Master of Science

thesisMining and the related industries play an important role in the Korean economy. The Korean mining industry is relatively small. There are several mines in Korea but they are very small, and production is low in comparison with other countries' mines. Thus Korea must take part in mineral projects all over the world to secure essential minerals such as coal, copper, uranium, iron ore, zinc, and nickel to support its manufacturing industries. For that reason, the Korean government established K Company1 as a government corporation to invest in mineral deposits throughout the world. However there are several major entities, including BHP Billiton, Rio Tinto, Anglo American, Xstrata, Freeport McMoRan, Chinese government-run companies, and Japanese trading companies that have large foreign holdings, controlling deposits in most of the world's established and productive mining districts. As a relative newcomer, K Company is having difficulty breaking into these markets. In general, the mining industry considers the United States, Canada, Australia, and European countries to have good mining investment environments, but because the major companies have already achieved market dominance in those countries large investments are required. Thus K Company is increasingly turning to new areas like South America (Peru and Bolivia) and Africa. The competition among businesses to secure a share of the new market is intense but there are still good investment opportunities. However, because most of the countries in these areas are not well developed, there are several additional risks associated with participation in mining projects there. Risk is a major factor in all mining activities, arising from many internal and external variables. In this thesis, those variables are identified, and their effects evaluated, based on a survey of 31 experts. A statistical model to analyze the effect of risk on the economic feasibility of mine development and operation at a given location is presented, and validated using analysis from projects at K Company. The guidelines and model, as presented here, will enable K Company and other investors to make better investment decisions in the future

Unsupervised Speech Representation Pooling Using Vector Quantization

With the advent of general-purpose speech representations from large-scale self-supervised models, applying a single model to multiple downstream tasks is becoming a de-facto approach. However, the pooling problem remains; the length of speech representations is inherently variable. The naive average pooling is often used, even though it ignores the characteristics of speech, such as differently lengthed phonemes. Hence, we design a novel pooling method to squash acoustically similar representations via vector quantization, which does not require additional training, unlike attention-based pooling. Further, we evaluate various unsupervised pooling methods on various self-supervised models. We gather diverse methods scattered around speech and text to evaluate on various tasks: keyword spotting, speaker identification, intent classification, and emotion recognition. Finally, we quantitatively and qualitatively analyze our method, comparing it with supervised pooling methods

Removal of Total Dissolved Solids from Reverse Osmosis Concentrates from a Municipal Wastewater Reclamation Plant by Aerobic Granular Sludge

Reverse osmosis (RO) has been widely utilized in water reclamation plants and produces a concentrated brine (or reject) stream as a by-product. RO concentrates (ROC) contain vast quantities of salts and dissolved organic matter, such as biomass and humic-like substances, which hinder biological wastewater treatment (such as biological nitrogen removal). In this study, we cultivated granular sludge in an aerobic sequencing batch reactor to treat municipal wastewater with an organic loading rate of 2.1–4.3 kgCOD/m3 day at room temperature (25 °C), and remove total dissolved solids (TDS) from ROC by biosorption, with aerobic granular sludge as a novel biosorbent. The results of the kinetic experiments demonstrated that TDS removal by aerobic granular sludge was more rapid than that by other coagulants and adsorbents (i.e., calcium hydroxide, polyaluminum chloride, activated sludge, powdered activated carbon, granular activated carbon, and zeolite) under optimal treatment conditions. The biosorption of TDS on the aerobic granular sludge was well-modeled by the Lagergren first-order model, with a maximum biosorption capacity of 1698 mg/g. Thus, aerobic granular sludge could be effective as a regenerable biosorbent for removing the TDS in ROC from municipal wastewater

Minimax particle filtering for tracking a highly maneuvering target

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152473/1/rnc4785_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152473/2/rnc4785.pd

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Cryogenic machining uses liquid nitrogen (LN2) as a coolant. This machining process can reduce the cutting temperature and increase tool life. Titanium alloys have been widely used in the aerospace and automobile industries because of their high strength-to-weight ratio. However, they are difficult to machine because of their poor thermal properties, which reduce tool life. In this study, we applied cryogenic machining to titanium alloys. Orthogonal cutting experiments were performed at a low cutting speed (1.2 - 2.1 m/min) in three cooling conditions: dry, cryogenic, and cryogenic plus heat. Cutting force and friction coefficients were observed to evaluate the machining characteristics for each cooling condition. For the cryogenic condition, cutting force and friction coefficients increased, but decreased for the cryogenic plus heat condition

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The surface roughness and cutting forces are the important factors for the machine-part quality during the hard-turning process. The aim of this paper is to optimize hard-cutting conditions via implementation of response surface methodology (RSM). The experiments were conducted for the hard-turning process with the Box-Behnken design. The validation of the surface roughness and cutting forces was performed with the obtained 2nd order polynomial regression model. The results showed that the surface roughness was strongly dependent upon the RPM. The diminution of the cutting force was attributed to the low feed rate and the depth of cut. On the basis of the RSM, optimized cutting conditions of RPM, feed rate, and depth of cut are 3440, 0.0352 [mm/rev], and 0.03 [mm]. In this optimal cutting condition, the surface roughness can be around Ra= 0.202 ??m