• Korean Journal of Heritage: History & Science
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• v.53 no.3
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• pp.80-109
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• 2020

The Sacheonwangsa Temple in Silla is an esoteric temple that was founded provisionally in 670, and was completed in 679. This study attempted to elucidate the planning method of the Sacheonwangsa Temple based on the results of research on excavations and investigations into its construction processes and construction measures thereof. The research results are as follows. (1) In the site construction, assuming the size of one Bang (坊) on the south of Nangsan Mountain, after dividing the north-south width into three equal parts, there is a possibility that two of these parts were set to the flat portion. (2) In the 'Jochang (祖創, 670)', it is estimated that an area of 300 cheoks by 300 cheoks was postulated on the flat surface, and, as an initial conception, the mandala's plane design of the outer square 2 hasta (3 cheoks) and inner square 1 hasta (1.5 cheoks) was originally devised for the setting of 'Mudra (神印)', and an area 100 times greater has been set as the basis in the scale and layout planning of the central block. (3) During 'Gaechang (攺刱, ~679)', it is judged that because of the narrowness of the distance between the Pagoda and Geumdang Hall, which occurs when the center of the Geumdang Hall coincides with the center of 'the first stage of the foundation (先築基壇)', the scale and layout planning were adjusted from the initial conception. (4) The arrangement of the building was determined by dividing the fixed size of the central block (280 cheoks by 320 cheoks). Specifically, the east-west direction is set on the quartile's line of the east-west width of the central block, and in contrast, the north-south direction is based on the structural characteristics of the central block. It is presumed that the position of the transept was determined through the division and adjustment of the column spacing of the east-west corridor, then the Geumdang Hall and Altar were based on this. (5) The scale of the Geumdang Hall and Pagoda is determined by the petition of the division by the unit fraction starting from the quartile's line of the central block's east-west width. This planning is understood to be based on the self-similarity, which is rooted in the mandala's plane design as the model.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.1
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• pp.57-70
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• 2009

Considerable advance has been made on research on effect of steel pipe Umbrella Arch Method (UAM) and mechanical reinforcement mechanism through numerical analyses and experiments. Due to long analysis time of three-dimensional analysis and its complexity, un-quantitative two-dimensional analysis is dominantly used in the design and application, where equivalent material properties of UAM reinforced area and ground are used, For this reason, development of reasonable, theoretical, quantitative and easy to use design and analysis method is required. In this study, both field UAM tests and laboratory tests were performed in the residual soil to highly weathered rock; field tests to observe the range of reinforcement, and laboratory tests to investigate the change of material properties between prior to and after UAM reinforcement. It has been observed that the increase in material property of neighboring ground is negligible, and that only stiffness of steel pipe and cement column formed inside the steel pipe and the gap between steel pipe and borehole contributes to ground reinforcement. Based on these results and concept of Convergence Confinement Method (CCM), two dimensional axisymmetric analyses have been performed to obtain the longitudinal displacement profile (LDP) corresponding to arching effect of tunnel face, UAM effect and effect of supports. In addition, modified load distribution method in two dimensional plane-strain analysis has been suggested, in which effect of UAM is transformed to internal pressure and modified load distribution ratios are suggested. Comparison between the modified method and conventional method shows that larger displacement occur in the conventional method than that in the modified method although it may be different depending on ground condition, depth and size of tunnel, types of steel pipe and initial stress state. Consequently, it can be concluded that the effect of UAM as a beam in a longitudinal direction is not considered properly in the conventional method.

• Journal of Bio-Environment Control
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• v.22 no.1
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• pp.27-33
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• 2013

In order to provide basic data for the development of a controlled environment cultivation system and standardization of the structures, structural status and improvement methods were investigated for the fruit tree greenhouses of grape, pear, and peach. The greenhouses for citrus and grape cultivation are increasing while pear and persimmon greenhouses are gradually decreasing due to the advance of storage facilities. In the future, greenhouse cultivation will expand for the fruit trees which are more effective in cultivation under rain shelter and are low in storage capability. Fruit tree greenhouses were mostly complying with standards of farm supply type models except for a pear greenhouse and a large single-span peach greenhouse. It showed that there was no greenhouse specialized in each species of fruit tree. Frame members of the fruit tree greenhouses were mostly complying with standards of the farm supply type model or the disaster tolerance type model published by MIFAFF and RDA. In most cases, the concrete foundations were used. The pear greenhouse built with the column of larger cross section than the disaster tolerance type. The pear greenhouse had also a special type of foundation with the steel plate welded at the bottom of columns and buried in the ground. As the results of the structural safety analysis of the fruit tree greenhouses, the grape greenhouses in Gimcheon and Cheonan and the peach greenhouses in Namwon and Cheonan appeared to be vulnerable for snow load whereas the peach greenhouse in Namwon was not safe enough to withstand wind load. The peach greenhouse converted from a vegetable growing facility turned out to be unsafe for both snow and wind loads. Considering the shape, height and planting space of fruit tree, the appropriate size of greenhouses was suggested that the grape greenhouse be 7.0~8.0 m wide and 2.5~2.8 m high for eaves, while 6.0~7.0 m wide and 3.0~3.3 m of eaves height for the pear and peach greenhouses.

• Korean Journal of Environmental Agriculture
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• v.19 no.2
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• pp.171-176
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• 2000

A wastewater treatment pond system was developed for treatment and recycling of dairy cattle excreta of $5\;m^1$ per day. The wastes were diluted by the water used for clearing stalls. The system was composed of three ponds in series. A submerged gas collector for the recovery of methane was installed at the bottom of secondary pond with water depth of 2.4m. This paper deals mainly with performance of methane fermentation of secondary pond which is faclutative one. The average $BOD_5$, SS, TN, and TP concentrations of influent into secondary pond were 49.1, 53.4, 48.6, and 5.3 mg/l, and those of effluent from it were 27.9, 45.7, 30.8, 3.2 mg/l respectively. Methane fermentation of 2.4-meter-deep secondary pond bottom was well established at $16^{\circ}C$ and gas garnered from the collector at that temperature was 80% methane. Literature on methane fermentation of wastewater treatment ponds shows that methane bacteria grow well around $24^{\circ}C$, the rate of daily accumulation and decomposition of sludge is approximately equal at $19^{\circ}C$, and activities of methanogenic bacteria are ceased below $14^{\circ}C$. The good methane fermentation of the pond bottom around $16^{\circ}C$, about $3^{\circ}C$ lower than $19^{\circ}C$, results from temperature stability, anaerobic condition, and neutral pH of the bottom sludge layer. It is recommended that the depth of pond water could be 2.4m. Gas from the collector during active methane fermentation was almost 83% methane, less than 17% nitrogen. Carbon dioxide was less than 1% of the gas, which indicates that carbon dioxide produced in bottom sludges was dissolved in the overlaying water column. Thus a purified methane can be collected and used as energy source. Sludge accumulation on the pond bottom for a nine month period was 1.3cm and annual sludge depth can be estimated to be 1.7cm. Design of additional pond depth of 0.3m can lead to 15 - 20 year sludge removal.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.