• Journal of Animal Environmental Science
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• v.16 no.3
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• pp.205-215
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• 2010

Geothermal heat pump system (GHPS) is an energy-efficient technology that use the relatively constant and renewable energy stored in the earth to provide heating and cooling. With the aim of using GHPS as a heating source, it's possibilities of application in farrowing house were examined by measuring environmental assessment and sow's performance. A total of 96 sows were assigned to 2 pig housings (GHPS and conventional housing) with 48 for four weeks in winter season. During the experimental period, indoor maximum temperature in GHPS-housing was measured up to $26.7^{\circ}C$, average temperature could maintain $21.2^{\circ}C$. The mean value of dust levels and $CO_2$, $NH_3$ and $H_2S$ gas emissions were decreased in GHPS-housing compare with those of conventional housing. Litter size, birth weight, parity and weaning weight did not differ between housings. However, feed intake of sow in GHPS-housing was lower than that of conventional housing. In energy consumption for heating, electric power consumption increased in GHPS-housing than the conventional housing, a 2,250 kwh increase, whereas there is no fuel usage for heater in GHPS-housing. Amount of ground water circulated for heating in cold weather for earth heat exchanger was 8.4-12.9 ton per day. In conclusion, GHPS may have environmental benefits and effectiveness of heating in farrowing housing and affect the performance in sows.

• Journal of the Korean Magnetics Society
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• v.16 no.5
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• pp.255-260
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• 2006

The basic composition of Ni-Zn ferrite was $(Ni_{0.35}Cu_{0.2}Zn_{0.45})_{1.02}(Fe_2O_3)_{0.98}$ (group A) and $(Ni_{0.4}Cu_{0.2}Zn_{0.4})_{1.02}(Fe_2O_3)_{0.98}$(group B) with additional 0.1 mol% $CaCO_3$ and 0.03 mol% $V_2O_5$. For high permeability and acceleration of grain growth, $CaCO_3$ and $V_2O_5$ was added. The mixture of the law materials was calcinated at $600^{\circ}C$ for 2 hours and then milled. The compacts of toroidal type were sintered at different temperature ($1,050^{\circ}C,\;1,070^{\circ}C,\;1,100^{\circ}C$) for 2 hours in air followed by an air cooling. Then, effects of various composition and sintering temperatures on the microstructure and physical properties such as density, resistivity, magnetic induction, coercive force, initial permeability, quality factor, and curie temperature of the Ni-Zn ferrite were investigated. The density of the Ni-Zn ferrite was $4.90{\sim}5.10g/cm^3$, resistivity revealed $10^8{\sim}10^{12}{\Omega}-cm$. The average grain size increased with the increase of sintering temperatures. The magnetic properties obtained from the aforementioned Ni-Zn ferrite specimens were 4,000 gauss for the maximum induction, 0.25 oersted for the coercive force, 2,997 for the initial permeability, 208 for the quality factor, and $202^{\circ}C$ for the curie temperature. The physical properties indicated that the specimens could be utilized as the core of microwave communication and high permeability deflection yoke of high permeability.

• Journal of Chest Surgery
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• v.2 no.1
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• pp.85-104
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• 1969

This experiment was carried out to study the effect of rapid hemorrhage on cardiopulmonary hemodynamics of the cooled dogs. Hypothermia was induced by means of body surface cooling with ice water. Lowest esophageal temperatures ranged from 24 to 26 degree. Dogs were bled via the femoral artery into a reservoir in amount of the equivalent blood volume of 3% of body weight of the dogs. Some dogs were reinfused with the same amount of blood which they lost and others infused with 5% dextrose solution. Fourty adult mongrel dogs were divided into three groups: group I[15 dogs]; dogs were bled in normothermic state. Five dogs had no further treatment, but five dogs were reinfused with blood and five infused with 5% dextrose solution 30 minutes after bleeding. GroupII[10 dogs]; dogs were bled as group I after having been cooled. Five dogs were reinfused with blood as group I. Group III[15 dogs]; dogs were first bled and then cooled. Reinfusion procedures were the same as in group l Results were as follow: 1. The heart rate showed a slight decrease after bleeding in group I and then increased over the control level after 60 minutes. After reinfusion and infusion, the heart rate was also increased gradually and after three hours almost returned to the control level. In group II and groupIll, the heart rate decreased remarkably and after reinfusion showed a light increase but after infusion tended to decrease cotinually. 2. The stroke volume showed remarkable decrease after bleeding in group I., and recovered to control level after reinfusion and infusion,and then gradually decreased again. In group III, the stroke volume showed no remarkable change after hypothermia, and tended to decrease after reinfusion. In group III, the stroke volume decreased remarkably after bleeding and hypothermia,and clearly increased after reinfusion and infusion and then returned to control level. 3. Femoral mean pressure declined very rapidly and significantly right after bleeding and showed a remarkable prompt rise after reinfusion and infusion in group I [67% recovery]. On the other hand, it declined remarkably after hypothermia and bleeding and showed a slight rise after reinfusion and infusion in group II[46% recovery] and III [41% recovery]. 4. Venous pressure declined slightly after bleeding and tended to return to the control level after reinfusion and infusion,in group I. In group II, it did not change significantly during hypothermia but showed a slight decline after bleeding and returned toward control level after reinfusion. In group III, it declined slightly after bleeding and showed no significant change after hypothermia and rose over the control level after reinfusion and infusion. 5. Right ventricular systolic pressure decreased markedly after bleeding and then increased progressively after 30 minutes. It increased after reinfusion and infusion as well, approaching the control level in group I. In group II, it showed no significant change during hypothermia, but decreased remarkably after bleeding and then returned to near control level after reinfusion. In group III, it was decreased markedly after bleeding but did not change significantly during hypothermia and showed a slight increase after reinfusion. 6. The respiratory rate increased gradually after bleeding and decreased gradually after reinfusion but did not return to the control level, whereas it decreased near to the control level after infusion,and tended to increase in group I. In group II, it decreased significantly after hypothermia and bleeding but returned near to the control level after reinfusion. In group III, it showed a remarkable decrease after hypothermia and increased slightly after reinfusion and infusion but did not returned to the control level. In group I, the tidal volume decreased slightly after hemorrhage, and increased gradually to near the control level after 3 hours following reinfusion.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.481-493
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• 2023

In high-density urban areas, the urban heat island effect increases urban temperatures, leading to negative impacts such as worsened air pollution, increased cooling energy consumption, and increased greenhouse gas emissions. In urban environments where it is difficult to secure additional green spaces, rooftop greening is an efficient greenhouse gas reduction strategy. In this study, we not only analyzed the current status of the urban heat island effect but also utilized high-resolution satellite data and spatial information to estimate the available rooftop greening area within the study area. We evaluated the mitigation effect of the urban heat island phenomenon and carbon sequestration capacity through temperature predictions resulting from rooftop greening. To achieve this, we utilized WorldView-2 satellite data to classify land cover in the urban heat island areas of Busan city. We developed a prediction model for temperature changes before and after rooftop greening using machine learning techniques. To assess the degree of urban heat island mitigation due to changes in rooftop greening areas, we constructed a temperature change prediction model with temperature as the dependent variable using the random forest technique. In this process, we built a multiple regression model to derive high-resolution land surface temperatures for training data using Google Earth Engine, combining Landsat-8 and Sentinel-2 satellite data. Additionally, we evaluated carbon sequestration based on rooftop greening areas using a carbon absorption capacity per plant. The results of this study suggest that the developed satellite-based urban heat island assessment and temperature change prediction technology using Random Forest models can be applied to urban heat island-vulnerable areas with potential for expansion.

• 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.