• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.23 no.4
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• pp.153-178
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• 2018

Most operational uses of wind speed data require measurements at, or estimates generated for, the reference height of 10 m above mean sea level (AMSL). On the Ieodo Ocean Research Station (IORS), wind speed is measured by instruments installed on the lighthouse tower of the roof deck at 42.3 m AMSL. This preliminary study indicates how these data can best be converted into synthetic 10 m wind speed data for operational uses via the Korea Hydrographic and Oceanographic Agency (KHOA) website. We tested three well-known conventional empirical neutral wind profile formulas (a power law (PL); a drag coefficient based logarithmic law (DCLL); and a roughness height based logarithmic law (RHLL)), and compared their results to those generated using a well-known, highly tested and validated logarithmic model (LMS) with a stability function (${\psi}_{\nu}$), to assess the potential use of each method for accurately synthesizing reference level wind speeds. From these experiments, we conclude that the reliable LMS technique and the RHLL technique are both useful for generating reference wind speed data from IORS observations, since these methods produced very similar results: comparisons between the RHLL and the LMS results showed relatively small bias values ($-0.001m\;s^{-1}$) and Root Mean Square Deviations (RMSD, $0.122m\;s^{-1}$). We also compared the synthetic wind speed data generated using each of the four neutral wind profile formulas under examination with Advanced SCATterometer (ASCAT) data. Comparisons revealed that the 'LMS without ${\psi}_{\nu}^{\prime}$ produced the best results, with only $0.191m\;s^{-1}$ of bias and $1.111m\;s^{-1}$ of RMSD. As well as comparing these four different approaches, we also explored potential refinements that could be applied within or through each approach. Firstly, we tested the effect of tidal variations in sea level height on wind speed calculations, through comparison of results generated with and without the adjustment of sea level heights for tidal effects. Tidal adjustment of the sea levels used in reference wind speed calculations resulted in remarkably small bias (<$0.0001m\;s^{-1}$) and RMSD (<$0.012m\;s^{-1}$) values when compared to calculations performed without adjustment, indicating that this tidal effect can be ignored for the purposes of IORS reference wind speed estimates. We also estimated surface roughness heights ($z_0$) based on RHLL and LMS calculations in order to explore the best parameterization of this factor, with results leading to our recommendation of a new $z_0$ parameterization derived from observed wind speed data. Lastly, we suggest the necessity of including a suitable, experimentally derived, surface drag coefficient and $z_0$ formulas within conventional wind profile formulas for situations characterized by strong wind (${\geq}33m\;s^{-1}$) conditions, since without this inclusion the wind adjustment approaches used in this study are only optimal for wind speeds ${\leq}25m\;s^{-1}$.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• Korean Journal of Heritage: History & Science
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• v.43 no.1
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• pp.160-189
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• 2010

This study has been designed to grasp the present situation, shapes and meaning of the standing stones and rock pillars in the whole area of Noseong Mountain Fortress in Nonsan City which have never been academically reported yet. Accordingly, the research was carried out to grasp the spatial identity of Noseong Mt. and Noseong Mountain Fortress and the dispersion of standing stones scattered around inside and outside Noseong Mountain Fortress, while the shapes and structural characteristics of stones were investigated and analyzed focusing on Chongsuk Temple, which was considered to have the highest density of standing stones and greatest values for preservation as a cultural property. In consideration of the reference to the 'Top Sa' (tower temple) at the 'Bul Woo Jo' (Article about Buddhism Houses) of 'Shinjoong Dongguk Yeoji Seungram', theoretical existence of the temple according to surveying investigation, and the excavation records of roof tile pieces with the name of 'Gwan Eum Temple', it is presumed that there had been a Buddhist sanctum inside the fortress and it could be connected to the carved letters, 'Chongsuk Temple'. According the observation survey, the 6th place of standing stones among many other places inside the fortress shows that Chongsuk Temple appears to have the strong characteristics of artificially constructed space in consideration of the size of trees and stones, the composite trend of tree and stone composition, and trace of the adjacent well and strand and the construction of stairway leading to the stone gate. Along with the constellation of the Big Dipper carved on a rock at the same space, the stones, on which the letters of 'Shinseonam', 'Chilseongam' and 'Daejangam' were carved, including 'Chongsuksa', and the carved statue of Buddha, which was assumed to be Avalokitesvara Guan Yin, have offered clue which make it possible to infer that the space was a space for Chilseong and Mountain god(Folk Belief) that had originated from the combination of Buddhism, Taoism and folk religion. According to the actual measurement of standing stones at Chonsuk Temple, it was identified that there were big differences in height among 24 stones in total, ranging from 402~29cm and the averaged distance between each stone appeared to be 23.6cm. And the shape of stones appeared to be standing or flat, and various stones such as mountain-like stones and Buddha-like stones were placed in a special arrangement or assorted arrangement, but the direction of the stones had a consistency pointing to the west. And comparing to the trace of construction of ZEN Landscape Garden well known in the country, the three flat stones except for the standing and shaped stones appeared to have the shape of meditation statue, which is the typical formational factors of a ZEN Landscape Garden, on the basis of formational technique of stones. Among them, the flat stone facing the Buddhist saint statue, was formed by way of symbolization of three-mountain stone, which was assumed to be an offering stone for sacrificial food rather than carrying out ZEN Meditation. In consideration of the formation of standing stones at Chong-suk Temple, which was carried out in the composite stoning method based using the scalene triangle with ratio of 3:5:7 in order to seek the in-depth beauty based on the stone statues of three Buddhas where the three factors such as heaven, earth and humans are embodied in the elevated or flat formation, the stones at Chongsuk Temple and the space seemed to the trace of contracted garden construction that was formed with stones for a temple, so that could be used for ZEN meditation.

• Korean Journal of Heritage: History & Science
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• v.51 no.3
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• pp.4-31
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• 2018

It is difficult to ascertain exactly when the Hongjuupseong (Town Wall) was first constructed, due to it had undergone several times of repair and maintenance works since it was piled up newly in 1415, when the first year of the reign of King Munjong (the 5th King of the Joseon Dynasty). Parts of its walls were demolished during the Japanese occupation, leaving the wall as it is today. Hongseong region is also susceptible to historical earthquakes for geological reasons. There have been records of earthquakes, such as the ones in 1978 and 1979 having magnitudes of 5.0 and 4.0, respectively, which left part of the walls collapsed. Again, in 2010, heavy rainfall destroyed another part of the wall. The fortress walls of the Hongjuupseong comprise various rocks, types of facing, building methods, and filling materials, according to sections. Moreover, the remaining wall parts were reused in repair works, and characteristics of each period are reflected vertically in the wall. Therefore, based on the vertical distribution of the walls, the Hongjuupseong was divided into type I, type II, and type III, according to building types. The walls consist mainly of coarse-grained granites, but, clearly different types of rocks were used for varying types of walls. The bottom of the wall shows a mixed variety of rocks and natural and split stones, whereas the center is made up mostly of coarse-grained granites. For repairs, pink feldspar granites was used, but it was different from the rock variety utilized for Suguji and Joyangmun Gate. Deterioration types to the wall can be categorized into bulging, protrusion of stones, missing stones at the basement, separation of framework, fissure and fragmentation, basement instability, and structural deformation. Manually and light-wave measurements were used to check the amount and direction of behavior of the fortress walls. A manual measurement revealed the sections that were undergoing structural deformation. Compared with the result of the light-wave measurement, the two monitoring methods proved correlational. As a result, the two measuring methods can be used complementarily for the long-term conservation and management of the wall. Additionally, the measurement system must be maintained, managed, and improved for the stability of the Hongjuupseong. The measurement of Nammunji indicated continuing changes in behavior due to collapse and rainfall. It can be greatly presumed that accumulated changes over the long period reached the threshold due to concentrated rainfall and subsequent behavioral irregularities, leading to the walls' collapse. Based on the findings, suggestions of the six grades of management from 0 to 5 have been made, to manage the Hongjuupseong more effectively. The applied suggested grade system of 501.9 m (61.10%) was assessed to grade 1, 29.5 m (3.77%) to grade 2, 10.4 m (1.33%) to grade 3, 241.2 m (30.80%) and grade 4. The sections with grade 4 concentrated around the west of Honghwamun Gate and the east of the battlement, which must be monitored regularly in preparation for a potential emergency. The six-staged management grade system is cyclical, where after performing repair and maintenance works through a comprehensive stability review, the section returned to grade 0. It is necessary to monitor thoroughly and evaluate grades on a regular basis.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.87-109
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• 2020

Currently, thanks to the major stride made in developing wired and wireless communication technology, a variety of IT services are available on land. This trend is leading to an increasing demand for IT services to vessels on the water as well. And it is expected that the request for various IT services such as two-way digital data transmission, Web, APP, etc. is on the rise to the extent that they are available on land. However, while a high-speed information communication network is easily accessible on land because it is based upon a fixed infrastructure like an AP and a base station, it is not the case on the water. As a result, a radio communication network-based voice communication service is usually used at sea. To solve this problem, an additional frequency for digital data exchange was allocated, and a ship ad-hoc network (SANET) was proposed that can be utilized by using this frequency. Instead of satellite communication that costs a lot in installation and usage, SANET was developed to provide various IT services to ships based on IP in the sea. Connectivity between land base stations and ships is important in the SANET. To have this connection, a ship must be a member of the network with its IP address assigned. This paper proposes a SANET-CC protocol that allows ships to be assigned their own IP address. SANET-CC propagates several non-overlapping IP addresses through the entire network from land base stations to ships in the form of the tree. Ships allocate their own IP addresses through the exchange of simple requests and response messages with land base stations or M-ships that can allocate IP addresses. Therefore, SANET-CC can eliminate the IP collision prevention (Duplicate Address Detection) process and the process of network separation or integration caused by the movement of the ship. Various simulations were performed to verify the applicability of this protocol to SANET. The outcome of such simulations shows us the following. First, using SANET-CC, about 91% of the ships in the network were able to receive IP addresses under any circumstances. It is 6% higher than the existing studies. And it suggests that if variables are adjusted to each port's environment, it may show further improved results. Second, this work shows us that it takes all vessels an average of 10 seconds to receive IP addresses regardless of conditions. It represents a 50% decrease in time compared to the average of 20 seconds in the previous study. Also Besides, taking it into account that when existing studies were on 50 to 200 vessels, this study on 100 to 400 vessels, the efficiency can be much higher. Third, existing studies have not been able to derive optimal values according to variables. This is because it does not have a consistent pattern depending on the variable. This means that optimal variables values cannot be set for each port under diverse environments. This paper, however, shows us that the result values from the variables exhibit a consistent pattern. This is significant in that it can be applied to each port by adjusting the variable values. It was also confirmed that regardless of the number of ships, the IP allocation ratio was the most efficient at about 96 percent if the waiting time after the IP request was 75ms, and that the tree structure could maintain a stable network configuration when the number of IPs was over 30000. Fourth, this study can be used to design a network for supporting intelligent maritime control systems and services offshore, instead of satellite communication. And if LTE-M is set up, it is possible to use it for various intelligent services.

• (The)Study of the Eastern Classic
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• no.62
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• pp.145-176
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• 2016

This paper aims to define the leadership of Gunja (君子, translated into prince, gentleman, or ideal man) based on Confucian Classics which offer the general values and norms of individuals' virtue and social virtuous acts. Thus, humanitarianism is regarded as true value, and the values of a virtuous person who properly practices social human relationships are discussed. The real worth of Gunja image is discussed as a true human image of "self-completion and completion of all things" (成己成物) which involves the convergence of truth, good and beauty where there is a sense of harmony and balance, where there is stern self discipline and self cultivation and where win-win values of human relationships are created. Confucian saint (聖人), wise man (賢人), great man (大人), and gentleman (君子) mean social leaders. They practice human morals, enlighten and beautify society with teachings, and are indicated as equipped with mental and material harmony, good character and competence, and economic power and morality. People today pursue their own personal growth according to their material preferences rather than pure intellectual cultural values, and are engrossed in visually beautiful external unlimited competition. In this digital age, we are supposed to demonstrate our individuality, but many people are obsessed with appearance, go on severe diet, and lose their health beauty, and consequently suffer mental stress. This trend fuels obsession with appearance and the sick practice of valuing appearance. As an alternative method to overcome this phenomenon, we need a leader image with the convergence of truth, good and beauty, which is characterized by internal self cultivation, external professionalism, and handsome and solid character. Confucian thoughts consist in practicing the Way of disciplining oneself for governing others (修己治人). Self discipline involves developing personal virtuous ability for cultivating a virtuous character, and governing others involves interacting to work together in society and to have right human relationships. Thus, leaders should impress not only themselves but also others. Self discipline for governing others means cultivating virtue for oneself and leading others. A true leader has self introspection and establishes himself through self discipline so that he can govern others or reach the realm of settling others where people live together. As all things have a value and a virtue, humans endeavor to cultivate character and virtue by learning and studying for securing their professionalism, reliability, character and ability, so as to create their own brand value. Personal character does not come from a high position, wealth and power. Character is a personal virtue, and is cultivated as immaculate and fresh through self discipline. As such, it well matches with a clean and clear spirit. This offers the ideal leader as the Guja image who has an extremely humane character, as well as being equipped with inherent virtues of intellect, benevolence and courage. Self development can foster virtue and self management through self leadership and self discipline. The leader in the relationship area can practice his virtue through virtuous acts, in other words, even think from another person's perspective. Such leader is mentioned as the principle of measuring square in the Great Learning. In our viewpoint, the beauty of character can breed the seed of virtue through intellect, benevolence and courage, the beauty of win-win can realize the right virtue by showing exemplary acts to others through considerateness, and the beauty of harmony can love and care for others like me through the principle of measuring square, thereby realizing the universal principle of virtue and harmony, which is like my mind. As such, the ideal leader, when his virtue and mind of being considerate of others all blending well, can exercise his ability to the full, can live together and coexist with many people, and can grow again into a triumphant relationship.

• (The)Study of the Eastern Classic
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• no.69
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• pp.83-114
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• 2017

The purpose of this article is to analyze the standings of working-level officials belonging to Jongbusi (Court of the Royal Clan) holding the rank of "jeong" and below between the 18th and mid-19th Century. Jongbusi, which was headed by a Grade-3 official, was in charge of the compilation of royal genealogy and supervision of royal relatives. During the late Joseon Period, its officials were composed of its chief, jeong, jubu (Grade-6 official), and jikjang (Grade-7 official). By 1864, it was incorporated into Jongchinbu (Office of the Royal Relatives). Jongbusi nangcheong seonsaengan (Register of Staff Officials at the Court of the Royal Clan), which is preserved at the Jangseogak Archives of the Academy of Korean Studies, lists the officials who served at the office between 1794 and its incorporation into Jongchinbu in 1864. The register also includes the officials' ranks, names, DOBs, family clans, their ranks in the offices they were transferred from, their ranks in the office they were transferred to, etc. Those interested view it as a precious relic that provides valuable information on the officialdom of the dynasty. A majority of the officials who served at Jongbusi were those who passed the higher civil service examination. Many of them at the level of jikjang were those who passed the licentiate examination. Their designation as an official was part of the "muneum" system, which granted official posts to descendants of those who accomplished a distinguished service for the country or served as a high-ranking official. They were those transferred from equal or lower positions in another office. Many of jubu-level officials of Jongbusi were those transferred from honorable and important posts of other offices or local administrative offices. Many of jikjang-level officials of Jongbusi were those who previously served as dosa (assistant officials) at Uigeumbu (Bureau of Crime Investigation) headed by a Grade-1 official. The officials' transfer to an office with a lower position like Jongbusi appears to have been for the provision of placing them in working-level positions rather than letting them remain in positions only carrying an honorary title. As for the transfer of officials of Jongbusi to other offices, many of those with the rank of jeong were transferred to lower positions. Supposedly it was because not many Grade-3 positions were vacant. Many of them were transferred to honorable and important posts. Some of them were also transferred to positions at local government offices, supposedly to avoid an excess of personnel at the central government. Those at the level of jubu or jikjang of Jongbusi were transferred to equal or higher posts in other offices. Particularly, most of those holding the position of jikjang (Grade-7) were transferred to higher posts. The family clan that produced the largest number (10%) of Jongbusi officials was the Jeonju Yi Clan, which produced the largest number of those who passed the higher civil service examination. It was also found that the top 20 family clans produced about half of the entirety of Jongbusi officials. According to the aforesaid Jongbusi nangcheong seonsaengan, about 90% of the cases of promotion of Jongbusi officials occurred after the revision of Seonwon boryak (Royal Genealogy of the Joseon Dynasty). It is speculated that the supervision of royal family members, one of the two leading functions assigned to Jongbusi, was suspended in the late Joseon Period. The relevant function does not appear even in chronicles pertaining to the Joseon Dynasty. The reason being had something to do with the sharp decrease in the number of royal family members during the reign of King Injo (r. 1623-1649). Their number was decreased to the extent that royal ceremonies could not be adequately carried out. Naturally, the meaning of supervising royal family members faded. Witnessing such a sorry state of the royal family, Heungseon Daewongun, King Gojong's father who served as the regent, incorporated Jongbusi into Jongchinbu in an effort to enhance the status and authority of the royal family.

• MISULJARYO - National Museum of Korea Art Journal
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• v.98
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• pp.22-53
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• 2020

This paper analyzes the contents, characteristics, and historical significance of the dedicatory inscriptions (josanggi) on the Amitabha Buddha and the Maitreya Bodhisattva statues of Gamsansa Temple, two masterpieces of Buddhist sculpture from the Unified Silla period. In the first section, I summarize research results from the past century (divided into four periods), before presenting a new perspective and methodology that questions the pre-existing notion that the Maitreya Bodhisattva has a higher rank than the Amitabha Buddha. In the second section, through my own analysis of the dedicatory inscriptions, arrangement, and overall appearance of the two images, I assert that the Amitabha Buddha sculpture actually held a higher rank and greater significance than the Maitreya Bodhisattva sculpture. In the third section, for the first time, I provide a new interpretation of two previously undeciphered characters from the inscriptions. In addition, by comparing the sentence structures from the respective inscriptions and revising the current understanding of the author (chanja) and calligrapher (seoja), I elucidate the possible meaning of some ambiguous phrases. Finally, in the fourth section, I reexamine the content of both inscriptions, differentiating between the parts relating to the patron (josangju), the dedication (josang), and the prayers of the patrons or donors (balwon). In particular, I argue that the phrase "for my deceased parents" is not merely a general axiom, but a specific reference. To summarize, the dedicatory inscriptions can be interpreted as follows: when Kim Jiseong's parents died, they were cremated and he scattered most of their remains by the East Sea. But years later, he regretted having no physical memorial of them to which to pay his respects. Thus, in his later years, he donated his estate on Gamsan as alms and led the construction of Gamsansa Temple. He then commissioned the production of the two stone sculptures of Amitabha Buddha and Maitreya Bodhisattva for the temple, asking that they be sculpted realistically to reflect the actual appearance of his parents. Finally, he enshrined the remains of his parents in the sculptures through the hole in the back of the head (jeonghyeol). The Maitreya Bodhisattva is a standing image with a nirmanakaya, or "transformation Buddha," on the crown. As various art historians have pointed out, this iconography is virtually unprecedented among Maitreya images in East Asian Buddhist sculpture, leading some to speculate that the standing image is actually the Avalokitesvara. However, anyone who reads the dedicatory inscription can have no doubt that this image is in fact the Maitreya. To ensure that the sculpture properly embodied his mother (who wished to be reborn in Tushita Heaven with Maitreya Bodhisattva), Kim Jiseong combined the iconography of the Maitreya and Avalokitesvara (the reincarnation of compassion). Hence, Kim Jiseong's deep love for his mother motivated him to modify the conventional iconography of the Maitreya and Avalokitesvara. A similar sentiment can be found in the sculpture of Amitabha Buddha. To this day, any visitor to the temple who first looks at the sculptures from the front before reading the text on the back will be deeply touched by the filial love of Kim Jiseong, who truly cherished the memory of his parents.