• Journal of the Korean Geosynthetics Society
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• v.22 no.2
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• pp.1-12
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• 2023

Research on the causes of landslides and prediction of vulnerable areas is being conducted globally. This study aims to predict the effective soil depth, a critical element in analyzing and forecasting landslide disasters, using topographic information. Topographic data from various institutions were collected and assigned as attribute information to a 100 m × 100 m grid, which was then reduced through data grading. The study predicted effective soil depth for two cases: three depths (shallow, normal, deep) and five depths (very shallow, shallow, normal, deep, very deep). Three classification models, including K-Nearest Neighbor, Random Forest, and Deep Artificial Neural Network, were used, and their performance was evaluated by calculating accuracy, precision, recall, and F1-score. Results showed that the performance was in the high 50% to early 70% range, with the accuracy of the three classification criteria being about 5% higher than the five criteria. Although the grading criteria and classification model's performance presented in this study are still insufficient, the application of the classification model is possible in predicting the effective soil depth. This study suggests the possibility of predicting more reliable values than the current effective soil depth, which assumes a large area uniformly.

• Korean Journal of Culture and Social Issue
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• v.24 no.1
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• pp.29-47
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• 2018

Do social situations have an impact on an individual's information processing and emotional experiences? Two studies were conducted to investigate relationships between self-reference effects, emotional experiences and social information processing. Study 1 examined whether biases favoring self-related stimuli could occur automatically. Participants had to judge whether sequential geometric shape-label pairs matched or mismatched. The results showed that self-related stimuli are more rapidly processed than friends/others-related stimuli. In Study 2, the participants had to recall items which were presented with different instructions (either chosen by a friend or by the computer). Here we explored whether the self-reference effect is reduced in a social learning condition. When comparing the social learning condition (seated in pairs) with the nonsocial learning condition (seated alone), the participants recalled more self-related words in the nonsocial learning condition than in the social learning condition. Importantly, the automatic self-reference effect disappeared in the social learning condition. More friends-related words were recalled in the social condition than self-related words. In addition, while tasting chocolates, the participants judged them to be more likeable in the social condition than in the nonsocial condition. These results implicated that social processing can be useful for reducing the automatic self-reference effects and shared experiences are perceived more intensely than unshared experiences.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.617-629
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• 2020

In order to overcome the limitations of the rule-based intrusion detection system due to changes in Internet computing environments, the emergence of new services, and creativity of attackers, network anomaly detection (NAD) using machine learning and deep learning technologies has received much attention. Most of these existing machine learning and deep learning technologies for NAD use supervised learning methods to learn a set of training data set labeled 'normal' and 'attack'. This paper presents the feasibility of the unsupervised learning AutoEncoder(AE) to NAD from data sets collecting of secured network traffic without labeled responses. To verify the performance of the proposed AE mode, we present the experimental results in terms of accuracy, precision, recall, f1-score, and ROC AUC value on the NSL-KDD training and test data sets. In particular, we model a reference AE through the deep analysis of diverse AEs varying hyper-parameters such as the number of layers as well as considering the regularization and denoising effects. The reference model shows the f1-scores 90.4% and 89% of binary classification on the KDDTest+ and KDDTest-21 test data sets based on the threshold of the 82-th percentile of the AE reconstruction error of the training data set.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.1092-1100
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• 2004

An E-mails have regarded as one of the most popular methods for exchanging information because of easy usage and low cost. Meanwhile, exponentially growing unwanted mails in user's mailbox have been raised as main problem. Recognizing this issue, Korean government established a law in order to prevent e-mail abuse. In this paper we suggest hybrid spam mail filtering system using weighted Bayesian classifier which is extended from naive Bayesian classifier by adding the concept of preprocessing and intelligent agents. This system can classify spam mails automatically by using training data without manual definition of message rules. Particularly, we improved filtering efficiency by imposing weight on some character by feature extraction from spam mails. Finally, we show efficiency comparison among four cases - naive Bayesian, weighting on e-mail header, weighting on HTML tags, weighting on hyperlinks and combining all of four cases. As compared with naive Bayesian classifier, the proposed system obtained 5.7% decreased precision, while the recall and F-measure of this system increased by 33.3% and 31.2%, respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.155-160
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• 2014

There are many image processing based algorithms and applications that fail when illumination change occurs. Therefore, the illumination change has to be detected then the illumination change occurred images need to be enhanced in order to keep the appropriate algorithm processing in a reality. In this paper, a new method for detecting illumination changes efficiently in a real time by using local region information and fuzzy logic is introduced. The effective way for detecting illumination changes in lighting area and the edge of the area was selected to analyze the mean and variance of the histogram of each area and to reflect the changing trends on previous frame's mean and variance for each area of the histogram. The ways are used as an input. The changes of mean and variance make different patterns w hen illumination change occurs. Fuzzy rules were defined based on the patterns of the input for detecting illumination changes. Proposed method was tested with different dataset through the evaluation metrics; in particular, the specificity, recall and precision showed high rates. An automatic parameter selection method was proposed for contrast limited adaptive histogram equalization method by using entropy of image through adaptive neural fuzzy inference system. The results showed that the contrast of images could be enhanced. The proposed algorithm is robust to detect global illumination change, and it is also computationally efficient in real applications.

• Korean Journal of Community Nutrition
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• v.5 no.3
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• pp.419-431
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• 2000

The purpose of this study was to investigate nutrient intake and food behavior according to the nutrition knowledge of middle school students residing in Seoul and Kyunggi-do, Korea. Anthropometric measurements and questionnaires, including 24-hr recall of dietary intake, were collected from 543 male and female middle school students. They were assigned to one of five groups according to their nutrition knowledge : very high group (VHG ; 90 - 100 score), high group (HG ; 80 - 90 score), normal group (NG ; 70 - 80 score), low group (LG ; 60 - 70 score) and very low group (VLG ; < 60 score), and comparisons were made. The mean age of the subjects was 13.7 years old. The mean height, weight, and BMI of male and female students were 161.9 cm, 52.6 kg and 20.2 kg/$m^2$, 157.0 cm, 50.4 kg, and 20.4 kg/$m^2$ respectively. Female students skip breakfast and dinner more frequently than male students do. Male students skipp lunch and eat fast foods more frequently than females do. Protein, calcium, vitamin B$_1$, vitamin B$_2$, niacin, vitamin C, fat, and animal protein intakes in females are shown to be related to their nutrition knowledge. Calcium, protein, animal protein, vitamin B$_2$and niacin intakes are significantly lower in the VLG than in the others. However, vitamin B$_1$and vitamin C intakes are significantly lower both in VHG and VLG. Fat intake in VHG is lowest. Nutrition knowledge of male students is correlated with mothers knowledge, nutrition attitude and nutritional status. On the other hand, in female students, nutrient consumption was lowest in subjects whose nutrition knowledge was highest and lowest. Therefore, nutrient consumption is affected by nutrition knowledge. However, in female students, possibly due to wrong information on diet or prejudice and outlook, nutrient consumption was low even when they scored high in nutrition knowledge. In conclusion, nutrition knowledge of male students is affected by the mothers nutritional knowledge and attitude. Therefore, nutrition education for mothers is very important. In male students, as their nutrition knowledge is low, their nutrient consumption is affect. These results indicate nutrition education and correct information for body image, balanced diet, regularity of meals and food selection for middle school students are required at both school and home.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.353-361
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• 2020

Due to the rapid urbanization, various traffic problems such as traffic jams during commute and regular traffic jams are occurring. In order to solve these traffic problems, it is necessary to quickly and accurately estimate and analyze traffic volume. ITS (Intelligent Transportation System) is a system that performs optimal traffic management by utilizing the latest ICT (Information and Communications Technology) technologies, and research has been conducted to analyze fast and accurate traffic volume through various techniques. In this study, we proposed a deep learning-based vehicle detection method using UAV (Unmanned Aerial Vehicle) video for real-time traffic analysis with high accuracy. The UAV was used to photograph orthogonal videos necessary for training and verification at intersections where various vehicles pass and trained vehicles by classifying them into sedan, truck, and bus. The experiment on UAV dataset was carried out using YOLOv3 (You Only Look Once V3), a deep learning-based object detection technique, and the experiments achieved the overall object detection rate of 90.21%, precision of 95.10% and the recall of 85.79%.

• Journal of The Korean Association For Science Education
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• v.35 no.2
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• pp.267-275
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• 2015

This research purports to find out the characteristics of Korean students cognitive attributes and compare it with that of high-achieving countries who took TIMSS 2011 based on the Cognitive Diagnostic Theory. Based on TIMSS 2011 Science framework, nine cognitive attributes were extracted and the researcher analyzed that 216 of the TIMSS 2011 science items require these attributes. This analysis was conducted to come up with a Q-matrix. After producing the Q-matrix, multi-level IRT was used to figure out each countries' characteristics for each of the cognitive attribute. According to the study results, four attributes, such as 'Use Models,' 'Interpret Information,' 'Draw Conclusions,' and 'Evaluate and justify' were easier attributes for Korean middle school students. However, the other five attributes such as 'Recall/Recognize', 'Explain', 'Classify', 'Integrate', 'Hypothesize and Design' were considered as harder attributes compared to other countries. Korean students also considered 'Interpret Information' as the easiest attributes, and 'Explain' as the hardest attributes of all. For Korean students, those attributes considered to be easy were the easiest and hard attributes as the hardest compared to other countries, showing very extreme cases. Therefore, to give students more meaningful learning experience, it is better to use all the attributes altogether rather than use specific attributes while constructing Science curriculum or textbooks.

• Korean Journal of Community Nutrition
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• v.13 no.2
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• pp.207-215
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• 2008

This study was conducted to measure and evaluate the food and nutrient intakes of the people living near the nuclear plant and to investigate the relationship between the household income level and the food and nutrient intake patterns. A total of 552 cases (263 males and 289 females) were surveyed during the period from April 1 to December 21 of 2005. Dietary intake was measured by means of the 24-hour recall method. The data were analyzed using SPSS Windows (ver. 14.0). The household income level of the subjects was classified into two groups : Low income group (LIG; $\leq$2,000,000 won) and high income group (HIG; > 2,000,000). The subjects at large had less energy and nutrient intakes than did the population in town and village who participated in the 2005 National Health and Nutrition Survey. The intake of calcium, zinc, vitamin A, riboflavin, vitamin $B_6$, vitamin C, and folic acid was less than the Estimated Average Requirement in case of $50{\sim}95%$ of the subjects. The LIG consumed less beans, vegetables, fruits, meats, and beverages than did the HIG in male, while the LIG consumed less eggs and beverages than did the HIG in female. The LIG consumed less nutrients than did the HIG in male, except for carbohydrate, while the LIG consumed less nutrients including zinc, vitamin A, riboflavin, vitamin B6, vitamin C, folic acid than did the HIG in female. In addition, the LIG had higher percentage energy consumption from carbohydrate. These results suggest that higher food and nutrient intake is associated with higher income.

• Journal of KIISE:Databases
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• v.34 no.3
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• pp.193-205
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• 2007

Increasing amount of digital images requires more accurate and faster way of image retrieval. So far, image retrieval method includes content-based retrieval and keyword based retrieval, the former utilizing visual features such as color and brightness and the latter utilizing keywords which describe the image. However, the effectiveness of these methods as to providing the exact images the user wanted has been under question. Hence, many researchers have been working on relevance feedback, a process in which responses from the user are given as a feedback during the retrieval session in order to define user’s need and provide improved result. Yet, the methods which have employed relevance feedback also have drawbacks since several feedbacks are necessary to have appropriate result and the feedback information can not be reused. In this paper, a novel retrieval model has been proposed which annotates an image with a keyword and modifies the confidence level of the keyword in response to the user’s feedback. In the proposed model, not only the images which have received positive feedback but also the other images with the visual features similar to the features used to distinguish the positive image are subjected to confidence modification. This enables modifying large amount of images with only a few feedbacks ultimately leading to faster and more accurate retrieval result. An experiment has been performed to verify the effectiveness of the proposed model and the result has demonstrated rapid increase in recall and precision while receiving the same number of feedbacks.