• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.7
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• pp.1075-1081
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• 2013

Twitter, Facebook, and KakaoStory are the major SNS in Korea. Social knowledge production is being produced by those services from numerous collaboration and co-participation in those SNS. Wikipedia or Naver JishikIN service was regarded as the representative product of collective knowledge production during the wired internet era. However now at the wireless internet era centered with smart phones, various forms of collective knowledge production would be achieved by connecting to SNS in real-time. In this thesis, the survey data of collective knowledge production for users of three SNS have been compared and analyzed. The difference of the collective knowledge production mechanism among Twitter, Facebook and KakaoStory has been studied and compared through three variables: the motivation of collective knowledge production, the preference of collective knowledge production model, and collective knowledge production cultural perception. As a result of the analysis of the discriminant factors for three SNS user groups, it turns out that the diversity-toward usage motivation, personal contribution motivation, and collective knowledge production tendency perception are the most influential variables. This thesis is of significance in that it unites the value of social science such as social capital and collective knowledge production from the viewpoint of computer science and opens the new chapter of collective knowledge production with the real-time SNS of wireless internet from the wired internet.

• Convergence Security Journal
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• v.21 no.2
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• pp.11-18
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• 2021

To protect tangible and intangible assets, most of the companies are conducting information protection monitoring by using various security equipment in the IT service network. As the security equipment that needs to be protected increases in the process of upgrading and expanding the service network, it is difficult to monitor the possible exposure to the attack for the entire service network. As a countermeasure to this, various studies have been conducted to detect external attacks and illegal communication of equipment, but studies on effective monitoring of the open service ports and construction of illegal communication monitoring system for large-scale service networks are insufficient. In this study, we propose a framework that can monitor information leakage and illegal communication attempts in a wide range of service networks without large-scale investment by analyzing 'Netflow statistical information' of backbone network equipment, which is the gateway to the entire data flow of the IT service network. By using machine learning algorithms to the Netfllow data, we could obtain the high classification accuracy of 94% in identifying whether the Telnet service port of operating equipment is open or not, and we could track the illegal communication of the damaged equipment by using the illegal communication history of the damaged equipment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.183-190
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• 2011

In this paper, we propose a prototype system for abnormal sound detection and identification which detects and recognizes the abnormal situations by means of analyzing audio information coming in real time from CCTV cameras under surveillance environment. The proposed system is composed of two layers: The first layer is an one-class support vector machine, i.e., support vector data description (SVDD) that performs rapid detection of abnormal situations and alerts to the manager. The second layer classifies the detected abnormal sound into predefined class such as 'gun', 'scream', 'siren', 'crash', 'bomb' via a sparse representation classifier (SRC) to cope with emergency situations. The proposed system is designed in a hierarchical manner via a mixture of SVDD and SRC, which has desired characteristics as follows: 1) By fast detecting abnormal sound using SVDD trained with only normal sound, it does not perform the unnecessary classification for normal sound. 2) It ensures a reliable system performance via a SRC that has been successfully applied in the field of face recognition. 3) With the intrinsic incremental learning capability of SRC, it can actively adapt itself to the change of a sound database. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.1-26
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• 2020

The term 'Unicorns' in the corporate ecosystem was firstly introduced by Aileen Lee in 2013. It has been actively discussed in South Korea particularly to compare the level of the 'start-up ecosystem' from a global perspective. Accordingly, the Korean government has recently set a policy goal 'to nurture 20 Korean unicorn companies by 2022'. While the phenomenon of 'Unicorn Club Company' has been brought to the level of policy objectives and spread more widely to the public, existing academic research to understand its substantial and underlying implications has been insufficient. First, in this study, the characteristics of 479 'Unicorn Club' companies in 2019 were analyzed in-depth. Previous research has focused on the general status and trend by analyzing the number of unicorn companies by country and industry classifications. However, this study conducted a qualitative exploratory analysis by investigating descriptive statistics about unicorn companies, including their investors, while providing case studies. Also, cluster analysis, ANOVA, and multi-level regression were employed for quantitative exploration. The characteristics of individual companies were examined based on the "ERIS Model (Entrepreneur - Industry(Market) - Resource - Strategy Model)". Secondly, factors influencing its valuations were examined in connection with the previously analyzed characteristic variables and investor characteristics. Finally, based on these, the future direction of the "Unicorn Phenomenon" from the perspective of "Enterprise Ecosystem" and productively using it from the perspective of the public policy is suggested.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.1
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• pp.1-12
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• 2022

Tilia amurensis is an important honey plant. As T. amurensis mainly distributes mountainous area with various elevations in Korea, accurate prediction of blooming time at the different elevation would benefit forest beekeepers. In this study, we measured time-dependent blooming progress of T. amurensis in Mt. Gariwang area ranging from 500-1500m. Additionally we collected blooming data from web and published literatures and estimated the variation of blooming time relative to the geographic locations. Flowers began to bloom from July 6 to July 22 with full blooming on July 14 in location where elevation is 638m in Mt. G ariwang area in 2021. Based on these databases, a growing degree day (G DD) model was developed for prediction of T. amurensis blooming progress using average daily temperatures. Using the starting date of G DD accumulation of January 1 and base temperature of 5 ℃, blooming period ranging from 10% to 90% of cumulative blooming rate was estimated as 860-1198 degree days (DD). This corresponded to the beginning to the end of July in Mt. Gariwaning area in 2021. This model could explain the phenological variations of T. amurensis flower blooming possibly affected by elevation within geographic area, latitude or year relative to the climate change, and aid forest beekeepers for better timing of nectar foraging by honey bees.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.3
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• pp.85-96
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• 2023

This study developed and evaluated a safe route guidance service for electric personal assistive mobility device used mainly by the mobility impaired people to improve their mobility. Thirteen underlying factors affecting the mobility of electric personal assistive mobility device have been derived through a survey with the mobility impaired people and employees in related organizations in Busan Metropolitan City. After assigning safety scores to individual factors and identifying the relevant factors along routes of interest with an object detection AI model, the safe route for electric personal assistive mobility device was provided through an optimal path-finding algorithm. As a result of comparing the general route of T-map and the recommended route of this study for the identical routes, the latter had relatively fewer obstacles and the gentler slope than the former, implicating that the recommended route is safer than the general one. As future works, it is necessary to enhance the function of a route guidance service based on the real-time location of users and to conduct spot investigations to evaluate and verify its social acceptability.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.519-531
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• 2023

With the aim of preventing safety accidents at construction sites, the company aims to create safe behaviors intended through variables called smart safety and health activities to help reduce industrial accidents. Purpose: It analyzes how smart safety and health activities affect accidents caused by unsafe behavior and changes in worker behavior, which is the root cause, and verifies the hypothesis that it helps prevent safety accidents and protect workers' lives. Method: Smart safety and health activities were selected as independent variables (X), and intended safety and anxiety, which are workers' behavioral intentions, were set as dependent variables (Y), attitude and subjective norms, and planned behavioral control as parameters (M). Exploratory factor analysis, discriminant validity analysis, and intensive validity analysis of safety and health activities were used to analyze the scale's reliability and validity. To verify the hypothesis of behavior change, the study was verified through Bayesian model analysis and MC simulation's probability density distribution. Result: It was found that workers who experienced smart safety and health activities at construction sites had the highest analysis of reducing unstable behavior and performing intended safety behavior. The research hypothesis that this will affect changes in worker behavior has been proven, the correlation between variables has been verified in the structural equation and path analysis of the research analysis, and it has been confirmed that smart safety and health activities can control and reduce worker instability. Conclusion: Smart safety and health activities are a very important item to prevent accidents and change workers' behavior at construction sites.

• Journal of Navigation and Port Research
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• v.47 no.4
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• pp.256-270
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• 2023

In this study, a sliding mode (SM) controller for dynamic positioning (DP) was specifically designed for a turret connection operation of a ship or an offshore structure in which an arbitrary point on the structure could be controlled as the motion center instead of the center of mass. The SM controller allows control of the arbitrary point and provides capability to manage uncertainties in the dynamics of ships and offshore structures, external forces caused by unknown changing marine environments, and transient performance of DP systems. The Jacobian matrix included in kinematic equations of the controlled object was modified to design the SM controller to control based on an arbitrary point of ships or offshore structures. To ensure robustness of the controller, the Lyapunov stability theory was applied in the design of the SM controller. In general, for robustness in DP control, gain scheduling based on a proportional-derivative (PD) control algorithm is employed. However, finding appropriate gains for gain scheduling complicates the application of DP systems. Therefore, in this study, the SM control algorithm was considered to mitigate the complexity of the DP controller for ships and offshore structures. To validate the proposed SM control algorithm, time-domain simulations were conducted and utilized to evaluate the performance of the control algorithm. The effectiveness of the proposed SM controller was assessed by comparing simulation results with results of a conventional PD control algorithm applied in DP control.

• Composites Research
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• v.20 no.5
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• pp.64-69
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• 2007

Matrix dominant properties of composites are largely degraded under harmful environments such as temperature and humidity. Therefore we should consider the harmful environmental factors in the design of an UAV integral fuel tank subjected to high temperature and high humidity. The harmful environment experiment was performed for carbon/epoxy composites made of a unidirectional prepreg USN175B, and a plain woven fabric prepreg WSN3. The immersion experiment was performed under $90^{\circ}C$. The specimens were tested when the weight gam of specimen was saturated. The specimens were tested under $74^{\circ}C$ to obtain tensile and inplane shear properties. The results showed that the matrix dominant properties were extremely degraded by hygrothermal environment. To consider the variability of load, the anti-optimization method was applied. By using this method, the worst load case was found by comparing the load convex model and stability boundary. The stability boundary was obtained by analysis of the integral wing fuel tank of UAV using degraded properties. To do this, it was known that the worst load case of the integral wing fuel tank was the hovering mode load case.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.236-238
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• 2022

According to the current Road Traffic Act, the 2020 amendment bill is currently in effect as a system that designates vehicle types for each lane for the purpose of securing road use efficiency and traffic safety. When comparing the number of traffic accident fatalities per 10,000 vehicles in Germany and Korea, the number of traffic accident deaths in Germany is significantly lower than in Korea. The representative case of the German autobahn, which did not impose a speed limit, suggests that Korea's speeding laws are not the only answer to reducing the accident rate. The designated lane system, which is observed in accordance with the keep right principle of the Autobahn Expressway, plays a major role in reducing traffic accidents. Based on this fact, we propose a traffic enforcement system to crack down on vehicles violating the designated lane system and improve the compliance rate. We develop a designated lane enforcement system that recognizes vehicle types using Yolo5, a deep learning object recognition model, recognizes license plates and lanes using OpenCV, and stores the extracted data in the server to determine whether or not laws are violated.Accordingly, it is expected that there will be an effect of reducing the traffic accident rate through the improvement of driver's awareness and compliance rate.