• Journal of Appropriate Technology
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• v.7 no.1
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• pp.51-58
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• 2021

This study proposes the design and implementation of a low-cost emergency ventilator which can be helpful during the COVID-19 pandemic where the supply of automatic ventilators is not smooth compared with the urgent demand worldwide. Easy implementation and lower price were made possible by using AMBU-bag and off-the-shelf embedded micro-controller board. Moreover, while 3D printing is used by companies and experts around the world to build prototype hardware, materials which are readily available from surrounding environments so that people in countries where it is difficult to access many advanced technologies could manufacture the system. The design features AMBU-bag automation, not use 3D printing, and it can contrl speed. By allowing speed control, ventilation can be performed according to the conditions of the patient being used. A complementary point in the study is that it is difficult to fix the start point of the wiper motor used first. A method for complementing this is a method for replacing the brush DC motor with a position feedback function. Secondly, the AMBU-bag may wear out in the long-term process of compressing the AMBU-bag because the arm and the fixing frame are made of wood. To complement this, the part of fixing frame and arm parts that the AMBU-bag touches need to be wrapped in a material such as silicon to minimize friction.

• Knowledge Management Research
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• v.24 no.2
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• pp.27-48
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• 2023

The COVID-19 pandemic has highlighted the importance of firm resilience, particularly for small and medium-sized enterprises (SMEs). This study aimed to investigate the concept of SME resilience, the role of entrepreneurial orientation in enhancing firm resilience, and the impact of top management networking capability on this relationship. The study defined firm resilience as consisting of adaptation capacity and recovery capacity and conducted a survey of 187 domestic SMEs for empirical verification. The findings indicate that entrepreneurial orientation is a critical factor in enhancing firm resilience. Furthermore, the networking capability of top management may also contribute to firm resilience, but it weakens the relationship between entrepreneurial orientation and firm resilience as a moderating variable. In crisis situations, SMEs tend to rely more strongly on existing networks, rather than engaging in new network to acquire new resources, information, and knowledge, which can hinder their ability to adapt and recover. This study contributes to the further development and understanding of SME resilience, which is essential for enterprises to overcome crises and return to pre-shock levels.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.383-384
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• 2022

Following the development of technologies such as digital twin, IoT, and AI after the 4th industrial revolution, decision-making problems are being solved based on high-dimensional data analysis. This has recently been applied to the port logistics sector, and a number of studies on big data analysis, deep learning predictions, and simulations have been conducted on container terminals to improve port productivity. These high-dimensional data analysis techniques generally require a large number of data. However, the global port environment has changed due to the COVID-19 pandemic in 2020. It is not appropriate to apply data before the COVID-19 outbreak to the current port environment, and the data after the outbreak was not sufficiently collected to apply it to data analysis such as deep learning. Therefore, this study intends to present a port data augmentation method for data analysis as one of these problem-solving methods. To this end, we generate the container stowage situation of the yard through a generative adversarial neural network model in terms of container terminal operation, and verify similarity through statistical distribution verification between real and augmented data.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.185-205
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• 2023

As the tourism industry recovers post the COVID-19 pandemic, an increasing number of tourists are utilizing various platforms to leave reviews. However, amidst the vast amount of data, finding useful information remains challenging, often leading to time and cost inefficiencies in selecting travel destinations. Despite ongoing research, there are limitations due to the absence of ratings or the presence of different rating formats across platforms. Moreover, inconsistencies between ratings and the content of reviews pose challenges in developing recommendation models. To address these issues, this study utilized 7,104 reviews of tourist spots in Jeju Island to develop a specialized satisfaction index for Jeju tourist attractions and employed this index to construct a 'Rating Prediction Model.' To validate the model's performance, we predicted the ratings of 700 experimental data points using both the developed model and an LSTM approach. The proposed model demonstrated superior performance with a weighted accuracy of 73.87%, which is approximately 4.67% higher than that of the LSTM. The results of this study are expected to resolve the discrepancies between ratings and review contents, standardize ratings in reviews without ratings or in various formats, and provide reliable rating indicators applicable across all areas of travel in different domains.

• IMMUNE NETWORK
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• v.22 no.3
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• pp.22.1-22.25
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• 2022

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndromecoronavirus-2 (SARS-CoV-2), has spread over the world causing a pandemic which is still ongoing since its emergence in late 2019. A great amount of effort has been devoted to understanding the pathogenesis of COVID-19 with the hope of developing better therapeutic strategies. Transcriptome analysis using technologies such as RNA sequencing became a commonly used approach in study of host immune responses to SARS-CoV-2. Although substantial amount of information can be gathered from transcriptome analysis, different analysis tools used in these studies may lead to conclusions that differ dramatically from each other. Here, we re-analyzed four RNA-sequencing datasets of COVID-19 samples including human bronchoalveolar lavage fluid, nasopharyngeal swabs, lung biopsy and hACE2 transgenic mice using the same standardized method. The results showed that common features of COVID-19 include upregulation of chemokines including CCL2, CXCL1, and CXCL10, inflammatory cytokine IL-1β and alarmin S100A8/S100A9, which are associated with dysregulated innate immunity marked by abundant neutrophil and mast cell accumulation. Downregulation of chemokine receptor genes that are associated with impaired adaptive immunity such as lymphopenia is another common feather of COVID-19 observed. In addition, a few interferon-stimulated genes but no type I IFN genes were identified to be enriched in COVID-19 samples compared to their respective control in these datasets. These features are in line with results from single-cell RNA sequencing studies in the field. Therefore, our re-analysis of the RNA-seq datasets revealed common features of dysregulated immune responses to SARS-CoV-2 and shed light to the pathogenesis of COVID-19.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.3
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• pp.121-129
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• 2024

The rapid advancement of digital technology and the COVID-19 pandemic have significantly accelerated the growth of online commerce, highlighting the need for support mechanisms that enable small business owners to effectively respond to these market changes. In response, this paper presents a foundational technology leveraging the Online to Offline (O2O) strategy to automatically capture products displayed on retail shelves and utilize these images to create virtual stores. The essence of this research lies in precisely identifying and recognizing the location and names of displayed products, for which a single-class-targeted, lightweight model based on YOLOv8, named ESD-YOLOv8, is proposed. The detected products are identified by their names through feature-point-based technology, equipped with the capability to swiftly update the system by simply adding photos of new products. Through experiments, product name recognition demonstrated an accuracy of 74.0%, and position detection achieved a performance with an F2-Score of 92.8% using only 0.3M parameters. These results confirm that the proposed method possesses high performance and optimized efficiency.

• Journal of Plant Biotechnology
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• v.48 no.3
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• pp.179-185
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• 2021

Indoor gardening includes wall greening, terrariums, and flower arrangements. Among these types of indoor gardens, the terrarium is easy to access for the general public, but in Korea, because of the focus on esthetics, the original purpose of creating terrariums, which was to grow plants sustainably in an enclosed space, has been lost. In addition, miniaturization of plants is required to grow plants in an enclosed space. Since the available plant species suitable for a terrarium are limited, only plants such as succulents, cacti, and moss have been used. In this study, Bronze (X Graptosedum) was used, and these problems were solved using the following three methods: placement and growth of virus-free plants in the terrarium; extending the diversity of plants with minimal size that can be planted in terrariums; and reducing the price of in vitro plants with minimal size by achieving large-scale production. In particular, tissue-cultured succulents were developed into a Vivorium by replacing the tissue culture container and renewing the composition of the plant. This paper suggests a new indoor horticultural field, Vivorium, that can improve the current limitations of terrariums and make them more accessible to the general public. The introduction and popularization of new indoor gardening fields with the increase in single-person households and indoor activities in the Pandemic era can also improve psychological stability among people and in the society.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.146-160
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• 2023

The spread of non-face-to-face services due to the COVID-19 pandemic has brought many changes in consumers' purchasing behavior and attracted much attention to new services. Could trying new services caused by this sudden environmental change alter consumers's choice patterns? This study proposes the research question of whether these new service experiences can change consumers' existing choice behavior, especially for risk-averse consumers who maintain their existing choice behavior or prefer safe alternatives. In this study, we examined whether trying out an unmanned payment services, one of innovative services that emerged after the pandemic crisis, can change the existing choice behavior of risk-averse consumers, i.e., make them more likely to prefer risky alternatives to safe alternatives. To accomplish these research goals, this research conducted one pilot survey and one study. The results of pilot survey showed that the stronger the prevention-focus tendency, the lower the self-efficacy to use the innovative service, with a negative relationship between them. Based on these findings, the study used an experimental method to examine the interaction effects between the use of innovation services and consumers' regulatory focus in a choice behavior and to explore the psychological mechanisms behind them. According to the results, it is found that prevention-focused consumers were more likely to choose risky alternatives and dissimilar extended brands following a trial of an unmanned payment service compared to not using that service. In contrast, promotion-focused consumers did not show different choice patterns regardless of following a trial of an innovative service. Furthermore, these results for prevention-focused consumers confirm the role of self-efficacy as a psychological mechanism. These findings shed light on the role of self-efficacy which has discussed in positive psychology into marketing area. Moreover, practical and academic implications are suggested by the finding that behavioral change occurs in risk-averse consumers, who are known to be hesitant to try new behaviors, indicating market expansion related to potential consumers for the use of the innovation services.

• Korean Journal of Microbiology
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• v.44 no.3
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• pp.178-185
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• 2008

Avian influenza virus (AIV) is recognized as key to the emergence of pandemic influenza for humans; there are growing concerns that AIV H9N2 may become more efficient to transmit to humans in the near future, since the infection of poultry with AIV H9N2 has been common in recent years. In this study, we aimed to produce antisera recognizing the HA and NA proteins of AIV H9N2. Initially, coding sequences corresponding to the N-terminal regions of the HA and NA proteins of the Korean AIV H9N2 (A/Ck/Kr/MS96/96) isolated from a domestic chicken were amplified from the genomic RNA. Following cloning of the amplified cDNA fragments into pGEX4T-1 vector, two GST-fusion proteins (GST-HAln and GST-NAn) were expressed in E. coli BL21 and purified with glutathione sepharose columns; the recombinant GST-HAln and GST-NAn proteins were both used as immunogens in rabbits. The antigenicity of the rabbit antisera was analyzed by immunoblotting of the cell lysates prepared from AIV H9N2-infected MDCK cells. Overall, the recombinant HAln and NAn proteins fused to the C-terminus of GST and the rabbit antisera raised against the corresponding recombinant proteins would provide a valuable reagent for AIV diagnosis and basic research.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.5
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• pp.574-583
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• 2021

Predicting shipping markets is an important issue. Such predictions form the basis for decisions on investment methods, fleet formation methods, freight rates, etc., which greatly affect the profits and survival of a company. To this end, in this study, we propose a shipping freight rate prediction model for container ships using gated recurrent units (GRUs) and long short-term memory structure. The target of our freight rate prediction is the China Container Freight Index (CCFI), and CCFI data from March 2003 to May 2020 were used for training. The CCFI after June 2020 was first predicted according to each model and then compared and analyzed with the actual CCFI. For the experimental model, a total of six models were designed according to the hyperparameter settings. Additionally, the ARIMA model was included in the experiment for performance comparison with the traditional analysis method. The optimal model was selected based on two evaluation methods. The first evaluation method selects the model with the smallest average value of the root mean square error (RMSE) obtained by repeating each model 10 times. The second method selects the model with the lowest RMSE in all experiments. The experimental results revealed not only the improved accuracy of the deep learning model compared to the traditional time series prediction model, ARIMA, but also the contribution in enhancing the risk management ability of freight fluctuations through deep learning models. On the contrary, in the event of sudden changes in freight owing to the effects of external factors such as the Covid-19 pandemic, the accuracy of the forecasting model reduced. The GRU1 model recorded the lowest RMSE (69.55, 49.35) in both evaluation methods, and it was selected as the optimal model.