• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.315-325
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• 2023

The purpose of this study was to predict abdominal obesity with 3-Dimensional computed tomography (3D CT) measurements of kidneys by analyzing the correlation between kidney sizes and abdominal obesity level. The subjects were 178 healthy adults without underlying diseases who had a comprehensive health examination at the Health Medical Center of Jesus Hospital in Jeonju. Abdominal obesity was measured by CT cross-sectional image at the level of the umbilicus and divided into visceral fat area, subcutaneous fat area, visceral fat/total fat ratio. The average comparison of kidney sizes classified according to abdominal obesity were performed through one-way analysis of variance (ANOVA) and Scheffe test. Pearson correlation analysis was performed to correlate all measurement values. The results of kidney size ANOVA analysis according to abdominal obesity were as follows. The means of kidney measurements according to visceral fat classification were significantly different in all kidney measurements (p<0.05). And in case of subcutaneous fat classification, the means of kidney measurements by 3D CT of the severe obesity group were significantly different in the right kidney width (p<0.05). In case of visceral fat area/total fat area ratio, the means of kidney measurements by 3D CT of the severe obesity group were significantly different in both kidneys width (p<0.05). Pearson correlation between kidneys measurements and CT abdominal obesity showed that visceral fat area had the highest correlation with the left kidney width measured by 3D CT (r=0.467) and subcutaneous fat area had correlation with the right kidney width measured by 3D CT (r=0.249). The visceral fat area/total fat area ratio had correlation with the left kidney width measured by 3D CT (r=0.291).

• Journal of The Korean Association For Science Education
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• v.43 no.3
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• pp.253-263
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• 2023

This study explored the discrepancy between the text of a teacher's guide about straight and reflective light and the content knowledge of pre-service elementary teachers. A total of 455 questions and 543 answers generated by 279 pre-service elementary teachers after reading a 'Shadow and Mirror' unit in the teacher's guide were analyzed. The questions were classified according to the types of concepts and discrepancies, and the answers were analyzed for accuracy. The results of analyzing the concepts of questions revealed that the pre-service elementary teachers were most curious about the shadow in the straight concept, the mirror image in the reflection concept, and the light source in other concepts. The questions with a low correct answer rate due to incorrect- or non-answers, such as those concerning the superposition principle of light by reflection, the principle of experimental tools, and images by lenses, were only partially or not included in the teacher's guide. When the questions were classified according to the type of discrepancy, the frequency of questions due to knowledge deficit was higher than that due to knowledge clash. This demonstrates that the concepts that teachers need to know must be supplemented with the contents of the teacher's guide. Discrepancies due to knowledge clashes are often caused by conflicts between what is experienced in everyday life and what is presented in textbooks. Therefore, it is necessary to reduce the discrepancy between the texts of the teacher's guide and the knowledge of pre-service elementary teachers by including the differences between the actual context of everyday life and the context of the textbook in the teacher's guide.

• Journal of the Korea Knowledge Information Technology Society
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• v.13 no.1
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• pp.11-23
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• 2018

This paper empirically investigates the factors that influence the consumer choice of an app market based on the rational choice theory. The app market is the only channel where a consumer can buy smartphone apps, which give various functional convenience and are considered to be a major contributor to the proliferation of smartphones. Analyses of 281 questionnaires show that usability and structural guarantees as benefit factors significantly influence the app market choice. From the cost perspectives, both monetary and non-monetary conversion costs are found to significantly influence the app market choice. On the other hand, customer trust, information quality, and market image were found to have no significant effect on app market selection. In particular, Korean app market platform providers (KT, LG U +) seem to be superior in terms of structural guarantees, such as customer center operation and damage compensation regulations, compared to overseas app market platform operators (Google). However, in the case of the Google App Market, it is pre-installed on all Android phones, so it is not inconvenient to install additional apps to use other app market. This is disadvantageous to domestic app market platform operators, and it is necessary to establish a policy solution point. In terms of operator costs, both monetary and non-monetary conversion costs have a significant impact on app market choice. In particular, non-monetary conversion costs have a negative impact on Korean app market platform operators. It can be explained that the service expectation level of the domestic app market is low and it is recognized that the time cost factor such as membership is large for new users to use. It seems to be necessary to improve the domestic app market business. Meanwhile, extant research on smartphone apps focuses on the purchase of apps themselves, but not on the selection of the app market itself. In order to fill in this gap, this study focuses on the determinants of app market selection, including the characteristics of an app market and the switching costs.

• Journal of the Daesoon Academy of Sciences
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• v.21
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• pp.99-156
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• 2013

This paper is to indicate research tendencies of faith in Daesoonjinrihoe and controversial points of those, and to consider the outlook on Sangje after defining it as theological understanding and explanation for Gu-Cheon-Sang-Je (High-est ruling Entity that is the object of devotion in Daesoon-jinrihoe). As the first introduction to the work, various descriptions for Sangje are arranged and the meanings of those are analyzed. In brief, first, the name of Gu-Cheon-Eung-Won-Nweh-Seong-Bo-Hwa-Cheon-Jon, expresses the fact that the authority of Sangje (the Supreme Entity) is exposed by spatial concept Sangje dwells in Ninth Heaven. This fact can be compared with the doctrines Allah in Islam and Jehovah in Christianity each are dwelled in Seventh Heaven. And the name shows Sangje is the ruler who reigns over the universe by using yin and yang. Second, the name, Gu-Cheon-Eung-Won-Nweh-Seong-BoHwa-Cheon-Jon, is imported from China Taoism because it has been in Ok-Chu-Gyeong (the Gaoshang shenlei yushu). But in fact it's root is in Korea because Buyeo and Goguryeo, the ancient Korean nations, have the source of the name. While the name is not the Supreme Entity in China Taoism, it is the Supreme Entity in Daesoonjinrihoe. This fact is a important difference. Third, arbitrarily or not, the name, Gu-Cheon-Eung-Won-Nweh-Seong-Bo-Hwa-Cheon-Jon, is put on the image of 'resolution of grievances'. The reason is that many peoples in Korea and China has called the name for about 1,000 years ago to help their fortunes and escape predicaments. Forth, not only Gu-Cheon-Eung-Won-Nweh-Seong-Bo-Hwa-Cheon-Jon but also the name, Three Pure Ones and Ok-Cheon-Jin-Wang (Yuqingzhenwang) in China Taoism used as the Highest ruling Entity in Daesoonjinrihoe. But the relations between three Pure Ones and Ok-Cheon-Jin-Wang and Gu-Cheon-Eung-Won-Nweh-Seong-Bo-Hwa-Cheon-Jon in Dae-soonjinrihoe are different from that in China Taoism. Fifth, Sangje is associated with the Polaris divinity of Tae-Eul, view on God in Oriental Cosmology. The description Tae-Eul as well as Gu-Cheon-Eung-Won-Nweh-Seong-Bo-Hwa-Cheon-Jon is indicated Sangje is linked to the faith of Buyeo and Goguryeo. Sixth, Sangje is not only Mugeuk-Sin (The God of The Endless) who supervise the Endless but also Taegeuk-Ji-Cheon-Jon (The God of The Ultimate Reality) who supervise the Ultimate Reality. These descriptions directly display the fact Sangje is a creator. Seventh, in case explaining Sangje, the point of view is necessary that grasps the whole viewpoints Sangje 'was' Hidden God(deus otiosus) and 'is' Unhidden God after Incarnation. Eighth, Sangje is Cheon-Ju in Donghak, but different from that. Cheon-Ju in Donghak has both transcendence and immanence in tightrope tension, but Cheon-Ju in Daesoonjinrihoe emphasize transcendence than immanence. That difference is the result of the fact Cheon-Ju in Donghak was a being having revealed a man and Cheon-Ju in Daesoonjinrihoe was a being having incarnated after revealing a man. Ninth, Sangje is Gae-Byeok-Jang who is the manager of the transforming and ordering the Three Realms of the World by the Great Do which is the mutual beneficence of all life and Hae-Won-Sin who is the God of resolution of grievances.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.387-397
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• 2023

Plant height is a growth parameter that provides visible insights into the plant's growth status and has a high correlation with yield, so it is widely used in crop breeding and cultivation research. Investigation of the growth characteristics of crops such as plant height has generally been conducted directly by humans using a ruler, but with the recent development of sensing and image analysis technology, research is being attempted to digitally convert growth measurement technology to efficiently investigate crop growth. In this study, the canopy height of rice grown at various nitrogen fertilization levels was measured using a laser scanner capable of precise measurement over a wide range, and a comparative analysis was performed with the actual plant height. As a result of comparing the point cloud data collected with a laser scanner and the actual plant height, it was confirmed that the estimated plant height measured based on the average height of the top 1% points showed the highest correlation with the actual plant height (R² = 0.93, RMSE = 2.73). Based on this, a linear regression equation was derived and used to convert the canopy height measured with a laser scanner to the actual plant height. The rice growth curve drawn by combining the actual and estimated plant height collected by various nitrogen fertilization conditions and growth period shows that the laser scanner-based canopy height measurement technology can be effectively utilized for assessing the plant height and growth of rice. In the future, 3D images derived from laser scanners are expected to be applicable to crop biomass estimation, plant shape analysis, etc., and can be used as a technology for digital conversion of conventional crop growth assessment methods.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.521-535
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• 2023

When converting rice fields into fields,sorghum (sorghum bicolor L. Moench) has excellent moisture resistance, enabling stable production along with soybeans. Therefore, it is a crop that is expected to improve the self-sufficiency rate of domestic food crops and solve the rice supply-demand imbalance problem. However, there is a lack of fundamental statistics,such as cultivation fields required for estimating yields, due to the traditional survey method, which takes a long time even with a large manpower. In this study, U-Net was applied to RGB images based on unmanned aerial vehicle to confirm the possibility of non-destructive segmentation of sorghum cultivation fields. RGB images were acquired on July 28, August 13, and August 25, 2022. On each image acquisition date, datasets were divided into 6,000 training datasets and 1,000 validation datasets with a size of 512 × 512 images. Classification models were developed based on three classes consisting of Sorghum fields(sorghum), rice and soybean fields(others), and non-agricultural fields(background), and two classes consisting of sorghum and non-sorghum (others+background). The classification accuracy of sorghum cultivation fields was higher than 0.91 in the three class-based models at all acquisition dates, but learning confusion occurred in the other classes in the August dataset. In contrast, the two-class-based model showed an accuracy of 0.95 or better in all classes, with stable learning on the August dataset. As a result, two class-based models in August will be advantageous for calculating the cultivation fields of sorghum.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.669-681
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• 2023

Studies have tried to apply remote sensing technology, a non-destructive survey method, instead of the existing destructive survey, which requires relatively large labor input and a long time to estimate chlorophyll content, which is an important indicator for evaluating the growth of fruit trees. This study was conducted to non-destructively evaluate the chlorophyll content of pear tree leaves using unmanned aerial vehicle-based hyperspectral imagery for two years(2021, 2022). The reflectance of the single bands of the pear tree canopy extracted through image processing was band rationed to minimize unstable radiation effects depending on time changes. The estimation (calibration and validation) models were developed using machine learning algorithms of elastic-net, k-nearest neighbors(KNN), and support vector machine with band ratios as input variables. By comparing the performance of estimation models based on full band ratios, key band ratios that are advantageous for reducing computational costs and improving reproducibility were selected. As a result, for all machine learning models, when calibration of coefficient of determination (R²)≥0.67, root mean squared error (RMSE)≤1.22 ㎍/cm², relative error (RE)≤17.9% and validation of R²≥0.56, RMSE≤1.41 ㎍/cm², RE≤20.7% using full band ratios were compared, four key band ratios were selected. There was relatively no significant difference in validation performance between machine learning models. Therefore, the KNN model with the highest calibration performance was used as the standard, and its key band ratios were 710/714, 718/722, 754/758, and 758/762 nm. The performance of calibration showed R²=0.80, RMSE=0.94 ㎍/cm², RE=13.9%, and validation showed R²=0.57, RMSE=1.40 ㎍/cm², RE=20.5%. Although the performance results based on validation were not sufficient to estimate the chlorophyll content of pear tree leaves, it is meaningful that key band ratios were selected as a standard for future research. To improve estimation performance, it is necessary to continuously secure additional datasets and improve the estimation model by reproducing it in actual orchards. In future research, it is necessary to continuously secure additional datasets to improve estimation performance, verify the reliability of the selected key band ratios, and upgrade the estimation model to be reproducible in actual orchards.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.21-35
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• 2023

Expectations surrounding generative AI technology and its profound ramifications are sweeping across various industrial domains. Given the anticipated pivotal role of the startup ecosystem in the utilization and advancement of generative AI technology, it is imperative to cultivate a deeper comprehension of the present state and distinctive attributes characterizing venture capital (VC) investments within this domain. The current investigation delves into South Korea's landscape of VC investment deals and prognosticates the projected VC investments by juxtaposing these against the United States, the frontrunner in the generative AI industry and its associated ecosystem. For analytical purposes, a compilation of 286 investment deals originating from 117 U.S. generative AI startups spanning the period from 2008 to 2023, as well as 144 investment deals from 42 South Korean generative AI startups covering the years 2011 to 2023, was amassed to construct new datasets. The outcomes of this endeavor reveal an upward trajectory in the count of VC investment deals within both the U.S. and South Korea during recent years. Predominantly, these deals have been concentrated within the early-stage investment realm. Noteworthy disparities between the two nations have also come to light. Specifically, in the U.S., in contrast to South Korea, the quantum of recent VC deals has escalated, marking an augmentation ranging from 285% to 488% in the corresponding developmental stage. While the interval between disparate investment stages demonstrated a slight elongation in South Korea relative to the U.S., this discrepancy did not achieve statistical significance. Furthermore, the proportion of VC investments channeled into generative AI enterprises, relative to the aggregate number of deals, exhibited a higher quotient in South Korea compared to the U.S. Upon a comprehensive sectoral breakdown of generative AI, it was discerned that within the U.S., 59.2% of total deals were concentrated in the text and model sectors, whereas in South Korea, 61.9% of deals centered around the video, image, and chat sectors. Through forecasting, the anticipated VC investments in South Korea from 2023 to 2029 were derived via four distinct models, culminating in an estimated average requirement of 3.4 trillion Korean won (ranging from at least 2.408 trillion won to a maximum of 5.919 trillion won). This research bears pragmatic significance as it methodically dissects VC investments within the generative AI domain across both the U.S. and South Korea, culminating in the presentation of an estimated VC investment projection for the latter. Furthermore, its academic significance lies in laying the groundwork for prospective scholarly inquiries by dissecting the current landscape of generative AI VC investments, a sphere that has hitherto remained void of rigorous academic investigation supported by empirical data. Additionally, the study introduces two innovative methodologies for the prediction of VC investment sums. Upon broader integration, application, and refinement of these methodologies within diverse academic explorations, they stand poised to enhance the prognosticative capacity pertaining to VC investment costs.

• Journal of the Korean Society of Radiology
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• v.82 no.5
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• pp.1207-1217
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• 2021

Purpose To evaluate the safety and efficacy of ultrasound-guided percutaneous core needle biopsy (USPCB) of pancreatic and peripancreatic lesions adjacent to critical vessels. Materials and Methods Data were collected retrospectively from 162 patients who underwent USPCB of the pancreas (n = 98), the peripancreatic area adjacent to the portal vein, the paraaortic area adjacent to pancreatic uncinate (n = 34), and lesions on the third duodenal portion (n = 30) during a 10-year period. An automated biopsy gun with an 18-gauge needle was used for biopsies under US guidance. The USPCB results were compared with those of the final follow-up imaging performed postoperatively. The diagnostic accuracy and major complication rate of the USPCB were calculated. Multiple factors were evaluated for the prediction of successful biopsies using univariate and multivariate analyses. Results The histopathologic diagnosis from USPCB was correct in 149 (92%) patients. The major complication rate was 3%. Four cases of mesenteric hematomas and one intramural hematoma of the duodenum occurred during the study period. The following factors were significantly associated with successful biopsies: a transmesenteric biopsy route rather than a transgastric or transenteric route; good visualization of targets; and evaluation of the entire US pathway. In addition, the number of biopsies required was less when the biopsy was successful. Conclusion USPCB demonstrated high diagnostic accuracy and a low complication rate for the histopathologic diagnosis of pancreatic and peripancreatic lesions adjacent to critical vessels.

• Korean Journal of Radiology
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• v.24 no.8
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• pp.807-820
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• 2023

Objective: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. Materials and Methods: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions. CT images in groups 2-7 were converted into the target CT style (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. Results: Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2-7 improved after CT conversion (original vs. converted: 0.63 vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists' scores were significantly higher (P < 0.001) and less variable on converted CT. Conclusion: CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.