01

In this paper, an experimental investigation is implemented on the abrasive waterjet polishing technique to evaluate its capability in polishing of surfaces and edges of hydraulic turbine blades. For this, the properties of this method are studied and the main parameters affecting its performance are determined. Then, an experimental test-rig is designed, manufactured and tested to be used in this study. This test-rig can be used to polish linear and planar areas on the surface of the desired workpieces. Considering the number of parameters and their levels, the Taguchi method is used to design the preliminary experiments. All experiments are then implemented according to the Taguchi L 18 orthogonal array. The signal-to-noise ratios obtained from the results of these experiments are used to determine the importance of the controlled polishing parameters on the final quality of the polished surface. The evaluations on these ratios reveal that the nozzle angle and the nozzle diameter have the most important impact on the results. The outcomes of these experiments can be used as a basis to design a more precise set of experiments in which the optimal values of each parameter can be estimated

Quantum Machine Learning

OpenAIRE

Romero García, Cristian

2017-01-01

[EN] In a world in which accessible information grows exponentially, the selection of the appropriate information turns out to be an extremely relevant problem. In this context, the idea of Machine Learning (ML), a subfield of Artificial Intelligence, emerged to face problems in data mining, pattern recognition, automatic prediction, among others. Quantum Machine Learning is an interdisciplinary research area combining quantum mechanics with methods of ML, in which quantum properties allow fo...

Increased productivity in construction of civil and mining tunnels through the use of high-capacity tunnel-boring machines and continuous belt conveyor muck haulage

Energy Technology Data Exchange (ETDEWEB)

Beatty, J.G.; Ganey, R.J.; Killingsworth, J.E. [Perini Corp., Chicago, IL (United States). US Heavy Division

1994-12-31

The use of a large diameter high production tunnel boring machine interfaced with a high capacity continuous belt conveyor system provides a highly productive and cost effective construction system for both civil and mining tunnels. Continuous advance of the tunnel boring machine for a distance of 1,000 feet (305 m) allows for very efficient operation of the system. The available cost reductions will likely prove that this approach to waste handling will make marginally viable projects economically feasible. 9 refs., 10 figs., 1 tab.

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

Science.gov (United States)

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Boring-type machines equipped for auxiliary..., DEPARTMENT OF LABOR TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Construction and Design Requirements § 18.22 Boring-type machines equipped for...

A Review of Extra-Terrestrial Mining Concepts

Science.gov (United States)

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.

Data Stream Mining

Science.gov (United States)

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).

A Comparison of the Effects of K-Anonymity on Machine Learning Algorithms

OpenAIRE

Hayden Wimmer; Loreen Powell

2014-01-01

While research has been conducted in machine learning algorithms and in privacy preserving in data mining (PPDM), a gap in the literature exists which combines the aforementioned areas to determine how PPDM affects common machine learning algorithms. The aim of this research is to narrow this literature gap by investigating how a common PPDM algorithm, K-Anonymity, affects common machine learning and data mining algorithms, namely neural networks, logistic regression, decision trees, and Baye...

Feasibility of introducing continuous systems in surface mines of India

Energy Technology Data Exchange (ETDEWEB)

Bordia, S K

1987-06-01

The paper presents a brief outline of the mineral types, production trends and techno-economic feasiblity associated with the possible introduction of continuous mining systems to India. Production trends are outlined for coal, limestone, bauxite, phosphate, and iron ore. Continuous mining systems described are heavy-duty bucket wheel excavators, road milling type machines and shearing type machines. 8 refs.

30 CFR 77.401 - Stationary grinding machines; protective devices.

Science.gov (United States)

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...

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

Science.gov (United States)

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.

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

Science.gov (United States)

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Approval of machines constructed of components... APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Field Approval of Electrically Operated Mining Equipment § 18.95 Approval of machines constructed of components approved...

Post-mining in France

International Nuclear Information System (INIS)

2007-01-01

This plentifully illustrated book aims at showing how new equilibria are building up during the transition between mining activity and post-mining, and at stressing on the necessity to keep up the cultural elements, the competencies and knowledge of mining works. The first chapter - mine and men - shows the importance of mineral substances in the objects of the everyday life, illustrates the importance of the mining tradition in France and describes the technical and administrative organisation of the end of the mining activity (works, rehabilitation, regulation, monitoring..). Chapter two - exploitation methods - presents the surface and underground facilities and their impact on the environment (extraction machines, workshops, ore processing plants, decantation ponds..). The third chapter deals with the rehabilitation and monitoring aspects: impact of mining activity stoppage on underground and surface waters, land stability, soils cleansing.. The last chapter summarizes the history of French mining region by region: Nord-Pas-de-Calais, Lorraine-Alsace, Massif central, Bretagne-Normandie, Provence-Alpes-Cote d'Azur and Pyrenees

Modelling open pit shovel-truck systems using the Machine Repair Model

Energy Technology Data Exchange (ETDEWEB)

Krause, A.; Musingwini, C. [CBH Resources Ltd., Sydney, NSW (Australia). Endeaver Mine

2007-08-15

Shovel-truck systems for loading and hauling material in open pit mines are now routinely analysed using simulation models or off-the-shelf simulation software packages, which can be very expensive for once-off or occasional use. The simulation models invariably produce different estimations of fleet sizes due to their differing estimations of cycle time. No single model or package can accurately estimate the required fleet size because the fleet operating parameters are characteristically random and dynamic. In order to improve confidence in sizing the fleet for a mining project, at least two estimation models should be used. This paper demonstrates that the Machine Repair Model can be modified and used as a model for estimating truck fleet size in an open pit shovel-truck system. The modified Machine Repair Model is first applied to a virtual open pit mine case study. The results compare favourably to output from other estimation models using the same input parameters for the virtual mine. The modified Machine Repair Model is further applied to an existing open pit coal operation, the Kwagga Section of Optimum Colliery as a case study. Again the results confirm those obtained from the virtual mine case study. It is concluded that the Machine Repair Model can be an affordable model compared to off-the-shelf generic software because it is easily modelled in Microsoft Excel, a software platform that most mines already use.

Ensemble Data Mining Methods

Data.gov (United States)

National Aeronautics and Space Administration — Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve...

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

Science.gov (United States)

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.

30 CFR 18.80 - Approval of machines assembled with certified or explosion-proof components.

Science.gov (United States)

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Approval of machines assembled with certified... ADMINISTRATION, DEPARTMENT OF LABOR TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Machines Assembled With Certified or Explosion-Proof Components, Field...

A Review of Extra-Terrestrial Mining Robot Concepts

Science.gov (United States)

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.

GPK heading machine

Energy Technology Data Exchange (ETDEWEB)

Krmasek, J.; Novosad, K.

1981-01-01

This article evaluates performance tests of the Soviet made GPK heading machine carried out in 4 coal mines in Czechoslovakia (Ostrava-Karvina region and Kladno mines). GPK works in coal seams and rocks with compression strength of 40 to 50 MPa. Dimensions of the tunnel are height 1.8 to 3.8 m and width 2.6 to 4.7 m, tunnel gradient plus to minus 10 degrees. GPK weighs 16 t, its conical shaped cutting head equipped with RKS-1 cutting tools is driven by an electric motor with 55 kW capacity. Undercarriage of the GPK, gathering-arm loader, hydraulic system, electric system and dust supression system (water spraying or pneumatic section) are characterized. Specifications of GPK heading machines are compared with PK-3r and F8 heading machines. Reliability, number of failures, dust level, noise, productivity depending on compression strength of rocks, heading rate in coal and in rocks, energy consumption, performance in inclined tunnels, and cutting tool wear are evaluated. Tests show that GPK can be used to drive tunnels in coal with rock constituting up to 50% of the tunnel crosscut, as long as rock compression strength does not exceed 50 MPa. In rocks characterized by higher compression strength cutting tool wear sharply increases. GPK is characterized by higher productivity than that of the PK-3r heading machine. Among the weak points of the GPK are: unsatisfactory reliability and excessive wear of its elements. (4 refs.) (In Czech)

INTEGRATED ROBOT-HUMAN CONTROL IN MINING OPERATIONS

Energy Technology Data Exchange (ETDEWEB)

George Danko

2005-04-01

This report contains a detailed description of the work conducted in the first year of the project on Integrated Robot-Human Control in Mining Operations at University of Nevada, Reno. This project combines human operator control with robotic control concepts to create a hybrid control architecture, in which the strengths of each control method are combined to increase machine efficiency and reduce operator fatigue. The kinematics reconfiguration type differential control of the excavator implemented with a variety of ''software machine kinematics'' is the key feature of the project. This software re-configured excavator is more desirable to execute a given digging task. The human operator retains the master control of the main motion parameters, while the computer coordinates the repetitive movement patterns of the machine links. These repetitive movements may be selected from a pre-defined family of trajectories with different transformations. The operator can make adjustments to this pattern in real time, as needed, to accommodate rapidly-changing environmental conditions. A Bobcat{reg_sign} 435 excavator was retrofitted with electro-hydraulic control valve elements. The modular electronic control was tested and the basic valve characteristics were measured for each valve at the Robotics Laboratory at UNR. Position sensors were added to the individual joint control actuators, and the sensors were calibrated. An electronic central control system consisting of a portable computer, converters and electronic driver components was interfaced to the electro-hydraulic valves and position sensors. The machine is operational with or without the computer control system depending on whether the computer interface is on or off. In preparation for emulated mining tasks tests, typical, repetitive tool trajectories during surface mining operations were recorded at the Newmont Mining Corporation's ''Lone Tree'' mine in Nevada.

Data Mining Aplications in Livestock

Directory of Open Access Journals (Sweden)

Feyza ALEV ÇETİN

2016-03-01

Full Text Available Data mining provides discovering the required and applicable knowledge from very large amounts of information collected in one centre. Data mining has been used in the information industry and society. Although many methods of data mining has been used, these techniques has been remarkable in animal husbandry in recent years. For the solution of complex problems in animal husbandry many methods were discussed and developed. Brief information on data mining techniques such as k-means approach, k-nearest neighbor approach, multivariate adaptive regression function (MARS, naive Bayesian classifiers (NBC, artificial neural networks (ANN, support vector machines (SVM, decision trees are given in the study. Some data mining methods are presented and examples of the application of data mining in the field of animal husbandry in the world are provided with this study.

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

Science.gov (United States)

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 (pmachine 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

Robotics for mining control

Energy Technology Data Exchange (ETDEWEB)

1986-11-01

In 1982 surveys of the mining industry revealed no applications of robotics existed and none were planned. This report provides a general overview of automation in the mining industry since this point in time. Roof control electronics, gas monitoring, jumbo drill automation, remote and sensor- controlled continuous miners, automated trolley trucks, roof bolting and screening machines are examples of technology available today. The report concludes with recommendations as to six potential research areas. 25 refs.

High precision laser processing of sensitive materials by Microjet

Science.gov (United States)

Sibailly, Ochelio D.; Wagner, Frank R.; Mayor, Laetitia; Richerzhagen, Bernold

2003-11-01

Material laser cutting is well known and widely used in industrial processes, including micro fabrication. An increasing number of applications require nevertheless a superior machining quality than can be achieved using this method. A possibility to increase the cut quality is to opt for the water-jet guided laser technology. In this technique the laser is conducted to the work piece by total internal reflection in a thin stable water-jet, comparable to the core of an optical fiber. The water jet guided laser technique was developed originally in order to reduce the heat damaged zone near the cut, but in fact many other advantages were observed due to the usage of a water-jet instead of an assist gas stream applied in conventional laser cutting. In brief, the advantages are three-fold: the absence of divergence due to light guiding, the efficient melt expulsion, and optimum work piece cooling. In this presentation we will give an overview on several industrial applications of the water-jet guided laser technique. These applications range from the cutting of CBN or ferrite cores to the dicing of thin wafers and the manufacturing of stencils, each illustrates the important impact of the water-jet usage.

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

Science.gov (United States)

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.

Frequent pattern mining

CERN Document Server

Aggarwal, Charu C

2014-01-01

Proposes numerous methods to solve some of the most fundamental problems in data mining and machine learning Presents various simplified perspectives, providing a range of information to benefit both students and practitioners Includes surveys on key research content, case studies and future research directions

Separation and reconstruction of high pressure water-jet reflective sound signal based on ICA

Science.gov (United States)

Yang, Hongtao; Sun, Yuling; Li, Meng; Zhang, Dongsu; Wu, Tianfeng

2011-12-01

The impact of high pressure water-jet on the different materials target will produce different reflective mixed sound. In order to reconstruct the reflective sound signals distribution on the linear detecting line accurately and to separate the environment noise effectively, the mixed sound signals acquired by linear mike array were processed by ICA. The basic principle of ICA and algorithm of FASTICA were described in detail. The emulation experiment was designed. The environment noise signal was simulated by using band-limited white noise and the reflective sound signal was simulated by using pulse signal. The reflective sound signal attenuation produced by the different distance transmission was simulated by weighting the sound signal with different contingencies. The mixed sound signals acquired by linear mike array were synthesized by using the above simulated signals and were whitened and separated by ICA. The final results verified that the environment noise separation and the reconstruction of the detecting-line sound distribution can be realized effectively.

Ergonomics of mining machinery and transport in the South African mining industry.

CSIR Research Space (South Africa)

Schutte, PC

2003-03-01

Full Text Available this background, a study was conducted to assess the ergonomics of a number of mining machines and transport systems to identify the ergonomics-related hazards that could impact on the operators’ ability to work safely and efficiently....

Recent advances in remote coal mining machine sensing, guidance, and teleoperation

Energy Technology Data Exchange (ETDEWEB)

Ralston, J C; Hainsworth, D W; Reid, D C; Anderson, D L; McPhee, R J [CSIRO Exploration & Minerals, Kenmore, Qld. (Australia)

2001-10-01

Some recent applications of sensing, guidance and telerobotic technology in the coal mining industry are presented. Of special interest is the development of semi or fully autonomous systems to provide remote guidance and communications for coal mining equipment. The use of radar and inertial based sensors are considered in an attempt to solve the horizontal and lateral guidance problems associated with mining equipment automation. Also described is a novel teleoperated robot vehicle with unique communications capabilities, called the Numbat, which is used in underground mine safety and reconnaissance missions.

Tunnelling support methods and their possible application to machine rock face excavation in coal mining

Energy Technology Data Exchange (ETDEWEB)

Maidl, B.; Edeling, H.

1981-06-11

Mechanized pushing through the rocks is possible even in teary rock if protective measures are taken directly behind the drill bit. Present arch-type supports are best reinforced with sprayed concrete as it will take up rock deformations. In this case, however, the question soon arises whether arch-type steel supports should be used at all. So far, mature solutions have not been found but they will be possible if the mining industry is really interested. Sprayed concrete with admixtures of reinforcing steel fibers plays a major role here as it will protect miner's heads already at an early stage and is suitable as support even at a later stage. Equally interesting would be reinforced concrete pumped behind advancing formwork. A combination of both techniques may turn out to be the most suitable method to replace arch-type supports. A problem of particular importance is machine bracing in the fresh concrete lining. If the concrete is filled in directly behind the drill bit, it is only 4 to 6 h old when it reaches the bracing device, i.e., its pressure resistance is lower than the contact pressure of present mining machinery. It may be difficult to find a solution here but it is considered to be possible. With shell concrete, the formwork should be constructed so as to withstand the contact pressure.

Kernel Methods for Mining Instance Data in Ontologies

Science.gov (United States)

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.

Quantum machine learning what quantum computing means to data mining

CERN Document Server

Wittek, Peter

2014-01-01

Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine L

Design of experiments in production engineering

CERN Document Server

2016-01-01

This book covers design of experiments (DoE) applied in production engineering as a combination of manufacturing technology with applied management science. It presents recent research advances and applications of design experiments in production engineering and the chapters cover metal cutting tools, soft computing for modelling and optmization of machining, waterjet machining of high performance ceramics, among others.

Highly-productive mechanization systems for coal mining in the Polish coal industry

Energy Technology Data Exchange (ETDEWEB)

Sikora, W

1985-01-01

Effects of mechanization on underground coal mining in Poland from 1960 to 1980 and mining equipment used in Poland is reviewed. In 1983 black coal output increased to 191.1 Mt. There were 765 working faces, 442 of which with powered supports. Six hundred thirty-four shearer loaders were in use. About 82.7% of coal output fell on faces mined by sets of mining equipment (shearer loaders, powered supports and chain conveyors). The average coal output per working face amounted to 889 t/d. About 50% of mine roadways was driven by heading machines (346 heading machines were in use). The average coal output per face mined by a set of mining equipment amounted to 1248 t/d. About 86% of shearer loaders fell on double drum shearer loaders. Types of mining equipment used in underground mining are reviewed: powered supports (Pioma, Fazos, Glinik and the SOW), shearer loaders (drum shearer loaders and double-drum shearer loaders with chain haulage and chainless haulage systems for unidirectional and bi-directional mining), chain conveyors (Samson, Rybnik). Statistical data on working faces with various sets of equipment are given. 3 references.

A systematic mapping study of process mining

Science.gov (United States)

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.

Development trends in mining technologies. [Poland

Energy Technology Data Exchange (ETDEWEB)

Sikora, W

1983-01-01

Research programs on underground black coal mining in Poland are discussed. It is assumed that no major technology changes will take place by the year 2000. Second generation of mining technologies will be used after the year 2000. The following technologies of the second generation are discussed: in-situ gasification, chemical coal disintegration, manless hydraulic mining with hydraulic transport. Research programs on technologies of the second generation should guarantee their commercial and economic use by the year 2000. The following targets for research programs aimed at increasing productivity, reducing mining cost, reducing labor and increasing safety in mining, covering the next 2 decades are comparatively evaluated: increasing advance rate of longwall faces, increasing machine time of integrated face systems by at least 20%, improving design of powered supports for eliminating rock falls, development of heavy-duty face systems for longwall mining with hydraulic stowing, development of face systems for mining thick coal seams on their whole thickness, slice mining thick coal seams with artificial roofs, bidirectional longwall mining by shearer loaders, use of more elastic and reliable mining systems.

Combination of water-jet dissection and needle-knife as a hybrid knife simplifies endoscopic submucosal dissection.

Science.gov (United States)

Lingenfelder, Tobias; Fischer, Klaus; Sold, Moritz G; Post, Stefan; Enderle, Markus D; Kaehler, Georg F B A

2009-07-01

The safety and efficacy of endoscopic submucosal dissection (ESD) is very dependent on an effective injection beneath the submucosal lamina and on a controlled cutting technique. After our study group demonstrated the efficacy of the HydroJet in needleless submucosal injections under various physical conditions to create a submucosal fluid cushion (Selective tissue elevation by pressure = STEP technique), the next step was to develop a new instrument to combine the capabilities of an IT-Knife with a high-pressure water-jet in a single instrument. In this experimental study, we compared this new instrument with a standard ESD technique. Twelve gastric ESD were performed in six pigs under endotracheal anesthesia. Square areas measuring 4-cm x 4-cm were marked out on the anterior and posterior wall in the corpus-antrum transition region. The HybridKnife was used as an standard needle knife with insulated tip (i.e., the submucosal injection was performed with an injection needle and only the radiofrequency (RF) part of the HybridKnife was used for cutting (conventional technique)) or the HybridKnife was used in all the individual stages of the ESD, making use of the HybridKnife's combined functions (HybridKnife technique). The size of the resected specimens, the operating time, the frequency with which instruments were changed, the number of bleeding episodes, and the number of injuries to the gastric wall together with the subjective overall assessment of the intervention by the operating physician were recorded. The resected specimens were the same size, with average sizes of 16.96 cm(2) and 15.85 cm(2) resp (p = 0.8125). Bleeding episodes have been less frequent in the HybridKnife group (2.83 vs. 3.5; p = 0.5625). The standard knife caused more injuries to the lamina muscularis propria (0.17 vs. 1.33; p = 0.0313). The operating times had a tendency to be shorter with the HybridKnife technique (47.18 vs. 58.32 minute; p = 0.0313). The combination of a needle

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

Science.gov (United States)

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

Mining in 2015

Energy Technology Data Exchange (ETDEWEB)

Hood, M.; Hatherly, P.; Gurgenci, H. [Centre for Mining Technology and Equipment (Australia)

1999-10-01

New technology in open-pit and underground hard rock mining in 2015 is anticipated in this article, based on a paper presented to the 1998 invitation symposium - 'Technology - Australia's future: new technology for traditional industry', held in Freemantle, WA, 24-25 November 1998. It is expected that essential mining operations of rock breakage and transport and ore processing will still exist but the use of drills, shovels/LHDs and trucks is likely to be replaced by continuous, intelligent, automated mining systems. Rock blasting models need to be fed data on rock properties at each blasthole for high accuracy. The authors believe that in 2015 measurements of rock properties will be a routine part of the drilling process. Blasthole drills will be fitted with a range of mechanical and geophysical sensors. New, non-explosive methods of rock breaking such as oscillating disc cutting, may be available. Mining automation will improve safety and productivity, perhaps with the automation of dragline swing LHDs and trucks may be able to drive themselves, with operators monitoring and intervening when necessary. Performance and machine condition data may be applied to improve equipment design. Australian mining stands to gain by these advances in mining technology. 1 fig., 3 photos.

Cart'Eaux: an automatic mapping procedure for wastewater networks using machine learning and data mining

Science.gov (United States)

Bailly, J. S.; Delenne, C.; Chahinian, N.; Bringay, S.; Commandré, B.; Chaumont, M.; Derras, M.; Deruelle, L.; Roche, M.; Rodriguez, F.; Subsol, G.; Teisseire, M.

2017-12-01

In France, local government institutions must establish a detailed description of wastewater networks. The information should be available, but it remains fragmented (different formats held by different stakeholders) and incomplete. In the "Cart'Eaux" project, a multidisciplinary team, including an industrial partner, develops a global methodology using Machine Learning and Data Mining approaches applied to various types of large data to recover information in the aim of mapping urban sewage systems for hydraulic modelling. Deep-learning is first applied using a Convolution Neural Network to localize manhole covers on 5 cm resolution aerial RGB images. The detected manhole covers are then automatically connected using a tree-shaped graph constrained by industry rules. Based on a Delaunay triangulation, connections are chosen to minimize a cost function depending on pipe length, slope and possible intersection with roads or buildings. A stochastic version of this algorithm is currently being developed to account for positional uncertainty and detection errors, and generate sets of probable networks. As more information is required for hydraulic modeling (slopes, diameters, materials, etc.), text data mining is used to extract network characteristics from data posted on the Web or available through governmental or specific databases. Using an appropriate list of keywords, the web is scoured for documents which are saved in text format. The thematic entities are identified and linked to the surrounding spatial and temporal entities. The methodology is developed and tested on two towns in southern France. The primary results are encouraging: 54% of manhole covers are detected with few false detections, enabling the reconstruction of probable networks. The data mining results are still being investigated. It is clear at this stage that getting numerical values on specific pipes will be challenging. Thus, when no information is found, decision rules will be used to

SparkText: Biomedical Text Mining on Big Data Framework.

Science.gov (United States)

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.

Data Mining and Statistics for Decision Making

CERN Document Server

Tufféry, Stéphane

2011-01-01

Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized lin

Aachen international mining symposia: 5. International symposium on roofbolting in mining. Proceedings

International Nuclear Information System (INIS)

2004-01-01

The authors of the symposium report on the following topics: roofbolting in German, Australian, British, Indian, Chinese coal mines, historical aspects, research programs, concrete lining, cable bolts, bolting machines, rectangular roadways,shear behavior of bolts, rock mechanics, resin grouted bolts, effects of earthquakes, support design guidelines, strata movements, numerical modelling, safety aspects. (uke)

Using Machine Learning Methods Jointly to Find Better Set of Rules in Data Mining

Directory of Open Access Journals (Sweden)

SUG Hyontai

2017-01-01

Full Text Available Rough set-based data mining algorithms are one of widely accepted machine learning technologies because of their strong mathematical background and capability of finding optimal rules based on given data sets only without room for prejudiced views to be inserted on the data. But, because the algorithms find rules very precisely, we may confront with the overfitting problem. On the other hand, association rule algorithms find rules of association, where the association resides between sets of items in database. The algorithms find itemsets that occur more than given minimum support, so that they can find the itemsets practically in reasonable time even for very large databases by supplying the minimum support appropriately. In order to overcome the problem of the overfitting problem in rough set-based algorithms, first we find large itemsets, after that we select attributes that cover the large itemsets. By using the selected attributes only, we may find better set of rules based on rough set theory. Results from experiments support our suggested method.

Applied data mining for business and industry

CERN Document Server

Giudici, Paolo

2009-01-01

The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications.Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods.Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining.Features detailed case studies based on applied projects within industry.Incorporates discussion of data mining software, with case studies a...

Information mining in remote sensing imagery

Science.gov (United States)

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

Mine games

Energy Technology Data Exchange (ETDEWEB)

Patchett, A. [Hitachi Construction Equipment (United Kingdom)

2006-09-15

The article describes various excavators used in the UK by Hall Construction for coal mining and reclamation projects. They include machines from Hitachi Construction Machinery that have been modified with a coal shovel at the front end. The ZX350LC-3, for example incorporates a coal shovel, manufactured by Kocurek, to allow it to work at the rock face and lift coal into road wagons or dump trucks. 5 figs.

Underground coal mining technology - the future

Energy Technology Data Exchange (ETDEWEB)

Lama, R P [Kembla Coal and Coke Pty Limited, Wollongong, NSW (Australia)

1989-01-01

Discusses development of underground coal mining in Australia in the last four decades. The following aspects are reviewed: technology for underground mining (longwall mining, unidirectional cutting, bidirectional cutting, operation of more than one shearer on a working face, optimum dimensions of longwall blocks), longwall productivity (productivity increase will depend on increasing the availability factor of equipment, reducing failures due to human errors, organizational models, improving on-site decision making, improving monitoring, maintenance, planning and scheduling, concept of 'Transparent Mine'), roadway development systems (types of heading machines, standard systems for mine drivage and roof bolting and their productivity), size of coal mines, man and material transport systems (20,000-30,000 t/d from a single longwall face, mine shafts with a diameter 9-10 m), mine layout design (layout of longwall blocks, main intakes and returns situated in rock layers), mine environmental systems (ventilation systems, gas control), management, training and interpersonal relationships. Future coal mines will be developed with an integral capacity of 8-10 Mt/a from a single longwall operation with main development arteries placed in rocks. Development of gate roadways will require novel solutions with continuous cutting, loading and bolting. Information technology, with the concept of 'transparent mine', will form the backbone of decision making.

An Evolutionary Machine Learning Framework for Big Data Sequence Mining

Science.gov (United States)

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…

Biomarker Identification Using Text Mining

Directory of Open Access Journals (Sweden)

Hui Li

2012-01-01

Full Text Available Identifying molecular biomarkers has become one of the important tasks for scientists to assess the different phenotypic states of cells or organisms correlated to the genotypes of diseases from large-scale biological data. In this paper, we proposed a text-mining-based method to discover biomarkers from PubMed. First, we construct a database based on a dictionary, and then we used a finite state machine to identify the biomarkers. Our method of text mining provides a highly reliable approach to discover the biomarkers in the PubMed database.

Improvement of a separation method for the reduction of secondary waste from the water-jet abrasive suspension cutting technique

International Nuclear Information System (INIS)

Brandauer, M.; Gentes, S.; Heneka, A.; Krauss, C.O.; Geckeis, H.; Plaschke, M.; Schild, D.; Tobie, W.

2017-01-01

Full text of publication follows. Disassembling the reactor pressure vessel and its built-in components is a huge challenge in the deconstruction of a nuclear power plant. After being exposed to neutron irradiation for years, the activated components need to be disassembled and packed by remote controlled techniques. Underwater disassembling systems have the advantage of the shielding effect of water against radiation. To avoid the generation of aerosols, cold cutting processes are preferred. A cutting method that meets these requirements is the water-jet abrasive suspension cutting technique (WASS). This method provides high flexibility and is immune towards mechanical stress in the components. During the cutting process, a mixture of abrasive particles and radioactive steel particles from the cut components is generated. Depending on the operational conditions, the amount of this secondary waste increases substantially. Therefore, despite of its intrinsic technical benefits, WASS has a serious disadvantage towards other cutting techniques due to the huge disposal costs of secondary waste. During our previous joint research project between KIT and AREVA GmbH called NENAWAS ('New Disposal Methods for the Secondary Waste Treatment of the Water-jet Abrasive Suspension Cutting Technique', funded by the German ministry for education and research, BMBF), a prototype separation device for WASS secondary waste was developed and tested. Using a magnetic filter, steel particles could be successfully separated from the rest of the secondary waste. The separation process is examined using elemental analysis (ICP-OES) for quantification of the separation grade. Additionally, morphologies of particles and particle aggregates before and after the separation process were examined by scanning electron microscopy (SEM). In the abrasive particle fraction after separation of the steel particles a remaining contamination by tiny steel particles could be detected by elemental and

A Data Mining Approach for Cardiovascular Diagnosis

Directory of Open Access Journals (Sweden)

Pereira Joana

2017-12-01

Full Text Available The large amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analysed by traditional methods. Data mining can improve decision-making by discovering patterns and trends in large amounts of complex data. In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiency, improve patient quality of life, and perhaps most importantly, save the lives of more patients. The main goal of this project is to apply data mining techniques in order to make possible the prediction of the degree of disability that patients will present when they leave hospitalization. The clinical data that will compose the data set was obtained from one single hospital and contains information about patients who were hospitalized in Cardio Vascular Disease’s (CVD unit in 2016 for having suffered a cardiovascular accident. To develop this project, it will be used the Waikato Environment for Knowledge Analysis (WEKA machine learning Workbench since this one allows users to quickly try out and compare different machine learning methods on new data sets

SparkText: Biomedical Text Mining on Big Data Framework

Science.gov (United States)

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

SparkText: Biomedical Text Mining on Big Data Framework.

Directory of Open Access Journals (Sweden)

Zhan Ye

Full Text Available 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.

Data Mining Solutions for the Business Environment

OpenAIRE

Ruxandra-Stefania PETRE

2013-01-01

Over the past years, data mining became a matter of considerable importance due to the large amounts of data available in the applications belonging to various domains. Data mining, a dynamic and fast-expanding field, that applies advanced data analysis techniques, from statistics, machine learning, database systems or artificial intelligence, in order to discover relevant patterns, trends and relations contained within the data, information impossible to observe using other techniques. The p...

An overview of data mining algorithms in drug induced toxicity prediction.

Science.gov (United States)

Omer, Ankur; Singh, Poonam; Yadav, N K; Singh, R K

2014-04-01

The growth in chemical diversity has increased the need to adjudicate the toxicity of different chemical compounds raising the burden on the demand of animal testing. The toxicity evaluation requires time consuming and expensive undertaking, leading to the deprivation of the methods employed for screening chemicals pointing towards the need to develop more efficient toxicity assessment systems. Computational approaches have reduced the time as well as the cost for evaluating the toxicity and kinetic behavior of any chemical. The accessibility of a large amount of data and the intense need of turning this data into useful information have attracted the attention towards data mining. Machine Learning, one of the powerful data mining techniques has evolved as the most effective and potent tool for exploring new insights on combinatorial relationships among various experimental data generated. The article accounts on some sophisticated machine learning algorithms like Artificial Neural Networks (ANN), Support Vector Machine (SVM), k-mean clustering and Self Organizing Maps (SOM) with some of the available tools used for classification, sorting and toxicological evaluation of data, clarifying, how data mining and machine learning interact cooperatively to facilitate knowledge discovery. Addressing the association of some commonly used expert systems, we briefly outline some real world applications to consider the crucial role of data set partitioning.

Bigger hybrid loader on the drawing board : Mining Technologies International hybrid gets rave reviews for power and comfort

Energy Technology Data Exchange (ETDEWEB)

Tollinsky, N.

2010-12-01

This article presented a hybrid loader that reduces diesel emissions in underground mining. Sudbury-based Mining Technologies International (MTI) plans to build a 4 cubic yard loader in 2011, following the successful trial of a smaller 1.5 cubic yard machine at the CANMET experimental mine in Val d'Or, Quebec. The prototype hybrid loader was equipped with a metal hydride battery pack and a 2-cylinder, 35 hp Deutz engine. Performance testing revealed that the machine is capable of providing much more torque than originally expected and that it has more power compared to a mechanical drive machine. Operators at the CANMET mine also gave the hybrid loader high marks for comfort. The MTI loaders are equipped with a load sensing hydraulics system to eliminate jarring movement. The prototype experienced some premature failures in the flex coupling, which was subsequently replaced at the MTI shop in Sudbury. The primary reason for building the hybrid loader was to reduce diesel emissions underground in anticipation of stricter emission standards planned by the Mine Safety and Health Administration, the United States Environmental Protection Agency and CANMET for 2014. Compared to a conventional machine, there is virtually no exhaust from the hybrid loader. It is an ideal machine for a mine with very limited ventilation. Since the loader runs off the battery, MTI is currently looking at battery technologies other than metal hydrides to obtain a much higher energy density. Diesel is used to recharge the loader, and eliminates the need to plug in the unit between shifts. 1 ref., 2 figs.

Turning of materials with high-speed abrasive waterjet

Czech Academy of Sciences Publication Activity Database

Sitek, Libor; Hlaváček, Petr

-, October 2016 (2016), s. 1160-1165 ISSN 1805-0476 R&D Projects: GA MŠk ED2.1.00/03.0082; GA MŠk(CZ) LO1406 Institutional support: RVO:68145535 Keywords : abrasive water jet machining * turning * steel * rock * wood Subject RIV: JQ - Machines ; Tools http://www.mmscience.eu/content/file/archives/MM_Science_201692.pdf

Health in mines

Energy Technology Data Exchange (ETDEWEB)

Breuer, H

1978-01-01

This report summarizes the information gained from research carried out under the five-year programme (1971-1976) relating to health in mines. This programme concentrates on technical means of dust control, and the various aspects of this topic are reviewed. The purpose of the epidemiology of pneumoconiosis is to determine the origin and development of this endemic disease with a view to formulating a policy of worker protection. The constituents of dust and their possible influences are studied. Measuring and determining the characteristics of dusts are the subject of the second group of research projects. Prevention techniques have been proposed, tried out and developed, thus providing the mining industry with a set of dust control methods. water infusion, dedusting on coal-getting and heading machines, control of dust produced by caving, etc. A chapter devoted to iron ore mines discusses means of protection against noxious emissions from Diesel engines and explosives. (Editions available in English, in German, in French, in Italian, and in Dutch)

Overview of the INEX 2008 XML Mining Track

Science.gov (United States)

Denoyer, Ludovic; Gallinari, Patrick

We describe here the XML Mining Track at INEX 2008. This track was launched for exploring two main ideas: first identifying key problems for mining semi-structured documents and new challenges of this emerging field and second studying and assessing the potential of machine learning techniques for dealing with generic Machine Learning (ML) tasks in the structured domain i.e. classification and clustering of semi structured documents. This year, the track focuses on the supervised classification and the unsupervised clustering of XML documents using link information. We consider a corpus of about 100,000 Wikipedia pages with the associated hyperlinks. The participants have developed models using the content information, the internal structure information of the XML documents and also the link information between documents.

Machine learning and medicine: book review and commentary.

Science.gov (United States)

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.

Review of data mining applications for quality assessment in manufacturing industry: support vector machines

Directory of Open Access Journals (Sweden)

Rostami Hamidey

2015-01-01

Full Text Available In many modern manufacturing industries, data that characterize the manufacturing process are electronically collected and stored in databases. Due to advances in data collection systems and analysis tools, data mining (DM has widely been applied for quality assessment (QA in manufacturing industries. In DM, the choice of technique to be used in analyzing a dataset and assessing the quality depend on the understanding of the analyst. On the other hand, with the advent of improved and efficient prediction techniques, there is a need for an analyst to know which tool performs better for a particular type of dataset. Although a few review papers have recently been published to discuss DM applications in manufacturing for QA, this paper provides an extensive review to investigate the application of a special DM technique, namely support vector machine (SVM to deal with QA problems. This review provides a comprehensive analysis of the literature from various points of view as DM concepts, data preprocessing, DM applications for each quality task, SVM preliminaries, and application results. Summary tables and figures are also provided besides to the analyses. Finally, conclusions and future research directions are provided.

Integrated Robot-Human Control in Mining Operations

Energy Technology Data Exchange (ETDEWEB)

George Danko

2007-09-30

This report contains a detailed description of the work conducted for the project on Integrated Robot-Human Control in Mining Operations at University of Nevada, Reno. This project combines human operator control with robotic control concepts to create a hybrid control architecture, in which the strengths of each control method are combined to increase machine efficiency and reduce operator fatigue. The kinematics reconfiguration type differential control of the excavator implemented with a variety of 'software machine kinematics' is the key feature of the project. This software re-configured excavator is more desirable to execute a given digging task. The human operator retains the master control of the main motion parameters, while the computer coordinates the repetitive movement patterns of the machine links. These repetitive movements may be selected from a pre-defined family of trajectories with different transformations. The operator can make adjustments to this pattern in real time, as needed, to accommodate rapidly-changing environmental conditions. A working prototype has been developed using a Bobcat 435 excavator. The machine is operational with or without the computer control system depending on whether the computer interface is on or off. In preparation for emulated mining tasks tests, typical, repetitive tool trajectories during surface mining operations were recorded at the Newmont Mining Corporation's 'Lone Tree' mine in Nevada. Analysis of these working trajectories has been completed. The motion patterns, when transformed into a family of curves, may serve as the basis for software-controlled machine kinematics transformation in the new human-robot control system. A Cartesian control example has been developed and tested both in simulation and on the experimental excavator. Open-loop control is robustly stable and free of short-term dynamic problems, but it allows for drifting away from the desired motion kinematics of the

Drivage of development workings in Kuzbass mines. Opyt provedeniya podgotovitel'nykh vyrabotok na shakhtakh Kuzbassa

Energy Technology Data Exchange (ETDEWEB)

Abramov, V M; Gusakov, F K; Goloshchapov, R E

1983-01-01

Mine drivage is evaluated in underground coal mines in the Kuzbass and factors which influence drivage advance rate and cost. Mine drivage in the Kuzbass in 1982 is described: 1,706.3 km of workings were driven by explosive fracturing and heading machines, 853.8 km of workings were driven with mechanized rock handling systems, 205.8 km with rock handling by loaders, 532.1 km were driven by heading machines. The average cross-section of workings driven in the Kuzbass ranged from 4 to 12 m/SUP/2; from 8 to 12% of workings had a cross-section exceeding 12 m/SUP/2. Strata control systems used in mine drivage are evaluated: 22.8% of workings was supported by steel arched supports, 20.3% by roof bolts, 0.07% by concrete supports or tubbings, 47.5% by timber props and 9.2% by other support systems. Statistical data on mine drivage of mine roadways, development workings, ventilation tunnels, inclined workings and coal chutes in the Kuzbass are given in 7 tables. Mine drivage in selected coal mines of the basin is evaluated: drivage systems, materials handling, mine haulage, strata control, transport of materials, safety.

Empirical approach for designing of support system in mechanized coal pillar mining

Energy Technology Data Exchange (ETDEWEB)

Kushwaha, A.; Singh, S.K.; Tewari, S.; Sinha, A. [Central Institute of Mining & Fuel Research, Dhanbad (India)

2010-10-15

Mechanized room-and-pillar system of coal pillar mining using side dump loading machine or load haul dumper machine, or by continuous miner, is the presently most dominant under ground method of extraction in India. Under this method of extraction, strata control is a major problem affecting safety and productivity of the mine. As per existing Director General of Mine Safety guidelines, systematic support rules must be followed at the depillaring faces irrespective of immediate roof rock type and competency. Therefore, there is a high chance that sometimes these systematic support rules give unnecessarily high support, or sometimes inadequate support, which may lead to roof failure at the face. As a result, there is a big loss of life and material including coal in terms of left-outribs/stooks and other associated mining equipment deployed at the faces. Therefore, in the present paper, authors attempted to develop generalized empirical equations for estimating the required support load density at different places of the face based on geotechnical parameters of the mine and physico-mechanical properties of the immediate roof rocks for designing of support system during mechanized coal pillar mining.

WIRELESS MINE-WIDE TELECOMMUNICATIONS TECHNOLOGY

Energy Technology Data Exchange (ETDEWEB)

Zvi H. Meiksin

2004-03-01

A comprehensive mine-wide, two-way wireless voice and data communication system for the underground mining industry was developed. The system achieves energy savings through increased productivity and greater energy efficiency in meeting safety requirements within mines. The mine-wide system is comprised of two interfaced subsystems: a through-the-earth communications system and an in-mine communications system. The mine-wide system permits two-way communication among underground personnel and between underground and surface personnel. The system was designed, built, and commercialized. Several systems are in operation in underground mines in the United States. The use of these systems has proven they result in considerable energy savings. A system for tracking the location of vehicles and people within the mine was also developed, built and tested successfully. Transtek's systems are being used by the National Institute of Occupational Safety and Health (NIOSH) in their underground mine rescue team training program. This project also resulted in a spin-off rescue team lifeline and communications system. Furthermore, the project points the way to further developments that can lead to a GPS-like system for underground mines allowing the use of autonomous machines in underground mining operations, greatly reducing the amount of energy used in these operations. Some products developed under this program are transferable to applications in fields other than mining. The rescue team system is applicable to use by first responders to natural, accidental, or terrorist-caused building collapses. The in-mine communications system can be installed in high-rise buildings providing in-building communications to security and maintenance personnel as well as to first responders.

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

Science.gov (United States)

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

Intelligent Mining Engineering Systems in the Structure of Industry 4.0

OpenAIRE

Rylnikova Marina; Radchenko Dmitriy; Klebanov Dmitriy

2017-01-01

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, machine...

Data mining in bioinformatics using Weka.

Science.gov (United States)

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.

Continuous Rating for Diggability Assessment in Surface Mines

Science.gov (United States)

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.

Research on and Design of a Self-Propelled Nozzle for the Tree-Type Drilling Technique in Underground Coal Mines

Directory of Open Access Journals (Sweden)

Yiyu Lu

2015-12-01

Full Text Available Due to the increasing depths of coal mines and the low permeability of some coal seams, conventional methods of gas drainage in underground mines are facing many problems. To improve gas extraction, a new technique using water jets to drill tree-type boreholes in coal seams is proposed. A self-propelled water-jet drilling nozzle was designed to drill these boreholes. The configuration of the self-propelled nozzle was optimized by conducting drilling experiments and self-propelling force measurements. Experimental results show that the optimal self-propelled nozzle has a forward orifice axial angle at 25°, a radial angle at 90°, a center distance of 1.5 mm, and backward pointing orifices with an axial angle of 25°. The self-propelling force generated by the jets of the nozzle with 30 MPa pump pressure can reach 29.8 N, enough to pull the hose and the nozzle forward without any external forces. The nozzle can drill at speeds up to 41.5 m/h with pump pressures at 30 MPa. The radial angles of the forward orifices improve the rock breaking performance of the nozzle and, with the correct angle, the rock breaking area of the orifices overlap to produce a connecting hole. The diameter of boreholes drilled by this nozzle can reach 35.2 mm. The nozzle design can be used as the basis for designing other self-propelled nozzles. The drilling experiments demonstrate the feasibility of using the tree-type drilling technique in underground mines.

30 CFR 75.703-3 - Approved methods of grounding offtrack mobile, portable and stationary direct-current machines.

Science.gov (United States)

2010-07-01

..., portable and stationary direct-current machines. 75.703-3 Section 75.703-3 Mineral Resources MINE SAFETY... stationary direct-current machines. In grounding offtrack direct-current machines and the enclosures of their... requirements: (1) Installation of silicon diodes shall be restricted to electric equipment receiving power from...

Grinding efficiency improvement of hydraulic cylinders parts for mining equipment

Directory of Open Access Journals (Sweden)

Korotkov Aleksandr

2017-01-01

Full Text Available The aim of the article is to find out ways to improve parts treatment and components of mining equipment on the example of hydraulic cylinders parts, used as pillars for mine roof supports, and other actuator mechanisms. In the course of the research work methods of machine retaining devices design were used, the scientific approaches for the selection of progressive grinding schemes were applied; theoretical and practical experience in the design and production of new constructions of grinding tools was used. As a result of this work it became possible to create a progressive construction of a machine retaining device for grinding of large parts of hydraulic cylinders, to apply an effective scheme of rotary abrasive treatment, to create and implement new design of grinding tools by means of grains with controllable shape and orientation. Implementation of the results obtained in practice will improve the quality and performance of repairing and manufacturing of mining equipment.

A REVIEW ON PREDICTIVE ANALYTICS IN DATA MINING

OpenAIRE

Arumugam.S

2016-01-01

The data mining its main process is to collect, extract and store the valuable information and now-a-days it’s done by many enterprises actively. In advanced analytics, Predictive analytics is the one of the branch which is mainly used to make predictions about future events which are unknown. Predictive analytics which uses various techniques from machine learning, statistics, data mining, modeling, and artificial intelligence for analyzing the current data and to make predictions about futu...

Traffic Flow Management: Data Mining Update

Science.gov (United States)

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

BOOK REVIEW EDUCATIONAL DATA MINING: APPLICATIONS AND TRENDS

Directory of Open Access Journals (Sweden)

Aylin OZTURK

2016-04-01

Full Text Available Educational Data Mining (EDM is a developing field based on data mining techniques. EDM emerged as a combination of areas such as machine learning, statistics, computer science, education, cognitive science, and psychometry. EDM focuses on learner characteristics, behaviors, academic achievements, process of learning, educational functionalities, domain knowledge content, assessments, and applications. Educational data mining is defined by Baker (2010 as ‘‘an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in’’. EDM is concerned with improving the learning process and environment.

Emerging Paradigms in Machine Learning

CERN Document Server

Jain, Lakhmi; Howlett, Robert

2013-01-01

This  book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The  multidisciplinary nature of machine learning makes it a very fascinating and popular area for research.  The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems.  Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary ...

Energy Consumption in the Process of Excavator-Automobile Complexes Distribution at Kuzbass Open Pit Mines

Directory of Open Access Journals (Sweden)

Panachev Ivan

2017-01-01

Full Text Available Every year worldwide coal mining companies seek to maintain the tendency of the mining machine fleet renewal. Various activities to maintain the service life of already operated mining equipment are implemented. In this regard, the urgent issue is the problem of efficient distribution of available machines in different geological conditions. The problem of “excavator-automobile” complex effective distribution occurs when heavy dump trucks are used in mining. For this reason, excavation and transportation of blasted rock mass are the most labor intensive and costly processes, considering the volume of transported overburden and coal, as well as diesel fuel, electricity, fuel and lubricants costs, consumables for repair works and downtime, etc. Currently, it is recommended to take the number of loading buckets in the range of 3 to 5, according to which the dump trucks are distributed to faces.

Identifying child abuse through text mining and machine learning

NARCIS (Netherlands)

Amrit, Chintan; Paauw, Tim; Aly, Robin; Lavric, Miha

2017-01-01

In this paper, we describe how we used text mining and analysis to identify and predict cases of child abuse in a public health institution. Such institutions in the Netherlands try to identify and prevent different kinds of abuse. A significant part of the medical data that the institutions have on

Data Mining Solutions for the Business Environment

Directory of Open Access Journals (Sweden)

Ruxandra-Stefania PETRE

2014-02-01

Full Text Available Over the past years, data mining became a matter of considerable importance due to the large amounts of data available in the applications belonging to various domains. Data mining, a dynamic and fast-expanding field, that applies advanced data analysis techniques, from statistics, machine learning, database systems or artificial intelligence, in order to discover relevant patterns, trends and relations contained within the data, information impossible to observe using other techniques. The paper focuses on presenting the applications of data mining in the business environment. It contains a general overview of data mining, providing a definition of the concept, enumerating six primary data mining techniques and mentioning the main fields for which data mining can be applied. The paper also presents the main business areas which can benefit from the use of data mining tools, along with their use cases: retail, banking and insurance. Also the main commercially available data mining tools and their key features are presented within the paper. Besides the analysis of data mining and the business areas that can successfully apply it, the paper presents the main features of a data mining solution that can be applied for the business environment and the architecture, with its main components, for the solution, that would help improve customer experiences and decision-making

Developments in the application of underground battery vehicles in the UK coal mining industry

Energy Technology Data Exchange (ETDEWEB)

Fortune, J A.B.; Crawshaw, S A.M. [Long-Airdox International Ltd. (United Kingdom)

1996-10-01

Trackless battery powered haulage vehicles have been in operation in British coal mines principally for longwall face transfer and personnel transportation. Changes within the industry have resulted in the introduction of room and pillar coal mining methods and the introduction of increasingly heavier longwall roof supports. This has resulted in the introduction of: battery powered coal haulage machines, which, without the need for trailing cables, increase productivity within room and pillar mining; and battery powered longwall shield haulers which are capable of carrying the heaviest shield supports currently being utilised within the British coal mining industry. The conventional machines have been adapted from an American design to meet the requirements of European legislation. This has seen the emphasis being placed upon the supplier with the European Machinery Directive being introduced, necessitating the assigning of a `CE` mark to each vehicle. Battery vehicle technology has advanced to meet the demands of the ever changing market and will no doubt be further adapted to meet the requirement of the British coal mining industry. 1 ref., 12 figs., 3 tabs.

Current status of thin seam longwall mining in the US

Energy Technology Data Exchange (ETDEWEB)

Peng, S.S. [West Virginia Univ., Morgantown, WV (United States); Orndorff, A.

1996-12-31

Thin seams in this paper refers to those seams the economic mining height of which is below 50-55 in. that are traditionally considered to be the proprietary of plowing and present a whole net set of problems for longwall mining. In thin seams it is difficult to design and manufacture an efficient high capacity cutting machine for maintenance and production operations. Thin seam mining by longwall plowing began in the late fifties in southern West Virginia, and continues to the present time. In the seventies when longwall mining began to take off a large percentage of U.S. longwalls were operating in the thin seams. Tables 1 and 2 show the historical trends of cutting machines used for seams less than 55 in and 50 in, respectively. In addition to the plow system, the single-ended fixed drum and single-ended ranging drum shearers were introduced in the mid and late seventies and operated continuously until 2-4 years ago. The double-ended ranging drum shearers have also been employed for thin seam longwall mining during this period including several in-web (or off-pan) shearers between late seventies and early eighties. In this paper three thin-seam longwalls in three states employing the latest thin-seam longwall technology will be reviewed. However only two of them are still in operation while the third one ceased operation recently.

A Novel Method for Borehole Blockage Removal and Experimental Study on a Hydraulic Self-Propelled Nozzle in Underground Coal Mines

Directory of Open Access Journals (Sweden)

Zhaolong Ge

2016-08-01

Full Text Available When coal bed methane (CBM drainage boreholes cross fractured, soft, or water-swelling strata, they collapse and block frequently. Borehole blockages result in a rapid decrease in CBM extraction ability, which leads to a reduction in CBM output and threatens coal mine safety production. To solve these problems, a novel method that uses a self-propelled water-jet nozzle to dredge blocked boreholes in coal seams has been proposed on the basis of the existing technology. Based on a theoretical analysis of the reason for borehole caving and the theory of blockage removal, we optimized the nozzle inlet pressure and selected an appropriate high-pressure resin pipe. A field experiment on the blockage removal of blocked CBM drainage boreholes using the proposed method was run in the Fengchun coal mine, Qijiang, Chongqing, southwest China. In this field trial, the time spent to unblock a borehole varied between 18.52 and 34.98 min, which is much shorter than using a drilling rig. After blockage removal, the average pure volume of the methane drainage of a single borehole was increased from 0.03 L/min to ~1.91–7.30 L/min, and the methane drainage concentration of a single borehole increased from 5% to ~44%–85%. The extraction effect increased significantly.

Annotating images by mining image search results

NARCIS (Netherlands)

Wang, X.J.; Zhang, L.; Li, X.; Ma, W.Y.

2008-01-01

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

2nd International Conference on Computational Intelligence in Data Mining

CERN Document Server

Mohapatra, Durga

2016-01-01

The book is a collection of high-quality peer-reviewed research papers presented in the Second International Conference on Computational Intelligence in Data Mining (ICCIDM 2015) held at Bhubaneswar, Odisha, India during 5 – 6 December 2015. The two-volume Proceedings address the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. The book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.

Process and device for hacking and arranging the mining face in mines. Verfahren und Einrichtung zum Auffahren eines Aufhauens und Einrichten des Gewinnungsstrebs in Bergbaubetrieben

Energy Technology Data Exchange (ETDEWEB)

Beckmann, K

1982-04-01

A process for driving a heading machine and a device for arranging the mining face is described. A movable heading and conveyor device is used for this purpose and to secure the roof in the area of the conveyor device, a moving hydraulic prop is used as the support. After preparing the heading, the frames remain as movable roadway supports to secure the entry to the mining face.

Advances in independent component analysis and learning machines

CERN Document Server

Bingham, Ella; Laaksonen, Jorma; Lampinen, Jouko

2015-01-01

In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithmUnsupervised deep learning Machine vision and image retrieval A review of developments in the t

Data Mining Process Optimization in Computational Multi-agent Systems

OpenAIRE

Kazík, O.; Neruda, R. (Roman)

2015-01-01

In this paper, we present an agent-based solution of metalearning problem which focuses on optimization of data mining processes. We exploit the framework of computational multi-agent systems in which various meta-learning problems have been already studied, e.g. parameter-space search or simple method recommendation. In this paper, we examine the effect of data preprocessing for machine learning problems. We perform the set of experiments in the search-space of data mining processes which is...

Report. First international symposium on innovating mining systems

Energy Technology Data Exchange (ETDEWEB)

Blackwood, R L

1985-01-01

The author presents a summary of proceedings of the First International Symposium on Innovative Mining Systems held at Massachusetts Institute of Technology, USA 4-5 November 1985, together with some comments on the conclusions and discussion throughout. The Symposium agenda included the following (i) Symposium intentions and expectations; (ii) International; (iii) Developments in safety; (iv) Overview of current major research and trends; (v) Panel discussion: Mechanisms for industrial and international collaboration; (vi) Closing remarks; (vii) Review of innovations: university programs; (viii) Review of selected mine operator programs and needs; Review of equipment innovations; capabilities and trends in areas of mining equipment and robotics; Concurrent sessions: operations and manufacturing. A series of workshops was also held, the titles of which were as follows: (i) Establishment of research network; (ii) Entry development-machine excavation; (iii) Sensing, monitoring, diagnostics, artificial intelligence; (iv) Remote control, automation, mining systems; (v) Computer aided design, simulation, system development; (vi) Surface mining; (vii) Rock breakage.

Multipass mining sequence room closures: In situ data report

International Nuclear Information System (INIS)

Munson, D.E.; Jones, R.L.; Northrop-Salazar, C.L.; Woerner, S.J.

1992-12-01

During the construction of the Thermal/Structural In Situ Test Rooms at the Waste Isolation Pilot Plant (WIPP) facility, measurements of the salt displacements were obtained at very early times, essentially concurrent with the mining activity. This was accomplished by emplacing manually read closure gage stations directly at the mining face, actually between the face and the mining machine, immediately upon mining of the intended gage location. Typically, these mining sequence closure measurements were taken within one hour of mining of the location and within one meter of the mining face. Readings were taken at these gage stations as the multipass mining continued, with the gage station reestablished as each successive mining pass destroyed the earlier gage points. Data reduction yields the displacement history during the mining operation. These early mining sequence closure data, when combined with the later data of the permanently emplaced closure gages, gives the total time-dependent closure displacements of the test rooms. This complete closure history is an essential part of assuring that the in situ test databases will provide an adequate basis for validation of the predictive technology of salt creep behavior, as required by the WIPP technology development program for disposal of radioactive waste in bedded salt

Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

Directory of Open Access Journals (Sweden)

Kuchin Yan

2017-12-01

Full Text Available The paper explores geophysical methods of wells survey, as well as their role in the development of Kazakhstan’s uranium deposit mining efforts. An analysis of the existing methods for solving the problem of interpreting geophysical data using machine learning in petroleum geophysics is made. The requirements and possible applications of machine learning methods in regard to uranium deposits of Kazakhstan are formulated in the paper.

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

Science.gov (United States)

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.

A tm Plug-In for Distributed Text Mining in R

Directory of Open Access Journals (Sweden)

Stefan Theussl

2012-11-01

Full Text Available R has gained explicit text mining support with the tm package enabling statisticians to answer many interesting research questions via statistical analysis or modeling of (text corpora. However, we typically face two challenges when analyzing large corpora: (1 the amount of data to be processed in a single machine is usually limited by the available main memory (i.e., RAM, and (2 the more data to be analyzed the higher the need for efficient procedures for calculating valuable results. Fortunately, adequate programming models like MapReduce facilitate parallelization of text mining tasks and allow for processing data sets beyond what would fit into memory by using a distributed file system possibly spanning over several machines, e.g., in a cluster of workstations. In this paper we present a plug-in package to tm called tm.plugin.dc implementing a distributed corpus class which can take advantage of the Hadoop MapReduce library for large scale text mining tasks. We show on the basis of an application in culturomics that we can efficiently handle data sets of significant size.

Technical improvement of arch setting machine for roadway drive. Kusshin kirihayo sewakuki no hensen

Energy Technology Data Exchange (ETDEWEB)

Fujino, T [Taiheiyo Coal Mining Co. Ltd., Tokyo (Japan)

1991-09-25

The following contents are described for a new type arch setting machine introduced to the Taiheiyo Coal Mining Co. (1) The excavation of drift in this coal mine is carried out by the excavating system combining the excavating machine (CM) and the transporter (SC) and the supporting method where arch type steel beams are used as the supporting material is used. (2) In order to automatize the arch setting work, this machine which combined the both functions of the arch lifter (developed in 1983) and the arch setting manipulator (ASM, developed in 1990). (3) This machine has the total weight of 1,500kg, is mounted on CM, and consists of the running part, the arm part, the boom part, and the end saucer part of steel arches. Further, simple fitting or removing can be made and the coal falling down is prevented by holding the coal wall during the arch setting work. (4) When this machine and the ASM were compared for the arch setting time in the working site, the former required about the half time period of the latter. 5 figs., 1 tab.

Discussion on the safety production risk managmeent of uranium mines

International Nuclear Information System (INIS)

Liu Bin; Luo Yun; Hu Penghua; Zhu Disi

2009-01-01

Based on the modern safety risk management theories and according to the actual situation, risk management for work safety in uranium mines is discussed from three aspects: risk identification,risk analysis and evaluation, and risk control. Referring to the '4M(Men,Machine,Medium,Management) factors' and 'Three types of hazards' theory, the classification of uranium mine accidents and risk factors are analyzed. In addition, the types and evaluation indexes of major risks of uranium mines as well as the 'spot, line, area' model of risk identification and analysis and the 'hierarchical' risk control mechanism are also studied. (authors)

A complex of optimization problems in planning for the development of mining operations in coal mines

Energy Technology Data Exchange (ETDEWEB)

Todorov, A K; Arnaudov, B K; Brankova, B A; Gyuleva, B I; Zakhariyev, G K

1977-01-01

The system for planning for the development of coal mines is a complex of interrelated plan optimization, plan calculation and supporting (accounting-analytical and standards) tasks. An important point in this complex is held by the plan optimization tasks. The questions about the synthesis and the structural peculiarities of the system, the essence and machine realization of the tasks are examined.

Machine vision and mechatronics in practice

CERN Document Server

Brett, Peter

2015-01-01

The contributions for this book have been gathered over several years from conferences held in the series of Mechatronics and Machine Vision in Practice, the latest of which was held in Ankara, Turkey. The essential aspect is that they concern practical applications rather than the derivation of mere theory, though simulations and visualization are important components. The topics range from mining, with its heavy engineering, to the delicate machining of holes in the human skull or robots for surgery on human flesh. Mobile robots continue to be a hot topic, both from the need for navigation and for the task of stabilization of unmanned aerial vehicles. The swinging of a spray rig is damped, while machine vision is used for the control of heating in an asphalt-laying machine.  Manipulators are featured, both for general tasks and in the form of grasping fingers. A robot arm is proposed for adding to the mobility scooter of the elderly. Can EEG signals be a means to control a robot? Can face recognition be ac...

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

Science.gov (United States)

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.

Supporting Solar Physics Research via Data Mining

Science.gov (United States)

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.

Data mining-aided materials discovery and optimization

Directory of Open Access Journals (Sweden)

Wencong Lu

2017-09-01

Full Text Available Recent developments in data mining-aided materials discovery and optimization are reviewed in this paper, and an introduction to the materials data mining (MDM process is provided using case studies. Both qualitative and quantitative methods in machine learning can be adopted in the MDM process to accomplish different tasks in materials discovery, design, and optimization. State-of-the-art techniques in data mining-aided materials discovery and optimization are demonstrated by reviewing the controllable synthesis of dendritic Co3O4 superstructures, materials design of layered double hydroxide, battery materials discovery, and thermoelectric materials design. The results of the case studies indicate that MDM is a powerful approach for use in materials discovery and innovation, and will play an important role in the development of the Materials Genome Initiative and Materials Informatics.

International Conference on Computational Intelligence in Data Mining

CERN Document Server

Mohapatra, Durga

2017-01-01

The book presents high quality papers presented at the International Conference on Computational Intelligence in Data Mining (ICCIDM 2016) organized by School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India during December 10 – 11, 2016. The book disseminates the knowledge about innovative, active research directions in the field of data mining, machine and computational intelligence, along with current issues and applications of related topics. The volume aims to explicate and address the difficulties and challenges that of seamless integration of the two core disciplines of computer science. .

Computer-based expert system aids underground mine planning

Energy Technology Data Exchange (ETDEWEB)

Britton, S.G.

1987-04-01

Artificial intelligence (AI) systems have been rapidly expanded over the last few years to aid in the decision making processes in a whole range of industries, including the use of machines for operating in high risk/dangerous areas such as at the working faces in longwall mining. Using Prolog software a program for mine management is being developed called CHOOZ. This system can help managers identify staffing problems and change manpower shifts to cope with unexpected labor problems such as absenteeism and sickness. All the programs have been developed for the IBM PC microcomputer.

DECISION SUPPORT SYSTEM TO SUPPORT DECISION PROCESSES WITH DATA MINING

OpenAIRE

Rupnik, Rok; Kukar, Matjaž

2007-01-01

Traditional techniques of data analysis do not enable the solution of all kind of problems and for that reason they have become insufficient. This caused a newinterdisciplinary field of data mining to arise, encompassing both classical statistical, and modern machine learning techniques to support the data analysis and knowledge discovery from data. Data mining methods are powerful in dealing with large quantities of data, but on the other hand they are difficult to master by business users t...

METODE DATA MINING UNTUK SELEKSI CALON MAHASISWA PADA PENERIMAAN MAHASISWA BARU DI UNIVERSITAS PAMULANG

Directory of Open Access Journals (Sweden)

Aries Saifudin

2018-01-01

Full Text Available Universitas Pamulang berusaha memberikan pendidikan tinggi dengan biaya yang terjangkau oleh kalangan bawah. Tetapi mahasiswanya banyak yang keluar di tiap semester, sehingga menyebabkan rasio jumlah mahasiswa baru dengan jumlah yang lulus tidak seimbang. Selain itu banyak mahasiswa yang tidak lulus tepat waktu, hal ini mengakibatkan rasio dosen dan mahasiswa tidak seimbang. Kedua hal ini akan mengurangi penilaian pada saat akreditasi. Penyebab keluarnya mahasiswa tanpa menyelesaikan pendidikannya, atau tidak dapat menyelesaikan pendidikannya tepat waktu belum dapat dideteksi dengan sistem seleksi saat ini. Pada penelitian ini diusulkan penggunaan teknik data mining untuk memprediksi ketepatan waktu lulus calon mahasiswa. Teknik data mining dan machine learning dapat digunakan untuk memprediksi berdasarkan data-data masa lalu. Metode data mining yang digunakan untuk memprediksi adalah klasifikasi, yaitu Naïve Bayes (NB, k-Nearest Neighbor (k-NN, Random Forest (RF, Decision Stump (DS, Decision Tree (DT, Rule Induction (RI, Linear Regression (LR, Linear Discriminant Analysis (LDA, Neural Network (NN, dan Support Vector Machine (SVM. Berdasarkan hasil implementasi dan pengukuran algoritma/model yang diusulkan diperoleh algoritma/model terbaik, yaitu Support Vector Machine (SVM dengan akurasi 65.00%. Tetapi akurasi ini masih jauh dari nilai excellent (sangat baik.

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

Science.gov (United States)

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

Machine Learning Methods for Knowledge Discovery in Medical Data on Atherosclerosis

Czech Academy of Sciences Publication Activity Database

Serrano, J.I.; Tomečková, Marie; Zvárová, Jana

2006-01-01

Roč. 1, - (2006), s. 6-33 ISSN 1801-5603 Institutional research plan: CEZ:AV0Z10300504 Keywords : knowledge discovery * supervised machine learning * biomedical data mining * risk factors of atherosclerosis Subject RIV: BB - Applied Statistics, Operational Research

Methods for protecting mine roadways used by two faces

Energy Technology Data Exchange (ETDEWEB)

Katkov, G A; Dimanshtein, A S

1983-09-01

Use of a mine roadway by two longwall faces reduces mine drivage by half and positively influences ventilation and mine haulage. Economic effects of repeated use of mine roadways depend on strata control cost and repair of support systems damaged or deformed by rock strata stress. Use of strips of pneumatic stowing along mine roadways is uneconomical due to low support strength and significant subsidence of the waste rock strip under rock strata stress. Use of timber cribbings as well as blocks of reinforced concrete for strata control is uneconomical due to their high damage rate and low support capacity. Investigations carried out by the IGD Im. A.A. Skochinski Institute and other research institutes show that use of hardening stowing is superior to other methods for strata control in mine roadways used by two longwall faces. Hardening stowing (on a cement basis) is prepared by the SB-67 or the PBM-2Eh machines and by other systems used for guniting in coal mines. The optimum dimensions of a strip of hardening stowing depend on coal seam thickness and mechanical properties of the surrounding rock strata. Use of hardening stowing successfully controls roof subsidence in mine roadways and reduces support deformation. Examples of hardening stowing use in some Donbass coal mines are discussed.

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

Science.gov (United States)

Ball, N. M.

2013-10-01

To-date, computing systems have allowed either sophisticated analysis of small datasets, as exemplified by most astronomy software, or simple analysis of large datasets, such as 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. CANFAR provides a generic environment for the storage and processing of large datasets, removing the requirement for an individual or project to set up and maintain a computing system when implementing an extensive undertaking such as a survey pipeline. 500 processor cores and several hundred terabytes of persistent storage are currently available to users, and both the storage and processing infrastructure are expandable. The storage is implemented via the International Virtual Observatory Alliance's VOSpace protocol, and is available as a mounted filesystem accessible both interactively, and to all processing jobs. The user interacts with CANFAR by utilizing virtual machines, which appear to them as equivalent to a desktop. Each machine is replicated as desired to perform large-scale parallel processing. Such an arrangement enables the user to immediately install and run the same astronomy code that they already utilize, in the same way as on a desktop. In addition, unlike many cloud systems, batch job scheduling is handled for the user on multiple virtual machines by the Condor job queueing system. Skytree is installed and run just as any other software on the system, and thus acts as a library of command line data mining functions that can be integrated into one's wider analysis. Thus we have created a generic environment for large-scale analysis by data mining, in the same way that CANFAR itself has done for storage and processing. Because Skytree scales to large data in

Chapter 16: text mining for translational bioinformatics.

Science.gov (United States)

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.

Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data

OpenAIRE

Veale, M; Binns, RDP

2017-01-01

Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining (DADM) and fairness, accountability and transparency machine learning (FATML), their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data o...

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

Directory of Open Access Journals (Sweden)

Santana Isabel

2011-08-01

Full Text Available Abstract 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 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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Permit to use experimental electric face... ADMINISTRATION, DEPARTMENT OF LABOR TESTING, EVALUATION, AND APPROVAL OF MINING PRODUCTS ELECTRIC MOTOR-DRIVEN MINE EQUIPMENT AND ACCESSORIES Machines Assembled With Certified or Explosion-Proof Components, Field...

Laser cutting or water-jet cutting. Laser setsudan ka water-jet setsudan ka

Energy Technology Data Exchange (ETDEWEB)

Yamada, T. (Shibuya Kogyo Co. Ltd., Ishikawa (Japan))

1991-05-01

The recent spread of carbon oxide laser cutter is so startlingly fast, but at the same time, water jet cutting using ultra high pressure water stream is drawing attention as it has identical characteristics, and opens the way to cutting materials that have been hitherto difficult to cut. The authors, who are fabricators of cutters of both types, gave the comparisons and explanations on several examples referring to materials that can be cut, cutting accuracy, speed, shape and thermal effects to cut face, and running cost in detail. However, simple comparison is difficult. For instance, cutting 6 mm thick SUS sheet costs a running cost of 65 yen per meter in laser cutting, and 535 yen per meter in water jet cutting, but this situation is often reversed when other material or sheet thickness is selected. The actual situation in the sheet metal processing industry at the present time is that it uses by far more laser processing machines, and uses water jet cutters to supplement for cutting materials more difficult to cut. 10 figs., 3 tabs.

Application of the chain saw machine for underground quarry

Energy Technology Data Exchange (ETDEWEB)

Lim, Han-Uk; Baek, Hwan-Jo [Kangwon National University, Chuncheon(Korea); Kim, Chi-Hwan [Woosuk University, Wonju(Korea); Kim, Tae-Soo [Sungshin Mining Co., Jungseun(Korea)

2001-10-31

Many regulatory activities for preservation of the environment make it recently difficult for the stone industry in our country. To reduce environmental hazards and to conserve original surface and woods, some effective underground methods must be adopted. Some new techniques such as chain saw machine, diamond wire saw and water jet cutting can be considered. But application of chain saw machine for underground quarry is proposed in this study. Some technical adoptions with chain saw were carried out at Jungseun marble mine. It is proved that this machine can be effectively adopted to cut dimension stone. With chain saw and diamond wire saw, it can be expected to achieve more effectively cutting the dimension stone. (author). 6 refs., 3 tabs., 6 figs.

The Application of Machine Learning Algorithms for Text Mining based on Sentiment Analysis Approach

Directory of Open Access Journals (Sweden)

Reza Samizade

2018-06-01

Full Text Available Classification of the cyber texts and comments into two categories of positive and negative sentiment among social media users is of high importance in the research are related to text mining. In this research, we applied supervised classification methods to classify Persian texts based on sentiment in cyber space. The result of this research is in a form of a system that can decide whether a comment which is published in cyber space such as social networks is considered positive or negative. The comments that are published in Persian movie and movie review websites from 1392 to 1395 are considered as the data set for this research. A part of these data are considered as training and others are considered as testing data. Prior to implementing the algorithms, pre-processing activities such as tokenizing, removing stop words, and n-germs process were applied on the texts. Naïve Bayes, Neural Networks and support vector machine were used for text classification in this study. Out of sample tests showed that there is no evidence indicating that the accuracy of SVM approach is statistically higher than Naïve Bayes or that the accuracy of Naïve Bayes is not statistically higher than NN approach. However, the researchers can conclude that the accuracy of the classification using SVM approach is statistically higher than the accuracy of NN approach in 5% confidence level.

Recent development in the design of hard rock tunnel boring machines for the mining industry

International Nuclear Information System (INIS)

Snyder, L.L.; Williams, R.I.

1991-01-01

Underground development for nuclear waste storage will possibly require tunnels to be excavated in a variety of rock conditions and configurations. Recent innovations in Tunnel Boring Machine (TBM) design have allowed for an evolved style of TBM which has distinct advantages over the standard machines. Present day conventional hard rock TBM's were developed primarily for the long, relatively straight tunnels of the civil construction industry, thereby making them for the most part, unsuitable for the sharp curves, turnouts, declines, inclines and ramps required in many underground environments. The five foot to 36 foot (1.52 to 11 m) diameter machines are capable of boring tunnels with curve radiuses as small as 40 to 90 feet (12.2 to 27.5 m) depending on size. These short turning radiuses can be accomplished while gripping the tunnel walls horizontally in the traditional manner or vertically as required when intersecting existing tunnels, or making turnouts from the tunnel that the machine has just bored. The machine's length is approximately half of a traditional machine's length while still employing a full measure of thrust, horsepower and rock cutting ability. The machine's short length, combined with a patented machine structure allows it to steer while boring without causing harmful eccentric loads on the cutterhead and main bearing assembly. The machine configuration is versatile and can be easily modified to operate in a wide variety of conditions

TSC mobile mining and extraction technology

Energy Technology Data Exchange (ETDEWEB)

Lavender, W.J. [TSC Company Ltd., Calgary, AB (Canada)

2001-11-01

This Power-Point presentation described an innovative mining and extraction technology developed by Calgary-based TSC Company Ltd. that has provided a major breakthrough in bitumen production from mineable oil sands. The presentation described the process and key mechanical components as demonstrated on oil sands leases. It also described the step change in cost structure and profitability. Oil sands mining provide a hugh resource base with no exploration costs and no decline in production. Despite these advantages, oil sands mining faces the challenge of high capital and operating costs and materials handling. Other challenges include the variability of the ore and environmental impacts. This paper described the fundamentals of the new technology called the Tar Sand Combine (TSC), a continuous mining machine, crusher, cyclone, tailings filter and stacker all in one mobile module. Several viewgraphs were included with the presentation to depict the recovery process as successfully demonstrated at a pilot project. Patent is pending on the process and components. The advantages of the TSC are reduced materials handling, and no tailings ponds are generated since tailings remain where they are mined. The final product is clean bitumen. The specifications of a commercial TSC are: 2000 ton/stream hour mining produce 25,000 bpsd bitumen at 12 per cent ore grade; mined ore bitumen recovery is greater than 95 per cent and the availability factor is 85 per cent. It was concluded that the TSC can maximize oil sands reserves, while providing significant cost savings and environmental benefits. 2 tabs., 24 figs.

Intelligent Mining Engineering Systems in the Structure of Industry 4.0

Directory of Open Access Journals (Sweden)

Rylnikova Marina

2017-01-01

Full Text Available 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.

Intelligent Mining Engineering Systems in the Structure of Industry 4.0

Science.gov (United States)

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.

Mechanization of operations in underground workings in coal mines and research project trends. [Poland

Energy Technology Data Exchange (ETDEWEB)

Reich, K; Skoczynski, W; Sikora, W

1985-01-01

Structure of black coal reserves of Poland, imported and Polish made equipment for underground mining, prospects for mechanization of selected operations in underground mines and research programs of the KOMAG Center for Mechanization of Mining are evaluated. Prospects for longwall mining with caving or stowing in thick coal seams (slice mining), thin (0.8 to 1.2 m), level or inclined coal seams and steep seams are analyzed. The following equipment for mechanization of underground mining is evaluated: integrated face systems, shearer loaders, chain conveyors, belt conveyors, coal plows, equipment for mine drivage, hoists, drive systems for mining equipment. The following research programs of the KOMAG Center are reviewed: modernization of face systems for coal seams with uncomplicated mining conditions, development of equipment for thin seam mining, development of types of mining equipment for coal seams from 1.5 to 3.0 m thick with dip angles to 25 degrees, modernization of equipment for thick seam mining, increasing efficiency of mine drivage (new types of heading machines, materials handling equipment for mine drivage), mechanization of auxiliary operations in underground coal mines, improving quality of mining equipment, development of equipment for coal preparation, increasing occupational safety in underground mining.

Feature Reduction Based on Genetic Algorithm and Hybrid Model for Opinion Mining

Directory of Open Access Journals (Sweden)

P. Kalaivani

2015-01-01

Full Text Available With the rapid growth of websites and web form the number of product reviews is available on the sites. An opinion mining system is needed to help the people to evaluate emotions, opinions, attitude, and behavior of others, which is used to make decisions based on the user preference. In this paper, we proposed an optimized feature reduction that incorporates an ensemble method of machine learning approaches that uses information gain and genetic algorithm as feature reduction techniques. We conducted comparative study experiments on multidomain review dataset and movie review dataset in opinion mining. The effectiveness of single classifiers Naïve Bayes, logistic regression, support vector machine, and ensemble technique for opinion mining are compared on five datasets. The proposed hybrid method is evaluated and experimental results using information gain and genetic algorithm with ensemble technique perform better in terms of various measures for multidomain review and movie reviews. Classification algorithms are evaluated using McNemar’s test to compare the level of significance of the classifiers.

Smart material screening machines using smart materials and controls

Science.gov (United States)

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.

Data Mining in Earth System Science (DMESS 2011)

Science.gov (United States)

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...

Archetypal Analysis for Machine Learning

DEFF Research Database (Denmark)

Mørup, Morten; Hansen, Lars Kai

2010-01-01

Archetypal analysis (AA) proposed by Cutler and Breiman in [1] estimates the principal convex hull of a data set. As such AA favors features that constitute representative ’corners’ of the data, i.e. distinct aspects or archetypes. We will show that AA enjoys the interpretability of clustering - ...... for K-means [2]. We demonstrate that the AA model is relevant for feature extraction and dimensional reduction for a large variety of machine learning problems taken from computer vision, neuroimaging, text mining and collaborative filtering....

Seminal quality prediction using data mining methods.

Science.gov (United States)

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

INTEGRATED ROBOT-HUMAN CONTROL IN MINING OPERATIONS

Energy Technology Data Exchange (ETDEWEB)

George Danko

2006-04-01

This report describes the results of the 2nd year of a research project on the implementation of a novel human-robot control system for hydraulic machinery. Sensor and valve re-calibration experiments were conducted to improve open loop machine control. A Cartesian control example was tested both in simulation and on the machine; the results are discussed in detail. The machine tests included open-loop as well as closed-loop motion control. Both methods worked reasonably well, due to the high-quality electro-hydraulic valves used on the experimental machine. Experiments on 3-D analysis of the bucket trajectory using marker tracking software are also presented with the results obtained. Open-loop control is robustly stable and free of short-term dynamic problems, but it allows for drifting away from the desired motion kinematics of the machine. A novel, closed-loop control adjustment provides a remedy, while retaining much of the advantages of the open-loop control based on kinematics transformation. Additional analysis of previously recorded, three-dimensional working trajectories of the bucket of large mine shovels was completed. The motion patterns, when transformed into a family of curves, serve as the basis for software-controlled machine kinematics transformation in the new human-robot control system.

Dependence of the mean time to failure of a hydraulic balancing machine unit on different factors for sectional pumps of the Alrosa JSC

Science.gov (United States)

Ovchinnikov, N. P.; Portnyagina, V. V.; Sobakina, M. P.

2017-12-01

This paper presents factors that have a greater impact on the mean time to failure of a hydraulic balancing machine unit working in underground kimberlite mines of the Alrosa JSC, the hydraulic balancing machine unit being the least reliable structural elements in terms of error-free operation. In addition, a multifactor linear dependence of mean time to failure of a hydraulic balancing machine unit is shown regarding it being parts of stage sectional pumps in the underground kimberlite mines of the Alrosa JSC. In prospect, this diagram can allow us to predict the durability of the least reliable structural element of a sectional pump.

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

Science.gov (United States)

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

Introduction to machine learning for brain imaging.

Science.gov (United States)

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.

Calculation of parameters of technological equipment for deep-sea mining

Science.gov (United States)

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.

Underground coal mining - methods, equipment developments and trends

Energy Technology Data Exchange (ETDEWEB)

Singhal, R

1988-12-01

Underground mines are truly beginning to accept the so-called 'high tech' technology evident in other industries. Automation, remote control and robotics have taken an added significance. Wireless communication, mine-wide equipment health and performance monitoring, and transmission of data from deeper levels to surface is moving towards becoming the norm. There is emphasis on developing and applying continuous mining systems, as well as on modifying cyclical discontinuous methods to continuous systems. Multi-purpose equipment is also being developed. Technology transfer is playing its role - equipment and systems from surface coal mining are being applied to underground mining and vice-versa. At the American Mining Congress Exhibition held in Chicago in April 1988, a variety of equipment for underground mining was displayed including coal face equipment such as shearer loaders, conveyors and powered supports, and equipment for room-and-pillar coal mining. The trend continues to be towards high power machines equipped with a variety of electronics and sensors, safety devices, and alarm systems. Ancillary equipment on display covered a variety of cutting drums, cutting tools, conveying equipment and so on. In room-and-pillar mining, the overall emphasis was on moving away from the cyclical nature of the work. Transportation by shuttle cars must be replaced by continuous transport systems such as conveyors. Experience from Australia has shown that the application of continuous haulage and breaker line supports has permitted a doubling of production from room-and-pillar systems. Production levels of 3,000tpd have already been achieved, and 4,000tpd is considered achievable.

The BUK-GPK suspended drilling equipment for the GPK heading machine

Energy Technology Data Exchange (ETDEWEB)

1982-11-01

The BUK-GPK system developed by PechorNIIproekt is used for borehole drilling in rocks with compression strength to 12 degrees on the Protod'yakonov scale. The BUK-GPK is used for drilling boreholes for roof bolts in development workings driven by heading machines under difficult conditions. The GPK heading machine with the BUK-GPK drill is 10 m long, 2.1 m high and 2.1 m wide. A borehole is up to 50 mm in diameter, rotating speed ranges from 170 to 315 rpm. Drillings are removed from a boreholes by flushing using water. The system is equipped with remote control. The BUK-GPK system was tested at the Pechora basin during mine drivage in a coal seam from 1.4 to 1.5 m thick. The BUK-GPK was reliable. It permitted labor productivity and occupational safety during during mine drivage to be increased. Commercial production of the BUK-GPK drill is recommended. (In Russian)

Efficiency of measures aimed at improving health by normalization of temperature conditions in the Kochegarka mine

Energy Technology Data Exchange (ETDEWEB)

Litvinov, G.I.; Nifonov, V.P.; Kobets, A.N.

1981-06-01

This paper evaluates effects of air conditioning in the Kochegarka black coal mine on miners' health. Up to 1975 air temperature in the lowest mine horizon located at a depth of 970 m ranged from 26 to 32 C, in summer from 34 to 36 C. Air humidity ranged from 94 to 98%. Since 1975 KhTMF-248-4000 freon air cooling machines have been used in the mine; their capacity amounts to 3.8 x 10/SUP/6 kcal/h. Use of air cooling systems reduces air temperature to permissible limits. Air temperature measured at a distance of 1 km from mine shaft ranges from 24 to 26 C, and air humidity from 90 to 95%. At a distance of 1.5 km from the mine shaft air temperature in conveyor roadways is 26.4 C, in dead-end development workings 27 C, and at working faces 26 C (with air humidity ranging from 96 to 98%). ARVP systems for local air cooling are used at places distant from the mine shaft. The ARVP reduces air temperature from 2 to 4.5 C at a distance ranging from 4 to 8 m from the machine. Reducing air temperature, combined with other measures aimed at improving miners' health, has caused a decrease in miner absenteeism due to illness by 25.4%.

Forming the management model in industrial partnerships of the machine-building complex of Ukraine

OpenAIRE

Reshetilova, T.; Kuvaieva, T.

2016-01-01

Stages of development the processes of forming the industrial networks, technological and logistic chains, partnership and their varieties are analyzed. Factors that determine the rate and scale of the process of forming the partnerships in the machine-building complex of Ukraine are established. A group of the factors that lead to forming the vertical partnership based on Partner Relationship Management (PRM) in mining machinery and mining industry are determined and analyzed. It is possible...

The Analisis Sentimen Sosial Media Twitter Dengan Algoritma Machine Learning Menggunakan Software R

Directory of Open Access Journals (Sweden)

Jaka Aulia Pratama

2017-10-01

Full Text Available Media sosial adalah wadah untuk mengungkapkan opini terhadap suatu topik tertentu. Ketersediaan informasi dan opini dari para pengguna media sosial merupakan kumpulan dokumen data berupa teks yang amat sangat besar dan berguna untuk kepentingan penelitian maupun membuat suatu keputusan bagi pihak – pihak tertentu. Text Mining bisa didefinisikan sebagai proses penggalian informasi di mana pengguna berinteraksi dengan kumpulan dokumen dari waktu ke waktu dengan menggunakan suatu alat analisis. Analisis sentimen atau Opinion Mining adalah salah satu studi di bidang komputasi yang berhubungan dengan kasus publik mengenai opini, penilaian, sikap, dan emosi. Penelitian ini akan menggunakan metode Machine Learning pada analisis sentimen pengguna layanan jejaring sosial Twitter terhadap Donald Trump dan Barack Obama dalam 20000 tweets. Nilai akurasi metode Machine Learning yang diperoleh cukup tinggi yaitu 87.52% untuk Data Training dan 87.4% untuk Data Testing.

Underground roadway drivage with heading machines in Indian coal industry

Energy Technology Data Exchange (ETDEWEB)

Mukherjee, T.K.

1983-03-01

Heading machines have assumed a very important place in underground roadway drivage. They are not only a compromise between ''drill-and-blast'' technique and full-face machines, but are also an economic and versatile form of mechanised roadway drivage. Since the advantages gained by heading machines are considerable, the use of these machines is becoming popular in underground roadway drivage. Experience with continuous miner and heading machines in Indian coal mines is very limited compared to that of Western countries. In 1964-65, for the first time, two units of Lee Norse Miner were used at Kunostoria Colliery of Bengal Coal Company. In 1966, two units of Joy Continuous Miner were introduced at Chalkari Colliery of National Coal Development Corporation, but had to be adandoned because of heavy make of water at the installation site. A Russian PK-3 heading machine was used limitedly during the development of Banki Colliery, Madhya Pradesh. A Demag Unicorn VS-1 machine operated for the development of roadways at Jitpur and Chasnala Collieries of IISCO between 1967-70. With this machine, progress of 7 m per day was attained in level roadways and of about 2 m per day in steep raises.

Comparison of surface roughness quality created by abrasive water jet and CO2 laser beam cutting

Czech Academy of Sciences Publication Activity Database

Zeleňák, M.; Valíček, Jan; Klich, Jiří; Židková, P.

2012-01-01

Roč. 19, č. 3 (2012), s. 481-485 ISSN 1330-3651 R&D Projects: GA MŠk ED2.1.00/03.0082 Institutional support: RVO:68145535 Keywords : abrasive waterjet cut ting * CO2 laser beam cut ting * optical profilometry * titanium sample Subject RIV: JQ - Machines ; Tools Impact factor: 0.601, year: 2012 http://hrcak.srce.hr/index.php?show=clanak&id_clanak_jezik=129054

Survey of Machine Learning Methods for Database Security

Science.gov (United States)

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.

Participation of Ostrava Mining College in training of personnel for nuclear technology

International Nuclear Information System (INIS)

Kuchar, L.

1983-01-01

The mining and geology faculty of the Mining College educates specialists for surveying, extraction and treatment of uranium raw materials. In 1980 the faculty introduced an interdisciplinary study course for the technology of drilling, geological surveying and mine surveying. A contract has been signed between the College and the Czechoslovak Uranium Industry on specialized and scientific cooperation, expertise, postgraduate courses, etc. The metallurgy faculty of the College introduced the nuclear metallurgy specialization in 1964. Students attending the course will acquire knowledge not only on the metallurgy of nuclear fuels, cladding, shielding and structural materials, their production and processing but also on the science of metals, heat treatment, metal testing, etc. A study course is now being prepared relating to materials problems of nuclear power which is oriented to modern methods of material assessment for nuclear power facilities, light water reactors and their components. In 1976 the Mining College also introduced the nuclear power specialization at its mechanical engineering and electrical engineering faculties. In the years 1976-1982 more than fifty students graduated from the faculty whose theses were oriented to the problems of welding, surfacing, machining and upgrading of WWER-440 and WWER-1000 components. In the years 1979-82 the College ran a postgraduate study course on ''Machines and equipment of nuclear power plants''. (E.S.)

Social networking mining, visualization, and security

CERN Document Server

Dehuri, Satchidananda; Wang, Gi-Nam

2014-01-01

With the proliferation of social media and on-line communities in networked world a large gamut of data has been collected and stored in databases. The rate at which such data is stored is growing at a phenomenal rate and pushing the classical methods of data analysis to their limits. This book presents an integrated framework of recent empirical and theoretical research on social network analysis based on a wide range of techniques from various disciplines like data mining, social sciences, mathematics, statistics, physics, network science, machine learning with visualization techniques, and security. The book illustrates the potential of multi-disciplinary techniques in various real life problems and intends to motivate researchers in social network analysis to design more effective tools by integrating swarm intelligence and data mining.  

Mining and global environmental challenges

Energy Technology Data Exchange (ETDEWEB)

Greeff, J C; Bailey-McEwan, M [Chamber of Mines of South Africa, Johannesburg (South Africa)

1992-04-01

At least half of South Africa's gold production is presently dependent on CFC11 an CFC12 as refrigerants in water chilling machines used in cooling the underground workings. The South African Government will ratify the revised Montreal Protocol on substances that deplete the ozone layer which will mean CFCs will have to be phased out probably by 1997. HFC134 or HFC22 are possible replacements for CFC but present costs of converting machines are high. The article goes on to discuss the contribution of CFCs and CO{sub 2} to global warming and model simulations and predictions of climate change. Likely effects of growing concern about global warming on the coal mining industry are the possible limitations on the use of coal and the increased need for clean coal technology. 12 refs., 5 figs., 3 tabs.

Application of Machine Learning for Dragline Failure Prediction

Directory of Open Access Journals (Sweden)

Taghizadeh Amir

2017-01-01

Full Text Available Overburden stripping in open cast coal mines is extensively carried out by walking draglines. Draglines’ unavailability and unexpected failures result in delayed productions and increased maintenance and operating costs. Therefore, achieving high availability of draglines plays a crucial role for increasing economic feasibility of mining projects. Applications of methodologies which can forecast the failure type of dragline based on the available failure data not only help to reduce the maintenance and operating costs but also increase the availability and the production rate. In this study, Machine Learning approaches have been applied for data which has been gathered from an operating coal mine in Turkey. The study methodology consists of three algorithms as: i implementation of K-Nearest Neighbors, ii implementation of Multi-Layer Perceptron, and iii implementation of Radial Basis Function. The algorithms have been utilized for predicting the draglines’ failure types. In this sense, the input data, which are mean time-to-failure, and the output data, failure types, have been fed to the algorithms. The regression analysis of methodologies have been compared and showed the K- Nearest Neighbors has a higher rate of regression which is around 70 percent. Thus, the K-Nearest Neighbor algorithm can be applied in order to preventive components replacement which causes to minimized preventive and corrective cost parameters. The accurate prediction of failure type, indeed, causes to optimized number of inspections. The novelty of this study is application of machine learning approaches in draglines’ reliability subject for first time.

Bucket wheel excavator performances at Neyveli lignite mine

Energy Technology Data Exchange (ETDEWEB)

Kumaraswamy, S; Mozumdar, B K

1987-03-01

Bucket-wheel excavators have been in use at the Neyveli Lignite Mine in the State of Tamil Nadu, India, since the early nineteen-sixties. The mining environment has been particularly harsh for BWE application. The adverse influencing factors are the hardness of the over-burden formation, high abrasivity of rock and artesian ground water conditions. In this paper, the performances of the BWEs at Neyveli have been statistically analysed to determine the effects of physico-mechanical properties of overburden, blasting and rainfall on machine productivity, availability, wear-and-tear of bucket teeth, power consumption, production efficiency and cost of mining. An empirical relationship between the production efficiency, defined as the ratio of actual production rate to the theoretical one, and the bench height and width, height of slices, specific cutting resistance of the overburden material and its clay content, consumption of explosives, and conveyor length has been established.

Data Mining Learning Models and Algorithms on a Scada System Data Repository

Directory of Open Access Journals (Sweden)

Mircea Rîşteiu

2010-06-01

Full Text Available This paper presents three data mining techniques applied
on a SCADA system data repository: Naijve Bayes, k-Nearest Neighbor and Decision Trees. A conclusion that k-Nearest Neighbor is a suitable method to classify the large amount of data considered is made finally according to the mining result and its reasonable explanation. The experiments are built on the training data set and evaluated using the new test set with machine learning tool WEKA.

Explaining and predicting workplace accidents using data-mining techniques

International Nuclear Information System (INIS)

Rivas, T.; Paz, M.; Martin, J.E.; Matias, J.M.; Garcia, J.F.; Taboada, J.

2011-01-01

Current research into workplace risk is mainly conducted using conventional descriptive statistics, which, however, fail to properly identify cause-effect relationships and are unable to construct models that could predict accidents. The authors of the present study modelled incidents and accidents in two companies in the mining and construction sectors in order to identify the most important causes of accidents and develop predictive models. Data-mining techniques (decision rules, Bayesian networks, support vector machines and classification trees) were used to model accident and incident data compiled from the mining and construction sectors and obtained in interviews conducted soon after an incident/accident occurred. The results were compared with those for a classical statistical techniques (logistic regression), revealing the superiority of decision rules, classification trees and Bayesian networks in predicting and identifying the factors underlying accidents/incidents.

Explaining and predicting workplace accidents using data-mining techniques

Energy Technology Data Exchange (ETDEWEB)

Rivas, T., E-mail: trivas@uvigo.e [Dpto. Ingenieria de los Recursos Naturales y Medio Ambiente, E.T.S.I. Minas, University of Vigo, Campus Lagoas, 36310 Vigo (Spain); Paz, M., E-mail: mpaz.minas@gmail.co [Dpto. Ingenieria de los Recursos Naturales y Medio Ambiente, E.T.S.I. Minas, University of Vigo, Campus Lagoas, 36310 Vigo (Spain); Martin, J.E., E-mail: jmartin@cippinternacional.co [CIPP International, S.L. Parque Tecnologico de Asturias, Parcela 43, Oficina 11, 33428 Llanera (Spain); Matias, J.M., E-mail: jmmatias@uvigo.e [Dpto. Estadistica e Investigacion Operativa, E.T.S.I. Minas, University of Vigo, Campus Lagoas, 36310 Vigo (Spain); Garcia, J.F., E-mail: jgarcia@cippinternacional.co [CIPP International, S.L. Parque Tecnologico de Asturias, Parcela 43, Oficina 11, 33428 Llanera (Spain); Taboada, J., E-mail: jtaboada@uvigo.e [Dpto. Ingenieria de los Recursos Naturales y Medio Ambiente, E.T.S.I. Minas, University of Vigo, Campus Lagoas, 36310 Vigo (Spain)

2011-07-15

Current research into workplace risk is mainly conducted using conventional descriptive statistics, which, however, fail to properly identify cause-effect relationships and are unable to construct models that could predict accidents. The authors of the present study modelled incidents and accidents in two companies in the mining and construction sectors in order to identify the most important causes of accidents and develop predictive models. Data-mining techniques (decision rules, Bayesian networks, support vector machines and classification trees) were used to model accident and incident data compiled from the mining and construction sectors and obtained in interviews conducted soon after an incident/accident occurred. The results were compared with those for a classical statistical techniques (logistic regression), revealing the superiority of decision rules, classification trees and Bayesian networks in predicting and identifying the factors underlying accidents/incidents.

Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

Directory of Open Access Journals (Sweden)

C. V. Subbulakshmi

2015-01-01

Full Text Available Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO algorithm with the extreme learning machine (ELM classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN, proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

1st International Conference on Computational Intelligence in Data Mining

CERN Document Server

Behera, Himansu; Mandal, Jyotsna; Mohapatra, Durga

2015-01-01

The contributed volume aims to explicate and address the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. Data Mining aims at the automatic discovery of underlying non-trivial knowledge from datasets by applying intelligent analysis techniques. The interest in this research area has experienced a considerable growth in the last years due to two key factors: (a) knowledge hidden in organizations’ databases can be exploited to improve strategic and managerial decision-making; (b) the large volume of data managed by organizations makes it impossible to carry out a manual analysis. The book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of relate...

Universal machine ''Shtrek'' and the tractor-lifter with pneumatic-equipment control. [Auxiliary multipurpose materials handling equipment

Energy Technology Data Exchange (ETDEWEB)

Bal' bert, B M; Borumenskiy, V A; Lishenko, A P; Mitchenko, G A

1982-01-01

The machine ''Shtrek'' is described. It makes it possible to mechanize over 20 auxiliary operations: loading-unloading operations: extraction of old and deformed timbering; dissmantling of obstructions; erection of different types of timbering; making and restoring of drainage channels; laying and straightening of a drift and its leveling; assembly and disassembly of pipelines and mine equipment, etc. Depending on the type of operation, the machine has the corresponding suspended equipment. The elementary variant has a limited area of application at mines of the central region of the Dunbass. Currently a pneumatic variant of the machine ''Shtrek'' has been developed. The electric motor and the starter of the pumping equipment of the machine have been replaced by a pneumatic motor and pneumatically controlled valve KTM-50. In this case there was significant reduction in the weight of the pumping equipment and in its overall dimensions; the electric drive of the hydraulic distributors for controlling the mechanisms were replaced by simpler pneumatic ones; the logical circuit of the control system was constructed on the USEPPA elements. A specialized tractor-lifter designed for moving suspended loads is described for auxiliary operations in the near-face zone of the preparatory drifts. The machine also lifts and lowers the boom, rotates the boom by 270/sup 0/ and additionally lifts and lowers the weight-lifting hook.

Machine learning in geosciences and remote sensing

Directory of Open Access Journals (Sweden)

David J. Lary

2016-01-01

Full Text Available Learning incorporates a broad range of complex procedures. Machine learning (ML is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficult-to-program applications, and software applications. It is a collection of a variety of algorithms (e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc. that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore, nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.

Machine Learning Methods to Predict Diabetes Complications.

Science.gov (United States)

Dagliati, Arianna; Marini, Simone; Sacchi, Lucia; Cogni, Giulia; Teliti, Marsida; Tibollo, Valentina; De Cata, Pasquale; Chiovato, Luca; Bellazzi, Riccardo

2018-03-01

One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice.

Development of the testing procedure for units and elements of mining equipment

Directory of Open Access Journals (Sweden)

P. B. Gerike

2017-09-01

Full Text Available The author considers in detail the stages of creating a testing procedure for mining equipment based on the complex implementation of principles of nondestructive testing and technical diagnostics. The author substantiates effectiveness of application of a complex diagnostic approach for assessing the state of metal structures and energy-mechanical equipment of mining machines. The opportunity for timely detection of defects, regardless of their type and degree of danger, presents itself only with a wide application of the modern methods of vibration diagnostics and nondestructive testing. The author substantiates the effectiveness of specific combination of methods of nondestructive testing, most optimally suited for solving given tasks. The article contains the developed complex of more than 120 diagnostic rules, suitable for performing automated analysis of vibroacoustic signal and revealing the main damages of energy-mechanical equipment based on selective groups of informative frequencies. The author formulates the main criteria that one can use as a basic platform for improving the methodology for normalizing the parameters of mechanical oscillations. The developed diagnostic criteria became a basis for the development of individual spectral masks suitable for performing the analysis of parameters of vibroacoustic waves generated during operation of mining equipment. The author proves necessity of transition of repair and maintenance divisions of industrial enterprises to the system of maintenance of machinery according to its actual technical state, and the developed complex of diagnostic rules for detecting defects can serve as a platform for the implementation of basic elements of this system. The author substantiates the principal validity of the developed methodology for testing mining machines equipment and its individual elements, such as the predictive modeling of degradation of technical state of mining equipment and the

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

Science.gov (United States)

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.

Data Mining and Machine Learning in Time-Domain Discovery and Classification

Science.gov (United States)

Bloom, Joshua S.; Richards, Joseph W.

2012-03-01

The changing heavens have played a central role in the scientific effort of astronomers for centuries. Galileo's synoptic observations of the moons of Jupiter and the phases of Venus starting in 1610, provided strong refutation of Ptolemaic cosmology. These observations came soon after the discovery of Kepler's supernova had challenged the notion of an unchanging firmament. In more modern times, the discovery of a relationship between period and luminosity in some pulsational variable stars [41] led to the inference of the size of the Milky way, the distance scale to the nearest galaxies, and the expansion of the Universe (see Ref. [30] for review). Distant explosions of supernovae were used to uncover the existence of dark energy and provide a precise numerical account of dark matter (e.g., [3]). Repeat observations of pulsars [71] and nearby main-sequence stars revealed the presence of the first extrasolar planets [17,35,44,45]. Indeed, time-domain observations of transient events and variable stars, as a technique, influences a broad diversity of pursuits in the entire astronomy endeavor [68]. While, at a fundamental level, the nature of the scientific pursuit remains unchanged, the advent of astronomy as a data-driven discipline presents fundamental challenges to the way in which the scientific process must now be conducted. Digital images (and data cubes) are not only getting larger, there are more of them. On logistical grounds, this taxes storage and transport systems. But it also implies that the intimate connection that astronomers have always enjoyed with their data - from collection to processing to analysis to inference - necessarily must evolve. Figure 6.1 highlights some of the ways that the pathway to scientific inference is now influenced (if not driven by) modern automation processes, computing, data-mining, and machine-learning (ML). The emerging reliance on computation and ML is a general one - a central theme of this book - but the time

Identification of Work-Related Musculoskeletal Disorders in Mining

Science.gov (United States)

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

Automation of strata bolting in iron mines

Energy Technology Data Exchange (ETDEWEB)

Belin, M; Lethuaire, M

1978-01-01

The Moyeure iron mine (Lorraine), with an output of 16,000 t/day, works 2 seams separated by a dirt band 6.50 m thick. The tyre-mounted Diesel Secoma jumbo for bolting operations can insert resin-embedded bolts 1.70 m in length. The jumbo is fitted with a standard universal boom with two settings - one for drilling and one for bolting. As the operator has to work in the unbolted zone in order to offer up the bolt on the boom and insert the resin cartridge, the mine in conjunction with the manufacturers, Secoma, have improved the safety and performance of the machine by adding three special attachments to the standard boom: a reamer, a resin-injection system and a bolt supply-magazine. Gives details of the results achieved. (In French)

THE VERIFICATION BRAKE MECHANISM OF WINDING MACHINES WITH SINGLE CABLE DRIVING WHEELS ON

Directory of Open Access Journals (Sweden)

Răzvan Bogdan ITU

2017-12-01

Full Text Available The development in safe conditions of the extracting process continuously imposes the need of optimal functioning of the extracting installations as important links in the transport flow. Diagnosis of winding engine brake mechanism in mines is important to provide normal extraction vessel movement in the shaft, or stopping machines in a certain position of the vessels in disturbances or failures. The paper presents the calculus of safety coefficients in the use of safety and maneuver brakes. Mine winding engines brake mechanisms is important to provide normal extraction vessel movement along the shaft, or stopping the engine in a certain position of the vessel in disturbances or failures. To assess the real safety coefficient, results obtained by tensiometric measurements were used. After diagnosis, necessary information is obtained to improve present maintenance system and repair this category of machines in view of increasing safety in use of winding installations, with possibility of monitoring brake mechanism...

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

Science.gov (United States)

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.

Mining Trust Relationships from Online Social Networks

Institute of Scientific and Technical Information of China (English)

Yu Zhang; Tong Yu

2012-01-01

With the growing popularity of online social network,trust plays a more and more important role in connecting people to each other.We rely on our personal trust to accept recommendations,to make purchase decisions and to select transaction partners in the online community.Therefore,how to obtain trust relationships through mining online social networks becomes an important research topic.There are several shortcomings of existing trust mining methods.First,trust is category-dependent.However,most of the methods overlook the category attribute of trust relationships,which leads to low accuracy in trust calculation.Second,since the data in online social networks cannot be understood and processed by machines directly,traditional mining methods require much human effort and are not easily applied to other applications.To solve the above problems,we propose a semantic-based trust reasoning mechanism to mine trust relationships from online social networks automatically.We emphasize the category attribute of pairwise relationships and utilize Semantic Web technologies to build a domain ontology for data communication and knowledge sharing.We exploit role-based and behavior-based reasoning functions to infer implicit trust relationships and category-specific trust relationships.We make use of path expressions to extend reasoning rules so that the mining process can be done directly without much human effort.We perform experiments on real-life data extracted from Epinions.The experimental results verify the effectiveness and wide application use of our proposed method.

Development of a Workbench to Address the Educational Data Mining Bottleneck

Science.gov (United States)

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…

Mining user-generated geographic content : an interactive, crowdsourced approach to validation and supervision

NARCIS (Netherlands)

Ostermann, F.O.; Garcia Chapeton, Gustavo Adolfo; Zurita-Milla, R.; Kraak, M.J.; Bergt, A.; Sarjakoski, T.; van Lammeren, R.; Rip, F.

2017-01-01

This paper describes a pilot study that implements a novel approach to validate data mining tasks by using the crowd to train a classifier. This hybrid approach to processing successfully addresses challenges faced during human curation or machine processing of user-generated geographic content

The participation of the Experimental Design Factory of the Uranium Industry of Czechoslovakia in the design of a tunneling machine with disk bits

Energy Technology Data Exchange (ETDEWEB)

Kastner, P

1983-01-01

A tunneling machine, two prototypes of which were designed and built jointly on the basis of scientific and technical cooperation between the Experimental Design Factory of the Uranium Industry of Czechoslovakia and the VEB-Schachtbau enterprise (East Germany), is described. The experimental design operations were conducted under the methodological leadership of the Mine Construction in the Uranium Industry (Czechoslovakia) enterprise. The experimental design factory developed a general design system for the machine and its individual subassemblies. The detailed technical documentation for the machine units was developed by both enterprises. Each enterprise made two complexes of specific units and spare parts. The prototypes were assembled in both countries with the technical assistance of the producer enterprise of the appropriate subassembly. Industrial tests were conducted by each enterprise independently with technical assistance and delivery of spare parts on the part of the producer enterprise. A machine under the title of VM 24-27 was used to drill more than 2,300 meters of water supply tunnel in East Germany in 1982 and a machine called the RS 24-27 (29) was used in Prague in the same year to drill approximately 1,400 meters of cable collectors. The machine is designed for the passage of rounded mine drifts with a diameter of 2.4 to 2.7 (2.9) meters) to the full cross section in stable rocks. Its overall length is 32.5 meters, while the total weight is 85 tons. The shift productivity was 9.55 meters. Since 1979 the Mining Construction in the Uranium Industry and the Experimental Design Plant of the Uranium Industry Enterprises of Czechoslovakia have supplied disk bits for the TVM Demag tunnel drilling machines (West Germany) and RS 24-27 and the HG 210 Wirth (West Germany) cross cut drills.

Recent advances in environmental data mining

Science.gov (United States)

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

Improving mining technology and organization of labor in the light of medical-biological aspects of physical health of miners

Energy Technology Data Exchange (ETDEWEB)

Egorov, P.V.; Nirenburg, K.G.; Davydova, N.N.; Dyatlova, L.A. (Kuzbasskii Politekhnicheskii Institut (USSR))

1991-12-01

Transfer to a contract-bonus system in mines of the Severokuzbassugol' and Leninskugol' associations (USSR) increased coal mining productivity by 42.2-54.4%, but, at the same time, problems concerning miners' health were noted. Presents data on the productivity and labor conditions of contract teams working at coal mining and in development faces. The influence of noise and vibration induced stresses on organisms of underground workers is analyzed. Investigations showed that 3 stages of exhaustion are likely to develop and that the most vulnerable are the cardiovascular system and the respiratory tract. The 3 stages of exhaustion and ability to recover were studied on mining machine operators and drivers of heading machines. Data showed that during the 1985-89 period, 972 miners received disability certificates; the rate of disability was 2.6 miners per 1 Mt of coal; 40.5% of miners over 40 years working on labor-intensive jobs had three or more chronic diseases which could cause permanent disability. In the structure of disability, cardio-vascular system cases accounted for 25%, osseous-muscular system cases for 20% and pulmonary diseases for 13%. Stresses the need for every mine to maintain its own medical center equipped with inhalation therapy, psychological relief, acupuncture and physiotherapy facilities.

Monitoring of Acoustic Emission During the Disintegration of Rock

Czech Academy of Sciences Publication Activity Database

Tripathi, R.; Srivastava, M.; Hloch, Sergej; Adamčík, P.; Chattopadhyaya, S.; Das, A. K.

2016-01-01

Roč. 149, č. 149 (2016), s. 481-488 E-ISSN 1877-7058. [International Conference on Manufacturing Engineering and Materials, ICMEM 2016. Nový Smokovec, 06.06.2016-10.06.2016] R&D Projects: GA MŠk ED2.1.00/03.0082; GA MŠk(CZ) LO1406 Institutional support: RVO:68145535 Keywords : acoustic emission * rock disintegration * waterjet Subject RIV: JQ - Machines ; Tools http://www.sciencedirect.com/science/article/pii/S1877705816312127

Vodní paprsky ve Velkých Losinách

Czech Academy of Sciences Publication Activity Database

Sitek, Libor

2015-01-01

Roč. 12, č. 12 (2015), s. 22-22 ISSN 1212-2572. [Vodní paprsek 2015 - výzkum, vývoj, aplikace. Velké Losiny, 06.10.2015-08.10.2015] R&D Projects: GA MŠk ED2.1.00/03.0082; GA MŠk(CZ) LO1406 Institutional support: RVO:68145535 Keywords : waterjet technology * research * application Subject RIV: JQ - Machines ; Tools http://www.mmspektrum.com/clanek/vodni-paprsky-ve-velkych-losinach.html

Data Mining and Data Fusion for Enhanced Decision Support

Energy Technology Data Exchange (ETDEWEB)

Khan, Shiraj [ORNL; Ganguly, Auroop R [ORNL; Gupta, Amar [University of Arizona

2008-01-01

The process of Data Mining converts information to knowledge by utilizing tools from the disciplines of computational statistics, database technologies, machine learning, signal processing, nonlinear dynamics, process modeling, simulation, and allied disciplines. Data Mining allows business problems to be analyzed from diverse perspectives, including dimensionality reduction, correlation and co-occurrence, clustering and classification, regression and forecasting, anomaly detection, and change analysis. The predictive insights generated from Data Mining can be further utilized through real-time analysis and decision sciences, as well as through human-driven analysis based on management by exceptions or by objectives, to generate actionable knowledge. The tools that enable the transformation of raw data to actionable predictive insights are collectively referred as Decision Support tools. This chapter presents a new formalization of the decision process, leading to a new Decision Superiority model, partially motivated by the Joint Directors of Laboratories (JDL) Data Fusion Model. In addition, it examines the growing importance of Data Fusion concepts.

Unified communication and standardized data exchange for underground mines; Einheitliche, standardisierte Kommunikation zur Effizienzsteigerung von Bergwerken

Energy Technology Data Exchange (ETDEWEB)

Mueller, C. [Embigence GmbH, Ladbergen (Germany)

2006-11-07

Communication in many forms during the past ten years has changed our daily life: Cellphone technology as well as the Internet are just two examples. In the same way, modern network based communication now starts to change the way how underground mines are run: The lack of proper communication traditionally caused downtime and production loss. In the future, mine communication will be crucial for efficiency and profitability of underground operations. This enables intelligent machines to be used like e.g. a machine server equipped drill rig at LKAB or highly advanced, networked monorail systems at DSK. Standardized communication and information exchange is a basis for using three dimensional visualization tools to support decision finding. The paper explains these visions and goals for the future and explains the feasibility by two running example applications. (orig.)

Privacy-Preserving Restricted Boltzmann Machine

Directory of Open Access Journals (Sweden)

Yu Li

2014-01-01

Full Text Available 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.

The influence of the waterjet propulsion system on the ships' energy consumption and emissions inventories.

Science.gov (United States)

Durán-Grados, Vanesa; Mejías, Javier; Musina, Liliya; Moreno-Gutiérrez, Juan

2018-08-01

In this study we consider the problems associated with calculating ships' energy and emission inventories. Various related uncertainties are described in many similar studies published in the last decade, and applying to Europe, the USA and Canada. However, none of them have taken into account the performance of ships' propulsion systems. On the one hand, when a ship uses its propellers, there is no unanimous agreement on the equations used to calculate the main engines load factor and, on the other, the performance of waterjet propulsion systems (for which this variable depends on the speed of the ship) has not been taken into account in any previous studies. This paper proposes that the efficiency of the propulsion system should be included as a new parameter in the equation that defines the actual power delivered by a ship's main engines, as applied to calculate energy consumption and emissions in maritime transport. To highlight the influence of the propulsion system on calculated energy consumption and emissions, the bottom-up method has been applied using data from eight fast ferries operating across the Strait of Gibraltar over the course of one year. This study shows that the uncertainty about the efficiency of the propulsion system should be added as one more uncertainty in the energy and emission inventories for maritime transport as currently prepared. After comparing four methods for this calculation, the authors propose a new method for eight cases. For the calculation of the Main Engine's fuel oil consumption, differences up to 22% between some methods were obtained at low loads. Copyright © 2018 Elsevier B.V. All rights reserved.

Differential Privacy and Machine Learning: a Survey and Review

OpenAIRE

Ji, Zhanglong; Lipton, Zachary C.; Elkan, Charles

2014-01-01

The objective of machine learning is to extract useful information from data, while privacy is preserved by concealing information. Thus it seems hard to reconcile these competing interests. However, they frequently must be balanced when mining sensitive data. For example, medical research represents an important application where it is necessary both to extract useful information and protect patient privacy. One way to resolve the conflict is to extract general characteristics of whole popul...

Throughput centered prioritization of machines in transfer lines

Energy Technology Data Exchange (ETDEWEB)

Pascual, R., E-mail: rpascual@ing.puc.cl [Physical Asset Management Lab, Centro de Mineria, Pontificia Universidad Catolica de Chile, Av. Vicuna Mackenna 4860, Santiago (Chile); Godoy, D. [Physical Asset Management Lab, Centro de Mineria, Pontificia Universidad Catolica de Chile, Av. Vicuna Mackenna 4860, Santiago (Chile); Louit, D.M. [Komatsu Chile S.A., Av. Americo Vespucio 0631, Quilicura, Santiago (Chile)

2011-10-15

In an environment of scarce resources and complex production systems, prioritizing is key to confront the challenge of managing physical assets. In the literature, there exist a number of techniques to prioritize maintenance decisions that consider safety, technical and business perspectives. However, the effect of risk mitigating elements-such as intermediate buffers in production lines-on prioritization has not yet been investigated in depth. In this line, the work proposes a user-friendly graphical technique called the system efficiency influence diagram (SEID). Asset managers may use SEID to identify machines that have a greater impact on the system throughput, and thus set prioritized maintenance policies and/or redesign of buffers capacities. The tool provides insight to the analyst as it decomposes the influence of a given machine on the system throughput as a product of two elements: (1) system influence efficiency factor and (2) machine unavailability factor. We illustrate its applicability using three case studies: a four-machine transfer line, a vehicle assembly line, and an open-pit mining conveyor system. The results confirm that the machines with greater unavailability factors are not necessarily the most important for the efficiency of the production line, as it is the case when no intermediate buffers exist. As a decision aid tool, SEID emphasizes the need to move from a maintenance vision focused on machine availability, to a systems engineering perspective. - Highlights: > We propose a graphical technique to prioritize machines in production lines. > The tool is called 'system efficiency influence diagram' (SEID). > It helps setting prioritized maintenance policies and/or redesign of buffers. > The SEID technique focuses on system efficiency and throughput. > We illustrate its applicability using three case studies.

Data Mining Methods for Recommender Systems

Science.gov (United States)

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.

A comparison of machine learning techniques for predicting downstream acid mine drainage

CSIR Research Space (South Africa)

van Zyl, TL

2014-07-01

Full Text Available windowing approach over historical values to generate a prediction for the current value. We evaluate a number of Machine Learning techniques as regressors including Support Vector Regression, Random Forests, Stochastic Gradient Decent Regression, Linear...

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

Science.gov (United States)

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)

Machine Learning in Nutritional Follow-up Research

Directory of Open Access Journals (Sweden)

Reis Rita

2017-12-01

Full Text Available Healthcare is one of the world’s fastest growing industries, having large volumes of data collected on a daily basis. It is generally perceived as being ‘information rich’ yet ‘knowledge poor’. Hidden relationships and valuable knowledge can be discovered in the collected data from the application of data mining techniques. These techniques are being increasingly implemented in healthcare organizations in order to respond to the needs of doctors in their daily decision-making activities. To help the decision-makers to take the best decision it is fundamental to develop a solution able to predict events before their occurrence. The aim of this project was to predict if a patient would need to be followed by a nutrition specialist, by combining a nutritional dataset with data mining classification techniques, using WEKA machine learning tools. The achieved results showed to be very promising, presenting accuracy around 91%, specificity around 97% and precision about 95%.

Mechatronics in the mining industry. Modelling of underground machines; Mechatronik im Bergbau. Modellbildung von Untertage-Maschinen

Energy Technology Data Exchange (ETDEWEB)

Bruckmann, Tobias; Brandt, Thorsten [mercatronics GmbH, Duisburg (Germany)

2009-12-17

The development of new functions for machines operating underground often requires a prolonged and cost-intensive test phase. Precisely the development of complex functions as occur in operating assistance systems, for example, is highly iterative. If a corresponding prototype is required for each iteration step of the development, the development costs will, of course, increase rapidly. Virtual prototypes and simulators based on mathematical models of the machine offer an alternative in this case. The article describes the same principles for modelling the kinematics of underground machines. (orig.)

Mining and mining authorities in Saarland 2016. Mining economy, mining technology, occupational safety, environmental protection, statistics, mining authority activities. Annual report

International Nuclear Information System (INIS)

2016-01-01

The annual report of the Saarland Upper Mining Authority provides an insight into the activities of mining authorities. Especially, the development of the black coal mining, safety and technology of mining as well as the correlation between mining and environment are stressed.

Data preparation for municipal virtual assistant using machine learning

OpenAIRE

Jovan, Leon Noe

2016-01-01

The main goal of this master’s thesis was to develop a procedure that will automate the construction of the knowledge base for a virtual assistant that answers questions about municipalities in Slovenia. The aim of the procedure is to replace or facilitate manual preparation of the virtual assistant's knowledge base. Theoretical backgrounds of different machine learning fields, such as multilabel classification, text mining and learning from weakly labeled data were examined to gain a better ...

Research on Health State Perception Algorithm of Mining Equipment Based on Frequency Closeness

Directory of Open Access Journals (Sweden)

Gang Wang

2014-06-01

Full Text Available The health state perception of mining equipment is intended to have an online real- time knowledge and analysis of the running conditions of large mining equipments. Due to its unknown failure mode, a challenge was raised to the traditional fault diagnosis of mining equipments. A health state perception algorithm of mining equipment was introduced in this paper, and through continuous sampling of the machine vibration data, the time-series data set was set up; subsequently, the mode set based on the frequency closeness was constructed by the d neighborhood method combined with the TSDM algorithm, thus the forecast method on the basis of the dual mode set was eventually formed. In the calculation of the frequency closeness, the Goertzel algorithm was introduced to effectively decrease the computation amount. It was indicated through the simulation test on the vibration data of the drum shaft base that the health state of the device could be effectively distinguished. The algorithm has been successfully applied to equipment monitoring in the Huoer Xinhe Coal Mine of Shanxi Coal Imp&Exp. Group Co., Ltd.

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

Science.gov (United States)

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.

On the classification techniques in data mining for microarray data classification

Science.gov (United States)

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.

Throughput centered prioritization of machines in transfer lines

International Nuclear Information System (INIS)

Pascual, R.; Godoy, D.; Louit, D.M.

2011-01-01

In an environment of scarce resources and complex production systems, prioritizing is key to confront the challenge of managing physical assets. In the literature, there exist a number of techniques to prioritize maintenance decisions that consider safety, technical and business perspectives. However, the effect of risk mitigating elements-such as intermediate buffers in production lines-on prioritization has not yet been investigated in depth. In this line, the work proposes a user-friendly graphical technique called the system efficiency influence diagram (SEID). Asset managers may use SEID to identify machines that have a greater impact on the system throughput, and thus set prioritized maintenance policies and/or redesign of buffers capacities. The tool provides insight to the analyst as it decomposes the influence of a given machine on the system throughput as a product of two elements: (1) system influence efficiency factor and (2) machine unavailability factor. We illustrate its applicability using three case studies: a four-machine transfer line, a vehicle assembly line, and an open-pit mining conveyor system. The results confirm that the machines with greater unavailability factors are not necessarily the most important for the efficiency of the production line, as it is the case when no intermediate buffers exist. As a decision aid tool, SEID emphasizes the need to move from a maintenance vision focused on machine availability, to a systems engineering perspective. - Highlights: → We propose a graphical technique to prioritize machines in production lines. → The tool is called 'system efficiency influence diagram' (SEID). → It helps setting prioritized maintenance policies and/or redesign of buffers. → The SEID technique focuses on system efficiency and throughput. → We illustrate its applicability using three case studies.

Evaluating operational efficiency of drainage holes in the Belchatow coal mine

Energy Technology Data Exchange (ETDEWEB)

Marek, A.; Paluch, W.

1979-03-01

This paper characterizes drainage holes used for lowering water level in the Belchatow brown coal surface mine in central Poland. Machines and installations used for drilling holes, and filter construction are described. Two types of filters are evaluated, one based an a steel construction, the other an a concrete- asbestos construction. The problem of evaluating operational efficiency of drainage holes is discussed. Yield of the well is presented as the factor characterizing operational efficiency of the hole. Factors influencing yield of the well are described. The proposed analysis of drainage hole efficiency makes it possible to compare efficiency of work of the filters with steel construction and asbestos-concrete construction. Under conditions of the Belchatow mine the asbestos-concrete filters are more efficient than steel filters. All drainage holes at the mine are characterized by declining efficiency. This can be caused prematurely by silting up. (2 refs.) (In Polish)

Pattern recognition algorithms for data mining scalability, knowledge discovery and soft granular computing

CERN Document Server

Pal, Sankar K

2004-01-01

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks.Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.

Some Considerations about Modern Database Machines

Directory of Open Access Journals (Sweden)

Manole VELICANU

2010-01-01

Full Text Available Optimizing the two computing resources of any computing system - time and space - has al-ways been one of the priority objectives of any database. A current and effective solution in this respect is the computer database. Optimizing computer applications by means of database machines has been a steady preoccupation of researchers since the late seventies. Several information technologies have revolutionized the present information framework. Out of these, those which have brought a major contribution to the optimization of the databases are: efficient handling of large volumes of data (Data Warehouse, Data Mining, OLAP – On Line Analytical Processing, the improvement of DBMS – Database Management Systems facilities through the integration of the new technologies, the dramatic increase in computing power and the efficient use of it (computer networks, massive parallel computing, Grid Computing and so on. All these information technologies, and others, have favored the resumption of the research on database machines and the obtaining in the last few years of some very good practical results, as far as the optimization of the computing resources is concerned.

Study of Environmental Data Complexity using Extreme Learning Machine

Science.gov (United States)

Leuenberger, Michael; Kanevski, Mikhail

2017-04-01

The main goals of environmental data science using machine learning algorithm deal, in a broad sense, around the calibration, the prediction and the visualization of hidden relationship between input and output variables. In order to optimize the models and to understand the phenomenon under study, the characterization of the complexity (at different levels) should be taken into account. Therefore, the identification of the linear or non-linear behavior between input and output variables adds valuable information for the knowledge of the phenomenon complexity. The present research highlights and investigates the different issues that can occur when identifying the complexity (linear/non-linear) of environmental data using machine learning algorithm. In particular, the main attention is paid to the description of a self-consistent methodology for the use of Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. By applying two ELM models (with linear and non-linear activation functions) and by comparing their efficiency, quantification of the linearity can be evaluated. The considered approach is accompanied by simulated and real high dimensional and multivariate data case studies. In conclusion, the current challenges and future development in complexity quantification using environmental data mining are discussed. References - 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.

Reliability analysis of mining equipment: A case study of a crushing plant at Jajarm Bauxite Mine in Iran

International Nuclear Information System (INIS)

Barabady, Javad; Kumar, Uday

2008-01-01

The performance of mining machines depends on the reliability of the equipment used, the operating environment, the maintenance efficiency, the operation process, the technical expertise of the miners, etc. As the size and complexity of mining equipments continue to increase, the implications of equipment failure become ever more critical. Therefore, reliability analysis is required to identify the bottlenecks in the system and to find the components or subsystems with low reliability for a given designed performance. It is important to select a suitable method for data collection as well as for reliability analysis. This paper presents a case study describing reliability and availability analysis of the crushing plant number 3 at Jajarm Bauxite Mine in Iran. In this study, the crushing plant number 3 is divided into six subsystems. The parameters of some probability distributions, such as Weibull, Exponential, and Lognormal distributions have been estimated by using ReliaSoft's Weibull++6 software. The results of the analysis show that the conveyer subsystem and secondary screen subsystem are critical from a reliability point of view, and the secondary crusher subsystem and conveyer subsystem are critical from an availability point of view. The study also shows that the reliability analysis is very useful for deciding maintenance intervals

Mine Water Treatment in Hongai Coal Mines

Science.gov (United States)

Dang, Phuong Thao; Dang, Vu Chi

2018-03-01

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

Mine Water Treatment in Hongai Coal Mines

OpenAIRE

Dang Phuong Thao; Dang Vu Chi

2018-01-01

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

PMLB: a large benchmark suite for machine learning evaluation and comparison.

Science.gov (United States)

Olson, Randal S; La Cava, William; Orzechowski, Patryk; Urbanowicz, Ryan J; Moore, Jason H

2017-01-01

The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. From this study, we find that existing benchmarks lack the diversity to properly benchmark machine learning algorithms, and there are several gaps in benchmarking problems that still need to be considered. This work represents another important step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.

Mine Water Treatment in Hongai Coal Mines

Directory of Open Access Journals (Sweden)

Dang Phuong Thao

2018-01-01

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

The role of waste sorting in the South African gold-mining industry

International Nuclear Information System (INIS)

Freer, J.S.; Boehme, R.C.

1985-01-01

The absolute potential for sorting waste from run-of-mine Witwatersrand gold ores normally lies between 60 and 90 per cent by mass. At present, the practical potential lies between 40 and 50 per cent. Yet few mines achieve a waste rejection of even 30 per cent. The average waste rejection for industry, including underground sorting, fell from 19,6 per cent in 1959 to 10,1 per cent in 1983, as industry moved from labour-intensive, multistage comminution, incorporating washing, screening, and sorting, to single-stage run-of-mine milling. Most of the sorting is still being done by hand; yet photometric and radiometric sorting machines of high capacity are available. More recently, a sorter based on neutron activation and the subsequent isomeric radioactive decay of gold itself was designed. This paper examines the case for an increased role for sorting in the South African gold-mining industry brought about by the increasing cost of power for milling and the possibility of extracting gold from low-grade reject fractions by heap leaching

Feasibility of a continuous surface mining machine using impact breakers. First quarterly report, October 1-December 31, 1979

Energy Technology Data Exchange (ETDEWEB)

Fisk, A. T.

1980-01-01

This is the first quarterly report on the efforts to evaluate the feasibility of excavating coal and overburden from surface mines using impact breakers. The initial stages of the project are devoted to a literature search, equipment selection, test site selection, and conceptual test system design. Hence, this report details the progress made in these areas; the next quarter will see the finalization of Phase I. Included as appendices to this report are FMA internal reports on the individual mines visited. These reports are the basis of the test site selection, and have been censored here to remove data the mine operators deemed as confidential.

Application of data mining techniques for nuclear data and instrumentation

International Nuclear Information System (INIS)

Toshniwal, Durga

2013-01-01

Data mining is defined as the discovery of previously unknown, valid, novel, potentially useful, and understandable patterns in large databases. It encompasses many different techniques and algorithms which differ in the kinds of data that can be analyzed and the form of knowledge representation used to convey the discovered knowledge. Patterns in the data can be represented in many different forms, including classification rules, association rules, clusters, etc. Data mining thus deals with the discovery of hidden trends and patterns from large quantities of data. The field of data mining is emerging as a new, fundamental research area with important applications to science, engineering, medicine, business, and education. It is an interdisciplinary research area and draws upon several roots, including database systems, machine learning, information systems, statistics and expert systems. Data mining, when performed on time series data, is known as time series data mining (TSDM). A time series is a sequence of real numbers, each number representing a value at a point of time. During the past few years, there has been an explosion of research in the area of time series data mining. This includes attempts to model time series data, to design languages to query such data, and to develop access structures to efficiently process queries on such data. Time series data arises naturally in many real-world applications. Efficient discovery of knowledge through time series data mining can be helpful in several domains such as: Stock market analysis, Weather forecasting etc. An important application area of data mining techniques is in nuclear power plant and related data. Nuclear power plant data can be represented in form of time sequences. Often it may be of prime importance to analyze such data to find trends and anomalies. The general goals of data mining include feature extraction, similarity search, clustering and classification, association rule mining and anomaly

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Prediction of tunnel boring machine performance using machine and rock mass data

International Nuclear Information System (INIS)

Dastgir, G.

2012-01-01

Performance of the tunnel boring machine and its prediction by different methods has been a hot issue since the first TBM came into being. For the sake of safe and sound transport, improvement of hydro-power, mining, civil and many other tunneling projects that cannot be driven efficiently and economically by conventional drill and blast, TBMs are quite frequently used. TBM parameters and rock mass properties, which heavily influence machine performance, should be estimated or known before choice of TBM-type and start of excavation. By applying linear regression analysis (SPSS19), fuzzy logic tools and a special Math-Lab code on actual field data collected from seven TBM driven tunnels (Hieflau expansion, Queen water tunnel, Vereina, Hemerwald, Maen, Pieve and Varzo tunnel), an attempt was made to provide prediction of rock mass class (RMC), rock fracture class (RFC), penetration rate (PR) and advance rate (AR). For detailed analysis of TBM performance, machine parameters (thrust, machine rpm, torque, power etc.), machine types and specification and rock mass properties (UCS, discontinuity in rock mass, RMC, RFC, RMR, etc.) were analyzed by 3-D surface plotting using statistical software R. Correlations between machine parameters and rock mass properties which effectively influence prediction models, are presented as well. In Hieflau expansion tunnel AR linearly decreases with increase of thrust due to high dependence of machine advance rate upon rock strength. For Hieflau expansion tunnel three types of data (TBM, rock mass and seismic data e.g. amplitude, pseudo velocity etc.) were coupled and simultaneously analyzed by plotting 3-D surfaces. No appreciable correlation between seismic data (Amplitude and Pseudo velocity) and rock mass properties and machine parameters could be found. Tool wear as a function of TBM operational parameters was analyzed which revealed that tool wear is minimum if applied thrust is moderate and that tool wear is high when thrust is

Special method of coal winning in Hambach open-cast mine in 1987 using large machines in tandem operation

Energy Technology Data Exchange (ETDEWEB)

Krug, M; Muellensiefen, K

1988-05-01

In 1987 an additional 1,75 Mt coal, which were only overlain by slight layers of overburden, were won from the marginal batter system at Hambach open-cast mine by using a bucket wheel dredger with a daily output of 240 000 m/sup 3/ (solid) and at the same time bringing up the inside dump. During this working method the dredger and the spreader operated with a common belt conveyor in so-called tandem operation. Thanks to the efficient co-operation of the head planning department, field mining department, opencast mine surveying department and rock and soil mechanics department of the head office of the Rheinische Braunkohlenwerke AG, this special coal winning operation could be completed successfully.

International mining forum 2004, new technologies in underground mining, safety in mines proceedings

Energy Technology Data Exchange (ETDEWEB)

Jerzy Kicki; Eugeniusz Sobczyk (eds.)

2004-01-15

The book comprises technical papers that were presented at the International Mining Forum 2004. This event aims to bring together scientists and engineers in mining, rock mechanics, and computer engineering, with a view to explore and discuss international developments in the field. Topics discussed in this book are: trends in the mining industry; new solutions and tendencies in underground mines; rock engineering problems in underground mines; utilization and exploitation of methane; prevention measures for the control of rock bursts in Polish mines; and current problems in Ukrainian coal mines.

Development of opencast mining technology during the last decade. El desarrollo de la tecnologia de la mineria de superficie de la ultima decada

Energy Technology Data Exchange (ETDEWEB)

Van Leyen, H. (Rheinbraun Engineering und Wasser GmbH, Koeln (Germany). Departamento de Ingernieria Mecancia)

1992-03-01

This review only covers the development of equipment and technology and is limited to those projects involving bucket wheel excavators, including compact conveyors for use with bucket wheel excavators, main and intermediate conveyors, overburden stripping systems for continuous mining operations, cutting and trenching equipment, crushing and conveying systems for hard rock mining, aspects of excavator and dumper systems, surface winning machines, draglines and special developments in the field of materials handling and storage, blasting and other deep-mining operations. 24 refs., 19 figs., 1 tab.

Influence of ergonomic design on trackless mining machines on the health and safety of the operators, drivers and workers.

CSIR Research Space (South Africa)

Mason, S

1998-07-01

Full Text Available The project has produced information and methodologies for use by designers, mine managers and engineers to improve the health and safety associated with the use of trackless vehicles in mines. The project deliverables focus on assisting; designers...

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

Science.gov (United States)

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.

Quality of research results in agro-economy by data mining

Directory of Open Access Journals (Sweden)

Vukelić Gordana

2015-01-01

Full Text Available Data Mining (DM through data in agroeconomy is a scientific method that enables researchers not to go through set research scenarioes that are predetermined assumptions and hypotheses on the basis of insignificant atributes. On the contrary, by data mining detection of these atributes is made possible, in general, those hiden facts that enable setting a hypothesis. The DM method does this by an iterative way, including key atributes and factors and their influence on the quality of agro-resources. The research was conducted on a random sample, by analyzing the quality of eggs. The research subject is the posibility of classifying and predicting significant variablesatributes that determine the level of egg quality. The research starts from the use of Data Mining, as an area of machine studies, which significantly helps researchers in optimizing research. The applied methodology during research includes analyticalsintetic procedures and methods of Data Mining, with a special focus on using Supervised linear discrimination analysis and the Decision Tree. The results indicate significant posibilities of using DM as an additional analytical procedure in performing agroresearch and it can be concluded that it contributes to an improvement in effectiveness and validity of process in performing these researches.

Production and repair of metal supports as an indispensable activity of the Georgi Dimitrov mining and power combine

Energy Technology Data Exchange (ETDEWEB)

Mladenov, O

1979-07-01

Georgi Dimitrov underground mines have favoured metal supports over concrete slabs and timber since 1972 because of their well known advantages and because metal supports lend themselves to easy handling by 4-PU combines and 1PNB-2 loading machines. To eliminate bottlenecks and high costs of procurement from a central base individual mines were charged with production of their own metal supports. This resulted in some new developments, for example, in the production of supports with a 3.16 times greater capacity in the Marshall Tolbukhin and Al. Milenov mines in 1978. Hydraulic presses are generally used to produce conventional arch and ring type supports, and the Polish make PHPG-100 press is used for repairs. Decentralization also caused problems: different length timber and metal supports often necessitate additional cutting operations, a multitude of machines cause increased manual handling, and equipment is too often adapted to special requirements of individual shifts. However, costs of metal supports have dropped about 15%. Further improvement would require that the production of metal supports be centralized for the entire combine, supports be used according to their strength, and screw joinings be replaced with cotter type fastenings.

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

Science.gov (United States)

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.

Analysis of machining and machine tools

CERN Document Server

Liang, Steven Y

2016-01-01

This book delivers the fundamental science and mechanics of machining and machine tools by presenting systematic and quantitative knowledge in the form of process mechanics and physics. It gives readers a solid command of machining science and engineering, and familiarizes them with the geometry and functionality requirements of creating parts and components in today’s markets. The authors address traditional machining topics, such as: single and multiple point cutting processes grinding components accuracy and metrology shear stress in cutting cutting temperature and analysis chatter They also address non-traditional machining, such as: electrical discharge machining electrochemical machining laser and electron beam machining A chapter on biomedical machining is also included. This book is appropriate for advanced undergraduate and graduate mechani cal engineering students, manufacturing engineers, and researchers. Each chapter contains examples, exercises and their solutions, and homework problems that re...

Sustainable Mining Environment: Technical Review of Post-mining Plans

Directory of Open Access Journals (Sweden)

Restu Juniah

2017-12-01

Full Text Available The mining industry exists because humans need mining commodities to meet their daily needs such as motor vehicles, mobile phones, electronic equipment and others. Mining commodities as mentioned in Government Regulation No. 23 of 2010 on Implementation of Mineral and Coal Mining Business Activities are radioactive minerals, metal minerals, nonmetallic minerals, rocks and coal. Mineral and coal mining is conducted to obtain the mining commodities through production operations. Mining and coal mining companies have an obligation to ensure that the mining environment in particular after the post production operation or post mining continues. The survey research aims to examine technically the post-mining plan in coal mining of PT Samantaka Batubara in Indragiri Hulu Regency of Riau Province towards the sustainability of the mining environment. The results indicate that the post-mining plan of PT Samantaka Batubara has met the technical aspects required in post mining planning for a sustainable mining environment. Postponement of post-mining land of PT Samantaka Batubara for garden and forest zone. The results of this study are expected to be useful and can be used by stakeholders, academics, researchers, practitioners and associations of mining, and the environment.

Dynamic aspects of design and maintenance of the rotating machinery applied in the mining industry

Directory of Open Access Journals (Sweden)

Szolc Tomasz

2017-01-01

Full Text Available In the paper a dynamic behaviour of the selected typical group of rotating machines applied in the mining industry is investigated. These are the beater mills and crushers as well as blowers and compressors, all driven by the asynchronous motors. In particular, there is considered an influence of various types of machine working tool loadings on the system lateral steady-state dynamic responses as well as a mutual torsional electromechanical interaction between the driving motor and the driven machine in transient operational conditions. The theoretical calculations have been performed by means of the advanced structural mechanical models. The conclusions drawn from computational results can be very useful during design phase of these devices as well as helpful for their users during regular maintenance.

Contract Mining versus Owner Mining

African Journals Online (AJOL)

Owner

mining companies can concentrate on their core businesses while using specialists for ... 2 Definition of Contract and Owner. Mining ... equipment maintenance, scheduling and budgeting ..... No. Region. Amount Spent on. Contract Mining. ($ billion). Percent of. Total. 1 ... cost and productivity data based on a large range.

Optimization of mining design of Hongwei uranium mine

International Nuclear Information System (INIS)

Wu Sanmao; Yuan Baixiang

2012-01-01

Combined with the mining conditions of Hongwei uranium mine, optimization schemes for hoisting cage, mine drainge,ore transport, mine wastewater treatment, power-supply system,etc are put forward in the mining design of the mine. Optimized effects are analyzed from the aspects of technique, economy, and energy saving and reducing emissions. (authors)

Mining for Strategic Competitive Intelligence Foundations and Applications

CERN Document Server

Ziegler, Cai-Nicolas

2012-01-01

The textbook at hand aims to provide an introduction to the use of automated methods for gathering strategic competitive intelligence. Hereby, the text does not describe a singleton research discipline in its own right, such as machine learning or Web mining. It rather contemplates an application scenario, namely the gathering of knowledge that appears of paramount importance to organizations, e.g., companies and corporations. To this end, the book first summarizes the range of research disciplines that contribute to addressing the issue, extracting from each those grains that are of utmost relevance to the depicted application scope. Moreover, the book presents systems that put these techniques to practical use (e.g., reputation monitoring platforms) and takes an inductive approach to define the gestalt of mining for competitive strategic intelligence by selecting major use cases that are laid out and explained in detail. These pieces form the first part of the book. Each of those use cases is backed by a nu...

One method for life time estimation of a bucket wheel machine for coal moving

Science.gov (United States)

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.

Surface Mines, Other - Longwall Mining Panels

Data.gov (United States)

NSGIC Education | GIS Inventory — Coal mining has occurred in Pennsylvania for over a century. A method of coal mining known as Longwall Mining has become more prevalent in recent decades. Longwall...

Study on friction behaviour of brake shoe materials for mining hoist

Science.gov (United States)

Ungureanu, M.; Ungureanu, N. S.; Crăciun, I.

2017-02-01

The friction coefficient in the brake linkages has an important influence on the braking efficiency and safety of machines. The paper presents a method for the study of the friction coefficient of the friction couple brake shoe-drum for mining hoist. In this context, it is interesting to define the friction coefficient, not just according to the materials in contact, but according to the entire ensemble of tribological factors of the friction couple.

Application for trackless mining technique in Benxi uranium mine

International Nuclear Information System (INIS)

Chen Bingguo

1998-01-01

The author narrates the circumstances achieving constructional target in Benxi Uranium Mine under relying on advance of science and technology and adopting small trackless mining equipment, presents the application of trackless mining equipment at mining small mine and complex mineral deposit and discusses the unique superiority of trackless mining technique in development work, mining preparation work and backstoping

Mining engineer requirements in a German coal mine

Energy Technology Data Exchange (ETDEWEB)

Rauhut, F J

1985-10-01

Basic developments in German coal mines, new definitions of working areas of mining engineers, and groups of requirements in education are discussed. These groups include: requirements of hard-coal mining at great depth and in extended collieries; application of process technology and information systems in semi-automated mines; thinking in processes and systems; organizational changes; future requirements of mining engineers; responsibility of the mining engineer for employees and society.

Mining Social Media and DBpedia Data Using Gephi and R

Directory of Open Access Journals (Sweden)

Sadiq HUSSAIN

2018-04-01

Full Text Available The big data is playing a big role in the field of machine learning and data mining. To extract meaningful and interesting information from big data mining is a challenge. The size of the data at social media and Wikipedia are increasing exponentially. To visualize such huge data is another aspect of big data. The roles of graphs are becoming important in case of visualization and modelling of such data. Gephi and R are two important visualization and exploration tools in this field. Using graph, one may find and calculate modularity, eccentricity, Indegree, Outdegree, betweenness centrality etc. In this paper, we had used Dbpedia, facebook and twitter datasets. We had used Gephi and R to look inside the structure of such data and comparing different statistics based on the graph by exploring the graphs.

Geminivirus data warehouse: a database enriched with machine learning approaches.

Science.gov (United States)

Silva, Jose Cleydson F; Carvalho, Thales F M; Basso, Marcos F; Deguchi, Michihito; Pereira, Welison A; Sobrinho, Roberto R; Vidigal, Pedro M P; Brustolini, Otávio J B; Silva, Fabyano F; Dal-Bianco, Maximiller; Fontes, Renildes L F; Santos, Anésia A; Zerbini, Francisco Murilo; Cerqueira, Fabio R; Fontes, Elizabeth P B

2017-05-05

The Geminiviridae family encompasses a group of single-stranded DNA viruses with twinned and quasi-isometric virions, which infect a wide range of dicotyledonous and monocotyledonous plants and are responsible for significant economic losses worldwide. Geminiviruses are divided into nine genera, according to their insect vector, host range, genome organization, and phylogeny reconstruction. Using rolling-circle amplification approaches along with high-throughput sequencing technologies, thousands of full-length geminivirus and satellite genome sequences were amplified and have become available in public databases. As a consequence, many important challenges have emerged, namely, how to classify, store, and analyze massive datasets as well as how to extract information or new knowledge. Data mining approaches, mainly supported by machine learning (ML) techniques, are a natural means for high-throughput data analysis in the context of genomics, transcriptomics, proteomics, and metabolomics. Here, we describe the development of a data warehouse enriched with ML approaches, designated geminivirus.org. We implemented search modules, bioinformatics tools, and ML methods to retrieve high precision information, demarcate species, and create classifiers for genera and open reading frames (ORFs) of geminivirus genomes. The use of data mining techniques such as ETL (Extract, Transform, Load) to feed our database, as well as algorithms based on machine learning for knowledge extraction, allowed us to obtain a database with quality data and suitable tools for bioinformatics analysis. The Geminivirus Data Warehouse (geminivirus.org) offers a simple and user-friendly environment for information retrieval and knowledge discovery related to geminiviruses.

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

Science.gov (United States)

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.

Mining Together : Large-Scale Mining Meets Artisanal Mining, A Guide for Action

OpenAIRE

World Bank

2009-01-01

The present guide mining together-when large-scale mining meets artisanal mining is an important step to better understanding the conflict dynamics and underlying issues between large-scale and small-scale mining. This guide for action not only points to some of the challenges that both parties need to deal with in order to build a more constructive relationship, but most importantly it sh...

Respirable quartz hazard associated with coal mine roof bolter dust

International Nuclear Information System (INIS)

Joy, G.J.; Beck, T.W.; Listak, J.M.

2010-01-01

Pneumoconiosis has been reported to be increasing among underground coal miners in the Southern Appalachian Region. The National Institute for Occupational Safety and Health conducted a study to examine the particle size distribution and quartz content of dust generated by the installation of roof bolts in mines. Forty-six bulk samples of roof bolting machine pre-cleaner cyclone dump dust and collector box dust were collected from 26 underground coal mines. Real-time and integrated airborne respirable dust concentrations were measured on 3 mining sections in 2 mines. The real-time airborne dust concentrations profiles were examined to identify any concentration changes that might be associated with pre-cleaner cyclone dust discharge events. The study showed that bolter dust is a potential inhalation hazard due to the fraction of dust less than 10 μm in size, and the quartz content of the dust. The pre-cleaner cyclone dust was significantly larger than the collector box dust, indicating that the pre-cleaner functioned properly in removing the larger dust size fraction from the airstream. However, the pre-cleaner dust still contained a substantial amount of respirable dust. It was concluded that in order to maintain the effectiveness of a roof bolter dust collector, periodic removal of dust is required. Appropriate work procedures and equipment are necessary to minimize exposure during this cleaning task. 13 refs., 3 tabs., 2 figs.

Machine rates for selected forest harvesting machines

Science.gov (United States)

R.W. Brinker; J. Kinard; Robert Rummer; B. Lanford

2002-01-01

Very little new literature has been published on the subject of machine rates and machine cost analysis since 1989 when the Alabama Agricultural Experiment Station Circular 296, Machine Rates for Selected Forest Harvesting Machines, was originally published. Many machines discussed in the original publication have undergone substantial changes in various aspects, not...

Data mining for the identification of metabolic syndrome status.

Science.gov (United States)

Worachartcheewan, Apilak; Schaduangrat, Nalini; Prachayasittikul, Virapong; Nantasenamat, Chanin

2018-01-01

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS.

Data mining for the identification of metabolic syndrome status

Science.gov (United States)

Worachartcheewan, Apilak; Schaduangrat, Nalini; Prachayasittikul, Virapong; Nantasenamat, Chanin

2018-01-01

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS. PMID:29383020

Ensemble Data Mining Methods

Science.gov (United States)

Oza, Nikunj C.

2004-01-01

Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

data mining in distributed database

International Nuclear Information System (INIS)

Ghunaim, A.A.A.

2007-01-01

as we march into the age of digital information, the collection and the storage of large quantities of data is increased, and the problem of data overload looms ominously ahead. it is estimated today that the volume of data stored by a company doubles every year but the amount of meaningful information is decreases rapidly. the ability to analyze and understand massive datasets lags far behind the ability to gather and store the data. the unbridled growth of data will inevitably lead to a situation in which it is increasingly difficult to access the desired information; it will always be like looking for a needle in a haystack, and where only the amount of hay will be growing all the time . so, a new generation of computational techniques and tools is required to analyze and understand the rapidly growing volumes of data . and, because the information technology (it) has become a strategic weapon in the modern life, it is needed to use a new decision support tools to be an international powerful competitor.data mining is one of these tools and its methods make it possible to extract decisive knowledge needed by an enterprise and it means that it concerned with inferring models from data , including statistical pattern recognition, applied statistics, machine learning , and neural networks. data mining is a tool for increasing productivity of people trying to build predictive models. data mining techniques have been applied successfully to several real world problem domains; but the application in the nuclear reactors field has only little attention . one of the main reasons, is the difficulty in obtaining the data sets

Productivity Improvement in Underground Coal Mines - A Case Study

Directory of Open Access Journals (Sweden)

Devi Prasad Mishra

2013-01-01

Full Text Available Improvement of productivity has become an important goal for today's coal industry in the race to increase price competitiveness. The challenge now lying ahead for the coal industry is to identify areas of waste, meet the market price and maintain a healthy profit. The only way to achieve this is to reduce production costs by improving productivity, efficiency and the effectiveness of the equipment. This paper aims to identify the various factors and problems affecting the productivity of underground coal mines adopting the bord and pillar method of mining and to propose suitable measures for improving them. The various key factors affecting productivity, namely the cycle of operations, manpower deployment, machine efficiency, material handling and management of manpower are discussed. In addition, the problem of side discharge loader (SDL cable handling resulting in the wastage of precious manpower resources and SDL breakdown have also been identified and resolved in this paper.

Implementation of Paste Backfill Mining Technology in Chinese Coal Mines

Science.gov (United States)

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

Data Mining and Machine Learning Tools for Combinatorial Material Science of All-Oxide Photovoltaic Cells.

Science.gov (United States)

Yosipof, Abraham; Nahum, Oren E; Anderson, Assaf Y; Barad, Hannah-Noa; Zaban, Arie; Senderowitz, Hanoch

2015-06-01

Growth in energy demands, coupled with the need for clean energy, are likely to make solar cells an important part of future energy resources. In particular, cells entirely made of metal oxides (MOs) have the potential to provide clean and affordable energy if their power conversion efficiencies are improved. Such improvements require the development of new MOs which could benefit from combining combinatorial material sciences for producing solar cells libraries with data mining tools to direct synthesis efforts. In this work we developed a data mining workflow and applied it to the analysis of two recently reported solar cell libraries based on Titanium and Copper oxides. Our results demonstrate that QSAR models with good prediction statistics for multiple solar cells properties could be developed and that these models highlight important factors affecting these properties in accord with experimental findings. The resulting models are therefore suitable for designing better solar cells. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

SVM models for analysing the headstreams of mine water inrush

Energy Technology Data Exchange (ETDEWEB)

Yan Zhi-gang; Du Pei-jun; Guo Da-zhi [China University of Science and Technology, Xuzhou (China). School of Environmental Science and Spatial Informatics

2007-08-15

The support vector machine (SVM) model was introduced to analyse the headstrean of water inrush in a coal mine. The SVM model, based on a hydrogeochemical method, was constructed for recognising two kinds of headstreams and the H-SVMs model was constructed for recognising multi- headstreams. The SVM method was applied to analyse the conditions of two mixed headstreams and the value of the SVM decision function was investigated as a means of denoting the hydrogeochemical abnormality. The experimental results show that the SVM is based on a strict mathematical theory, has a simple structure and a good overall performance. Moreover the parameter W in the decision function can describe the weights of discrimination indices of the headstream of water inrush. The value of the decision function can denote hydrogeochemistry abnormality, which is significant in the prevention of water inrush in a coal mine. 9 refs., 1 fig., 7 tabs.

Self-powered remotely controlled machines and tools for safety improvement in mining

Energy Technology Data Exchange (ETDEWEB)

Mirzaeva, G. [University of Newcastle, Callaghan, NSW (Australia)

2005-07-01

This paper addresses the problem of meeting the safety requirements of mining industry for implementation of control and monitoring equipment without external wiring. Local power generation and accumulation combined with remote control and wireless data transmission are suggested as an appropriate way to make the implementation of such device safe and convenient, which in its turn would facilitate their wider application for automation and safety improvement. A rope shovel dipper trip system is discussed in detail as an example of a self-powered remotely-controlled system. Other possible applications of the concept are also identified, such as Armoured Face Conveyor (AFC) and water jet drilling operation monitoring. 5 refs., 6 figs.

EVALUATION OF ROOF BOLTING REQUIREMENTS BASED ON IN-MINE ROOF BOLTER DRILLING

Energy Technology Data Exchange (ETDEWEB)

Syd S. Peng

2005-04-15

In this quarter, the field, theoretical and programming works have been performed toward achieving the research goals set in the proposal. The main accomplishments in this quarter included: (1) one more field test has been conducted in an underground coal mine, (2) optimization studies of the control parameters have been conducted, (3) the relationship among feed pressure, penetration rate and rotation rate seems to be a good indicator for estimating rock strength when both penetration rate and rotation rate are controlled or kept constant, (4) the empirical equations for eliminating the machine effect on drilling parameters were developed and verified, and (5) a real time roof geology mapping system for roof bolters in limestone mine, including a special version of the geology mapping program and hardware, performs very well in underground production condition.

Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery.

Directory of Open Access Journals (Sweden)

Rubén Armañanzas

Full Text Available Epilepsy surgery is effective in reducing both the number and frequency of seizures, particularly in temporal lobe epilepsy (TLE. Nevertheless, a significant proportion of these patients continue suffering seizures after surgery. Here we used a machine learning approach to predict the outcome of epilepsy surgery based on supervised classification data mining taking into account not only the common clinical variables, but also pathological and neuropsychological evaluations. We have generated models capable of predicting whether a patient with TLE secondary to hippocampal sclerosis will fully recover from epilepsy or not. The machine learning analysis revealed that outcome could be predicted with an estimated accuracy of almost 90% using some clinical and neuropsychological features. Importantly, not all the features were needed to perform the prediction; some of them proved to be irrelevant to the prognosis. Personality style was found to be one of the key features to predict the outcome. Although we examined relatively few cases, findings were verified across all data, showing that the machine learning approach described in the present study may be a powerful method. Since neuropsychological assessment of epileptic patients is a standard protocol in the pre-surgical evaluation, we propose to include these specific psychological tests and machine learning tools to improve the selection of candidates for epilepsy surgery.

30 CFR 819.21 - Auger mining: Protection of underground mining.

Science.gov (United States)

2010-07-01

... 30 Mineral Resources 3 2010-07-01 2010-07-01 false Auger mining: Protection of underground mining. 819.21 Section 819.21 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT... STANDARDS-AUGER MINING § 819.21 Auger mining: Protection of underground mining. Auger holes shall not extend...

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

Science.gov (United States)

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.

[A new machinability test machine and the machinability of composite resins for core built-up].

Science.gov (United States)

Iwasaki, N

2001-06-01

A new machinability test machine especially for dental materials was contrived. The purpose of this study was to evaluate the effects of grinding conditions on machinability of core built-up resins using this machine, and to confirm the relationship between machinability and other properties of composite resins. The experimental machinability test machine consisted of a dental air-turbine handpiece, a control weight unit, a driving unit of the stage fixing the test specimen, and so on. The machinability was evaluated as the change in volume after grinding using a diamond point. Five kinds of core built-up resins and human teeth were used in this study. The machinabilities of these composite resins increased with an increasing load during grinding, and decreased with repeated grinding. There was no obvious correlation between the machinability and Vickers' hardness; however, a negative correlation was observed between machinability and scratch width.

Contract Mining versus Owner Mining – The Way Forward | Suglo ...

African Journals Online (AJOL)

Ghana Mining Journal ... By contracting out one or more of their mining operations, the mining companies can concentrate on their core businesses. This paper reviews ... The general trends in the mining industry show that contract mining will be the way forward for most mines under various circumstances in the future.

Selection of mining method for No.3 uranium ore body in the independent mining area at a uranium mine

International Nuclear Information System (INIS)

Ding Fulong; Ding Dexin; Ye Yongjun

2010-01-01

Mining operation in the existed mining area at a uranium mine is near completion and it is necessary to mine the No.3 uranium ore body in another mining area at the mine. This paper, based on the geological conditions, used analogical method for analyzing the feasible methods and the low cost and high efficiency mining method was suggested for the No.3 ore body in the independent mining area at the uranium mine. (authors)

Development of science and technology in underground coal mining in Czechoslovakia during the 7th 5 year plan

Energy Technology Data Exchange (ETDEWEB)

Klimek, M.

1982-01-01

Reviews main tasks of underground coal mining in Czechoslovakia from 1981 to 1985 in the following basins: Ostrava-Karvina, Kladno, Prievidza, Most and Sokolov. The planned increase of brown and black coal output in each of the basins is discussed. Selected problems associated with mining are evaluated: significant increase of mining depth, rock burst hazards, methane hazards and water influx in the Ostrava-Karvina basin. Investment program in the current 5 year plan as well as until the year 2000 is analyzed: sinking of 38.8 km of mine shafts and 4.4 km of blind shafts. Equipment for shaft sinking produced in the USA (by Robins the 241 SB-184) and in the USSR (the Uralmash Sk-1U system) is compared. Design and technical specifications of the two systems are given. Equipment for mine drivage is also reviewed. The following machines are described: the TVM-55H by Demag (FRG), the TBS V-600E/Sch by Wirth (FRG), the TBM ser. 18a781 by Robins (USA) and the MARK-18T by JARVA (USA). Selected types of powered supports which will be widely used in coal mines in the current 5 year plan are evaluated. Research programs in underground coal mining are reviewed (safety, mining thin coal seams, slice mining of thick coal seams in the Namurian B series, mining extremely thick seams with stowing of the top slice and mining with caving the 4.5 m thick bottom slice). (4 refs.) (In Czech)

Ensemble Methods in Data Mining Improving Accuracy Through Combining Predictions

CERN Document Server

Seni, Giovanni

2010-01-01

This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although e

Using of science technologies for mining machinery constructions' strength improvement

Science.gov (United States)

Yurchenko, E. V.; Mehtiev, A. D.; Yugai, V. V.; Bulatbayev, F. N.

2015-04-01

Recommendations for strengthening the brake construction in accident dangerous areas of fatigue destruction were developed. Computer modeling was made using the ANSYS program that helps to visualize stained condition of the construction for further practical testing of the strength and reliability improving technology of mining elevating machines' constructions, which are being in a long-term use, with a help of the strengthening elements. A way of construction strengthening, which eliminates the possibility of further fatigue destruction of the brake system elements, because of the load cycle in exploitation process.

Experimental use of road header (AM-50) as face cutting machine for extraction of coal in longwall panel

Energy Technology Data Exchange (ETDEWEB)

Passi, K.K.; Kumar, C.R.; Prasad, P. [DGMS, Dhanbad (India)

2001-07-01

The scope of this paper has been limited to the use of available machines and techniques for attaining higher and more efficient production in underground coal mines. Under certain conditions of strata and higher degree of gassiness, the longwall method with hydraulic sand stowing is the only appropriate method of work for extraction of thick seam. In Moonidih Jitpur Colliery of M/S IISCO, No. 14 seam, Degree III gassy seam, 9.07 m thick, is extracted in multilift system with hydraulic sand stowing. In general, the bottom lift is extracted by Single Ended Ranging Arm Shearer and the middle and top lift are extracted by conventional method. However, in one of the panels spare road header machine was used as face cutting machine in bottom lift, on an experimental basis. This paper presents a successful case study of extraction of bottom lift coal by the longwall method with hydraulic sand stowing using road header (AM 50) as the face cutting machines. 9 figs.

Combined data mining/NIR spectroscopy for purity assessment of lime juice

Science.gov (United States)

Shafiee, Sahameh; Minaei, Saeid

2018-06-01

This paper reports the data mining study on the NIR spectrum of lime juice samples to determine their purity (natural or synthetic). NIR spectra for 72 pure and synthetic lime juice samples were recorded in reflectance mode. Sample outliers were removed using PCA analysis. Different data mining techniques for feature selection (Genetic Algorithm (GA)) and classification (including the radial basis function (RBF) network, Support Vector Machine (SVM), and Random Forest (RF) tree) were employed. Based on the results, SVM proved to be the most accurate classifier as it achieved the highest accuracy (97%) using the raw spectrum information. The classifier accuracy dropped to 93% when selected feature vector by GA search method was applied as classifier input. It can be concluded that some relevant features which produce good performance with the SVM classifier are removed by feature selection. Also, reduced spectra using PCA do not show acceptable performance (total accuracy of 66% by RBFNN), which indicates that dimensional reduction methods such as PCA do not always lead to more accurate results. These findings demonstrate the potential of data mining combination with near-infrared spectroscopy for monitoring lime juice quality in terms of natural or synthetic nature.

Data Mining of the Thermal Performance of Cool-Pipes in Massive Concrete via In Situ Monitoring

Directory of Open Access Journals (Sweden)

Zheng Zuo

2014-01-01

Full Text Available Embedded cool-pipes are very important for massive concrete because their cooling effect can effectively avoid thermal cracks. In this study, a data mining approach to analyzing the thermal performance of cool-pipes via in situ monitoring is proposed. Delicate monitoring program is applied in a high arch dam project that provides a good and mass data source. The factors and relations related to the thermal performance of cool-pipes are obtained in a built theory thermal model. The supporting vector machine (SVM technology is applied to mine the data. The thermal performances of iron pipes and high-density polyethylene (HDPE pipes are compared. The data mining result shows that iron pipe has a better heat removal performance when flow rate is lower than 50 L/min. It has revealed that a turning flow rate exists for iron pipe which is 80 L/min. The prediction and classification results obtained from the data mining model agree well with the monitored data, which illustrates the validness of the approach.

Quantifying the Mechanical Properties of Materials and the Process of Elastic-Plastic Deformation under External Stress on Material

Directory of Open Access Journals (Sweden)

Jan Valíček

2015-11-01

Full Text Available The paper solves the problem of the nonexistence of a new method for calculation of dynamics of stress-deformation states of deformation tool-material systems including the construction of stress-strain diagrams. The presented solution focuses on explaining the mechanical behavior of materials after cutting by abrasive waterjet technology (AWJ, especially from the point of view of generated surface topography. AWJ is a flexible tool accurately responding to the mechanical resistance of the material according to the accurately determined shape and roughness of machined surfaces. From the surface topography, it is possible to resolve the transition from ideally elastic to quasi-elastic and plastic stress-strain states. For detecting the surface structure, an optical profilometer was used. Based on the analysis of experimental measurements and the results of analytical studies, a mathematical-physical model was created and an exact method of acquiring the equivalents of mechanical parameters from the topography of surfaces generated by abrasive waterjet cutting and external stress in general was determined. The results of the new approach to the construction of stress-strain diagrams are presented. The calculated values agreed very well with those obtained by a certified laboratory VÚHŽ.

Quantifying the Mechanical Properties of Materials and the Process of Elastic-Plastic Deformation under External Stress on Material

Science.gov (United States)

Valíček, Jan; Harničárová, Marta; Öchsner, Andreas; Hutyrová, Zuzana; Kušnerová, Milena; Tozan, Hakan; Michenka, Vít; Šepelák, Vladimír; Mitaľ, Dušan; Zajac, Jozef

2015-01-01

The paper solves the problem of the nonexistence of a new method for calculation of dynamics of stress-deformation states of deformation tool-material systems including the construction of stress-strain diagrams. The presented solution focuses on explaining the mechanical behavior of materials after cutting by abrasive waterjet technology (AWJ), especially from the point of view of generated surface topography. AWJ is a flexible tool accurately responding to the mechanical resistance of the material according to the accurately determined shape and roughness of machined surfaces. From the surface topography, it is possible to resolve the transition from ideally elastic to quasi-elastic and plastic stress-strain states. For detecting the surface structure, an optical profilometer was used. Based on the analysis of experimental measurements and the results of analytical studies, a mathematical-physical model was created and an exact method of acquiring the equivalents of mechanical parameters from the topography of surfaces generated by abrasive waterjet cutting and external stress in general was determined. The results of the new approach to the construction of stress-strain diagrams are presented. The calculated values agreed very well with those obtained by a certified laboratory VÚHŽ. PMID:28793645

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

Science.gov (United States)

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.

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

Science.gov (United States)

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

Data Mining for Imbalanced Datasets: An Overview

Science.gov (United States)

Chawla, Nitesh V.

A dataset is imbalanced if the classification categories are not approximately equally represented. Recent years brought increased interest in applying machine learning techniques to difficult "real-world" problems, many of which are characterized by imbalanced data. Additionally the distribution of the testing data may differ from that of the training data, and the true misclassification costs may be unknown at learning time. Predictive accuracy, a popular choice for evaluating performance of a classifier, might not be appropriate when the data is imbalanced and/or the costs of different errors vary markedly. In this Chapter, we discuss some of the sampling techniques used for balancing the datasets, and the performance measures more appropriate for mining imbalanced datasets.

Treatment of mine-water from decommissioning uranium mines

International Nuclear Information System (INIS)

Fan Quanhui

2002-01-01

Treatment methods for mine-water from decommissioning uranium mines are introduced and classified. The suggestions on optimal treatment methods are presented as a matter of experience with decommissioned Chenzhou Uranium Mine

Topic categorisation of statements in suicide notes with integrated rules and machine learning.

Science.gov (United States)

Kovačević, Aleksandar; Dehghan, Azad; Keane, John A; Nenadic, Goran

2012-01-01

We describe and evaluate an automated approach used as part of the i2b2 2011 challenge to identify and categorise statements in suicide notes into one of 15 topics, including Love, Guilt, Thankfulness, Hopelessness and Instructions. The approach combines a set of lexico-syntactic rules with a set of models derived by machine learning from a training dataset. The machine learning models rely on named entities, lexical, lexico-semantic and presentation features, as well as the rules that are applicable to a given statement. On a testing set of 300 suicide notes, the approach showed the overall best micro F-measure of up to 53.36%. The best precision achieved was 67.17% when only rules are used, whereas best recall of 50.57% was with integrated rules and machine learning. While some topics (eg, Sorrow, Anger, Blame) prove challenging, the performance for relatively frequent (eg, Love) and well-scoped categories (eg, Thankfulness) was comparatively higher (precision between 68% and 79%), suggesting that automated text mining approaches can be effective in topic categorisation of suicide notes.

The Machine within the Machine

CERN Multimedia

Katarina Anthony

2014-01-01

Although Virtual Machines are widespread across CERN, you probably won't have heard of them unless you work for an experiment. Virtual machines - known as VMs - allow you to create a separate machine within your own, allowing you to run Linux on your Mac, or Windows on your Linux - whatever combination you need.   Using a CERN Virtual Machine, a Linux analysis software runs on a Macbook. When it comes to LHC data, one of the primary issues collaborations face is the diversity of computing environments among collaborators spread across the world. What if an institute cannot run the analysis software because they use different operating systems? "That's where the CernVM project comes in," says Gerardo Ganis, PH-SFT staff member and leader of the CernVM project. "We were able to respond to experimentalists' concerns by providing a virtual machine package that could be used to run experiment software. This way, no matter what hardware they have ...

Practical data mining and machine learning for optics applications: introduction to the feature issue.

Science.gov (United States)

Abdulla, Ghaleb; Awwal, Abdul; Borne, Kirk; Ho, Tin Kam; Vestrand, W Thomas

2011-08-01

Data mining algorithms utilize search techniques to explore hidden patterns and correlations in the data, which otherwise require a tremendous amount of human time to explore. This feature issue explores the use of such techniques to help understand the data, build better simulators, explain outlier behavior, and build better predictive models. We hope that this issue will spur discussions and expose a set of tools that can be useful to the optics community.

A planetary nervous system for social mining and collective awareness

Science.gov (United States)

Giannotti, F.; Pedreschi, D.; Pentland, A.; Lukowicz, P.; Kossmann, D.; Crowley, J.; Helbing, D.

2012-11-01

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how

Efficient system modules to meet the communication requirements in the mining industry; Leistungsfaehige Systembausteine zur Erfuellung der Kommunikationsanforderungen des Bergbaus

Energy Technology Data Exchange (ETDEWEB)

Becker, F. [Becker Mining Systems GmbH, Friedrichsthal (Germany)

2006-11-07

Communication technology has become an important module of efficient operation of a mine. The exchange of information with technical aids takes place between man and machines as participants in communication. The diversity of the requirements associated with the need for communication in a mine can be mastered only by a wide portfolio of suitable technical components. In addition to the technical serviceability of the individual components the ergonomic handling and economic efficiency of the entire production operation must also be ensured. For this purpose it is necessary to design the individual technical modules in such a way that despite their different appearance they interact as a system and thus make available an integrated and transparent communication network to the mine. (orig.)

30 CFR 77.1712 - Reopening mines; notification; inspection prior to mining.

Science.gov (United States)

2010-07-01

... to mining. 77.1712 Section 77.1712 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION... prior to mining. Prior to reopening any surface coal mine after it has been abandoned or declared... an authorized representative of the Secretary before any mining operations in such mine are...

Fundamentals of using bio-diesel for operating large fleets of mining equipment and building machines and the experience gained so far

International Nuclear Information System (INIS)

Drebenstedt, C.; Jauer, J.

2008-01-01

Against the topical background of the finite reserves of fossil mineral oil as well as internationally available vegetable fat and oil resources, of the current developments in the field of the biodiesel production technology and of the international conditions for the reduction of CO 2 emissions, this paper is to examine, whether the suitability of bio-diesel for fuelling mining equipment has come true. The examination will focus on the biogenic fuel profile, on the organizational necessity to actively retrofit the machinery during operations as well as on the precise verification of the expected technical conversion problems and of the saving potentials actually achieved. The examination will be conducted in the world's first open-cast mine that has converted its entire fleet of equipment to be fuelled with bio-diesel. The open-cast mine is operated by the Ronneburg branch of Wismut GmbH, a company based in Germany (referred to hereinafter as the Lichtenberg open-cast mine). (orig.)

Text Mining to Support Gene Ontology Curation and Vice Versa.

Science.gov (United States)

Ruch, Patrick

2017-01-01

In this chapter, we explain how text mining can support the curation of molecular biology databases dealing with protein functions. We also show how curated data can play a disruptive role in the developments of text mining methods. We review a decade of efforts to improve the automatic assignment of Gene Ontology (GO) descriptors, the reference ontology for the characterization of genes and gene products. To illustrate the high potential of this approach, we compare the performances of an automatic text categorizer and show a large improvement of +225 % in both precision and recall on benchmarked data. We argue that automatic text categorization functions can ultimately be embedded into a Question-Answering (QA) system to answer questions related to protein functions. Because GO descriptors can be relatively long and specific, traditional QA systems cannot answer such questions. A new type of QA system, so-called Deep QA which uses machine learning methods trained with curated contents, is thus emerging. Finally, future advances of text mining instruments are directly dependent on the availability of high-quality annotated contents at every curation step. Databases workflows must start recording explicitly all the data they curate and ideally also some of the data they do not curate.

Acid mine drainage: mining and water pollution issues in British Columbia

Energy Technology Data Exchange (ETDEWEB)

NONE

1998-12-31

The importance of protecting water quality and some of the problems associated with mineral development are described. Negative impacts of mining operations such as sedimentation, water disturbances, and water pollution from waste rock and tailings are considered. Mining wastes, types of water pollution from mining, the legacy of acid mine drainage, predicting acid mine drainage, preventing and mitigating acid mine drainage, examples from the past, and cyanide heap-leaching are discussed. The real costs of mining at the Telkwa open pit coal mine are assessed. British Columbia mines that are known for or are potentially acid generating are shown on a map. 32 refs., 10 figs.

Testing of Alternative Abrasives for Water-Jet Cutting at C Tank Farm

Energy Technology Data Exchange (ETDEWEB)

Krogstad, Eirik J.

2013-08-01

Legacy waste from defense-related activities at the Hanford Site has predominantly been stored in underground tanks, some of which have leaked; others may be at risk to do so. The U.S. Department of Energy’s goal is to empty the tanks and transform their contents into more stable waste forms. To do so requires breaking up, and creating a slurry from, solid wastes in the bottoms of the tanks. A technology developed for this purpose is the Mobile Arm Retrieval System. This system is being used at some of the older single shell tanks at C tank farm. As originally planned, access ports for the Mobile Arm Retrieval System were to be cut using a high- pressure water-jet cutter. However, water alone was found to be insufficient to allow effective cutting of the steel-reinforced tank lids, especially when cutting the steel reinforcing bar (“rebar”). The abrasive added in cutting the hole in Tank C-107 was garnet, a complex natural aluminosilicate. The hardness of garnet (Mohs hardness ranging from H 6.5 to 7.5) exceeds that of solids currently in the tanks, and was regarded to be a threat to Hanford Waste Treatment and Immobilization Plant systems. Olivine, an iron-magnesium silicate that is nearly as hard as garnet (H 6.5 to 7), has been proposed as an alternative to garnet. Pacific Northwest National Laboratory proposed to test pyrite (FeS2), whose hardness is slightly less (H 6 to 6.5) for 1) cutting effectiveness, and 2) propensity to dissolve (or disintegrate by chemical reaction) in chemical conditions similar to those of tank waste solutions. Cutting experiments were conducted using an air abrader system and a National Institute of Standards and Technology Standard Reference Material (SRM 1767 Low Alloy Steel), which was used as a surrogate for rebar. The cutting efficacy of pyrite was compared with that of garnet and olivine in identical size fractions. Garnet was found to be most effective in removing steel from the target; olivine and pyrite were less

Responsible Mining: A Human Resources Strategy for Mine Development Project

OpenAIRE

Sampathkumar, Sriram (Ram)

2012-01-01

Mining is a global industry. Most mining companies operate internationally, often in remote, challenging environments and consequently frequently have respond to unusual and demanding Human Resource (HR) requirements. It is my opinion that the strategic imperative behind success in mining industry is responsible mining. The purpose of this paper is to examine how an effective HR strategy can be a competitive advantage that contributes to the success of a mining project in the global mining in...

Effect of Machining Velocity in Nanoscale Machining Operations

International Nuclear Information System (INIS)

Islam, Sumaiya; Khondoker, Noman; Ibrahim, Raafat

2015-01-01

The aim of this study is to investigate the generated forces and deformations of single crystal Cu with (100), (110) and (111) crystallographic orientations at nanoscale machining operation. A nanoindenter equipped with nanoscratching attachment was used for machining operations and in-situ observation of a nano scale groove. As a machining parameter, the machining velocity was varied to measure the normal and cutting forces. At a fixed machining velocity, different levels of normal and cutting forces were generated due to different crystallographic orientations of the specimens. Moreover, after machining operation percentage of elastic recovery was measured and it was found that both the elastic and plastic deformations were responsible for producing a nano scale groove within the range of machining velocities from 250-1000 nm/s. (paper)

Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems.

Science.gov (United States)

Fallah, Mina; Niakan Kalhori, Sharareh R

2017-10-01

Smartphones represent a promising technology for patient-centered healthcare. It is claimed that data mining techniques have improved mobile apps to address patients' needs at subgroup and individual levels. This study reviewed the current literature regarding data mining applications in patient-centered mobile-based information systems. We systematically searched PubMed, Scopus, and Web of Science for original studies reported from 2014 to 2016. After screening 226 records at the title/abstract level, the full texts of 92 relevant papers were retrieved and checked against inclusion criteria. Finally, 30 papers were included in this study and reviewed. Data mining techniques have been reported in development of mobile health apps for three main purposes: data analysis for follow-up and monitoring, early diagnosis and detection for screening purpose, classification/prediction of outcomes, and risk calculation (n = 27); data collection (n = 3); and provision of recommendations (n = 2). The most accurate and frequently applied data mining method was support vector machine; however, decision tree has shown superior performance to enhance mobile apps applied for patients' self-management. Embedded data-mining-based feature in mobile apps, such as case detection, prediction/classification, risk estimation, or collection of patient data, particularly during self-management, would save, apply, and analyze patient data during and after care. More intelligent methods, such as artificial neural networks, fuzzy logic, and genetic algorithms, and even the hybrid methods may result in more patients-centered recommendations, providing education, guidance, alerts, and awareness of personalized output.

Scalable Frequent Subgraph Mining

KAUST Repository

Abdelhamid, Ehab

2017-06-19

A graph is a data structure that contains a set of nodes and a set of edges connecting these nodes. Nodes represent objects while edges model relationships among these objects. Graphs are used in various domains due to their ability to model complex relations among several objects. Given an input graph, the Frequent Subgraph Mining (FSM) task finds all subgraphs with frequencies exceeding a given threshold. FSM is crucial for graph analysis, and it is an essential building block in a variety of applications, such as graph clustering and indexing. FSM is computationally expensive, and its existing solutions are extremely slow. Consequently, these solutions are incapable of mining modern large graphs. This slowness is caused by the underlying approaches of these solutions which require finding and storing an excessive amount of subgraph matches. This dissertation proposes a scalable solution for FSM that avoids the limitations of previous work. This solution is composed of four components. The first component is a single-threaded technique which, for each candidate subgraph, needs to find only a minimal number of matches. The second component is a scalable parallel FSM technique that utilizes a novel two-phase approach. The first phase quickly builds an approximate search space, which is then used by the second phase to optimize and balance the workload of the FSM task. The third component focuses on accelerating frequency evaluation, which is a critical step in FSM. To do so, a machine learning model is employed to predict the type of each graph node, and accordingly, an optimized method is selected to evaluate that node. The fourth component focuses on mining dynamic graphs, such as social networks. To this end, an incremental index is maintained during the dynamic updates. Only this index is processed and updated for the majority of graph updates. Consequently, search space is significantly pruned and efficiency is improved. The empirical evaluation shows that the

Operational reliability of the GPK and the 4PP-2 heading machines

Energy Technology Data Exchange (ETDEWEB)

Ivanov, N.A.; Demchenko, N.T.

1985-09-01

Reliability of the GPK and 4PP-2 heading machines used in 98 development workings in 40 coal mines is analyzed. Failure analysis was based on records of 199 heading machines after overhauls. The mean-time-between-failures of the GPK and the 4PP-2 was 270 min (93 t) and 155 min (35 t), availability coefficient was 0.83 and 0.77 respectively. Reliability of the GPK on the average was higher than that of the 4PP-2. The mean-time-to-overhaul of the GPK was 17.7 months (54,000 t), of the 4PP-2 - 18 months (59,000 t). Time between overhauls in the case of the GPK was 14 months (47,200 t), in the case of the 4PP-2 it was 14.5 months (38,300 t). During the 17.7 months between overhauls the GPK failed 580 times, the repair operations lasted 530 h. During the 18 month time to overhaul the 4PP-2 failed 1600 times, the repair operations lasted 1300 h. Reliability of major elements of the 2 heading machines is analyzed: cutters, materials handling equipment, electrical equipment, hydraulic systems, dust suppression systems, etc.

Prediction of Machine Tool Condition Using Support Vector Machine

International Nuclear Information System (INIS)

Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

2011-01-01

Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

MONITORING OF MINING

Directory of Open Access Journals (Sweden)

Berislav Šebečić

1996-12-01

Full Text Available The way mining was monitored in the past depended on knowledge, interest and the existing legal regulations. Documentary evidence about this work can be found in archives, libraries and museums. In particular, there is the rich archival material (papers and books concerning the work of the one-time Imperial and Royal Mining Captaincies in Zagreb, Zadar, Klagenfurt and Split, A minor part of the documentation has not yet been transferred to Croatia. From mining handbooks and books we can also find out about mining in Croatia. In the context of Austro-Hungary. For example, we can find out that the first governorships in Zagreb and Zadar headed the Ban, Count Jelacic and Baron Mamula were also the top mining authorities, though this, probably from political motives, was suppressed in the guides and inventories or the Mining Captaincies. At the end of the 1850s, Croatia produced 92-94% of sea salt, up to 8.5% of sulphur, 19.5% of asphalt and 100% of oil for the Austro-Hungarian empire. From data about mining in the Split Mining Captaincy, prepared for the Philadephia Exhibition, it can be seen that in the exploratory mining operations in which there were 33,372 independent mines declared in 1925 they were looking mainly for bauxite (60,0%, then dark coal (19,0%, asphalts (10.3% and lignites (62%. In 1931, within the area covered by the same captaincy, of 74 declared mines, only 9 were working. There were five coal mines, three bauxite mines and one for asphalt. I suggest that within state institution, the Mining Captaincy or Authority be renewed, or that a Mining and Geological Authority be set ap, which would lead to the more complete affirmation of Croatian mining (the paper is published in Croatian.

Environmentally Friendly Machining

CERN Document Server

Dixit, U S; Davim, J Paulo

2012-01-01

Environment-Friendly Machining provides an in-depth overview of environmentally-friendly machining processes, covering numerous different types of machining in order to identify which practice is the most environmentally sustainable. The book discusses three systems at length: machining with minimal cutting fluid, air-cooled machining and dry machining. Also covered is a way to conserve energy during machining processes, along with useful data and detailed descriptions for developing and utilizing the most efficient modern machining tools. Researchers and engineers looking for sustainable machining solutions will find Environment-Friendly Machining to be a useful volume.

Gold-Mining

DEFF Research Database (Denmark)

Raaballe, J.; Grundy, B.D.

2002-01-01

  Based on standard option pricing arguments and assumptions (including no convenience yield and sustainable property rights), we will not observe operating gold mines. We find that asymmetric information on the reserves in the gold mine is a necessary and sufficient condition for the existence...... of operating gold mines. Asymmetric information on the reserves in the mine implies that, at a high enough price of gold, the manager of high type finds the extraction value of the company to be higher than the current market value of the non-operating gold mine. Due to this under valuation the maxim of market...

The mining methods at the Fraisse mine

International Nuclear Information System (INIS)

Heurley, P.; Vervialle, J.P.

1985-01-01

The Fraisse mine is one of the four underground mines of the La Crouzille mining divisions of Cogema. Faced with the necessity to mechanize its workings, this mine also had to satisfy a certain number of stringent demands. This has led to concept of four different mining methods for the four workings at present in active operation at this pit, which nevertheless preserve the basic ideas of the methods of top slicing under concrete slabs (TSS) or horizontal cut-and-fill stopes (CFS). An electric scooptram is utilized. With this type of vehicle the stringent demands for the introduction of means for fire fighting and prevention are reduced to a minimum. Finally, the dimensions of the vehicles and the operation of these methods result in a net-to-gross tonnages of close to 1, i.e. a maximum output, combined with a minimum of contamination [fr

ANALYSIS OF THE POSSIBILITY OF INTEGRATING A MINING RIGHT-ANGLE PLANETARY GEARBOX WITH TECHNICAL DIAGNOSTICS SYSTEMS

Directory of Open Access Journals (Sweden)

Andrzej WIECZOREK

2016-12-01

Full Text Available A key factor enabling the achievement of the required capacity by longwall mining systems is to obtain a satisfactory service life for individual components of such systems. Such components include right-angle planetary gearboxes for armoured face conveyors. An increase in the service life of such equipment can be achieved by ensuring adequacy in terms of design, materials and organization. As a part of organizational changes, the use of individual diagnostics systems may have the greatest impact on the service life of mining gearboxes; however, their widespread implementation is limited by economic and operational barriers. This paper presents an analysis of the possibility of integrating mining gearboxes with electronic systems of technical diagnostics, as well as expanding the scope of the technical condition monitoring by the machines operating together with these gearboxes. As a result of the calculation and design work performed, it has been demonstrated that it is possible to integrate technical diagnostics systems with advanced data transmission capabilities inside gearboxes.

Hygienic evaluation of new technology for control of methane and dust in coal mines

Energy Technology Data Exchange (ETDEWEB)

Gadzhiev, G P; Deynega, V G; Sukhanov, V V; Levshina, I M; Yarym, N T; Petrenko, G A

1977-07-01

Exploitation of available new technology for mining is hindered by the dangers of gas evolution, and the need for maintenance of hygienic standards. The Moscow Mining Institute has developed, and proposed for industrial introduction, a new process for control of methane and dust in mine shafts; the method will help to raise significantly the productivity of excavating machines in high gas factor shafts. The process to combat methane and dust consists essentially in drilling boreholes from the side of the gallery, or from the outer surface, into the coal-bearing stratum. These boreholes are injected with a 24% solution of urea-formaldehyde resin (binder M/sub 2/), with 1% solution of ammonium chloride hardener. After several months this plastic is removed. The new technology involves the escape of toxic substances into the air, hence the need for hygienic testing. Additional study must estimate the danger of accidents, e.g., shaft fires, toxicity of combustion products of coal or binder. Study is also needed on pathologies which might occur to miners engaged in removal of the plastic with the new technology.

Recovery and recycling of aluminum, copper, and precious metals from dismantled weapon components

International Nuclear Information System (INIS)

Gundiler, I.H.; Lutz, J.D.; Wheelis, W.T.

1994-01-01

Sandia National Laboratories (SNL) is tasked to support The Department of Energy in the dismantlement and disposal of SNL designed weapon components. These components are sealed in a potting compound, and contain heavy metals, explosive, radioactive, and toxic materials. SNL developed a process to identify and remove the hazardous sub-components utilizing real-time radiography and abrasive water-jet cutting. The components were then crushed, granulated, screened, and separated into an aluminum and a precious-and-base-metals fraction using air-tables. Plastics were further cleaned for disposal as non-hazardous waste. New Mexico Bureau of Mines and Mineral Resources assisted SNL in investigation of size-reduction and separation technologies

Mined-out land

International Nuclear Information System (INIS)

Reinsalu, Enno; Toomik, Arvi; Valgma, Ingo

2002-01-01

Estonian mineral resources are deposited in low depth and mining fields are large, therefore vast areas are affected by mining. There are at least 800 deposits with total area of 6,000 km 2 and about the same number of underground mines, surface mines, peat fields, quarries, and sand and gravel pits. The deposits cover more than 10% of Estonian mainland. The total area of operating mine claims exceeds 150 km 2 that makes 0.3 % of Estonian area. The book is written mainly for the people who are living or acting in the area influenced by mining. The observations and research could benefit those who are interested in geography and environment, who follow formation and look of mined-out landscapes. The book contains also warnings for careless people on and under the surface of the mined-out land. Part of the book contains results of the research made in 1968-1993 by the first two authors working at the Estonian branch of A.Skochinsky Institute of Mining. Since 1990, Arvi Toomik continued this study at the Northeastern section of the Institute of Ecology of Tallinn Pedagogical University. Enno Reinsalu studied aftereffects of mining at the Mining Department of Tallinn Technical University from 1998 to 2000. Geographical Information System for Mining was studied by Ingo Valgma within his doctoral dissertation, and this book is one of the applications of his study

Data mining and education.

Science.gov (United States)

Koedinger, Kenneth R; D'Mello, Sidney; McLaughlin, Elizabeth A; Pardos, Zachary A; Rosé, Carolyn P

2015-01-01

An emerging field of educational data mining (EDM) is building on and contributing to a wide variety of disciplines through analysis of data coming from various educational technologies. EDM researchers are addressing questions of cognition, metacognition, motivation, affect, language, social discourse, etc. using data from intelligent tutoring systems, massive open online courses, educational games and simulations, and discussion forums. The data include detailed action and timing logs of student interactions in user interfaces such as graded responses to questions or essays, steps in rich problem solving environments, games or simulations, discussion forum posts, or chat dialogs. They might also include external sensors such as eye tracking, facial expression, body movement, etc. We review how EDM has addressed the research questions that surround the psychology of learning with an emphasis on assessment, transfer of learning and model discovery, the role of affect, motivation and metacognition on learning, and analysis of language data and collaborative learning. For example, we discuss (1) how different statistical assessment methods were used in a data mining competition to improve prediction of student responses to intelligent tutor tasks, (2) how better cognitive models can be discovered from data and used to improve instruction, (3) how data-driven models of student affect can be used to focus discussion in a dialog-based tutoring system, and (4) how machine learning techniques applied to discussion data can be used to produce automated agents that support student learning as they collaborate in a chat room or a discussion board. © 2015 John Wiley & Sons, Ltd.

Trust Mines

Science.gov (United States)

The United States and the Navajo Nation entered into settlement agreements that provide funds to conduct investigations and any needed cleanup at 16 of the 46 priority mines, including six mines in the Northern Abandoned Uranium Mine Region.

Inverse Analysis and Modeling for Tunneling Thrust on Shield Machine

Directory of Open Access Journals (Sweden)

Qian Zhang

2013-01-01

Full Text Available With the rapid development of sensor and detection technologies, measured data analysis plays an increasingly important role in the design and control of heavy engineering equipment. The paper proposed a method for inverse analysis and modeling based on mass on-site measured data, in which dimensional analysis and data mining techniques were combined. The method was applied to the modeling of the tunneling thrust on shield machines and an explicit expression for thrust prediction was established. Combined with on-site data from a tunneling project in China, the inverse identification of model coefficients was carried out using the multiple regression method. The model residual was analyzed by statistical methods. By comparing the on-site data and the model predicted results in the other two projects with different tunneling conditions, the feasibility of the model was discussed. The work may provide a scientific basis for the rational design and control of shield tunneling machines and also a new way for mass on-site data analysis of complex engineering systems with nonlinear, multivariable, time-varying characteristics.

Lunabotics Mining Competition: Inspiration Through Accomplishment

Science.gov (United States)

Mueller, Robert P.

2011-01-01

NASA's Lunabotics Mining Competition is designed to promote the development of interest in space activities and STEM (Science, Technology, Engineering, and Mathematics) fields. The competition uses excavation, a necessary first step towards extracting resources from the regolith and building bases on the moon. The unique physical properties of lunar regolith and the reduced 1/6th gravity, vacuum environment make excavation a difficult technical challenge. Advances in lunar regolith mining have the potential to significantly contribute to our nation's space vision and NASA space exploration operations. The competition is conducted annually by NASA at the Kennedy Space Center Visitor Complex. The teams that can use telerobotic or autonomous operation to excavate a lunar regolith geotechnical simulant, herein after referred to as Black Point-1 (or BP-1) and score the most points (calculated as an average of two separate 10-minute timed competition attempts) will eam points towards the Joe Kosmo Award for Excellence and the scores will reflect ranking in the on-site mining category of the competition. The minimum excavation requirement is 10.0 kg during each competition attempt and the robotic excavator, referred to as the "Lunabot", must meet all specifications. This paper will review the achievements of the Lunabotics Mining Competition in 2010 and 2011, and present the new rules for 2012. By providing a framework for robotic design and fabrication, which culminates in a live competition event, university students have been able to produce sophisticated lunabots which are tele-operated. Multi-disciplinary teams are encouraged and the extreme sense of accomplishment provides a unique source of inspiration to the participating students, which has been shown to translate into increased interest in STEM careers. Our industrial sponsors (Caterpillar, Newmont Mining, Harris, Honeybee Robotics) have all stated that there is a strong need for skills in the workforce related

Data mining, mining data : energy consumption modelling

Energy Technology Data Exchange (ETDEWEB)

Dessureault, S. [Arizona Univ., Tucson, AZ (United States)

2007-09-15

Most modern mining operations are accumulating large amounts of data on production and business processes. Data, however, provides value only if it can be translated into information that appropriate users can utilize. This paper emphasized that a new technological focus should emerge, notably how to concentrate data into information; analyze information sufficiently to become knowledge; and, act on that knowledge. Researchers at the Mining Information Systems and Operations Management (MISOM) laboratory at the University of Arizona have created a method to transform data into action. The data-to-action approach was exercised in the development of an energy consumption model (ECM), in partnership with a major US-based copper mining company, 2 software companies, and the MISOM laboratory. The approach begins by integrating several key data sources using data warehousing techniques, and increasing the existing level of integration and data cleaning. An online analytical processing (OLAP) cube was also created to investigate the data and identify a subset of several million records. Data mining algorithms were applied using the information that was isolated by the OLAP cube. The data mining results showed that traditional cost drivers of energy consumption are poor predictors. A comparison was made between traditional methods of predicting energy consumption and the prediction formed using data mining. Traditionally, in the mines for which data were available, monthly averages of tons and distance are used to predict diesel fuel consumption. However, this article showed that new information technology can be used to incorporate many more variables into the budgeting process, resulting in more accurate predictions. The ECM helped mine planners improve the prediction of energy use through more data integration, measure development, and workflow analysis. 5 refs., 11 figs.

Mining with communities

International Nuclear Information System (INIS)

Veiga, Marcello M.; Scoble, Malcolm; McAllister, Mary Louise

2001-01-01

To be considered as sustainable, a mining community needs to adhere to the principles of ecological sustainability, economic vitality and social equity. These principles apply over a long time span, covering both the life of the mine and post-mining closure. The legacy left by a mine to the community after its closure is emerging as a significant aspect of its planning. Progress towards sustainability is made when value is added to a community with respect to these principles by the mining operation during its life cycle. This article presents a series of cases to demonstrate the diverse potential challenges to achieving a sustainable mining community. These case studies of both new and old mining communities are drawn mainly from Canada and from locations abroad where Canadian companies are now building mines. The article concludes by considering various approaches that can foster sustainable mining communities and the role of community consultation and capacity building. (author)

Ideate about building green mine of uranium mining and metallurgy

International Nuclear Information System (INIS)

Shi Zuyuan

2012-01-01

Analysing the current situation of uranium mining and metallurgy; Setting up goals for green uranium mining and metallurgy, its fundamental conditions, Contents and measures. Putting forward an idea to combine green uranium mining and metallurgy with the state target for green mining, and keeping its own characteristics. (author)

Social big data mining

CERN Document Server

Ishikawa, Hiroshi

2015-01-01

Social Media. Big Data and Social Data. Hypotheses in the Era of Big Data. Social Big Data Applications. Basic Concepts in Data Mining. Association Rule Mining. Clustering. Classification. Prediction. Web Structure Mining. Web Content Mining. Web Access Log Mining, Information Extraction and Deep Web Mining. Media Mining. Scalability and Outlier Detection.

Mining and mining authorities in Saarland 2016. Mining economy, mining technology, occupational safety, environmental protection, statistics, mining authority activities. Annual report; Bergbau und Bergbehoerden im Saarland 2016. Bergwirtschaft, Bergtechnik, Arbeitsschutz, Umweltschutz, Statistiken, Taetigkeiten der Bergbehoerden. Jahresbericht

Energy Technology Data Exchange (ETDEWEB)

NONE

2016-07-01

The annual report of the Saarland Upper Mining Authority provides an insight into the activities of mining authorities. Especially, the development of the black coal mining, safety and technology of mining as well as the correlation between mining and environment are stressed.

Hybrid machining processes perspectives on machining and finishing

CERN Document Server

Gupta, Kapil; Laubscher, R F

2016-01-01

This book describes various hybrid machining and finishing processes. It gives a critical review of the past work based on them as well as the current trends and research directions. For each hybrid machining process presented, the authors list the method of material removal, machining system, process variables and applications. This book provides a deep understanding of the need, application and mechanism of hybrid machining processes.

Mine water treatment

Energy Technology Data Exchange (ETDEWEB)

Komissarov, S V

1980-10-01

This article discusses composition of chemical compounds dissolved or suspended in mine waters in various coal basins of the USSR: Moscow basin, Kuzbass, Pechora, Kizelovsk, Karaganda, Donetsk and Chelyabinsk basins. Percentage of suspended materials in water depending on water source (water from water drainage system of dust suppression system) is evaluated. Pollution of mine waters with oils and coli bacteria is also described. Recommendations on construction, capacity of water settling tanks, and methods of mine water treatment are presented. In mines where coal seams 2 m or thicker are mined a system of two settling tanks should be used: in the upper one large grains are settled, in the lower one finer grains. The upper tank should be large enough to store mine water discharged during one month, and the lower one to store water discharged over two months. Salty waters from coal mines mining thin coal seams should be treated in a system of water reservoirs from which water evaporates (if climatic conditions permit). Mine waters from mines with thin coal seams but without high salt content can be treated in a system of long channels with water plants, which increase amount of oxygen in treated water. System of biological treatment of waste waters from mine wash-houses and baths is also described. Influence of temperature, sunshine and season of the year on efficiency of mine water treatment is also assessed. (In Russian)

Uranium mining

International Nuclear Information System (INIS)

2008-01-01

Full text: The economic and environmental sustainability of uranium mining has been analysed by Monash University researcher Dr Gavin Mudd in a paper that challenges the perception that uranium mining is an 'infinite quality source' that provides solutions to the world's demand for energy. Dr Mudd says information on the uranium industry touted by politicians and mining companies is not necessarily inaccurate, but it does not tell the whole story, being often just an average snapshot of the costs of uranium mining today without reflecting the escalating costs associated with the process in years to come. 'From a sustainability perspective, it is critical to evaluate accurately the true lifecycle costs of all forms of electricity production, especially with respect to greenhouse emissions, ' he says. 'For nuclear power, a significant proportion of greenhouse emissions are derived from the fuel supply, including uranium mining, milling, enrichment and fuel manufacture.' Dr Mudd found that financial and environmental costs escalate dramatically as the uranium ore is used. The deeper the mining process required to extract the ore, the higher the cost for mining companies, the greater the impact on the environment and the more resources needed to obtain the product. I t is clear that there is a strong sensitivity of energy and water consumption and greenhouse emissions to ore grade, and that ore grades are likely to continue to decline gradually in the medium to long term. These issues are critical to the current debate over nuclear power and greenhouse emissions, especially with respect to ascribing sustainability to such activities as uranium mining and milling. For example, mining at Roxby Downs is responsible for the emission of over one million tonnes of greenhouse gases per year and this could increase to four million tonnes if the mine is expanded.'

Internet technologies in the mining industry. Towards unattended mining systems

Energy Technology Data Exchange (ETDEWEB)

Krzykawski, Michal [FAMUR Group, Katowice (Poland)

2009-08-27

Global suppliers of longwall systems focus mainly on maximising the efficiency of the equipment they manufacture. Given the fact that, since 2004, coal demand on world markets has been constantly on the increase, even during an economic downturn, this endeavour seems fully justified. However, it should be remembered that maximum efficiency must be accompanied by maximum safety of all underground operations. This statement is based on the belief that the mining industry, which exploits increasingly deep and dangerous coal beds, faces the necessity to implement comprehensive IT systems for managing all mining processes and, in the near future, to use unmanned mining systems, fully controllable from the mine surface. The computerisation of mines is an indispensable element of the development of the world mining industry, a belief which has been put into practice with e-mine, developed by the FAMUR Group. (orig.)

Identification of Village Building via Google Earth Images and Supervised Machine Learning Methods

Directory of Open Access Journals (Sweden)

Zhiling Guo

2016-03-01

Full Text Available In this study, a method based on supervised machine learning is proposed to identify village buildings from open high-resolution remote sensing images. We select Google Earth (GE RGB images to perform the classification in order to examine its suitability for village mapping, and investigate the feasibility of using machine learning methods to provide automatic classification in such fields. By analyzing the characteristics of GE images, we design different features on the basis of two kinds of supervised machine learning methods for classification: adaptive boosting (AdaBoost and convolutional neural networks (CNN. To recognize village buildings via their color and texture information, the RGB color features and a large number of Haar-like features in a local window are utilized in the AdaBoost method; with multilayer trained networks based on gradient descent algorithms and back propagation, CNN perform the identification by mining deeper information from buildings and their neighborhood. Experimental results from the testing area at Savannakhet province in Laos show that our proposed AdaBoost method achieves an overall accuracy of 96.22% and the CNN method is also competitive with an overall accuracy of 96.30%.

Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.

Science.gov (United States)

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.

Knowledge Discovery and Data Mining in Iran's Climatic Researches

Science.gov (United States)

Karimi, Mostafa

2013-04-01

Advances in measurement technology and data collection is the database gets larger. Large databases require powerful tools for analysis data. Iterative process of acquiring knowledge from information obtained from data processing is done in various forms in all scientific fields. However, when the data volume large, and many of the problems the Traditional methods cannot respond. in the recent years, use of databases in various scientific fields, especially atmospheric databases in climatology expanded. in addition, increases in the amount of data generated by the climate models is a challenge for analysis of it for extraction of hidden pattern and knowledge. The approach to this problem has been made in recent years uses the process of knowledge discovery and data mining techniques with the use of the concepts of machine learning, artificial intelligence and expert (professional) systems is overall performance. Data manning is analytically process for manning in massive volume data. The ultimate goal of data mining is access to information and finally knowledge. climatology is a part of science that uses variety and massive volume data. Goal of the climate data manning is Achieve to information from variety and massive atmospheric and non-atmospheric data. in fact, Knowledge Discovery performs these activities in a logical and predetermined and almost automatic process. The goal of this research is study of uses knowledge Discovery and data mining technique in Iranian climate research. For Achieve This goal, study content (descriptive) analysis and classify base method and issue. The result shown that in climatic research of Iran most clustering, k-means and wards applied and in terms of issues precipitation and atmospheric circulation patterns most introduced. Although several studies in geography and climate issues with statistical techniques such as clustering and pattern extraction is done, Due to the nature of statistics and data mining, but cannot say for

Requirements and opportunities for mining engineers in the mining industry abroad

Energy Technology Data Exchange (ETDEWEB)

Albrecht, E

1987-04-09

The decline of the German mining industry and the increasing industrialization of mining is forcing ever greater numbers of young German mining graduates to build their careers abroad. The requirements for this - apart from the technical qualifications are a good knowledge of foreign languages and a readiness to leave Germany for a long time, even for ever. If the young mining graduate accepts these conditions, numerous professional opportunities will open up for him, both with German mining companies with interests abroad, in mining supply companies and consultancy firms and with foreign companies. 6 references.

Object and operation supported maintenance for mining equipment

Directory of Open Access Journals (Sweden)

Walter Bartelmus

2014-09-01

Full Text Available The paper aroused in answer to discussion in Mining Magazine (MM September 2011 and July/August 2013. The paper shows that discussion given in the MM issue July/August 2013 does not fulfill expectations expressed in MM issue 2011. The presented paper is the review on maintenance that is based on condition monitoring as tool for detection of faults and failure prevention. Fault and failure are regarded as inevitable during the machine operation as the process of wear and the process of degradation. The question is, if one can influence the wear and degradation process, using condition monitoring. The paper will present technology (in reference to cited papers which demonstrates that the use of the proper method can influence the wear and machine degradation process, using proper condition monitoring techniques and knowing scenarios of wear and degradation process, the maintenance can be rationalized. The presented paper shows possible improvements which are needed to fulfill expectations expressed in MM September 2011 and they are not taken into consideration in MM July/August 3013. These improvements can be fulfilling on the bases of object and operation supported maintenance.

Diagnostic information system dynamics in the evaluation of machine learning algorithms for the supervision of energy efficiency of district heating-supplied buildings

International Nuclear Information System (INIS)

Kiluk, Sebastian

2017-01-01

Highlights: • Energy efficiency classification sustainability benefits from knowledge prediction. • Diagnostic classification can be validated with its dynamics and current data. • Diagnostic classification dynamics provides novelty extraction for knowledge update. • Data mining comparison can be performed with knowledge dynamics and uncertainty. • Diagnostic information refinement benefits form comparing classifiers dynamics. - Abstract: Modern ways of exploring the diagnostic knowledge provided by data mining and machine learning raise some concern about the ways of evaluating the quality of output knowledge, usually represented by information systems. Especially in district heating, the stationarity of efficiency models, and thus the relevance of diagnostic classification system, cannot be ensured due to the impact of social, economic or technological changes, which are hard to identify or predict. Therefore, data mining and machine learning have become an attractive strategy for automatically and continuously absorbing such dynamics. This paper presents a new method of evaluation and comparison of diagnostic information systems gathered algorithmically in district heating efficiency supervision based on exploring the evolution of information system and analyzing its dynamic features. The process of data mining and knowledge discovery was applied to the data acquired from district heating substations’ energy meters to provide the automated discovery of diagnostic knowledge base necessary for the efficiency supervision of district heating-supplied buildings. The implemented algorithm consists of several steps of processing the billing data, including preparation, segmentation, aggregation and knowledge discovery stage, where classes of abstract models representing energy efficiency constitute an information system representing diagnostic knowledge about the energy efficiency of buildings favorably operating under similar climate conditions and

Mining

Directory of Open Access Journals (Sweden)

Khairullah Khan

2014-09-01

Full Text Available Opinion mining is an interesting area of research because of its applications in various fields. Collecting opinions of people about products and about social and political events and problems through the Web is becoming increasingly popular every day. The opinions of users are helpful for the public and for stakeholders when making certain decisions. Opinion mining is a way to retrieve information through search engines, Web blogs and social networks. Because of the huge number of reviews in the form of unstructured text, it is impossible to summarize the information manually. Accordingly, efficient computational methods are needed for mining and summarizing the reviews from corpuses and Web documents. This study presents a systematic literature survey regarding the computational techniques, models and algorithms for mining opinion components from unstructured reviews.

PREVENTION OF ACID MINE DRAINAGE GENERATION FROM OPEN-PIT MINE HIGHWALLS

Science.gov (United States)

Exposed, open pit mine highwalls contribute significantly to the production of acid mine drainage (AMD) thus causing environmental concerns upon closure of an operating mine. Available information on the generation of AMD from open-pit mine highwalls is very limit...

A direction of developing a mining method and mining complexes

Energy Technology Data Exchange (ETDEWEB)

Gabov, V.V.; Efimov, I.A. [St. Petersburg State Mining Institute, St. Petersburg (Russian Federation). Vorkuta Branch

1996-12-31

The analyses of a mining method as a main factor determining the development stages of mining units is presented. The paper suggests a perspective mining method which differs from the known ones by following peculiarities: the direction selectivity of cuts with regard to coal seams structure; the cutting speed, thickness and succession of dusts. This method may be done by modulate complexes (a shield carrying a cutting head for coal mining), their mining devices being supplied with hydraulic drive. An experimental model of the module complex has been developed. 2 refs.

Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming.

Directory of Open Access Journals (Sweden)

Stephen Gang Wu

2016-04-01

Full Text Available 13C metabolic flux analysis (13C-MFA has been widely used to measure in vivo enzyme reaction rates (i.e., metabolic flux in microorganisms. Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux (http://mflux.org that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM, k-Nearest Neighbors (k-NN, and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on 13C-MFA are published for non-model species.

Data Mining Approaches for Landslide Susceptibility Mapping in Umyeonsan, Seoul, South Korea

Directory of Open Access Journals (Sweden)

Sunmin Lee

2017-07-01

Full Text Available The application of data mining models has become increasingly popular in recent years in assessments of a variety of natural hazards such as landslides and floods. Data mining techniques are useful for understanding the relationships between events and their influencing variables. Because landslides are influenced by a combination of factors including geomorphological and meteorological factors, data mining techniques are helpful in elucidating the mechanisms by which these complex factors affect landslide events. In this study, spatial data mining approaches based on data on landslide locations in the geographic information system environment were investigated. The topographical factors of slope, aspect, curvature, topographic wetness index, stream power index, slope length factor, standardized height, valley depth, and downslope distance gradient were determined using topographical maps. Additional soil and forest variables using information obtained from national soil and forest maps were also investigated. A total of 17 variables affecting the frequency of landslide occurrence were selected to construct a spatial database, and support vector machine (SVM and artificial neural network (ANN models were applied to predict landslide susceptibility from the selected factors. In the SVM model, linear, polynomial, radial base function, and sigmoid kernels were applied in sequence; the model yielded 72.41%, 72.83%, 77.17% and 72.79% accuracy, respectively. The ANN model yielded a validity accuracy of 78.41%. The results of this study are useful in guiding effective strategies for the prevention and management of landslides in urban areas.

Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data

Directory of Open Access Journals (Sweden)

Michael Veale

2017-11-01

Full Text Available Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining (DADM and fairness, accountability and transparency machine learning (FATML, their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data on sensitive attributes such as gender, ethnicity, sexuality or disability needed to diagnose and mitigate emergent indirect discrimination-by-proxy, such as redlining. Such organisations might also lack the knowledge and capacity to identify and manage fairness issues that are emergent properties of complex sociotechnical systems. This paper presents and discusses three potential approaches to deal with such knowledge and information deficits in the context of fairer machine learning. Trusted third parties could selectively store data necessary for performing discrimination discovery and incorporating fairness constraints into model-building in a privacy-preserving manner. Collaborative online platforms would allow diverse organisations to record, share and access contextual and experiential knowledge to promote fairness in machine learning systems. Finally, unsupervised learning and pedagogically interpretable algorithms might allow fairness hypotheses to be built for further selective testing and exploration. Real-world fairness challenges in machine learning are not abstract, constrained optimisation problems, but are institutionally and contextually grounded. Computational fairness tools are useful, but must be researched and developed in and with the messy contexts that will shape their deployment, rather than just for imagined situations. Not doing so risks real, near-term algorithmic harm.

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

Science.gov (United States)

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.

30 CFR 780.27 - Reclamation plan: Surface mining near underground mining.

Science.gov (United States)

2010-07-01

... 30 Mineral Resources 3 2010-07-01 2010-07-01 false Reclamation plan: Surface mining near underground mining. 780.27 Section 780.27 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR SURFACE COAL MINING AND RECLAMATION OPERATIONS PERMITS AND COAL...

Minimizing the Impact of Mining Activities for Sustainable Mined-Out ...

African Journals Online (AJOL)

Minimizing the Impact of Mining Activities for Sustainable Mined-Out Area ... sensing and Geographical Information System (GIS) in assessing environmental impact of ... Keywords: Solid mineral, Impact assessment, Mined-out area utilization, ...

Archveyor{trademark} automated mining system - implementation at the Conant mine

Energy Technology Data Exchange (ETDEWEB)

Hofmann, W.J. [Arch of Illinois, Percy, IL (United States)

1997-12-01

Arch Mineral Corporation, through the Arch Technology Department, has developed an automated continuous haulage mining system called the `Archveyor{trademark}`. The original technology came from a Russian patent. Kloeckner-Becorit (K-B) further developed the system and called it the `Mobile Conveyor`. This system was utilized in both coal and trona mines in the United States and Canada. Consolidation Coal designed their version of this continuous haulage system, called the `Tramveyor`. The Tramveyor is presently operating in their Dilworth Mine, in Pennsylvania. This system has no computer guidance system related to the continuous miner or the Tramveyor. Arch Mineral Corporation has further developed this continuous haulage mining system. Their system is a programmable, logic-controlled (PLC) automated mining system. A highwall version of the Archveyor{trademark} is being operated at Arch of Wyoming near Hanna, Wyoming. This paper introduces the first underground version of Archveyor{trademark} to be implemented at Conant Mine in southern Illinois. During the development process, the Archveyor{trademark} mining system consists of a continuous miner, a bolter car, the Archveyor{trademark} (itself), a stageloader, and an operator`s cab. During the secondary mining process the bolter car is taken out of the system.

Machine terms dictionary

Energy Technology Data Exchange (ETDEWEB)

NONE

1979-04-15

This book gives descriptions of machine terms which includes machine design, drawing, the method of machine, machine tools, machine materials, automobile, measuring and controlling, electricity, basic of electron, information technology, quality assurance, Auto CAD and FA terms and important formula of mechanical engineering.

Management of mining-related damages in abandoned underground coal mine areas using GIS

International Nuclear Information System (INIS)

Lee, U.J.; Kim, J.A.; Kim, S.S.; Kim, W.K.; Yoon, S.H.; Choi, J.K.

2005-01-01

The mining-related damages such as ground subsidence, acid mine drainage (AMD), and deforestation in the abandoned underground coal mine areas become an object of public concern. Therefore, the system to manage the mining-related damages is needed for the effective drive of rehabilitation activities. The management system for Abandoned Underground Coal Mine using GIS includes the database about mining record and information associated with the mining-related damages and application programs to support mine damage prevention business. Also, this system would support decision-making policy for rehabilitation and provide basic geological data for regional construction works in abandoned underground coal mine areas. (authors)

Text Mining.

Science.gov (United States)

Trybula, Walter J.

1999-01-01

Reviews the state of research in text mining, focusing on newer developments. The intent is to describe the disparate investigations currently included under the term text mining and provide a cohesive structure for these efforts. A summary of research identifies key organizations responsible for pushing the development of text mining. A section…

Surface mining

Science.gov (United States)

Robert Leopold; Bruce Rowland; Reed Stalder

1979-01-01

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

Uranium mining

International Nuclear Information System (INIS)

Lange, G.

1975-01-01

The winning of uranium ore is the first stage of the fuel cycle. The whole complex of questions to be considered when evaluating the profitability of an ore mine is shortly outlined, and the possible mining techniques are described. Some data on uranium mining in the western world are also given. (RB) [de

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

Directory of Open Access Journals (Sweden)

Georgia Tsiliki

Full Text Available 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.

Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose

Directory of Open Access Journals (Sweden)

Osowski Stanisław

2017-03-01

Full Text Available The paper analyses the distorted data of an electronic nose in recognizing the gasoline bio-based additives. Different tools of data mining, such as the methods of data clustering, principal component analysis, wavelet transformation, support vector machine and random forest of decision trees are applied. A special stress is put on the robustness of signal processing systems to the noise distorting the registered sensor signals. A special denoising procedure based on application of discrete wavelet transformation has been proposed. This procedure enables to reduce the error rate of recognition in a significant way. The numerical results of experiments devoted to the recognition of different blends of gasoline have shown the superiority of support vector machine in a noisy environment of measurement.

The use of filters to reduce the potential α-energy due to radon daughters in the cabs of mining vehicles

International Nuclear Information System (INIS)

Zettwoog, P.; Duport, P.; Campbell, F.E.; Caplan, H.S.

1982-01-01

This paper presents a theoretical analysis of the performance of filters for use in ventilation systems and describes an experiment performed in an inactive stope in an operating uranium mine in France. A series of truck engine air filters were used, designed to remove dust in the tens of microns range with an efficiency of about 5-20. They were chosen because they were designed from the outset to operate under the conditions of shock and vibration likely to be encountered in a mine. It was concluded that the use of the Liebherr filter will reduce the working level in the machine cab by a factor of two. (U.K.)

Using data mining techniques to characterize participation in observational studies.

Science.gov (United States)

Linden, Ariel; Yarnold, Paul R

2016-12-01

Data mining techniques are gaining in popularity among health researchers for an array of purposes, such as improving diagnostic accuracy, identifying high-risk patients and extracting concepts from unstructured data. In this paper, we describe how these techniques can be applied to another area in the health research domain: identifying characteristics of individuals who do and do not choose to participate in observational studies. In contrast to randomized studies where individuals have no control over their treatment assignment, participants in observational studies self-select into the treatment arm and therefore have the potential to differ in their characteristics from those who elect not to participate. These differences may explain part, or all, of the difference in the observed outcome, making it crucial to assess whether there is differential participation based on observed characteristics. As compared to traditional approaches to this assessment, data mining offers a more precise understanding of these differences. To describe and illustrate the application of data mining in this domain, we use data from a primary care-based medical home pilot programme and compare the performance of commonly used classification approaches - logistic regression, support vector machines, random forests and classification tree analysis (CTA) - in correctly classifying participants and non-participants. We find that CTA is substantially more accurate than the other models. Moreover, unlike the other models, CTA offers transparency in its computational approach, ease of interpretation via the decision rules produced and provides statistical results familiar to health researchers. Beyond their application to research, data mining techniques could help administrators to identify new candidates for participation who may most benefit from the intervention. © 2016 John Wiley & Sons, Ltd.

Realizatinon of “zero emission” of mining water effluents from Sasa mine

OpenAIRE

Mirakovski, Dejan; Doneva, Nikolinka; Hadzi-Nikolova, Marija; Gocevski, Borce

2015-01-01

Sasa mine continuously takes actions to minimize the environmental impact of mining activities, in order to fulfill the national legislation in the field of environmental protection which comply with European legislation. This paper shows the drainage system of the horizon 830, which is performed in order to prevent free leakage of mining groundwater, as a part of these actions. This system provides a zero emission of mining water in the environment from Sasa mine. Key words: mining water...

Sustainable rehabilitation of mining waste and acid mine drainage using geochemistry, mine type, mineralogy, texture, ore extraction and climate knowledge.

Science.gov (United States)

Anawar, Hossain Md

2015-08-01

The oxidative dissolution of sulfidic minerals releases the extremely acidic leachate, sulfate and potentially toxic elements e.g., As, Ag, Cd, Cr, Cu, Hg, Ni, Pb, Sb, Th, U, Zn, etc. from different mine tailings and waste dumps. For the sustainable rehabilitation and disposal of mining waste, the sources and mechanisms of contaminant generation, fate and transport of contaminants should be clearly understood. Therefore, this study has provided a critical review on (1) recent insights in mechanisms of oxidation of sulfidic minerals, (2) environmental contamination by mining waste, and (3) remediation and rehabilitation techniques, and (4) then developed the GEMTEC conceptual model/guide [(bio)-geochemistry-mine type-mineralogy- geological texture-ore extraction process-climatic knowledge)] to provide the new scientific approach and knowledge for remediation of mining wastes and acid mine drainage. This study has suggested the pre-mining geological, geochemical, mineralogical and microtextural characterization of different mineral deposits, and post-mining studies of ore extraction processes, physical, geochemical, mineralogical and microbial reactions, natural attenuation and effect of climate change for sustainable rehabilitation of mining waste. All components of this model should be considered for effective and integrated management of mining waste and acid mine drainage. Copyright © 2015 Elsevier Ltd. All rights reserved.

Philippine Mining Capitalism: The Changing Terrains of Struggle in the Neoliberal Mining Regime

Directory of Open Access Journals (Sweden)

Alvin A. Camba

2016-06-01

Full Text Available This article analyzes how the mining sector and anti-mining groups compete for mining outcomes in the Philippines. I argue that the transition to a neoliberal mineral regime has empowered the mining sector and weakened the mining groups by shifting the terrains of struggle onto the domains of state agencies and scientific networks. Since the neoliberal era, the mining sector has come up with two strategies. First, technologies of subjection elevate various public institutions to elect and select the processes aimed at making mining accountable and sensitive to the demands of local communities. However, they often refuse or lack the capacity to intervene effectively. Second, technologies of subjectivities allow a selective group of industry experts to single-handedly determine the environmental viability of mining projects. Mining consultants, specialists, and scientists chosen by mining companies determine the potential environmental damage on water bodies, air pollution, and soil erosion. Because of the mining capital’s access to economic and legal resources, anti-mining communities across the Philippines have been forced to compete on an unequal terrain for a meaningful social dialogue and mining outcomes.

Successive overrelaxation for laplacian support vector machine.

Science.gov (United States)

Qi, Zhiquan; Tian, Yingjie; Shi, Yong

2015-04-01

Semisupervised learning (SSL) problem, which makes use of both a large amount of cheap unlabeled data and a few unlabeled data for training, in the last few years, has attracted amounts of attention in machine learning and data mining. Exploiting the manifold regularization (MR), Belkin et al. proposed a new semisupervised classification algorithm: Laplacian support vector machines (LapSVMs), and have shown the state-of-the-art performance in SSL field. To further improve the LapSVMs, we proposed a fast Laplacian SVM (FLapSVM) solver for classification. Compared with the standard LapSVM, our method has several improved advantages as follows: 1) FLapSVM does not need to deal with the extra matrix and burden the computations related to the variable switching, which make it more suitable for large scale problems; 2) FLapSVM’s dual problem has the same elegant formulation as that of standard SVMs. This means that the kernel trick can be applied directly into the optimization model; and 3) FLapSVM can be effectively solved by successive overrelaxation technology, which converges linearly to a solution and can process very large data sets that need not reside in memory. In practice, combining the strategies of random scheduling of subproblem and two stopping conditions, the computing speed of FLapSVM is rigidly quicker to that of LapSVM and it is a valid alternative to PLapSVM.

Some relations between quantum Turing machines and Turing machines

OpenAIRE

Sicard, Andrés; Vélez, Mario

1999-01-01

For quantum Turing machines we present three elements: Its components, its time evolution operator and its local transition function. The components are related with the components of deterministic Turing machines, the time evolution operator is related with the evolution of reversible Turing machines and the local transition function is related with the transition function of probabilistic and reversible Turing machines.

Knowledge Mining from Clinical Datasets Using Rough Sets and Backpropagation Neural Network

Directory of Open Access Journals (Sweden)

Kindie Biredagn Nahato

2015-01-01

Full Text Available The availability of clinical datasets and knowledge mining methodologies encourages the researchers to pursue research in extracting knowledge from clinical datasets. Different data mining techniques have been used for mining rules, and mathematical models have been developed to assist the clinician in decision making. The objective of this research is to build a classifier that will predict the presence or absence of a disease by learning from the minimal set of attributes that has been extracted from the clinical dataset. In this work rough set indiscernibility relation method with backpropagation neural network (RS-BPNN is used. This work has two stages. The first stage is handling of missing values to obtain a smooth data set and selection of appropriate attributes from the clinical dataset by indiscernibility relation method. The second stage is classification using backpropagation neural network on the selected reducts of the dataset. The classifier has been tested with hepatitis, Wisconsin breast cancer, and Statlog heart disease datasets obtained from the University of California at Irvine (UCI machine learning repository. The accuracy obtained from the proposed method is 97.3%, 98.6%, and 90.4% for hepatitis, breast cancer, and heart disease, respectively. The proposed system provides an effective classification model for clinical datasets.

Experimental study on variations in Charpy impact energies of low carbon steel, depending on welding and specimen cutting method

Energy Technology Data Exchange (ETDEWEB)

Yang, Zhaorui; Kang, Hansaem; Lee, Young Seog [Chung-Ang University, Seoul (Korea, Republic of)

2016-05-15

This paper presents an experimental study that examines variations of Charpy impact energy of a welded steel plate, depending upon the welding method and the method for obtaining the Charpy specimens. Flux cored arc welding (FCAW) and Gas tungsten arc welding (GTAW) were employed to weld an SA516 Gr. 70 steel plate. The methods of wire cutting and water-jet cutting were adopted to take samples from the welded plate. The samples were machined according to the recommendations of ASTM SEC. II SA370, in order to fit the specimen dimension that the Charpy impact test requires. An X-ray diffraction (XRD) method was used to measure the as-weld residual stress and its redistribution after the samples were cut. The Charpy impact energy of specimens was considerably dependent on the cutting methods and locations in the welded plate where the specimens were taken. The specimens that were cut by water jet followed by FCAW have the greatest resistance-to-fracture (Charpy impact energy). Regardless of which welding method was used, redistributed transverse residual stress becomes compressive when the specimens are prepared using water-jet cutting. Meanwhile, redistributed transverse residual stress becomes tensile when the specimens are prepared using wire cutting.

PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine

Directory of Open Access Journals (Sweden)

Baskin Berivan

2003-03-01

Full Text Available Abstract Background The majority of experimentally verified molecular interaction and biological pathway data are present in the unstructured text of biomedical journal articles where they are inaccessible to computational methods. The Biomolecular interaction network database (BIND seeks to capture these data in a machine-readable format. We hypothesized that the formidable task-size of backfilling the database could be reduced by using Support Vector Machine technology to first locate interaction information in the literature. We present an information extraction system that was designed to locate protein-protein interaction data in the literature and present these data to curators and the public for review and entry into BIND. Results Cross-validation estimated the support vector machine's test-set precision, accuracy and recall for classifying abstracts describing interaction information was 92%, 90% and 92% respectively. We estimated that the system would be able to recall up to 60% of all non-high throughput interactions present in another yeast-protein interaction database. Finally, this system was applied to a real-world curation problem and its use was found to reduce the task duration by 70% thus saving 176 days. Conclusions Machine learning methods are useful as tools to direct interaction and pathway database back-filling; however, this potential can only be realized if these techniques are coupled with human review and entry into a factual database such as BIND. The PreBIND system described here is available to the public at http://bind.ca. Current capabilities allow searching for human, mouse and yeast protein-interaction information.

Support vector machine in machine condition monitoring and fault diagnosis

Science.gov (United States)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

Kiruna research mine

Energy Technology Data Exchange (ETDEWEB)

Oestensen, A

1983-12-01

The research mine at Kiruna is the first large-scale mining research project sponsored by the Swedish government. Under the leadership of the Swedish Mining Research Foundation, a five-year project involving development of new mining systems and machinery will be carried out in cooperation with the Lulea Institute of Technology and a number of Swedish industrial companies.

Lunar construction/mining equipment

Science.gov (United States)

Ozdemir, Levent

1990-01-01

For centuries, mining has utilized drill and blast as the primary method of rock excavation. Although this technique has undergone significant improvements, it still remains a cyclic, labor intensive operation with inherent safety hazards. Other drawbacks include damage to the surrounding ground, creation of blast vibrations, rough excavation walls resulting in increased ventilation requirements, and the lack of selective mining ability. Perhaps the most important shortcoming of drill and blast is that it is not conducive to full implementation of automation or robotics technologies. Numerous attempts have been made in the past to automate drill and blast operations to remove personnel from the hazardous work environment. Although most of the concepts devised look promising on paper, none of them was found workable on a sustained production basis. In particular, the problem of serious damage to equipment during the blasting cycle could not be resolved regardless of the amount of charge used in excavation. Since drill and blast is not capable of meeting the requirements of a fully automated rock fragmentation method, its role is bound to gradually decrease. Mechanical excavation, in contrast, is highly suitable to automation because it is a continuous process and does not involve any explosives. Many of the basic principles and trends controlling the design of an earth-based mechanical excavator will hold in an extraterrestrial environment such as on the lunar surface. However, the economic and physical limitations for transporting materials to space will require major rethinking of these machines. In concept, then, a lunar mechanical excavator will look and perform significantly different from one designed for use here on earth. This viewgraph presentation gives an overview of such mechanical excavator systems.

Overview of mine drainage geochemistry at historical mines, Humboldt River basin and adjacent mining areas, Nevada. Chapter E.

Science.gov (United States)

Nash, J. Thomas; Stillings, Lisa L.

2004-01-01

Reconnaissance hydrogeochemical studies of the Humboldt River basin and adjacent areas of northern Nevada have identified local sources of acidic waters generated by historical mine workings and mine waste. The mine-related acidic waters are rare and generally flow less than a kilometer before being neutralized by natural processes. Where waters have a pH of less than about 3, particularly in the presence of sulfide minerals, the waters take on high to extremely high concentrations of many potentially toxic metals. The processes that create these acidic, metal-rich waters in Nevada are the same as for other parts of the world, but the scale of transport and the fate of metals are much more localized because of the ubiquitous presence of caliche soils. Acid mine drainage is rare in historical mining districts of northern Nevada, and the volume of drainage rarely exceeds about 20 gpm. My findings are in close agreement with those of Price and others (1995) who estimated that less than 0.05 percent of inactive and abandoned mines in Nevada are likely to be a concern for acid mine drainage. Most historical mining districts have no draining mines. Only in two districts (Hilltop and National) does water affected by mining flow into streams of significant size and length (more than 8 km). Water quality in even the worst cases is naturally attenuated to meet water-quality standards within about 1 km of the source. Only a few historical mines release acidic water with elevated metal concentrations to small streams that reach the Humboldt River, and these contaminants and are not detectable in the Humboldt. These reconnaissance studies offer encouraging evidence that abandoned mines in Nevada create only minimal and local water-quality problems. Natural attenuation processes are sufficient to compensate for these relatively small sources of contamination. These results may provide useful analogs for future mining in the Humboldt River basin, but attention must be given to

Present and future mine effluents management at Zirovski Vrh uranium mine

International Nuclear Information System (INIS)

Logar, Z.; Likar, B.; Gantar, I.

2002-01-01

Zirovski Vrh uranium mine and its facilities are situated on the northeastern slopes of the Zirovski Vrh ridge (960 m) and on the southern slopes of Crna gora (611 m) respectively. Mine elevation is from 430 m (bottom of the valley) to 580 m (P-1 adit). All effluents from the mine and mill objects flow into the Brebovscica river (with average yearly flow of 0.74 m 3 /s): run off mine water; mine waste pile Jazbec outflow; mill tailings Borst outflows; effluents from mine temporary mine waste piles P-1, P-9, P-36 are of minor significance. The first three effluents and the recipient surface water flows (the Todrascica brook and the Brebovscica river) are monitored extensively. The impact of radioactive polluted outflows on named waters is proved, but far under the maximal permitted limit values. The authorised maximal limits values for mine effluents were obtained in 1996. Detail design will ensure that this values will not be exceeded in the future. The long term planes are to minimise the uranium concentrations in the run off mine water by target underground drilling. The mine waste pile and the mill tailings will be covered by engineered cover system to avoid clean water contamination by weathering and ablution as well. The existing effluents from the mill tailings will diminish after the remediation and consolidation of the tailing. The Government of Slovenia funds the remediation of the uranium production site Zirovski Vrh. Estimated needed funds for remediation of the main objects are shown in the table below. The total investment includes also the costs for effluents control. Area Mio US$ Underground mine remediation 19.00 Mine waste pile remediation 6.50 Mill tailings remediation 2.24 Total investment costs 27.74 Above figures do not include operation costs of the Zirovski Vrh Mine, approximately US$ 2.2 Mio per year nowadays. The last implementation schedule foresights the end of remediation works in year 2005. After that starts trial monitoring of 5 years

Hydrogeochemical assessment of mine-impacted water and sediment of iron ore mining

Science.gov (United States)

Nur Atirah Affandi, Fatin; Kusin, Faradiella Mohd; Aqilah Sulong, Nur; Madzin, Zafira

2018-04-01

This study was carried out to evaluate the hydrogeochemical behaviour of mine-impacted water and sediment of a former iron ore mining area. Sampling of mine water and sediment were carried out at selected locations within the mine including the former mining ponds, mine tailings and the nearby stream. The water samples were analysed for their hydrochemical facies, major and trace elements including heavy metals. The water in the mining ponds and the mine tailings was characterised as highly acidic (pH 2.54-3.07), but has near-neutral pH in the nearby stream. Results indicated that Fe and Mn in water have exceeded the recommended guidelines values and was also supported by the results of geochemical modelling. The results also indicated that sediments in the mining area were contaminated with Cd and As as shown by the potential ecological risk index values. The total risk index of heavy metals in the sediment were ranked in the order of Cd>As>Pb>Cu>Zn>Cr. Overall, the extent of potential ecological risks of the mining area were categorised as having low to moderate ecological risk.