• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1072-1075
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• 2005

This paper presents that TD method is applied to the human adaptive devices for smart home with context awareness (or recognition) technique. For smart home, the very important problem is how the appliances (or devices) can adapt to user. Since there are many humans to manage home appliances (or devices), managing the appliances automatically is difficult. Moreover, making the users be satisfied by the automatically managed devices is much more difficult. In order to do so, we can use several methods, fuzzy controller, neural network, reinforcement learning, etc. Though the some methods could be used, in this case (in dynamic environment), reinforcement learning is appropriate. Among some reinforcement learning methods, we select the Temporal Difference learning method as a core algorithm for adapting the devices to user. Since this paper assumes the environment is a smart home, we simply explained about the context awareness. Also, we treated with the TD method briefly and implement an example by VC++. Thereafter, we dealt with how the devices can be applied to this problem.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.4
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• pp.82-95
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• 2008

As society has changed to being more knowledge-based, it is necessary that change of paradigm is incorporated into engineering education and the education goals and the assessment method of educational outcomes is developed to promptly meet the needs of the times. A purpose of this study is to measure learning outcomes in coursework of engineering college every semester, which ultimately provides to validate program outcomes. We looked into teaching-learning style of course in the engineering college and analyzed its grade method and tool. By use of a survey, we derived a reasonable method to measure for the learning outcomes in course and presented tools for course-embedded assessment to measure that learning outcomes had been tied to their objectives. These tools are effective to determine that program outcomes and education goals have been achieved, ultimately. In addition, it will help that instruction builds a loop system for better.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.88.5-88
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• 2001

We propose a neural network solver for an inverse problem. The problem is that input data with complete teaching include defects and predict the defect value. The solver is constructed of a three layer neural network whose learning method is combined from BP and reconstruction learning. The input data for the defects are unknown; therefore, the circulation of an arithmetic progression replaces them; rightly, the learning procedure is not converged for the circulation data vut for the normal data. The learning is quitted after such a learning status id kept. Then, we search a minimum of the differences between teaching data and output of the circulation. Then, we search a minimum of the ...

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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• pp.215-223
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• 2021

3D data can be categorized into two parts : Euclidean data and non-Euclidean data. In general, 3D data exists in the form of non-Euclidean data. Due to irregularities in non-Euclidean data such as mesh and point cloud, early 3D deep learning studies transformed these data into regular forms of Euclidean data to utilize them. This approach, however, cannot use memory efficiently and causes loses of essential information on objects. Thus, various approaches that can directly apply deep learning architecture to non-Euclidean 3D data have emerged. In this survey, we introduce various deep learning methods for mesh and point cloud data. After analyzing the operating principles of these methods designed for irregular data, we compare the performance of existing methods for shape classification and segmentation tasks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2942-2960
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• 2022

The core of cognitive radio is the problem concerning intelligent decision-making for communication parameters, the objective of which is to find the most appropriate parameter configuration to optimize transmission performance. The current algorithms have the disadvantages of high dependence on prior knowledge, large amount of calculation, and high complexity. We propose a new decision-making model by making full use of the interactivity of reinforcement learning (RL) and applying the Q-learning algorithm. By simplifying the decision-making process, we avoid large-scale RL, reduce complexity and improve timeliness. The proposed model is able to find the optimal waveform parameter configuration for the communication system in complex channels without prior knowledge. Moreover, this model is more flexible than previous decision-making models. The simulation results demonstrate the effectiveness of our model. The model not only exhibits better decision-making performance in the AWGN channels than the traditional method, but also make reasonable decisions in the fading channels.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.68-74
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• 2019

Along with the deeper architecture in the deep learning approaches, the need for the data becomes very big. In the real problem, to get huge data in some disciplines is very costly. Therefore, learning on limited data in the recent years turns to be a very appealing area. Meta-learning offers a new perspective to learn a model with this limitation. A state-of-the-art model that is made using a meta-learning framework, Meta-SGD, is proposed with a key idea of learning a hyperparameter or a learning rate of the fast adaptation stage in the outer update. However, this learning rate usually is set to be very small. In consequence, the objective function of SGD will give a little improvement to our weight parameters. In other words, the prior is being a key value of getting a good adaptation. As a goal of meta-learning approaches, learning using a single gradient step in the inner update may lead to a bad performance. Especially if the prior that we use is far from the expected one, or it works in the opposite way that it is very effective to adapt the model. By this reason, we propose to add a weight term to decrease, or increase in some conditions, the effect of this prior. The experiment on few-shot learning shows that emphasizing or weakening the prior can give better performance than using its original value.

• IE interfaces
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• v.19 no.4
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• pp.306-315
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• 2006

In this paper we analyze whether we can use the mobile phone having been highly distributed into young generation as a device for the u-learning in Korean public education. For this purpose we deal with the technical maturity in three axes. Firstly, we examine the authoring nature of mobile internet-based contents such as both text and motion picture for the contents developers in the public education. As a research result the authoring of text has almost no difficulty, but that of the motion picture shows some problems. Secondly, we deal with whether u-learners can easily get and use u-contents on both mobile phone and PC respectively. After analysing this factor, we found that the downloading of motion picture contents into mobile phone is very limited. Therfore we talk about the usability and problem of various PC Sync tools and propose their standardization. Finally, the needs of the introduction of the ubiquitous SCORM which could enable to reuse u-contents among different Korean telco’s mobile phones are discussed. Here we describe some functionality of both ubiquitous SCORM and u-LMS. Our study looks like almost the first work examining the technological maturity for the introduction of u-learning with mobile phone in Korean public education and it could be used as a reference for the study of any other wireless telecommunication-based u-learning other than mobile telecommunication.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1123-1146
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• 2023

In South Korea, there have been many studies on efficient building-energy management using renewable energy facilities in single zero-energy houses or buildings. However, such management was limited due to spatial and economic problems. To realize a smart zero-energy city, studying efficient energy integration for the entire city, not just for a single house or building, is necessary. Therefore, this study was conducted in the eco-friendly energy town of Chungbuk Innovation City. Chungbuk successfully realized energy independence by converging new and renewable energy facilities for the first time in South Korea. This study analyzes energy data collected from public buildings in that town every minute for a year. We propose a smart city building-energy management model based on the results that combine various renewable energy sources with grid power. Supervised learning can determine when it is best to sell surplus electricity, or unsupervised learning can be used if there is a particular pattern or rule for energy use. However, it is more appropriate to use reinforcement learning to maximize rewards in an environment with numerous variables that change every moment. Therefore, we propose a power distribution algorithm based on reinforcement learning that considers the sales of Energy Storage System power from surplus renewable energy. Finally, we confirm through economic analysis that a 10% saving is possible from this efficiency.

• Journal of Korean Elementary Science Education
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• v.34 no.2
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• pp.238-251
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• 2015

This study, based on literature review and theoretical discussion, reinterprets the learning environment instruments from cultural perspectives and suggests the applicability of learning environment instruments for understanding science classroom cultures. To do this, the existing learning environment instruments are first investigated and compared in terms of their features and utilizations appeared in previous studies. The learning environment instruments are then reinterpreted in the light of culture. Finally, we suggest the possibilities to use the learning environment instruments to understand science classroom cultures. The results of this study can be summarized as follows. First, the learning environment instruments, by interpreting them culturally, could be interpreted in cultural ways and used as the alternative ways to explore science classroom cultures. Second, the learning environment instruments, such as WIHIC and CLEQ, could be interpreted both along the dimension of phenomena in classrooms and the dimension of students' psychology in order to investigate science classroom cultures. Third, the instrument items could be interpreted culturally in different ways according to the description types of instrument items. Thus, when learning environment instruments are used in culture research, the description types should be sufficiently taken into account. Based on the results of this study, educational implications are discussed in terms of exploring classroom cultures and of culture research.

• Journal of Advanced Navigation Technology
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• v.14 no.2
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• pp.278-283
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• 2010

In this paper, we designed and proposed framework of extended user model to support student tailored learning in ubiquitous environment. For the purpose, existents model that is domain model, user model, adaptation model and interaction model connected to LMS(Learning Management System) and LCMS(Learning Contents Management System). Students information management process that is extended user model is in between LMS and adaptive learning system. And the process connected u-LMS to use u-learning. u-LMS and u-LCMS could support the learning contents through exchange the contents according to connect and request from the students.