• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.189-196
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• 2016

The purpose of this study is to investigate the causal relationships among self-efficacy, perceived usefulness, supervisor and peer support, satisfaction, and transfer intentions in the corporate mobile learning. For this study, the web survey was administered to 302 mobile learning learners of the A domestic corporation in South Korea. Structural equation modeling(SEM) analysis was conducted in order to examine the causal relationships among the variables. The results indicated that first, self-efficacy, perceived usefulness, and supervisor and peer support had positive effects on satisfaction. Second, supervisor and peer support and satisfaction had positive effects on transfer intentions. Third, satisfaction mediated the relationship between self-efficacy and perceived usefulness, while it did partially the relationship between supervisor and peer support and transfer intentions. Based on the result of the research, the study proposes organizational environment with cooperative supervisor and peer support should be made in order to improve the level of learners' transfer intentions. In addition, learning strategies that facilitate learners' self-efficacy and mobile information technology acceptance are needed to develop for enhancing the learners' satisfaction.

• Journal of Elementary Mathematics Education in Korea
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• v.14 no.3
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• pp.937-957
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• 2010

The purpose of this study is to discover the features of symbol sense. This study tries to sum up the meaning and elements of symbol sense and the measures to improve them through documents. Also based on this, it analyzes the learning conditions about symbol sense for 6th grade mathematically able students and suggests the method that activates symbol sense in the math of elementary schools. Considering various studies on symbol sense, symbol sense means the exact knowledge and essential understanding in a comprehensive way. Symbol sense is an intuition about symbols that grasps the meaning of symbols, understands the situation of question, and realizes the usefulness of symbols in resolving a process. Considering all other scholars' opinions, this study sums up 5 elements of the symbol sense. (The recognition of needs to introduce symbol, ability to read the meaning of symbols, choice of suitable symbols according to the context, pattern guess through visualization, recognize the role of symbols in other context) This study draws the following conclusions after applying the symbol questionnaires targeting 6th grade mathematically able students : First, although they are math talents, there are some differences in terms of the symbol sense level. Second, 5 elements of the symbol sense are not completely separated. They are rather closely related in terms of mainly the symbol understanding, thereby several elements are combined.

• Journal of The Korean Association For Science Education
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• v.36 no.3
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• pp.371-387
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• 2016

Learning at the elbow of scientist is a well-known educational approach to improve students' understanding of science and scientific practice. This study, in the perspective of legitimate peripheral participation in a community of practice, explores how students' scientific practice and perception could be shifted through R&E program with the development of participation. Data from participant observation for 18 months and in-depth interviews were analyzed based on constant comparative method to extract common characteristics of students' participation and major shifts in their scientific practices and perceptions. Students' development of participation was categorized into three stages: legitimate, peripheral, and full participation. In the stage of peripheral participation, students perceived themselves as mere students and showed passive engagement. They just followed the directions of researchers and didn't know what they should be doing. But through continuous participation, students showed enhanced engagement like voluntary article reading, role assignments, and establishing norms in a community of practice with the reference of scientists'. In this stage of transitional participation, students also showed a deepened perception on everyday life of scientist and the community of scientist. And finally in the stage of full participation, students showed responsibility and ownership on research and continuous efforts to refine their research. They recognized themselves as beginning scientists. With these findings, this paper highlighted the dynamic processes of students' development of scientific practices and identity through R&E participation. It also suggests implications for research programs for education, especially for students who have already articulated a science-related career but still have only foggy notions about science.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.99-109
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• 2021

In recent machine learning studies, in order to consider the quality and completeness of data, derivation of non-functional requirements for data has been proposed from the viewpoint of requirements engineering. In particular, requirements engineers have defined data requirements in machine learning. In this study, data requirements were derived at the data acquisition (DAQ) stage, where data is collected and stored before data preprocessing. Through this, it is possible to express the requirements of all data required in the existing DAQ system, the presence of tasks (functions) satisfying them, and the relationship between the requirements and functions. In addition, it is possible to elicit requirements and to define the relationship, so that a software design document can be produced, and a systematic approach and direction can be established in terms of software design and maintenance. This research using existing DAQ system cases, scenarios and use cases for requirements engineering approach are created, and data requirements for each case are extracted based on them, and the relationship between requirements, functions, and goals is illustrated through goal modeling. Through the research results, it was possible to extract the non-functional requirements of the system, especially the data requirements, from the DAQ system using requirements engineering.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.225-235
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• 2021

The purpose of this study is to verify the mediated effect of academic failure tolerance in the influence of professor support on academic procrastination of university students. A Google survey was conducted on 211 students at D University in Gyeonggi-do, and descriptive statistics, correlation analysis, and multiple regression were conducted using the SPSS program with collected data. The mediated effect of academic failure tolerance has been verified through three stages of regression analysis (Baron and Kenny) and Sobel Test. The results of the study are as follows; First, professor support has a negative correlation with academic procrastination, and a positive correlation with academic failure tolerance. In addition, there was a negative correlation between academic failure tolerance and academic procrastination. Second, it showed that academic failure tolerance has a full mediated effect in the influence of professor support on academic procrastination. This implies that it is necessary to create an educational environment that can raise the level of professor support and academic failure tolerance of university students in the academic situation. Therefore, this study suggested the need of providing a program of professor support that can improve academic procrastination problems and strengthen the academic failure tolerance of students.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.194-199
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• 2022

Preprocessing for high-quality data is required for high accuracy and usability in various and complex image data-based industries. However, when a contaminated hostile example that combines noise with existing image or video data is introduced, which can pose a great risk to the company, it is necessary to restore the previous damage to ensure the company's reliability, security, and complete results. As a countermeasure for this, restoration was previously performed using Defense-GAN, but there were disadvantages such as long learning time and low quality of the restoration. In order to improve this, this paper proposes a method using adversarial examples created through FGSM according to image segmentation in addition to using the VQ-VAE model. First, the generated examples are classified as a general classifier. Next, the unsegmented data is put into the pre-trained VQ-VAE model, restored, and then classified with a classifier. Finally, the data divided into quadrants is put into the 4-split-VQ-VAE model, the reconstructed fragments are combined, and then put into the classifier. Finally, after comparing the restored results and accuracy, the performance is analyzed according to the order of combining the two models according to whether or not they are split.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.246-246
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• 2021

본 연구는 강우 발생시 유량을 추정하는 것에 목적이 있다. 이를 위해 본 연구는 선행연구의 모형 개발방법론에서 벗어나 딥러닝 알고리즘 중 하나인 합성곱 신경망 (convolution neural network)과 수문학적 이미지 (hydrological image)를 이용하여 강우 발생시 유량을 추정하였다. 합성곱 신경망은 일반적으로 분류 문제 (classification)을 해결하기 위한 목적으로 개발되었기 때문에 불특정 연속변수인 유량을 모의하기에는 적합하지 않다. 이를 위해 본 연구에서는 합성곱 신경망의 완전 연결층 (Fully connected layer)를 개선하여 연속변수를 모의할 수 있도록 개선하였다. 대부분 합성곱 신경망은 RGB (red, green, blue) 사진 (photograph)을 이용하여 해당 사진이 나타내는 것을 예측하는 목적으로 사용하지만, 본 연구의 경우 일반 RGB 사진을 이용하여 유출량을 예측하는 것은 경험적 모형의 전제(독립변수와 종속변수의 관계)를 무너뜨리는 결과를 초래할 수 있다. 이를 위해 본 연구에서는 임의의 유역에 대해 2차원 공간에서 무차원의 수문학적 속성을 갖는 grid의 집합으로 정의되는 수문학적 이미지는 입력자료로 활용했다. 합성곱 신경망의 구조는 Convolution Layer와 Pulling Layer가 5회 반복하는 구조로 설정하고, 이후 Flatten Layer, 2개의 Dense Layer, 1개의 Batch Normalization Layer를 배열하고, 다시 1개의 Dense Layer가 이어지는 구조로 설계하였다. 마지막 Dense Layer의 활성화 함수는 분류모형에 이용되는 softmax 또는 sigmoid 함수를 대신하여 회귀모형에서 자주 사용되는 Linear 함수로 설정하였다. 이와 함께 각 층의 활성화 함수는 정규화 선형함수 (ReLu)를 이용하였으며, 모형의 학습 평가 및 검정을 판단하기 위해 MSE 및 MAE를 사용했다. 또한, 모형평가는 NSE와 RMSE를 이용하였다. 그 결과, 모형의 학습 평가에 대한 MSE는 11.629.8 m3/s에서 118.6 m3/s로, MAE는 25.4 m3/s에서 4.7 m3/s로 감소하였으며, 모형의 검정에 대한 MSE는 1,997.9 m3/s에서 527.9 m3/s로, MAE는 21.5 m3/s에서 9.4 m3/s로 감소한 것으로 나타났다. 또한, 모형평가를 위한 NSE는 0.7, RMSE는 27.0 m3/s로 나타나, 본 연구의 모형은 양호(moderate)한 것으로 판단하였다. 이에, 본 연구를 통해 제시된 방법론에 기반을 두어 CNN 모형 구조의 확장과 수문학적 이미지의 개선 또는 새로운 이미지 개발 등을 추진할 경우 모형의 예측 성능이 향상될 수 있는 여지가 있으며, 원격탐사 분야나, 위성 영상을 이용한 전 지구적 또는 광역 단위의 실시간 유량 모의 분야 등으로의 응용이 가능할 것으로 기대된다.

• Journal of The Korean Association of Information Education
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• v.19 no.3
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• pp.261-270
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• 2015

In this paper, a strategy of cooperation activities was conducted to analyze on the impact of what effect appears in teacher training. We classified with satisfaction, effectiveness and academic achievement as effects of teacher training. We were divided into three groups that are cooperative-reflection activity group using the SNS, self-reflection activity group and general training group. Depending on the type of reflection activity, we have one-way ANOVA analysis for the effectiveness of teacher training. By the results of the analysis, we found to have a positive impact that cooperative reflection activity group were more an academic achievement, satisfaction and effectiveness of training. Accordingly, we have found the SNS-based collaborative reflection activity is very effective in teacher training.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.4
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• pp.65-73
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• 1997

This paper proposes a new method of the structure pruning of dynamic recurrent neural networks (DRNN) using evolutionary computations. In general, evolutionary computations are population-based search methods, therefore it is very useful when several different properties of neural networks need to be optimized. In order to prune the structure of the DRNN in this paper, we used the evolutionary programming that searches the structure and weight of the DRNN and evolution strategies which train the weight of neuron and pruned the net structure. An addition or elimination of the hidden-layer's node of the DRNN is decided by mutation probability. Its strategy is as follows, the node which has mhnimum sum of input weights is eliminated and a node is added by predesignated probability function. In this case, the weight is connected to the other nodes according to the probability in all cases which can in- 11:ract to the other nodes. The proposed pruning scheme is exemplified on the stabilization and position control of the inverted-pendulum system and visual servoing of a robot manipulator and the effc: ctiveness of the proposed method is demonstrated by numerical simulations.