• Journal of Information Technology Services
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• v.13 no.2
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• pp.185-194
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• 2014

Recently, as value for practical use of big data is evaluated, companies and organizations that create benefit and profit are gradually increasing with application of big data. But specifical and theoretical study about possible risk factors as introduction of big data is not being conducted. Accordingly, the study extracts the possible risk factors as introduction of big data based on literature reviews and classifies according to big data processing, data collection, data storage, data analysis, analysis data visualization and application. Also, the risk factors have order of priority according to the degree of risk from the survey of experts. This study will make a chance that can avoid risks by bid data processing and preparation for risks in order of dangerous grades of risk.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.1
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• pp.147-162
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• 2017

With the advent of various services and devices in the change of IT environment, increasing the complexity of the data, and increasing scale of IT, Many organizations are experiencing the difficulty of analyzing and processing with a large amounts of data for security situations awareness. Therefore, propose the enhancement of security situational awareness through visualization in order to solve the problems of slow response and security situational awareness in organizational risk management. In this paper, we selected the evaluation factors and alternatives for effective visualization by considering user type, situational awareness step, and information visualization attributes through various studies on visualization. And established AHP layer model. Based on this, by using the AHP method for solving the problem of multi-criteria decision making, by calculating the factors for effectively visualizing and the importance of alternative by factor, try to propose a visualization method that can improve the effectiveness of the security situational awareness according to the purpose of visualization and the type of user.

• Journal of Welding and Joining
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• v.34 no.1
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• pp.54-58
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• 2016

In this research paper, suggest method of generate same bead as an actual measurement data in virtual welding conditions, exploit morphology information of the bead that acquired through robot welding. It has many multiple risk factors to Beginners welding training, by we make possible to train welding in virtual reality, we can reduce welding training risk and welding material to exploit bead visualization algorithm that we suggest so it will be expected to achieve educational, environmental and economical effect. The proposed method is acquire data to each case performing robot welding by set the voltage, current, working angle, process angle, speed and arc length of welding condition value. As Welding condition value is most important thing in decide bead form, we would selected one of baseline each item and then acquired metal followed another factors change. Welding type is FCAW, SMAW and TIG. When welding trainee perform the training, it's difficult to save all of changed information into database likewise working angle, process angle, speed and arc length. So not saving data into database are applying the method to infer the form of bead using a neural network algorithm. The way of bead's visualization is applying the spline algorithm. To accurately represent Morphological information of the bead, requires much of morphological information, so it can occur problem to save into database that is why we using the spline algorithm. By applying the spline algorithm, it can make simplified data and generate accurate bead shape. Through the research paper, the shape of bead generated by the virtual reality was able to improve the accuracy when compared using the form of bead generated by the robot welding to using the morphological information of the bead generated through the robot welding. By express the accurate shape of bead and so can reduce the difference of the actual welding training and virtual welding, it was confirmed that it can be performed safety and high effective virtual welding education.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.663-671
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• 2020

Recently, there have been many natural disasters in Korea, not only in forest areas but also in urban areas, and the national requirements for them are increasing. In particular, there is no pre-disaster information system that can systematically manage the collapse of the slope of the national highway. In this study, big data analysis was conducted on the factors causing slope collapse based on the detailed investigation report on the slope collapse of national roads in Gangwon-do and Gyeongsang-do areas managed by the Cut Slope Management System (CSMS) and the basic survey of slope failures. Based on the analysis results, a slope collapse risk prediction model was established through Adaboost, a classification-based machine learning model, reflecting the collapse slope location and weather information. It also developed a visualization map for the risk of slope collapse, which is a visualization program, to show that it can be used for preemptive disaster prevention measures by identifying the risk of slope due to changes in weather conditions.

• Journal of the Korea Safety Management & Science
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• v.14 no.3
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• pp.151-158
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• 2012

The objective of this paper is to propose a method of how to perform risk assessment in the early stage of defense research and development for the acquisition of weapon systems. An advanced method for risk assessment and its associated objective functions are developed first based on the concept of systems engineering. The developed method is then applied to carry out the analysis of alternatives in the trade-off environments. As a case study, the multi-purpose training ship is considered, where it is performed using the notions of technology readiness levels, degrees of difficulty, and technology need values to facilitate design space visualization and decision maker interaction. It is noted that decision makers can benefit from our approach as an improved risk assessment method in the context of multi-criteria decision making.

• Journal of Navigation and Port Research
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• v.42 no.2
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• pp.97-106
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• 2018

Despite the development of safety measures and improvements in preventive systems technologies, maritime traffic accidents that involve ships carrying hazardous and noxious substances (HNS) continuously occur owing to increased amount of HNS goods transported and the growing number of HNS fleet. To prevent maritime traffic accidents involving ships carrying HNS, this study proposes an intuitive route risk assessment technique using risk contours that can be visually and quantitatively analyzed. The proposed technique offers continuous information based on quantified values. It determines and structures route risk factors classified as absolute danger, absolute factors, and influential factors within the assessment area. The route risk is assessed in accordance with the proposed algorithmic procedures by means of contour maps overlaid on electronic charts for visualization. To verify the effectiveness of the proposed route risk assessment technique, experimental case studies under various conditions were conducted to compare results obtained by the proposed technique to actual route plans used by five representative companies operating the model ship carrying HNS. This technique is beneficial not only for assessing the route risk of ships carrying HNS, but also for identifying better route options such as recommended routes and enhancing navigation safety. Furthermore, this technique can be used to develop optimized route plans for current maritime conditions in addition to future autonomous navigation application.

• Journal of the Korean Society of Safety
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• v.33 no.6
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• pp.77-84
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• 2018

Report documents of industrial and occupational accidents have continuously been accumulated in private and public institutes. Amongst others, information on narrative-texts of accidents such as accident processes and risk factors contained in disaster report documents is gaining the useful value for accident analysis. Despite this increasingly potential value of analysis of text information, scientific and algorithmic text analytics for safety management has not been carried out yet. Thus, this study aims to develop data processing and visualization techniques that provide a systematic and structural view of text information contained in a disaster report document so that safety managers can effectively analyze accident risk factors. To this end, the risk factor map using text mining and self-organizing map is developed. Text mining is firstly used to extract risk keywords from disaster report documents and then, the Self-Organizing Map (SOM) algorithm is conducted to visualize the risk factor map based on the similarity of disaster report documents. As a result, it is expected that fruitful text information buried in a myriad of disaster report documents is analyzed, providing risk factors to safety managers.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.4
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• pp.11-33
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• 2013

We analyzed the structures and properties of the global financial market networks using social network analysis approach. The Minimum Spanning Tree (MST) lengths and networks of the global financial markets based on the correlation coefficients have been analyzed. Firstly, similar to the previous studies on the global stock indices using MST length, the diversification effects in the global multi-asset portfolio can disappear during the crisis as the correlations among the asset class and within the asset class increase due to the system risks. Second, through the network visualization, we found the clustering of the asset class in the global financial markets network, which confirms the possible diversification effect in the global multi-asset portfolio. Meanwhile, we found the changes in the structure of the network during the crisis. For the last one, in terms of the degree centrality, the stock indices were the most influential to other assets in the global financial markets network, while in terms of the betweenness centrality, Gold, Silver and AUD. In the practical perspective, we propose the methods such as MST length and network visualization to monitor the change of the correlation risk for the risk management of the multi-asset portfolio.

• The Journal of Asian Finance, Economics and Business
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• v.10 no.2
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• pp.355-365
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• 2023

This study examined the relationship between corporate governance and risk disclosure via a systematic literature review and bibliometric visualization analysis. The study aimed to present evidence of risk disclosure intellectual structure, volume, and development knowledge trends. Data was extracted from Scopus and analyzed with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and RTools. In turn, 64 articles were extracted from the Scopus database. The results demonstrated that the number of corporate governance and risk disclosure publications increased significantly from 2015 to 2019 compared to before 2015. RTools revealed the most prominent journals, authors, and interests in the field. The co-occurrences map was constructed based on 208 keywords from 64 articles, where the keywords were required to appear once in the research. Interestingly, the keyword search yielded new concepts relatively unexplored in the risk disclosure field. The 13 clusters were generated, which contained 1987 total links and 1567 direct citations. Based on the scientific analysis discussion, corporate governance and risk disclosure is an interesting topic that has produced many publications. Applying research keywords arguably aided in producing and publishing papers in top journals. Despite the number of publications decreasing due to the COVID-19 pandemic, the pandemic also presented new opportunities for future research.

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.17-22
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• 2019

Network attack analysis and visualization methods using network traffic session data detect network anomalies by visualizing the sender's and receiver's IP addresses and the relationship between them. The traffic flow is a critical feature in detecting anomalies, but simply visualizing the source and destination IP addresses symmetrically from up-down or left-right would become a problematic factor for the analysis. Also, there is a risk of losing timely security situation when designing a visualization interface without considering the temporal characteristics of time-series traffic sessions. In this paper, we propose a visualization interface and analysis method that visualizes time-series traffic data by using the radial axis, divide IP addresses into network and host portions which then projects on the cylindrical coordinate system that could effectively monitor network attacks. The proposed method has the advantage of intuitively recognizing network attacks and identifying attack activity over time.