• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.529-530
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• 2022

단어 카드나 그림을 통한 낱말 교육은 집중력과 주의력을 오래 유지하는 것이 어렵다. 유아들은 사물을 심상 혹은 이미지로 인식하는 성향이 있으므로 개념을 무리하게 주입시키기 보다는 감각적이고 입체적인 교육이 필요하다. 본 논문에서는 홀로렌즈와 객체 인식 기능을 이용한 낱말 학습 시스템 개발에 대해 설명한다. 이 시스템을 통해 사용자는 실제 객체와의 상호작용을 통해 낱말 학습이 가능하며, 한국어를 제외한 언어에도 적용하여 외국어 교육에도 효과적일 것으로 기대한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.85-88
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• 2017

보행자 검출을 위한 기술이 많이 개발되고 있으며 HOG(Histograms of oriented)와 haar-like feature를 이용한 특징값 검출을 통해 보행자를 검출하는 방법들이 대표적이라 할 수 있다. 하지만 이 방법들은 보행자가 사물에 가려졌을 때 보행자를 검출하지 못한다는 단점이 있다. 이에 본 논문에서는 haar-like feature와 adaboost 학습알고리듬을 이용하여 보행자를 검출하고 kalman filter를 이용하여 보행자가 특정 사물에 가려지는 것 과 같은 occlusion 문제를 해결하여 보행자 검출 성능을 높이고자 하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.47-52
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• 2023

With the growing data size and the increased computing load in machine learning, energy-efficient resource planning in IoT systems is becoming increasingly important. In this paper, we suggest a new resource planning policy for real-time workloads that can be fluctuated over time in IoT systems. To handle such situations, we categorize real-time tasks into fixed tasks and variable tasks, and optimize the resource planning for various workload conditions. Based on this, we initiate the IoT system with the configuration for the fixed tasks, and when variable tasks are activated, we update the resource planning promptly for the situation. Simulation experiments show that the proposed policy saves the processor and memory energy significantly.

• Journal of Internet of Things and Convergence
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• v.5 no.2
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• pp.89-94
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• 2019

The purpose of this study was to empirically analyze the motivation of university lifelong education and the factors that are important in selecting it. In the motivation for participation of adult learners, learning new knowledge and skills in the first place and learning new things in the second place were good, so that they could learn together in the third place. Active school investment in faculty and staff will be needed to increase program diversity and quality through professionalism. Adult learners are getting information from the system they are interacting with, and furthermore, they learn the information from the public relations of university professors. Since they are acquiring paths and information through acquaintances, it is necessary to continuously promote the curriculum to unspecified adult learners. Advertisement should take advantage of various convergence strategies such as hanging banners in the area, publicity of the subway, local newspapers, word of mouth, SNS, and the Internet.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.49-55
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• 2022

This study aimed to examine the meaning of adult learners' experience in which they performed a convergence program for the self-confidence improvement of disabled persons with brain lesion who were daycare center users. For the goal, the study collected data through a 5-session profound interview with those disabled persons and then through this author's observation. This study analyzed all the data and, as a result, categorized three significant themes that best represented the above mentioned meaning, which are 'tension of beginning', 'joy of being in company with others' and 'I as the present being'. With those meaningful themes taken into serious consideration. Finally, this study suggested that field programs for social welfare practice in better connection with adult learners' major should be researched and developed.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.47-49
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• 2020

본 논문은 유아기 언어 발달 시기에 한글의 기초 단계를 학습하기 위해 기존의 학습지 형태의 한글 교육 선행 학습과는 달리 시각적, 청각적 효과로 몸의 감각을 통해 창의적이고 동적으로 사물을 배우며 이러한 자극으로 정보를 기억하고 축적할 수 있는 한글 학습 교육용 콘텐츠를 개발하고자 한다. 이는 유아의 호기심을 자극할 뿐만 아니라 모바일 플랫폼과의 상호작용을 통해 재미와 즐거움을 키우며 나아가 지식을 얻을 수 있다. 더불어 유아가 한글 학습의 놀이 과정을 통해 창의력을 높이고, 다방면으로 문제를 해결할 수 있는 능력을 키울 뿐만 아니라 학습을 통해 스스로 이끌어가는 자기 주도적 학습 능력을 키울 수 있을 것으로 기대한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.798-800
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• 2024

현대의 머신러닝 환경에서는 특히 모바일 컴퓨팅 및 사물 인터넷(IoT)의 애플리케이션 영역에서 개인 정보를 보호하고 효율적이며 확장 가능한 모델에 대한 관심이 높아지고 있다. 본 연구는 연합 학습(FL)과 자기지도 학습(self-supervised learning)을 결합하여 이질적(heterogeneous)인 분산 자원에서 레이블이 없는 데이터를 활용하면서 사용자의 개인 정보를 보호하는 새로운 프레임워크를 소개한다. 이 프레임워크의 핵심은 SimCLR 과 같은 자기지도 학습 기법으로 학습된 공유 인코더로, 입력 데이터에서 고수준 특성을 추출하도록 설계되었다. 또한 이 구조를 통해 주석(annotation)이 없는 방대한 데이터셋을 활용하여 모델 성능을 향상시키고, 여러 개의 격리된 모델이 필요하지 않아 리소스를 크게 최적화할 수 있는 가능성을 확인했다. 본 연구를 통해 생성된 모델은 중앙 집중 방식(CL)이면서 자기지도학습으로 학습되지 않은 기존 모델과 비교하여 전체 평균 정확도가 14.488% 향상됐다.

• Smart Media Journal
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• v.7 no.2
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• pp.47-53
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• 2018

Along with the development of information and communication technology, smart education that can learn without restrictions of time, place and equipment is activated even in the field of education. Although smart education is provided with content-based training solutions, construction of a system that grasps individual characteristics of learners and provides personalized learning is relatively weak. The activity of free choice is an important play activity of early childhood education, but it is not implemented efficiently by relying on the clinical observation of the teacher. If the IoT(Internet of Things) technology based on Hyper-Connected is applied to free-choice activities, it is possible to provide the child's personalized activity type and play-form analysis based on objective and stylized data. In this paper, we design and implement a system to monitor the child's activity of free choice by building an IoT environment that is based on open source hardware. The proposed system provides children's activity information as objective data and will be used as teacher's work mitigation and custom training material for each child.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.69-75
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• 2024

This paper proposes an innovative cognitive IoT framework specifically designed for fog computing (FC) in the context of industrial Internet of Things (IIoT). The discourse in this paper is centered on the intricate design and functional architecture of the Cognitive IoT platform. A crucial feature of this platform is the integration of machine learning (ML) and artificial intelligence (AI), which enhances its operational flexibility and compatibility with a wide range of industrial applications. An exemplary application of this platform is highlighted through the Predictive Maintenance-as-a-Service (PdM-as-a-Service) model, which focuses on real-time monitoring of machine conditions. This model transcends traditional maintenance approaches by leveraging real-time data analytics for maintenance and management operations. Empirical results substantiate the platform's effectiveness within a fog computing milieu, thereby illustrating its transformative potential in the domain of industrial IoT applications. Furthermore, the paper delineates the inherent challenges and prospective research trajectories in the spheres of Cognitive IoT and Fog Computing within the ambit of Industrial Internet of Things (IIoT).

• Journal of Internet of Things and Convergence
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• v.8 no.1
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• pp.37-43
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• 2022

The fundamental basis of AI technology is learningable data. Recently, the types and amounts of data collected and produced by the government or private companies are increasing exponentially, however, verified data that can be used for actual machine learning has not yet led to it. This study discusses the conditions that data actually can be used for machine learning should meet, and identifies factors that degrade data quality through case studies. To this end, two representative cases of developing a prediction model using public big data was selected, and data for actual problem solving was collected from the public data portal. Through this, there is a difference from the results of applying valid data screening criteria and post-processing. The ultimate purpose of this study is to argue the importance of data quality management that must be most fundamentally preceded before the development of machine learning technology, which is the core of artificial intelligence, and accumulating valid data.