Analysis and evaluation of fragment size distributions in rock blasting at the Erdenet Mine

Master's Project (M.S.) University of Alaska Fairbanks, 2015Rock blasting is one of the most important operations in mining. It significantly affects the subsequent comminution processes and, therefore, is critical to successful mining productions. In this study, for the evaluation of the blasting performance at the Erdenet Mine, we analyzed rock fragment size distributions with the digital image processing method. The uniformities of rock fragments and the mean fragment sizes were determined and applied in the Kuz-Ram model. Statistical prediction models were also developed based on the field measured parameters. The results were compared with the Kuz-Ram model predictions and the digital image processing measurements. A total of twenty-eight images from eleven blasting patterns were processed, and rock size distributions were determined by Split-Desktop program in this study. Based on the rock mass and explosive properties and the blasting parameters, the rock fragment size distributions were also determined with the Kuz-Ram model and compared with the measurements by digital image processing. Furthermore, in order to improve the prediction of rock fragment size distributions at the mine, regression analyses were conducted and statistical models w ere developed for the estimation of the uniformity and characteristic size. The results indicated that there were discrepancies between the digital image measurements and those estimated by the Kuz-Ram model. The uniformity indices of image processing measurements varied from 0.76 to 1.90, while those estimate by the Kuz-Ram model were from 1.07 to 1.13. The mean fragment size of the Kuz-Ram model prediction was 97.59% greater than the mean fragment size of the image processing. The multivariate nonlinear regression analyses conducted in this study indicated that rock uniaxial compressive strength and elastic modulus, explosive energy input in the blasting, bench height to burden ratio and blast area per hole were significant predictor variables in determining the fragment characteristic size and the uniformity index. The regression models developed based on the above predictor variables showed much closer agreement with the measurements

Evaluation of ERTS-1 data for inventory of forest and rangeland and detection of forest stress

The author has identified the following significant results. Results of photointerpretation indicated that ERTS is a good classifier of forest and nonforest lands (90 to 95 percent accurate). Photointerpreters could make this separation as accurately as signature analysis of the computer compatible tapes. Further breakdowns of cover types at each site could not be accurately classified by interpreters (60 percent) or computer analysts (74 percent). Exceptions were water, wet meadow, and coniferous stands. At no time could the large bark beetle infestations (many over 300 meters in size) be detected on ERTS images. The ERTS wavebands are too broad to distinguish the yellow, yellow-red, and red colors of the dying pine foliage from healthy green-yellow foliage. Forest disturbances could be detected on ERTS color composites about 90 percent of the time when compared with six-year-old photo index mosaics. ERTS enlargements (1:125,000 scale, preferably color prints) would be useful to forest managers of large ownerships over 5,000 hectares (12,500 acres) for broad area planning. Black-and-white enlargements can be used effectively as aerial navigation aids for precision aerial photography where maps are old or not available

The Globular Cluster System of the Spiral Galaxy NGC7814

We present the results of a wide-field photometric study of the globular cluster (GC) system of the edge-on Sab spiral NGC7814. This is the first spiral to be fully analyzed from our survey of the GC systems of a large sample of galaxies beyond the Local Group. NGC7814 is of particular interest because a previous study estimated that it has 500-1000 GCs, giving it the largest specific frequency (S_N) known for a spiral. Understanding this galaxy's GC system is important in terms of our understanding of the GC populations of spirals in general and has implications for the formation of massive galaxies. We observed the galaxy in BVR filters with the WIYN 3.5-m telescope, and used image classification and three-color photometry to select GC candidates. We also analyzed archival HST WFPC2 images of NGC7814, both to help quantify the contamination level of the WIYN GC candidate list and to detect GCs in the inner part of the galaxy halo. Combining HST data with high-quality ground-based images allows us to trace the entire radial extent of this galaxy's GC system and determine the total number of GCs directly through observation. We find that rather than being an especially high-S_N spiral, NGC7814 has <200 GCs and S_N ~ 1, making it comparable to the two most well-studied spirals, the Milky Way and M31. We explore the implications of these results for models of the formation of galaxies and their GC systems. The initial results from our survey suggest that the GC systems of typical ellipticals can be accounted for by the merger of two or more spirals, but that for highly-luminous ellipticals, additional physical processes may be needed.Comment: 28 pages, incl. 4 figures; accepted for publication in The Astronomical Journal, November 2003 issu

Design and analysis of a content-based image retrieval system

The automatic retrieval of images according to the similarity of their content is a challenging task with many application fields. In this book the automatic retrieval of images according to human spontaneous perception without further effort or knowledge is considered. A system is therefore designed and analyzed. Methods for the detection and extraction of regions and for the extraction and comparison of color, shape, and texture features are also investigated

\u3ci\u3eCharacterizing Feedlot Feed using Depth Cameras and Imaging Technology\u3c/i\u3e

Imaging technology is a growing field that provides solutions in many areas from manufacturing to agriculture. Through previous research, imaging technologies have been studied in livestock farming to monitor the animal’s health and welfare in the production process. However, the feedlot industry is still behind in validating the feasibility to use some of these technologies nor to adopt such technologies to address challenges the industry is facing, such as lack of skilled labor.This work proposes using novel imaging methods to identify feed types and estimate the amount of feed remaining in a typical Midwestern feedlot feed bunk. These methods have promising potential to provide alternative tools to feedlot operations to alleviate labor requirements for tasks like bunk calling, feed sourcing, and feed mixing. This approach, if successful, provides an alternative option that allows existing systems to incorporate these methods into their framework to accurately perform daily tasks.The main contribution of this work is to leverage imaging technologies, specifically, depth imaging and machine learning techniques to build and validate models that can be used in the feedlot production systems in the Midwestern U.S. To date, several studies have explored the use of imaging technologies and machine learning to monitor individual cow intake in dairy production, but there is limited research body comprehensively conducted to explore these technologies for feedlot applications.The proposed methods were used to collect imagery data for eleven common feedlot ingredients and seven diets. Collected images were processed (a) to estimate the weights of residual feed in the bunk, (b) to evaluate the accuracy of depth cameras in estimating residual feed, (c) to characterize the different feed textures, and (d) to classify feed textures using machine learning techniques. Regression models using pixel transformation were developed to correlate image-model-estimated and the scale-measured feed weights, whereas texture analysis techniques and residual neural network model in 10 classes were used to identify the individual ingredients. Methodologies and results are presented in this thesis as a paper format. The major findings indicate that using low-cost depth cameras and machine learning techniques is promising in the development of alternative tools to estimate the amount of residual feed in concrete bunks and identify individual feed ingredients commonly used in commercial feedlots in the U.S. Advisor: Yijie Xion

Machine Learning Based Defect Detection in Robotic Wire Arc Additive Manufacturing

In the last ten years, research interests in various aspects of the Wire Arc Additive Manufacturing (WAAM) processes have grown exponentially. More recently, efforts to integrate an automatic quality assurance system for the WAAM process are increasing. No reliable online monitoring system for the WAAM process is a key gap to be filled for the commercial application of the technology, as it will enable the components produced by the process to be qualified for the relevant standards and hence be fit for use in critical applications in the aerospace or naval sectors. However, most of the existing monitoring methods only detect or solve issues from a specific sensor, no monitoring system integrated with different sensors or data sources is developed in WAAM in the last three years. In addition, complex principles and calculations of conventional algorithms make it hard to be applied in the manufacturing of WAAM as the character of a long manufacturing cycle. Intelligent algorithms provide in-built advantages in processing and analysing data, especially for large datasets generated during the long manufacturing cycles. In this research, in order to establish an intelligent WAAM defect detection system, two intelligent WAAM defect detection modules are developed successfully. The first module takes welding arc current / voltage signals during the deposition process as inputs and uses algorithms such as support vector machine (SVM) and incremental SVM to identify disturbances and continuously learn new defects. The incremental learning module achieved more than a 90% f1-score on new defects. The second module takes CCD images as inputs and uses object detection algorithms to predict the unfused defect during the WAAM manufacturing process with above 72% mAP. This research paves the path for developing an intelligent WAAM online monitoring system in the future. Together with process modelling, simulation and feedback control, it reveals the future opportunity for a digital twin system