This thesis investigated the abilities of artificial neural networks as rock classifiers in an open-pit hard rock environment using monitoring-while-drilling (MWD) data. Blast hole drilling data has been collected from an open-pit taconite mine. The data was smoothed with respect to depth and filtered for non-drilling data. Preliminary analysis was performed to determine classifier input variables and a method of labelling training data. Results obtained from principal component analysis suggested that the best set of possible classifier input variables was: penetration rate, torque, specific fracture energy, vertical vibration, horizontal vibration, penetration rate deviation and thrust deviation. Specific fracture energy and self-organizing-maps were explored as a means of labelling training data and found to be inadequate. Several backpropagation neural networks were trained and tested with various combinations of input parameters and training sets. Input sets that included all seven parameters achieved the best overall performances. 7-input neural networks that were trained with and tested on the entire data set achieved an average overall performance of 81%. A sensitivity analysis was performed to test the generalization abilities of the neural networks as rock classifiers. The best overall neural network performance on data not included in the training set was 67%. The results indicated that neural networks by themselves are not capable rock classifiers on MWD data in such a hard rock iron ore environment. / Thesis (Master, Mining Engineering) -- Queen's University, 2009-04-23 11:59:07.806

In the past decade, society has become increasingly dependent on portable electronic devices that are almost exclusively powered by batteries. The performance and duration of operation of these devices are constrained by the limited energy per unit mass of batteries. Recent advances in the field of energy harvesting have led to the development of efficient and sustainable technologies that are capable of collecting mechanical energy from human motion, and producing the electrical power required to operate portable devices. This thesis focuses on the design and evaluation of a motion-based biomechanical energy harvester that collects energy from the user’s lower limbs. Two lower-limb energy-driven harvesting backpacks, a belt-driven prototype and a gear-driven prototype, were developed. Human treadmill walking testing showed that the belt-driven prototype was able to produce 19.3-12.2W of electrical power with a device efficiency of 34.4-48.4%. The belt-driven prototype had a low metabolic cost of carrying the device, approximately 18W, but had a large metabolic cost of producing electrical power, approximately 188W. This large metabolic cost of energy production is likely a consequence of the large mechanical power required to drive the device, namely to overcome the moment of inertia and the frictional loss of the device. Preliminary testing of the gear-driven prototype showed that the device was able to produce 7-11.2W of electrical power with a device efficiency of 58-78%. A theoretical model was developed that was able to predict the harvester0s electrical power output and the respective load on the user, from a given input motion wave-form. This model was able to predict the peak voltage and peak force with a percent difference of 2% ± 2% and 6.4% ± 4% respectively. Further reduction of the volume, weight, and number of parts of the energy harvester is essential in making the harvester a viable commercial product for powering portable devices. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2013-08-27 10:46:27.16

A novel method of predicting pore pressure has been invented. The method utilizes currently recorded drilling measurements to predict the pore pressure of the formation through which the bit is drilling. The method applies Mohr’s Theory to describe the stresses at the bottom of the borehole. From the stress state and knowledge of Mohr’s Envelope, the pore pressure is predicted. To verify the method, a test procedure was developed. The test procedure enabled systematic collection and processing of the drilling data to calculate the pore pressure prediction. The test procedure was then applied to industry data that was recorded at the surface. The industry data were composed of wells from different geographical regions. Two conclusions were deduced from the research. First, Mohr’s Theory indicates that the model is valid. Second, because of too much variation in the torque measurements the model cannot be proved and requires further investigation.

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<p> We present a symbolic analysis of a class of while loop programs which can automatically derive a closed-form symbolic expression for the input-output relation embodied in that program.</p> <p> We show that this is especially well-suited to analyzing programs from scientific computation, in particular programs which compute special functions (like Bessel functions) from its Taylor series expansion. Other than making heavy use of algebraic manipulations, as available in any computer algebra system, we also require the use of recurrence relations. It is from these recurrence relations that we derive most of our information.</p> <p> It is important to note that we can often get interesting information about a program (like termination) without requiring closed-form solutions to the recurrences.</p> / Thesis / Master of Science (MSc)

This study investigates the relationships between planning strategies on how to avoid future DWI arrest and actual DWI recidivism for a group of problem and non-problem drinkers. A sample of 75 individuals who were arrested for DWI and completed a DWI training program in 1987 was gathered.

<p dir="ltr">Learning involves complex cognitive processes such as perception, attention, and memory, with reading being a common method to acquire knowledge. Textbooks are often supplemented with additional materials such as self-test questions and demonstration tasks to enhance learning. However, the impact of these supplemental materials, especially when they interrupt reading, is not well understood. This study examined how interruptions by supplemental materials affect reading comprehension and learning outcomes.</p>

Drilling and blasting procedures are a critical part of mine planning activities and improvements in this stage can lead to better productivity downstream and lower costs. One potential improvement would be better understanding the characteristics of the rock for blast design purposes. The distribution of material properties within a rock mass is very unpredictable so to more accurately determine its characteristics a controlled drilling environment is needed. Many mines possess the capacity to record Measurement While Drilling (MWD) data but don't utilize it. This project investigates and analyzes MWD data from an anonymous iron ore mine. Machine learning was used to analyze the MWD data for the sake of improving blast optimization and productivity and has been used to successfully implement MWD data in other studies. Based on previous work, it has been demonstrated that the utilization of MWD data can assist with developing a better understanding of rock mass properties and other variables of importance during the drill, blast, and mine planning processes. This report investigates using MWD data to classify and predict lithology and utilize regression modeling to identify potential soft spots within blast patterns for blast optimization. The MWD data of six blast patterns from an anonymous mine underwent data processing and then were modeled. The lithology was able to be approximately classified with new information of potentially revealed bed boundaries and blast pattern soft spots. / Master of Science / In the mining industry, liberating ore from the ground is necessary to process the material and generate products. To accomplish this liberation objective a process of drilling and blasting is utilized. A pattern is designed, and holes are drilled that match the spacing and depth of the design. The blast holes are loaded with explosives and detonated to create fractured rock for the liberation of desired material. During the drilling process, drilling parameters are recorded called Measure While Drilling (MWD) data. Previous research has demonstrated that modeling techniques using MWD data can assist with developing a better understanding of rock mass properties and other variables of importance during the drill, blast, and mine planning processes. Utilizing MWD data and machine learning to improve blasting procedures by classifying and predicting bed assignment and potential soft spots in a blast hole will be investigated in this research. The MWD data comes from 6 blast patterns from an anonymous iron ore mine. After the data was processed and modeled the lithology was classified with a validation accuracy of approximately 78% and potential soft spots estimated.

abstract: Two different techniques utilizing vocalization in clarinet performance were examined through a research study in which one subject (the author) played several tasks utilizing each technique with different played pitches, vocalized pitches, and dynamic levels for each task. The first technique was singing while playing, which is also sometimes referred to as growling. This technique is produced by engaging the vocal folds during regular clarinet performance to create a second vocalized pitch that resonates in the oral cavity and exits through the mouthpiece as part of the same air stream as that used by the vibrating reed. The second technique studied was a much more recently pioneered technique that the author has labelled humming while playing due to its similarity to traditional humming in vocal pedagogy. This technique is produced by filling the oral cavity with air, sealing it off from the rest of the vocal tract using the tongue and soft palate, and humming through the nasal cavity. The cheeks are simultaneously used to squeeze air into the mouthpiece to maintain the clarinet pitch, much like in the technique of circular breathing. For the study, audio, nasalance, and intraoral pressure data were collected and analyzed. Audio was analyzed using spectrograms and root mean square measurements of sound pressure for intensity (IRMS). Analysis of the nasalance data confirmed the description of the physiological mechanisms used to generate the humming while playing technique, with nasalance values for this technique far exceeding those for both singing while playing and regular playing. Intraoral pressure data showed significant spikes in pressure during the transitions from the regular air stream to air stored in the oral cavity when humming while playing. Audio analysis showed that the dynamic range of each technique is similar to that of regular playing, and that each technique produces very different and distinct aural effects. This information was then used to help create a method to assist performers in learning how to produce both singing and humming while playing and a resource to help educate composers about the possibilities and limitations of each technique. / Dissertation/Thesis / Example of Singing While Playing / Example of Humming While Playing / Doctoral Dissertation Music 2019

Tunnel Seismic Prediction (TSP) is a geophysical investigation method used to predict the rock conditions ahead of the tunnel face. The method has been used in different types of rock and rock conditions. The Swedish Transport Administration, Trafikverket, has used the investigation method in multiple locations in the large infrastructure project E4 The Stockholm Bypass. The method is however rather new to Swedish rock conditions, and there is therefore a need to evaluate the method to assess its strengths and weaknesses. In this thesis, the TSP method is compared to other investigation methods used in the Stockholm Bypass project at the location Sätra-Kungshatt where the tunnels cross underneath Lake Mälaren. The investigation methods include geological mapping and Measurement While Drilling (MWD). The TSP results are also compared to the engineering geological prognosis. An evaluation of how the seismic primary and secondary waves, Vp and Vs, correlates to rock quality was carried out, and a linear regression analysis was performed to determine if the wave velocities found using the TSP method correlate with the Q value retrieved through the geological mapping. It was found that the TSP method is capable of detecting weaker zones of rock mass, however no correlation between the wave velocities and the Q value used to describe the quality of the rock mass was found. When comparing the TSP results to the MWD results, it was found that the methods could be used as complements to each other as the different methods sometimes detected weakness zones where the other method did not. As the geology in this location of Stockholm Bypass overall was found to be complex with rather poor rock mass quality, it could be concluded that the TSP method might be better suited for less complex geology where the contrast in rock quality is greater. / Tunnel Seismic Prediction (TSP) är en geofysisk undersökningsmetod för att tillhandhålla en prognos av berget framför tunnelstuffen. Metoden har använts i olika typer av berg och bergförhållanden. I Sverige har metoden använts av Trafikverket vid flertalet tillfällen i infrastrukturprojektet E4 Förbifart Stockholm. Metoden är dock relativt ny för de svenska bergförhållandena, vilket gör att det finns ett behov av att utvärdera metodens styrkor och svagheter i dessa förhållanden. I detta masterprojekt har resultaten som tillhandahållits från TSP-metoden jämförts med resultat från andra undersökningsmetoder som använts vid vattenpassagen vid Sätra-Kungshatt där tunneln korsar under Mälaren. Dessa undersökningsmetoder inkluderar geologisk kartering och Measurement While Drilling (MWD). TSP-resultaten har även jämförts med den ingenjörsgeologiska prognosen för området. Vikt har lagts på hur den seismiska primärvågen, Vp, och sekundärvågen, Vs, förhåller sig till den karterade bergkvaliteten. En regressionsanalys har även utförts för att avgöra om resultaten från TSP-metoden korrelerar med resultaten från den geologiska karteringen. Jämförelsen mellan de olika undersökningsmetoderna visade på att TSP kan påvisa svaghetszoner i bergmassan. Dock kunde ingen korrelation mellan våghastigheterna och Q värdet påvisas. Jämförelsen mellan TSP och MWD visade att de båda metoderna generellt visade liknande resultat. Dock kunde vissa avvikelser mellan resultaten från metoderna hittas, vilket göra att metoderna skulle kunna användas som komplement till varandra. Detta då de olika metoderna ibland kunde identifiera svaghetszoner som den andra metoden inte kunde identifiera. De svåra geologiska förhållandena på platsen kan ha bidragit till att TSP-resultaten över lag är relativt svårtolkade, vilket gör att TSP-metoden möjligtvis är bättre lämpad för mindre komplexa bergförhållanden där kontrasten mellan bra och dålig bergkvalitet är tydligare.