• The Mathematical Education
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• v.51 no.4
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• pp.317-335
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• 2012

The purposes of this study were to investigate the perceptions of teachers and students on the implementation of the intensive course completion system in mathematics courses and to provide suggestions for the improvement of the system. Five high school mathematics teachers and 338 10th graders and 87 11th graders in 2 high schools located in Gyeonggi-do participated in this study. The results of this study indicated that the intensive course completion system is more appropriate to the subjects which require less time allotment or practical exercise than mathematics courses. For better implementation of the intensive course completion system in mathematics courses, first of all, enough time allotment for teaching and learning mathematics should be guaranteed. Otherwise, the system can make students feel more burden of learning due to increase in learning volume of mathematics courses.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.600-616
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• 2021

SIMON and SPECK are two families of lightweight block ciphers that have excellent performance on hardware and software platforms. At CRYPTO 2019, Gohr first introduces the differential cryptanalysis based deep learning on round-reduced SPECK32/64, and finally reduces the remaining security of 11-round SPECK32/64 to roughly 38 bits. In this paper, we are committed to evaluating the safety of SIMON cipher under the neural differential cryptanalysis. We firstly prove theoretically that SIMON is a non-Markov cipher, which means that the results based on conventional differential cryptanalysis may be inaccurate. Then we train a residual neural network to get the 7-, 8-, 9-round neural distinguishers for SIMON32/64. To prove the effectiveness for our distinguishers, we perform the distinguishing attack and key-recovery attack against 15-round SIMON32/64. The results show that the real ciphertexts can be distinguished from random ciphertexts with a probability close to 1 only by 2^8.7 chosen-plaintext pairs. For the key-recovery attack, the correct key was recovered with a success rate of 23%, and the data complexity and computation complexity are as low as 2⁸ and 2^20.1 respectively. All the results are better than the existing literature. Furthermore, we briefly discussed the effect of different residual network structures on the training results of neural distinguishers. It is hoped that our findings will provide some reference for future research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2882-2903
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• 2022

Image super-resolution (SR) processing is of great value in the fields of digital image processing, intelligent security, film and television production and so on. This paper proposed a densely connected deep learning network based on cascade architecture, which can be used to solve the problem of super-resolution in the field of image quality enhancement. We proposed a more efficient residual scaling dense block (RSDB) and the multi-channel cascade architecture to realize more efficient feature reuse. Also we proposed a hybrid loss function based on L₁ error and L_∞ error to achieve better L_∞ error performance. The experimental results show that the overall performance of the network is effectively improved on cascade architecture and residual scaling. Compared with the residual dense net (RDN), the PSNR / SSIM of the new method is improved by 2.24% / 1.44% respectively, and the L_∞ performance is improved by 3.64%. It shows that the cascade connection and residual scaling method can effectively realize feature reuse, improving the residual convergence speed and learning efficiency of our network. The L_∞ performance is improved by 11.09% with only a minimal loses of 1.14% / 0.60% on PSNR / SSIM performance after adopting the new loss function. That is to say, the L_∞ performance can be improved greatly on the new loss function with a minor loss of PSNR / SSIM performance, which is of great value in L_∞ error sensitive tasks.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.35-43
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• 2023

This study aims to evaluate the structural safety of a structural thermal barrier, installed inside the structure of a building and performed the role of a load-bearing element and an insulation simultaneously, contributing to the realization of net-zero buildings. To ensure the reliability of the analysis model, the analysis results derived from LS-DYNA were compared with the experimental results. Based on the results shown through the flexural experiment, the reliability of the thermal cross-section insulation structure model for slabs was validated. In addition, the effect of the UHPC block on the load support performance and its contribution to vertical deflection was verified.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.29-32
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• 2020

Due to the lack of improper image acquisition process, noise induction is an inevitable step. As a result, objective image quality assessment (IQA) plays an important role in estimating the visual quality of noisy image. Plenty of IQA methods have been proposed including traditional signal processing based methods as well as current deep learning based methods where the later one shows promising performance due to their complex representation ability. The deep learning based methods consists of several convolution layers and down sampling layers for feature extraction and fully connected layers for regression. Usually, the down sampling is performed by using max-pooling layer after each convolutional block. We reveal that this max-pooling causes information loss despite of knowing their importance. Consequently, we propose a better IQA method that replaces the max-pooling layers with strided convolutions to down sample the feature space and since the strided convolution layers have learnable parameters, they preserve optimal features and discard redundant information, thereby improve the prediction accuracy. The experimental results verify the effectiveness of the proposed method.

• Geomechanics and Engineering
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• v.34 no.5
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• pp.547-559
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• 2023

We evaluated the applicability of machine learning techniques and the Kuz-Ram model for predicting the mean fragmentation size in open-pit mines. The characteristics of the in-situ rock considered here were uniaxial compressive strength, tensile strength, rock factor, and mean in-situ block size. Seventy field datasets that included these characteristics were collected to predict the mean fragmentation size. Deep neural network, support vector machine, and extreme gradient boosting (XGBoost) models were trained using the data. The performance was evaluated using the root mean squared error (RMSE) and the coefficient of determination (r²). The XGBoost model had the smallest RMSE and the highest r² value compared with the other models. Additionally, when analyzing the error rate between the measured and predicted values, XGBoost had the lowest error rate. When the Kuz-Ram model was applied, low accuracy was observed owing to the differences in the characteristics of data used for model development. Consequently, the proposed XGBoost model predicted the mean fragmentation size more accurately than other models. If its performance is improved by securing sufficient data in the future, it will be useful for improving the blasting efficiency at the target site.

• The Journal of the Korea Contents Association
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• v.18 no.1
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• pp.164-172
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• 2018

This study explores how to implement a learning experience-based professionalism program for a medical students and evaluates its program through effectiveness and usability test. This study aims to provide practical implications for experience-based learning in an undergraduate level. Seventy four first-year medical students enrolled in PDS1(Patient-Doctor-Society 1): professionalism, one-week block (30 hours), one-credit program based on a experience-based learning model. All of the students were given six learning themes and learning resources and supporting tools, and conducted stepwise learning activities; preparation, organization, sharing, reflection and evaluation of experiences. The effectiveness of learning was evaluated by comparing the pre and post results of student's self-assessment on 24 questionnaire items about professionalism. After the course, the students and instructors conducted a usability evaluation of the program through questionnaires or group interviews. Learners' self-assessment results of professionalism such as leadership, self-directed learning, professional attitude, and social accountability all showed significant differences between the pre- and post-test. Satisfaction of the program was distributed to 3.58~3.78 according to items. Instructors and learner interviews confirmed practical usability throughout the course design, implementation and students evaluation. The results of the study showed the feasibility of implementing learning experience-based professionalism program in medical school. This study provides practical implications to develope and evaluate the learning experience-based professionalism program in medical education.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.115-125
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• 2018

This paper presents a fast partition decision framework for High Efficiency Video Coding (HEVC) Screen Content Coding (SCC) based on machine learning. Currently, the HEVC performs quad-tree block partitioning process to achieve optimal coding efficiency. Since this process requires a high computational complexity of the encoding device, the fast encoding process has been studied as determining the block structure early. However, in the case of the screen content video coding, it is difficult to apply the conventional early partition decision method because it shows different partition characteristics from natural content. The proposed method solves the problem by classifying the screen content blocks after partition decision, and it shows an increase of 3.11% BD-BR and 42% time reduction compared to the SCC common test condition.

• School Mathematics
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• v.8 no.1
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• pp.45-68
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• 2006

Singaporean students have demonstrated their superior mathematical achievement and positive mathematical dispositions. Against this background, this study compared Korean elementary mathematics textbooks with Singaporean counterparts focusing on the geometry and measurement strand. The analysis was conducted in many aspects, including an overall unit structure, the contents to be covered in each grade, and the methods of introducing essential learning themes. The textbooks were also compared and contrasted with regard to the main characteristics of constructing mathematical contents. Whereas Korean textbooks used block teaming, Singaporean textbooks used repeated teaming. The latter also employed the activity of classifying multiple figures as the main method of introducing concepts. Whereas Korean textbooks consist of typical examples of figures, Singaporean counterparts include various examples consistent with the principle of mathematical variability.

• Journal of the Korea Convergence Society
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• v.6 no.3
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• pp.103-109
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• 2015

TOLED(Transparent, Organic Light Emitting Diodes) based edutainment system has been studied to solve the actual feeling training and educational experience problem of e-learning. However, edutainment system using TOLED has a problem for the non-detection of multi marker array and rotate marker array, and it has problem for the dissonance phenomena caused by Illumination Environment between real world and virtual object. It also has a do not provide services through a variety of devices problem. Therefore, in this paper, we designed a system that provides a realistic actual feeling edutainment contents by recognizes the marker array rotation and a plurality of marker arrangement via an improved marker detection technique. And to unify the real space and virtual space of the lighting environment through a nested block layer.