• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.5
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• pp.394-402
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• 2021

The entry of an aging society and the extension of human life expectancy, the increasing interest in women's social advancement and men's appearance, and the natural interest in K-culture through media media, while receiving worldwide attention, Focus on K-Bueaty. Recently, looking at the occupation of the medical tourism field, in the case of aesthetic medicine tourism such as molding and dermatology, it has gained popularity not only in Asia such as China and Japan, but also in North America and Europe. The first external confirmation of human aging is the wrinkles on the skin of the face. Clean, wrinkle-free, elastic and healthy skin is a desire of most people. Skin condition and condition such as focused ultrasonic stimulation (HIFU: High Intensity Focused Utrasound) and low frequency, high frequency (RF: Radio Frequency), galvanic therapy using microcurrent, cryotherapy using rapid cooling, etc. Depending on the method of management, the effect of the treatment differs depending on the output and the stimulation site, etc., even in the treatment of medical equipment and beauty equipment using the same mechanism. In this research, in order to develop invasive high-frequency dermatological devices using a large number of beauty medical devices and microneedles of beauty devices, the international standards IEC 60601-2 (standards for individual medical devices) and MFDS (Ministry of) We designed and developed a high-frequency output device in compliance with the high-frequency stimulation standard announced in the Food and Drug Safety (Ministry of Food and Drug Safety). The circuit design consists of an amplifier (AMP: Amplifier) using Class-A Topology and a power supply device using Half-Bridge Topology. As a result of measuring the developed high-frequency output device, an average efficiency of 63.86% was obtained, and the maximum output was measured at 116.7W and 50.67dBm.

• Korea Science and Art Forum
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• v.37 no.1
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• pp.87-97
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• 2019

The study began in recent years with the aim of grasping the nation's medical status following the fast-growing trend of international medical tourism and attracting foreign patients, among other things, Busan, which ranks second in attracting foreign patients after the nation's capital, Seoul, has been highly active in the past eight years, with foreign patients rising by about 426 percent, and Russian patients entering the sea. In addition, Gimhae and Changwon, the Busan-based Gyeongsangnam-do region, ranked first and second in number of foreign residents, and are inhabited by a variety of foreign workers. Medical institutions, such as hospitals, should be able to find directions within hospitals. It is also a space where information in various languages, including Korean, English, Chinese, or Russian, must be delivered in a single medium. Based on this research, the purpose of this research is to provide converged information that helps foreigners who are not familiar with Korean language easily understand the proposed recognition system when visiting hospitals. Therefore, this paper is applied to a multi-language survey of six medical institutions (A, B, C, D, E, F) at the university hospital in Busan, and 10 medical institutions (R, J) in Gimhae, South Gyeongsang Province with high foreign residents. Research results and contents are as follows. First, the results of analyzing the design of the sinusoidal system show that the font uses colorless Gothic fonts, arrows, and pictograms to introduce the design of a typical hospital sign system. Second, the results of the multi-lingual situation were found to have only two languages in the system, such as Korean and English, and to have four languages, including Korean, English, Chinese, and Russian, according to their geographical location. However, it was judged that most medical institutions currently have only two languages (Korean, English) that may cause some discomfort in terms of language for foreign patients in non-English speaking countries. Based on these findings, it is necessary to propose designs that are considered by Koreans as well as foreign users in the use of multilingual hospital sign systems.

• 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.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.63-88
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• 2019

The goal of this paper is to investigate changes in North Korea's domestic and foreign policies through automated text analysis over North Korea represented in South Korean mass media. Based on that data, we then analyze the status of text mining research, using a text mining technique to find the topics, methods, and trends of text mining research. We also investigate the characteristics and method of analysis of the text mining techniques, confirmed by analysis of the data. In this study, R program was used to apply the text mining technique. R program is free software for statistical computing and graphics. Also, Text mining methods allow to highlight the most frequently used keywords in a paragraph of texts. One can create a word cloud, also referred as text cloud or tag cloud. This study proposes a procedure to find meaningful tendencies based on a combination of word cloud, and co-occurrence networks. This study aims to more objectively explore the images of North Korea represented in South Korean newspapers by quantitatively reviewing the patterns of language use related to North Korea from 2016. 11. 1 to 2019. 5. 23 newspaper big data. In this study, we divided into three periods considering recent inter - Korean relations. Before January 1, 2018, it was set as a Before Phase of Peace Building. From January 1, 2018 to February 24, 2019, we have set up a Peace Building Phase. The New Year's message of Kim Jong-un and the Olympics of Pyeong Chang formed an atmosphere of peace on the Korean peninsula. After the Hanoi Pease summit, the third period was the silence of the relationship between North Korea and the United States. Therefore, it was called Depression Phase of Peace Building. This study analyzes news articles related to North Korea of the Korea Press Foundation database(www.bigkinds.or.kr) through text mining, to investigate characteristics of the Kim Jong-un regime's South Korea policy and unification discourse. The main results of this study show that trends in the North Korean national policy agenda can be discovered based on clustering and visualization algorithms. In particular, it examines the changes in the international circumstances, domestic conflicts, the living conditions of North Korea, the South's Aid project for the North, the conflicts of the two Koreas, North Korean nuclear issue, and the North Korean refugee problem through the co-occurrence word analysis. It also offers an analysis of South Korean mentality toward North Korea in terms of the semantic prosody. In the Before Phase of Peace Building, the results of the analysis showed the order of 'Missiles', 'North Korea Nuclear', 'Diplomacy', 'Unification', and ' South-North Korean'. The results of Peace Building Phase are extracted the order of 'Panmunjom', 'Unification', 'North Korea Nuclear', 'Diplomacy', and 'Military'. The results of Depression Phase of Peace Building derived the order of 'North Korea Nuclear', 'North and South Korea', 'Missile', 'State Department', and 'International'. There are 16 words adopted in all three periods. The order is as follows: 'missile', 'North Korea Nuclear', 'Diplomacy', 'Unification', 'North and South Korea', 'Military', 'Kaesong Industrial Complex', 'Defense', 'Sanctions', 'Denuclearization', 'Peace', 'Exchange and Cooperation', and 'South Korea'. We expect that the results of this study will contribute to analyze the trends of news content of North Korea associated with North Korea's provocations. And future research on North Korean trends will be conducted based on the results of this study. We will continue to study the model development for North Korea risk measurement that can anticipate and respond to North Korea's behavior in advance. We expect that the text mining analysis method and the scientific data analysis technique will be applied to North Korea and unification research field. Through these academic studies, I hope to see a lot of studies that make important contributions to the nation.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.127-137
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• 2019

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.5
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• pp.159-175
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• 2021

This study attempted to analyze the relationship between influencer's characteristic factors such as professionalism, authenticity, and interactivity and content quality factors consisting of accuracy, completeness, and diversity on brand attitude and purchase attitude through trust and self-consistency. To reveal the structural relationship between main variables, a survey was conducted on 201 users. An EFA, CFA, and reliability analysis were performed to confirm reliability and validity. And structural equation was conducted to verify hypothesis. The main results are as follows. First, it was found that professionalism and interactivity had a significant positive effect on trust. And, accuracy, completeness, and variety were all found to have a significant positive effect on trust. Second, in the relationship between individual characteristic factors and self-consistency, it was found that professionalism and authenticity had a significant positive effect on self-consistency. In addition, in the relationship between content quality and self-consistency, accuracy, completeness, and diversity were found to have a positive effect on self-consistency along with trust. Third, in the relationship between trust and self-consistency on brand attitude and purchase intention, both trust and self-consistency were found to have a statistically significant positive effect on brand attitude. It was found that only self-consistency and brand attitude had a statistically significant positive effect on purchase intention. These findings showed that when users perceive professionalism and interaction with influencer, trust increases, and professionalism and progress increase self-consistency with influencer. In addition, in the case of content quality, it was found that trust and self-consistency responded positively when perceived content quality through content accuracy, completeness, and diversity. Also, trust and self-consistency increased attitudes toward brands and could influence consumption behavior such as purchase intention. Therefore, for effective marketing performance using influencer's influence in the field of influencer marketing, which has a strong information delivery on products and brands, not only personal characteristics such as professionalism, authenticity, and interactivity, but also quality of content should be considered. The above research results are expected to suggest implications for marketing strategies and practices as one available basic data to exert the expected effect of marketing using influencer.

• Journal of the Society of Disaster Information
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• v.17 no.1
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• pp.10-24
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• 2021

Purpose: The purpose of this study is to determine the dangers of biodiesel and general diesel mixtures currently used as alternative fuels by equipment (tag method and penski Marten method) and to determine the difference between flash point and combustion point (closed, open) according to test methods. It is intended to be used as a reference material for identification and evaluation of firecausing substances by confirming the risk of mixtures by comparative analysis and measurement, and establishing a risk assessment method for chemical substances. Method: Flash point test method and result treatment were tested based on ASTM and KS M mode, which are tag sealing and pen schematense test methods used as flash point and combustion point test methods for crude oil and petroleum products. The manufacturer of the equipment used in this experiment was a test equipment that satisfies the test standards of KS M 2010 with equipment produced by TANAKA of Japan. The flash point and combustion point were measured, and the flash point according to the test method of biodiesel and general diesel mixture ( Closed, open), and the ignition point of a mixture of biodiesel and general diesel was compared and analyzed for ignition risk compared with conventional diesel. Results: Looking at the experimental results, first, as an analysis of the risk of flammability of the mixture, the flash point of a substance containing 70% biodiesel was found to be about 92℃ based on general diesel with a flash point of 64.5℃, and gasoline and biodiesel or When the biodiesel mixture was synthesized, it was confirmed that the flash point tends to decrease. In addition, the difference between the flash point and the combustion point was analyzed as about 20 ~ 30℃, and when a small amount of gasoline or methanol was mixed, the flash point was lowered, but it was confirmed that the combustion point was similar to that of the existing mixture. Conclusion: In this study, in order to secure the effectiveness of the details of the criteria for judging dangerous materials in the existing Dangerous Materials Safety Management Act, and to secure the reliability and reproducibility of the judgment of dangerous materials, we confirm the criteria for judging the risk of the mixture through an experimental study on flammable mixtures. It will be able to provide reference data for experimental criteria for flammable liquids that are regulated in the field. In addition, if this study accumulates know-how on experiment by test method, it is expected that it can be used as a basis for research on risk assessment and research on dangerous goods.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.30-43
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• 2018

Many studies have been carried out to retrieve images using colors, shapes, and textures which are characteristic of images. In addition, there is also progress in research related to the disease images of the crop. In this paper, to be a help to identify the disease occurred in crops grown in the agricultural field, we propose a similarity-based crop disease search system using the diseases image of horticulture crops. The proposed system improves the similarity retrieval performance compared to existing ones through the combination descriptor without using a single descriptor and applied the weight based calculation method to provide users with highly readable similarity search results. In this paper, a total of 13 Descriptors were used in combination. We used to retrieval of disease of six crops using a combination Descriptor, and a combination Descriptor with the highest average accuracy for each crop was selected as a combination Descriptor for the crop. The retrieved result were expressed as a percentage using the calculation method based on the ratio of disease names, and calculation method based on the weight. The calculation method based on the ratio of disease name has a problem in that number of images used in the query image and similarity search was output in a first order. To solve this problem, we used a calculation method based on weight. We applied the test image of each disease name to each of the two calculation methods to measure the classification performance of the retrieval results. We compared averages of retrieval performance for two calculation method for each crop. In cases of red pepper and apple, the performance of the calculation method based on the ratio of disease names was about 11.89% on average higher than that of the calculation method based on weight, respectively. In cases of chrysanthemum, strawberry, pear, and grape, the performance of the calculation method based on the weight was about 20.34% on average higher than that of the calculation method based on the ratio of disease names, respectively. In addition, the system proposed in this paper, UI/UX was configured conveniently via the feedback of actual users. Each system screen has a title and a description of the screen at the top, and was configured to display a user to conveniently view the information on the disease. The information of the disease searched based on the calculation method proposed above displays images and disease names of similar diseases. The system's environment is implemented for use with a web browser based on a pc environment and a web browser based on a mobile device environment.

• Korean Journal of Environmental Biology
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• v.38 no.4
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• pp.634-649
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• 2020

Recently, stream longitudinal connectivity has been a topic of investigation due to the frequent disconnections and the impact of aquatic ecosystems caused by the construction of small and medium-sized weirs and various artificial structures (fishways) directly influencing the stream ecosystem health. In this study, the international and domestic research trends of the longitudinal connectivity in aquatic ecosystems were evaluated and the applicability of fish-based longitudinal connectivity models used in developed countries was analyzed. For these purposes, we analyzed the current status of research on longitudinal connectivity and structural problems, fish monitoring methodology, monitoring approaches, longitudinal disconnectivity of fish movement, and biodiversity. In addition, we analyzed the current status and some technical limitations of physical habitat suitability evaluation, ecology-based water flow, eco-hydrological modeling for fish habitat connectivity, and the s/w program development for agent-based model. Numerous references, data, and various reports were examined to identify worldwide longitudinal stream connectivity evaluation models in European and non-European countries. The international approaches to longitudinal connectivity evaluations were categorized into five phases including 1) an approach integrating fish community and artificial structure surveys (two types input variables), 2) field monitoring approaches, 3) a stream geomorphological approach, 4) an artificial structure-based DB analytical approach, and 5) other approaches. the overall evaluation of survey methodologies and applicability for longitudinal stream connectivity suggested that the ICE model (Information sur la Continuite Ecologique) and the ICF model (Index de Connectivitat Fluvial), widely used in European countries, were appropriate for the application of longitudinal connectivity evaluations in Korean streams.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.12-28
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• 2021

Based on the global smart city promotion trend, in 2018, the "Fourth Industrial Revolution Committee" selected "sustainability" and "people-centered" as keywords in relation to the direction of domestic smart city policy. Accordingly, the Living Lab program, which is an active citizen-centered innovation methodology, is applied to each stage of the domestic smart city construction project. Through the Living Lab program, and in collaboration with the public and experts, the smart city discovers local issues as it focuses on citizens, devises solutions to sustainable urban problems, and formulates a regional development plan that reflects the needs of citizens. However, compared to citizen participation in urban regeneration projects that have been operated for a relatively long time, participation in smart city projects was found to significantly differ in level and sustainability. Therefore, this study conducted a comparative analysis of the characteristics of citizen participation at each stage of an urban regeneration project and, based on Arnstein's "Participation Ladder" model, examined the level of citizen participation activities in the Living Lab program carried out in a smart city commercial area from 2018 to 2019. The results indicated that citizen participation activities in the Living Lab conducted in the smart city project had a great influence on selecting smart city services, which fit the needs of local residents, and on determining the technological level of services appropriate to the region based on a relatively high level of authority, such as selection of smart city services or composition of solutions. However, most of the citizen participation activities were halted after the project's completion due to the one-off recruitment of citizen participation groups for the smart city construction project only. On the other hand, citizens' participation activities in the field of urban regeneration were focused on local communities, and continuous operation and management measures were being drawn from the project planning stage to the operation stage after the project was completed. This study presented a plan to revitalize citizen participation for the realization of a more sustainable smart city through a comparison of the characteristics and an examination of the level of citizen participation in such urban regeneration and smart city projects.