By Taylor, Charles D.; Chilton, J. Emery; Goodman, Gerrit V.R.
Compiled through the U.S. Dept of well-being and Human providers, CDC/NIOSH workplace of Mine safeguard and wellbeing and fitness study, this 2010 book demonstrates how current and new engineering controls can be utilized to minimize face methane degrees on non-stop mining operations. The sampling tools that have been investigated promises larger how one can degree methane degrees close to front of the continual mining laptop. during this file numerous sensible directions are suggested for controlling and tracking methane degrees within the face components of underground coal mines. lots of the techniques have been in response to stories performed within the NIOSH air flow try gallery.
Content:
• entrance subject
• checklist of Figures
• Acronyms and Abbreviations utilized in This document
• Unit of degree Abbreviations utilized in This file
• desk of Contents
• 1. government precis
2. history
three. try amenities
four. relocating Air to the Mining Face
five. impression of Scrubber Operation on Face Airflow and Methane Concentrations
6. impact of Water Sprays on Face Airflow and Methane Concentrations
7. Methane tracking
eight. Measuring gasoline degrees Outby the Face
nine. comparing Methane degrees in components Outby the Mining Face
10. ideas for Measuring Airflow
eleven. Methane tracking in the course of Roof Bolting
• 12. useful directions for the keep an eye on and tracking of Methane fuel on non-stop Mining Operations
• References
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Extra resources for Guidelines for the Control and Monitoring of Methane Gas on Continuous Mining Operations
Example text
However, a large amount of data does not always make for a difficult learning or mining problem, for the complexity of what the algorithm must learn or mine is also critical. These difficulties of transforming a raw data source into a set of examples have prompted some researchers to investigate automated methods of finding representations for examples. Methods of feature construction, feature engineering, or constructive induction automatically transform raw data into examples suitable for learning.
There are two books about data mining approaches to computer security complementary to this one. Mena [90] surveys several machine learning and data mining technologies and describes companies, software, and case studies of such technologies applied to a wide range of applications, including intrusion detection, fraud detection, and criminal profiling. Reference [91] is a collection of research articles, similar to the second part of this volume. Readers with a concrete or practical understanding of such topics may want to investigate sources that discuss these issues more generally and more formally.
A+b+c+d Percent correct is simply accuracy expressed as a percentage. Other performance metrics include: • Error rate, being the portion of the examples in the test set the model predicts incorrectly: (b + c)/(a + b + c + d). • True-positive rate (tp), hit rate, detect rate, or sensitivity, being the portion of the positive examples the model predicts correctly: a/(a + b). • True-negative rate (tn), correct-reject rate, or specificity, being the portion of the negative examples the model predicts correctly: d/(c + d).