Academic literature on the topic 'Mining engineering'

<p> Normalization of deviance has been thoroughly studied and proven to have a dramatic impact on the medical industry, particularly in the field of anesthesiology, and for the National Aeronautics and Space Administration (NASA). Few such studies have been conducted in the mining industry. This research was designed to show whether normalization of deviance is occurring within the subculture of mining engineers.</p><p> This research project was based on a cross-sectional surveillance of a group of mining engineers and consultants belonging to the Society for Mining, Metallurgy and Exploration (SME).</p><p> There were three hypotheses for this research: 1) there is a correlation between ethics, compensation, risk tolerance, and normalization of deviance; 2) there are either positive or negative associations between each of the independent variables&mdash;ethics, compensation, and risk tolerance&mdash;to the dependent variable&mdash;normalization of deviance; 3) the data would make it possible to predict normalization of deviance among mining engineers. All three hypotheses were proven true in this study.</p><p> This research is important because it shows that normalization of deviance exists among mining engineers.</p><p>

How do we find patterns and anomalies, on graphs with billions of nodes and edges, which do not fit in memory? How to use parallelism for such Tera- or Peta-scale graphs? In this thesis, we propose PEGASUS, a large scale graph mining system implemented on the top of the HADOOP platform, the open source version of MAPREDUCE. PEGASUS includes algorithms which help us spot patterns and anomalous behaviors in large graphs. PEGASUS enables the structure analysis on large graphs. We unify many different structure analysis algorithms, including the analysis on connected components, PageRank, and radius/diameter, into a general primitive called GIM-V. GIM-V is highly optimized, achieving good scale-up on the number of edges and available machines. We discover surprising patterns using GIM-V, including the 7-degrees of separation in one of the largest publicly available Web graphs, with 7 billion edges. PEGASUS also enables the inference and the spectral analysis on large graphs. We design an efficient distributed belief propagation algorithm which infer the states of unlabeled nodes given a set of labeled nodes. We also develop an eigensolver for computing top k eigenvalues and eigenvectors of the adjacency matrices of very large graphs. We use the eigensolver to discover anomalous adult advertisers in the who-follows-whom Twitter graph with 3 billion edges. In addition, we develop an efficient tensor decomposition algorithm and use it to analyze a large knowledge base tensor. Finally, PEGASUS allows the management of large graphs. We propose efficient graph storage and indexing methods to answer graph mining queries quickly. We also develop an edge layout algorithm for better compressing graphs.

This project describes a novel approach to the development of a multimodal video segmentation system for the analysis of multimedia data. The current practices of multimedia data analysis rely either solely on one of the video and audio components or on the presence of both together. The proposed approach makes use of both the video and audio inputs in parallel, complementing each other during the video processing stage, towards optimising both the accuracy and speed of the method. Unlike in the other commonly established methods, the video analysis here is carried out using both the luminance and the chrominance values of the colour images, instead of relying on either of them. The approach considered in the proposed method of video cut detection primarily uses a modified luminance based histogram analysis algorithm, supported by the additional sub-sampling and median filtering options. They improve the efficiency of the method through enhancing its speed and the accuracy of detection respectively. The algorithm mentioned above uses a progressively varying threshold for indicating a significant variation in the measurement of successive histograms for a window length of 2 image frames. The method worked successfully for the videos with varying rates and sizes of the frames that have been under investigation. Because of the degrading effect of chrominance histogram analysis on the processing speed its use is kept to a minimum. This is restricted only to verify the existence of possible cuts, failed to be identified by the luminance analysis. The indication of such cuts could be obtained through audio classification analysis.

The large-scale wind energy industry is relatively new and is rapidly expanding. The ability of a wind turbine to extract power from the wind is a function of three main factors: the measured wind speed, the power curve of the turbine, and the ability of the machine to handle wind fluctuations. The key parameter determining wind turbine performance is wind speed and it is normally measured with an anemometer placed at the nacelle of a turbine. The dynamic nature of wind speed, however, is a barrier for applying predictive engineering in wind energy. Traditional approaches based on physical science and mathematical modelings have limitations on wind power prediction models. Conventional approach based on dynamic modeling has disadvantage of power generation process modeling due to time-shift nature of the process. Data mining is a promising approach for modeling wind energy, e.g., power prediction and optimization, wind speed forecasting, power curve monitoring and fault diagnosis. It involves a number of steps including data pre-processing, data sampling, feature selection, dimension reduction and, etc. This thesis focus on applying data mining to predictive engineering in wind industry, and ultimately builds wind speed prediction and wind farm power prediction models, develops turbine dynamic control and power optimization strategy, explores methodology for system level fault diagnosis. However the philosophy, methods and frameworks discussed in this research can also be applied to other industrial processes. This thesis proposes a series of predictive models under the framework of data mining. Chapter 2 introduces a methodology for short term wind speed prediction based on wind farm layout information. Chapter 3 and Chapter 4 present prediction models for wind turbine parameters. Chapter 5 proposes strategies for dynamic control of wind turbines. Chapter 6 explores the fault diagnosis and prediction using SCADA data.

Each year, organizations in Australian mining industry (asset intensive industry) spend substantial amount of capital (A$86 billion in 2009-10) (Statistics, 2011) in acquiring engineering assets. Engineering assets are put to use in operations to generate value. Different functions (departments) of an organization have different expectations and requirements from each of the engineering asset e.g. return on investment, reliability, efficiency, maintainability, low cost of running the asset, low or nil environmental impact and easy of disposal, potential salvage value etc. Assets are acquired from suppliers or built by service providers and or internally. The process of acquiring assets is supported by procurement function. One of the most costly mistakes that organizations can make is acquiring the inappropriate or non-conforming assets that do not fit the purpose. The root cause of acquiring non confirming assets belongs to incorrect acquisition decision and the process of making decisions. It is very important that an asset acquisition decision is based on inputs and multi-criteria of each function within the organization which has direct or indirect impact on the acquisition, utilization, maintenance and disposal of the asset. Literature review shows that currently there is no comprehensive process framework and tool available to evaluate the inclusiveness and breadth of asset acquisition decisions that are taken in the Mining Organizations. This thesis discusses various such criteria and inputs that need to be considered and evaluated from various functions within the organization while making the asset acquisition decision. Criteria from functions such as finance, production, maintenance, logistics, procurement, asset management, environment health and safety, material management, training and development etc. need to be considered to make an effective and coherent asset acquisition decision. The thesis also discusses a tool that is developed to be used in the multi-criteria and cross functional acquisition decision making. The development of multi-criteria and cross functional inputs based decision framework and tool which utilizes that framework to formulate cross functional and integrated asset acquisition decisions are the contribution of this research.

Mining automation has incrementally progressed from line-of-sight remote operation to teleoperation and automatic control of mobile machines, mainly due to significant advances in underground communication systems. The present trend points towards a robotic mining environment where mobile machinery and stationary equipment will be fully integrated with a mine-wide information system overseeing all aspects of mining via a communication network. The successful design and implementation of the software and hardware components necessary to realize this vision depends on the level of seamless integration achieved. The complexity involved in terms of systems functionality and coherence necessitates systems analysis and computer-aided software engineering tools to actively support this integration effort.<br>Hence, the primary objective of this thesis is to introduce and relate systems analysis concepts and tools to the business of mining. This investigation begins by setting the industrial context of this work with respect to past initiatives and future trends. It discusses different approaches to the design and implementation of mining information systems. It reviews the fundamentals of software and information engineering as well as structured and object-oriented analysis and design. It presents a survey of computerized tools for systems analysis. It then applies systems analysis concepts and tools to a high-level top-down analysis of a Mine Information System and examines a specific mining process in detail. Finally, it compares the applicability of structured versus object-oriented analysis and design methodologies to the complex problem of mining.