• The Journal of Korean Philosophical History
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• no.32
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• pp.337-370
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• 2011

The academic circle of the Qing Dynasty has seen a new trend, mostly based on Han-Hak through the event of the publication of "Sagojeyo" and sought a new change in the sector of Spring Autumn-Hak as well. This study is focused on understanding and evaluation of the developmental process of especially the traditional Spring Autumn-Hak view, among some views of "Sagojeyo" by the book. "Sagojeyo" does a critical analysis of Gongyang-Hak and Gokryang-Hak and the interpretation of "Spring Autumn" made by Song-Hak scholars, and which seems a kind of preparatory step to build up a new research methodology for Spring Autumn-Hak. "Sagojeyo" emphasizes the importance of "Jwacijeon" and also points out its weaknesses and strongly demands the acceptance of strengths "Gongyangjeon" and "Gokryangjeon" have in order to compensate such demerits. Even further, it tries to evaluate Spring Autumn-Hak of the Song Dynasty objectively and reasonably as possible with no prejudice. Such analysis attitude made it possible for Spring Autumn-Hak to get a new turning point starting from the publication of "Sagojeyo". The new research methodology that comprehensively judges historical research and fidelity discourse could be one of important achievements discovered in the Han-Hak-centered academic circle in the early Qing Dynasty.

• The Journal of Korean Philosophical History
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• no.50
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• pp.235-268
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• 2016

Gwon Geuk-jung wrote Juyeokchamdonggyejuhae(hereinafter Juhae) and established his Internal Alchemy(Nae-dan) Theory systematically in the former half of the 17th century. In its annotations, there are many quotes about Taoist books and figures, which leads to an estimation that the Chinese Taoist ideology must have exercised its influences on the formation of his Internal Alchemy Theory. It is thus needed to compare it with the Chinese Taoist ideology to have a clear picture of its characteristics and significance. The first step of the comparative research involved the estimation of Taoist books that Gown must have consulted and the examination of circumstances around Taoist books circulated in Joseon through the quotes in his Juhae, which were categorized into the ones that began with a book title, those that began with a name, and those that began in "In an old poem." They were compared with those of Dojang, and the results show that Gwon must have consulted various annotation books of Chamdonggye including Chamdonggyebalhui and various Taoist books including Gyujungjinam and Geumdandaeyo. The investigator then looked into Taoist books circulated in Joseon at the time when Gwon wrote Juhae and found a circumstance of circulation of annotation books of Chamdonggye by three families including Yu Yeom and collections of Taoist books including Doseojeonjip and Sujinshipseo. They were then compared with the Taoist books in the quotes of the author, and the results show that most of his quotes contained the Taoist books of Doseojeonjip. That is, the specific circumstances of books circulated in the former half of Joseon were clearly revealed in the quotes of Juhae. Of the Taoist books found in his quotes, Jin Chi-heo was most quoted, being followed by Jang Baek-dan, Seol Do-gwang, Jin Nam, Baek Ok-seom, and So Jeong-ji. It thus seems that Gwon was under the huge influence of Internal Alchemy Theory of Jin Chi-heo and Nam Jong.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.427-438
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• 2021

Two-stage sampling allows us to estimate population characteristics by both unit and cluster level together. Given a complex auxiliary information, integrated calibration weighting would better reflect the level-wise characteristics as well as multivariate characteristics between levels. This paper explored the integrated calibration weighting methods by Estevao and Särndal (2006) and Kim (2019) through a simulation study, where the efficiency of those weighting methods was compared using an artificial population data. Two weighting methods among others are shown efficient: single step calibration at the unit level with stacked individualized auxiliary information and iterative integrated calibration at each level. Under both methods, cluster calibrated weights are defined as the average of the calibrated weights of the unit(s) within cluster. Both were very good in terms of the goodness-of-fit of estimating the population totals of mutual auxiliary information between clusters and units, and showed small relative bias and relative mean square root errors for estimating the population totals of survey variables that are not included in calibration adjustments.

• Journal of the Korea Computer Graphics Society
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• v.27 no.2
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• pp.1-11
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• 2021

In a virtual environment, a 3D workspace and 3D interaction are possible, but most virtual reality applications use a 2D color picker. This paper implements a 3D color picker based on 3D color space in a virtual environment, and compares color selection performance with the existing 2D color picker. The 3D color picker is intuitive by using the 3D color space as it is, and it can position the 3D pointer at a specific point in the color space using a controller, which is a virtual reality device, so a user can select a color in one step. On the other hand, the 2D color picker has the advantage of being familiar with existing users who work with colors in a computer environment, but has a disadvantage that requires several steps of user interaction since it has to set color properties through 2D interfaces. Based on user experiments, we confirmed the usefulness of a 3D color picker in addition to a 2D color picker in a virtual environment, and it was possible to perform natural 3D work in a virtual environment using the 3D color picker.

• Journal of Dental Rehabilitation and Applied Science
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• v.36 no.4
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• pp.262-271
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• 2020

To restore a tooth with a fracture line extending below the marginal bone level, a surgical crown lengthening procedure accompanied by ostectomy could be considered to expose the fracture line and reestablish the biologic width. However, this procedure could lead to esthetic failure, especially in the anterior teeth. Therefore, orthodontic extrusion, which elevates the fracture line from within the alveolar socket without sacrificing the supporting bone and gingiva, is recommended. This technique allows for the proper placement of the crown on a sound tooth structure, with the reestablishment of the biologic width. Alternatively, surgical extrusion is an one-step procedure that is simpler and less time-consuming than orthodontic extrusion; placing and adjusting the orthodontic appliance does not require multiple visits. This study presents successful restoration in 2 cases with a crown-tooth root fracture of the maxillary central incisor treated using a multidisciplinary approach through orthodontic extrusion or surgical extrusion followed by successful restoration.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.206-216
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• 2020

As the number of aging railway bridges in Korea increases, maintenance costs due to aging are increasing and continuous management is becoming more important. However, while the number of old facilities to be managed increases, there is a shortage of professional personnel capable of inspecting and diagnosing these old facilities. To solve these problems, this study presents an improved model that can detect Local damage to structures using machine learning techniques of AI technology. To construct a damage detection machine learning model, an analysis model of the bridge was set by referring to the design drawing of a non-ballasted plate-girder railroad bridge. Static strain data according to the damage scenario was extracted with the analysis model, and the Local damage index based on the reliability of the bridge was presented using statistical techniques. Damage was performed in a three-step process of identifying the damage existence, the damage location, and the damage severity. In the estimation of the damage severity, a linear regression model was additionally considered to detect random damage. Finally, the random damage location was estimated and verified using a machine learning-based damage detection classification learning model and a regression model.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1523-1535
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• 2020

In order to produce crop information using remote sensing, we use classification and growth monitoring based on crop phenology. Therefore, time-series satellite images with a short period are required. However, there are limitations to acquiring time-series satellite data, so it is necessary to use fusion with other earth observation satellites. Before fusion of various satellite image data, it is necessary to overcome the inherent difference in radiometric characteristics of satellites. This study performed Korea Multi-Purpose Satellite-3 (KOMPSAT-3) cross calibration with Landsat-8 as the first step for fusion. Top of Atmosphere (TOA) Reflectance was compared by applying Spectral Band Adjustment Factor (SBAF) to each satellite using hyperspectral sensor band aggregation. As a result of cross calibration, KOMPSAT-3 and Landsat-8 satellites showed a difference in reflectance of less than 4% in Blue, Green, and Red bands, and 6% in NIR bands. KOMPSAT-3, without on-board calibrator, idicate lower radiometric stability compared to ladnsat-8. In the future, efforts are needed to produce normalized reflectance data through BRDF (Bidirectional reflectance distribution function) correction and SBAF application for spectral characteristics of agricultural land.

• Applied Chemistry for Engineering
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• v.33 no.2
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• pp.151-158
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• 2022

In this paper, parameter characteristics such as pH effect, isotherm, kinetic and thermodynamic parameters and competitive adsorption of dyes including malachite green (MG), direct red 81 (DR 81) and thioflavin S (TS), which have different functional groups, being adsorbed onto activated carbon were investigated. Langmuir, Freundlich and Temkin isotherm models were employed to find the adsorption mechanism. Effectiveness of adsorption treatment of three dyes by activated carbon were confirmed by the Langmuir dimensionless separation factor. The mechanism was found to be a physical adsorption which can be verified through the adsorption heat calculated by Temkin equation. The adsorption kinetics followed the pseudo second order and the rate limiting step was intra-particle diffusion. The positive enthalpy and entropy changes showed an endothermic reaction and increased disorder via adsorption at the S-L interface, respectively. For each dye molecule, negative Gibbs free energy increased with the temperature, which means that the process is spontaneous. In the binary component system, it was found that the same functional groups of the dye could interfere with the mutual adsorption, and different functional groups did not significantly affect the adsorption. In the ternary component system, the adsorption for MG lowered a bit, likely to be disturbed by the other dyes meanwhile DR 81 and TS were to be positively affected by the presence of MG, thus resulting in much higher adsorption.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.77-86
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• 2022

The Republic of Korea is a country in which coastal surveillance is an imperative national task as it is surrounded by seas on three sides under the confrontation between South and North Korea. However, due to Defense Reform 2.0, the number of R/D (Radar) operating personnel has decreased, and the period of service has also been shortened. Moreover, there is always a possibility that a human error will occur. This paper presents specific guidelines for developing an AI learning model for the intelligent coastal surveillance system. We present a three-step strategy to realize the guidelines. The first stage is a typical stage of building an AI learning model, including data collection, storage, filtering, purification, and data transformation. In the second stage, R/D signal analysis is first performed. Subsequently, AI learning model development for classifying real and false images, coastal area analysis, and vulnerable area/time analysis are performed. In the final stage, validation, visualization, and demonstration of the AI learning model are performed. Through this research, the first achievement of making the existing weapon system intelligent by applying the application of AI technology was achieved.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.101-125
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• 2022

Recently, with the development of computing technology and the improvement of the cloud environment, deep learning technology has developed, and attempts to apply deep learning to various fields are increasing. A typical example is anomaly detection, which is a technique for identifying values or patterns that deviate from normal data. Among the representative types of anomaly detection, it is very difficult to detect a contextual anomaly that requires understanding of the overall situation. In general, detection of anomalies in image data is performed using a pre-trained model trained on large data. However, since this pre-trained model was created by focusing on object classification of images, there is a limit to be applied to anomaly detection that needs to understand complex situations created by various objects. Therefore, in this study, we newly propose a two-step pre-trained model for detecting abnormal situation. Our methodology performs additional learning from image captioning to understand not only mere objects but also the complicated situation created by them. Specifically, the proposed methodology transfers knowledge of the pre-trained model that has learned object classification with ImageNet data to the image captioning model, and uses the caption that describes the situation represented by the image. Afterwards, the weight obtained by learning the situational characteristics through images and captions is extracted and fine-tuning is performed to generate an anomaly detection model. To evaluate the performance of the proposed methodology, an anomaly detection experiment was performed on 400 situational images and the experimental results showed that the proposed methodology was superior in terms of anomaly detection accuracy and F1-score compared to the existing traditional pre-trained model.