• Journal of Digital Convergence
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• v.18 no.12
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• pp.87-95
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• 2020

The Incheon airport is a gateway to and from the Republic of Korea and has a great influence on the image of the country. Therefore, it is necessary to predict the number of airport passengers in the long term in order to maintain the quality of service at the airport. In this study, we compared the predictive performance of various time series models to predict the air passenger demand at Incheon Airport. From 2002 to 2019, passenger data include trend and seasonality. We considered the naive method, decomposition method, exponential smoothing method, SARIMA, PROPHET. In order to compare the capacity and number of passengers at Incheon Airport in the future, the short-term, mid-term, and long-term was forecasted by time series models. For the short-term forecast, the exponential smoothing model, which weighted the recent data, was excellent, and the number of annual users in 2020 will be about 73.5 million. For the medium-term forecast, the SARIMA model considering stationarity was excellent, and the annual number of air passengers in 2022 will be around 79.8 million. The PROPHET model was excellent for long-term prediction and the annual number of passengers is expected to be about 99.0 million in 2024.

• Journal of the Korean Chemical Society
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• v.66 no.2
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• pp.136-149
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• 2022

Chemistry focuses on explaining macroscopic phenomena at the microscopic level with particles, such as atoms or molecules. Explanation using particles are bound to be considered as abstract by students, because it was dealing with invisible objects. For that reason, the science national curriculum presented to middle school students the explanation of the units related to the composition of matter. Therefore, understanding about the composition of matter in middle school students becomes an important basis for learning of chemistry, and it is necessary to investigate their understanding about composition of matter. In this study, students' understanding about 'composition of matter' region, which is first presented to middle school students, was confirmed at an overall level. In this line, this study analyzed the results of the items in the composition of matter region, and analyzed items were used in the National Assessment of Educational Achievement (NAEA) from 2015 to 2019. We analyzed the 9 items presented in the NAEA according to the response rate of options and response rate distribution curve, and explained the characteristics of understanding derived by each achievement level were examined. According to the analyzed results by dividing the conceptions about elements, atoms, and ions, students above the proficient achievement-level had scientific conceptions overall, but students below the basic achievement-level had inconsistent or naive conceptions. Based on the results for each item, this study discussed some implications to be considered or to be improved on teaching-learning for 'composition of matter'.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.17-27
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• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

• Journal of the Korean Society of Earth Science Education
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• v.16 no.1
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• pp.30-42
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• 2023

The purpose of this study is to quantitatively and qualitatively analyze the science journals written by pre-service elementary school teachers, and to categorize the view on the nature of science and the process of their change. For this purpose, 112 science journals written by 13 pre-service elementary school teachers were analyzed. The frequency of each area was analyzed using the research framework of the four areas of the nature of science, and the pattern of change in perspective on the nature of science was inductively derived and classified using the VNOS-C test analysis framework. As a result, The nature of scientific thinking, nature of scientific knowledge, nature of STS, and nature of scientific inquiry were described in relatively similar proportions, but among them, The nature of scientific thinking appeared in the largest percentage, and the nature of scientific inquiry was described in the smallest percentage. The variability of scientific knowledge, the importance of empirical evidence, and the positive and negative effects of science were especially intensively addressed. In addition, the changing aspects of pre-service elementary school teachers' perspectives on the nature of science could be categorized into 'naive view maintenance type', 'informed view maintenance type', 'regression type', 'development type', and 'mixed type'. The element of 'the empirical nature of scientific knowledge' showed various patterns of change depending on the students, and most of the students maintained a informed view on the tentativeness of scientific knowledge for several sessions.

• Journal of the Korean Society of Earth Science Education
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• v.16 no.1
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• pp.72-86
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• 2023

The purpose of this study is to examine what epistemological beliefs pre-service teachers have about science depending on the situation, and to explore in-depth changes in epistemological beliefs through disciplinary reading. For this purpose, 77 essays written by pre-service elementary school teachers after reading Feynman's 'the meaning of it all' were analyzed using an inductive analysis method. As a result of the study, the epistemological beliefs of pre-service teachers were divided into two situations: 'science in subject learning' and 'science in daily life', and the epistemological beliefs formed in the 'science handled by scientists' situation were analyzed after reading the book. Each situation was divided into sub-categories of 'Impression of Knowledge', 'Source of Knowledge', 'Justification of Knowledge', 'Variability of Knowledge', 'Structure of Knowledge', and 'Value of Knowledge Acquisition' to reveal differences in sophisticated beliefs and naive belief levels. As a result, it was derived that Feynman's science lecture influenced pre-service teachers in terms of establishing new perspectives and recontextualizing existing epistemological beliefs. This study is meaningful in that pre-service teachers' scientific epistemological beliefs may vary depending on the situation, and that the scope and depth of epistemological beliefs may be expanded to include scientists' beliefs in science through disciplinary reading.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.4
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• pp.29-39
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• 2017

Various electronic warfare situations drive the need to develop an integrated electronic warfare simulator that can perform electronic warfare modeling and simulation on radar threats. In this paper, we analyze the components of a simulation system to reversely model the radar threats that emit electromagnetic signals based on the parameters of the electronic information, and propose a method to gradually maintain the reverse extrapolation model of RF threats. In the experiment, we will evaluate the effectiveness of the incremental model update and also assess the integration method of reverse extrapolation models. The individual model of RF threats are constructed by using decision tree, naive Bayesian classifier, artificial neural network, and clustering algorithms through Euclidean distance and cosine similarity measurement, respectively. Experimental results show that the accuracy of reverse extrapolation models improves, while the size of the threat sample increases. In addition, we use voting, weighted voting, and the Dempster-Shafer algorithm to integrate the results of the five different models of RF threats. As a result, the final decision of reverse extrapolation through the Dempster-Shafer algorithm shows the best performance in its accuracy.

• Korean Journal of Radiology
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• v.22 no.10
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• pp.1628-1639
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• 2021

Objective: Based on the Liver Imaging Reporting and Data System version 2018 (LI-RADS, v2018), this study aimed to analyze LR-5 diagnostic performance for hepatocellular carcinoma (HCC) when threshold growth as a major feature is replaced by a more HCC-specific ancillary feature, as well as the frequency of threshold growth in HCC and non-HCC malignancies and its association with tumor size. Materials and Methods: This retrospective study included treatment-naive patients who underwent gadoxetate disodium-enhanced MRIs for focal hepatic lesions and surgery between January 2009 and December 2016. The frequency of major and ancillary features was evaluated for HCC and non-HCC malignancies, and the LR-category was assessed. Ancillary features that were significantly more prevalent in HCC were then used to either replace threshold growth or were added as additional major features, and the diagnostic performance of the readjusted LR category was compared to the LI-RADS v2018. Results: A total of 1013 observations were analyzed. Unlike arterial phase hyperenhancement, washout, or enhancing capsule which were more prevalent in HCCs than in non-HCC malignancies (521/616 vs. 18/58, 489/616 vs. 19/58, and 181/616 vs. 5/58, respectively; p < 0.001), threshold growth was more prevalent in non-HCC malignancies than in HCCs (11/23 vs. 17/119; p < 0.001). The mean size of non-HCC malignancies showing threshold growth was significantly smaller than that of non-HCC malignancies without threshold growth (22.2 mm vs. 42.9 mm, p = 0.040). Similar results were found for HCCs; however, the difference was not significant (26.8 mm vs. 33.1 mm, p = 0.184). Additionally, Fat-in-nodule was more frequent in HCCs than in non-HCC malignancies (99/616 vs. 2/58, p = 0.010). When threshold growth and fat-in-nodule were considered as ancillary and major features, respectively, LR-5 sensitivity (73.2% vs. 73.9%, p = 0.289) and specificity (98.2% vs. 98.5%, p > 0.999) were comparable to the LI-RADS v2018. Conclusion: Threshold growth is not a significant diagnostic indicator of HCC and is more common in non-HCC malignancies. The diagnostic performance of LR-5 was comparable when threshold growth was recategorized as an ancillary feature and replaced by a more HCC-specific ancillary feature.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.71-89
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• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.

• Radiation Oncology Journal
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• v.24 no.2
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• pp.103-109
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• 2006

Purpose: To evaluate the treatment outcome according to the salvage treatment modalities and identify the prognostic factors influencing the survival. Materials and Methods: Forty-five patients with locally recurrent rectal cancer treated between 1994 to 2003 were reviewed retrospectively. Median time from initial surgery to loal recurrence was 16months. Of the patients, 25 (56%) recurred at presacral and perirectal space. Among the 18 (40%) patients who received salvage surgery, 14 patients were treated with postoperative chemoradiotherapy. Among 27 (60%) patients who didn't receive salvage surgery, 16 were treated with chemoradiotherapy and 11 were treated with radiotherapy alone. Radiotherapy was given with total dose ranging from 37.5 to 64.8 Gy. Results: Five-year locoregional progression-free survival rate and overall survival rate of all patents were 49.5% and 34.3%, respectively. The 5-year locoregional progression-free survival rate and overall survival rate of patients undergoing salvage surgery were 77.0% and 52.1% compared with 36.0% and 37.9% f3r patients treated with chemoradiotherapy and 0% and 0% for patients treated with radiotherapy alone, respectively. The 5-year locoregional progression free survival and overall survival of patients who recurred earlier than 24 months were higher (67.5% and 59.1%) than the other patients (39.5% and 24.9%). Among the 27 patients who didn't receive salvage surgery, there was no significant difference for locoregional progression free survival and overall survival between re-irradiated patients and radiation-naive patients. Conclusion: Surgical resection is preferred to treatment for locally recurrent rectal cancer. If salvage surgery is not possible, chemoradiotherapy may achieve higher locoregional progression free survival and overall survival than radiotherapy alone.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.107-120
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• 2014

This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user's preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data. In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user's preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis. This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users' preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user's cluster, and compare the recommendation performances of comparative models. For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data - UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure - Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses 'all-but-one' approach for the CF experiment. In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.