• Journal of Digital Contents Society
• /
• v.17 no.6
• /
• pp.489-497
• /
• 2016

This study focused on the focus group interview based on the questionnaire. Prior to the interview, we used questionnaires from the previous researchers in order to select the questionnaires and interviews of the focus group. In order to measure the possibility, which is the expression characteristic of the safety education app, the items related to cognitive, sensual, physical, and safety behaviors were used as constituent factors. And the safety education app to analyze was selected 'Water Go GO!' App developed by the National Emergency Management Agency. The results of this study are as follows: First, the learner should help to participate in learning continuously in order to make meaningful learning activities in safety education app learning environment. Second, learners must interact with mobile devices in their apps to facilitate learning while reducing the number of factors that can interfere with learners' learning. This study is meaningful in that it can utilize this design principle as a guideline for enhancing safety behaviors.

• Information and Communications Magazine
• /
• v.33 no.5
• /
• pp.48-54
• /
• 2016

우리는 물리적인 현실 세계와 디지털의 가상 세계에서 매일 끊임없이 데이터를 양산해내고 있다. 구글, 아마존, MS, IBM 등의 유수 기업들은 이미 데이터를 수집하고 분석하여 특정 사용자나 불특정 다수에게 다양한 서비스를 제공하면서 새로운 형태의 이윤을 창출하고 있다. 가까운 미래에 사물인터넷(Internet of Things)이 본격적으로 활성화된다면 사람뿐만 아니라 모든 사물들이 인터넷을 통해 데이터를 양산하고 서로 교환하는 그야말로 데이터 빅뱅의 시대가 도래할 것으로 예상된다. 이러한 변혁의 시대에 우리는 사물인터넷을 통해 수집되는 수많은 데이터를 어떻게 활용할 것인지에 대해 진지하게 고민하고 연구할 필요가 있다. 본고에서는 사물인터넷을 통해 수집된 데이터를 효과적으로 활용하기 위해 필요한 핵심기술 중 하나인 기계학습(Machine Learning)에 대해 기본 개념, 종류, 평가방법 등을 설명하고 기계학습 알고리즘 중 딥 러닝(Deep Learning)에 대한 기술 동향을 살펴본 후, 사물인터넷에서 기계학습 프레임워크에 대해 간략히 소개한다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.354-354
• /
• 2022

딥러닝(Deep Learning)은 일반적으로 인공신경망(Artificial Neural Network) 를 의미하는데, 이에 따른 결과는 데이터의 양, 변수, 학습모델의 학습횟수, 은닉층(Hidden Layer)의 개수 등 여러 요소로 인해 결정된다. 본 연구에서는 물리적 장기유출 모형인 SWAT의 결과를 참값으로 LSTM모형의 매개변수인 은닉층 갯수와 학습횟수등의 시나리오를 바탕으로 검보정을 수행하였으며, 최적의 목적함수를 갖는 매개변수를 도출하였다. 이를 이용하여 유량지속곡선을 도출한결과를 SWAT의 결과와 비교해본 결과 매우 높은 상관성을 도출하였으며 이를 통해 수자원분야에서 인공신경망의 활용 가능성을 확인하였다.

• 대한공업교육학회지
• /
• v.33 no.2
• /
• pp.120-136
• /
• 2008

Using the tangible programming tools, which combines physical objects (e.g. robot) and educational programming language, may help to encourage learners' creative thinking as well as to enhance problem solving ability. That is, learners can have opportunities to simulate problem solving processes through the physical objects, such as robots. Therefore, they can minimize an fixation about problem solving process. These experience is effective to induce creative thinking that is useful to find new solutions and change environment actively. Therefore, we developed a robot based programming teaching and learning curriculum and implemented it in college level introductory programming courses. The result shows that the robot based programming learning has a positive effect in all three factors of learners' creative problem solving potential, especially in a cognitive factor. The cognitive factor includes general problem solving abilities as well as factors that explain creativity, such as divergent thinking, problem recognition, problem representation. These result means that the developed robot based programming teaching and learning curriculum give positive effect to creative problem solving abilities.

• Journal of the Korea Computer Graphics Society
• /
• v.27 no.1
• /
• pp.9-17
• /
• 2021

In this paper, we present a simple and fast supervised learning framework based on model predictive control so as to learn motion controllers for a physic-based character to track given example motions. The proposed framework is composed of two components: training data generation and offline learning. Given an example motion, the former component stochastically controls the character motion with an optimal controller while repeatedly updating the controller for tracking the example motion through model predictive control over a time window from the current state of the character to a near future state. The repeated update of the optimal controller and the stochastic control make it possible to effectively explore various states that the character may have while mimicking the example motion and collect useful training data for supervised learning. Once all the training data is generated, the latter component normalizes the data to remove the disparity for magnitude and units inherent in the data and trains an artificial neural network with a simple architecture for a controller. The experimental results for walking and running motions demonstrate how effectively and fast the proposed framework produces physics-based motion controllers.

• KIPS Transactions on Software and Data Engineering
• /
• v.8 no.12
• /
• pp.491-498
• /
• 2019

Digital twin is a technology that virtualizes physical objects of the real world on a computer. It is used by collecting sensor data through IoT, and using the collected data to connect physical objects and virtual objects in both directions. It has an advantage of minimizing risk by tuning an operation of virtual model through simulation and responding to varying environment by exploiting experiments in advance. Recently, artificial intelligence and machine learning technologies have been attracting attention, so that tendency to virtualize a behavior of physical objects, observe virtual models, and apply various scenarios is increasing. In particular, recognition of each robot's motion is needed to build digital twin for co-robot which is a heart of industry 4.0 factory automation. Compared with modeling based research for recognizing motion of co-robot, there are few attempts to predict motion based on sensor data. Therefore, in this paper, an experimental environment for collecting current and inertia data in co-robot to detect the motion of the robot is built, and a motion prediction model based on the collected sensor data is proposed. The proposed method classifies the co-robot's motion commands into 9 types based on joint position and uses current and inertial sensor values to predict them by accumulated learning. The data used for accumulating learning is the sensor values that are collected when the co-robot operates with margin in input parameters of the motion commands. Through this, the model is constructed to predict not only the nine movements along the same path but also the movements along the similar path. As a result of learning using SVM, the accuracy, precision, and recall factors of the model were evaluated as 97% on average.

• Journal of The Korean Association of Information Education
• /
• v.5 no.1
• /
• pp.17-29
• /
• 2001

As the result of the rapid development of communication technology makes the circumstance and method of education change, distance education is adapted to new fields of education so that students can be educated what they need in the time and space which they want instead of relying on the existing physical frameworks. In this paper Learner-Analysing System was designed and implemented in the distance education based on the web. For the easy access and the demand of customers, this Learner-Analysing System is composed of the Q&A-processing module which can produce proper results for questions and acquisitions of their own study materials through efficient search, and the student-analyzing module for reinforcement of feedback through correct analysis.

• 한국정보교육학회:학술대회논문집
• /
• 2006.08a
• /
• pp.263-268
• /
• 2006

토론학습시 보다 자발적인 아동의 참여를 향상시킬 수 있는 웹토론시스템은 시간의 편의성을 제공하고 학습자간의 상호작용을 활발하게 한다. 그러나 기존 웹토론시스템은 능숙한 자판사용능력과 물리적인 교육적 환경을 요구한다. Wiki는 하와이어로 '빨리'라는 뜻으로 누구나 '자유롭게' 정보와 지식을 편집할 수 있는 동적 프로그래밍 도구이다. Wiki를 사용하여 기존의 웹토론시스템의 단점을 보안한 본 시스템의 목적은 학습자의 자발적인 토론참여와 토론학습에 대한 흥미를 유발하는 것이다. 본 시스템의 특징은 다음과 같다. 첫째, 본 시스템은 웹토론에 대한 학생들의 흥미를 높일수 있다. 즉 누구나 관리자가 될 수 있는 기능을 이용해서 학생들의 흥미를 유발하였기 때문이다. 둘째, Wiki 웹토론시스템은 기존의 웹토론시스템보다 사용이 편리하여 학생의 참여도를 향상시키고 토론학습에 대한 관심을 증대시킬 수 있다. 기존의 웹토론시스템은 회원가입을 해야하고 로그인을 해야만 토론학습에 참여할 수 있지만 본 시스템은 웹페이지접속만으로도 가능하게 하였다. 셋째, Wiki 웹토론시스템은 웹토론를 학습하는 과정을 공개하여 올려지는 자료나 다른 사람의 의견을 통해 지식공유를 가능하게 한다. 즉, 자신이 찾은 주장의 근거을 찾는 과정에서나 또 그 근거를 통해 새로운 지식을 알게 되고 본 시스템에서 의견을 개진하고 다른 사람의 의견의 근거를 살펴보면서 지식을 공유하게 한다.

• Journal of the Korea Computer Graphics Society
• /
• v.26 no.4
• /
• pp.1-8
• /
• 2020

Motion synthesis using physics-based controllers can generate a character animation that interacts naturally with the given environment and other characters. Recently, various methods using deep neural networks have improved the quality of motions generated by physics-based controllers. In this paper, we present a control policy learned by deep reinforcement learning (DRL) that enables Luxo, the mascot character of Pixar animation studio, to run towards a random goal location while imitating a reference motion and maintaining its balance. Instead of directly training our DRL network to make Luxo reach a goal location, we use a reference motion that is generated to keep Luxo animation's jumping style. The reference motion is generated by linearly interpolating predetermined poses, which are defined with Luxo character's each joint angle. By applying our method, we could confirm a better Luxo policy compared to the one without any reference motions.

• Journal of Gifted/Talented Education
• /
• v.20 no.2
• /
• pp.571-594
• /
• 2010

Learning styles affect how students access and handle their task, so it is very important to understand how they study, when planning teaching-learning process, to enhance their potential to the maximum. In addition, in order to improve the quality of gifted education, there is a need to examine the curriculum and teaching-learning process which reflect learner characteristics. In this study, gifted student's preferred learning styles are investigated using questionnaires and learning style inventory. Also by analyzing the characteristics of the learners, it is hoped to get parents and teachers to understand the gifted who have various talents, and to support teaching programs for the gifted in order to develop their potential. This study shows that there are differences in the studying style between the gifted child and the average child. Namely, learner's physical and psychological environment can affect learning styles. Also there is a difference between the studying style which the gifted students prefer and the teaching style which teachers use most frequently. Programs for the gifted should be planned through better understanding of the characteristics of the learners.