• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.1-7
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• 2022

As the number of IoT devices increases, power management of the cluster head, which acts as a gateway between the cluster and sink nodes in the IoT network, becomes crucial. Particularly when the cluster head is a mobile wireless terminal, the power consumption of the IoT network must be minimized over its lifetime. In addition, the delay of information transmission in the IoT network is one of the primary metrics for rapid information collecting in the IoT network. In this paper, we propose a low-power buffer management algorithm that takes into account the information transmission delay in an IoT network. By forwarding or skipping received packets utilizing deep Q learning employed in deep reinforcement learning methods, the suggested method is able to reduce power consumption while decreasing transmission delay level. The proposed approach is demonstrated to reduce power consumption and to improve delay relative to the existing buffer management technique used as a comparison in slotted ALOHA protocol.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.117-123
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• 2023

While applying new digital technologies, interest in software and artificial intelligence is quite high. In particular, many changes are being made for the development of software and artificial intelligence in the field of education. From 2025, software and artificial intelligence-related curricula will be applied to public education in elementary, middle and high schools. The Ministry of Education is also conducting various camps to experience software and artificial intelligence in various ways in elementary, middle and high schools before they are applied to public education. Several platforms for experience camps related to software and artificial intelligence are also being used. In this study, we intend to increase the educational efficiency of the learning method for software and artificial intelligence to be developed in the future by designing a model for software and artificial intelligence experiential learning platforms.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.550-552
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• 2020

본 논문에서는 YOLO(You Only Look Once) 사물 인식 알고리즘을 활용하여 영상 압축에 적용한다. YOLO 는 물체의 일반화된 특징을 학습한 뉴럴 네트워크이다. 영상을 압축하는 동시에 YOLO 를 활용하여, 영상 내의 사물을 인식한다. 사물이 인식된 영역을 영상 압축을 할 때, 더 구체적으로 예측을 하는 방법을 제안한다. 본 논문에서 제안하는 방법은 QP(Quantization Parameter)를 조절하여, YOLO 로부터 인식된 사물을 더 정교하게 사물을 부호화/복호화한다. VVC(Versatile Video Coding) 기반에서 Rate-Control 를 사용하며, QP 를 조절한다. QP 는 CTU-Level 단위로 조절하며, 사물이 포함된 CTU 는 더 낮은 QP 를 바탕으로 효율적인 화질을 가져온다. 본 논문에서 제안하는 방법은 VVC 기반으로 한 Rate-Control 보다 주관적 화질이 선명한 것으로 보인다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.400-402
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• 2002

지금까지의 코스웨어는 주로 우수아와 일반아를 대상으로 제작되어 왔다. 그러나 본 연구는 초등학교 학습 장애아들을 대상으로 개별화 학습 ICT 활용을 위한 멀티미디어 코스웨어를 제작하였다. 특히 학습 동기와 흥미도를 강화하여 학습 부진 요소를 제거할 수 있는 방안을 모색하였다. 그리고 다양한 교육정보화 매체를 활용하여 자기 주도의 학습을 할 수 있도록 멀티미디어 저작도구를 사용하여 단계별 개별화 학습자료를 설계하고 개발하였다. 이를 통해 학습 장애아들이 정확한 지식을 습득할 수 있고, 사물에 대한 정확한 개념과 관심을 가질 수 있도록 하였다.

• Journal of Convergence for Information Technology
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• v.7 no.1
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• pp.55-60
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• 2017

In order to provide intelligent services without human intervention in the Internet of Things environment, it is necessary to analyze the big data generated by the IoT device and learn the normal pattern, and to predict the abnormal symptoms such as faulty or malfunction based on the learned normal pattern. The purpose of this study is to implement a machine learning model that can predict product failure by analyzing big data generated in various devices of product process. The machine learning model uses the big data analysis tool R because it needs to analyze based on existing data with a large volume. The data collected in the product process include the information about product faulty, so supervised learning model is used. As a result of the study, I classify the variables and variable conditions affecting the product failure, and proposed a prediction model for the product failure based on the decision tree. In addition, the predictive power of the model was significantly higher in the conformity and performance evaluation analysis of the model using the ROC curve.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.552-555
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• 2019

디지털 트윈이란 현실 세계의 물리적인 사물을 컴퓨터 상에 동일하게 가상화 시키는 기술을 의미하는 것으로, 물리적 사물이나 시스템을 모델링하거나 IoT 기술에 접목되어 활용되고 있는 기술이다. 디지털 트윈 기술은 가상의 모델을 무한정 시뮬레이션을 통해 동작을 튜닝하고 환경변화에 대한 대응을 미리 실험하여 리스크를 최소화할 수 있는 장점을 지닌다. 최근 인공지능이나 기계학습에 관련된 기술들이 주목받기 시작하면서, 이와 같은 물리적인 사물의 모델링 작업을 데이터 기반으로 수행하려는 시도가 증가하고 있다. 특히, 산업현장에서 많이 활용되는 인더스트리 4.0 공장 자동화의 핵심인 협력 로봇의 디지털 트윈을 구축하기 위해서는 로봇의 동작을 인지하는 과정이 필수적으로 요구된다. 그러나 현재 협력 로봇의 동작을 인지하기 위한 시도는 미비하며, 센서 데이터를 기반으로 동작을 역으로 예측하는 기술은 더욱 그렇다. 따라서 본 논문에서는 로봇의 동작을 인지하기 위해 가정용 협력 로봇에서 전류 및 관성 데이터를 수집하기 위한 실험 환경을 구축하고, 수집한 센서 데이터를 기반으로 한 동작 예측 모델을 제안하고자 한다. 제안하는 방식은 로봇의 동작 명령어를 조인트 위치 기반으로 분류하고 전류와 위치 센서 값을 사용하여 학습을 통해 예측하는 방식이다. SVM 을 이용하여 학습한 결과, 모델의 성능은 평균적으로 정확도, 정밀도, 및 재현율이 모두 96%로 평가되었다.

• The Journal of the Korea Contents Association
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• v.12 no.3
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• pp.114-122
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• 2012

Traditional music should be succeeded and developed through the systematic education since music represents culture and history of one country. In Korea, even through the importance of Korean traditional music has been emphasized through the seven times of revision of national curriculum, music education has been still Western-oriented. This study aims to make students have experience Uttari Samul-nori and investigate the perception changes about the Korean traditional music. It is ultimately expected that students inherit the Korean traditional music and further recreate our own traditional culture. Samul-nori class was organized in the regular music curriculum twice a week, for 10 hours and various activities were tried during that time. 1st-year high school students participated in this research and they were allowed to play Samul instruments directly and play the basic rhythm suggested in the newly designed curriculum. Before and after the research, the questionnaires were sent to examine the attitude changes toward the Korean traditional music. The result of the questionnaire are as follows: First, Samul-nori activities affect positively the students' interest in the Korean traditional music and Samul-nori itself. Second, Danso(short bamboo flute) education which has been implemented most frequently during the Korean music education, is not satisfactory to the students. Third, students were satisfied with the new teaching method of Samul-nori and most students wanted to continue to take Samul-nori class. Last, students recognized the importance of Korean traditional music education after the research activities.

• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.1-13
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• 2022

The Paper Using the meta-analysis research method, first through literature retrieval to obtain 23 relevant empirical studies in China, and then using Review Manager for quantitative analysis, it is found that VR technology has a positive impact on students' overall learning effect and VR technology has a significant positive impact on all dimensions of learning effect (theoretical performance, operational performance, learning motivation, learning interest, learning attitude). There is no significant difference between the dimensions. Significant differences were found for moderating variables such as Discipline types, Teaching Length, and Teaching Method. No significant differences were found for the Academic segments and VR technology types.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.101-108
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• 2022

We propose an intrusion detection model that detects denial-of-service(DoS) and distributed reflection denial-of-service(DRDoS) attacks, based on the empirical data of each internet of things(IoT) device by training system and network metrics that can be commonly collected from various IoT devices. First, we collect 37 system and network metrics from each IoT device considering IoT attack scenarios; further, we train them using six types of machine learning models to identify the most effective machine learning models as well as important metrics in detecting and distinguishing IoT attacks. Our experimental results show that the Random Forest model has the best performance with accuracy of over 96%, followed by the K-Nearest Neighbor model and Decision Tree model. Of the 37 metrics, we identified five types of CPU, memory, and network metrics that best imply the characteristics of the attacks in all the experimental scenarios. Furthermore, we found out that packets with higher transmission speeds than larger size packets represent the characteristics of DoS and DRDoS attacks more clearly in IoT networks.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.37-38
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• 2015

본 논문에서는 IoT(Internet of Things) 환경 기반의 문화재 학습 시스템 기술을 제안한다. 각 상황에 해당하는 시스템 모듈을 기반으로 하여 문화재에 대한 정보의 수집 및 분석을 통해 사용자에게 적합한 서비스 제공하여 문화재에 대한 관심을 증대시키고 문화재 정보에 대한 효율적인 이해관계를 제공한다. 또한 제안하는 시스템으로 국민의 문화재에 대한 이해를 높이고 정보 기술의 발전을 활용한 지식 정보 기반의 문화재 서비스를 제공 할 수 있다.