• Proceedings of the KSEEG Conference
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• 2001.05b
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• pp.42-63
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• 2001

The detail survey on the Songsanri tomb site including the Muryong royal tomb was carried out during the period from May 1 , 1996 to April 30, 1997. A quantitative analysis was tried to find changes of tomb itself since the excavation. Main subjects of the survey are to find out the cause of infiltration of rain water and groundwater into the tomb and the tomb site, monitoring of the movement of tomb structure and safety, removal method of the algae inside the tomb, and air controlling system to solve high humidity condition and dew inside the tomb. For these purposes, detail survery inside and outside the tombs using a electronic distance meter and small airplane, monitoring of temperature and humidity, geophysical exploration including electrical resistivity, geomagnetic, gravity and georadar methods, drilling, measurement of physical and chemical properties of drill core and measurement of groundwater permeability were conducted. We found that the center of the subsurface tomb and the center of soil mound on ground are different 4.5 meter and 5 meter for the 5th tomb and 7th tomb, respectively. The fact has caused unequal stress on the tomb structure. In the 7th tomb (the Muryong royal tomb), 435 bricks were broken out of 6025 bricks in 1972, but 1072 bricks are broken in 1996. The break rate has been increased about 250% for just 24 years. The break rate increased about 290% in the 6th tomb. The situation in 1996 is the result for just 24 years while the situation in 1972 was the result for about 1450 years. Status of breaking of bircks represents that a severe problem is undergoing. The eastern wall of the Muryong royal tomb is moving toward inside the tomb with the rate of 2.95 mm/myr in rainy season and 1.52 mm/myr in dry season. The frontal wall shows biggest movement in the 7th tomb having a rate of 2.05 mm/myr toward the passage way. The 6th tomb shows biggest movement among the three tombs having the rate of 7.44mm/myr and 3.61mm/myr toward east for the high break rate of bricks in the 6th tomb. Georadar section of the shallow soil layer represents several faults in the top soil layer of the 5th tomb and 7th tomb. Raninwater flew through faults tnto the tomb and nearby ground and high water content in nearby ground resulted in low resistance and high humidity inside tombs. High humidity inside tomb made a good condition for algae living with high temperature and moderate light source. The 6th tomb is most severe situation and the 7th tomb is the second in terms of algae living. Artificial change of the tomb environment since the excavation, infiltration of rain water and groundwater into the tombsite and bad drainage system had resulted in dangerous status for the tomb structure. Main cause for many problems including breaking of bricks, movement of tomb walls and algae living is infiltration of rainwater and groundwater into the tomb site. Therefore, protection of the tomb site from high water content should be carried out at first. Waterproofing method includes a cover system over the tomvsith using geotextile, clay layer and geomembrane and a deep trench which is 2 meter down to the base of the 5th tomb at the north of the tomv site. Decrease and balancing of soil weight above the tomb are also needed for the sfety of tomb structures. For the algae living inside tombs, we recommend to spray K101 which developed in this study on the surface of wall and then, exposure to ultraviolet light sources for 24 hours. Air controlling system should be changed to a constant temperature and humidity system for the 6th tomb and the 7th tomb. It seems to much better to place the system at frontal room and to ciculate cold air inside tombs to solve dew problem. Above mentioned preservation methods are suggested to give least changes to tomb site and to solve the most fundmental problems. Repairing should be planned in order and some special cares are needed for the safety of tombs in reparing work. Finally, a monitoring system measuring tilting of tomb walls, water content, groundwater level, temperature and humidity is required to monitor and to evaluate the repairing work.

• 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.