• Korean Journal of Heritage: History & Science
• /
• v.51 no.3
• /
• pp.4-31
• /
• 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
• /
• v.25 no.1
• /
• pp.63-83
• /
• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.109-125
• /
• 2019

As interest on intelligent search engines increases, various studies have been conducted to extract and utilize the features related to products intelligencely. In particular, when users search for goods in e-commerce search engines, the 'color' of a product is an important feature that describes the product. Therefore, it is necessary to deal with the synonyms of color terms in order to produce accurate results to user's color-related queries. Previous studies have suggested dictionary-based approach to process synonyms for color features. However, the dictionary-based approach has a limitation that it cannot handle unregistered color-related terms in user queries. In order to overcome the limitation of the conventional methods, this research proposes a model which extracts RGB values from an internet search engine in real time, and outputs similar color names based on designated color information. At first, a color term dictionary was constructed which includes color names and R, G, B values of each color from Korean color standard digital palette program and the Wikipedia color list for the basic color search. The dictionary has been made more robust by adding 138 color names converted from English color names to foreign words in Korean, and with corresponding RGB values. Therefore, the fininal color dictionary includes a total of 671 color names and corresponding RGB values. The method proposed in this research starts by searching for a specific color which a user searched for. Then, the presence of the searched color in the built-in color dictionary is checked. If there exists the color in the dictionary, the RGB values of the color in the dictioanry are used as reference values of the retrieved color. If the searched color does not exist in the dictionary, the top-5 Google image search results of the searched color are crawled and average RGB values are extracted in certain middle area of each image. To extract the RGB values in images, a variety of different ways was attempted since there are limits to simply obtain the average of the RGB values of the center area of images. As a result, clustering RGB values in image's certain area and making average value of the cluster with the highest density as the reference values showed the best performance. Based on the reference RGB values of the searched color, the RGB values of all the colors in the color dictionary constructed aforetime are compared. Then a color list is created with colors within the range of ${\pm}50$ for each R value, G value, and B value. Finally, using the Euclidean distance between the above results and the reference RGB values of the searched color, the color with the highest similarity from up to five colors becomes the final outcome. In order to evaluate the usefulness of the proposed method, we performed an experiment. In the experiment, 300 color names and corresponding color RGB values by the questionnaires were obtained. They are used to compare the RGB values obtained from four different methods including the proposed method. The average euclidean distance of CIE-Lab using our method was about 13.85, which showed a relatively low distance compared to 3088 for the case using synonym dictionary only and 30.38 for the case using the dictionary with Korean synonym website WordNet. The case which didn't use clustering method of the proposed method showed 13.88 of average euclidean distance, which implies the DBSCAN clustering of the proposed method can reduce the Euclidean distance. This research suggests a new color synonym processing method based on RGB values that combines the dictionary method with the real time synonym processing method for new color names. This method enables to get rid of the limit of the dictionary-based approach which is a conventional synonym processing method. This research can contribute to improve the intelligence of e-commerce search systems especially on the color searching feature.

• Korean Journal of Mineralogy and Petrology
• /
• v.34 no.3
• /
• pp.177-191
• /
• 2021

The Zhenzigou Pb-Zn deposit, one of the largest Pb-Zn deposit in the northeast of China, is located at the Qingchengzi mineral field in Jiao Liao Ji belt. The geology of this deposit consists of Archean granulite, Paleoproterozoinc migmatitic granite, Paleo-Mesoproterozoic sodic granite, Paleoproterozoic Liaohe group, Mesozoic diorite and monzoritic granite. The Zhenzigou deposit which is a strata bound SEDEX or SEDEX type deposit occurs as layer ore and vein ore in Langzishan formation and Dashiqiao formation of the Paleoproterozoic Liaohe group. Based on mineral petrography and paragenesis, dolomites from this deposit are classified three type (1. dolomite (D₀) as hostrock, 2. dolomite (D₁) in layer ore associated with white mica, quartz, K-feldspar, sphalerite, galena, pyrite, arsenopyrite from greenschist facies, 3. dolomite (D₂) in vein ore associated with quartz, apatite and pyrite from quartz vein). The structural formulars of dolomites are determined to be Ca_1.00-1.03Mg_0.94-0.98Fe_0.00-0.06As_0.00-0.01(CO₃)₂(D₀), Ca_0.97-1.16Mg_0.32-0.83Fe_0.10-0.50Mn_0.01-0.12Zn_0.00-0.01Pb_0.00-0.03As_0.00-0.01(CO₃)₂(D₁), Ca_1.00-1.01Mg_0.85-0.92Fe_0.06-0.11 Mn_0.01-0.03As_0.01(CO₃)₂(D₂), respectively. It means that dolomites from the Zhenzigou deposit have higher content of trace elements compared to the theoretical composition of dolomite. Feo and MnO contents of these dolomites (D₀, D₁ and D₂) contain 0.05-2.06 wt.%, 0.00-0.08 wt.% (D₀), 3.53-17.22 wt.%, 0.49-3.71 wt.% (D₁) and 2.32-3.91 wt.%, 0.43-0.95 wt.% (D₂), respectively. The dolomite (D₁) from layer ore has higher content of these trace elements (FeO, MnO, ZnO and PbO) than dolomite (D₀) from hostrock and dolomite (D₂) from quartz vein. Dolomites correspond to Ferroan dolomite (D₀ and D₂), and ankerite and Ferroan dolomite (D₁), respectively. Therefore, 1) dolomite (D₀) from hostrock is a Ferroan dolomite formed by marine evaporative lagoon environment in Paleoproterozoic Jiao Liao Ji basin. 2) Dolomite (D₁) from layer ore is a ankerite and Ferroan dolomite formed by hydrothermal metasomatism origined metamorphism (greenschist facies) associated with Paleoproterozoic intrusion. 3) Dolomte (D₂) from quartz vein is a Ferroan dolomite formed by hydrothermal fluid origined Mesozoic intrusion.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.231-252
• /
• 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 the Korean Institute of Traditional Landscape Architecture
• /
• v.29 no.2
• /
• pp.81-89
• /
• 2011

The results of tracking the symbolism of plants in the introduction factors of Sokhwa(folk painting) are as the following. 1. The term Sokhwa(俗畵) is not only a type of painting with a strong local customs, but also carries a symbolic meaning and was discovered in "Donggukisanggukjip" of Lee, Gyu-Bo(1268~1241) in the Goryo era as well as the various usage in the "Sok Dongmunseon" in the early Chosun era, "Sasukjaejip" of Gang, Hee-mang(1424~1483), "Ilseongrok(1786)" in the late Chosun era, "Jajeo(自著)" of Yoo, Han-joon(1732~1811), and "Ojuyeonmunjangjeonsango(五洲衍文長箋散稿)" of Lee, Gyu-gyung(1788~?). Especially, according to the Jebyungjoksokhwa allegation〈題屛簇俗畵辯證說〉in the Seohwa of the Insa Edition of Ojuyeonmunjangjeonsango, there is a record that the "people called them Sokhwa." 2. Contemporarily, the Korean Sokhwa underwent the prehistoric age that primitively reflected the natural perspective on agricultural culture, the period of Three States that expressed the philosophy of the eternal spirits and reflected the view on the universe in colored pictures, the Goryo Era that religiously expressed the abstract shapes and supernatural patterns in spacein symbolism, and the Chosun Era that established the traditional Korean identity of natural perspective, aesthetic values and symbolism in a complex integration in the popular culture over time. 3. The materials that were analyzed in 1,009 pieces of Korean Sokhwa showed 35 species of plants, 37 species of animals, 6 types of natural objects and other 5 types with a total of 83 types. 4. The shape aesthetics according to the aesthetic analysis of the plants in Sokhwa reflect the primitive world view of Yin/yang and the Five Elements in the peony paintings and dynamic refinement and biological harmonies in the maehwado; the composition aesthetics show complex multi-perspective composition with a strong noteworthiness in the bookshelf paintings, a strong contrast of colors with reverse perspective drawing in the battlefield paintings, and the symmetric beauty of simple orderly patterns in nature and artificial objects with straight and oblique lines are shown in the leisurely reading paintings. In terms of color aesthetics, the five colors of directions - east, west, south, north and the center - or the five basic colors - red, blue, yellow, white and black - are often utilized in ritual or religious manners or symbolically substitute the relative relationships with natural laws. 5. The introduction methods in the Korean Sokhwa exceed the simple imitation of the natural shapes and have been sublimated to the symbolism that is related to nature based on the colloquial artistic characteristics with the suspicion of the essence in the universe. Therefore, the symbolism of the plants and animals in the Korean Sokhwas is a symbolic recognition system, not a scientific recognition system with a free and unique expression with a complex interaction among religious, philosophical, ecological and ideological aspects, as a identity of the group culture of Koreans where the past and the future coexist in the present. This is why the Koran Sokhwa or the folk paintings can be called a cultural identity and can also be interpreted as a natural and folk meaningful scenic factor that has naturally integrated into our cultural lifestyle. However, the Sokhwa(folk paintings) that had been closely related to our lifestyle drastically lost its meaning and emotions through the transitions over time. As the living lifestyle predominantly became the apartment culture and in the historical situations where the confusion of the identity has deepened, the aesthetic and the symbolic values of the Sokhwa folk paintings have the appropriateness to be transmitted as the symbolic assets that protect our spiritual affluence and establish our identity.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.1-25
• /
• 2020

Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.

• The Journal of Korean Society for Radiation Therapy
• /
• v.32
• /
• pp.7-15
• /
• 2020

Purpose: In modern radiotherapy technology, several methods of image guided radiation therapy (IGRT) are used to deliver accurate doses to tumor target locations and normal organs, including CBCT (Cone Beam Computed Tomography) and other devices, ExacTrac System, other than CBCT equipped with linear accelerators. In previous studies comparing the two systems, positional errors were analysed rearwards using Offline-view or evaluated only with a Yaw rotation with the X, Y, and Z axes. In this study, when using CBCT and ExacTrac to perform 6 Degree of the Freedom(DoF) Online IGRT in a treatment center with two equipment, the difference between the set-up calibration values seen in each system, the time taken for patient set-up, and the radiation usefulness of the imaging device is evaluated. Materials and Methods: In order to evaluate the difference between mobile calibrations and exposure radiation dose, the glass dosimetry and Rando Phantom were used for 11 cancer patients with head circumference from March to October 2017 in order to assess the difference between mobile calibrations and the time taken from Set-up to shortly before IGRT. CBCT and ExacTrac System were used for IGRT of all patients. An average of 10 CBCT and ExacTrac images were obtained per patient during the total treatment period, and the difference in 6D Online Automation values between the two systems was calculated within the ROI setting. In this case, the area of interest designation in the image obtained from CBCT was fixed to the same anatomical structure as the image obtained through ExacTrac. The difference in positional values for the six axes (SI, AP, LR; Rotation group: Pitch, Roll, Rtn) between the two systems, the total time taken from patient set-up to just before IGRT, and exposure dose were measured and compared respectively with the RandoPhantom. Results: the set-up error in the phantom and patient was less than 1mm in the translation group and less than 1.5° in the rotation group, and the RMS values of all axes except the Rtn value were less than 1mm and 1°. The time taken to correct the set-up error in each system was an average of 256±47.6sec for IGRT using CBCT and 84±3.5sec for ExacTrac, respectively. Radiation exposure dose by IGRT per treatment was measured at 37 times higher than ExacTrac in CBCT and ExacTrac at 2.468mGy and 0.066mGy at Oral Mucosa among the 7 measurement locations in the head and neck area. Conclusion: Through 6D online automatic positioning between the CBCT and ExacTrac systems, the set-up error was found to be less than 1mm, 1.02°, including the patient's movement (random error), as well as the systematic error of the two systems. This error range is considered to be reasonable when considering that the PTV Margin is 3mm during the head and neck IMRT treatment in the present study. However, considering the changes in target and risk organs due to changes in patient weight during the treatment period, it is considered to be appropriately used in combination with CBCT.

• Journal of the Daesoon Academy of Sciences
• /
• v.36
• /
• pp.1-34
• /
• 2020

It has been suggested that Jeungsan's prophetic poem that starts with the verse "For about seven or eight years, there will be a castle in the ancient country [七八年間古國城] ⋯" originally comes from Prophecies of Jeong Gam (鄭鑑錄). Despite Jeungsan, himself, obviously having been critical of that text, this claim has become the basic grounds for discourse suggesting that Jeungsan was not only interested in Prophecies of Jeong Gam but also considerably influenced by the text. However, the claim itself was formulated due to misunderstandings of the Chinese poems that had been included in A Compilation of Secret Prophecies Hidden in the Family-clan of Seogye (西溪家臧訣). These poems pursue a different ideological orientation than the poem from Prophecies of Jeong Gam. Ultimately, the Chinese poem in the verse 84 the chapter titled, Prophetic Elucidations in The Canonical Scripture of Daesoon Jinrihoe cannot provide a basis for the claim that Jeungsan was strongly influenced by Prophecies of Jeong Gam. This claim that Prophecies of Jeong Gam made a deep impact on Jeungsan and Daesoon Thought was based on three other texts outside of those that appear within verse 84 of Prophetic Elucidations. The first supposedly-related line is: "Heaven opens at the period of the Rat (Ja 子), Earth opens at the period of the Ox (Chuk 丑), humankind starts at the period of the Tiger (Ihn 寅)." This line comes from from Shao Kangjie's Book of Supreme World Ordering Principles (皇極經世), and the line could be quoted idiomatically as an expression in the Joseon Dynasty. Accordingly, attempts to relate Daesoon Thought to Prophecies of Jeong Gam are a distortion that arise from the assumption that Jeungsan had a significant interest in Prophecies of Jeong Gam. The second related line is "At the foot of Mount Mother (母岳山), a golden icon of Buddha has the ability to speak [母岳山下 金佛能言]." That line is nearly identical to the verse "On the summit of Mount Mother, a golden icon of Buddha has the ability to speak [母岳山頭 金佛能言]." Yet, Jeungsan changed '頭 (du, the summit)' to '下 (ha, the foot or under)' and express his own unique religious prophecy. This allusion to the prophecies of Jeong Gam is actually a criticism designed to disprove the earlier prophecy. Third, is the verse, "The form of Buddhism, creation of daoism, and propriety of Confucianism [佛之形體仙之造化儒之凡節]," which is characteristically related to Daesoon Thought. This verse can only be found in the prophetic text, Prophecies of Chochang (蕉蒼訣), and it is provided a main source when alleging that Prophecies of Jeong Gam was an influence on Daesoon Thought. However, considering the context of Prophecies of Chochang and the year of its publication (it is assumed to be compiled after 1950s), this does not hold water as Jeungsan had already passed into Heaven several decades before that time. This disqualifies the verse from being a basis for asserting Prophecies of Jeong Gam as an influence on Daesoon Thought. Contrary to the original assertion, there is a considerable amount of evidence that Prophecies of Chochang absorbed aspects of Daesoon Thought, which were simply revised in a novel way. There is no truly compelling evidence underpinning the argument that Prophecies of Jeong Gam had a unilateral impact on Daesoon Thought. There seems to be a great deal of confusion and numerous misinterpretations on this matter. Therefore, the claim that Daesoon Thought, as developed by Jeungsan, was influenced by the discourse on dynastic revolution and feng shui contained in Prophecies of Jeong Gam should be re-examined at the level of its very premise.

• Korean Journal of Mineralogy and Petrology
• /
• v.35 no.2
• /
• pp.153-168
• /
• 2022

Petrological and mineralogical analyses were conducted to identify minerals containing radioactive elements (uranium) in the Chiaksan gneiss complex and to confirm their association with the surrounding groundwater. Fourteen minerals were identified through the microscopic and electron microscopy (SEMEDS) investigation. The principal minerals included plagioclase, biotite, quartz, alkali feldspar, chlorite, and calcite. Minor minerals were sphene, allanite, apatite, zircon, thorite, titanite, pyrite, and galena. A small amount of thorite was observed in the size of ~1 mm within macrocrystalline allanite. Allanite, which includes a large amount of rare earth elements, appeared in three distinctive patterns. The results of the EPMA analyses indicated that macrocrystalline allanite had higher elemental contents of TiO₂~1.70 wt.%, Ce₂O₃~11.86 wt.%, FeO ~13.31 wt.%, MgO ~0.90 wt.% and ThO₂ ~1.06 wt.% with the lowest average content of Al₂O₃ 17.35 ± 2.15 wt.% (n = 7), CaO 12.13 ± 1.81 wt.% (n = 7). An allanite existing at the edge of the sphenes encompassing titanites had a higher element content of Al₂O₃ ~24.00 wt.%, Nd₂O₃ ~5.10 wt.%, Sm₂O₃~0.66 wt.%, Dy₂O₃~0.86 wt.% and Y₂O₃~1.38 wt.% with the lowest average content of TiO₂ 0.35 ± 0.21 wt.% (n = 11), Ce₂O₃ 5.25 ± 1.03 wt.% (n = 11), FeO 9.84 ± 0.26 wt.% (n = 11), MgO 0.12 ± 0.05 wt.% (n = 11), and La₂O₃ 1.49 ± 0.29 wt.% (n = 11). Allanites in a matrix of parental rocks exhibited intermediate values between the two elemental compositions mentioned above. None of the uranium-rich minerals were observed in the migmatitic gneiss within the study area. Consequently, the origin of uranium in the groundwater was not associated with the geology of the surrounding environment, but our investigation proved the existence of abundant allanites containing significant amounts of radioactive thorium and rare earth elements.