• Journal of The Korean Association of Information Education
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• v.16 no.4
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• pp.451-462
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• 2012

In this study, our primary areas of mathematical shapes as a way to solve the problem of sixth grade math and geometry around the area in addition to the real world, the virtual objects to explore on their own learning, heuristic principles and learning concepts are developed. To this end, second-class sixth grade in Seoul class M is selected and the area of Augmented Reality class shapes students' academic achievement sure to affect how much agreed. experimental study was developed and then applied to the actual class content across pre and post implementation evaluation, and subsequent academic achievement levels were compared and analyzed. As a result, learners in the experimental group and control group than the class of interested students and class satisfaction, a statistically higher achievement. Learning on augmented reality, which shapes have the gumption to participate in classes, and concepts related to shape the formation and indicates that academic achievement is related.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.239-245
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• 2020

Recently, BIM (Building Information Modeling) are widely being utilized in Construction industry. However, most structures that have been constructed in the past do not have BIM. For structures without BIM, the use of SfM (Structure from Motion) techniques in the 2D image obtained from the camera allows the generation of 3D model point cloud data and BIM to be established. However, since these generated point cloud data do not contain semantic information, it is necessary to manually classify what elements of the structure. Therefore, in this study, deep learning was applied to automate the process of classifying structural components. In the establishment of deep learning network, Inception-ResNet-v2 of CNN (Convolutional Neural Network) structure was used, and the components of bridge structure were learned through transfer learning. As a result of classifying components using the data collected to verify the developed system, the components of the bridge were classified with an accuracy of 96.13 %.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.297-303
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• 2023

Pediatric Computed Tomography (CT) examinations can often result in exam failures or the need for frequent retests due to the difficulty of cooperation from young patients. Deep Learning Image Reconstruction (DLIR) methods offer the potential to obtain diagnostically valuable images while reducing the retest rate in CT examinations of pediatric patients with high radiation sensitivity. In this study, we investigated the possibility of applying DLIR to reduce artifacts caused by respiration or motion and obtain clinically useful images in pediatric chest CT examinations. Retrospective analysis was conducted on chest CT examination data of 43 children under the age of 7 from P Hospital in Gyeongsangnam-do. The images reconstructed using Filtered Back Projection (FBP), Adaptive Statistical Iterative Reconstruction (ASIR-50), and the deep learning algorithm TrueFidelity-Middle (TF-M) were compared. Regions of interest (ROI) were drawn on the right ascending aorta (AA) and back muscle (BM) in contrast-enhanced chest images, and noise (standard deviation, SD) was measured using Hounsfield units (HU) in each image. Statistical analysis was performed using SPSS (ver. 22.0), analyzing the mean values of the three measurements with one-way analysis of variance (ANOVA). The results showed that the SD values for AA were FBP=25.65±3.75, ASIR-50=19.08±3.93, and TF-M=17.05±4.45 (F=66.72, p=0.00), while the SD values for BM were FBP=26.64±3.81, ASIR-50=19.19±3.37, and TF-M=19.87±4.25 (F=49.54, p=0.00). Post-hoc tests revealed significant differences among the three groups. DLIR using TF-M demonstrated significantly lower noise values compared to conventional reconstruction methods. Therefore, the application of the deep learning algorithm TrueFidelity-Middle (TF-M) is expected to be clinically valuable in pediatric chest CT examinations by reducing the degradation of image quality caused by respiration or motion.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.1-9
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• 2022

Localization systems can be used with various circumstances like measuring population movement and rescue technology, even in security technology (like infiltration detection system). Vision sensors such as camera often used for localization is susceptible with light and temperature, and can cause invasion of privacy. In this paper, we used ultra-wideband radar technology (which is not limited by aforementioned problems) and machine learning techniques to measure the number and location of occupants in other indoor spaces behind the wall. We used four different algorithms and compared their results, including extremely randomized tree for four different situations; detect the number of occupants in a classroom, split the classroom into 28 locations and check the position of occupant, select one out of the 28 locations, divide it into 16 fine-grained locations, and check the position of occupant, and checking the positions of two occupants (existing in different locations). Overall, four algorithms showed good results and we verified that detecting the number and location of occupants are possible with high accuracy using machine learning. Also we have considered the possibility of service expansion using the oneM2M standard platform and expect to develop more service and products if this technology is used in various fields.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.103-108
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• 2020

This paper related with the performance evaluation of mDSE-MMA adaptive equalization algorithm which is possible to reduce the distortion that occurs in nonlinear communication channel like as additive noise, intersymbol interference and fading when transmitting the QAM signal. The DSE-MMA algorithm is possible to reduce the computational load compared to the presently MMA algorithm, it has the degraded equalization performance by this. In order to improve the performance degradation of DSE-MMA, the mDSE-MMA controls the step size according to the existence of arbitrary radius circle of equalizer output is centered at transmitted symbol point. The performance of proposed mDSE-MMA algorithm were compared to present DSE-MMA using the same channel and noise environment by computer simulation. For this, the recoverd signal constellation which is the output of equalizer, residual isi and MD, MSE learning curve which is represents the convergence performance and SER were applied as performance index. As a result of simulation, the mDSE-MMA has more superior to the DSE-MMA in every performance index.

• Journal of radiological science and technology
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• v.45 no.2
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• pp.159-164
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• 2022

The study developed a radiation dose measurement program in the radiology laboratory to measure how much exposure the students are exposed to during the radiology class, to request for the improvement and the revision of the current Nuclear Safety Act. The experimental program is shown in the following figure, and experiments were conducted to determine the degree of radiation exposure in the control room with a lead gown at a distance of 1 m, 2 m, and 1 m, and in a control room with a radiographic lead glass wall. The duration of the experiment was 3 months from April to June, when radiation imaging practice classes were conducted, and 128 hours of imaging practice per month were conducted. In order to find out the dose of radiation dose during radiology imaging practice class, the experiment was carried out from April to June for 3 months, and according to the program, the results of exposure dose were 0.34 mSv at 1 m distance, 0.01 mSv at shielding of lead gown at 1 m distance, 0.16 mSv at 2 m distance, and 0.01 mSv at control room with radiation lead glass wall. The exposure dose from the test results was much below the annual general public limit dose of 1 mSv. The restriction on the operation of the radiation equipment in the practice of the students is a regulation that infringes the right of students to learn, and amendments or exemptions of Nuclear Safety Act should be enacted to ensure that it does not violate the fundamental right to learn for students in radiology.

• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.117-126
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• 2020

This study was conducted to explore the effect of the type of dashboard on online group discussion activities based on learning analysis. The experimental research was conducted among 51 learners from a university by dividing them into 2 groups. Group A provided participation and interaction dashboard, and group B provided the discussion topics and message type dashboard. First, pre-tests were conducted on attitudes toward computer writing and the level of motivation that could affect online discussion activities. Then the students participated three different topics of online group discussions. The participation and interaction data were automatically collected through the dashboard, and learning outcome data were collected through post-tests. The results showed level of participation in Group B (M=47.56, SD=2.37) that provided discussion topics and message type dashboard was significantly higher than the level of participation in Group A (M=38.13, SD=2.21) that provided participation and interaction dashboard. On the other hand, there were no differences in the level of interaction and learning outcomes. In future studies, we suggest that the dashboard effects based on the learners' characteristics should be carried out because the learners' characteristics may affect the use of the dashboard.

• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.641-653
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• 2023

As climate change and urbanization are causing unprecedented natural disasters in urban areas, it is crucial to have urban flood predictions with high fidelity and accuracy. However, conventional physically- and deep learning-based urban flood modeling methods have limitations that require a lot of computer resources or data for high-resolution flooding analysis. In this study, we propose and implement a method for improving the spatial resolution of urban flood analysis using a deep learning based super-resolution technique. The proposed approach converts low-resolution flood maps by physically based modeling into the high-resolution using a super-resolution deep learning model trained by high-resolution modeling data. When applied to two cases of retrospective flood analysis at part of City of Portland, Oregon, U.S., the results of the 4-m resolution physical simulation were successfully converted into 1-m resolution flood maps through super-resolution. High structural similarity between the super-solution image and the high-resolution original was found. The results show promising image quality loss within an acceptable limit of 22.80 dB (PSNR) and 0.73 (SSIM). The proposed super-resolution method can provide efficient model training with a limited number of flood scenarios, significantly reducing data acquisition efforts and computational costs.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2016.09a
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• pp.82-85
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• 2016

• Electronics and Telecommunications Trends
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• v.35 no.5
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• pp.151-151
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• 2020