• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.12 no.4
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• pp.380-389
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• 2007

Stable carbon and nitrogen isotopes were analyzed in suspended particulate organic matter, macroalgae and macrobenthic invertebrates in order to determine the importance of primary organic matter sources in supporting food webs of rocky subtidal and intertidal macroalgal beds in the Korean coasts. Investigations were conducted at the inter tidal sites within Gwangyang bay, a semi-enclosed and eutrophicated bay, and the subtidal sites of the east coast, a relatively oligotrophic and open environment, in May and June 2005. Water-column suspension feeders showed more negative $\delta^{13}C$ values than those of the other feeding guilds, indicating trophic linkage with phytoplankton and thereby association with pelagic food chains. In contrast, animals of the other feeding guilds, including interface suspension feeders, herbivores, deposit feeders, omnivores and predators, displayed relatively less negative $\delta^{13}C$ values than those of the water-column suspension feeders and similar with that of macroalgae, indicating exclusive use of macroalgae-derived organic matter and association with benthic food chains. Most the macrobenthic species were considered to form strong trophic links with benthic food chains. In addition, the distribution of higher $\delta^{15}N$ values in macrobenthic consumers and macroalgae at the intertidal sites of Gwangyang Bay than those at the subtidal sites of the east coast suggests that anthropogenic nutrients may enhance the macroalgal production at the intertidal sites and in turn be incorporated into the particular littoral food web in Gwangyag Bay. These results confirm the dominant role of macroalgae in supporting rocky subtidal and intertidal food webs in the Korean coasts.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.99-107
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• 2024

Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.

• Journal of Practical Agriculture & Fisheries Research
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• v.25 no.4
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• pp.138-147
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• 2024

Agrobacterium-mediated transformation (AMT) is a method that allows for the stable integration of DNA fragments into the plant genome. Transgenic plants generated through AMT typically exhibit a lower copy number of the transgene compared to those induced by particle bombardment. Furthermore, AMT offers a straightforward and efficient approach for generating transgenic plants. While the transformation efficiency of wheat is comparatively lower than that of other monocot plants such as Rice (Oryza sativa L.) and Maize (Zea mays L.), the cultivars 'Bobwhites' and 'Fielder' are commonly employed for wheat transformation. To date, there have been no reported instances of successful development of transgenic plants using Korean wheat varieties through AMT. This study aims to assess the transformation efficiency of 43 Korean wheat cultivars using the GUS assay, with the goal of identifying suitable Korean wheat cultivars for AMT. The pCAMBIA1301 vector, carrying the β-glucuronidase (GUS) gene, was incorporated into Agrobacterium strain EH105. Following the inoculation of Agrobacterium into immature embryos, GUS assays were conducted 'Saeol', 'Jopum', and 'Jonong' showed 100% (the number of embryos showing GUS spots/the number of embryos used for AMT) among 43 cultivars. In addition, cultivars with more than 70% were 'Saekeumgang', 'Jojung', 'Tapdong', 'Anbaek', 'Dabun', 'Sugang', 'Keumgang', 'Jeokjung', 'Seodun', 'Joeun', 'Dajung', and 'Baekjung'. It seems that the 15 cultivars above showed the possibility of using AMT. On the other hand, 'Yeonbaek', 'Goso', 'Baekgang', and 'Johan' showed less than 20% and GUS spots were not observed in 'Gru', 'Gobun', 'Milseong', and 'Shinmichal-1'. This study explores transient GUS expression in Korean wheat cultivars seven days after AMT. The observed initial high efficiency of transient transformation suggests the potential for subsequent stable transformation efficiency. Korean wheat cultivars demonstrating elevated transient transformation efficiency could serve as promising candidates for the development of stable transgenic wheat.

• Journal of Food Hygiene and Safety
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• v.39 no.2
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• pp.61-71
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• 2024

In this study, we analyzed chlorogenic acid indicator components in preparation for the additional listing of green coffee bean extract in the Health Functional Food Code and optimized caffeine for simultaneous analysis. We extracted chlorogenic acid and caffeine using 30% methanol, phosphoric acid solution, and acetonitrile-containing phosphoric acid and analyzed them at 330 and 280 nm, respectively, using liquid chromatography. Our analysis validation results yielded a correlation coefficient (R²) revealing a significance level of at least 0.999 within the linear quantitative range. The chlorogenic acid and caffeine detection and quantification limits were 0.5 and 0.2 ㎍/mL and 1.4, and 0.4 ㎍/mL, respectively. We confirmed that the precision and accuracy results were suitable using the AOAC validation guidelines. Finally, we developed a simultaneous chlorogenic acid and caffeine analysis approach. In addition, we confirmed that our analysis approach could simultaneously quantify chlorogenic acid and caffeine by examining the applicability of each formulation through prototypes and distribution products. In conclusion, the results of this study demonstrated that the standardized analysis would expectably increase chlorogenic acidcontaining health functional food quality control reliability.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.295-305
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• 2024

This study aims to analyze the micro-sedimentary structures of the Hwangdo tidal flats using ultra-high resolution unmanned aerial vehicle (UAV) data. Tidal flats, located in the transitional area between land and sea, constantly change due to tidal activities and provide a unique environment important for understanding sedimentary processes and environmental conditions. Traditional field observation methods are limited in spatial and temporal coverage, and existing satellite imagery does not provide sufficient resolution to study micro-sedimentary structures. To overcome these limitations, high-resolution images of the Hwangdo tidal flats in Chungcheongnam-do were acquired using UAVs. This area has experienced significant changes in its sedimentary environment due to coastal development projects such as sea wall construction. From May 17 to 18, 2022, sediment samples were collected from 91 points during field surveys and 25 in-situ points were intensively analyzed. UAV data with a spatial resolution of approximately 0.9 mm allowed identifying and extracting parameters related to micro-sedimentary structures. For mud cracks, the length of the major axis of the polygons was extracted, and the wavelength and ripple symmetry index were extracted for ripple marks. The results of the study showed that in areas with mud content above 80%, mud cracks formed at an average major axis length of 37.3 cm. In regions with sand content above 60%, ripples with an average wavelength of 8 cm and a ripple symmetry index of 2.0 were formed. This study demonstrated that micro-sedimentary structures of tidal flats can be effectively analyzed using ultra-high resolution UAV data without field surveys. This highlights the potential of UAV technology as an important tool in environmental monitoring and coastal management and shows its usefulness in the study of sedimentary structures. In addition, the results of this study are expected to serve as baseline data for more accurate sedimentary facies classification.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.2
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• pp.129-152
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• 2024

In the application of deep learning object detection via CCTV in tunnels, a large number of false positive detections occur due to the poor environmental conditions of tunnels, such as low illumination and severe perspective effect. This problem directly impacts the reliability of the tunnel CCTV-based accident detection system reliant on object detection performance. Hence, it is necessary to reduce the number of false positive detections while also enhancing the number of true positive detections. Based on a deep learning object detection model, this paper proposes a false positive data training method that not only reduces false positives but also improves true positive detection performance through retraining of false positive data. This paper's false positive data training method is based on the following steps: initial training of a training dataset - inference of a validation dataset - correction of false positive data and dataset composition - addition to the training dataset and retraining. In this paper, experiments were conducted to verify the performance of this method. First, the optimal hyperparameters of the deep learning object detection model to be applied in this experiment were determined through previous experiments. Then, in this experiment, training image format was determined, and experiments were conducted sequentially to check the long-term performance improvement through retraining of repeated false detection datasets. As a result, in the first experiment, it was found that the inclusion of the background in the inferred image was more advantageous for object detection performance than the removal of the background excluding the object. In the second experiment, it was found that retraining by accumulating false positives from each level of retraining was more advantageous than retraining independently for each level of retraining in terms of continuous improvement of object detection performance. After retraining the false positive data with the method determined in the two experiments, the car object class showed excellent inference performance with an AP value of 0.95 or higher after the first retraining, and by the fifth retraining, the inference performance was improved by about 1.06 times compared to the initial inference. And the person object class continued to improve its inference performance as retraining progressed, and by the 18th retraining, it showed that it could self-improve its inference performance by more than 2.3 times compared to the initial inference.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.3
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• pp.27-40
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• 2024

This study empirically derives the factors affecting the practice of corporate social responsibility (CSR) of venture firms in Korea from the perspective of Slack Resource Theory and the company's growth strategy, and provides implications for future expansion of venture firm's CSR activities. In Korea, venture firms have grown into important players in the national economy since the late 1990s through social contributions such as economic value creation, job creation, and technological development. As venture companies grow in status, positive relationships with stakeholders and responsibility for environmental and social values are required. Now, CSR is becoming an important strategic choice for SMEs and venture firms. However, until now, CSR-related academic research has mainly focused on large or listed corporations, and there is not much research on SMEs or venture firms. In particular, research on the factors that lead venture companies to make important business decisions of participating in CSR activities is not there yet. This study applied logistic multiple regression analysis using the '2023 Survey on Venture Firms' conducted by the Ministry of SMEs and Startups. As a result of this study, operating profit, which is an available resources of venture companies, and government support, which is a potential resource, have a positive impact on venture firms's CSR activities. Also, business relationships with large corporations and expectation for future cooperation also have a positive impact on CSR activities as the determinants. On the other hand, it was analyzed that in venture firms where ownership and management are not separated, the higher the CEO's shareholding ratio, the more negatively it affects CSR activities. This study contributes academically as the first empirical study on the determinants of CSR activities of venture firms in Korea and provides implications that government policy support and collaboration between large corporations and venture firms are important in order to expand CSR activities of venture firms.

• Korean Journal of Ecology and Environment
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• v.57 no.2
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• pp.102-110
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• 2024

This study presents a fine scale distribution of the endangered species, Odontobutis obscura, through field surveys and literature reviews. Using the mark-recapture method, the population size in major habitats was determined. Field surveys conducted on 18 streams in Geoje Island revealed that the O. obscura was only found in the main streams and tributaries of the Sanyang, Gucheon, and Buchun Streams, which are part of the Sanyang Stream watershed. The O. obscura exhibited relative abundances ranging from 0.5% to 35.3% at different locations, with certain spots showing higher relative abundances (18.8% to 35.3%), indicating major habitat areas. A review of six literature studies confirmed the presence of the O. obscura, although there were differences in occurrence status depending on the purpose, scope, and duration of the studies. Combining the results of field and literature surveys, it was found that the O. obscura inhabits the main and tributary streams of the Sanyang, Gucheon, and Buchun Streams, from the upper to lower reaches. Currently, the O. obscura population in the Sanyang Stream watershed maintains a stable habitat, but its limited distribution range suggests potential issues such as genetic diversity deficiency in the long term. The population size of the O. obscura was confirmed at two specific locations, with densities of 0.5 to 1.5 individuals per m². The average movement distance from the release point was 13.1 m, indicating the limited mobility characteristic of ambush predators. Understanding the distribution and population size of endangered species is the first step towards their conservation and protection. Based on this information, further research could significantly contribute to the conservation of the O. obscura.

• Korean Journal of Environmental Biology
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• v.41 no.4
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• pp.400-412
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• 2023

In this study, the biogeochemistry management (BGC-MAN) model was applied to North and South Korea pine and oak forest stands to evaluate the Net Primary Productivity (NPP), an indicator of forest ecosystem productivity. For meteorological information, historical records and East Asian climate scenario data of Shared Socioeconomic Pathways (SSPs) were used. For vegetation information, pine (Pinus densiflora) and oak(Quercus spp.) forest stands were selected at the Gwangneung and Seolmacheon in South Korea and Sariwon, Sohung, Haeju, Jongju, and Wonsan, which are known to have tree nurseries in North Korea. Among the biophysical information, we used the elevation model for topographic data such as longitude, altitude, and slope direction, and the global soil database for soil data. For management factors, we considered the destruction of forests in North and South Korea due to the Korean War in 1950 and the subsequent reforestation process. The overall mean value of simulated NPP from 1991 to 2100 was 5.17 Mg C ha^-1, with a range of 3.30-8.19 Mg C ha^-1. In addition, increased variability in climate scenarios resulted in variations in forest productivity, with a notable decline in the growth of pine forests. The applicability of the BGC-MAN model to the Korean Peninsula was examined at a time when the ecosystem process-based models were becoming increasingly important due to climate change. In this study, the data on the effects of climate change disturbances on forest ecosystems that was analyzed was limited; therefore, future modeling methods should be improved to simulate more precise ecosystem changes across the Korean Peninsula through process-based models.

• Korean Journal of Heritage: History & Science
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• v.57 no.3
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• pp.24-35
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• 2024

Recently, research using deep learning technologies such as artificial intelligence, convolutional neural networks, etc. has been actively conducted in various fields including healthcare, manufacturing, autonomous driving, and security, and is having a significant influence on society. In line with this trend, the present study attempted to apply deep learning to the classification of archaeological artifacts, specifically ancient Korean roof-end tiles. Using 100 images of roof-end tiles from each of the Goguryeo, Baekje, and Silla dynasties, for a total of 300 base images, a dataset was formed and expanded to 1,200 images using data augmentation techniques. After building a model using transfer learning from the pre-trained EfficientNetB0 model and conducting five-fold cross-validation, an average training accuracy of 98.06% and validation accuracy of 97.08% were achieved. Furthermore, when model performance was evaluated with a test dataset of 240 images, it could classify the roof-end tile images from the three dynasties with a minimum accuracy of 91%. In particular, with a learning rate of 0.0001, the model exhibited the highest performance, with accuracy of 92.92%, precision of 92.96%, recall of 92.92%, and F1 score of 92.93%. This optimal result was obtained by preventing overfitting and underfitting issues using various learning rate settings and finding the optimal hyperparameters. The study's findings confirm the potential for applying deep learning technologies to the classification of Korean archaeological materials, which is significant. Additionally, it was confirmed that the existing ImageNet dataset and parameters could be positively applied to the analysis of archaeological data. This approach could lead to the creation of various models for future archaeological database accumulation, the use of artifacts in museums, and classification and organization of artifacts.