• Proceedings of the Korean Vacuum Society Conference
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• 2010.08a
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• pp.232-232
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• 2010

Recently light emitting diodes (LEDs) have been expected as the new generation light sources because of their advantages such as small size, long lifetime and energy-saving. GaN, as a wide band gap material, is widely used as a material of LEDs and GaN nanorods are the one of the most widely investigated nanostructure which has advantages for the light extraction of LEDs and increasing the active area by making the cylindrical core-shell structure. Lately GaN nanorods are fabricated by various techniques, such as selective area growth, vapor-liquid-solid (VLS) technique. But these techniques have some disadvantages. Selective area growth technique is too complicated and expensive to grow the rods. And in the case of VLS technique, GaN nanorods are not vertically aligned well and the metal catalyst may act as the impurity. So we just tried to grow the GaN nanorods on Si substrate without catalyst to get the vertically well aligned nanorods without impurity. First we deposited the AlN buffer layer on Si substrate which shows more vertical growth mode than sapphire substrate. After the buffer growth, we flew trimethylgallium (TMGa) as the III group source and ammonia as the V group source. And during the GaN growth, we kept the ammonia flow stable and periodically changed the flow rate of TMGa to change the growth mode of the nanorods. Finally, as the optimization, we changed the various growth conditions such as the growth temperature, the working pressure, V/III ratio and the doping level. And we are still in the process to reduce the diameter of the nanorods and to extend the length of the nanorods simultaneously. In this study, we focused on the shape changing of GaN nanorods with different growth conditions. So we confirmed the shape of the nanorods by scanning electron microscope (SEM) and carried out the Photoluminescence (PL) measurement and x-ray diffraction (XRD) to examine the crystal quality difference between samples. Detailed results will be discussed.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.81-88
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• 2021

Convertible bonds are financial products that contain the nature of both bonds and shares, which are generally issued by companies with lower credit ratings to increase liquidity. Conversion bonds rely on qualitative judgment in the past, although decision-making on whether and when to exercise the right to convert is the most important issue. Therefore, this paper proposes to apply artificial neural network techniques to scientifically determine the exercise of conversion rights. We distinguish between a total of 1,800 learning data published in the past and 200 predictive experimental data and build an artificial neural network learning model. As a result, the parity performance in most groups was excellent, achieving an average excess of about 10% or more. In particular, groups 3-6 recorded an average excess of about 20% and group 6 recorded an average excess of about 37%. This paper is meaningful in that it focused on solving decision problems by converging and applying machine learning techniques, a representative technology of the fourth industry, to the financial sector.

• Journal of Preventive Medicine and Public Health
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• v.54 no.5
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• pp.301-308
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• 2021

Objectives: Despite its advantages, it is not yet common practice in Korea for researchers to investigate disease associations using spatio-temporal analyses. In this study, we aimed to review health-related epidemiological research using spatio-temporal analyses and to observe methodological trends. Methods: Health-related studies that applied spatial or spatio-temporal methods were identified using 2 international databases (PubMed and Embase) and 4 Korean academic databases (KoreaMed, NDSL, DBpia, and RISS). Two reviewers extracted data to review the included studies. A search for relevant keywords yielded 5919 studies. Results: Of the studies that were initially found, 150 were ultimately included based on the eligibility criteria. In terms of the research topic, 5 categories with 11 subcategories were identified: chronic diseases (n=31, 20.7%), infectious diseases (n=27, 18.0%), health-related topics (including service utilization, equity, and behavior) (n=47, 31.3%), mental health (n=15, 10.0%), and cancer (n=7, 4.7%). Compared to the period between 2000 and 2010, more studies published between 2011 and 2020 were found to use 2 or more spatial analysis techniques (35.6% of included studies), and the number of studies on mapping increased 6-fold. Conclusions: Further spatio-temporal analysis-related studies with point data are needed to provide insights and evidence to support policy decision-making for the prevention and control of infectious and chronic diseases using advances in spatial techniques.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.49-57
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• 2021

With the advent of the era of the fourth industry, more and more scientific techniques are being used to solve decision-making problems. In particular, big data analysis technology is developing as it becomes easier to collect numerical data. Therefore, in this study, in order to overcome the limitations of qualitatively analyzing investment trends, the association of various products was analyzed using associated analysis techniques. For the experiment, two experimental periods were divided based on the COVID-19 economic crisis, and sales information from individuals, institutions, and foreign investors was collected, and related analysis algorithms were implemented through r software. As a result of the experiment, institutions and foreigners recently invested in the KOSPI and KOSDAQ markets and bought futures and products such as ETF. Individuals purchased ETN and ETF products together, which is presumed to be the result of the recent great interest in sector investment. In addition, after COVID-19, all investors tended to be passive in investing in high-risk products of futures and options. This paper is thought to be a useful reference for product sales and product design in the financial field.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.589-598
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• 2019

The amount of digital data a is explosively growing, and these data have large potential values. Countries and companies are creating various added values from vast amounts of data, and are making a lot of investments in data analysis techniques. The privacy problem that occurs in data analysis is a major factor that hinders data utilization. Recently, as privacy violation attacks on neural network models have been proposed. researches on artificial neural network technology that preserves privacy is required. Therefore, various privacy preserving artificial neural network technologies have been studied in the field of differential privacy that ensures strict privacy. However, there are problems that the balance between the accuracy of the neural network model and the privacy budget is not appropriate. In this paper, we study differential privacy techniques that preserve the performance of a model within a given privacy budget and is resistant to model inversion attacks. Also, we analyze the resistance of model inversion attack according to privacy preservation strength.

• Journal of Korean Society of Rural Planning
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• v.24 no.4
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• pp.69-80
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• 2018

In this study, we typified the project for revitalizing the rural area through text analysis using news data, and analyzed the main direction and characteristics of the project. In order to examine the factors emphasized among the issues related to the revitalization of rural areas, we used news data related to 'tourism' and 'livelihood', which are the main keyword of the project to promote rural areas. In the analysis, text mining techniques were used. Topic modeling was conducted on LDA techniques for major projects in 'tourism' and 'livelihood' keyword. Based on this, this study typified the projects that are carried out for the activation of rural areas by topic. As a result of the analysis, it was fount that the topics included in the project were distributed in 11 sub-types(Tourism Promotion, Regional Specialization, Local Festival, Development of Regional Scale, Urban and Rural Exchange, Agricultural Support, Community Forest Management, Improve the Settlement Environment, General Welfare Service, Low Class Support, Others). The characteristics of the rural revitalization projects were examined, and it was confirmed that domestic projects were carried out by tourism-oriented projects. To summarize, the government is making projects to revitalize rural areas through related ministries. Within the structure where the project is spreading to the region, a lot of projects are being carried out. It is understood that the tourism and welfare oriented projects are being carried out in the revitalization project of the domestic rural area. Therefore, in order to achieve the goal of rural revitalization, it is believed that it will be effective to carry out a balanced project to improve the settlement environment of the residents.

• The Journal of Advanced Prosthodontics
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• v.11 no.3
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• pp.179-186
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• 2019

PURPOSE. The purpose of this study was to compare two novel impression methods and a conventional impression method for edentulous jaws using 3-dimensional (3D) analysis software. MATERIALS AND METHODS. Five edentulous patients (four men and one woman; mean age: 62.7 years) were included. Three impression techniques were used: conventional impression method (CI; control), simple modified closed-mouth impression method with a novel tray (SI), and digital impression method using an intraoral scanner (DI). Subsequently, a gypsum model was made, scanned, and superimposed using 3D analysis software. Mean area displacement was measured using CI method to evaluate differences in the impression surfaces as compared to those values obtained using SI and DI methods. The values were confirmed at two to five areas to determine the differences. CI and SI were compared at all areas, while CI and DI were compared at the supporting areas. Kruskal-Wallis test was performed for all data. Statistical significance was considered at P value <.05. RESULTS. In the comparison of the CI and SI methods, the greatest difference was observed in the mandibular vestibule without statistical significance (P>.05); the difference was < 0.14 mm in the maxilla. The difference in the edentulous supporting areas between the CI and DI methods was not significant (P>.05). CONCLUSION. The CI, SI, and DI methods were effective in making impressions of the supporting areas in edentulous patients. The SI method showed clinically applicability.

• The Research Journal of the Costume Culture
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• v.28 no.6
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• pp.787-800
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• 2020

This study aims to derive the criteria of folding techniques and their characteristics through analysis of literature and previous studies. This will be realized by performing a case study on male fashion design and folding. It will propose diverse directions and data for male fashion design, by making men's jackets using a folding technique. The concept and terms of folding were clarified through examination of existing literature and previous studies. Specifically, four pieces were created with motifs of the four seasons. Among the types of pleats expressed in the works, composition pleats include double ruffles, gathers, and draperies, while processed ones include box pleats, knife pleats, and accordion pleats. This study expresses continuity, fluidity, scalability, and ambiguity through the use of such pleats. The results of the production are as follows. First, in terms of the continuous use of regular and repetitive pleats, a possibility of rich pleats was confirmed because they varied depending on the gap between the pleat and target material. Second, in liquid but irregular pleats, diverse moods were created by the pleat movement. The overlapping of repeated pleats expresses diverse spaces and shapes in a 3D extended silhouette. Third, in pleat classification, ambiguity was confirmed with the use of continuous accordion pleats in the printed gradation fabric. It is anticipated that more diverse and creative designs could be created using more extended techniques in future studies.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.65-76
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• 2022

Recently, not only traditional statistical techniques but also machine learning algorithms have been used to make more accurate bankruptcy predictions. But the insolvency rate of companies dealing with financial institutions is very low, resulting in a data imbalance problem. In particular, since data imbalance negatively affects the performance of artificial intelligence models, it is necessary to first perform the data imbalance process. In additional, as artificial intelligence algorithms are advanced for precise decision-making, regulatory pressure related to securing transparency of Artificial Intelligence models is gradually increasing, such as mandating the installation of explanation functions for Artificial Intelligence models. Therefore, this study aims to present guidelines for eXplainable Artificial Intelligence-based corporate bankruptcy prediction methodology applying SMOTE techniques and LIME algorithms to solve a data imbalance problem and model transparency problem in predicting corporate bankruptcy. The implications of this study are as follows. First, it was confirmed that SMOTE can effectively solve the data imbalance issue, a problem that can be easily overlooked in predicting corporate bankruptcy. Second, through the LIME algorithm, the basis for predicting bankruptcy of the machine learning model was visualized, and derive improvement priorities of financial variables that increase the possibility of bankruptcy of companies. Third, the scope of application of the algorithm in future research was expanded by confirming the possibility of using SMOTE and LIME through case application.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.2
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• pp.69-76
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• 2023

Hundreds of thousands of passwords are lost or forgotten every year, making the necessary information unavailable to legitimate owners or authorized law enforcement personnel. In order to recover such a password, a tool for password cracking is required. Using GPUs instead of CPUs for password cracking can quickly process the large amount of computation required during the recovery process. This paper optimizes on GPUs using CUDA, with a focus on decryption of the currently most popular PDF 1.4-1.6 version. Techniques such as eliminating unnecessary operations of the MD5 algorithm, implementing 32-bit word integration of the RC4 algorithm, and using shared memory were used. In addition, autotune techniques were used to search for the number of blocks and threads that affect performance improvement. As a result, we showed throughput of 31,460 kp/s (kilo passwords per second) and 66,351 kp/s at block size 65,536, thread size 96 in RTX 3060, RTX 3090 environments, and improved throughput by 22.5% and 15.2%, respectively, compared to the cracking tool hashcat that achieves the highest throughput.