• Economic and Environmental Geology
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• v.30 no.4
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• pp.289-301
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• 1997

The Boguk cobalt mine is located within the Cretaceous Gyeongsang Sedimentary Basin. Major ore minerals including cobalt-bearing minerals (loellingite, cobaltite, and glaucodot) and Co-bearing arsenopyrite occur together with base-metal sulfides (pyrrhotite, chalcopyrite, pyrite, sphalerite, etc.) and minor amounts of oxides (magnetite and hematite) within fracture-filling $quartz{\pm}actinolite{\pm}carbonate$ veins. These veins are developed within an epicrustal micrographic granite stock which intrudes the Konchonri Formation (mainly of shale). Radiometric date of the granite (85.98 Ma) indicates a Late Cretaceous age for granite emplacement and associated cobalt mineralization. The vein mineralogy is relatively complex and changes with time: cobalt-bearing minerals with actinolite, carbonates, and quartz gangues (stages I and II) ${\rightarrow}$ base-metal sulfides, gold, and Fe oxides with quartz gangues (stage III) ${\rightarrow}$ barren carbonates (stages IV and V). The common occurrence of high-temperature minerals (cobalt-bearing minerals, molybdenite and actinolite) with low-temperature minerals (base-metal sulfides, gold and carbonates) in veins indicates a xenothermal condition of the hydrothermal mineralization. High enrichment of Co in the granite (avg. 50.90 ppm) indicates the magmatic hydrothermal derivation of cobalt from this cooling granite stock, whereas higher amounts of Cu and Zn in the Konchonri Formation shale suggest their derivations largely from shale. The decrease in temperature of hydrothermal fluids with a concomitant increase in fugacity of oxygen with time (for cobalt deposition in stages I and II, $T=560^{\circ}C-390^{\circ}C$ and log $fO_2=$ >-32.7 to -30.7 atm at $350^{\circ}C$; for base-metal sulfide deposition in stage III, $T=380^{\circ}-345^{\circ}C$ and log $fO_2={\geq}-30.7$ atm at $350^{\circ}C$) indicates a transition of the hydrothermal system from a magmatic-water domination toward a less-evolved meteoric-water domination. Sulfur isotope data of stage II sulfide minerals evidence that early, Co-bearing hydrothermal fluids derived originally from an igneous source with a ${\delta}^{34}S_{{\Sigma}S}$ value near 3 to 5‰. The remarkable increase in ${\delta}^{34}S_{H2S}$ values of hydrothermal fluids with time from cobalt deposition in stage II (3-5‰) to base-metal sulfide deposition in stage III (up to about 20‰) also indicates the change of the hydrothermal system toward the meteoric water domination, which resulted in the leaching-out and concentration of isotopically heavier sulfur (sedimentary sulfates), base metals (Cu, Zn, etc.) and gold from surrounding sedimentary rocks during the huge, meteoric water circulation. We suggest that without the formation of the later, meteoric water circulation extensively through surrounding sedimentary rocks the Boguk cobalt deposits would be simple veins only with actinolite + quartz + cobalt-bearing minerals. Furthermore, the formation of the meteoric water circulation after the culmination of a magmatic hydrothermal system resulted in the common occurrence of high-temperature minerals with later, lower-temperature minerals, resulting in a xenothermal feature of the mineralization.

• Economic and Environmental Geology
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• v.36 no.6
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• pp.391-405
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• 2003

The Daebong gold-silver deposit consists of mesothermal massive quartz veins thar are filling the fractures along fault shear (NE, NW) Bones within banded or granitic gneiss of Precambrian Gyeonggi massif. Based on vein mineralogy, ore textures and paragenesis, ore mineralization of this deposits is composed of massive white quartz vein(stage I) which was formed in the same stage by multiple episodes of fracturing and healing, and transparent quartz vein(stage II) which is separated by a major faulting event. Stage I is divided into the 3 substages. Ore minerals of each substages are as follows: 1) early stage I=magnetite, pyrrhotite, arsenopyrite, pyrite, sphalerite, chalcopyrite, 2) middle stage I=pyrrhotite, arsenopyrite, pyrite, marcasite, sphalerite, chalcopyrite, galena, electrum and 3) late stage I=pyrite, sphalerite, chalcopyrite, galena, electrum, argentite, respectively. Ore minerals of the stage II are composed of pyrite, sphalerite, chalcopyrite, galena and electrum. Systematic studies (petrography and microthermometry) of fluid inclusions in stage I and II quartz veins show fluids from contrasting physical-chemical conditions: 1) $H_2O-CO_2-CH_4-NaCl{\pm}N-2$ fluid(early stage I=homogenization temperature: 203∼3$88^{\circ}C$, pressure: 1082∼2092 bar, salinity: 0.6∼13.4 wt.%, middle stage I=homogenization temperature: 215∼28$0^{\circ}C$, salinity: 0.2∼2.8 wt.%) related to the stage I sulfide deposition, 2) $H_2O-NaCl{\pm}CO_2$ fluid (late stage I=homogenization temperature: 205∼2$88^{\circ}C$, pressure: 670 bar, salinity: 4.5∼6.7 wt.%, stage II=homogenization temperature: 201-3$58^{\circ}C$, salinity: 0.4-4.2 wt.%) related to the late stage I and II sulfide deposition. $H_2O-CO_2-CH_4-NaCl{\pm}N_2$ fluid of early stage I is evolved to $H_2O-NaCl{\pm}CO_2$ fluid represented by the $CO_2$ unmixing due to decrease in fluid pressure and is diluted and cooled by the mixing of deep circulated meteoric waters ($H_2O$-NaCl fluid) possibly related to uplift and unloading of the mineralizing suites. $H_2O-NaCl{\pm}CO_2$ fluid of stage II was hotter than that of late stage I and occurred partly unmixing, mainly dilution and cooling for sulfide deposition. Calculated sulfur isotope compositions ({\gamma}^{34}S_{H2S}$) of hydrothermal fluids (3.5∼7.9%o) indicate that ore sulfur was derived from mainly an igneous source and partly sulfur of host rock. Measured and calculated oxygen and hydrogen isotope compositions ({\gamma}^{18}O_{H_2O}$, {\gamma}$D) of ore fluids (stage I: 1.1∼9.0$\textperthousand$, -92∼-86{\textperthansand}$, stage II: 0.3{\textperthansand}$, -93{\textperthansand}$) and ribbon-banded structure (graphitic lamination) indicate that mesothermal auriferous fluids of Daebong deposit were two different origin and their evolution. 1) Fluids of this deposit were likely mixtures of $H_2O$-rich, isotopically less evolved meteoric water and magmatic fluids and 2) were likely mixtures of $H_2O$-rich. isotopically heavier $\delta$D meteoric water and magmaticmetamorphic fluids.

• Economic and Environmental Geology
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• v.35 no.3
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• pp.211-220
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• 2002

The Xiaoxinancha Cu-Au deposit in the Jilin province, located in NNE 800 km of Beijing, is hosted by diorite. The ore mineralization of Xiaoxinancha Cu-Au deposit show a stockwork occurrence that is concentrated on the potassic and phyllic alteration zones. The Xiaoxinancha Cu-Au deposit in the south is being mined with its reserves grading 0.8% Cu, 3.64 g/t Au and 16.8 g/t Ag and in the north, grading 0.63% Cu, 3.80 g/t Au and 6.8 glt Ag. The alteration assemblage occurs as a supergene blanket over deposit. Hydrothermal alteration at the Xiaoxinancha Cu-Au deposit is centered about the stock and was extensively related to the emplacement of the stock. Early hydrothermal alteration was dominantly potassic and followed by propylitic alteration. Chalcocite, often associated with hematite, account for the ore-grade copper, while chalcopyrite, bornite, quartz, epidote, chlorite and calcite constitute the typical gangue assemblage. Other minor opaque phases include pyrite, marcasite, native gold, electrum, hessite, hedleyite, volynskite, galenobismutite, covellite and goethite. Fluid inclusion data indicate that the formation of this porphyry copper deposit is thought to be a result of cooling followed by mixing with dilute and cooler meteoric water with time. In stage II vein, early boiling occurred at 497$^{\circ}$C was succeeded by the occurrence of halite-bearing type III fluid inclusion with homogenization temperature as much as 100$^{\circ}$C lower. The salinities of type 1II fluid inclusion in stage II vein are 54.3 to 66.9 wt.% NaCI + KCI equiv. at 383$^{\circ}$ to 495$^{\circ}$C, indicating the formation depth less than 1 km. Type I cupriferous fluids in stage III vein have the homogenization temperatures and salinity of 168$^{\circ}$ to 365$^{\circ}$C and 1.1 to 9.0 wt.% NaCI equiv. These fluid inclusions in stage III veins were trapped in quartz veins containing highly fractured breccia, indicating the predominance of boiling evidence. This corresponds to hydrostatic pressure of 50 to 80 bars. The $\delta$$^{34}S$ value of sulfide minerals increase slightly with paragenetic time and yield calculated $\delta$$^{34}S_{H2S}$ values of 0.8 to 3.7$\textperthousand$. There is no mineralogical evidence that fugacity of oxygen decreased, and it is thought that the oxygen fugacity of the mineralizing fluids have been buffered through reaction with magnetite. We interpreted the range of the calculated $\delta$$^{34}S_{H2S}$ values for sulfides to represent the incorporation of sulfur from two sources into the Xiaoxinancha Cu-Au hydrothermal fluids: (1) an isotopically light source with a $\delta$$^{34}S$ value of I to 2$\textperthousand$, probably a Mesozoic granitoid related to the ore mineralization. We can infer from the fact that diorite as the host rock in the Xiaoxinancha Cu-Au deposit area intruded plagiogranite; (2) an isotopically heavier source with a $\delta$$^{34}S$ value of > 4.0$\textperthousand$, probably the local porphyry.

• Journal of the Korean Wood Science and Technology
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• v.2 no.2
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• pp.32-34
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• 1974

The important results which have been obtained in the investigation can be recapitulated as follows. 1. As demonstrated by the experimental results and analyses concerning their effects in the on-ground type mushroom house, the constructions in relation to the side wall and ceiling of the experimental house showed a sufficient heat insulation on effect to protect insides of the house from outside climatic conditions. 2. As the effect on the solar type experimental mushroom house which was constructed in a half basement has been shown by the experimental results and analyses, it has been proved to be effective for making use of solar heat. However there were found two problems to be improved for putting solar house to practical use in the farm mushroom growing: (1) the construction of the roof and ceiling should be the same as for the on ground type house, and (2) the solar heat generating system should be reconstructed properly. 3. Among several ventilation systems which have been studied in the experiments, the underground earthen pipe and ceiling ventilation, and vertical side wall and ceiling ventilation systems have been proved to be most effective for natural ventilation. 4. The experimental results have shown that ventilation systems such as the vertical side wall and underground ventilation systems are suitable to put to practical use as natural ventilation systems for farm mushroom house. These ventilation systems can remarkably improve the temperature of fresh air which is introduced into the house by heat transfers within the ventilation passages, so as to approach to the desired temperature of the house without any cooling or heating operation. For example, if it is assuming that X is the outside temperature and Y is the amount of temperature adjustment made by the influence of the ventilation system, the relationships that exist between X and Y can be expressed by the following regression lines. Underground iron pipe ventilation system. Y=0.9X-12.8 Underground earthen pipe ventilation system. Y=0.96X-15.11 Vertical side wall ventilation system. Y=0.94X-17.57 5. The experimental results have 8hown that the relationships existing between the admitted and expelled air and the $CO_2$ concentration can be described with experimental regression lines or an exponent equation as follows: 5.1 If it is assumed that X is an air speed cm/sec. and Y is an expelled air speed in cm/sec. in a natural ventilation system, since the Y is a function of the X, the relationships that exist between X and Y can be expressed by the regression lines shown below: 5.2 If it IS assumed that X is an admitted volume of air in $m^3$/hr. and Y is an expelled volume of air in $m^3$/hr. in a natural ventilation system, since the Y is a function of the X, the relationships that exist between X and Y can be expressed by the regression lines shown below. 5.3 If it is assumed that expelled air speed in emisec. and replacement air speed in cm/sec. at the bed surface in a natural ventilation system are shown as X and Y. respectively, since the Y is a function of the X. the relationships that exist between X and Y can be expressed by the following regression line: GE(100%)-CV (50%) ventilation system. Y=-0.54X+0.84 5.4 If it is assumed that the replacement air speed in cm/sec. at the bed surface is shown as X, and $CO_2$ concentration which is expressed by multiplying 1000 times the actual value of $CO_2$ % is shown as Y, in a natural ventilation system, since the Y is a function of the X, the relationships that exist between X and Y can be expressed by the following regression line: GE(100%)-CV(50%) ventilation system. Y=114.53-6.42X 5.5 If it is assumed that the expelled volume of air is shown as X and the $CO_2$ concencration which is expressed by multiplying 1000 times the actual of $CO_2$% is shown as Y in a natural ventilation system, since the Y is a function of the X, the relationships that exist between X and Y can be expressed by the following exponent equation: GE(100%)-CV(50%) ventilation system. Y=$127.18{\times}1.0093^{-x}$ 5.6 The experimental results have shown that the ratios of the cross sectional area of the GE and CV vent to the total cubic capacity of the house, required for providing an adequate amount of air in a natural ventilation system, can be estimated as follows: GE(admitting vent of the underground ventilation) 0.3-0.5% (controllable) CV(expelling vent of the ceiling ventilation) 0.8-1.0% (controllable) 6. Among several heating devices which were studied in the experiments, the hot-water boilor which wasmodified to be fitted both as hot-water boiler and as a pressureless steam-water was found most suitable for farm mushroom growing.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of Korean Society of Forest Science
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• v.9 no.1
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• pp.1-44
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• 1969

The important results which have been obtained in the investigation can be recapitulated as follows. 1. As demostrated by the experimental results and analyses concerning their effects in the on-ground type mushroom house, the constructions in relation to the side wall and ceiling of the experimental houses showed a sufficient heat insulation on effect to protect insides of the houses from outside climatic conditions. 2. As the effect on the solar type experimental mushroom house which was constructed in a half basement has been shown by the experimental results and analyses, it has been proved to be effective for making use of solar heat. However there were found two problems to be improved for putting solar houses to practical use in the farm mushroom growing: (1) the construction of the roof and ceiling should be the same as for the on-ground type house, and (2) the solar heat generating system should be reconstructed properly. A trial solar heat generating system is shown in Fig. 40. 3. Among several ventilation systems which have been studied in the experiments, the underground earthen pipe and ceiling ventilation, and vertical side wall and ceiling ventilation systems have been proved to be most effective for natural ventilation. 4. The experimental results have shown that ventilation systems such as the vertical side wall and underground ventilation systems are suitable to put to practical use as natural ventilation systems for farm mushroom houses. These ventilation systems can remarkably improve the temperature of fresh air which is introduced into the house by heat transfers within the ventilation passages, so as to approach to the desired temperature of the house without any cooling or heating operation. For example, if it is assuming that x is the outside temperature and y is the amount of temperature adjustment made by the influence of the ventilation system, the relationships that exist between x and y can be expressed by the following regression lines. Underground iron pipe ventilation system ${\cdots}{\cdots}$ y=0.9x-12.8 Underground earthen pipe ventilation system ${\cdots}{\cdots}$y=0.96x-15.11 Vertical side wall ventilation system${\cdots}{\cdots}$ y=0.94x-17.57 5. The experimental results have shown that the relationships existing between the admitted and expelled air and the $Co_2$ concentration can be described with experimental regression lines or an exponent equation as follows: 1) If it is assumed that x is an air speed cm/sec. and y is an expelled air speed in cm/sec. in a natural ventilation system, since the y is a function of the x, the relationships that exist between x and y can be expressed by the regression lines shown below: 2) If it is assumed that x is an admitted volume of air in $m^3/hr$ and y is an expelled volume of air in $m^3/hr$ in a natural ventilation system, since the y is a function of the x, the relationships that exist between x and y can be expressed by the regression lines shown below. 3) If it is assumed that the expelled air speed in cm/sec and replacement air speed in cm/sec. at the bed surface in a natural ventilation system are shown as x and y, respectively, since the y is a function of the x, the relationships that exist between x and y can be expressed by the following regression line: G.E. (100%)- C.V. (50%) ventilation system${\cdots}$ y=0.54X+0.84 4) If it is assumed that the replacement air speed in cm/sec. at the bed surface is shown as x, and $CO_2$ concentration which is expressed by multiplying 1000 times the actual value of $CO_2$ % is shown as y, in a natural ventilation system, since the y is a function of the x the relationships that exist between x and y can be expressed by the following regression line: G.E. (100%)- C.V. (50%) ventilation system${\cdots}{\cdots}$ y=114.53-6.42x 5) If it is assumed that the expelled volume of air is shown as x and the $CO_2$ concentration which is expressed by multiplying 1000 times the actual of $CO_2$ % is shown as y in a natural ventilation system, since the y is a function of of the x, the relationships that exist between x and y can be expressed by the following exponent equation: G.E. (100%)-C.V. (50%) ventilation system${\cdots}{\cdots}$ $$y=127.18{\times}1.0093^{-X}$$ 6. The experimental results have shown that the ratios of the crass sectional area of the G.E. and C.V. vent to the total cubic capacity of the house, required for providing an adequate amount of air in a natural ventilation system, can be estimated as follows: G.E. (admitting vent of the underground ventilation)${\cdots}{\cdots}$ 0.30-0.5% (controllable) C.V. (expelling vent of the ceiling ventilation)${\cdots}{\cdots}$ 0.8-1.0% (controllable) 7. Among several heating devices which were studied in the experiments, the hot-water boilor which was modified to be fitted both as hot-water toiler and as a pressureless steam-water was found most suitable for farm mushroom growing.