• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.786-792
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• 2022

In the cloud computing environment, there has been a lot of research into the Fog/Edge Computing (FEC) paradigm for securing user proximity of application services and computation offloading to alleviate service delay difficulties. The method of predicting dynamic location change patterns of edge devices (moving objects) requesting application services is critical in this FEC environment for efficient computing resource distribution and deployment. This paper proposes an optimal moving pattern extraction algorithm in which variable weights (distance, time, congestion) are applied to selected paths in addition to a support factor threshold for frequency patterns (moving objects) of edge devices. The proposed algorithm is compared to the OPE_freq [8] algorithm, which just applies frequency, as well as the A^* and Dijkstra algorithms, and it can be shown that the execution time and number of nodes accessed are reduced, and a more accurate path is extracted through experiments.

• Industry Promotion Research
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• v.7 no.3
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• pp.17-26
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• 2022

As the well-being mindset to keep our minds and bodies free and healthy more than anything else in the society we live in is spreading, the meaning of health care has become a key part of the 4th industrial revolution such as big data, IoT, AI, and block chain. The advancement of the advanced medical information service industry is being promoted by utilizing convergence technology. In digital healthcare, the development of intelligent information technology such as artificial intelligence, big data, and cloud is being promoted as a digital transformation of the traditional medical and healthcare industry. In addition, due to rapid development in the convergence of science and technology environment, various issues such as health, medical care, welfare, etc., have been gradually expanded due to social change. Therefore, in this study, first, the general meaning and current status of digital health care medical information is examined, and then, developmental tasks to activate digital health care medical information are analyzed and reviewed. The purpose of this article is to improve usability to fully pursue our human freedom.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.2
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• pp.47-60
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• 2023

This study proposed to adopt consortium blockchain for the database in the research support system of national R&D projects in order to increase efficiency, to reduce administrative burden, and to promote transparent research environment focusing on servicing researchers. Specifically, storage methods were classified according to data characteristics. First, data that requires integrity and transparency is stored in the blockchain, Second, confidentiality and data that require modification and deletion are stored in the database, Third, data that requires confidentiality, integrity, and transparency at the same time stores the original in the database, and the hash value of the data is separately stored in the blockchain. If research support system adopts blockchain, it is possible to operate the system stably, to share quick exchange of information between research institutes, to reduce administrative burden, to improve transparency of process, to resolve asymmetry of information, and to secure integrity, confidentiality, and availability of data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.1
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• pp.87-97
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• 2023

Containers are an emerging virtualization technology that can build an isolated environment more lightweight and faster than existing virtual machines. For that reason, many organizations have recently adopted them for their services. Yet, the container architecture has also exposed many security problems since all containers share the same OS kernel. In this work, we focus on the fact that an attacker can abuse host resources to make them unavailable to benign containers-also known as host resource exhaustion attacks. Then, we analyze the impact of host resource exhaustion attacks through real attack scenarios exhausting critical host resources, such as CPU, memory, disk space, process ID, and sockets in Docker, the most popular container platform. We propose five attack scenarios performed in several different host environments and container images. The result shows that three of them put other containers in denial of service.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.153-158
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• 2023

The purpose of current study was to identify relations between pregnancy-childbirth infrastructures and intention to childbirth and relocation. We conducted a logistic regression analysis to determine the influence of the pregnancy and childbirth infrastructure level over the people's intentions to have any additional pregnancy and to relocate to other city. The results have showed that the younger the age and the higher the income is, the stronger the intention to have an additional pregnancy becomes, and that of the pregnancy and childbirth infrastructure, only the level of pediatrics service would affect the intention to have another pregnancy. As for the intention to relocate or move to another locations, the results have shown that such intention tends to decline where there are overall sufficient and good pregnancy and childbirth infrastructure in place.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.97-111
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• 2024

Developing countries and small local governments face financial constraints, limiting the adoption of their own bus information systems. However, despite poor social infrastructure and low income levels, developing countries have a high smartphone penetration rate, and the distribution and usage of online content and social media are widespread. Smartphones, equipped with GPS sensors, cameras, and other location-based information collection capabilities, can replace expensive on-site terminals. This study aims to replace expensive on-site terminals with smartphones, develop a center system based on cloud servers, and establish an extensible Open BIS (Bus Information System) service and platform that can be applied anywhere. The goal is to formulate a business model in the process.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.77-84
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• 2024

Over the past few years, IoT edges have begun to emerge based on new low-latency communication protocols such as 5G. However, IoT edges, despite their enormous advantages, pose new complementary threats, requiring new security solutions to address them. In this paper, we propose a cloud environment-based IoT edge architecture model that complements IoT systems. The proposed model acts on machine learning to prevent security threats in advance with network traffic data extracted from IoT edge devices. In addition, the proposed model ensures load and security in the access network (edge) by allocating some of the security data at the local node. The proposed model further reduces the load on the access network (edge) and secures the vulnerable part by allocating some functions of data processing and management to the local node among IoT edge environments. The proposed model virtualizes various IoT functions as a name service, and deploys hardware functions and sufficient computational resources to local nodes as needed.

• Journal of the Institute of Convergence Signal Processing
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• v.25 no.1
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• pp.15-20
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• 2024

In the field of ship inspection in South Korea, due to outdated workflow processes, there is a possibility of tampering with inspection results. Accordingly, research is being conducted to prevent tampering with inspection results by introducing blockchain technology and cloud-based systems that allow real-time tracking and sharing of data, and to establish a transparent and efficient communication system. In this study, unit and integrated processes for overall data management and inspection execution related to ship inspection were implemented to automatically collect, manage, and track various inspection results occurring during the ship inspection process. Through this, it aimed to increase the efficiency of the ship inspection process overall, inducing growth in the ship inspection industry as a whole. The implemented web portal reached a level where trend analysis and comparative analysis with other ships based on inspection results are possible, and subsequent research aims to demonstrate the excellence of the system.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.374-390
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• 2021

Growth and yield of field vegetable crops would be affected by climate conditions, which cause a relatively large fluctuation in crop production and consumer price over years. The yield prediction system for these crops would support decision-making on policies to manage supply and demands. The objectives of this study were to compile literatures related to onion and garlic and to perform data-mining analysis, which would shed lights on the development of crop models for these major field vegetable crops in Korea. The literatures on crop growth and yield were collected from the databases operated by Research Information Sharing Service, National Science & Technology Information Service and SCOPUS. The keywords were chosen to retrieve research outcomes related to crop growth and yield of onion and garlic. These literatures were analyzed using text mining approaches including word cloud and semantic networks. It was found that the number of publications was considerably less for the field vegetable crops compared with rice. Still, specific patterns between previous research outcomes were identified using the text mining methods. For example, climate change and remote sensing were major topics of interest for growth and yield of onion and garlic. The impact of temperature and irrigation on crop growth was also assessed in the previous studies. It was also found that yield of onion and garlic would be affected by both environment and crop management conditions including sowing time, variety, seed treatment method, irrigation interval, fertilization amount and fertilizer composition. For meteorological conditions, temperature, precipitation, solar radiation and humidity were found to be the major factors in the literatures. These indicate that crop models need to take into account both environmental and crop management practices for reliable prediction of crop yield.