• Korean Journal of Heritage: History & Science
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• v.42 no.1
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• pp.88-99
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• 2009

This study was conducted to formulate management guidelines for Natural monumental old trees in Korea through survey of tree vigor and analysis of growing environments. A total of 20 old trees designated as natural monuments in Seoul, Incheon, and Gyeonggi Province were surveyed. The biological characteristics were surveyed with 4 items of species, ages and height of trees. The surrounding environments were surveyed with 2 items of location types and surroundings. The root conditions were surveyed with 2 items of denudation and molding depth. The health conditions were surveyed with 5 items of withering rate, cavity size, bark breakaway rate, damages by blight and insects, and growing tips. The soil conditions were surveyed with 6 items of PH, organic contents, valid phosphoric acid, transposal cations(K, Ca) and soil compaction. On the basis of outcomes of these research items, mutual relations among locations, growings and soil conditions of old trees were analyzed by carring out cross tabulation, correlation, and simple and multiple regression. Management guidelines were presented searching the factors effecting on the health of the monumental old trees. On the biological characteristics, the old trees designated as natural monuments were Pinus bungeana(4 trees), Juniperus chinensis(3 trees), Ginkgo biloba(3 trees), Poncirus trifoliata(2 trees). Actinidia arguta, Wisteria floribunda, Thuja orientalis, Quercus mongolica, Sophora japonica, Fraxinus rhynchophylla, Zelkova serrata, and Pinus densiflora. The tree height ranged from 4.2 to 39.2m, and root collar rounds ranged from 1.01 to 15.2m. On the surrounding environments, The location types ; Gardens(4), historical sites(5), residental sections(3) open agricultural fields(3), mountain hills(3), and near ocean beaches(1) and stream site(1). The surroundings ; 75% denudation of roots, molded more than 10cm except 4 trees(25%). On the health conditions, 1)Withering rate ; Ginkgo biloba(20%) in Yongmoon temple, (5%) in Saki-ri, kanwha-gun, and others had no withering rate. 2) Cavity size ; all subject had $5{\sim}100cm^3$ of cavity. 3) Bark breakaway rate ; Pinus bungeana in Soosong-dong, in the shrine of Confucius, in Samchung-dong, especially high rate of cavity(5~50%) in Seoul area and in Saki-ri, Kangwha-gun were high 45% brakeaway rate. 4) Damages by blight and insects was slight due to managements. 5Growing tips ; In cases of Juniperus chinensis in Changdeok palace and SunnogDang, seoul, growing tips were 1/2, presumably cause by air pollution, and in cases of Fraxinus rhynchophylla in Paju city and Pinus densiflora in BacksaDorip-ri, Icheon city, growing tips were fine, presumably because there were no moldings. On the Soil conditions, Soil pH ranged from 5.2 to 8.3, organic matter contents from 12% to 56%, phosphorus contents from 104 to 618ppm, soil compaction ranged from 7 to 28mm( among them, Denudation was severe with 21~28mm soil compactions in cases of Pinus bungeana in Soosong -dong, Thuja orientalis in Samchung -dong, Ginkgo biloba in the shrine of Confucius and in Yongmoon temple.) Results of cross tabulation, correlation, and regression analysis showed that molding depth was the most serious factor to deteriorate the tree vigor and cambium conductivity. In addition, soil acidity, organic matter contents, disease and insect damages and cambial detachment were also related to the tree vigor. Additional research of these relationships will be needed to conduct more detailed studies. Based on the relationships between the tree vigor and growing environments, it is considered that old trees should be managed to give them more growing spaces and less abuses. Also, molded soils should be removed and further soil-molding around the tree collar should be prohibited. For the construction of systematic management and removal of harmful factors, appropriative management according to spices, persistent monitering of damaged cases and construction of management system through the accumulation of data on the relationships of soil conditions are required.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.197-218
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• 2019

In addition to economic growth and national income increase, China is also experiencing rapid growth in consumption of cosmetics. About 67% of the total trade volume of Chinese cosmetics is made by e-commerce and especially K-Beauty products, which are Korean cosmetics are very popular. According to previous studies, 80% of consumer goods such as cosmetics are affected by the word of mouth information, searching the product information before purchase. Mostly, consumers acquire information related to cosmetics through comments made by other consumers on SNS such as SINA Weibo and Wechat, and recently they also use information about beauty related video channels. Most of the previous online word-of-mouth researches were mainly focused on media itself such as Facebook, Twitter, and blogs. However, the informational characteristics and the expression forms are also diverse. Typical types are text, picture, and video. This study focused on these types. We analyze the unstructured data of SINA Weibo, the SNS representative platform of China, and Meipai, the video platform, and analyze the impact of K-Beauty brand sales by dividing online word-of-mouth information with quantity and direction information. We analyzed about 330,000 data from Meipai, and 110,000 data from SINA Weibo and analyzed the basic properties of cosmetics. As a result of analysis, the amount of online word-of-mouth information has a positive effect on the sales of cosmetics irrespective of the type of media. However, the online videos showed higher impacts than the pictures and texts. Therefore, it is more effective for companies to carry out advertising and promotional activities in parallel with the existing SNS as well as video related information. It is understood that it is important to generate the frequency of exposure irrespective of media type. The positiveness of the video media was significant but the positiveness of the picture and text media was not significant. Due to the nature of information types, the amount of information in video media is more than that in text-oriented media, and video-related channels are emerging all over the world. In particular, China has made a number of video platforms in recent years and has enjoyed popularity among teenagers and thirties. As a result, existing SNS users are being dispersed to video media. We also analyzed the effect of online type of information on the online cosmetics sales by dividing the product type of cosmetics into basic cosmetics and color cosmetics. As a result, basic cosmetics had a positive effect on the sales according to the number of online videos and it was affected by the negative information of the videos. In the case of basic cosmetics, effects or characteristics do not appear immediately like color cosmetics, so information such as changes after use is often transmitted over a period of time. Therefore, it is important for companies to move more quickly to issues generated from video media. Color cosmetics are largely influenced by negative oral statements and sensitive to picture and text-oriented media. Information such as picture and text has the advantage and disadvantage that the process of making it can be made easier than video. Therefore, complaints and opinions are generally expressed in SNS quickly and immediately. Finally, we analyzed how product diversity affects sales according to online word of mouth information type. As a result of the analysis, it can be confirmed that when a variety of products are introduced in a video channel, they have a positive effect on online cosmetics sales. The significance of this study in the theoretical aspect is that, as in the previous studies, online sales have basically proved that K-Beauty cosmetics are also influenced by word-of-mouth. However this study focused on media types and both media have a positive impact on sales, as in previous studies, but it has been proven that video is more informative and influencing than text, depending on media abundance. In addition, according to the existing research on information direction, it is said that the negative influence has more influence, but in the basic study, the correlation is not significant, but the effect of negation in the case of color cosmetics is large. In the case of temporal fashion products such as color cosmetics, fast oral effect is influenced. In practical terms, it is expected that it will be helpful to use advertising strategies on the sales and advertising strategy of K-Beauty cosmetics in China by distinguishing basic and color cosmetics. In addition, it can be said that it recognized the importance of a video advertising strategy such as YouTube and one-person media. The results of this study can be used as basic data for analyzing the big data in understanding the Chinese cosmetics market and establishing appropriate strategies and marketing utilization of related companies.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• 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.25 no.1
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• pp.109-125
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• 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.

• The Journal of Korean Society for Radiation Therapy
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• v.32
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• pp.93-109
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• 2020

Purpose: The radiochromic film (Gafchromic EBT3, Ashland Advanced Materials, USA) and 3-dimensional analysis system dosimetry checkTM (DC, MathResolutions, USA) were evaluated for patient-specific quality assurance (QA) of helical tomotherapy. Materials and Methods: Depending on the tumors' positions, three types of targets, which are the abdominal tumor (130.6㎤), retroperitoneal tumor (849.0㎤), and the whole abdominal metastasis tumor (3131.0㎤) applied to the humanoid phantom (Anderson Rando Phantom, USA). We established a total of 12 comparative treatment plans by the four geometric conditions of the beam irradiation, which are the different field widths (FW) of 2.5-cm, 5.0-cm, and pitches of 0.287, 0.43. Ionization measurements (1D) with EBT3 by inserting the cheese phantom (2D) were compared to DC measurements of the 3D dose reconstruction on CT images from beam fluence log information. For the clinical feasibility evaluation of the DC, dose reconstruction has been performed using the same cheese phantom with the EBT3 method. Recalculated dose distributions revealed the dose error information during the actual irradiation on the same CT images quantitatively compared to the treatment plan. The Thread effect, which might appear in the Helical Tomotherapy, was analyzed by ripple amplitude (%). We also performed gamma index analysis (DD: 3mm/ DTA: 3%, pass threshold limit: 95%) for pattern check of the dose distribution. Results: Ripple amplitude measurement resulted in the highest average of 23.1% in the peritoneum tumor. In the radiochromic film analysis, the absolute dose was on average 0.9±0.4%, and gamma index analysis was on average 96.4±2.2% (Passing rate: >95%), which could be limited to the large target sizes such as the whole abdominal metastasis tumor. In the DC analysis with the humanoid phantom for FW of 5.0-cm, the three regions' average was 91.8±6.4% in the 2D and 3D plan. The three planes (axial, coronal, and sagittal) and dose profile could be analyzed with the entire peritoneum tumor and the whole abdominal metastasis target, with planned dose distributions. The dose errors based on the dose-volume histogram in the DC evaluations increased depending on FW and pitch. Conclusion: The DC method could implement a dose error analysis on the 3D patient image data by the measured beam fluence log information only without any dosimetry tools for patient-specific quality assurance. Also, there may be no limit to apply for the tumor location and size; therefore, the DC could be useful in patient-specific QAl during the treatment of Helical Tomotherapy of large and irregular tumors.

• The Korean Journal of Nuclear Medicine Technology
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• v.26 no.2
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• pp.43-47
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• 2022

Purpose Radioimmunoassay implement quality control by systematizing the internal quality control system for quality assurance of test results. This study aims to contribute to the quality assurance of radioimmunoassay results and to implement systematic quality control by measuring the average CV of internal quality control and external quality control by plenty of institutions for reference when setting the laboratory's own acceptable range. Materials and Methods We measured the average CV of internal quality control and the bounce rate of more than 10.0% for a total of 42 items from October 2020 to December 2021. According to the CV result, we classified and compared the upper group (5.0% or less), the middle group (5.0~10.0%) and the lower group (10.0% or more). The bounce rate of 10.0% or more was compared by classifying the item of five or more institutions into tumor markers, thyroid hormones and other hormones. The average CV was measured by the overall average and standard deviation of the external quality control results for 28 items from the first quarter to the fourth quarter of 2021. In addition, the average CV was measured by the overall average and standard deviation of the proficiency results between institutions for 13 items in the first half and the second half of 2021. The average CV of internal quality control and external quality control was compared by item so we compared and analyzed the items that implement well to quality control and the items that require attention to quality control. Results As a result of measuring the precision average of internal quality control for 42 items of six institutions, the top group (5.0% or less) are Ferritin, HGH, SHBG, and 25-OH-VitD, while the bottom group (≤10.0%) are cortisol, ATA, AMA, renin, and estradiol. When comparing more than 10.0% bounce rate of CV for tumor markers, CA-125 (6.7%), CA-19-9 (9.8%) implemented well, while SCC-Ag (24.3%), CA-15-3 (26.7%) were among the items that require attention to control. As a result of comparing the bounce rate of more than 10.0% of CV for thyroid hormones examination, free T4 (2.1%), T3 (9.3%) showed excellent performance and AMA (39.6%), ATA (51.6%) required attention to control. When comparing the bounce rate of 10.0% or more of CV for other hormones, IGF-1 (8.8%), FSH (9.1%), prolactin (9.2%) showed excellent performance, however estradiol (37.3%), testosterone (37.7%), cortisol (44.4%) required attention to control. As a result of measuring the average CV of the whole institutions participating at external quality control for 28 items, HGH and SCC-Ag were included in the top group (≤10.0%), however ATA, estradiol, TSI, and thyroglobulin included in bottom group (≥30.0%). Conclusion As a result of evaluating 42 items of six institutions, the average CV was 3.7~12.2% showing a 3.3 times difference between the upper group and the lower group. Cortisol, ATA, AMA, Renin and estradiol tests with high CV will require continuous improvement activities to improve precision. In addition, we measured and compared the overall average CV of the internal quality control, the external quality control and the proficiency between institutions participating of six institutions for 41 items excluding HBs-Ab. As a result, ATA, AMA, Renin and estradiol belong to the same subgroup so we require attention to control and consider setting a higher acceptable range. It is recommended to set and control the acceptable range standard of internal quality control CV in consideration of many things in the laboratory due to the different reagents and instruments, and the results vary depending on the test's proficiency and quality control materials. It is thought that the accuracy and reliability of radioimmunoassay results can be improved if systematic quality control is implemented based on the set acceptable range.

• Journal of the Daesoon Academy of Sciences
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• v.24_2
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• pp.231-270
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• 2015

This thesis examined Chu Hsi's self-cultivation theory as the representative theory of cultivation in Confucianism, while juxtaposing it to self-cultivation theory in Daesoon thought, concentrating on its similarity and difference. Neo-Confucianism is a scholarship which has wielded a tremendous influence upon East Asia, while functioning as an official scholarship for long period up to Yuan, Ming, and Ching period, after achieving its synthesis by Chu Hsi. After 13th century, Neo-Confucianism has been a representative academic system in Confucianism, and self-cultivation theory was in its center. It suggested various virtues that classical scholars have to equip to fulfill the sainthood and the politics of royal road. The self-cultivation theory of Chu Hsi was developed upon the basis of the theory 'Li-Qi' and the theory of mind. Here, the practice of Geokyung(morally awakened state with a reverent spirit in every moment) and Gyeokmul-tsiji(reaching into the utmost knowledge through investigation of things) formed a nucleus of Chu Hsi's self-cultivation theory. While Geokyung was to reveal one's true nature through cultivation of mind, Gyeokmul-tsiji was to demonstrate the complete use of mind's essence and function. Chu Hsi's self-cultivation theory attempted to realize the unity of heaven and man, and through cultivating self and governing people, it also tried to achieve its ideal of the society of Great Union(Daedong). Daesoon Thought is originated from Sangjenim who has descended to this world as a human being called Jeungsan. He went on his circuit to rectify the disorder of heaven and earth for 9 years to rectify the Three worlds of heaven, earth and human being which were faced with total annihilation due to its rule of mutual conflict, while creating an earthly paradise. Respecting the will of Sangjenim, Dojunim established the foundation of Do through launching 'Meukeukdo' and setting tenet, creed and object so that the cultivators (Doins) could believe and respect the truth of Sangje's great itineration (Daesoon). Among those, the two components of creed, which are four fundamental principles and the three fundamental attitudes are of great account as precept and cardinal point. Through this means, the cultivators of Daesoonjinrihoe aspire to guard against self-deception through transforming the human spirit, to realize earthly immortality through renewing human beings, and to create an earthly paradise through transforming the world. This thesis attempted to compare and analyze the theory of self-cultivation in Neo-Confucianism and Daesoon Thought in the aspect of ground, method, and object of cultivation. First, as for the ground of cultivation, the doctrines of Chu-tzu and Daesoon thought place the essence of cultivation on 'heaven'. Yet, whereas the former postulates Taekeuk (the Great Ultimate) as a principle as well as the heaven of a natural order, Daesoon thought postulates Sangenim as the heaven of superintendence as well as the heaven of a natural order, signified as its equation of Daesoon with circle, along with the unity of Meukeuk (Endlessness) and Taegeuk (the Great Ultimate). Further, the doctrine of Chu-zhu and Daesoon thought is identical in the point that both thoughts see mind as the subject of cultivation, while trying to restore a pure essence. Nevertheless, whereas Neo-Confucianism intends to give scope to ability of the complete use of mind's essence and function, Daesoon Thought sees mind as the essence which is used by spiritual beings and as an organ that heaven, earth and human being rely upon as the center of the universe. In the aspect of method of cultivation, the doctrine of Chuzhu lays emphasis on the rational factor in that it brightens its 'myung-deoki'(bright inner virtue),' while trying to correspond to the law of heaven on the basis of 'Geokyung' and 'Gyeokmul-tsiji.' On the contrary, Daesoon thought lays much emphasis on faith factor in that it aspires for human perfection based on the restoration of conscience by cultivating Daesoonjinri with sincerity, reverence and faith along with 'quieting the heart-mind', 'quieting the body', 'respecting the God of the Ninth Heaven', and 'observing ritual practice on the basis of the faith in Sangjenim. Yet, both thoughts have similarities in that cultivation of body forms the basis and that they attempt to realize their ideals through cultivation in daily life while taking 'Guarding against self-deception' as the key method of self-cultivation.' However, the principle of Chu-zhu can be said to be a voluntary and autonomous practice based on scripture of the saint as well as self-reflection. On the other hand, Daesoon thought reveals certain difference in that it combines faith factor with one's self-effort by concentrating on cultivation under the presence of Sangjenim as the object of belief and the spirits of heaven and earth. In the aspect of object of cultivation, both thoughts share similarities in that the saint and the perfected gentleman with a moral virtue as an ideal image of men in both thoughts attempt to realize each of their 'myung-deok' in human nature as a heavenly mandate while respecting morality. Further, they also share similarity in the point that the desirable characters in both thoughts want to participate in harmonious creation and nurturance. Yet, the perfected gentleman with a moral virtue is also characterized by its aim for a new heaven and earth where there is no mutual conflict but mutual beneficence, by promoting the moral influence and virtue of Sangjenim over one's own virtue, while practising the mutual beneficence of all life through harmonious union of divine beings and human beings.

• The Journal of Korean Society for Radiation Therapy
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• v.34
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• pp.73-82
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• 2022

Purpose: The purpose of this study is to evaluate the effectiveness of reducing the absorbed dose to the ovaries and the quality of the CBCT image when using the Halcyon^TM Fast kV CBCT of cervical cancer patients of child-bearing age who performed ovarian transposition Materials and Methods : Contouring of the cervix and ovaries required for measurement was performed on the computed tomography images of the human phantom (Alderson Rando Phantom, USA), and three Optically Stimulated Luminescence Dosimeter(OSLD) were attached to the selected organ cross-section, respectively. In order to measure the absorbed dose to the cervix and ovaries in the Truebeam^TM pelvis mode (Hereinafter referred to as TP), The Halcyon^TM Pelvis mode (Hereinafter referred to as HP) and The Halcyon^TM Pelvis Fast mode (Hereinafter referred to as HPF), An image was taken with a scan range of 17.5 cm and also taken an image that reduced the Scan range to 12.5cm. A total of 10 cumulative doses were summed, It was replaced with a value of 23 Fx, the number of cervical cancer treatments, and compared In additon, uniformity, low contrast visibility, spatial resolution, and geometric distortion were compared and analyzed using Catphan 504 phantom to compare CBCT image quality between equipment. Each factor was repeatedly measured three times, and the average value was obtained by analysing with the Doselab (Mobius Medical Systems, LP. Versions: 6.8) program. Results: As a result of measuring absorbed dose by CBCT with OSLD, TP and HP did not obtain significant results under the same conditions. The mode showing the greatest reduction value was HPF versus TP. In HPF, the absorbed dose was reduced by 39.8% in the cervix and 19.8% in the ovary compared to the TP in the scan range of 17.5 cm. the scan range was reduced to 12.5 cm, absorbed dose was reduced by 34.2% in the cervix and 50.5% in the ovary. In addition, result of evaluating the quality of the image used in the above experiment, it complied with the equipment manufacturer's standards with Geometric Distortion within 1mm (SBRT standard), Uniformity HU, LCV within 2.0%, Spatial Resolution more than 3 lp/mm. Conclusion: According to the results of this experiment, Halcyon^TM can select more various conditions than Truebeam^TM in treatment of fertility woman who have undergone ovarian Transposition , because it is important to reduce the radiation dose by CBCT during radiation therapy. So finally we recommend Halcyon^TM Fast kV CBCT which maintains image quality even at low mAs. However, it is consider that the additional exposure to low doses can be reduced by controlling the imaging range for patients who have undergone ovarian transposition in other treatment machines.