Many areas of mining engineering gather and use statistical information, provided by observing the actual operation of equipment, their systems, the development of mining works, surface subsidence that accompanies underground mining, displacement of rocks surrounding surface pits and underground drives and longwalls, amongst others. In addition, the actual modern machines used in surface mining are equipped with diagnostic systems that automatically trace all important machine parameters and send this information to the main producer’s computer. Such data not only provide information on the technical properties of the machine but they also have a statistical character. Furthermore, all information gathered during stand and lab investigations where parts, assemblies and whole devices are tested in order to prove their usefulness, have a stochastic character. All of these materials need to be developed statistically and, more importantly, based on these results mining engineers must make decisions whether to undertake actions, connected with the further operation of the machines, the further development of the works, etc. For these reasons, knowledge of modern statistics is necessary for mining engineers; not only as to how statistical analysis of data should be conducted and statistical synthesis should be done, but also as to understanding the results obtained and how to use them to make appropriate decisions in relation to the mining operation. This book on statistical analysis and synthesis starts with a short repetition of probability theory and also includes a special section on statistical prediction. The text is illustrated with many examples taken from mining practice; moreover the tables required to conduct statistical inference are included.
Written by a mineral engineer for mineral engineers, and packed with real world examples, this book de-mystifies the statistics that most of us learned at university and then forgot. It shows how simple statistical methods, most of them available in Excel, can be used to make good decisions in the face of experimental uncertainty. Written in accessible language, it explains how experimental uncertainty arises from the normal measurement errors and how statistics provides a powerful methodology to manage that uncertainty. It assumes only that the readers are numerate, can use Excel, and want to do a better professional job. It is aimed squarely at mineral engineers and allied professionals (such as chemists) on the mine site, in head office, in engineering and supply companies and in universities. Most of the examples are illustrated in Excel but Minitab is also used for advanced techniques. The book includes over 100 Excel and Minitab hints. Example spreadsheets can be downloaded from the JKMRC and JKTech websites.
Recent rough estimates are that the U.S. Department of Defense (DoD) spends at least $38 billion a year on the research, development, testing, and evaluation of new defense systems; approximately 40 percent of that cost-at least $16 billion-is spent on software development and testing. There is widespread understanding within DoD that the effectiveness of software-intensive defense systems is often hampered by low-quality software as well as increased costs and late delivery of software components. Given the costs involved, even relatively incremental improvements to the software development process for defense systems could represent a large savings in funds. And given the importance of producing defense software that will carry out its intended function, relatively small improvements to the quality of defense software systems would be extremely important to identify. DoD software engineers and test and evaluation officials may not be fully aware of a range of available techniques, because of both the recent development of these techniques and their origination from an orientation somewhat removed from software engineering, i.e., from a statistical perspective. The panel's charge therefore was to convene a workshop to identify statistical software engineering techniques that could have applicability to DoD systems in development.
A practical field reference for mining and mineral engineers that is small enough to carry into the field. With its comprehensive store of charts, graphs, tables, equations, and rules of thumb, this handbook is the essential technical reference for mobile mining professionals.
In today’s global and highly competitive environment, continuous improvement in the processes and products of any field of engineering is essential for survival. This book gathers together the full range of statistical techniques required by engineers from all fields. It will assist them to gain sensible statistical feedback on how their processes or products are functioning and to give them realistic predictions of how these could be improved. The handbook will be essential reading for all engineers and engineering-connected managers who are serious about keeping their methods and products at the cutting edge of quality and competitiveness.
Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.
Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. Winner of a 2012 PROSE Award in Computing and Information Sciences from the Association of American Publishers, this book presents a comprehensive how-to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities. The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possible Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com Glossary of text mining terms provided in the appendix
Vol.1 : Données de Base - Vol. 2 : Consommation d'énergie
Publisher: OECD Publishing
PREVIOUSLY PUBLISHED AS INDUSTRIAL STRUCTURE STATISTICS, VOLUME 1: Core Data, VOLUME 2: Energy Consumption In this seventeenth edition, Volume 1, Core Data, provides official annual data for detailed industrial sectors (mining and quarrying ...