• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.545-554
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• 2019

This research aims to provide a building area extraction approach over the areas where data acquisition is impossible through field surveying, aerial photography and lidar scanning. Hence, high-resolution satellite images, which have high accessibility over the earth, are utilized for the automated building extraction in this study. 3D point clouds or DSM (Digital Surface Models), derived from the stereo image matching process, provides low quality of building area extraction due to their high level of noises and holes. In this regards, this research proposes a hybrid building area extraction approach which utilizes 3D point clouds (from image matching), and color and linear information (from imagery). First of all, ground and non-ground points are separated from 3D point clouds; then, the initial building hypothesis is extracted from the non-ground points. Secondly, color based building hypothesis is produced by considering the overlapping between the initial building hypothesis and the color segmentation result. Afterwards, line detection and space partitioning results are utilized to acquire the final building areas. The proposed approach shows 98.44% of correctness, 95.05% of completeness, and 1.05m of positional accuracy. Moreover, we see the possibility that the irregular shapes of building areas can be extracted through the proposed approach.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.116-126
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• 2021

Ground Component Command (GCC) has been developing operational planning and execution systems to implement "Decisive Integrated Operations", which is the concept of ground operations execution, and achieved remarkable results. In particular, "Simultaneous Offense-Defense Integrated Operations" is developed mainly to neutralize enemies in deep areas and develop favorable conditions for the allies early by simultaneously attacking and defending from the beginning of the war. On the other hand, it is limited to providing scientific and reasonable support for the commander's decision-making process because analyzing the effects of the deep operation with existing M&S systems is impossible. This study developed a model for analyzing the effects of deep operations that can be used in the KJCCS. Previous research was conducted on the effects of surveillance, physical strike, and non-physical strike, which are components of deep operations to find the characteristics and limitations and suggest a research direction. A methodology for analyzing the effects of deep operations reflecting the interactions of components using data was then developed by the GCC, and input data for each field was calculated through combat experiments and a literature review. Finally, the Deep operations Effect CAlculating Model(DECAM) was developed and distributed to the GCC and Corps battle staff during the ROK-US Combined Exercise. Through this study, the effectiveness of the methodology and the developed model were confirmed and contribute to the development of the GCC and Corps' abilities to perform deep operations.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.77-86
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• 2022

The Republic of Korea is a country in which coastal surveillance is an imperative national task as it is surrounded by seas on three sides under the confrontation between South and North Korea. However, due to Defense Reform 2.0, the number of R/D (Radar) operating personnel has decreased, and the period of service has also been shortened. Moreover, there is always a possibility that a human error will occur. This paper presents specific guidelines for developing an AI learning model for the intelligent coastal surveillance system. We present a three-step strategy to realize the guidelines. The first stage is a typical stage of building an AI learning model, including data collection, storage, filtering, purification, and data transformation. In the second stage, R/D signal analysis is first performed. Subsequently, AI learning model development for classifying real and false images, coastal area analysis, and vulnerable area/time analysis are performed. In the final stage, validation, visualization, and demonstration of the AI learning model are performed. Through this research, the first achievement of making the existing weapon system intelligent by applying the application of AI technology was achieved.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.61-67
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• 2024

Soldiers serving in military units and institutions are subject to strict security policies and technologies because they handle sensitive and confidential information related to national security, so they are likely to experience security stress. The purpose of this study is to recognize the need to manage the security stress of military personnel and to suggest management measures. To this end, a literature study was conducted on 12 KCI(Korean Journal Citation Index) journals dealing with security stress. Since 2016, research on security stress has been conducted mainly through empirical analysis through surveys. Studies related to security stress were divided into studies dealing with factors that affect stress, the relationship between security stress and security compliance intentions, and factors that reduce security stress. In particular, it was confirmed that factors such as organizational justice, organizational technical support, and security feedback can alleviate security stress. Next, by applying the results of this literature study to the defense security environment, we presented security stress management measures for military personnel in terms of improving security-related organizational justice awareness, technical support, and security feedback. The significance of this study is that we recognized the need to manage military personnel's security stress and reviewed practical measures related to this.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.63-88
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• 2019

The goal of this paper is to investigate changes in North Korea's domestic and foreign policies through automated text analysis over North Korea represented in South Korean mass media. Based on that data, we then analyze the status of text mining research, using a text mining technique to find the topics, methods, and trends of text mining research. We also investigate the characteristics and method of analysis of the text mining techniques, confirmed by analysis of the data. In this study, R program was used to apply the text mining technique. R program is free software for statistical computing and graphics. Also, Text mining methods allow to highlight the most frequently used keywords in a paragraph of texts. One can create a word cloud, also referred as text cloud or tag cloud. This study proposes a procedure to find meaningful tendencies based on a combination of word cloud, and co-occurrence networks. This study aims to more objectively explore the images of North Korea represented in South Korean newspapers by quantitatively reviewing the patterns of language use related to North Korea from 2016. 11. 1 to 2019. 5. 23 newspaper big data. In this study, we divided into three periods considering recent inter - Korean relations. Before January 1, 2018, it was set as a Before Phase of Peace Building. From January 1, 2018 to February 24, 2019, we have set up a Peace Building Phase. The New Year's message of Kim Jong-un and the Olympics of Pyeong Chang formed an atmosphere of peace on the Korean peninsula. After the Hanoi Pease summit, the third period was the silence of the relationship between North Korea and the United States. Therefore, it was called Depression Phase of Peace Building. This study analyzes news articles related to North Korea of the Korea Press Foundation database(www.bigkinds.or.kr) through text mining, to investigate characteristics of the Kim Jong-un regime's South Korea policy and unification discourse. The main results of this study show that trends in the North Korean national policy agenda can be discovered based on clustering and visualization algorithms. In particular, it examines the changes in the international circumstances, domestic conflicts, the living conditions of North Korea, the South's Aid project for the North, the conflicts of the two Koreas, North Korean nuclear issue, and the North Korean refugee problem through the co-occurrence word analysis. It also offers an analysis of South Korean mentality toward North Korea in terms of the semantic prosody. In the Before Phase of Peace Building, the results of the analysis showed the order of 'Missiles', 'North Korea Nuclear', 'Diplomacy', 'Unification', and ' South-North Korean'. The results of Peace Building Phase are extracted the order of 'Panmunjom', 'Unification', 'North Korea Nuclear', 'Diplomacy', and 'Military'. The results of Depression Phase of Peace Building derived the order of 'North Korea Nuclear', 'North and South Korea', 'Missile', 'State Department', and 'International'. There are 16 words adopted in all three periods. The order is as follows: 'missile', 'North Korea Nuclear', 'Diplomacy', 'Unification', 'North and South Korea', 'Military', 'Kaesong Industrial Complex', 'Defense', 'Sanctions', 'Denuclearization', 'Peace', 'Exchange and Cooperation', and 'South Korea'. We expect that the results of this study will contribute to analyze the trends of news content of North Korea associated with North Korea's provocations. And future research on North Korean trends will be conducted based on the results of this study. We will continue to study the model development for North Korea risk measurement that can anticipate and respond to North Korea's behavior in advance. We expect that the text mining analysis method and the scientific data analysis technique will be applied to North Korea and unification research field. Through these academic studies, I hope to see a lot of studies that make important contributions to the nation.

• Management & Information Systems Review
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• v.39 no.2
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• pp.39-60
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• 2020

Block chain technology is one of the core elements for realizing the 4^th industrial revolution, and many efforts have been made by government and companies to provide services based on block chain technology. In this study we analyzed the benefits of block chain technology for EVMS and designed EVMS block chain platform with increased data security and work efficiency for project management data, which are important assets in monitoring progress, foreseeing future events, and managing post-completion. We did the case studies on the benefits of block chain technology and then conducted the survey study on security, reliability, and efficiency of block chain technology, targeting 18 block chain experts and project developers. And then, we interviewed EVMS system operator on the compatibility between block chain technology and EVM Systems. The result of the case studies showed that block chain technology can be applied to financial, logistic, medical, and public services to simplify the insurance claim process and to improve reliability by distributing transaction data storage and applying security·encryption features. Also, our research on the characteristics and necessity of block chain technology in EVMS revealed the improvability of security, reliability, and efficiency of management and distribution of EVMS data. Finally, we designed a network model, a block structure, and a consensus algorithm model and combined them to construct a conceptual block chain model for EVM system. This study has the following contribution. First, we reviewed that the block chain technology is suitable for application in the defense sector and proposed a conceptual model. Second, the effect that can be obtained by applying block chain technology to EVMS was derived, and the possibility of improving the existing business process was derived.

• The Journal of Society for e-Business Studies
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• v.20 no.3
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• pp.59-77
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• 2015

Social networking sites (SNS), such as Facebook, provide abundant social comparison opportunities. Given the widespread use of SNSs, the purpose of the present study was to examine the impact of exposure to social media-based social comparison on user's negative emotions and discontinuous use intention on SNS. We present evidence that under the use of SNS, social comparison activities diverge into three patterns, with explicit self-evaluation desire made against similar target (lateral comparison), self-defense desire made against less fortunate target (downward comparison), and self-enhancement desire made with more fortunate target (upward comparison). Such social comparison processes frequently arise, as people are increasingly using on SNSs, the downward contacts ameliorating self-esteem with positive emotions, but the upward contacts and standard contacts with lateral status enabling a person to compare his or her situation with others and simultaneously increase negative emotions due to its differences with others. In other words, as people increasingly relying on SNSs for a variety of everyday tasks, they risk overexposure to upward or standard social comparison information that may have a cumulative detrimental impact on future intention on SNS use. This study with survey with 209 SNS users found that these negative emotions lead to negative fatigue (attitude) and then discontinuous use intention (behavior) on SNS. Our findings are among the first to explicitly examine discontinuous use intention on SNS using social comparison theory and our results are consistent with those of past research showing that upward social comparisons can be detrimental.