• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.613-621
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• 2019

The purpose of this study is to evaluate the effectiveness of supportive devices which are for minimizing the patient's movement during lower extremity angiography and to verify image quality of phantom by analyzing of Mask image, DSA image and Roadmap image into SNR and CNR. As a result of comparing SNR with CNR of mask image obtained by DSA technique using the phantom alone and phantom placed on the supportive devices, there was no significant difference between about 0~0.06 for SNR and about 0~0.003 for CNR. The study showed about 0.11~0.35 for SNR and 0.016~0.031 for CNR of DSA imaging by DSA technique about only water phantom of the blood vessel model and the water phantom placed on the device. Analyzing SNR and CNR of Roadmap technique about water phantom on the auxiliary device (hardboard paper, pomax, polycarbonate, acrylic) and water phantom alone, there was no significant difference between 0.02~0.05 for SNR and 0.002~0.004 for CNR. In conclusion, there was no significant difference on image quality by using supportive devices made by hardboard paper, pomax, polycarbonate or acryl regardless of whether using supportive devices or not. Supportive devices to minimize of the patient's movement may reduce the total amount of contrast, exam-time, radiation exposure and eliminate risk factors during angiogram. Supportive devices made by hardboard paper can be applied easily during angiogram due to advantages of reasonable price and simple processing. It is considered that will be useful to consider cost efficiency and types of materials and their properties in accordance with purpose and method of the study when the operator makes and uses supportive devices.

• Clean Technology
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• v.25 no.3
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• pp.256-262
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• 2019

Hydroxylammonium nitrate (HAN)-based liquid propellants are attracting attention as environmentally friendly propellants because they are not carcinogens and the combustion gases have little toxicity. The catalyst used to decompose the HAN-based liquid propellant in a thruster must have both low temperature activity and high heat resistance. The objective of this study is to prepare an Ru/alumina/metal foam catalyst by supporting alumina slurry on the surface of NiCrAl metal foam using a washing coating method and then to support a ruthenium precursor thereon. The decomposition activity of a HAN aqueous solution of the Ru/alumina/metal foam catalyst was evaluated. The effect of the number of repetitive coatings of alumina slurry on the physical properties of the alumina/metal foam was analyzed. As the number of alumina wash coatings increased, mesopores with a diameter of about 7 nm were well-developed, thereby increasing the surface area and pore volume. It was optimal to repeat the wash coating alumina on the metal foam 12 times to maximize the surface area and pore volume of the alumina/metal foam. Mesopores were also well developed on the surface of the Ru/alumina/metal foam catalyst. It was found that the metal form itself without the active metal and alumina can promote the decomposition reaction of the HAN aqueous solution. In the case of the Ru/alumina/metal foam-550 catalyst, the decomposition onset temperature was significantly lowered compared with that of the thermal decomposition reaction, and ${\Delta}P$ could be greatly increased in the decomposition of the HAN aqueous solution. However, when the catalyst was calcined at $1,200^{\circ}C$, the catalytic activity was lowered inevitably because the surface area and pore volume of the catalyst were drastically reduced and Ru was sintered. Further research is needed to improve the heat resistance of Ru/alumina/metal foam catalysts.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Korean Journal of Heritage: History & Science
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• v.50 no.2
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• pp.94-115
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• 2017

'Arirang folk song in the Democratic People's Republic of Korea' was inscribed to the Representative List of the Intangible Cultural Heritage of Humanity in 2014 and 'Tradition of kimchi-making in the Democratic People's Republic of Korea' followed in 2015. It is presumed that North Korea was influenced by the Republic of Korea inscribing 'Arirang, lyrical folk song in the Republic of Korea' to the list in 2012 as well as 'Kimjang, making and sharing kimchi in the Republic of Korea' in 2013. These cases show the necessity (or possibility) of cultural exchanges between the two Koreas through UNESCO ICH lists. The purpose of this article is to explore the possibility of inter-Korean cultural integration. Therefore, I would like to review UNESCO's ICH policy and examine the ways of cooperation and joint nominations to the Representative List of Intangible Cultural Heritage of Humanity between the two Koreas. First, I reviewed the amendments to the laws and regulations of the two Koreas and how the two countries applied the UNESCO Convention for the Safeguarding of the Intangible Cultural Heritage. Although the cultural exchange is a non-political field, given the situation between South and North Korea, it is influenced by politics. Therefore, we devised a stepwise development plan, divided into four phases: infrastructure development, cooperation and promotion, diversification, and policymaking and alternative development. First a target group will be needed. In this regard, joint nominations to the Representative List of the UNESCO Intangible Cultural Heritage of Humanity will be suitable for cooperation. Both countries have already started separate nominations on shared ICH elements to the UNESCO lists. Therefore, I have selected a few elements as examples that can be considered for joint nominations. The selected items are makgeolli (traditional liquor), jang (traditional soybean sauce), gayangju (homebrewed liquor), gudeul (Korean floor heating system), and jasu (traditional embroidery). Cooperation should start with sharing information on ICH elements. A pilot project for joint nomination can be implemented and then a mid-term plan can be established for future implementation. When shared ICH elements are inscribed on UNESCO ICH lists, various activities can be considered as follow-ups, such as institution visits, performances, exhibitions, and joint monitoring of the intangible cultural heritage. Mutual cooperation of the two Koreas' intangible cultural heritage will be a unique example between the divided countries, so its value will be recognized as a symbol of cultural cooperation. In addition, it will be a foundation for cultural integration of the two Koreas, and it will show the value of their unique ICH to the world. At the same time, it will become a good example for joint nominations to the Representative List recommended by UNESCO.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.85-96
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• 2019

Solar radiation is an important variable for estimation of energy balance and water cycle in natural and agricultural ecosystems. A deep neural network (DNN) model has been developed in order to estimate the daily global solar radiation. Temperature and precipitation, which would have wider availability from weather stations than other variables such as sunshine duration, were used as inputs to the DNN model. Five-fold cross-validation was applied to train and test the DNN models. Meteorological data at 15 weather stations were collected for a long term period, e.g., > 30 years in Korea. The DNN model obtained from the cross-validation had relatively small value of RMSE ($3.75MJ\;m^{-2}\;d^{-1}$) for estimates of the daily solar radiation at the weather station in Suwon. The DNN model explained about 68% of variation in observed solar radiation at the Suwon weather station. It was found that the measurements of solar radiation in 1985 and 1998 were considerably low for a small period of time compared with sunshine duration. This suggested that assessment of the quality for the observation data for solar radiation would be needed in further studies. When data for those years were excluded from the data analysis, the DNN model had slightly greater degree of agreement statistics. For example, the values of $R^2$ and RMSE were 0.72 and $3.55MJ\;m^{-2}\;d^{-1}$, respectively. Our results indicate that a DNN would be useful for the development a solar radiation estimation model using temperature and precipitation, which are usually available for downscaled scenario data for future climate conditions. Thus, such a DNN model would be useful for the impact assessment of climate change on crop production where solar radiation is used as a required input variable to a crop model.

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.581-588
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• 2019

MRI is superior when contrasted to help the organization generate artifacts resolution, but also affect the diagnosis and create a image that can not be read. Metal is inserted into the tooth, it is necessary to often be inhibited in imaging by causing the geometric distortion due to the majority and if the difference between the magnetic susceptibility of a ferromagnetic material or paramagnetic reducing them. The purpose of this study is to conduct a metal artefact in accordance with the analysis using a diamagnetic material. The magnetic material include a wire for the orthodontic bracket and a stainless steel was used as a diamagnetic material was used copper, zinc, bismuth. Testing equipment is sequenced using 1.5T, 3T was used was measured using a SE, TSE, GE, EPI. A self-produced phantom material was used for agarose gel (10%) to a uniform signal artifacts causing materials are stainless steel were tested by placing in the center of the phantom and cover inspection of the positive cube diamagnetic material of 10mm each length.After a measurement artefact artifact zone settings area was calculated using the Wand tool After setting the Low Threshold value of 10 in the image obtained by subtracting images, including magnetic material from a pure tool phantom images using Image J. Metal artifacts occur in stainless steel metal artifact reduction was greatest in the image with the bismuth diamagnetic materials of copper and zinc is slightly reduced, but the difference in degree will not greater. The reason for this is thought to be due to hayeotgi offset most of the susceptibility in bismuth diamagnetic susceptibility of most small ferromagnetic. Most came with less artifacts in image of bismuth in both 1.5T and 3T. Sequence-specific artifact reduction was most reduced artifacts from the TSE 1.5T 3T was reduced in the most artifacts from SE. Signal-to-noise ratio was the lowest SNR is low, appears in the implant, the 1.5T was the Implant + Bi Cu and Zn showed similar results to each other. Therefore, the results of artifacts variation of diamagnetic material, magnetic susceptibility (${\chi}$) is the most this shows the reduced aspect lower than the implant artificial metal artifacts criteria in the video using low bismuth susceptibility to low material the more metal artifacts It was found that the decrease. Therefore, based on the study on the increase, the metal artifacts reduction for the whole, as well as dental prosthesis future orthodontic materials in a way that can even reduce the artifact does not appear which has been pointed out as a disadvantage of the solutions of conventional metal artifact It is considered to be material.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.19-41
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• 2019

According to the rapidly increasing demand for text data analysis, research and investment in text mining are being actively conducted not only in academia but also in various industries. Text mining is generally conducted in two steps. In the first step, the text of the collected document is tokenized and structured to convert the original document into a computer-readable form. In the second step, tasks such as document classification, clustering, and topic modeling are conducted according to the purpose of analysis. Until recently, text mining-related studies have been focused on the application of the second steps, such as document classification, clustering, and topic modeling. However, with the discovery that the text structuring process substantially influences the quality of the analysis results, various embedding methods have actively been studied to improve the quality of analysis results by preserving the meaning of words and documents in the process of representing text data as vectors. Unlike structured data, which can be directly applied to a variety of operations and traditional analysis techniques, Unstructured text should be preceded by a structuring task that transforms the original document into a form that the computer can understand before analysis. It is called "Embedding" that arbitrary objects are mapped to a specific dimension space while maintaining algebraic properties for structuring the text data. Recently, attempts have been made to embed not only words but also sentences, paragraphs, and entire documents in various aspects. Particularly, with the demand for analysis of document embedding increases rapidly, many algorithms have been developed to support it. Among them, doc2Vec which extends word2Vec and embeds each document into one vector is most widely used. However, the traditional document embedding method represented by doc2Vec generates a vector for each document using the whole corpus included in the document. This causes a limit that the document vector is affected by not only core words but also miscellaneous words. Additionally, the traditional document embedding schemes usually map each document into a single corresponding vector. Therefore, it is difficult to represent a complex document with multiple subjects into a single vector accurately using the traditional approach. In this paper, we propose a new multi-vector document embedding method to overcome these limitations of the traditional document embedding methods. This study targets documents that explicitly separate body content and keywords. In the case of a document without keywords, this method can be applied after extract keywords through various analysis methods. However, since this is not the core subject of the proposed method, we introduce the process of applying the proposed method to documents that predefine keywords in the text. The proposed method consists of (1) Parsing, (2) Word Embedding, (3) Keyword Vector Extraction, (4) Keyword Clustering, and (5) Multiple-Vector Generation. The specific process is as follows. all text in a document is tokenized and each token is represented as a vector having N-dimensional real value through word embedding. After that, to overcome the limitations of the traditional document embedding method that is affected by not only the core word but also the miscellaneous words, vectors corresponding to the keywords of each document are extracted and make up sets of keyword vector for each document. Next, clustering is conducted on a set of keywords for each document to identify multiple subjects included in the document. Finally, a Multi-vector is generated from vectors of keywords constituting each cluster. The experiments for 3.147 academic papers revealed that the single vector-based traditional approach cannot properly map complex documents because of interference among subjects in each vector. With the proposed multi-vector based method, we ascertained that complex documents can be vectorized more accurately by eliminating the interference among subjects.

• The Korean Journal of Nuclear Medicine Technology
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• v.23 no.2
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• pp.43-50
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• 2019

Purpose The principle of nuclear medicine test is divided into two main categories: competition(radioimmunoassay, RIA) and noncompetitive reaction(Immunoradiometric assay, IRMA). It is known that the curve fitting method, which is commonly used in inspection field, uses Spline interpolation in RIA method and Linear interpolation method in IRMA method. Among them, the insulin test using the IRMA test showed a significant difference, especially at low concentrations, despite the same algorithm of linear interpolation between fully automated radio immunoassay analyzers. In this study, we aim to obtain results from applying two different of algorithm using fully automated radio immunoassay analyzers including Gamma pro, Gamma 10, Cobra, and SR300. Materials and Methods A total of 30 test samples were selected for the test of TSH, ferritin, C-peptide, and insulin serum levels. Test was performed by IRMA method. We compared the difference in the results of applying the linear interpolation method and the spline interpolation method to Gamma Pro, Gamma 10, Cobra, and SR300 equipment. Results Two-way ANOVA was used for statistical analysis. The significance level was applied as P <0.05. The results of TSH, ferritin, C-peptide, and insulin tests were compared between the fully automated radio immunoassay analyzers. There was a significant difference between ferritin, C-peptide, and insulin serum levels(P<0.001). TSH didn't show any significant different between the devices(P=0.29). In the difference between linear and spline interpolation, there was no significant difference between insulin test(P=0.08), TSH test(P=0.81), and Ferritin test(P=0.06). However, C-peptide test showed a significant difference(P=0.03). Especially, the insulin test showed significant difference in lower ranges. As a result of comparing and analyzing the difference between the two interpolation methods, the devices in the low concentration group showed significant difference(P<0.001). Conclusion In case of new equipment in the laboratory it is necessary to recognize that there is a difference in the curve fitting method for each automated radio immunoassay analyzers in the low concentration area when the principle of inspection is IRMA method.

• Journal of Nutrition and Health
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• v.52 no.5
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• pp.435-448
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• 2019

Purpose: This study examined the changes in the nutrient intake and the prevalences of obesity and metabolic syndrome according to the eating alone behavior in middle-aged men and women. Methods: The data from the 2013 ~ 2017 Korean National Health and Nutrition Examination Survey were analyzed. A total of 7,728 adults (3,404 male, 4,324 female) aged 40 ~ 64 years old were included. The subjects were classified into three groups according to the number of times eating alone per day (0, 1 ~ 2, and 3 times/day). Dietary data were collected by a 1-day 24-h recall. The nutrient intakes were compared among the three groups. Survey logistic regression analyses were conducted to examine the association of the eating alone behavior with obesity and metabolic syndrome, adjusting for the related confounding variables. Results: In men, eating alone was associated significantly with a lower intake of potassium (p-for-trend = 0.048) and lower intake of calories from protein (p-for-trend = 0.04). In women, the proportion of subjects consuming energy less than 75% of the estimated energy requirement (p = 0.001) and less than the estimated adequate requirement of riboflavin (p < 0.001) differed significantly according to the eating alone behavior. The eating alone behavior was positively associated with an increased risk of developing metabolic syndrome (p-for-trend = 0.033), increased blood pressure (p-for-trend < 0.001), and increased waist circumference (p-for-trend = 0.004). On the other hand, in women, however, the eating alone behavior was associated with a decreased risks of developing obesity (p-for-trend = 0.02). No association was found between the eating alone behavior and the risk of metabolic syndrome in women. Conclusion: These results suggest that the eating alone behavior is a risk factor for the development of metabolic syndrome in middle-aged Korean men.

• Korean Journal of Ecology and Environment
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• v.52 no.2
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• pp.71-80
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• 2019

Effects of environmental factors on phytoplankton succession and community structure were studied in Lake Chuncheon located in Bukhan River, South Korea. The data were sampled at three sites such as CC1 (lower side), CC2 (middle side), and CC3 (upper side of Lake Chuncheon) from 2014 to 2017. The annual average precipitation in Lake Chuncheon was 992 mm during the study period (2014~2017), and the annual precipitation was lower than 800 mm in 2014 and 2015. The annual average water temperature, total phosphorus (TP), and total nitrogen (TN) ranged from 17.0 to $21.1^{\circ}C$, 0.012 to $0.019mg\;L^{-1}$, and 1.272 to $1.922mg\;L^{-1}$, respectively. The TN concentration was relatively high in 2015 compared with the other study years, as a drought continued from 2014 to 2015. When comparing the correlation between precipitation and environmental factors, water temperature (p<0.01) and TP(p<0.05) showed positive correlations with rainfall. The average numbers of phytoplankton cells by branch were 2,094, 2,182, and $3,108cells\;mL^{-1}$ in CC1, CC2, and CC3, respectively. CC3 is considered advantageous for phytoplankton growth, even in small pollution sources due to low water depth. As a result of analyzing the relationship between precipitation and phytoplankton, the correlation between the two was shown to be high for 2016 (p<0.01) and 2017 (p<0.05), which is when precipitation was high. However, the correlation was not clear to 2014 and 2015. The relationship between water temperature and phytoplankton indicated a negative correlation with diatoms (p<0.01), yet positive correlations with green algae (p<0.01) and cyanobacteria (p<0.01). Diatoms increased in spring and autumn, which are characterized by low water temperature, and green algae and cyanobacteria increased in summer, when the water temperature is high. Our findings provide a scientific basis for characteristics of phytoplankton and water quality and management at the Lake Chuncheon.