• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.746-754
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• 2016

Smart healthcare systems, a convergent industry based on information and communications technologies (ICT), has emerged from personal health management to remote medical treatment as a distinguished industry. The smart healthcare environment provides technology to deliver vital information, such as pulse rate, body temperature, health status, and so on, from wearable devices to the hospital network where the physician is located. However, since it deals with the patient's personal medical information, there is a security issue for personal information management, and the system may be vulnerable to cyber-attacks in wireless networks. Therefore, this study focuses on a key-development and device-management system to generate keys in the smart environment to safely manage devices. The protocol is designed to provide safe communications with the generated key and to manage the devices, as well as the generated key. The security level is analyzed against attack methods that may occur in a healthcare environment, and it was compared with existing key methods and coding capabilities. In the performance evaluation, we analyze the security against attacks occurring in a smart healthcare environment, and the security and efficiency of the existing key encryption method, and we confirmed an improvement of about 15%, compared to the existing cipher systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.942-948
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• 2020

In this paper, we propose a clustered algorithm that possible more efficient user distinction within clustering using context-aware attribute information. In typically, the data provided to classify interrelationships within cluster information in the process of clustering data will be as a degrade factor if new or newly processing information is treated as contaminated information in comparative information. In this paper, we have developed a clustering algorithm that can extract user's recognition information to solve this problem in using K-means algorithm. The proposed algorithm analyzes the user's clustering attributed parameters from user clusters using accumulated information and clustering according to their attributes. The results of the simulation with the proposed algorithm showed that the user management system was more adaptable in terms of classifying and maintaining multiple users in clusters.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1696-1707
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• 2021

With the recent growth of information technology, various literacy such as digital literacy, data literacy, AI literacy is being studied. In this paper, we focus on data visualization literacy as visualization is an essential part of big data analysis and is used in several mobile apps. Visualization Literacy Assessment Test(VLAT) was developed in 2016 and we introduce how the test was developed and modified to a Korean version, K-VLAT. K-VLAT is consisted of 12 visualizations and 53 questions through a website. Additionally, to understand the research trend in visualization literacy we analyzed 81 papers that had cited the VLAT publication. We categorized the research into 4 categories with 11 sub-categories. The area of studies visualization literacy related to was understanding the relation with cognition, expanding the literacy measures, relation with education, utilization for developing user-centric dashboards or using the test to show effectiveness of visualizations. At last, we discuss about different ways to utilize K-VLAT for future research.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2176-2183
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• 2017

The value of Big-Data which is a main factor of the fourth Industrial Revolution enhances industrial productivity in private sector and provides administrative services for nations and corporates in public sector. ICT-developed countries are coming up with Big-Data application in public sector rapidly. Especially, when it comes to social crisis management, they are equipped with pre-forcasting system. Korean Government also emphasizes Big-Data application in public sector for the social crisis management. But the reality where the overall infrastructure vulnerability reveals requires preparation and operation of measurement for social problems. Accordingly, we need to analyze Big-Data application problem and benchmark the precedented cases, thereby, direct policy diversity. Hence, this paper proposes the roles and rules of E-government analyzing problems from Big-Data application. The following policy proposes open Information and legal&institutional improvement, Big-Data service considerations threatening privacy issues in Big-Data ecosystem, necessity of operational and analytical technology for Big-Data and related technology in technical implication of Big-Data.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.439-439
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• 2023

근래 국내에서는 기후변화로 인한 국지성 호우가 점점 늘어나는 추세로 급격한 하천 수위상승 및 유량 증가로 인해 지속적으로 홍수피해가 발생하고 있으며, 이를 예방하기 위한 실시간 자료수집의 중요성이 증대되고 있다. 이러한 사회적 환경을 고려하여 우리는 물 순환에 관한 자료를 실시간으로 수집하고 홍수예보를 위한 수문조사시설을 설치하여 운영하고 있으나, 대부분 하천과 인접한 곳에 설치되는 시설 특성상 시스템 오류, 전원 이상 발생 등 다양한 요인으로 발생하는 자료 결측·손실에 즉각적인 조치가 어려운 실정이다. 이에, 현장 기반 시설의 안정적인 운영을 통한 연속성 있는 자료 제공을 위해 수문조사시설 중 하천 내 설치된 유량측정시스템에 ICT·사물인터넷(IoT, Internet of Things)을 적용하여 현장 환경-정보 등 언택트(non-contact) 모니터링을 통해 실시간 점검을 수행하였다. 그 결과 2022년 기준 총 508회(현장점검 358회) 점검 중 150회 원격점검을 수행하였고, 이중 74회 즉각 점검 및 복구 조치가 이루어져 점검 시간 단축을 통한 자료 결측 최소화, 현장점검 최소화를 통해 효율적인 시설 운영이 가능하도록 하였다. 또한, 점검을 위해 현장 이동 시 발생하는 이산화탄소 배출량 저감으로 탄소중립 효과도 나타낼 수 있었다. 코로나바이러스감염증-19 이후 사회환경 패러다임 전환에 따라 비대면 활성화, 탄소중립, 안전하고 건전한 사회환경 조성 등과 같이 대면 위주로 운영되는 현장 시설의 관리 방향 또한 사회적 상황을 고려하여 효율적인 시설물 운영, 예산 절감, 자료의 연속성 확보 등을 위해 적극적인 운영 방향의 전환이 필요하다고 판단된다.

• Economic and Environmental Geology
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• v.51 no.3
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• pp.279-289
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• 2018

With the launch of the new government in 2017, National Planning and Advisory Committee (NPAC) announced the five-year plan for the government administration and the 100 national tasks. The Ministry of Science and ICT (Information & Communication Technology) (MSIT) and other government agencies issued work plans for 2018, including R&D plans. Analyzing the changes in government policy will be a major task of public research and development institutions. This study analyzed the changes of the R&D policy of the new government and the R&D policy direction and strategic plan of the government departments related to the geoscience and mineral resources in 2018. The results of the analysis are as follows: 1) to lead the innovation growth through the 4th industrial revolution, 2) to supply clean energy and to cope with climate change in the environment and energy field, 3) to improve the quality of life through problem solving in the life of the people and disaster prevention. Considering the investment direction of the government, it is necessary to lead the geoscience and mineral resources R&D to solve problems of the society and to grow by the science and technology.

• Journal of the Korean Geographical Society
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• v.50 no.3
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• pp.325-338
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• 2015

Telemedicine, which gives or receives medical information via ICT (information and communication technology), is regarded as innovation in a medical field and its application is various according to offline conditions. For example, the utilization of telemedicine in Korea is unfair because of the administrative discretion, which is the basic unit of telemedicine for its practical operation, in spite of the same diagnostic area. With this mind, this study investigates the cause of regional differences of telemedicine through a case of Kangwon province. Furthermore, the crucial matter is that regional differences of telemedicine are associated with digital divide; therefore, this research considers digital divide triggered by telemedicine. The core results are as follows. First, there are little measures such as increase of the staff, economic compensation for public officials, education of telemedicine facilities; accordingly, only regions, where can accept these insufficient conditions, manage the telemedicine system. Second, the interesting of a mayor or a governor and a head of a health center as a highest decision maker has something to do with different utilization of telemedicine. Third, public health doctors play a role as practical operators in telemedicine, but their stance is skeptic about telemedicine somewhat because of the relationship with the medical association opposing the implementation of telemedicine, unimproved regional health care condition, etc. Forth, it seems that the digital divide caused by the regional differences of the present telemedicine utilization does not led to tangible results and is not turned to another disparity so far, the proper measures are required considering that various health care services based on telemedicine will be extended.

• Journal of Korean Institute of Industrial Engineers
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• v.43 no.3
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• pp.229-237
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• 2017

Presented in this paper is a case study of constructing a smart factory for aircraft parts. The construction procedure involves four phases. First of all, its management goals are set, and layout design and simulation are carried out in the conceptual design phase. In the detail design phase, operating scenarios for each module are written out, and probable risks are analyzed by expert groups, and then requirements for developing equipments and subsystems are determined with consideration for element technologies and their integration schemes into the smart factory. In the fabrication and installation phase, system development, equipment fabrication and installation are proceeded in a separate manner, and then integrated together subsequently. In the operation and improvement phase, the factory is stabilized, sophisticated and improved constantly during real operation.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.