• Industry Promotion Research
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• v.7 no.3
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• pp.115-124
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• 2022

The most difficult things, when we study the future-telling science of human destiny, are in case of what one's individual's fate is bad which is shown by Saju-Palza(四柱八字), In that case, we have faced the problems on how we live ; to follow or to deny our fate under the brief of improving our lives by trying to make hard efforts, regardless of the bad Saju-Palza(四柱八字). However, we can hardly find the clear answer to those questions. 『Liao Fan 4 lessons(了凡四訓)』 shows that one's destiny can be improved by accumulating good deeds despite of the bad Saju-Palza(四柱八字). Someone says that future can be created, not be foreseen. As well, Dr. Steven Coby says that the best definite way to forecast future is in creating the future. Anyhow, the strong desire and curiosity to know one's individual's future is having been lasted until now since the Genesis. we guess these desires may be one of our basic instinct. If then, the function and role of the future-telling science will be to increase the accuracy of future prediction, whether our fate has been fixed or been able to be changeable. Therefore, this study summarizes the definition of confusing terms, focusing on Gyeokguk(格局) and Sangshin(相神), the core of Myeongrihak(命理學), which is considered to be one of the most popular future-telling science. Concering Gyeok(格), in this paper, Nae-Gyeok(內格) has been mainly considered and Oi-Gyeok(外格) or Special-Gyeok(別格) have not been addressed. Specifically, it summarized the views of the classical Myeongri(命理) books and modern scholars on Gyeokguk(格局) and Yongshin(用神). In particular, it also summarized the comparison of various concepts of Gyeokguk(格局), the advantages and disadvantages of each Nae-Gyeok(內格)'s characteristic, the determination order of Nae-Gyeok(內格) and the good case and bad case of it's Gyeok(格). In addition, it was necessary to summarize the concept of Sangshin(相神), which was talked about in 『Japyeongjinjeon』 and to briefly summarize Heeshin(喜神) with a broader concept than Sangshin(相神). The different usage of Sangshin(相神) was also analyzed, between the priority interpretation of Cheongan(天干) in Day-Column(日柱) and the interpretation based on Jijee(地支) in Month-Column(月柱). Finally, this paper was completed, leaving it later as a research task, the confusion that comes from the scholars' acceptance of the comprehensive diversity on the same term.

• Journal of Food Hygiene and Safety
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• v.38 no.2
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• pp.37-45
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• 2023

The surge in food delivery systems during the coronavirus 2019 pandemic necessitated this study of heavy metal migration from food contact materials (FCMs). A total of 104 samples of FCMs, comprising 51 polypropylene (PP), 21 polyethylene (PE), and 32 polystyrene (PS) samples of six different types of FCMs (containers, covers, table utensils, cups, pouches, and wrappers) used for food delivery distributed in Korea, were collected and investigated for migration of three heavy metals (Pb, Cd, and As) using inductively coupled plasma-mass spectrometry (ICP-MS) to determine whether they complied with Korea's Standards and Specifications for Utensils, Containers, and Packages. Acetic acid (4%, v/v) was used as the food simulant, and tests were performed at 100℃ (in harsh conditions) for 30 min. Linearity of Pb, Cd, and As showed acceptable results with a coefficient of determination (R²) value of 0.9999. Limit of detection (LOD) and limit of quantification (LOQ) of Pb, Cd, and As were 0.001, 0.001, and 0.001 ㎍/L and 0.002, 0.003, and 0.003 ㎍/L, respectively. Accuracy and precision results complied with the criteria presented in the European Commission Joint Research Centre guidelines. The average concentration of Pb, Cd, and As migration detected in a total of 104 samples was 0.009-0.260 ㎍/L, which was very low compared with the migration specification set in the Standards and Specifications for Utensils, Containers, and Packages. The maximum level of Pb corresponded to 0.23% of the migration limit. There were no samples exceeding the limit. Thus, this study confirmed that the heavy metal contents of FCMs used for delivery food distributed in Korea were safely managed. The data from this study represent an invaluable source for science-based safety management of hazardous heavy metals migrating from FCMs used in the food delivery industry.

• The Journal of Korean Philosophical History
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• no.29
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• pp.255-280
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• 2010

Zhang Bo-duan compiled about internal alchemy in Taoism. Although he lived in the mundane world, he wished to seek theory on neidan of Taoism(internal alchemy). After finding enlightenment, he elucidated that the enlightenment was a state of rising above world not needed to leave the world. After ages, he was admired as the founder of Taoism in Southern school and his Oh Jin Peon which contents internal alchemy was considered seriously to have more than 30 people who annotated with it until Ch'ing Empire. At his age of 80, he met the real person who gave him theory on neidan of Taoism(internal alchemy), its preface tells that he organized its main point, and then wrote Oh Jin Peon with it in 1075. Generally Zhang Bo-duan was known to leave three books as Oh Jin Peon, Guem Dan Sa Baek Ja, and Cheung Hwa Bi Mun, most of critics have been studying on the basis of them. However, it is not correct whether all of them is his writings and there is not exact analysis but simple belief about it. I think accuracy and details are indispensible in philosophical approach. The study not having verification about primary data is no more than a visionary projet which soon collapses. So the purpose of this study is adding the detail analysis on it and making its exact basis of philosophical approach. Zhang Bo-duan over his age of 80, became enlightened, in his old age handed down his student the secret as a record and theory on neidan of Taoism(internal alchemy). And not in his living but after his dying his status was soared. Because of his high status in internal alchemy Taoism, it seems that there are more interest in it and some published books which just leave his name. In this study, I accept Oh Jin Peon as a his real writing among unsure his writings and criticize systematically and classify its characteristics. And I demonstrate that Guem Dan Sa Baek Ja, Cheung Hwa Bi Mun couldn't be his real writings, these could be forgeries by posterity, with proposing some basis of the argument.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1255-1272
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• 2023

Satellite-based chlorophyll-a concentration, produced as a long-term time series, is crucial for global climate change research. The production of data without gaps through the merging of time-synthesized or multi-satellite data is essential. However, studies related to satellite-based chlorophyll-a concentration in the waters around the Korean Peninsula have mainly focused on evaluating seasonal characteristics or proposing algorithms suitable for research areas using a single ocean color sensor. In this study, a merging dataset of remote sensing reflectance from the geostationary sensor GOCI-II and polar-orbiting sensors (MODIS, VIIRS, OLCI) was utilized to achieve high spatial coverage of chlorophyll-a concentration in the waters around the Korean Peninsula. The spatial coverage in the results of this study increased by approximately 30% compared to polar-orbiting sensor data, effectively compensating for gaps caused by clouds. Additionally, we aimed to quantitatively assess accuracy through comparison with global chlorophyll-a composite data provided by Ocean Colour Climate Change Initiative (OC-CCI) and GlobColour, along with in-situ observation data. However, due to the limited number of in-situ observation data, we could not provide statistically significant results. Nevertheless, we observed a tendency for underestimation compared to global data. Furthermore, for the evaluation of practical applications in response to marine disasters such as red tides, we qualitatively compared our results with a case of a red tide in the East Sea in 2013. The results showed similarities to OC-CCI rather than standalone geostationary sensor results. Through this study, we plan to use the generated data for future research in artificial intelligence models for prediction and anomaly utilization. It is anticipated that the results will be beneficial for monitoring chlorophyll-a events in the coastal waters around Korea.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1651-1669
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• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1553-1563
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• 2023

The Geostationary Ocean Color Imager-II (GOCI-II) is a satellite designed for ocean color observation, covering the Northeast Asian region and the entire disk of the Earth. It commenced operations in 2020, succeeding its predecessor, GOCI, which had been active for the previous decade. In this study, we aimed to enhance the atmospheric correction algorithm, a critical step in producing satellite-based ocean color data, by performing cross-calibration on the GOCI-II near-infrared (NIR) band using the GOCI NIR band. To achieve this, we conducted a cross-calibration study on the top-of-atmosphere (TOA) radiance of the NIR band and derived a vicarious calibration gain for two NIR bands (745 and 865 nm). As a result of applying this gain, the offset of two sensors decreased and the ratio approached 1. It shows that consistency of two sensors was improved. Also, the Rayleigh-corrected reflectance at 745 nm and 865 nm increased by 5.62% and 9.52%, respectively. This alteration had implications for the ratio of Rayleigh-corrected reflectance at these wavelengths, potentially impacting the atmospheric correction results across all spectral bands, particularly during the aerosol reflectance correction process within the atmospheric correction algorithm. Due to the limited overlapping operational period of GOCI and GOCI-II satellites, we only used data from March 2021. Nevertheless, we anticipate further enhancements through ongoing cross-calibration research with other satellites in the future. Additionally, it is essential to apply the vicarious calibration gain derived for the NIR band in this study to perform vicarious calibration for the visible channels and assess its impact on the accuracy of the ocean color products.

• Journal of the Korean Society of Radiology
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• v.83 no.5
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• pp.1090-1103
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• 2022

Purpose To evaluate the diagnostic performance of digital breast tomosynthesis (DBT) with the two-dimensional synthesized mammogram (2DSM), compared to full-field digital mammography (FFDM), for suspicious microcalcifications in the breast ahead of stereotactic biopsy and to assess the diagnostic image visibility of the images. Materials and Methods This retrospective study involved 189 patients with microcalcifications, which were histopathologically verified by stereotactic breast biopsy, who underwent DBT with 2DSM and FFDM between January 8, 2015, and January 20, 2020. Two radiologists assessed all cases of microcalcifications based on Breast Imaging Reporting and Data System (BI-RADS) independently. They were blinded to the histopathologic outcome and additionally evaluated lesion visibility using a fivepoint scoring scale. Results Overall, the inter-observer agreement was excellent (0.9559). Under the setting of category 4A as negative due to the low possibility of malignancy and to avoid the dilution of malignancy criteria in our study, McNemar tests confirmed no significant difference between the performances of the two modalities in detecting microcalcifications with a high potential for malignancy (4B, 4C, or 5; p = 0.1573); however, the tests showed a significant difference between their performances in detecting microcalcifications with a high potential for benignancy (4A; p = 0.0009). DBT with 2DSM demonstrated superior visibility and diagnostic performance than FFDM in dense breasts. Conclusion DBT with 2DSM is superior to FFDM in terms of total diagnostic accuracy and lesion visibility for benign microcalcifications in dense breasts. This study suggests a promising role for DBT with 2DSM as an accommodating tool for stereotactic biopsy in female with dense breasts and suspicious breast microcalcifications.

• Journal of the Korea Organic Resources Recycling Association
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• v.31 no.4
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• pp.29-39
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• 2023

In this study, an analysis of medical waste generation characteristics was conducted, differentiating between ordinary situation and the outbreaks of massive infectious diseases. During ordinary situation, prediction models for medical waste quantities by type, general medical waste(G-MW), hazardous medical waste(H-MW), infectious medical waste(I-MW), were established through regression analysis, with all significance values (p) being <0.0001, indicating statistical significance. The determination coefficient(R2) values for prediction models of each category were analyzed as follows : I-MW(R2=0.9943) > G-MW(R2=0.9817) > H-MW(R2=0.9310). Additionally, factors such as GDP(G-MW), the number of medical institutions (H-MW), and the elderly population ratio(I-MW), utilized as influencing factors and consistent with previous literature, showed high correlations. The total MW generation, evaluated by combining each model, had an MAE of 2,615 and RMSE of 3,353. This indicated accuracy levels similar to the medical waste models of H-MW(2,491, 2,890) and I-MW(2,291, 3,267). Due to limitations in accurately estimating the quantity of medical waste during the rapid and outbreaks of massive infectious diseases, the generation unit of I-MW was derived to analyze its characteristics. During the early unstable stage of infectious disease outbreaks, the generation unit was 8.74 kg/capita·day, 2.69 kg/capita·day during the stable stage, and an average of 0.08 kg/capita·day during the reduction stage. Correlation analysis between generation unit of I-MW and lethality rates showed +0.99 in the unstable stage, +0.52 in the stable stage, and +0.96 in the reduction period, demonstrating a very high positive correlation of +0.95 or higher throughout the entire outbreaks of massive infectious diseases. The results derived from this study are expected to play a useful role in establishing an effective medical waste management system in the field of health care.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.227-252
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• 2018

The evolution of instant communication has mirrored the development of the Internet and messenger applications are among the most representative manifestations of instant communication technologies. In messenger applications, senders use emoticons to supplement the emotions conveyed in the text of their messages. The fact that communication via messenger applications is not face-to-face makes it difficult for senders to communicate their emotions to message recipients. Emoticons have long been used as symbols that indicate the moods of speakers. However, at present, emoticon-use is evolving into a means of conveying the psychological states of consumers who want to express individual characteristics and personality quirks while communicating their emotions to others. The fact that companies like KakaoTalk, Line, Apple, etc. have begun conducting emoticon business and sales of related content are expected to gradually increase testifies to the significance of this phenomenon. Nevertheless, despite the development of emoticons themselves and the growth of the emoticon market, no suitable emoticon recommendation system has yet been developed. Even KakaoTalk, a messenger application that commands more than 90% of domestic market share in South Korea, just grouped in to popularity, most recent, or brief category. This means consumers face the inconvenience of constantly scrolling around to locate the emoticons they want. The creation of an emoticon recommendation system would improve consumer convenience and satisfaction and increase the sales revenue of companies the sell emoticons. To recommend appropriate emoticons, it is necessary to quantify the emotions that the consumer sees and emotions. Such quantification will enable us to analyze the characteristics and emotions felt by consumers who used similar emoticons, which, in turn, will facilitate our emoticon recommendations for consumers. One way to quantify emoticons use is metadata-ization. Metadata-ization is a means of structuring or organizing unstructured and semi-structured data to extract meaning. By structuring unstructured emoticon data through metadata-ization, we can easily classify emoticons based on the emotions consumers want to express. To determine emoticons' precise emotions, we had to consider sub-detail expressions-not only the seven common emotional adjectives but also the metaphorical expressions that appear only in South Korean proved by previous studies related to emotion focusing on the emoticon's characteristics. We therefore collected the sub-detail expressions of emotion based on the "Shape", "Color" and "Adumbration". Moreover, to design a highly accurate recommendation system, we considered both emotion-technical indexes and emoticon-emotional indexes. We then identified 14 features of emoticon-technical indexes and selected 36 emotional adjectives. The 36 emotional adjectives consisted of contrasting adjectives, which we reduced to 18, and we measured the 18 emotional adjectives using 40 emoticon sets randomly selected from the top-ranked emoticons in the KakaoTalk shop. We surveyed 277 consumers in their mid-twenties who had experience purchasing emoticons; we recruited them online and asked them to evaluate five different emoticon sets. After data acquisition, we conducted a factor analysis of emoticon-emotional factors. We extracted four factors that we named "Comic", Softness", "Modernity" and "Transparency". We analyzed both the relationship between indexes and consumer attitude and the relationship between emoticon-technical indexes and emoticon-emotional factors. Through this process, we confirmed that the emoticon-technical indexes did not directly affect consumer attitudes but had a mediating effect on consumer attitudes through emoticon-emotional factors. The results of the analysis revealed the mechanism consumers use to evaluate emoticons; the results also showed that consumers' emoticon-technical indexes affected emoticon-emotional factors and that the emoticon-emotional factors affected consumer satisfaction. We therefore designed the emoticon recommendation system using only four emoticon-emotional factors; we created a recommendation method to calculate the Euclidean distance from each factors' emotion. In an attempt to increase the accuracy of the emoticon recommendation system, we compared the emotional patterns of selected emoticons with the recommended emoticons. The emotional patterns corresponded in principle. We verified the emoticon recommendation system by testing prediction accuracy; the predictions were 81.02% accurate in the first result, 76.64% accurate in the second, and 81.63% accurate in the third. This study developed a methodology that can be used in various fields academically and practically. We expect that the novel emoticon recommendation system we designed will increase emoticon sales for companies who conduct business in this domain and make consumer experiences more convenient. In addition, this study served as an important first step in the development of an intelligent emoticon recommendation system. The emotional factors proposed in this study could be collected in an emotional library that could serve as an emotion index for evaluation when new emoticons are released. Moreover, by combining the accumulated emotional library with company sales data, sales information, and consumer data, companies could develop hybrid recommendation systems that would bolster convenience for consumers and serve as intellectual assets that companies could strategically deploy.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.