• Journal of the Korea Society of Computer and Information
• /
• v.13 no.1
• /
• pp.233-241
• /
• 2008

In order to increase the learning effect in minimal time, it is required that the lecturer tailor the materials to suit the needs and achievement levels of each individual. However, in lecture environments such as in junior colleges. many students enroll under one professor, and many courses are open under the same course title at each department. where each enrolled student possesses different academic needs and achievement levels Therefore. this paper proposes a learning system based on a hypothesis that if a lecturer shares his/her course material to students in other classes of the same subject, and opens up other professors' grading and marks of the same subject to his students, their achievement levels will improve by utilizing other peers' achievements and needs. Also, in order to improve the learning performance, we utilized an e-catalog in order to access students' grading, corrections and coaching, ultimately saving time and cost.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.4
• /
• pp.587-594
• /
• 2018

The purpose of this study is to propose a power demand volatility evaluation system based on LSTM and not to verify the accuracy of the demand module which is a core module, but to recognize the sudden change of power pattern by using deeplearning in the actual power demand monitoring system. Then we confirm the availability of the module. Also, we tried to provide a visualized report so that the manager can determine the fluctuation of the power usage patten by applying it as a module to the web based system. It is confirmed that the power consumption data shows a certain pattern in the case of government offices and hospitals as a result of implementation of the volatility evaluation system. On the other hand, in areas with relatively low power consumption, such as residential facilities, it was not appropriate to evaluate the volatility.

• Journal of radiological science and technology
• /
• v.45 no.2
• /
• pp.151-158
• /
• 2022

The Cardiac Gated Blood Pool (GBP) scintigram, a nuclear medicine imaging, calculates the left ventricular Ejection Fraction (EF) by segmenting the left ventricle from the heart. However, in order to accurately segment the substructure of the heart, specialized knowledge of cardiac anatomy is required, and depending on the expert's processing, there may be a problem in which the left ventricular EF is calculated differently. In this study, using the DeepLabV3 architecture, GBP images were trained on 93 training data with a ResNet-50 backbone. Afterwards, the trained model was applied to 23 separate test sets of GBP to evaluate the reproducibility of the region of interest and left ventricular EF. Pixel accuracy, dice coefficient, and IoU for the region of interest were 99.32±0.20, 94.65±1.45, 89.89±2.62(%) at the diastolic phase, and 99.26±0.34, 90.16±4.19, and 82.33±6.69(%) at the systolic phase, respectively. Left ventricular EF was calculated to be an average of 60.37±7.32% in the ROI set by humans and 58.68±7.22% in the ROI set by the deep learning segmentation model. (p<0.05) The automated segmentation method using deep learning presented in this study similarly predicts the average human-set ROI and left ventricular EF when a random GBP image is an input. If the automatic segmentation method is developed and applied to the functional examination method that needs to set ROI in the field of cardiac scintigram in nuclear medicine in the future, it is expected to greatly contribute to improving the efficiency and accuracy of processing and analysis by nuclear medicine specialists.

• The Korean Journal of Food And Nutrition
• /
• v.35 no.6
• /
• pp.399-410
• /
• 2022

To enhance the bioavailability and bioactivities of mixed herbal medicines (RW), they were fermented with lactic-acid bacteria isolated from kimchi into postbiotics (FRW). Then, from the results of the 16s rRNA sequencing analysis, lactic acid bacteria isolated from kimchi were identified to be of two species, namely Lactobacillus sakei and Leuconostoc mesenteroides. The FRW prepared from the RW were extracted using hot water (HW) and 70% EtOH (EtOH) for comparison of their macrophage-stimulating activities. Based on a comparison of the activities of the FRW extracts, nitric oxide (NO) production of HW was significantly higher than that in EtOH. An analysis of the chemical properties of the extracts showed that HW had higher contents of neutral sugar and uronic acid than EtOH as well as contained a large amount of glucose. In addition, crude polysaccharide (CP) was prepared to enhance the macrophage-stimulating activity. The FRW-CP not only secreted immunostimulatory mediators but also increased the expression of immunostimulatory genes (iNOS, TNF-α, MCP-1, and IL-6). The fractionated FRW-CP contained about 90% neutral sugars, and these sugars were mainly composed of glucose, galacturonic acid, and arabinose. Thus, FRW prepared by fermentation of RW with kimchi lactic acid bacteria were found to be immunostimulatory modulators.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.3
• /
• pp.175-187
• /
• 2011

Since insects play important roles in existence of plants and other animals in the natural environment, they are considered as necessary biological resources from the perspectives of those biodiversity conservation and national utilization strategy. For the conservation and utilization of insect species, an observational learning environment is needed for non-experts such as citizens and students to take interest in insects in the natural ecosystem. The insect identification is a main factor for the observational learning. A current time-consuming search method by insect classification is inefficient because it needs much time for the non-experts who lack insect knowledge to identify insect species. To solve this problem, we proposed an smart phone-based insect identification inference system that helps the non-experts identify insect species from observational characteristics in the natural environment. This system is based on the similarity between the observational information by an observer and the biological insect characteristics. For this system, we classified the observational characteristics of insects into 27 elements according to order, family, and species, and proposed similarity indexes to search similar insects. In addition, we developed an insect identification inference prototype system to show this study's viability and performed comparison experimentation between our system and a general insect classification search method. As the results, we showed that our system is more effective in identifying insect species and it can be more efficient in search time.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.28 no.2
• /
• pp.1-8
• /
• 2024

The expansion joint is installed to offset the expansion of the superstructure and must ensure sufficient gap during its service life. In detailed guideline of safety inspection and precise safety diagnosis for bridge, damage due to lack or excessive gap is specified, but there are insufficient standards for determining the abnormal behavior of superstructures. In this study, a data-based maintenance was proposed by continuously monitoring the expansion-gap data of the same expansion joint. A total of 2,756 data were collected from 689 expansion joint, taking into account the effects of season. We have developed a method to evaluate changes in the expansion joint-gap that can analyze the thermal movement through four or more data at the same location, and classified the factors that affect the superstructure behavior and analyze the influence of each factor through deep learning and explainable artificial intelligence(AI). Abnormal behavior of the superstructure was classified into narrowing and functional failure through the expansion joint-gap evaluation graph. The influence factor analysis using deep learning and explainable AI is considered to be reliable because the results can be explained by the existing expansion gap calculation formula and bridge design.

• Journal of Intelligence and Information Systems
• /
• v.28 no.3
• /
• pp.161-183
• /
• 2022

Opinion polls have become a powerful means for election campaigns and one of the most important subjects in the media in that they predict the actual election results and influence people's voting behavior. However, the more active the polls, the more often they fail to properly reflect the voters' minds in measuring the effectiveness of election campaigns, such as repeatedly conducting polls on the likelihood of winning or support rather than verifying the pledges and policies of candidates. Even if the poor predictions of the election results of the polls have undermined the authority of the press, people cannot easily let go of their interest in polls because there is no clear alternative to answer the instinctive question of which candidate will ultimately win. In this regard, we attempt to retrospectively grasp public opinion on the 20th presidential election by applying the 'YouTube Analysis' function of Sometrend, which provides an environment for discovering insights through online big data. Through this study, it is confirmed that a result close to the actual public opinion (or opinion poll results) can be easily derived with simple YouTube data results, and a high-performance public opinion prediction model can be built.

• The Korean Journal of Applied Statistics
• /
• v.37 no.4
• /
• pp.525-539
• /
• 2024

Massive multiplayer online role playing game (MMORPG) is a common type of game these days. Predicting user churn in MMORPG is a crucial task. The retention rate of users is deeply associated with the lifespan and revenue of the service. If the churn of a specific user can be predicted in advance, targeted promotions can be used to encourage their stay. Therefore, not only the accuracy of churn prediction but also the speed at which signs of churn can be detected is important. In this paper, we propose methods to identify early signs of churn by utilizing the daily predicted user retention probabilities. We train various deep learning and machine learning models using log data and estimate user retention probabilities. By analyzing the change patterns in these probabilities, we provide empirical rules for early identification of users at high risk of churn. Performance evaluations confirm that our methodology is more effective at detecting high risk users than existing methods based on login days. Finally, we suggest novel methods for customized marketing strategies. For this purpose, we provide guidelines of the percentage of accessed users who are at risk of churn.

• The Journal of the Korea Contents Association
• /
• v.22 no.7
• /
• pp.55-62
• /
• 2022

The perovskite solar cell is an active part of research in renewable energy fields such as solar energy, wind, hydroelectric power, marine energy, bioenergy, and hydrogen energy to replace fossil fuels such as oil, coal, and natural gas, which will gradually disappear as power demand increases due to the increase in use of the Internet of Things and Virtual environments due to the 4th industrial revolution. The perovskite solar cell is a solar cell device using an organic-inorganic hybrid material having a perovskite structure, and has advantages of replacing existing silicon solar cells with high efficiency, low cost solutions, and low temperature processes. In order to optimize the light absorption layer thin film predicted by the existing empirical method, reliability must be verified through device characteristics evaluation. However, since it costs a lot to evaluate the characteristics of the light-absorbing layer thin film device, the number of tests is limited. In order to solve this problem, the development and applicability of a clear and valid model using machine learning or artificial intelligence model as an auxiliary means for optimizing the light absorption layer thin film are considered infinite. In this study, to estimate the light absorption layer thin-film optimization of perovskite solar cells, the regression models of the support vector machine's linear kernel, R.B.F kernel, polynomial kernel, and sigmoid kernel were compared to verify the accuracy difference for each kernel function.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.11
• /
• pp.1571-1576
• /
• 2022

This study aims to analyze the interest of modern people in non-face-to-face medical counseling in the medical industrys. Big data was collected on two social platforms, 지식인, a platform that allows experts to receive medical counseling, and YouTube. In addition to the top five keywords of telephone counseling, "internal medicine", "general medicine", "department of neurology", "department of mental health", and "pediatrics", a data set was built from each platform with a total of eight search terms: "specialist", "medical counseling", and "health information". Afterwards, pre-processing processes such as morpheme classification, disease extraction, and normalization were performed based on the crawled data. Data was visualized with word clouds, broken line graphs, quarterly graphs, and bar graphs by disease frequency based on word frequency. An emotional classification model was constructed only for YouTube data, and the performance of GRU and BERT-based models was compared.