• Proceedings of the Korean Society of Food Science and Nutrition Conference
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• 2001.12a
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• pp.69-73
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• 2001

Two opioid peptides, YPLDL and YPLDLF, were isolated from enzymatic digests of spinach ribulose-1, 5-bisphosphate carboxylase/oxygenase (RuBisCO) and named rubiscolin-5 and -6, respectively. These peptides were selective for delta-receptor and the latter was about 3 times more potent than the former. After oral administration in mice at the dose of 100 mg/kg, rubiscolin-6 showed analgesic activity in tail pinch test. It also stimutated learning performance at the same dose in passive avoidance experiment using step-through apparatus. An immunostimulating peptide, MITLAIPVNKPGR, was isolated from a trypsin digest of soybean protein and named soymetide. Immunostimulating activy of soymetide was mediated by fMLP receptor. Interestingly, after oral administration in rats at a dose of 300 mg/kg (po.), soymetide-4 (MITL) protected alopecia (hair-loss) induced by etoposide, a cancer chemotherapy agent. Stimulation of IL-1 release by the peptide was involved in the mechanism. Ovokinin(2-7), RADHPF, is a vasorelaxing peptide released from ovalbumin by the action of chymotrypsin. It lowered blood pressure of spontaneously hypersensive rats (SHR) after oral administration at a dose of 10 mg/kg. RPLKPW, which was designed by replacing 4 amino acid residues in ovokinin(2-7), exhibited hypotensive activity at a dose of 0.1 mg/kg (po.). This peptides was introduced into 3 homologous sites in soybean beta-conglycinin alpha' subunit by site-directed mutagenesis of the cDNA and expressed in E. coli. The minimum effective dose for hypotensive activity of the genetically modified beta-conglycinin alpha' subunit was 10 mg/kg (po.), which is about 1/200 that of ovalbumin.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.137-145
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• 2020

Providing safety training to construction workers is essential to reduce safety accidents at the construction site. With the prosperity of visualization technologies, Immersive Virtual Reality (IVR) has been adopted for construction safety training by providing interactive learning experiences in a virtual environment. Previous research efforts on IVR-based training have found that the level of fidelity of interaction between real and virtual worlds is one of the important factors contributing to the sense of presence that would affect training performance. Various interactive devices that link activities between real and virtual worlds have been applied in IVR-based training, ranging from existing computer input devices (e.g., keyboard, mouse, joystick, etc.) to specially designed devices such as high-end VR simulators. However, the need for high-fidelity interactive devices may hinder the applicability of IVR-based training as they would be more expensive than IVR headsets. In this regard, this study aims to understand the impact of the level of fidelity of interactive devices in the sense of presence in a virtual environment and the training performance during IVR-based forklift safety training. We conducted a comparative study by recruiting sixty participants, splitting them into two groups, and then providing different interactive devices such as a keyboard for a low fidelity group and a steering wheel and pedals for a high-fidelity group. The results showed that there was no significant difference between the two groups in terms of the sense of presence and task performance. These results indicate that the use of low-fidelity interactive devices would be acceptable for IVR-based safety training as safety training focuses on delivering safety knowledge, and thus would be different from skill transferring training that may need more realistic interaction between real and virtual worlds.

• Journal of Yeungnam Medical Science
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• v.24 no.2
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• pp.148-153
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• 2007

Background : Computer- and web-based simulation methods help students develop problem solving and decision making skills. In addition, they provide reality based learning to the student clinical experience with immediate medical feedback as well as repetitive training, on-site reviews and case closure. Materials and Methods : Seventy-five third-year medical students participated in a two-week simulation program. The students selected four modules from eight modules as follows: airway and breathing 1, cardiac arrest 1, cardiac arrhythmia 1, and chest pain 1, and then selected the first case within each of the modules. After 2 weeks, a pass score was obtained and the data analyzed. The average pass score of over 70% was considered a passing grade for each module. If the student did not pass each module, there was no score (i.e., pass score was zero). In addition, when at least one of the four modules was zero, the student was not included in this study. Results : Seventy-five students participated in the simulation program. Nineteen students were excluded based on their performance. The final number of students studied was 56 students (74.7%). The average scores for each module 1 to 4 were 86.7%, 85.3%, 84.0%, and 84.0%, and the average obtained pass score was 88.6 for the four modules in all 56 students. Conclusion : Medical simulation enabled students to experience realistic patient situations as part of medical learning. However, it has not been incorporated into traditional educational methodology. Here we describe the introduction and the development of various simulation modules and technologies for medical education.

• The Journal of Bigdata
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• v.7 no.1
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• pp.15-27
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• 2022

In the past five years, the fatality rate of single-vehicle accidents has been 4.7 times higher than that of all accidents, so it is necessary to establish a system that can detect and respond to single-vehicle accidents immediately. The IoT(Internet of Thing)-based real-time traffic accident recognition system proposed in this study is as following. By attaching an IoT sensor which detects the impact and vehicle ingress to the guardrail, when an impact occurs to the guardrail, the image of the accident site is analyzed through artificial intelligence technology and transmitted to a rescue organization to perform quick rescue operations to damage minimization. An IoT sensor module that recognizes vehicles entering the monitoring area and detects the impact of a guardrail and an AI-based object detection module based on vehicle image data learning were implemented. In addition, a monitoring and operation module that imanages sensor information and image data in integrate was also implemented. For the validation of the system, it was confirmed that the target values were all met by measuring the shock detection transmission speed, the object detection accuracy of vehicles and people, and the sensor failure detection accuracy. In the future, we plan to apply it to actual roads to verify the validity using real data and to commercialize it. This system will contribute to improving road safety.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1109-1123
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• 2020

In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1117-1132
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• 2019

Forest covers 30% of the Earth's land area and plays an important role in global carbon flux through its ability to store much greater amounts of carbon than other terrestrial ecosystems. The Gross Primary Production (GPP) represents the productivity of forest ecosystems according to climate change and its effect on the phenology, health, and carbon cycle. In this study, we estimated the daily GPP for a forest ecosystem using remote-sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS) and machine learning algorithms Support Vector Machine (SVM). MODIS products were employed to train the SVM model from 75% to 80% data of the total study period and validated using eddy covariance measurement (EC) data at the six flux tower sites. We also compare the GPP derived from EC and MODIS (MYD17). The MODIS products made use of two data sets: one for Processed MODIS that included calculated by combined products (e.g., Vapor Pressure Deficit), another one for Unprocessed MODIS that used MODIS products without any combined calculation. Statistical analyses, including Pearson correlation coefficient (R), mean squared error (MSE), and root mean square error (RMSE) were used to evaluate the outcomes of the model. In general, the SVM model trained by the Unprocessed MODIS (R = 0.77 - 0.94, p < 0.001) derived from the multi-sites outperformed those trained at a single-site (R = 0.75 - 0.95, p < 0.001). These results show better performance trained by the data including various events and suggest the possibility of using remote-sensed data without complex processes to estimate GPP such as non-stationary ecological processes.

• Journal of Korea Entertainment Industry Association
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• v.15 no.1
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• pp.207-220
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• 2021

The purpose of the study is to investigate the application and utility of emergency rescue education and derive limitations, improvements and development directions of university education based on the field experience of 119 emergency medical technician(EMT)s. The research subjects were six new 119 emergency medical technician(EMT)s within three years of starting their first-aid service in the field. After conducting in-depth narrative interviews, the analysis was performed using Colaizzi method. The 82 formulated meanings were derived from significant statements. From formulated meanings, 23 themes, 4 theme clusters, 2 categories were identified. The four theme clusters were 'The effectiveness of university education', 'The limitations of university education', 'The direction of improvement in educational methodology' and 'The direction of improvement in educational contents. University education has been helpful overall, but limitations are observed at the same time, suggesting that it should be developed through the improvement of educational methodologies (i.e. problem-based learning, field case review, education through role-playing, simulation education, strengthening skill ect.) and educational content (i.e. training tailored to the field, education focused on trauma or cardiac arrest, expansion of triage education in disaster management, reinforcement of education on-site safety, education on special patients, diverse guidance and faculty for different perspectives).

• Journal of The Korean Association of Information Education
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• v.1 no.1
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• pp.38-48
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• 1997

This study is an attempt to present a usage of the Web as CAI in the classroom and to give a direction to the future education in the face of information age. Characteristcs of information society, current curriculum, educational and teacher education are first analyzed in this article. The features of internet and 'Web are then summarized to present benefits of usage in the classroom as a CAI tool. The literature shows several characteristics of information society as follows : a technological computer, a provision and sharing of information, multi functional society, a participative democracy', an autonomy, a time value..A problem solving and 4 Cs(e.g., cooperation, copying, communication, creativity) are newly needed in this learning environment. The Internet is a large collection of networks that are tied together so that users can share their vast resources, a wealth of information, and give a key to a successful, efficient. individual study over a time and space. The 'Web increases an academic achievement, a creativity, a problem solving, a cognitive thinking, and a learner's motivation through an easy access to : documents available on the Internet, files containing programs, pictures, movies, and sounds from an FTP site, Usenet newsgroups, WAIS seraches, computers accessible through telnet, hypertext document, Java applets and other multimedia browser enhancements, and much more, In the Web browser will be our primary tool in searching for information on the Internet in this information age.

• Journal of the Korean earth science society
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• v.32 no.2
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• pp.212-224
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• 2011

In school science class, actual geological field trips tend to be restricted due to a number of problems including travel distance, cost, safety, and so on. Therefore, alternative way should be sought to provide students with the benefits of actual field trip. The purpose of this study is to develop web-based virtual field trip (VFT) about Jeju island in Korea by using flash panorama, and to explore a variety of ways to utilize the VFT. The characteristics of Jeju VFT are as follows: it provides virtual space for secondary school students to learn about volcanic topography and geology; students can access contents in a non-sequential order by virtue of web-based system, and students can control learning pace according to their ability; it is possible to investigate the same field site repeatedly, not limited by time and space; it presents differentiated worksheets for different school grade; it provides diverse complementary web contents, e. g., closeup features, thin sections, inquiry questions, and explanations of outcrops. We proposed several ways with instructional models to utilize Jeju VFT in science class and extra-school curricular as well.

• Informatization Policy
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• v.24 no.4
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• pp.3-16
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• 2017

With the development of social media services in the era of Web 2.0, the public opinion formation site has been partially shifted from the traditional mass media to social media. This phenomenon is continuing to expand, and public opinions on government polices created and shared on social media are attracting more attention. It is particularly important to grasp public opinions in policy formulation because setting up educational policies involves a variety of stakeholders and conflicts. The purpose of this study is to explore public opinions about education-related policies through an empirical analysis of social media documents on education policies using opinion mining techniques. For this purpose, we collected the education policy-related documents by keyword, which were produced by users through the social media service, tokenized and extracted sentimental qualities of the documents, and scored the qualities using sentiment dictionaries to find out public preferences for specific education policies. As a result, a lot of negative public opinions were found regarding the smart education policies that use the keywords of digital textbooks and e-learning; while the software education policies using coding education and computer thinking as the keywords had more positive opinions. In addition, the general policies having the keywords of free school terms and creative personality education showed more negative public opinions. As much as 20% of the documents were unable to extract sentiments from, signifying that there are still a certain share of blog posts or tweets that do not reflect the writers' opinions.