• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.145-146
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• 2010

This paper proposed a new concept of global healthcare system based on M2M technology with the combination of networks by using IPv6 techniques. The proposed system consists of 6LoWPAN based wearable sensors, gateway for the connection of different networks, and server program offering health information. Thus our approach presents an intelligent system which allows direct exchange of information between machines without human assistance with the epoch-making extension of measurement environment in healthcare areas appropriately.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.4
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• pp.15-24
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• 2019

Currently, museums and architectural heritage provide augmented user experiences by incorporating various media technologies. They still, however, suffer from the limitation of entertainment-based and the provision of location-based simple and repetitive contents. In addition, while acting as a key medium of experience for architectural heritage, the concept of space is not properly reflected in current services. The purpose of this study is to design user space experience considering such characteristics of architectural heritage. The spatial experience content and content production platform are defined. This software platform creates content that enhances the experience of the place by giving a context-based digital data associated with space and objects. The spatial experience content is designed as a series of experience sequences. The composition of the sequence borrows the method of film and narrative which segment and connect consecutive experiences on a scene basis considering user's detailed spatial experience. Therefore, content components can be combined and reproduced in various types. Augmented contents were extracted by using rule-based reasoning function of ontology at the moment. As a practical example of architectural heritage, the Seokjojeon Hall is used to reveal a spatial experience scenario.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.695-700
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• 2005

In the sensor network, a lot of sensor nodes are scattered sparsely and organizes a united communication network between each node. After that, environmental information around each sensor node are gathered and analyzed. Because each node operates under resource constraint, the efficiency and hardware specification of a node should be maximized. There exist technical constraints until now but recent technical progress in IC fabrication and wireless network enables to construct a tiny embedded system, which has the properties of low cost, low power consumption, multi functions. Wireless sensor network becomes a modern research field with technical improvements, is studied in numerous laboratories, and is called as diverse different project names - Wireless Integrated Network Sensors (WINS), Mobile Ad hoc NETwork (MANET), Ubiquitous Sensor Network (USN). TinyOS is one of leading project and is widely used. In this paper, we suggest a sensor network, which uses TinyOS platforms and aims for context awareness in a home environment.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.205-210
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• 2022

Drones, which have rapidly grown along with the 4th industrial revolution, spread over industries and also widely used for military purposes. In recent wars in Europe, drones are being evaluated as a game changer on the battlefield, and their importance for military use is being highlighted. The Republic of Korea Army also planned drone-bot systems including various drones suitable for echelons and missions of the military as future defense forces. The keyword of these drone-bot systems is autonomy by artificial intelligence. In addition, common use of operating platforms is required for the rapid development of various types of drones. In this paper, we propose software framework that applies diverse artificial intelligence technologies such as multi-agent system, cognitive architecture and knowledge-based context reasoning for mission autonomy and common use of military drones.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1259-1265
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• 2022

Recently, AI, big data, and IoT technologies are being used in various solutions such as fire detection and gas or dangerous substance detection for safety accident prevention. According to the status of occupational accidents published by the Ministry of Employment and Labor in 2021, the accident rate, the number of injured, and the number of deaths have increased compared to 2020. In this paper, referring to the dataset construction guidelines provided by the National Intelligence Service Agency(NIA), the dataset is directly collected from the field and learned with YOLOv4 to propose a collision risk object detection system through object detection. The accuracy of the dangerous situation rule violation was 88% indoors and 92% outdoors. Through this system, it is thought that it will be possible to analyze safety accidents that occur in industrial sites in advance and use them to intelligent platforms research.

• Education of Primary School Mathematics
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• v.26 no.4
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• pp.333-348
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• 2023

Clear analysis and diagnosis of various characteristic factors of individual students is the most important in order to realize individual customized teaching and learning, which is considered the most essential function of math artificial intelligence-based digital textbooks. In this study, analysis factors and tools for individual customized learning diagnosis and construction models for data collection and analysis were derived from mathematical AI digital textbooks. To this end, according to the Ministry of Education's recent plan to apply AI digital textbooks, the demand for AI digital textbooks in mathematics, personalized learning and prior research on data for it, and factors for learner analysis in mathematics digital platforms were reviewed. As a result of the study, the researcher summarized the factors for learning analysis as factors for learning readiness, process and performance, achievement, weakness, and propensity analysis as factors for learning duration, problem solving time, concentration, math learning habits, and emotional analysis as factors for confidence, interest, anxiety, learning motivation, value perception, and attitude analysis as factors for learning analysis. In addition, the researcher proposed noon data on the problem, learning progress rate, screen recording data on student activities, event data, eye tracking device, and self-response questionnaires as data collection tools for these factors. Finally, a data collection model was proposed that time-series these factors before, during, and after learning.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.272-275
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• 2022

Large-scale online platforms such as MOOCs-Massive Open Online Courses, which provide a variety of educational contents, have provided a learning environment that allows students to freely access and learn anytime and anywhere. Currently, the proportion of online lectures and home-based learning is increasing, and portfolio or experience-based learning such as bootcamp, field activities, and team project-based group learning are also being actively carried out for educational outcomes. At present, interest in nano or microdegree focused on core technology in units of hours or credits is increasing significantly because such strategic intensive education enables effective learning in terms of continuity and efficiency of education. In an era of large changes in job market due to the reorganization of the industrial structure by new technologies, intensive education in specialized new technology fields such as smart mobility, big data, and artificial intelligence is much more conducive to finding a job. With this reason it is attracting attention as an alternative to lifelong learning are receiving In this paper we propose an educational platform that can efficiently and effectively support the purpose learning for the personalized microdegree education in the online learning era.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.649-655
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• 2020

This study determined effective educational strategies by investigating and analyzing the related educational demands for SMEs (small and medium-sized enterprises) in the 4th Industrial Revolution based area of smart farms. In order to derive the approprate educational strategies, Importance-Performance Analysis (IPA) and Borich's Needs Assessment Model were conducted based on the smart farm technological field. As a result, the education demand survey showed high demand for production systems and intelligent farm machinery. In detail, Borich's analysis showed the need for pest prevention and diagnosis technology (8.03), network and analysis SW linkage technology (7.83), and intelligent farm worker-agricultural power system-electric energy hybrid technology (7.43). In contrast, smart plant factories (4.09), lighting technology for growth control (4.46) and structure construction technology (4.62) showed low demands. Based on this, the IPA portfolio shows that the network and analysis SW linkage technology and the CAN-based complex center are urgently needed. However, the technology that has already been developed, such as smart factory platform development, growth control lighting technology and structure construction technology, was oversized. Based on these results, it is possible to strategically suggest the customized training programs for industrial sectors of SMEs that reflect the needs for efficiently operating smart farms. This study also provides effective ways to operate the relevant training programs.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.559-561
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• 2001

현재 웹 기반 교육시스템 (Web-based Educational System WES)은 웹이 플랫폼 독립적이라는 점과 장소와 시간에 대해 자유롭다는 점을 가지고 급성장하고 있으며, 활발히 연구되고 있는 분야이다. 초기의 WES 시스템들은 코스웨어의 정적인 구성과 상호작용성의 부족 등의 문제점들이 있었으나 최근에는 학습자 개개인의 능력에 따라 적응력 있는 학습을 제시하는 연구 방향과 기존 단독형 ITS에서 연구된 결과를 웹 기반으로 적용하려는 두 가지 연구방향으로 접근하고 있다. 본 논문에서는 기존 ITS의 연구결과를 웹 기반 시스템에 적용하여 보다 지능적이며, 적응력 있는 웹 기반 교육시스템을 목표로 이를 설계 및 구현하였다. 특히 시스템의 여러 모듈 중, 교수 계획기의 설계 및 구현을 설명한다. 또한, 교수 계획기는 학습의 효과를 상승시키기 위한 방안으로, 학습자가 학습상황을 시각적으로 선택 및 이해할 수 있게 도와줄 수 있는 curriculum map 개념으로 설계되었다.