• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.3
• /
• pp.661-668
• /
• 2018

Biomedical signals using skin resistance have different characteristics according to stress diseases. Biological diagnostic devices for diagnosing stress diseases have been developed by using these characteristics, and devices have been developed so that the signals measured by the skin storage meter can be easily analyzed. Experts in the field will look directly at the output signal to determine the likelihood of any stress disorder. However, it is very difficult for a person to accurately determine whether a person to be measured has a stress disorder by analyzing a bio-signal measured by each person to be measured, and the result of the judgment is very likely to be wrong. In order to solve these problems, we implemented the function of determining the signal of a stress disorder by using the machine learning technique. SVM was used as a classification method in consideration of low computing ability of measurement equipment. Training data and test data were randomly generated for each disease using error range 5 based on 13 diseases. Simulation results showed more than 90% decision accuracy. In the future, if the measurement equipment is actually applied to the patients, we can retrain the classifier with the newly generated data.

• Convergence Security Journal
• /
• v.13 no.5
• /
• pp.187-196
• /
• 2013

Recently, school violence has come to the fore as a social phenomenon. "Comprehensive countermeasures for eradication of school violence" as a policy safety are created by Safety Administration bureau and Ministry of Education, Science and Technology under the chairmanship of the Office of Prime Minister on Feb,2012. This policy is supposed to be test-operated for a year from March, 2012. but voices of concern about effectiveness have been brought up by some critics greatly. So 172 teachers in high school in Seoul were surveyed in order to examine the effectiveness of "Comprehensive countermeasures for eradication of school violence" with a questionnaire composed of 5 point Likert-type. Among the fundamental measures, there were a total of 12 countermeasures about 'Practices for personality education' (with the exception of unrelated one question). 'Expanding opportunities of various art education and Supporting reading activities' of them ranked highest on average. Then, 'Reflecting results of special feature related to character develops to the Selection of Admission officers and Self-directed learning was the next. And among the three countermeasures about 'Reinforcement of roles of the family and society', 'Pan governmental conducting annual campaign related to broadcast, press, civic group to combat school violence was highest. Finally, among the 7 countermeasures about 'Countermeasure about harmful factors of games and internet addiction', 'Reinforcement of preventive discipline about game and internet addiction' was highest and 'Development and Promotion of various educational contents for preventive discipline about game and internet addiction' was the next.

• Korean Journal of Construction Engineering and Management
• /
• v.17 no.3
• /
• pp.32-42
• /
• 2016

In Korea, the absence of BIM use in existing civil structures and buildings is driving a demand for as-built BIM. As-built BIMs are often created using laser scanners that provide dense 3D point cloud data. Conventional static laser scanning approaches often suffer from limitations in their operability due to the difficulties in moving the equipment, the selection of scanning location, and the requirement of placing targets or extracting tie points for registration of each scanned point cloud. This paper aims at reducing the manual effort using a kinematic 3D laser scanning system based on graph-based simultaneous localization and mapping (SLAM) for continuous indoor mapping. The robotic platform carries three 2D laser scanners: the front scanner is mounted horizontally to compute the robot's trajectory and to build the SLAM graph; the other two scanners are mounted vertically to scan the profiles of surrounding environments. To reduce the accumulated error in the trajectory of the platform through loop closures, the graph-based SLAM system incorporates AdaBoost loop closure approach, which is particularly suitable for the developed multi-scanner system providing more features than the single-scanner system for training. We implemented the proposed method and evaluated it in two indoor test sites. Our experimental results show that the false positive rate was reduced by 13.6% and 7.9% for the two dataset. Finally, the 2D and 3D mapping results of the two test sites confirmed the effectiveness of the proposed graph-based SLAM.

• Journal of Science Education
• /
• v.40 no.3
• /
• pp.238-253
• /
• 2016

The purpose of this study was to explore the effect of flipped learning applied printed reading material (PRM) and getting implications for the application of flipped learning applied PRM. For the purpose of this study, we made the flipped learning programs applied printed reading material and applied them to the 6th grade science class in the elementary school. Selection of the applied test is considered the special feature of flipped learning and existing researches about the flipped learning. The PRM contains the Science words, conceptions, explanation about contents. And it was added to checking system by quiz. The results were as follows: The science class applied PRM was effective on increase in Korean elementary school students' the curiosity (in Intrinsic Goal Orientation) and attention (in Task Value) about science class content. There were no significant difference in the results of academic achievement. But the improvement of students' academic achievement in class applied PRM were bigger than that of those in class applied existing methods that follow the process of the teacher's guidebook in the same way. Students had the highest satisfaction about communication and environmental education in the class applied PRM.

• Journal of Soil and Groundwater Environment
• /
• v.14 no.1
• /
• pp.1-10
• /
• 2009

Groundwater flow due to intake of horizontal collector well in riverbank filtration site was analyzed by use of numerical groundwater modeling program (FEFLOW 5.1). Drawdowns of groundwater table nearby collector well were evaluated according to variations of several conditions; pumping rate, thickness of aquifer, offset distance from well to shore line of stream, conductance of streambed. It is observed that the drawdowns of groundwater table are clearly changed according to the variations of these conditions. The results of sensitive analysis shows that the thickness of alluvial aquifer and the offset distance are more sensitive than the conductance of streambed in evaluation of drawdown. This result implies that hydrogeological conditions, as like thickness of aquifer and its distribution in the site are important factors in site selection and evaluating the availability of riverbank filtration intake using horizontal collector well system. It is also revealed that numerical modeling using FEFLOW with 1-D discrete element feature can give efficient quantitative evaluation of horizontal collector well and estimation of availability of riverbank filtration site.

• Journal of Intelligence and Information Systems
• /
• v.20 no.4
• /
• pp.43-58
• /
• 2014

As the use of trading systems has increased rapidly, many researchers have become interested in developing effective stock market prediction models using artificial intelligence techniques. Stock market prediction involves multifaceted interactions between market-controlling factors and unknown random processes. A successful stock prediction model achieves the most accurate result from minimum input data with the least complex model. In this research, we develop a combination model of ${\pi}$-fuzzy logic and support vector machine (SVM) models, using a genetic algorithm to optimize the parameters of the SVM and ${\pi}$-fuzzy functions, as well as feature subset selection to improve the performance of stock market prediction. To evaluate the performance of our proposed model, we compare the performance of our model to other comparative models, including the logistic regression, multiple discriminant analysis, classification and regression tree, artificial neural network, SVM, and fuzzy SVM models, with the same data. The results show that our model outperforms all other comparative models in prediction accuracy as well as return on investment.

• Journal of the Korean Society for information Management
• /
• v.25 no.1
• /
• pp.211-233
• /
• 2008

This study examines various weighting methods for improving the performance of automatic classification based on Rocchio algorithm on two collections(LISA, Reuters-21578). First, three factors for weighting are identified as document factor, document factor, category factor for each weighting schemes, the performance of each was investigated. Second, the performance of combined weighting methods between the single schemes were examined. As a result, for the single schemes based on each factor, category-factor-based schemes showed the best performance, document set-factor-based schemes the second, and document-factor-based schemes the worst. For the combined weighting schemes, the schemes(idf*cat) which combine document set factor with category factor show better performance than the combined schemes(tf*cat or ltf*cat) which combine document factor with category factor as well as the common schemes (tfidf or ltfidf) that combining document factor with document set factor. However, according to the results of comparing the single weighting schemes with combined weighting schemes in the view of the collections, while category-factor-based schemes(cat only) perform best on LISA, the combined schemes(idf*cat) which combine document set factor with category factor showed best performance on the Reuters-21578. Therefore for the practical application of the weighting methods, it needs careful consideration of the categories in a collection for automatic classification.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.8
• /
• pp.521-529
• /
• 2020

Forest fires are one of the most important environmental risks that have adverse effects on many aspects of life, such as the economy, environment, and health. The early detection, quick prediction, and rapid response of forest fires can play an essential role in saving property and life from forest fire risks. For the rapid discovery of forest fires, there is a method using meteorological data obtained from local sensors installed in each area by the Meteorological Agency. Meteorological conditions (e.g., temperature, wind) influence forest fires. This study evaluated a Data Mining (DM) approach to predict the burned area of forest fires. Five DM models, e.g., Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Decision Tree (DT), Random Forests (RF), and Deep Neural Network (DNN), and four feature selection setups (using spatial, temporal, and weather attributes), were tested on recent real-world data collected from Gyeonggi-do area over the last five years. As a result of the experiment, a DNN model using only meteorological data showed the best performance. The proposed model was more effective in predicting the burned area of small forest fires, which are more frequent. This knowledge derived from the proposed prediction model is particularly useful for improving firefighting resource management.

• Journal of Korea Multimedia Society
• /
• v.13 no.7
• /
• pp.1062-1075
• /
• 2010

In the medical image application the difference of intensity is widely used for the image segmentation and feature extraction, and a well known method is the threshold technique that determines a threshold value and generates a binary image based on the threshold. A frequently-used threshold technique is the Otsu algorithm that provides efficient processing and effective selection criterion for choosing the threshold value. However, we cannot get good segmentation results by applying the Otsu algorithm to chest X-ray images. It is because there are various organic structures around lung regions such as ribs and blood vessels, causing unclear distribution of intensity levels. To overcome the ambiguity, we propose in this paper an effective algorithm to extract pulmonary regions that utilizes the Otsu algorithm after removing the background of an X-ray image, constructs intensity-level maps, and uses them for segmenting the X-ray image. To verify the effectiveness of our method, we compared it with the existing 1-dimensional and 2-dimensional Otsu algorithms, and also the results by expert's naked eyes. The experimental result showed that our method achieved the more accurate extraction of pulmonary regions compared to the Otsu methods and showed the similar result as the naked eye's one.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.6
• /
• pp.1418-1432
• /
• 1997

Most general content_based image retrieval techniques use color and texture as retrieval indices. In color techniques, color histogram and color pair based color retrieval techniques suffer from a lack of spatial information and text. And This paper describes the design and implementation of content_based image retrieval system using color space and keywords. The preprocessor for image retrieval has used the coordinate system of the existing HSI(Hue, Saturation, Intensity) and preformed to split One image into chromatic region and achromatic region respectively, It is necessary to normalize the size of image for 200*N or N*200 and to convert true colors into 256 color. Two color histograms for background and object are used in order to decide on color selection in the color space. Spatial information is obtained using a maximum entropy discretization. It is possible to choose the class, color, shape, location and size of image by using keyword. An input color is limited by 15 kinds keyword of chromatic and achromatic colors of the Korea Industrial Standards. Image retrieval method is used as the key of retrieval properties in the similarity. The weight values of color space ${\alpha}(%)and\;keyword\;{\beta}(%)$ can be chosen by the user in inputting the query words, controlling the values according to the properties of image_contents. The result of retrieval in the test using extracted feature such as color space and keyword to the query image are lower that those of weight value. In the case of weight value, the average of te measuring parameters shows approximate Precision(0.858), Recall(0.936), RT(1), MT(0). The above results have proved higher retrieval effects than the content_based image retrieval by using color space of keywords.