• Journal of Gifted/Talented Education
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• v.15 no.2
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• pp.35-57
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• 2005

As the results of studies based on the Russian Activities-Oriented Theory, the gifted students in many fields have common insights for the true nature of the problem, or the actual state. From Russian Activities-Oriented Theory of view, gifted students have the ability to discern the essential elements involved in each actual state and change of state of things, and to solve the problem, based on these elements. Enhancing these abilities of the students, the educator can develop the average student into a gifted one. This study result of the Russian specialist suggests the possibility of a stream of education that can develop gifted students. Hence, this paper discussed the points and processes of formation of the Russian Activities-Oriented Theory, and inquired on what is the true nature of the problem or the meaning of actual state and how it affects the studies of the student. The paper also investigated the actual conditions of wrong learning about some mathematical concepts and discussed the role of insights to the true nature of the problem in the learning process of the student.

• Journal of Internet of Things and Convergence
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• v.2 no.1
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• pp.41-48
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• 2016

The Korea Internet of Things Society. In this paper, we developed 'PyStudy' system as a python based self-study helper software. Proposed PyStudy system is consisted with several components such as PyStudy console, self-study window, helper and self-coding window. User can refer helper function to find questionable python libraries at on-line connection quickly. And self-progress checking on python study also provided on integrated PyStudy IDE software. The PyStudy software provides the information necessary to learn the Python language efficiently. Proposed software can be applicable to an advanced Python language education course.

• Journal of KIISE
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• v.42 no.11
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• pp.1423-1429
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• 2015

The Internet of Things (IoT) is a new concept associated with the future Internet, and it has recently become a popular concept to build a dynamic, global network infrastructure. However, the deployment of IoT creates difficulties in satisfying different Quality of Service (QoS) requirements and achieving rapid service composition and deployment. In this paper, we propose a new QoS control scheme for IoT systems. The Markov game model is applied in our proposed scheme to effectively allocate IoT resources while maximizing system performance. The results of our study are validated by running a simulation to prove that the proposed scheme can promptly evaluate current IoT situations and select the best action. Thus, our scheme approximates the optimum system performance.

• Smart Media Journal
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• v.8 no.2
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• pp.66-73
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• 2019

Recently, object recognition technology using computer vision has attracted attention as a technology to replace sensor-based object recognition technology. It is often difficult to commercialize sensor-based object recognition technology because such approach requires an expensive sensor. On the other hand, object recognition technology using computer vision may replace sensors with inexpensive cameras. Moreover, Real-time recognition is viable due to the growth of CNN, which is actively introduced into other fields such as IoT and autonomous vehicles. Because object recognition model applications demand expert knowledge on deep learning to select and learn the model, such method, however, is challenging for non-experts to use it. Therefore, in this paper, we analyze the structure of deep - learning - based object recognition models, and propose a platform that can automatically select a deep - running object recognition model based on a user 's desired condition. We also present the reason we need to select statistics-based object recognition model through conducted experiments on different models.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1055-1065
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• 2023

Recently, various studies using deep reinforcement learning (deep RL) technology have been conducted to solve complex problems using big data collected at industrial internet of things. Deep RL uses reinforcement learning"s trial-and-error algorithms and cumulative compensation functions to generate and learn its own data and quickly explore neural network structures and parameter decisions. However, studies so far have shown that the larger the size of the learning data is, the higher are the memory usage and search time, and the lower is the accuracy. In this study, model-agnostic learning for efficient federated deep RL was utilized to solve privacy invasion by increasing robustness as 55.9% and achieve 97.8% accuracy, an improvement of 5.5% compared with the comparative optimization-based meta learning models, and to reduce the delay time by 28.9% on average.

• Review of Korea Contents Association
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• v.15 no.3
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• pp.14-20
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• 2017

• Communications of Mathematical Education
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• v.9
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• pp.1-13
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• 1999

수학 교육에서 수학 지식의 추상적 특성으로 인하여 수학 학습에 중요한 발생적 측면으로서 “concrete“ 에 대한 학습론적인 연구가 부족하였다. 또한 구체적 감각 조작 단계에서 형식적 추상적 조작 단계로의 아동의 인지 발달을 강조하다보니 ”concrete“와 ”abstract“의 통상적인 의미가 이분화 됨으로서 수학 학습에서 모든 연령과 수준에 무관한 상보적이고 상호 작용하는 가치를 수학 교육 연구에서 잊고 있었다. 본 논문은 발생적인 그리고 구성주의적 수학 학습에서 ”concrete”가 가져야 할 새로운 의미를 제안하였다. 새로운 의미의 “concrete“는 다양한 경험과 사물 그리고 지식과의 관계 맺음을 의미하는 ”connected“와 같은 맥락을 갖는다고 보고 몇 가지 수학교육에 관련된 의의와 중요성을 제시하였다.

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

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11b
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• pp.839-843
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• 2003

듣고 발화하는 과정을 통해 언어를 재구성해가는 건청아동과는 달리 청각장애 아동은 청력의 상실로 인해 언어습득의 선천적 매커니즘이 작용될 수 없으며 이는 청각장애아동의 언어능력향상을 위한 웹기반언어학습이 의도적으로 구성되어야 항을 의미한다. 동작이나 상황을 나타내는 말을 동적으로 구현하여 사물 및 행동에 대한 관찰력을 증진시키고 주도적으로 상황언어를 익힐 수 있도록 유도하는 시스템 구성이 필요하며 대체사고 전략을 활용하고 얼굴표정과 반대어와 대비어를 강조해야 함이 웹기반 언어학습을 통한 청각장애아동의 문법적 오류를 감소시키는 효과적 방법임을 이 논문을 통해 제안하고자 한다.

• 한국정보교육학회:학술대회논문집
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• 2006.08a
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• pp.123-128
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• 2006

수학 학습은 구체적인 사물의 조작을 통해 추상적인 개념을 습득하는 과정이다. 이 과정에서 여러 가지 학습 도구들이 사용되어지는데, 그 중에서 컴퓨터를 활용한 Logo프로그램을 도입하여, 도형 문제해결과정에서의 부정확한 도형 개념과 정의로 인한 오류를 줄여 정확한 개념과 정의를 형성하는 지도 방안을 마련하고, 실제 수업을 통하여 일반적 수학 도형 수업보다 Logo를 활용한 수학 도형 수업이 도형 문제해결과정에서 학습자가 오류를 줄이는데 효과가 있는지 알아보고자 한다.