• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.262-269
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• 2021

The Ministry of National Defense is re-establishing the role of the Army in accordance with the defense reform and is promoting the Warrior Platform, a next-generation individual combat system. The Warrior Platform project is divided into three stages and is being promoted. In the first stage, the quality and performance of individual items are improved, in the second stage, items between system development are integrated, and in the third stage, the combat capability is maximized by developing an integrated unit weapon system. In this paper, detailed sub-items for the five essential required competencies (survival, lethality, mobility, sustainability, Communication) that are considered for building an effective warrior platform are presented. We also present a plan that can be used to prepare a specific master plan for the Army's Warrior Platform project by using Analytic Hierarchy Process(AHP) and selecting the priority of the five required capabilities and detailed sub-items for different unit types. As a result of analyzing the priorities of the four types of units with different mission types, we find that there are differences for each unit. These results are expected to be used as useful reference materials for setting the future direction for the development of warrior platform.

• 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.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.311-326
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• 2022

The Compact Advanced Satellite 500-4 (CAS500-4) is under development to efficiently manage and monitor forests in Korea and is scheduled to launch in 2025. The National Institute of Forest Science is developing 36 types of forestry applications to utilize the CAS500-4 efficiently. The products derived using the remote sensing method require validation with ground reference data, and the quality monitoring results for the products must be continuously reported. Due to it being the first time developing the national forestry satellite, there is no official calibration and validation site for forestry products in Korea. Accordingly, the author designed a calibration and validation site for the forestry products following international standards. In addition, to install calibration and validation sites nationwide, the authors selected appropriate sensors and evaluated the applicability of the sensors. As a result, the difference between the ground observation data and the Sentinel-2 image was observed to be within ±5%, confirming that the sensor could be used for nationwide expansion.

• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.711-719
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• 2021

EBS has been producing numerous educational contents with traditional virtual studio production systems since the early 2000s and applied AR video production system in October 2020, twenty-years after. Although the basic concept of synthesizing graphic elements and actual image in real time by tracking camera movement and lens information is similar to the previous one but the newly applied AR video production system contains some of advanced technologies that are improved over the previous ones. Marker tracking technology that enables camera movement free and position tracking has been applied that can track the location stably, and the operating software has been applied with Unreal Engine, one of the representative graphic engines used in computer game production, therefore the system's rendering burden has been reduced, enabling high-quality and real-time graphic effects. This system is installed on a crane camera that is mainly used in a crane shot at the live broadcasting studio and applied for live broadcasting programs for children and some of the videos such as program introductions and quiz events that used to be expressed in 2D graphics were converted to 3D AR videos which has been enhanced. This paper covers the effect of introduction and application of the AR video production system on EBS content production and the future development direction and possibility.

• Informatization Policy
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• v.29 no.3
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• pp.26-47
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• 2022

"The certification of compliance of new products of industrial convergence" (hereinafter referred to as "certification of compliance") is a legal certification system in accordance with the Industrial Convergence Promotion Act through which a convergence new product can be officially certified without legislation when the certification standards applicable to the product are not yet provided. Unlike other certification systems, the certification of compliance is characterized by the role of resolving the certification difficulties driven by the regulatory lag of convergence new products. Nevertheless, studies that analyzed the certification of compliance in detail from the viewpoint of regulatory improvement were surprisingly rare. Through the sequential matching of the steps of certification of compliance with the process from the occurrence of a regulatory problem to resolution, our study provided clear understanding as to how the regulatory lag could be reduced by the procedure for certification of compliance. Furthermore, we divided the perspective on regulatory lag management into quantitative and qualitative, and the structures and practices of certification of compliance were then analyzed from the two perspectives. By doing this, the present study emphasized that the fundamental reason the certification of compliance could effectively solve the regulatory lag problem of convergence new products was not only the quantitative elements such as legal deadlines for each step but also several qualitative approaches to securing the quality of every stage.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.3
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• pp.73-82
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• 2022

Auto-scaling is one of the most important functions for cloud computing technology. Even if the number of users or service requests is explosively increased or decreased, system resources and service instances can be appropriately expanded or reduced to provide services suitable for the situation and it can improves stability and cost-effectiveness. However, since the policy is performed based on a single metric data at the time of monitoring a specific system resource, there is a problem that the service is already affected or the service instance that is actually needed cannot be managed in detail. To solve this problem, in this paper, we propose a method to predict system resource and service response time using a multivariate time series analysis model and establish an auto-scaling policy based on this. To verify this, implement it as a custom scheduler in the Kubernetes environment and compare it with the Kubernetes default auto-scaling method through experiments. The proposed method utilizes predictive data based on the impact between system resources and response time to preemptively execute auto-scaling for expected situations, thereby securing system stability and providing as much as necessary within the scope of not degrading service quality. It shows results that allow you to manage instances in detail.

• Korean Journal of Artificial Intelligence
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• v.11 no.2
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• pp.39-44
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• 2023

The creativity of thesis is that the significance of cyber security challenges in blockchain. The variety of enterprises, including those in the medical market, are the targets of cyberattacks. Hospitals and clinics are only two examples of medical facilities that are easy targets for cybercriminals, along with IoT-based medical devices like pacemakers. Cyberattacks in the medical field not only put patients' lives in danger but also have the potential to expose private and sensitive information. Reviewing and looking at the present and historical flaws and vulnerabilities in the blockchain-based IoT and medical institutions' equipment is crucial as they are sensitive, relevant, and of a medical character. This study aims to investigate recent and current weaknesses in medical equipment, of blockchain-based IoT, and institutions. Medical security systems are becoming increasingly crucial in blockchain-based IoT medical devices and digital adoption more broadly. It is gaining importance as a standalone medical device. Currently the use of software in medical market is growing exponentially and many countries have already set guidelines for quality control. The achievements of the thesis are medical equipment of blockchain-based IoT no longer exist in a vacuum, thanks to technical improvements and the emergence of electronic health records (EHRs). Increased EHR use among providers, as well as the demand for integration and connection technologies to improve clinical workflow, patient care solutions, and overall hospital operations, will fuel significant growth in the blockchain-based IoT market for linked medical devices. The need for blockchain technology and IoT-based medical device to enhance their health IT infrastructure and design and development techniques will only get louder in the future. Blockchain technology will be essential in the future of cybersecurity, because blockchain technology can be significantly improved with the cybersecurity adoption of IoT devices, i.e., via remote monitoring, reducing waiting time for emergency rooms, track assets, etc. This paper sheds the light on the benefits of the blockchain-based IoT market.

• Journal of Ginseng Culture
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• v.6
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• pp.116-134
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• 2024

This study aimed to identify the issues in Korean ginseng exports through analyzing the ginseng market. Therefore, the study examined the current ginseng production status in South Korea and China, the major ginseng-producing countries in Northeast Asia, including cultivated areas, harvested areas, and production volumes. For South Korea, specific data on ginseng, such as average prices, operating costs, and production costs, were compiled to demonstrate the production competitiveness of Korean ginseng from a production perspective. Furthermore, as major ginseng-exporting countries, South Korea, China, and Hong Kong's export trends, including export quantities, export values, and export prices, as well as crucial export items and tariff rates, were summarized to showcase the export competitiveness of Korean ginseng. Additionally, this study aimed to understand the consumption patterns of ginseng in China, Hong Kong, and Taiwan by presenting various cases and events in these countries. Based on information related to production, export, and consumption, this study identified obstacles in the ginseng export process, including market downturns, weakened price competitiveness of Korean ginseng, increased market share of competing products like Chinese and Western ginseng, a lack of promotion and marketing, and insufficient development and export of various ginseng products. In response, strategies for overcoming these obstacles were proposed, including diversifying exports, establishing effective production systems, enhancing quality and branding, strengthening promotion and marketing efforts, and developing various ginseng products.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.1-18
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• 2016

Owing to the extensive use of Web media and the development of the IT industry, a large amount of data has been generated, shared, and stored. Nowadays, various types of unstructured data such as image, sound, video, and text are distributed through Web media. Therefore, many attempts have been made in recent years to discover new value through an analysis of these unstructured data. Among these types of unstructured data, text is recognized as the most representative method for users to express and share their opinions on the Web. In this sense, demand for obtaining new insights through text analysis is steadily increasing. Accordingly, text mining is increasingly being used for different purposes in various fields. In particular, issue tracking is being widely studied not only in the academic world but also in industries because it can be used to extract various issues from text such as news, (SocialNetworkServices) to analyze the trends of these issues. Conventionally, issue tracking is used to identify major issues sustained over a long period of time through topic modeling and to analyze the detailed distribution of documents involved in each issue. However, because conventional issue tracking assumes that the content composing each issue does not change throughout the entire tracking period, it cannot represent the dynamic mutation process of detailed issues that can be created, merged, divided, and deleted between these periods. Moreover, because only keywords that appear consistently throughout the entire period can be derived as issue keywords, concrete issue keywords such as "nuclear test" and "separated families" may be concealed by more general issue keywords such as "North Korea" in an analysis over a long period of time. This implies that many meaningful but short-lived issues cannot be discovered by conventional issue tracking. Note that detailed keywords are preferable to general keywords because the former can be clues for providing actionable strategies. To overcome these limitations, we performed an independent analysis on the documents of each detailed period. We generated an issue flow diagram based on the similarity of each issue between two consecutive periods. The issue transition pattern among categories was analyzed by using the category information of each document. In this study, we then applied the proposed methodology to a real case of 53,739 news articles. We derived an issue flow diagram from the articles. We then proposed the following useful application scenarios for the issue flow diagram presented in the experiment section. First, we can identify an issue that actively appears during a certain period and promptly disappears in the next period. Second, the preceding and following issues of a particular issue can be easily discovered from the issue flow diagram. This implies that our methodology can be used to discover the association between inter-period issues. Finally, an interesting pattern of one-way and two-way transitions was discovered by analyzing the transition patterns of issues through category analysis. Thus, we discovered that a pair of mutually similar categories induces two-way transitions. In contrast, one-way transitions can be recognized as an indicator that issues in a certain category tend to be influenced by other issues in another category. For practical application of the proposed methodology, high-quality word and stop word dictionaries need to be constructed. In addition, not only the number of documents but also additional meta-information such as the read counts, written time, and comments of documents should be analyzed. A rigorous performance evaluation or validation of the proposed methodology should be performed in future works.