• MISULJARYO - National Museum of Korea Art Journal
• /
• v.100
• /
• pp.112-138
• /
• 2021

This paper mainly analyzes the records titled 'Shijujil(施主秩)' from the Bokjangs of each of the Rocana and Shakyamuni statues enshrined as wooden seated Vairocana Buddha Triadcomposed of Vairocana(center), Rocana(right), and Shakyamuni(left) at the Daeungjeon Hall of Hwaeomsa Temple in Gurye. The Shijujil from the Shakyamuni statue was recovered through Bokjang investigation in September 2015 and has been kept in the museum of Hwaeomsa as an undisclosed relic. After the discovery of the Shijujil from the Rocana statue through an Bokjang investigation in July 2020, both of the Shijujils were only officially released through the special exhibition 'Grand Hwaeomsa Temple in Jirisan Mountain' in September 2021. Existing documents recording on the creation of Buddha statues in the 17th century are in the form of sheets or rolls. However, the Shijujils take the form of simple stitched booklets. The Shijujil from Rocana consists of 19 chapters and 38 pages in one book, and the Shijujil from Shakyamuni consists of 11 chapters and 22 pages in one book. The contents of the Shijujils consist of the purpose of the Buddha statue creation, the creation date, the year and place of enshrining, the names of the statues, the people in charge and their roles, the sculptors, the list of items donated, and the list of the contributors. In addition, the list of monks who were staying at Hwaeomsa Temple at that time are also recorded, so the Shijujil is like a time capsule that tells the situation of Hwaeomsa Temple about 400 years ago. According to the records of the Shijujils and the Writing on the wooden pedestal of Rocana, the Vairocana Triad began to be in March 1634(12th year of King Injo) and was completed in August of that year, and was enshrined in the Daeungjeon Hall in the fall of the following year. It is very important to confirm that the Vairocana Buddha Triad of Hwaeomsa was created in 1634. Since studies on the reconstruction of Hwaeomsa Temple in the 17th century and the roles of Byeokam Gakseong have been mainly based on 『湖南道求禮縣智異山大華嚴寺事蹟』 written by monk Haean in 1636, it has been estimated that the wooden seated Vairocana Buddha Triad was created in 1636. However, it is now known that the Virocana Buddha Triad was created in 1634. The Shijujils are also a good source of information about Byeokam Gakseong who played a pivotal roles in the reconstruction projects of Hwaeomsa Temple in the 17th century. He played leading roles in rebuilding the East Five-story Stone Pagoda(1630), in creating the wooden seated Vairocana Buddha Triad(1634), and in producing the Yeongsanhoe Gwaebul(1653, Hanging Scroll Painting depicting the Shakyamuni preaching). It is also very important that the Shijujils are records that can reveal the relationship between Byeokam Gakseong and royal family of Joseon Dynasty in the 17th century. The Shijujils from Rocana and Shakyamuni are the first documents ever discovered in which the names of royal family members, such as Uichanggun(Gwang Lee, son of King Seonjo), Ikseong Shin(son-in-law of King Seonjo), and Crown Prince Sohyeon(son of King Injo) are recorded in detail in relation to the production of Buddha statues. The Shijujils from Rocana and Shakyamuni contain specific information about the production of the wooden seated Vairocana Buddha Triad in the 17th century, such as the year of production of the Buddha statues, the role of Byeokam Gakseong, and the relationship between Byeokam Gakseong and the royal family, so it is of great value not only for art history but also for historical studies of Hwaeomsa Temple.

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