• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.137-144
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• 2007

In KORDI (Korea Ocean Research and Development Institute), the KOSC (Korea Ocean Satellite Center) construction project is being prepared for acquisition, processing and distribution of sensor data via L-band from GOCI (Geostationary Ocean Color Imager) instrument which is loaded on COMS (Communication, Ocean and Meteorological Satellite); it will be launched in 2008. Ansan (the headquarter of KORDI) has been selected for the location of KOSC between 5 proposed sites, because it has the best condition to receive radio wave. The data acquisition system is classified into antenna and RF. Antenna is designed to be $\phi$ 9m cassegrain antenna which has 19.35 G/T$(dB/^{\circ}K)$ at 1.67GHz. RF module is divided into LNA (low noise amplifier) and down converter, those are designed to send only horizontal polarization to modem. The existing building is re-designed and arranged for the KOSC operation concept; computing room, board of electricity, data processing room, operation room. Hardware and network facilities have been designed to adapt for efficiency of each functions. The distribution system which is one of the most important systems will be constructed mainly on the internet. and it is also being considered constructing outer data distribution system as a web hosting service for offering received data to user less than an hour.

• Convergence Security Journal
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• v.21 no.1
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• pp.191-200
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• 2021

Artificial intelligence technology is developing in an environment where a lot of data is produced due to the development of computing technology, a cloud environment that can store data, and the spread of personal mobile phones. Among these artificial intelligence technologies, the deep neural network provides excellent performance in image recognition and image classification. There have been many studies on image detection for forest fires and fire prevention using such a deep neural network, but studies on detection of cigarette smoking were insufficient. Meanwhile, military units are establishing surveillance systems for various facilities through CCTV, and it is necessary to detect smoking near ammunition stores or non-smoking areas to prevent fires and explosions. In this paper, by reflecting experimentally optimized numerical values such as activation function and learning rate, we did the detection of smoking pictures and non-smoking pictures in two cases. As experimental data, data was constructed by crawling using pictures of smoking and non-smoking published on the Internet, and a machine learning library was used. As a result of the experiment, when the learning rate is 0.004 and the optimization algorithm Adam is used, it can be seen that the accuracy of 93% and F1-score of 94% are obtained.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.49-57
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• 2019

The core technology development of the Fourth Industrial Revolution is linked to the development of other core technologies, which will change the industrial structure in the future and create a new smart business model. In this paper, tried to analyze the technology maturity level and analyze the technology maturity. To do this, used technology trend information to investigate and integrate the market, policy, etc. Of core technology of the 4th Industrial Revolution to achieve a comprehensive maturity level. Because technology maturity measures are scored by technology developers, prejudices may be acted upon according to a person's tendency, which may be a subjective evaluation. It is also a measure of the maturity of individual technologies, and thus is not suitable for evaluating the overall system integration perspective. However, it is possible to evaluate the maturity before integrating the core element technologies constituting the whole system and to use it as a means to compare the effect of the whole system and its feasibility and play an important role in the planning of technology development.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.2
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• pp.41-56
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• 2019

Cloud manufacturing is a new concept of manufacturing process that works like a single factory with connected multiple factories. The cloud manufacturing system is a kind of large-scale CPS that produces products through the collaboration of distributed manufacturing facilities based on technologies such as cloud computing, IoT, and virtualization. It utilizes diverse and distributed facilities based on centralized information systems, which allows flexible composition user-centric and service-oriented large-scale systems. However, the cloud manufacturing system is composed of a large number of highly heterogeneous subsystems. It has difficulties in interconnection, data exchange, information processing, and system verification for system construction. In this paper, we derive the user requirements of various aspects of the cloud manufacturing system, such as functional, human, trustworthiness, timing, data and composition, based on the CPS Framework, which is the analysis methodology for CPS. Next, by analyzing the user requirements we define the system requirements including scalability, composability, interactivity, dependability, timing, interoperability and intelligence. We map the defined CPS system requirements to the requirements of oneM2M, which is the platform standard for IoT, so that the support of the system requirements at the level of the IoT platform is verified through Mobius, which is the implementation of oneM2M standard. Analyzing the verification result, finally, we propose a large-scale cloud manufacturing platform based on oneM2M that can meet the cloud manufacturing requirements to support the overall features of the Cloud Manufacturing CPS with dependability.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.381-394
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• 2022

Recently, due to the personalization and customization of data, Internet-based services have increased requirements for real-time processing, such as real-time AI inference and data analysis, which must be handled immediately according to the user's situation or requirement. Real-time tasks have a set deadline from the start of each task to the return of the results, and the guarantee of the deadline is directly linked to the quality of the services. However, traditional container systems are limited in operating real-time tasks because they do not provide the ability to allocate and manage deadlines for tasks executed in containers. In addition, tasks such as AI inference and data analysis basically utilize graphical processing units (GPU), which typically have performance impacts on each other because performance isolation is not provided between containers. And the resource usage of the node alone cannot determine the deadline guarantee rate of each container or whether to deploy a new real-time container. In this paper, we propose a monitoring technique for tracking and managing the execution status of deadlines and real-time GPU tasks in containers to support real-time processing of GPU tasks running on containers, and a node list management technique for container placement on appropriate nodes to ensure deadlines. Furthermore, we demonstrate from experiments that the proposed technique has a very small impact on the system.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.10-10
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• 2023

본질적으로 복잡하고 다양한 특성을 가지는 우리나라(도시, 농어촌, 도서산간, 섬 등)의 물 공급 시스템은 생활수준의 향상, 기후변화 및 가뭄위기, 소비환경 중심의 요구와 한정된 수자원을 잘 활용하기 위한 운영 및 관리가 매우 복잡하다. 이로 인한 수자원 고갈과 가뭄위기 등에 관련한 대책 및 방안으로 대체수자원인 지하수 활용방안들이 제시되고 있다. 따라서, 물 관리 시스템과 관련한 디지털 기술은 오늘날 플랫폼과 디지털 트윈의 도입을 통해 네트워크와 가상현실 세계의 연결이 통합되어진 4차 산업혁명 사업이 현실화되고 있다. 물 관리 시스템에 사용된 새로운 디지털 기술 "BDA(Big Data Analytics), CPS(Cyber Physical System), IoT(Internet of Things), CC(Cloud Computing), AI(Artificial Intelligence)" 등의 성장이 증가함에 따라 가뭄대응 위기와 도시 지하수 물 순환 시스템 운영이 증가하는 소비자 중심의 수요를 충족시키기 위해서는 지속가능한 지하수 공급을 효과적으로 관리되어야 한다. 4차 산업혁명과 관련한 기술성장이 증가함으로 인한 물 부문은 시스템의 지속가능성을 향상시키기 위해 전체 디지털화 단계로 이동하고 있다. 이러한 디지털 전환의 핵심은 데이터에 관한 것이며, 이를 활용하여 가치 창출을 위해서 "Digital Groundwater Technology/Twin(DGT)"를 극대화하는 방식으로 제고해야 한다. 현재 당면하고 있는 기후위기에 따른 가뭄, 홍수, 녹조, 탁수, 대체수자원 등의 수자원 재해에 대한 다양한 대응 방안과 수자원 확보 기술이 논의되고 있다. 이에 따른 "물 순환 시스템"의 이해와 함께 문제해결 방안도출을 위하여 이번 "기획 세션"에서는 지하수 수량 및 수질, 정수, 모니터링, 모델링, 운영/관리 등의 수자원 데이터의 플랫폼 동시성 구축으로부터 역동적인 "DGT"을 통한 디지털 트윈화하여, 지표수-토양-지하수 분야의 특화된 연직 프로파일링 관측기술을 다각도로 모색하고자 한다. "Digital Groundwater(DG)"는 지하수의 물 순환, 수량 및 수질 관리, 지표수-지하수 순환 및 모니터링, 지하수 예측 모델링 통합연계를 위해 지하수 플랫폼 동시성, ChatGPT, CPS 및 DT 등의 복합 디지털화 단계로 나가고 있다. 복잡한 지하환경의 이해와 관리 및 보존을 위한 지하수 네트워크에서 수량과 수질 데이터를 수집하기 위한 스마트 지하수 관측기술 개발은 큰 도전이다. 스마트 지하수 관측기술은 BD분석, AI 및 클라우드 컴퓨팅 등의 디지털 기술에 필요한 획득된 데이터 분석에 사용되는 알고리즘의 복잡성과 데이터 품질에 따라 영향을 미칠 수 있기 때문이다. "DG"는 지하수의 정보화 및 네트워크 운영관리 자동화, 지능화 등을 위한 디지털 도구를 활용함으로써 지표수-토양층-지하수 네트워크 통합관리에 대한 비전을 만들 수 있다. 또한, DGT는 지하수 관측센서의 1차원 데이터 융합을 이용한 지하수 플랫폼 동시성과 디지털 트윈을 연계할 수 있다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1D
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• pp.129-136
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• 2008

Along with the development in cutting-edge digital technology, the street space is also being changed. Mobile telecommunication units and internet give a big change in a human being's lifestyle. And the ubiquitous computing is proceeding with expanding its application range from the indoor space to the street space. As the street furniture is the convenient facility that allows a human being's life in street space to be abundant, it is getting advanced. First of all, in terms of such phenomenon, this study analyzed the cases of a research on application of street space and the actual condition of a change in the number of individuals for the street furniture, through a literature research of ubiquitous. Also, it researched into the realities of using the street furniture of the walking-desired streets at Daehac-ro and Hongdae district, where are two representative places related to digital generation. The next was carried out FGI (Focus Group Interview) with users of the street space in front of Hongik University and managers of the street furniture, and was researched into the use & management behavior, and recognition level on the street furniture. Thus, the key elements were extracted such as interchange of information for cultural activities, automation for interaction variability in function. Finally the core elements for future vision of street furniture in this digital era were extracted in 3I, namely, Information, Intellectualization, and Integration. This is considered to be applied to the establishment of direction in the process of high-tech digitalization in street furniture related to information hereafter.

• Journal of Korean Home Economics Education Association
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• v.31 no.4
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• pp.149-161
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• 2019

At present, we are at the point of change of the 4th industrial revolution era due to the development of artificial intelligence(AI) and rapid technological innovation that no one can predict until now. This study started from the question of 'What role should home economics education play in the era of the Fourth Industrial Revolution?'. The Fourth Industrial Revolution is characterized by AI, cloud computing, Internet of Things(IoT), big data, and Online to Offline(O2O). It will drastically change the social system, science and technology and the structure of the profession. Since the dehumanization of robots and artificial intelligence may occur, the 4th Industrial Revolution Education should be sought to foster future human resources with humanity and citizenship for the future community. In addition, the implication of education in the fourth industrial revolution, which will bring about a change to a super-intelligent and hyper-connected society, is that the role of education should be emphasized so that humans internalize their values as human beings. Character education should be established as a generalized and internalized consciousness with a concept established in the integration of the curriculum, and concrete practical strategies should be prepared. In conclusion, home economics education in the 4th industrial revolution era should play a leading role in the central role of character education, and intrinsic improvement of various human lives. The fourth industrial revolution will change not only what we do, or human mental and physical activities, but also who we are, or human identity. In the information society and digital society, it is important how quickly and accurately it is possible to acquire scattered knowledge. In the information society, it is required to learn how to use knowledge for human beings in rapid change. As such, the fourth industrial revolution seeks to lead the family, organization, and community positively by influencing the systems that shape our lives. Home economics education should take the lead in this role.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.2
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• pp.59-75
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• 2019

This study was carried out to explore the 4th Industrial Revolution technology from the perspective of the landscape industry to provide the basic data necessary to increase the virtuous circle value. The 4th Industrial Revolution, the characteristics of the landscape industry and urban regeneration were considered and the methodology was established and studied including the technical classification system suitable for systematic research, which was selected as a framework. First, the 4th Industrial Revolution technology based on digital data was selected, which could be utilized to increase the value of the virtuous circle for the landscape industry. From 'Element Technology Level', and 'Core Technology' such as the Internet of Things, Cloud Computing, Big Data, Artificial Intelligence, Robot, 'Peripheral Technology', Virtual or Augmented Reality, Drones, 3D 4D Printing, and 3D Scanning were highlighted as the 4th Industrial Revolution technology. It has been shown that it is possible to increase the value of the virtuous circle when applied at the 'Trend Level', in particular to the landscape industry. The 'System Level' was analyzed as a general-purpose technology, and based on the platform, the level of element technology(computers, and smart devices) was systematically interconnected, and illuminated with the 4th Industrial Revolution technology based on digital data. The application of the 'Trend Level' specific to the landscape industry has been shown to be an effective technology for increasing the virtuous circle values. It is possible to realize all synergistic effects and implementation of the proposed method at the trend level applying the element technology level. Smart gardens, smart parks, etc. have been analyzed to the level they should pursue. It was judged that Smart City, Smart Home, Smart Farm, and Precision Agriculture, Smart Tourism, and Smart Health Care could be highly linked through the collaboration among technologies in adjacent areas at the Trend Level. Additionally, various utilization measures of related technology applied at the Trend Level were highlighted in the process of urban regeneration, public service space creation, maintenance, and public service. In other words, with the realization of ubiquitous computing, Hyper-Connectivity, Hyper-Reality, Hyper-Intelligence, and Hyper-Convergence were proposed, reflecting the basic characteristics of digital technology in the landscape industry can be achieved. It was analyzed that the landscaping industry was effectively accommodating and coordinating with the needs of new characters, education and consulting, as well as existing tasks, even when participating in urban regeneration projects. In particular, it has been shown that the overall landscapig area is effective in increasing the virtuous circle value when it systems the related technology at the trend level by linking maintenance with strategic bridgehead. This is because the industrial structure is effective in distributing data and information produced from various channels. Subsequent research, such as demonstrating the fusion of the 4th Industrial Revolution technology based on the use of digital data in creation, maintenance, and service of actual landscape space is necessary.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.101-107
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• 2014

With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms' total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash's buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting's memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).