• Journal of the Korean Geotechnical Society
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• v.35 no.5
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• pp.5-19
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• 2019

Considering the natural phenomenon in which steep slopes ($65^{\circ}{\sim}85^{\circ}$) consisting of rock mass remain stable for decades, slopes steeper than 1:0.5 (the standard of slope angle for blast rock) may be applied in geotechnical conditions which are similar to those above at the design and initial construction stages. In the process of analysing the stability of a good to fair continuum rock slope that can be designed as a steep slope, a general method of estimating rock mass strength properties from design practice perspective was required. Practical and genealized engineering methods of determining the properties of a rock mass are important for a good continuum rock slope that can be designed as a steep slope. The Genealized Hoek-Brown (H-B) failure criterion and GSI (Geological Strength Index), which were revised and supplemented by Hoek et al. (2002), were assessed as rock mass characterization systems fully taking into account the effects of discontinuities, and were widely utilized as a method for calculating equivalent Mohr-Coulomb shear strength (balancing the areas) according to stress changes. The concept of calculating equivalent M-C shear strength according to the change of confining stress range was proposed, and on a slope, the equivalent shear strength changes sensitively with changes in the maximum confining stress (${{\sigma}^{\prime}}_{3max}$ or normal stress), making it difficult to use it in practical design. In this study, the method of estimating the strength properties (an iso-angle division method) that can be applied universally within the maximum confining stress range for a good to fair continuum rock mass slope is proposed by applying the H-B failure criterion. In order to assess the validity and applicability of the proposed method of estimating the shear strength (A), the rock slope, which is a study object, was selected as the type of rock (igneous, metamorphic, sedimentary) on the steep slope near the existing working design site. It is compared and analyzed with the equivalent M-C shear strength (balancing the areas) proposed by Hoek. The equivalent M-C shear strength of the balancing the areas method and iso-angle division method was estimated using the RocLab program (geotechnical properties calculation software based on the H-B failure criterion (2002)) by using the basic data of the laboratory rock triaxial compression test at the existing working design site and the face mapping of discontinuities on the rock slope of study area. The calculated equivalent M-C shear strength of the balancing the areas method was interlinked to show very large or small cohesion and internal friction angles (generally, greater than $45^{\circ}$). The equivalent M-C shear strength of the iso-angle division is in-between the equivalent M-C shear properties of the balancing the areas, and the internal friction angles show a range of $30^{\circ}$ to $42^{\circ}$. We compared and analyzed the shear strength (A) of the iso-angle division method at the study area with the shear strength (B) of the existing working design site with similar or the same grade RMR each other. The application of the proposed iso-angle division method was indirectly evaluated through the results of the stability analysis (limit equilibrium analysis and finite element analysis) applied with these the strength properties. The difference between A and B of the shear strength is about 10%. LEM results (in wet condition) showed that Fs (A) = 14.08~58.22 (average 32.9) and Fs (B) = 18.39~60.04 (average 32.2), which were similar in accordance with the same rock types. As a result of FEM, displacement (A) = 0.13~0.65 mm (average 0.27 mm) and displacement (B) = 0.14~1.07 mm (average 0.37 mm). Using the GSI and Hoek-Brown failure criterion, the significant result could be identified in the application evaluation. Therefore, the strength properties of rock mass estimated by the iso-angle division method could be applied with practical shear strength.

• MISULJARYO - National Museum of Korea Art Journal
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• v.101
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• pp.12-46
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• 2022

Completed in 1467, the Thirteen-story Stone Pagoda of Wongaksa Temple is the last Buddhist pagoda erected at the center of the capital (present-day Seoul) of the Joseon Dynasty. It was commissioned by King Sejo, the final Korean king to favor Buddhism. In this paper, I aim to examine King Sejo's intentions behind celebrating the tenth anniversary of his enthronement with the construction of the thirteen-story stone pagoda in the central area of the capital and the enshrinement of sarira from Shakyamuni Buddha and the Newly Translated Sutra of Perfect Enlightenment (圓覺經). This paper provides a summary of this examination and suggests future research directions. The second chapter of the paper discusses the scriptural background for thirteen-story stone pagodas from multiple perspectives. I was the first to specify the Latter Part of the Nirvana Sutra (大般涅槃經後分) as the most direct and fundamental scripture for the erection of a thirteen-story stone pagoda. I also found that this sutra was translated in Central Java in the latter half of the seventh century and was then circulated in East Asia. Moreover, I focused on the so-called Kanishka-style stupa as the origin of thirteen-story stone pagodas and provided an overview of thirteen-story stone pagodas built around East Asia, including in Korea. In addition, by consulting Buddhist references, I prove that the thirteen stories symbolize the stages of the practice of asceticism towards enlightenment. In this regard, the number thirteen can be viewed as a special and sacred number to Buddhist devotees. The third chapter explores the Buddhist background of King Sejo's establishment of the Thirteen-story Stone Pagoda of Wongaksa Temple. I studied both the Dictionary of Sanskrit-Chinese Translation of Buddhist Terms (翻譯名義集) (which King Sejo personally purchased in China and published for the first time in Korea) and the Sutra of Perfect Enlightenment. King Sejo involved himself in the first translation of the Sutra of Perfect Enlightenment into Korean. The Dictionary of Sanskrit-Chinese Translation of Buddhist Terms was published in the fourteenth century as a type of Buddhist glossary. King Sejo is presumed to have been introduced to the Latter Part of the Nirvana Sutra, the fundamental scripture regarding thirteen-story pagodas, through the Dictionary of Sanskrit-Chinese Translation of Buddhist Terms, when he was set to erect a pagoda at Wongaksa Temple. King Sejo also enshrined the Newly Translated Sutra of Perfect Enlightenment inside the Wongaksa pagoda as a scripture representing the entire Tripitaka. This enshrined sutra appears to be the vernacular version for which King Sejo participated in the first Korean translation. Furthermore, I assert that the original text of the vernacular version is the Abridged Commentary on the Sutra of Perfect Enlightenment (圓覺經略疏) by Zongmi (宗密, 780-841), different from what has been previously believed. The final chapter of the paper elucidates the political semantics of the establishment of the Wongaksa pagoda by comparing and examining stone pagodas erected at neungsa (陵寺) or jinjeonsawon (眞殿寺院), which were types of temples built to protect the tombs of royal family members near their tombs during the early Joseon period. These stone pagodas include the Thirteen-story Pagoda of Gyeongcheonsa Temple, the Stone Pagoda of Gaegyeongsa Temple, the Stone Pagoda of Yeongyeongsa Temple, and the Multi-story Stone Pagoda of Silleuksa Temple. The comparative analysis of these stone pagodas reveals that King Sejo established the Thirteen-story Stone Pagoda at Wongaksa Temple as a political emblem to legitimize his succession to the throne. In this paper, I attempt to better understand the scriptural and political semantics of the Wongaksa pagoda as a thirteen-story pagoda. By providing a Korean case study, this attempt will contribute to the understanding of Buddhist pagoda culture that reached its peak during the late Goryeo and early Joseon periods. It also contributes to the research on thirteen-story pagodas in East Asia that originated with Kanishka stupa and were based on the Latter Part of the Nirvana Sutra.

• 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.