• The Mathematical Education
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• v.57 no.4
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• pp.353-369
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• 2018

This study started with the following questions. Suppose that students do not accept various forms of geometric series tasks as the same task. Also, let's say that the approach was different for each task. Then, when they realize that they are the same task, how will students connect the different approaches? This study is a process of pro-actively confirming whether or not such a question can be made. For this purpose, three students in the second grade of high school participated in the teaching experiment. The results of this study are as follows. It also confirmed how the students think about the various types of tasks in the geometric series. For example, students have stated that the value is 1 in a series type of task. However, in the case of the 0.999... type of task, the value is expressed as less than 1. At this time, we examined only mathematical expressions of students approaching each task. The problem of reachability was not encountered because the task represented by the series symbol approaches the problem solved by procedural calculation. However, in the 0.999... type of task, a variety of expressions were observed that revealed problems with reachability. The analysis of students' expressions related to geometric series can provide important information for infinite concepts and limit conceptual research. The problems of this study may be discussed through related studies. Perhaps more advanced research may be based on the results of this study. Through these discussions, I expect that the contents of infinity in the school field will not be forced unilaterally because there is no mathematical error, but it will be an opportunity for students to think about the learning method in a natural way.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1207-1216
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• 2018

Discharge or water level predictions at tidally affected river reaches are currently still a great challenge in hydrological practices. This research aims to predict water level of the tide dominated site, Jamsu bridge in the Han River downstream. Physics-based hydrodynamic approaches are sometimes not applicable for water level prediction in such a tidal river due to uncertainty sources like rainfall forecasting data. In this study, TensorFlow deep learning framework was used to build a deep neural network based LSTM model and its applications. The LSTM model was trained based on 3 data sets having 10-min temporal resolution: Paldang dam release, Jamsu bridge water level, predicted tidal level for 6 years (2011~2016) and then predict the water level time series given the six lead times: 1, 3, 6, 9, 12, 24 hours. The optimal hyper-parameters of LSTM model were set up as follows: 6 hidden layers number, 0.01 learning rate, 3000 iterations. In addition, we changed the key parameter of LSTM model, sequence length, ranging from 1 to 6 hours to test its affect to prediction results. The LSTM model with the 1 hr sequence length led to the best performing prediction results for the all cases. In particular, it resulted in very accurate prediction: RMSE (0.065 cm) and NSE (0.99) for the 1 hr lead time prediction case. However, as the lead time became longer, the RMSE increased from 0.08 m (1 hr lead time) to 0.28 m (24 hrs lead time) and the NSE decreased from 0.99 (1 hr lead time) to 0.74 (24 hrs lead time), respectively.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.188-198
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• 2018

With recent development of high-speed wide-area wireless networks and wide spread of highperformance wireless devices, the demand on seamless video streaming services in Long Term Evolution (LTE) network environments is ever increasing. To meet the demand and provide enhanced Quality of Experience (QoE) with mobile users, the Dynamic Adaptive Streaming over HTTP (DASH) has been actively studied to achieve QoE enhanced video streaming service in dynamic network environments. However, the existing DASH algorithm to select the quality of requesting video segments is based on a procedural algorithm so that it reveals a limitation to adapt its performance to dynamic network situations. To overcome this limitation this paper proposes a novel quality selection mechanism based on a Deep Q-Network (DQN) model, the DQN-based DASH ABR($DQN_{ABR}$) mechanism. The $DQN_{ABR}$ mechanism replaces the existing DASH ABR algorithm with an intelligent deep learning model which optimizes service quality to mobile users through reinforcement learning. Compared to the existing approaches, the experimental analysis shows that the proposed solution outperforms in terms of adapting to dynamic wireless network situations and improving QoE experience of end users.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.541-552
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• 2018

The purpose of this study was to investigate the effects of 'problem posing from mathematical problems' on the students' affective domain of mathematics, and to conduct evaluation and management of teachers' respectively. The quantitative and qualitative approaches were combined to analyze the changes in the affective achievement of all the students and individual students in the study. The conclusions of this study are as follows: First, problem-posing class improved the problem-solving ability and meaningful experience in the learning activity itself, thus improving students' self-confidence, interest, value, and desire to learn. Second, The students' affective domain of mathematics should be emphasized, and systematic evaluation and management should be carried out from the first grade of middle school to high school senior in mathematics. Third, it is necessary to present and disseminate them in detail on the national-level to evaluation system and method of affective domain of mathematics. Therefore, the teacher should actively implement the problem-posing teaching and learning in the classroom lesson and help students' affective achievement. and teachers need to measure and manage the affective achievement of all students on a regular basis.

• Hwankyungkyoyuk
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• v.24 no.4
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• pp.111-128
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• 2011

This study was aimed at developing a common curriculum for the department of environmental education from 5 colleges of education. The need and background of curriculum reform can be summarized as follow; first, it has been recognized that new national curriculum of 2009 and 2011 created need for training teachers equipped with more integrated competency. Second, global environmental problems such as climate change and energy crisis asked for more responsible choice and action from all citizens. Third, the extremely low hiring rate resulted in the consideration of new working fields for teacher students majoring in environmental education. Fourth, the expansion of new environmental education paradigms including education for sustainable development called for practicing reconstruction of both contends and methods. From a series of research processes including analysis of current curriculum, DACUM, opinion survey and interest groups review, several new approaches for developing new curriculum had been identified as follow; first, content areas of environmental education should be extended beyond environmental natural science. Second, new learning approaches such as project-based learning need to be emphasized for strengthening the identity of environment as a separate subject. Third, more selective majoring system need to be applied in connection with environment government officials, researchers, and social environmental educators. It was recommended that the application of new curriculum developed by the study would be evaluated and managed by teaching conditions surrounding each of the five university members joined this developing processes. However, it needs to be noted that there is not much time because we had experienced zero hiring rate for the last 4 years and environmental policy and education programs are moving rapidly toward sustainable development.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.8
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• pp.5565-5570
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• 2015

Implicit citation sentence recognition is to locate citation sentences which lacks explicit citation markers, from articles' full-text. State-of-the-art approaches exploit word ngrams, clue words, researcher's surnames, mentions of previous methods, and distance relative to nearest explicit citation sentences, etc., reaching over 50% performance. However, most previous works have been conducted on English. As for Korean, a rule-based method using positive/negative clue patterns was reported to attain the performance of 42%, requiring further improvement. This study attempted to learn to recognize implicit citation sentences from Korean literatures' full-text using Korean lexical features. Different lexical feature units such as Eojeol, morpheme, and Eumjeol were evaluated to determine proper lexical features for Korean implicit citation sentence recognition. In addition, lexical features were combined with the position features representing backward/forward proximities to explicit citation sentences, improving the performance up to over 50%.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.111-126
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• 2022

Land use and land cover (LULC) mapping is an important factor in geospatial analysis. Although highly precise ground-based LULC monitoring is possible, it is time consuming and costly. Conversely, because the synthetic aperture radar (SAR) sensor is an all-weather sensor with high resolution, it could replace field-based LULC monitoring systems with low cost and less time requirement. Thus, LULC is one of the major areas in SAR applications. We developed a LULC model using only KOMPSAT-5 single co-polarized data and digital elevation model (DEM) data. Twelve HH-polarized images and 18 VV-polarized images were collected, and two HH-polarized images and four VV-polarized images were selected for the model testing. To train the LULC model, we applied the conditional generative adversarial network (cGAN) method. We used U-Net combined with the residual unit (ResUNet) model to generate the cGAN method. When analyzing the training history at 1732 epochs, the ResUNet model showed a maximum overall accuracy (OA) of 93.89 and a Kappa coefficient of 0.91. The model exhibited high performance in the test datasets with an OA greater than 90. The model accurately distinguished water body areas and showed lower accuracy in wetlands than in the other LULC types. The effect of the DEM on the accuracy of LULC was analyzed. When assessing the accuracy with respect to the incidence angle, owing to the radar shadow caused by the side-looking system of the SAR sensor, the OA tended to decrease as the incidence angle increased. This study is the first to use only KOMPSAT-5 single co-polarized data and deep learning methods to demonstrate the possibility of high-performance LULC monitoring. This study contributes to Earth surface monitoring and the development of deep learning approaches using the KOMPSAT-5 data.

• Journal of Internet Computing and Services
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• v.2 no.2
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• pp.71-93
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• 2001

Commonly to design a database system. a conceptual database has to be designed and then it is transformed into a logical database schema prior to building a target database system. This paper proposes a method which transforms a Temporal Entity-Relationship Model(TERM) into a Temporal Object-Oriented Model(TOOM) to build an efficient database schema. I formalize the time concept in view of object versioning and specify the constraints required during transformation procedure. The proposed transformation method contributes to getting the logical temporal data from the conceptual temporal events Without any loss of semantics, Compared to other approaches of supporting various properties, this approach is more general and efficient because it is the semantically seamless transformation method by using the orthogonality of types of objects, semantics of relationships and constraints over roles.

• Journal of Technology Innovation
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• v.28 no.4
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• pp.151-163
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• 2020

The routinized tasks in the post-COVID era are to be replaced by digital technologies, while there is a high possibility that digital transformation technologies and non-routinized tasks have strong complementarity. In particular, looking at the job composition within Korea's industries, the intensities of routinized works appear to be continuously rising. It suggests that the potential side effects on the labor market caused by the acceleration of digital transformation in the post-COVID era will be greater within Korean innovation system. With this background, this study aims to provide a conceptual framework for dealing with potential crises such as, job polarization and widening gaps between workers in terms of economic earnings, based on an in-depth understanding of the inherent properties of digital transformation that will lead to structural changes in our economic and social system. In particular, focusing on the interaction between digital transformation technology and learning in the post-COVID era, this study attempts to redefine the role of the innovation policy for making a successful transition to a new equilibrium state. In addition, this study examines the institutional conditions of the Korean innovation system which affect the creative learning activities of economic actors to draw policy implications for establishing future-oriented innovation policy. Based on these approaches, this study highlights the importance of coevolution between the skills demand and skills supply to spur inclusiveness of Korean innovation system in the post-COVID era.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.93-106
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• 2021

Many recent studies on deep learning-based road crack detection have shown significantly more improved performances than previous works using algorithm-based conventional approaches. However, many deep learning-based studies are still focused on classifying the types of cracks. The classification of crack types is highly anticipated in that it can improve the crack detection process, which is currently relying on manual intervention. However, it is essential to calculate the severity of the cracks as well as identifying the type of cracks in actual pavement maintenance planning, but studies related to road crack detection have not progressed enough to automated calculation of the severity of cracks. In order to calculate the severity of the crack, the type of crack and the area of the crack in the image must be identified together. This study deals with a method of using Mobilenet-SSD that is deep learning-based object detection techniques to effectively automate the simultaneous detection of crack types and crack areas. To improve the accuracy of object-detection for road cracks, several experiments were conducted to combine the U-Net for automatic segmentation of input image and object-detection model, and the results were summarized. As a result, image masking with U-Net is able to maximize object-detection performance with 0.9315 mAP value. While referring the results of this study, it is expected that the automation of the crack detection functionality on pave management system can be further enhanced.