• Journal of the Korean Society of Radiology
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• v.13 no.2
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• pp.193-200
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• 2019

The study was conducted surveying ultrasound room workers on hospital infection awareness in Daejeon and Choong-chunng region. The contamination of ultrasonic probes used in clinical trials was measured using ATP, and the results were verified after using 70% alcohol sterilization. It was measured on the group's general characteristics and the specific categories such as academic background, job type, having professional certificate and infection education. After the examination, the gel removal and method, disinfection status of the probe and variable correlation analysis were performed to analyze the recognition of the ultrasonic probe disinfection. After examination in ultrasound room, it was found that towels were used the most for cleaning, and the gel container was not replaced for more than three months. After 70% alcohol disinfection, ATP contamination was reduced from $1055.4{\pm}944.2$ to $133.5{\pm}93.2$ and the result was analyzed to be statistically significant.(${\rho}<0.01$) The found bacteria were CNS, Gram positive bacillus, and Micrococcus specs. In order to solve this problem, 70% alcohol sterilization was applied and the bacteria were not detected after the treatment. The research shows that regular training on infection control and efforts to prevent infection are necessary, and that 70% alcohol is effective in disinfect the bacteria. Therefore, the medical institution should provide active hospital infection control education to improve the awareness of hospital infection among workers and contribute to the prevention of patient infection. It is also understood that proper use of the results of this study will help prevent infection by means of ultrasonic probes.

• The Journal of Korean Academy of Prosthodontics
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• v.57 no.3
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• pp.211-218
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• 2019

Purpose: The purpose of the present study was to compare the accuracy of four different metal copings fabricated by CAD/CAM technology and to evaluate clinical effectiveness. Materials and methods: Composite resin tooth of the maxillary central incisor was prepared for a metal ceramic crown and duplicated metal die was fabricated. Then scan the metal die for 12 times to obtain STL files using a confocal microscopy type oral scanner. Metal copings with a thickness of 0.5 mm and a cement space of $50{\mu}m$ were designed on a CAD program. The Co-Cr metal copings were fabricated by the following four methods: Wax pattern milling & Casting (WM), Resin pattern 3D Printing & casting (RP), Milling & Sintering (MS), Selective laser melting (SLM). Silicone replica technique was used to measure marginal and internal discrepancies. The data was statistically analyzed with One-way analysis of variance and appropriate post hoc test (Scheffe test) (${\alpha}=.05$). Results: Mean marginal discrepancy was significantly smaller in the Group WM ($27.66{\pm}9.85{\mu}m$) and Group MS ($28.88{\pm}10.13{\mu}m$) than in the Group RP ($38.09{\pm}11.14{\mu}m$). Mean cervical discrepancy was significantly smaller in the Group MS than in the Group RP. Mean axial discrepancy was significantly smaller in the Group WM and Group MS then in the Group RP and Group SLM. Mean incisal discrepancies was significantly smaller in the Group RP than in all other groups. Conclusion: The marginal and axial discrepancies of the Co-Cr coping fabricated by the Wax pattern milling and Milling/Sintering method were better than those of the other groups. The marginal, cervical and axial fit of Co-Cr copings in all groups are within a clinically acceptable range.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.82-98
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• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.36 no.4
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• pp.94-104
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• 2018

This study examined photogrammetric reconstruction techniques that can measure the original form of a cultural property utilizing photographs taken in the past. During the research process, photographs taken in the past as well as photograph on the internet of Soswaewon Garden in Damyang(scenic site 40) were collected and utilized. The landscaping structures of Maedae, Aiyangdan, Ogokmun Wall, and Yakjak and natural scenery Gwangseok, of which photographs can be taken from any 360 degree direction from a close distance or a far distance without any barriers in the way, were selected and tested for the possibility of reproducing three-dimensional shapes. The photography method of 151 landscape photographs (58.6%) from internet portal sites for the aforementioned five landscape subjects containing information on the date the photograph was taken, focal length, and exposure were analyzed. As a result of the analysis, it was revealed that the majority of the photographs tend to focus on important parts of each subject. In addition, we discovered that there are two or three photography methods that internet users preferred in regards to each landscape subject. For the purposes of the experiment, photographs in which a single scene consistently appears for each landscape subject and it was determined that there was a high level of preference related to the photography method were analyzed, and three-dimensional mesh shape model was produced with a photoscan program to analyze the reproducibility of three-dimensional shapes. Based on the results of the reproduction, it was relatively possible to reproduce three-dimensional shapes for artifacts such as Ogukmun wall, Maedae, and Aeyangdan, but it was impossible to reproduce three-dimensional images for natural scenery or an object that has similar texture such as Yakjak and Gwangseok. As a result of experimentation related to the reconstruction of three-dimensional shapes with the photographs taken on site using a photography method similar to that of the photographs selected as previously mentioned, there was success related to reproducing the three-dimensional shapes of Yakjak and Gwangseok, of which it was not possible to do so through the photographs that had been collected previously. In addition, through comparison of past and present images, it was possible to measure the exact sizes as well as discover any changes that have taken place. If past photographs taken by tourists or landscape architects of cultural properties can be obtained, the three-dimensional shapes from a particular period of time can be reproduced. If this technology becomes widespread, it will increase the level of accuracy and reliability in regards to measuring the past shapes of cultural landscape properties and examining any changes to the properties.

• Korean Journal of Plant Resources
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• v.32 no.2
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• pp.116-123
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• 2019

Angelica gigas Nakai (AGN) is a traditional medicinal herb especially in Korea. It contains pyranocoumarins, which are major active components including decursin (D) and decursinol angelate (DA). This study was carried out to determine the change in active component content and antioxidant activity depending on the root diameter of AGN. Several processing steps are involved to use AGN roots as medicine. The dried AGN roots are divided into body (B), thick root (TkR), medium root (MR) and thin root (TnR) according to their diameter before cutting into medicine. The recovery rates of each root parts per 100 kg were measured as $32.3{\pm}2.5$, $9.0{\pm}1.0$, $39.3{\pm}2.1$ and $15.0{\pm}1.0%$, respectively and the mean diameters were measured as $51.95{\pm}4.55$, $7.05{\pm}0.89$, $2.88{\pm}0.49$ and $1.57{\pm}0.32mm$. Two index components, D and DA, were analyzed. The change of both D and DA content showed a similar tendency. Both D and DA content were increased as the root diameter decreased (higher in TnR). In addition, antioxidant activity was higher in B and TnR, and lower in TkR and MR than control. This study showed that the thinner the root diameter, the higher the D and DA content in AGN roots and that TnR has excellent antioxidant activity compared to other root parts, suggesting that the thinner root part of AGN could be used as a potential material.

• Journal of the Korean Society of International Agriculture
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• v.30 no.4
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• pp.347-356
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• 2018

Genetically modified (GM) crops have been increased continuously over the world and concerns about the potential risks of GM crops have also been increasing. Even though GM crops have not been cultivated commercially in Korea, it should be necessary to develop the safety assesment technology for GM crops. In this study, we investigated the influence of heading date difference on gene flow from GM to non-GM rice. In the experimental plot design, The PAC GM rice was placed in the center as a pollen donor and non-GM rice were placed in eight directions as pollen receivers. Five pollen receiver rice cultivars were Unkawng, Daebo, Saegyejinmi, Nakdong-byeo, and Ilmi which had different flowering times. A total of 266,436, 300,237, 305,223, 273,373, and 290,759 seeds were collected from Unkawng, Daebo, Saegyejinmi, Nakdong, and Ilmi, respectively, which were planted around PAC GM rice. The GM${\times}$non-GM hybrids were detected by repeated spraying of herbicide and PAT immunostrip assay. Finally, the hybrids were confirmed by PCR analysis using PAC gene specific primer. The hybrids were found in Nakdong-byeo which had the same heading date with PAC GM rice. The hybridization rate was 0.0007% at Nakdong-byeo plot. All of GM${\times}$non-GM hybrids were located within 2 m distance from the PAC GM rice zone. The physiological elements including rice heading date were found to be important factors to determine GM?rice out crossing rate with GM rice. Consideration should be taken into for many factors like the physiological elements of field heading date of rice cultivars to set up the safety management guideline for prevention of GM rice gene flow.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.2
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• pp.51-78
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• 2021

Followed by 'start-up', the theme of 'scale-up' has been considered as an important agenda in both corporate and policy spheres. In particular, although it is a term commonly used in industry and policy fields, even a conceptual definition has not been achieved from the academic perspective. "Corporate Growth" in the academic aspect and "Business Growth" in the practical management field have different understandings (Achtenhagen et al., 2010). Previous research on corporate growth has not departed from Penrose(1959)'s "Firm as a bundle of resources" and "the role of managers". Based on the theory and background of economics, existing research has mainly examined factors that contribute to firms' growth and their growth patterns. Comparatively, we lack knowledge on the firms' growth with a focus on 'annual revenue growth rate'. In the early stage of the firms, they tend to exhibit a high growth rate as it started with a lower level of annual revenue. However, when the firms reach annual revenue of more than 100 billion KRW, a threshold to be classified as a 'middle-standing enterprise' by Korean standards, they are unlikely to reach a high level of revenue growth rate. In our study, we used our sample of 333 companies (6.7% out of 5,000 'fastest-growing' companies) which reached 15% of the compound annual growth rate in the last three years with more than USD 100 million. It shows that sustaining 'high-growth' above a certain firm size is difficult. The study focuses on firms with annual revenue of more than $100 billion (approximately 120 billion KRW) from the 'Inc. 2020 fast-growing companies 5,000' list. The companies have been categorized into 1) Fast-growing companies (revenue CAGR 15%~40% between 2016 and 2019), 2) Hyper-growing companies (40%~99.9%), and 3) Super-growing (100% or more) with in-depth analysis of each group's characteristics. Also, the relationship between the revenue growth rate, individual company's strategy choice (market orientation, generic strategy, growth strategy, pioneer strategy), industry/market environment, and firm age is investigated with a quantitative approach. Through conducting the study, it aims to provide a reference to the 'Hyper-Growing Model' that combines the paths and factors of growth strategies. For policymakers, our study intends to provide a reference to which factors or environmental variables should be considered for 'optimal effective combinations' to promote firms' growth.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.314-320
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• 2021

With the increasing age of motherhood in recent years, attributed to late marriages due to social or environmental factors, the Down's syndrome screening test using biochemical markers has become essential for pregnant women. The process of diagnosing Down's syndrome pregnancy in the high-risk group subjects involves chromosomal analysis, which is performed on samples obtained through invasive procedures such as chorionic biopsy or amniotic fluid. Thus, to reduce unnecessary invasive tests and lower the risk to mother and fetus, it is important to identify a screening test with low risk and high Down's syndrome detection rate. Recently, as the average age of mothers has increased, numerous inspection agencies have classified high-risk mothers as women over the age of 35 years. This study evaluated a total of 36,436 pregnant women aged between 17 to 46 years, and who requested prenatal screening at an inspection agency in Yongin in 2018. Test (13,690 people) Four tests were conducted by applying the time-resolved fluoroimmunoassay method using the direct sandwich and indirect sandwich technology, and the immunoassay method using the sandwich method. We aimed to confirm the difference in positivity rate with increasing age of the subjects. We believe that in future, data obtained from this study will be very useful for the prevention and treatment of Down's syndrome risk at varied inspection institutions, and for prospective mothers.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.2
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• pp.19-34
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• 2021

The government budget for promoting startup have been skyrocketed as catching up with increasing demands for high-tech startup by disruptive innovation resulted from rapid technology change. However, major trend of startup have still fallen on self-employed type of startup due to the lack of expertise and fund in spite of desperate government policy efforts. In reality, the access to high-tech startup has been very limited and too high huddle to would-be entrepreneur. This paper implement empirical analysis on the effects of entrepreneur competency and satisfaction level to government support, considering these as the KSF for the growth and success of high-tech startup, to the performance of the company. In particular, it focus on defining unique characteristics of high-tech startup through differential proving by the backgrounds of entrepreneur such as major, R&D experience, patent possession, CTO possession. This research carry out survey to 217 entrepreneurs in high-tech company in Daejon and Daegue at R&D Special Innopolis Zone. Research results are as follow. First, entrepreneurial achievement competencies, conceptualization competencies, network competencies and market recognition competencies positively affect the financial and non-financial performance and organizational and technical competencies, while organizational and technological competencies only positively impact on non-financial performance. Second, the satisfaction level of government support showed a positive moderating effect on entrepreneurial achievement competencies and financial performance, while no significant effect in other competencies. Third, positive differential effect by the technological background of entrepreneur such as Major, R&D experience, patent possession, CTO possession) have been confirmed. This paper deliver several significant implications and contributions, First, it propose classified and systematized entrepreneur competency through the domestic and foreign literature reviews. Second, it proves the need for the wider spread of team based startup culture rather then sole startup. Third, it also proves the important role of technological background of entrepreneur among the characteristics of high-tech startup.