• The Bulletin of The Korean Astronomical Society
• /
• v.42 no.2
• /
• pp.60.2-61
• /
• 2017

A Convolutional Neural Network(CNN) is one of the well-known deep-learning methods in image processing and computer vision area. In this study, we apply CNN to two kinds of flare forecasting models: flare classification and occurrence. For this, we consider several pre-trained models (e.g., AlexNet, GoogLeNet, and ResNet) and customize them by changing several options such as the number of layers, activation function, and optimizer. Our inputs are the same number of SOHO)/MDI images for each flare class (None, C, M and X) at 00:00 UT from Jan 1996 to Dec 2010 (total 1600 images). Outputs are the results of daily flare forecasting for flare class and occurrence. We build, train, and test the models on TensorFlow, which is well-known machine learning software library developed by Google. Our major results from this study are as follows. First, most of the models have accuracies more than 0.7. Second, ResNet developed by Microsoft has the best accuracies : 0.77 for flare classification and 0.83 for flare occurrence. Third, the accuracies of these models vary greatly with changing parameters. We discuss several possibilities to improve the models.

• The Journal of Korea Robotics Society
• /
• v.17 no.1
• /
• pp.76-85
• /
• 2022

One of the most fundamental challenges when designing controllers for dynamic systems is the adjustment of controller parameters. Usually the system model is used to get the initial controller, but eventually the controller parameters must be manually adjusted in the real system to achieve the best performance. To avoid this manual tuning step, data-driven methods such as machine learning were used. Recently, reinforcement learning became one alternative of this problem to be considered as an agent learns policies in large state space with trial-and-error Markov Decision Process (MDP) which is widely used in the field of robotics. However, on initial training step, as an agent tries to explore to the new state space with random action and acts directly on the controller parameters in real systems, MDP can lead the system safety-critical system failures. Therefore, the issue of 'safe exploration' became important. In this paper we meet 'safe exploration' condition with Control Barrier Function (CBF) which converts direct constraints on the state space to the implicit constraint of the control inputs. Given an initial low-performance controller, it automatically optimizes the parameters of the control law while ensuring safety by the CBF so that the agent can learn how to predict and control unknown and often stochastic environments. Simulation results on a quadrotor UAV indicate that the proposed method can safely optimize controller parameters quickly and automatically.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.752-759
• /
• 2022

The instantiation of spaces as a discrete entity allows users to utilize BIM models in a wide range of analyses. However, in practice, their utility has been limited as spaces are erroneously entered due to human error and often omitted entirely. Recent studies attempted to automate space allocation using artificial intelligence approaches. However, there has been limited success as most studies focused solely on the use of geometric features to distinguish spaces. In this study, in addition to geometric features, semantic relations between spaces and elements were modeled and used to improve space classification in BIM models. Graph Convolutional Networks (GCN), a deep learning algorithm specifically tailored for learning in graphs, was deployed to classify spaces via a similarity graph that represents the relationships between spaces and their surrounding elements. Results confirmed that accuracy (ACC) was +0.08 higher than the baseline model in which only geometric information was used. Most notably, GCN was able to correctly distinguish spaces with no apparent difference in geometry by discriminating the specific elements that were provided by the similarity graph.

• 제어로봇시스템학회:학술대회논문집
• /
• 1999.10a
• /
• pp.292-297
• /
• 1999

Evolving in artificial agent is an extremely difficult problem, but on the other hand, a challenging task. At present the studies mainly centered on single agent learning problem. In our case, we use simulated soccer to investigate multi-agent cooperative learning. Consider the fundamental differences in learning mechanism, existing reinforcement learning algorithms can be roughly classified into two types-that based on evaluation functions and that of searching policy space directly. Genetic Programming developed from Genetic Algorithms is one of the most well known approaches belonging to the latter. In this paper, we give detailed algorithm description as well as data construction that are necessary for learning single agent strategies at first. In following step moreover, we will extend developed methods into multiple robot domains. game. We investigate and contrast two different methods-simple team learning and sub-group loaming and conclude the paper with some experimental results.

• Journal of Environmental Science International
• /
• v.32 no.8
• /
• pp.573-583
• /
• 2023

This study was conducted to verify the effectiveness of improving indoor environmental awareness, relieving stress, and improving learning efficiency in school indoor green space, and suggest desirable ways to develop indoor green space in the future. As part of the study, a survey was conducted among 225 individuals across six schools in a metropolitan area with garden and panel-type indoor gardens inside the school building. The survey comprised the current status and use of indoor green spaces, the perception of indoor green spaces, improvement measures in indoor green spaces, and basic properties. Semantic Differential (SD) was used to investigate the impression of school indoor spaces. Resultantly, the more frequent the use of green spaces in the school, the more they feel the positive effects of indoor green spaces, such as improving the school's indoor environment, reducing stress, and improving learning efficiency. In addition, it appears that the more frequent contact with the natural environment, the more they feel the positive effects provided by indoor green space at school. Therefore, it is suggested that educational conditions must be improved by revitalizing various green welfare, including indoor green areas, at the school level.

• The Journal of the Korea Contents Association
• /
• v.5 no.2
• /
• pp.53-60
• /
• 2005

e-learning which is beyond time and space restrictions as a new education tool, has been practically used in many fields in order to support instrument or displacement for traditional education. This research shows that the survey on the significance and the effect of e-learning to teachers who have experience in e-learning through the internet compared with effect of tradition education. Furthermore, we investigate variable factors affecting on effect of e-learning, the correlation of factors and coefficient of correlation between factors associated with effects of e-learning based on the results of our survey.

• Journal of The Korea Institute of Healthcare Architecture
• /
• v.15 no.3
• /
• pp.71-77
• /
• 2009

This study is to understand the situations of special education classroom layout, find differences according to school levels and summarize the findings in order to build up the indicators for special classroom layout. As for elementary school level, special classrooms are using multi-purposes desk or group desk for diverse activities such as basic learning and formation of basic life practice. The most frequent type in classroom layout is Type E which is for diverse coner-learning and play activities and the next is Type C which secures activity space. Because security of dynamic activity which most teachers find problematic is important, it needs more research to secure dynamic activity space within classroom. As for middle school level, the most frequent type in classroom layout is Type B which is equiped for computer aided learning and the next is Type C which secures activity space. Research for systematic layout of activity space is needed in order to secure the spaces of dynamic activity and basic job training. As for high school levels, mostly Type B which emphasizes computer activities is adopted and next is Type F which is capable for job training. The survey about the size of special education classroom proves that most teachers want one and half size classroom which in not such a large classroom. It is expected that more systematic research of special classroom layout according to school levels may reach for rational space layout.

• Journal of the Korean Institute of Educational Facilities
• /
• v.23 no.1
• /
• pp.43-54
• /
• 2016

The purpose of this study was to survey teachers' perception of classroom space utilization through analyzing the physical environment of elementary school classrooms (56 classrooms in 10 schools). Most of the teachers arranged desks in the two person parallel type (sectional layout : standard type) for their classes. Although the number was small, some classrooms used the T type, H type, U type, group type, and the teachers of such cases used these layouts for children's play activities or group learning. Some teachers changed the desk layout depending on the contents of learning or for different atmosphere of class, but about 40% of the teachers used the same classroom layout without any change during a semester. When the teachers' perception of classroom space utilization was examined according to the type and change of desk layout, the quantity and characteristics of posts, the position of posting spaces, and the size of activity spaces in the classroom, most of the teachers tended to be conventional without any characteristic, and only 16% of them were relatively active in utilizing classroom spaces. In addition, teachers of a relatively small class were more active in utilizing classroom spaces. In particular, perception was very low to utilize the classroom as a space for children's life or play activities or various types of learning. These findings suggest that it is necessary to improve teachers' perception of classroom space utilization in the future.

• Journal of the Korean School Mathematics Society
• /
• v.6 no.1
• /
• pp.27-43
• /
• 2003

This paper introduces some theories that can be effectively applied for the development of teaching and learning mathematics using fuzzy theory developed by Zadeh who defined fuzzy set and knowledge space by Doignon and Falmagne. Especially, we expect that two theories mentioned above are expected to solve the situation that could not be taken care of the present evaluation method.

• The Bulletin of The Korean Astronomical Society
• /
• v.46 no.1
• /
• pp.41.1-41.1
• /
• 2021

We generate solar magnetograms and EUV images from Galileo sunspot drawings using a deep learning model based on conditional generative adversarial networks. We train the model using pairs of sunspot drawing from Mount Wilson Observatory (MWO) and their corresponding magnetogram (or UV/EUV images) from 2011 to 2015 except for every June and December by the SDO (Solar Dynamic Observatory) satellite. We evaluate the model by comparing pairs of actual magnetogram (or UV/EUV images) and the corresponding AI-generated one in June and December. Our results show that bipolar structures of the AI-generated magnetograms are consistent with those of the original ones and their unsigned magnetic fluxes (or intensities) are well consistent with those of the original ones. Applying this model to the Galileo sunspot drawings in 1612, we generate HMI-like magnetograms and AIA-like EUV images of the sunspots. We hope that the EUV intensities can be used for estimating solar EUV irradiance at long-term historical times.