• Communications of Mathematical Education
• /
• v.23 no.2
• /
• pp.175-196
• /
• 2009

In this paper we analyze Ziv's geometrical differentiated teaching and learning materials using inner structure of mathematics problems. In order to analyze inner structure of mathematics problems we in detail describe problem solving process, and extract main frame from problem solving process. We represent inner structure of mathematics problems as tree including induced relations. As a result, we characterize low-level problems and middle-level problems, and find some differences between low-level problems and middle-level problems.

• Journal of Korean Association for Spatial Structures
• /
• v.20 no.2
• /
• pp.51-58
• /
• 2020

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.466-467
• /
• 2018

Recently, artificial intelligence has been applied to various fields such as image recognition, image recognition speech recognition, and natural language processing, and interest in Deep Learning technology is increasing. Many researches on Convolutional Neural Network(CNN), which is one of the most representative algorithms among Deep Learning, have strong advantages in image recognition and classification and are widely used in various fields. In this paper, we propose a new network structure that transforms the general CNN structure. A typical CNN structure consists of a convolution layer, ReLU layer, and a pooling layer. Therefore in this paper, We intend to construct a new network by adding fully connected layer inside a general CNN structure. This modification is intended to increase the learning and accuracy of the convoluted image by including the generalization which is an advantage of the neural network.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.3
• /
• pp.483-490
• /
• 2022

Malaria is a disease caused by a parasite and it is prevalent in all over the world. The usual method used to recognize malaria cells is a thick and thin blood smears examination methods, but this method requires a lot of manual calculation, so the efficiency and accuracy are very low as well as the lack of pathologists in impoverished country has led to high malaria mortality rates. In this paper, a malaria cell image recognition model using transfer learning is proposed, which consists in the feature extractor, the residual structure and the fully connected layers. When the pre-training parameters of the VGG-19 model are imported to the proposed model, the parameters of some convolutional layers model are frozen and the fine-tuning method is used to fit the data for the model. Also we implement another malaria cell recognition model without residual structure to compare with the proposed model. The simulation results shows that the model using the residual structure gets better performance than the other model without residual structure and the proposed model has the best accuracy of 97.33% compared to other recent papers.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.941-946
• /
• 2005

In this paper an active random noise control using adaptive learning rate neural networks is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. It is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.

• Journal of Information Technology Applications and Management
• /
• v.15 no.3
• /
• pp.261-273
• /
• 2008

The starting point of knowledge generation and management is the enhancement of learning capability and capacity of organizational members. Organizational change for learning environment should be aligned with the change of organizational strategy, structure and processes. The study employed action learning methodology to constitute learning organization processes. The treatment effect to institute learning organization has been successful thanks to the members' zeal and consensus to change the processes. However, not every learning team has been so successful. Some cases complained time consuming where others expect to be helpful for their incentives. The researchers concluded that the most important point for success of the learning organization project should be the support of top management.

• Journal of Digital Convergence
• /
• v.2 no.1
• /
• pp.43-56
• /
• 2004

The emergence of distance learning through Information and Communication Technologies(ICT) requires a lot of abilities from learners, and these become main features to a successful learning. Accordingly, a self-regulated learning is one of the key abilities required by the learners, Hence this study is aimed to develop Web-Based Instruction(WBI) systems that support this self-regulated learning. The self-regulated learning not only provides significant positive learning effects, but also appropriates to apply on the WBI because it has learning-structure of specified instruction process and each process requires separated spaces. Therefore, in this study, a model of self-regulated learning system on the web is developed.

• The Journal of Sustainable Design and Educational Environment Research
• /
• v.3 no.1
• /
• pp.15-23
• /
• 2003

Focusing on both the form of open classroom and that of independent one which have been most planned and being built, this study was designed to see if the educational environment of their inner space, structure, and facilities gives a proper support to classroom activities during the various classes based on open education. Selecting representative teaching methods in elementary school, such as open simultaneous learning, learning through a medium, learning in the corner, subject learning, team teaching and learning hardening basics, this study surveyed problems and improvements using literature works, questionnaires, observing, and interviews. Through the study on learning space for open education, it has been known that the form of independent classroom fits into one classroom learning and open classroom into small group learning and individual learning, and that the form of open classroom connecting open space with a classroom are more desirable when there is change from large to small group.

• Journal of Korea Multimedia Society
• /
• v.24 no.7
• /
• pp.868-879
• /
• 2021

PCB (Printed Circuit Board) inspection using a deep learning model requires a large amount of data and storage. When the amount of stored data increases, problems such as learning time and insufficient storage space occur. In this study, the existing object detection model is changed to a continual learning model to enable the recognition and classification of PCB components that are constantly increasing. By changing the structure of the object detection model to a knowledge distillation model, we propose a method that allows knowledge distillation of information on existing classified parts while simultaneously learning information on new components. In classification scenario, the transfer learning model result is 75.9%, and the continual learning model proposed in this study shows 90.7%.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.12
• /
• pp.983-987
• /
• 2009

One of the critical themes in the language acquisition is its exposure to linguistic environments. Linguistic environments, which interact with infants, include not only human beings such as its parents but also artificially crafted linguistic media as their functioning elements. An infant learns a language by exploring these extensive language environments around it. Based on such large linguistic data exposure, we propose a machine learning based method on the cognitive mechanism that simulate flexibly and appropriately infant's language learning. The infant's initial stage of language learning comes with sentence learning and creation, which can be simulated by exposing it to a language corpus. The core of the simulation is a memory-based learning model which has language hypernetwork structure. The language hypernetwork simulates developmental and progressive language learning using the structure of new data stream through making it representing of high level connection between language components possible. In this paper, we simulates an infant's gradual and developmental learning progress by training language hypernetwork gradually using 32,744 sentences extracted from video scripts of commercial animation movies for children.