• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.541-550
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• 2009

This research attempted to find out any implications for strategies to design and develop the connections between the activities of the brain function and the fields of English learning (dictation, word level, speaking, word memory, listening). Thus, in developing the brain based learning model for the English education, attempts need to be made to help learners to keep the whole brain toward learning. On this point, this study indicated the significant results for the exclusive brain location and the brainwaves on the each English learning field by the quantitative EEG analysis. The results of this study presented the guidelines for the balanced development of the left brain and the right brain to train the specific site of the brain connected to the English learning fields. In addition, whole brain training model is developed by the quantitative EEG data not by the theoretical learning methods focused on the right brain training.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.06a
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• pp.270-272
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• 2013

In order to protect coastal facilities mainly from wave and current actions, the self-locking bio blocks constituting component elements of protecting structures against scouring were designed. These blocks are adapted to the sloping bottom, coastal dunes, and submerged coastal base counteracting the destructive and erosive impulse action. A series of laboratory experiments is necessary to investigate the reflection of water waves over and against a train of protruded or submerged shore structures and compare the reflecting capabilities of incident waves including wave forces. In this study the hydraulic model experiment was conducted to identify the performance of newly designed water affinity bio blocks to keep the coast slope and bottom mound from scouring by reduction of the reflection coefficient and to convince stability of the placements. Revised design of each element of blocks were also tested for field conditions. From the result of experiment, the field applicability of the developed blocks and placement is to be discussed afterward.

• Journal of Satellite, Information and Communications
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• v.6 no.2
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• pp.15-19
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• 2011

The T-DMB(Terrestrial Digital Multimedia Broadcasting) which was developed by the convergence of digital broadcasting technology and communication technology provides us with very good quality of music like CD, and provides TV services in a super express train like the KTX whose velocity is over 300 Km per hour. The T-DMB is diffusing toward the world as a technology which is be able to provide the various convergent services of broadcasting and communication through mobile phone, PDA, dedicated terminal, and so on. A business model needed for the diffusion of the T-DMB toward the world was established and utilized to expand the T-DMB into Vietnam in the paper. In addition, this paper describes not only some predicting methods for the technological valuation of the T-DMB Total Solution, but also a case study on the marketing related to establishing the T-DMB system in order to provide the paid services in Vietnam. Finally, A couple of business models needed to globally expand the T-DMB have been provided.

• KIISE Transactions on Computing Practices
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• v.21 no.9
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• pp.616-621
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• 2015

Recently developed software systems have many components, and their complexity is thus increasing. Last year, about 375 bug reports in one day were reported to a software repository in Eclipse and Mozilla open source projects. With so many bug reports submitted, developers' time and efforts have increased unnecessarily. Since the bug severity is manually determined by quality assurance, project manager or other developers in the general bug fixing process, it is biased to them. They might also make a mistake on the manual decision because of the large number of bug reports. Therefore, in this study, we propose an approach of bug severity prediction to solve these problems. First, we find similar topics within a new bug report and reduce the candidate reports of the topic by using the meta field of the bug report. Next, we train the reduced reports by applying Naive Bayes Multinomial. Finally, we predict the severity of the new bug report. We compare our approach with other prediction algorithms by using bug reports in open source projects. The results show that our approach better predicts bug severity than other algorithms.

• Journal of the Korean Society for Railway
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• v.17 no.5
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• pp.342-348
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• 2014

Contact between wheel and rail leads to the creep phenomenon. Linear creep theory, assuming linear increase in the creep force vs creep, results in a critical speed at which the vibration of a railway vehicle goes to infinity. However, the actual creep force converges to a limited value, so that the vibration of a railway vehicle cannot increase indefinitely. In this study, the dynamics of a railway vehicle is investigated with a 6 DOF bogie model includingthe nonlinear creep curves of Vermeulen, Polach, and a newly calculated creep curve with strip theory. Strip theory considers the profiles of the wheel and rail. The results show that the vibration of a railway vehicle results in a limit-cycle over a specific running speed, and this limit-cycle becomes smaller as the slope of the creep-curve steepens. Moreover, a hunting phenomenon is caused due to flange contact, which restricts the magnitude of the limit-cycle.

• Journal of the Korean Society for Railway
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• v.20 no.3
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• pp.349-355
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• 2017

Failures of railway systems can result in train delays or accidents, and therefore high reliability is required to ensure safety of railway systems. To improve reliability, railway systems are designed with redundant systems so that the standby system will continue to function normally even if the primary system fails. Generally, overall system reliability can be evaluated by the reliabilities of the parts of the whole system and the reliability of the redundant system considering common failures in case of each system is not conform physical, functional and process independent. In this study, the reliability of the hot-standby sparing system is analyzed the independent systems and dependent systems with common failures. The reliability for the standby system can be effectively analysed using Markov models, which can model the redundant configuration and the state transition.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.3
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• pp.348-355
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• 2021

Asphalt, widely used for road pavement, has different required physical properties depending on the environment to which the road is exposed. Therefore, it is essential to maximize the life of asphalt roads by evaluating the physical properties of asphalt according to additives and selecting an appropriate formulation considering road traffic and climatic environment. Dynamic shear rheometer(DSR) test is mainly used to measure resistance to rutting among various physical properties of asphalt. However, the DSR test has limitations in that the results are different depending on the experimental setting and can only be measured within a specific temperature range. Therefore, in this study, to overcome the limitations of the DSR test, the rheological characteristics were predicted by learning the images collected from atomic force microscopy. Images and rheology properties were trained through EfficientNet, one of the deep learning architectures, and transfer learning was used to overcome the limitation of the deep learning model, which require many data. The trained model predicted the rheological properties of the asphalt binder with high accuracy even though different types of additives were used. In particular, it was possible to train faster than when transfer learning was not used.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1109-1122
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• 2020

The aim of this study was to develop a marbling classification and prediction model using small parts of sirloin images based on a deep learning algorithm, namely, a convolutional neural network (CNN). Samples were purchased from a commercial slaughterhouse in Korea, images for each grade were acquired, and the total images (n = 500) were assigned according to their grade number: 1++, 1+, 1, and both 2 & 3. The image acquisition system consists of a DSLR camera with a polarization filter to remove diffusive reflectance and two light sources (55 W). To correct the distorted original images, a radial correction algorithm was implemented. Color images of sirloins of Hanwoo (mixed with feeder cattle, steer, and calf) were divided and sub-images with image sizes of 161 × 161 were made to train the marbling prediction model. In this study, the convolutional neural network (CNN) has four convolution layers and yields prediction results in accordance with marbling grades (1++, 1+, 1, and 2&3). Every single layer uses a rectified linear unit (ReLU) function as an activation function and max-pooling is used for extracting the edge between fat and muscle and reducing the variance of the data. Prediction accuracy was measured using an accuracy and kappa coefficient from a confusion matrix. We summed the prediction of sub-images and determined the total average prediction accuracy. Training accuracy was 100% and the test accuracy was 86%, indicating comparably good performance using the CNN. This study provides classification potential for predicting the marbling grade using color images and a convolutional neural network algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.433-441
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• 2021

Android applications are released across various categories, including productivity apps and games, and users are exposed to various applications and even malware depending on their usage patterns. On the other hand, most analysis engines train using existing datasets and do not reflect user patterns even if periodic updates are made. Thus, the detection rate for known malware is high, while types of malware such as adware are difficult to detect. In addition, existing engines incur increased service provider costs due to the cost of server farm, and the user layer suffers from problems where availability and real-timeness are not guaranteed. To address these problems, we propose an analysis system that performs on-device malware detection through transfer learning, which requires only one-time communication with the server. In addition, The system has a complete process on the device, including decompiler, which can distribute the load of the server system. As an evaluation result, it shows 90.3% accuracy without transfer learning, while the model transferred with adware catergories shows 95.1% of accuracy, which is 4.8% higher compare to original model.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.111-118
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• 2022

In computer vision research, the two-dimensional human pose is a very extensive research direction, especially in pose tracking and behavior recognition, which has very important research significance. The acquisition of human pose targets, which is essentially the study of how to accurately identify human targets from pictures, is of great research significance and has been a hot research topic of great interest in recent years. Human pose recognition is used in artificial intelligence on the one hand and in daily life on the other. The excellent effect of pose recognition is mainly determined by the success rate and the accuracy of the recognition process, so it reflects the importance of human pose recognition in terms of recognition rate. In this human body gesture recognition, the human body is divided into 17 key points for labeling. Not only that but also the key points are segmented to ensure the accuracy of the labeling information. In the recognition design, use the comprehensive data set MS COCO for deep learning to design a neural network model to train a large number of samples, from simple step-by-step to efficient training, so that a good accuracy rate can be obtained.