• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.293-302
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• 2016

Since the electricity is produced and consumed simultaneously, predicting the electric load and securing affordable electric power are necessary for reliable electric power supply. In particular, a university campus is one of the highest power consuming institutions and tends to have a wide variation of electric load depending on time and environment. For these reasons, an accurate electric load forecasting method that can predict power consumption in real-time is required for efficient power supply and management. Even though various influencing factors of power consumption have been discovered for the educational institutions by analyzing power consumption patterns and usage cases, further studies are required for the quantitative prediction of electric load. In this paper, we build an electric load forecasting model by implementing and evaluating various machine learning algorithms. To do that, we consider three building clusters in a campus and collect their power consumption every 15 minutes for more than one year. In the preprocessing, features are represented by considering periodic characteristic of the data and principal component analysis is performed for the features. In order to train the electric load forecasting model, we employ both artificial neural network and support vector machine. We evaluate the prediction performance of each forecasting model by 5-fold cross-validation and compare the prediction result to real electric load.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.148-156
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• 2017

This study was conducted to examine the effects of a simulation-based learning method that utilizes the task of making a video for respiratory patients care. A quasi-experimental non-equivalent control group pre-post test design was used. A total of 56 students-28 students in the experimental group and 28 students in the control group were included. The experimental group received the 2 education sessions with 120 minutes in each session. It was implemented in November, 2014. Data were analyzed with paired t-test and unpaired t-test using SPSS/Win 18.0. The experimental group who had the simulation-based learning method utilizing the task of making video. It showed significantly higher learning satisfaction (p=.008 p<.001), and self-efficacy (p=.010) compared with the control group who had a traditional simulation education. Through this study, The educational effects of video-making task are the stimulation of interest in learners, improvement of self-led learning and communication skills. Therefore, a simulation-based learning method utilizing the task of making a video was an effective teaching method for the growth of professional competency for students involved in health related fields.

• Journal of the Korean School Mathematics Society
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• v.17 no.3
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• pp.359-384
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• 2014

The purpose of this study is inquire the reaction and adaptability of the mathematically gifted student, in the case of introduce learning materials based on GeoGebra in real class. The study program using GeoGebra consist of 'construction of fundamental figures', 'making animation with using slider tools' (graph of a function, trace of a figure, definite integral, fixed point, and draw a parametric curve), make up the group report after class. In detail, 1st to 15th classes are mainly problem-solving, and topic-exploring classes. To analyze the application effects of developed learning materials, divide students in four groups and lead them to make out their own creative products. In detail, guide students to make out their own report about mathematical themes that based on given learning materials. Concretely, build up the program to make up group report about their own topics in six weeks, after learning on various topics. Expert panel concluded that developed learning materials are successfully stimulate student's creativity in various way, after analyze of the student's activities. Moreover, those learning programs also contributed to the develop of the mathematical ability to thinking that necessary to writing a report. As well, four creative products are assessed as connote mathematically gifted student's creative thinking and meaningful elements in mathematical aspects.

• Education of Primary School Mathematics
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• v.2 no.1
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• pp.53-64
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• 1998

Two different CAI programs have been developed to study the affect of CAI element for the types of learners'performance; (i) one is the 'CAI program 1' including the open questions for the fourth grade (the fourth period of the 'Time and Angle' in chapter 3 of the first term) of the mathematics class in the elementary school, and (il) the other is 'CAI program 2' for the existing methods. The fourth grade of Andong Songhyun elementary school has been chosen as the study subjects (243 learners), and the t-test and learners'interview have also been used to analysis the results of CAI programs. The CAI programs have only been used as the control variable. The developed CAI programs have been applied two different learners'groups to investigate the degree of performance among the superior, average, and inferior learners. For the superior group (p＜.0023) at the t＜3.2268 level and for the average group (p＜.0706) at the t＜1.8211 level the learner' group using CAI program 1 shows the higher performance compared with the learners' group using the CAI program 2, whereas fur the inferior group (p＜.8073) at the t＜.2458 level two programs did not show any difference. The learners interviews show that the superior and average groups have an interest for the open problems, whereas the inferior group do not shows an interest for the open problems. Thus, the CAI programs including the open questions (open fields, open evaluation) will be helped to the learners' group with the individual differences. Furthermore, it is expected that the CAI programs including the open questions as the mathematics and the program model of CAI can be used to develope the CAI program in future.

• Communications of Mathematical Education
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• v.34 no.3
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• pp.325-354
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• 2020

This study utilized longitudinal data from the 2013 year (Secondary Middle School) to 2017 year (Secondary High School) of the Seoul Education Termination Study. Using the latent growth model and the piecewise growth model, we investigated the changes in mathematics academic achievement, internal factors(self-concept, self-control, self-assessment of life satisfaction), and external factors(school climate, guardians) as students' grades increased, and examined whether internal factors and external factors influence the changes in mathematics academic achievement. We examined whether internal and external factors influence the change in academic achievement. As a result of analysis, it was found that mathematics academic achievement remained unchanged from the first grade of middle school to the second grade of middle school, and steadily increased from the second grade of middle school to the first grade of high school, and then decreased slightly in the second grade of high school. The internal and external factors had little change. It has been found that self-concept, self-control as internal factors, and school climate as external factors influence changes in mathematics academic achievement.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.651-660
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• 2013

Object oriented design brought up improvement of productivity and software quality by adopting some concepts such as inheritance and encapsulation. However, both the number of software's classes and object couplings are increasing as the software volume is becoming larger. The object coupling between classes is closely related with software complexity, and high complexity causes decreasing software quality. In order to solve the object coupling issue, IT-field researchers adopt a component based development and software quality metrics. The component based development requires explicit representation of dependencies between classes and the software quality metrics evaluates quality of software. As part of the research, we intend to gain a basic data that will be used on decomposing software. We focused on properties of the linkage between classes rather than previous studies evaluated and accumulated the qualities of individual classes. Our method exploits machine learning technique to analyze the properties of linkage and predict the strength of dependency between classes, as a new perspective on analyzing software property.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.713-722
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• 2013

This paper proposes a method of using depth information to detect the hand region in real-time based on the cascade method. In order to ensure stable and speedy detection of the hand region even under conditions of lighting changes in the test environment, this study uses only features based on depth information, and proposes a method of detecting the hand region by means of a classifier that uses boosting and cascading methods. First, in order to extract features using only depth information, we calculate the difference between the depth value at the center of the input image and the average of depth value within the segmented block, and to ensure that hand regions of all sizes will be detected, we use the central depth value and the second order linear model to predict the size of the hand region. The cascade method is applied to implement training and recognition by extracting features from the hand region. The classifier proposed in this paper maintains accuracy and enhances speed by composing each stage into a single weak classifier and obtaining the threshold value that satisfies the detection rate while exhibiting the lowest error rate to perform over-fitting training. The trained classifier is used to classify the hand region, and detects the final hand region in the final merger stage. Lastly, to verify performance, we perform quantitative and qualitative comparative analyses with various conventional AdaBoost algorithms to confirm the efficiency of the hand region detection algorithm proposed in this paper.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.153-159
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• 2018

The purpose of this study is to grasp the internet use time that young people use for purposes other than learning purpose, to grasp the perceived status of the youth according to internet use time and to grasp the interrelationships of them. Using the 2016 youth health behavior online survey, the odds ratios and 95% confidence intervals of perceived status according to internet use time were calculated by binary logistic regression analysis. The main results are as follows. In perceived health and perceived oral health the odds ratios of perceived who feel that they are perceived and unhealthy as the time spent using the Internet increased significantly compared to those who did not use the Internet for learning purposes. In the perceived body type, the odds ratio of being overweight increased significantly with longer internet use time. The odds ratios of perceived happiness were 1.19 times (CI = 1.10-1.30) higher than the perceived expectation of unhappiness when using the Internet for over 300 minutes. The use of the internet for a long time other than the purpose of learning may have a negative effect on the health and happiness of the youth, so we think that the recommended time for using the internet is necessary.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.597-608
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• 2022

Remotely sensed data have been used in various fields, such as disasters, agriculture, urban planning, and the military. Recently, the demand for the multitemporal dataset with the high-spatial-resolution has increased. This manuscript proposed an automatic image matching algorithm using a deep learning technique to utilize a multitemporal remotely sensed dataset. The proposed deep learning model was based on High Resolution Net (HRNet), widely used in image segmentation. In this manuscript, denseblock was added to calculate the correlation map between images effectively and to increase learning efficiency. The training of the proposed model was performed using the multitemporal orthophotos of the National Geographic Information Institute (NGII). In order to evaluate the performance of image matching using a deep learning model, a comparative evaluation was performed. As a result of the experiment, the average horizontal error of the proposed algorithm based on 80% of the image matching rate was 3 pixels. At the same time, that of the Zero Normalized Cross-Correlation (ZNCC) was 25 pixels. In particular, it was confirmed that the proposed method is effective even in mountainous and farmland areas where the image changes according to vegetation growth. Therefore, it is expected that the proposed deep learning algorithm can perform relative image registration and image matching of a multitemporal remote sensed dataset.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.471-484
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• 2023

Deep learning models based on generative adversarial neural networks are specialized in generating new information based on learned information. The deep generative models (DGMR) model developed by Google DeepMind is an generative adversarial neural network model that generates predictive radar images by learning complex patterns and relationships in large-scale radar image data. In this study, the DGMR model was trained using radar rainfall observation data from the Ministry of Environment, and rainfall prediction was performed using an generative adversarial neural network for a heavy rainfall case in August 2021, and the accuracy was compared with existing prediction techniques. The DGMR generally resembled the observed rainfall in terms of rainfall distribution in the first 60 minutes, but tended to predict a continuous development of rainfall in cases where strong rainfall occurred over the entire area. Statistical evaluation also showed that the DGMR method is an effective rainfall prediction method compared to other methods, with a critical success index of 0.57 to 0.79 and a mean absolute error of 0.57 to 1.36 mm in 1 hour advance prediction. However, the lack of diversity in the generated results sometimes reduces the prediction accuracy, so it is necessary to improve the diversity and to supplement it with rainfall data predicted by a physics-based numerical forecast model to improve the accuracy of the forecast for more than 2 hours in advance.