• Journal of Food Hygiene and Safety
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• v.36 no.5
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• pp.418-429
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• 2021

This paper investigates the antioxidant activity and quality characteristics of yanggaeng containing white ginseng and red ginseng extracts and their enzyme hydrolysates that were produced for the purpose of the study. White and red ginseng extracts were hydrolyzed using Rapidase C80 max, Pyr-flo, and Ultimase MFC. Ginsenoside F2 and compound K (CK) were not detected in white and red ginseng before enzymic reaction but were detected in white and red ginseng hydrolyzed through Rapidase C80 max, Pyr-flo, and Ultimase MFC, and the content of CK was the highest in the second enzymic reaction group of red ginseng. Upon preparing yanggaeng containing white and red ginseng before or after enzymatic hydrolysis, the polyphenol content and antioxidant abilities were analyzed. The yanggaeng containing enzyme-hydrolyzed white ginseng and red ginseng showed greater total polyphenol content, superior DPPH radical scavenging activity, superior ABTS radical scavenging activity, and superior FRAP analysis results compared to the yanggaeng that doesn't contain white or red ginseng. As the enzymic reaction was performed in the added white and red ginseng, the antioxidant activity increased significantly (P<0.05). In brightness(L^*), non-additive yanggaeng (control group) was the highest, red ginseng yanggaeng (RG) showed the highest redness(a^*), and the white ginseng yanggaeng (WG) showed the highest yellowness(b^*). In terms of texture, the yanggaeng containing red ginseng with second hydrolysis (RG-T2) showed significantly high results in hardness, springiness, chewiness, cohesiveness, and gumminess (P<0.05). In conclusion, treating white and red ginseng with Rapidase C80 max, Pyr-flo, and Ultimase MFC is very useful in ginsenoside deglycosylation and will produce CK with excellent biological activity. It can also be seen that yanggaeng containing white and red ginseng hydrolyzed with enzymes significantly increase total polyphenol and antioxidant activity compared to the control group (yanggaeng with no added ginseng). These results will be useful as excellent foundational data for the production of functional yanggaeng in the future.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.5
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• pp.1-14
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• 2021

Innovation and Scale-up of Start-up companies are becoming important national tasks. In the past, it was spread the start-up policy paradigm such as 'Start-up America', 'Start-up Chile', 'Start-up Britain' to overcome the recession globally. However as the economic recovery has become more visible recently in advanced economies, it is shifting from a start-up support policy to a scale-up oriented policy paradigm such as 'Scale-up America', Scale-up UK', 'Scale-up Denmark'. It is necessary to enter the scale-up phase beyond the start-up phase to increase the number of high-quality jobs and to continue economic growth. Therefore, it is necessary to grow the start-up into a strong medium-sized company and to lay the foundation for survival. Therefore, the purpose of this study is to consider the antecedent factors that influence the scale-up aspiration for the start-up firm to grow into a scale-up company, and empirically identifies the differences between the stages of economic development and entrepreneurs in the country. In order to accomplish the purpose, this study predicted scale-up by aspiration which is a predictor of scale-up behavior because it is difficult to achieve visible growth in a short period of time due to the characteristics of start-up companies. In order to empirically explore these relationships, the data were collected from nascent entrepreneurs who have less than 3.5 years of the Adult Population Survey(APS) among the subjects surveyed by the Global Entrepreneurship Monitor(GEM) and the national economic development stage are divided into Innovation-driven, Efficiency-driven, Factor-driven type economies. For the test hypotheses, this study adopted the multi-level model analysis for comparison between national economic development stages and using the R 3.5.0 program. The results of this study are as follows. There is difference between the national economic development and the entrepreneur in the relationship between innovation orientation of entrepreneurs and scale-up aspirations. As the economy of the country develops, the innovation activity of the entrepreneur becomes more active. Since start-ups are heavily influenced by entrepreneurs, there is a difference in the degree of aspiration depending on how innovative an entrepreneur is in the same environment. In terms of the relationship between innovation orientation and scale-up aspiration, the fear of failure was found to differ between national economic development and entrepreneurs. The fear of failure differ from country to country, and this is one of the important factors affecting entrepreneurial activities. It is expected that the factors influencing the growth of the start-up companies which are identified through the results of these studies, will be used to create a suitable scale-up ecosystem according to the national economic development stage.

• The Korean Journal of Archival Studies
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• no.16
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• pp.147-200
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• 2007

Archives provide users with easier and more convenient access to and use of archival data through their Internet websites. Now, archival websites function not only as a tool of information provision or marketing, or as a gateway of Internet-based materials, but also as cyber space for all services of archives, providing users with information and knowledge and direct accessibility to the services in the archives. At present, Korean archives are proceeding with establishing websites or upgrading existing websites, and the use rates and reliability of web services by users are becoming higher. However, although there have been various studies for the evaluation of general websites, few are found with regard to the evaluation of archival websites. It is necessary for archival websites that provide information service to users of every stripe to make more efforts and have more interest in user-centered convenience, right to know, and information provision-centered service. Accordingly, needless to say, it is necessary to study evaluation criteria of websites so that high quality archival websites can be established. With this background, this study establishes evaluation criteria for archival websites, which are appropriate to their objectives and functions and directly evaluates archives, presenting ways to establish and redevelop archival websites. More detailed purposes are as follows: First, analyzes existing theories of evaluation through reviews on previous literature and elicits evaluation criteria for websites, which are appropriate to archives; Second, based on the elicited evaluation criteria for archival websites, examines the current state of domestic archival websites through analytic evaluation; and Third, presents ways to improve archival websites that may be helpful in establishing or improving them in the future. The expectancy effects of this study are as follows: First, it will be helpful when one wishes to identify the current state of archival websites and to improve or redevelop existing websites, or to develop online service through website; Second, it will function as a checklist when a developer who is to establish an archival website wishes to develop evaluation criteria; and Third, it may be used as an inspection tool when an archives contracts out the establishment of its website.

• The Korean Journal of Nuclear Medicine Technology
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• v.23 no.2
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• pp.29-37
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• 2019

Purpose DMIDR(Discovery Molecular Imaging Digital Ready, General Electric Healthcare, USA) is a PET/CT scanner designed to allow application of PSF(Point Spread Function), TOF(Time of Flight) and Q.Clear algorithm. Especially, Q.Clear is a reconstruction algorithm which can overcome the limitation of OSEM(Ordered Subset Expectation Maximization) and reduce the image noise based on voxel unit. The aim of this paper is to evaluate the performance of reconstruction algorithms and optimize the algorithm combination to improve the accurate SUV(Standardized Uptake Value) measurement and lesion detectability. Materials and Methods PET phantom was filled with $^{18}F-FDG$ radioactivity concentration ratio of hot to background was in a ratio of 2:1, 4:1 and 8:1. Scan was performed using the NEMA protocols. Scan data was reconstructed using combination of (1)VPFX(VUE point FX(TOF)), (2)VPHD-S(VUE Point HD+PSF), (3)VPFX-S (TOF+PSF), (4)QCHD-S-400((VUE Point HD+Q.Clear(${\beta}-strength$ 400)+PSF), (5)QCFX-S-400(TOF +Q.Clear(${\beta}-strength$ 400)+PSF), (6)QCHD-S-50(VUE Point HD+Q.Clear(${\beta}-strength$ 50)+PSF) and (7)QCFX-S-50(TOF+Q.Clear(${\beta}-strength$ 50)+PSF). CR(Contrast Recovery) and BV(Background Variability) were compared. Also, SNR(Signal to Noise Ratio) and RC(Recovery Coefficient) of counts and SUV were compared respectively. Results VPFX-S showed the highest CR value in sphere size of 10 and 13 mm, and QCFX-S-50 showed the highest value in spheres greater than 17 mm. In comparison of BV and SNR, QCFX-S-400 and QCHD-S-400 showed good results. The results of SUV measurement were proportional to the H/B ratio. RC for SUV is in inverse proportion to the H/B ratio and QCFX-S-50 showed highest value. In addition, reconstruction algorithm of Q.Clear using 400 of ${\beta}-strength$ showed lower value. Conclusion When higher ${\beta}-strength$ was applied Q.Clear showed better image quality by reducing the noise. On the contrary, lower ${\beta}-strength$ was applied Q.Clear showed that sharpness increase and PVE(Partial Volume Effect) decrease, so it is possible to measure SUV based on high RC comparing to conventional reconstruction conditions. An appropriate choice of these reconstruction algorithm can improve the accuracy and lesion detectability. In this reason, it is necessary to optimize the algorithm parameter according to the purpose.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.1
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• pp.29-36
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• 2021

Recently, phytochemicals in whole grains have received increasing attention because the increased consumption of whole grains and whole grain products has been associated with a reduced risk of chronic diseases. Among the phytochemicals in whole wheat, arabinoxylan influences various physiological activities and can aid the treatment of numerous health conditions, either directly or indirectly. In this study, 614 wheat germplasms, collected from the world, were cultivated in 2018 and harvested in 2019 in South Korea. The qualities of these whole grains, including seed protein content, were evaluated using the NIR spectrophotometric method, and arabinoxylan content was determined using enzymatic methods. The ash content was 0.36 to 2.80% and protein content was 7.66 to 20. The SDS-sedimentation for gluten complex ability ranged from 26.85 to 80.63 mL, and protein content showed a high correlation with SDS-sedimentation in the germplasms. Verify the result of the enzymatic method of arabinoxylan, the reliability of the experimental method was determined through repeatability, reproducibility, and recovery. The average value of the 614 resources was 51.64 mg/g of whole grain, and when classified according to the country of origin, the South Korean origin resources tended to have a higher content compared to the genetic resources of other countries. These results are expected to be used as basic data for setting standards for whole grain quality in wheat breeding systems.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.1
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• pp.72-84
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• 2023

The Rural Revitalization Strategy (2018-2022), published by the Chinese State Council in 2018, represents a new period of rural development in China. Suburban areas are more convenient than other rural areas in integrated urban-rural development but are under greater pressure from construction and industrial pollution. As a rural area with a high proportion of rural areas, it would be valuable for Henan province to gain a comprehensive grasp of rural human settlementst while identifying problems and proposing solutions. The purpose of this study is to analyze the satisfaction of the evaluation items based on the usage status and life perception of the residents of Tai Nan village, a suburb-type rural village in Henan province. The study proposes improvement programs based on the evaluation results. As a result of the study, 24 evaluation items were derived and divided into five categories: "Living Service Facilities", "Housing Environment, "Road Environment", "Health & Ecology Environment", and "Social & Cultural Environment". The Fuzzy Comprehensive Evaluation Method was used to find the overall satisfaction level of the human living environment in Tai Nan village, which was "average", among which "Living Service Facilities" was the most important "Health & Ecology Environment" was the least satisfied. Based on these results, an improvement plan is proposed in three stages. First, the living service will be improved while strengthening the facility management of the hygiene and the ecological environment. Second, reasonable improvement of housing and the road environment will be applied. Third, programs will be introduced to cultivate residents' ability to build their own and improve the social and cultural environment. This study provides basic data for the future improvement of rural settlements in the suburban areas of Henan province and is of great significance in gradually improving the the residents' quality of life.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.297-303
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• 2023

Pediatric Computed Tomography (CT) examinations can often result in exam failures or the need for frequent retests due to the difficulty of cooperation from young patients. Deep Learning Image Reconstruction (DLIR) methods offer the potential to obtain diagnostically valuable images while reducing the retest rate in CT examinations of pediatric patients with high radiation sensitivity. In this study, we investigated the possibility of applying DLIR to reduce artifacts caused by respiration or motion and obtain clinically useful images in pediatric chest CT examinations. Retrospective analysis was conducted on chest CT examination data of 43 children under the age of 7 from P Hospital in Gyeongsangnam-do. The images reconstructed using Filtered Back Projection (FBP), Adaptive Statistical Iterative Reconstruction (ASIR-50), and the deep learning algorithm TrueFidelity-Middle (TF-M) were compared. Regions of interest (ROI) were drawn on the right ascending aorta (AA) and back muscle (BM) in contrast-enhanced chest images, and noise (standard deviation, SD) was measured using Hounsfield units (HU) in each image. Statistical analysis was performed using SPSS (ver. 22.0), analyzing the mean values of the three measurements with one-way analysis of variance (ANOVA). The results showed that the SD values for AA were FBP=25.65±3.75, ASIR-50=19.08±3.93, and TF-M=17.05±4.45 (F=66.72, p=0.00), while the SD values for BM were FBP=26.64±3.81, ASIR-50=19.19±3.37, and TF-M=19.87±4.25 (F=49.54, p=0.00). Post-hoc tests revealed significant differences among the three groups. DLIR using TF-M demonstrated significantly lower noise values compared to conventional reconstruction methods. Therefore, the application of the deep learning algorithm TrueFidelity-Middle (TF-M) is expected to be clinically valuable in pediatric chest CT examinations by reducing the degradation of image quality caused by respiration or motion.

• Journal of the Korean Society for information Management
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• v.39 no.4
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• pp.191-213
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• 2022

With a view to providing a high-quality policy information service beyond the existing national task service of the national policy information portal (POINT) of the National Library of Korea Sejong, it would be necessary to effectively provide the policy data needed for the implementation of the new national tasks. Accordingly, in this study, an attempt has been made to find a way to connect and develop the BRM-based national tasks and the policy information contents. Towards this end, first, the types of national tasks and the contents of each field and area of the government function's classification system were analyzed, with a focus placed on the 120 national tasks of the new administration. Furthermore, by comparing and analyzing the national tasks of the previous administration and the current information, the contents ought to be reflected for the development of contents related to the national tasks identified. Second, the method for linking and collecting the policy information was sought based on the analysis of the current status of policy information and the national information portal. As a result of the study, first, examining the 1st stage BRM of the national tasks, it turned out that there were 21 tasks for social welfare, 14 for unification and diplomacy, 17 for small and medium-sized businesses in industry and trade, 12 for general public administration, 8 for the economy, taxation and finance, 6 for culture, sports and tourism, science and technology, and education each, 5 for communication, public order and safety each, 4 for health, transportation and logistics, and environment each, 3 for agriculture and forestry, 2 for national defense and regional development each, and 1 for maritime and fisheries each, among others. As for the new administration, it is apparent that science technology and IT are important, and hence, it is necessary to consider such when developing the information services for the core national tasks. Second, to link the database with external organizations, it would be necessary to form a linked operation council, link and collect the information on the national tasks, and link and provide the national task-related information for the POINTs.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.193-205
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• 2023

A large amount of floating debris from land-based sources during heavy rainfall has negative social, economic, and environmental impacts, but there is a lack of monitoring systems for floating debris accumulation areas and amounts. With the recent development of artificial intelligence technology, there is a need to quickly and efficiently study large areas of water systems using drone imagery and deep learning-based object detection models. In this study, we acquired various images as well as drone images and trained with You Only Look Once (YOLO)v5s and the recently developed YOLO7 and YOLOv8s to compare the performance of each model to propose an efficient detection technique for land-based floating debris. The qualitative performance evaluation of each model showed that all three models are good at detecting floating debris under normal circumstances, but the YOLOv8s model missed or duplicated objects when the image was overexposed or the water surface was highly reflective of sunlight. The quantitative performance evaluation showed that YOLOv7 had the best performance with a mean Average Precision (intersection over union, IoU 0.5) of 0.940, which was better than YOLOv5s (0.922) and YOLOv8s (0.922). As a result of generating distortion in the color and high-frequency components to compare the performance of models according to data quality, the performance degradation of the YOLOv8s model was the most obvious, and the YOLOv7 model showed the lowest performance degradation. This study confirms that the YOLOv7 model is more robust than the YOLOv5s and YOLOv8s models in detecting land-based floating debris. The deep learning-based floating debris detection technique proposed in this study can identify the spatial distribution of floating debris by category, which can contribute to the planning of future cleanup work.