• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.3
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• pp.156-170
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• 2006

Determining the best visibility positions on terrain surface has been one of the frequently used analytical issues in GIS visibility analysis and the search for a solution has been carried out effectively using spatial search techniques. However, the spatial search process provides operational and methodological challenges for finding computational algorithms suitable for solving the best visibility site problem. For this problem, current GIS visibility analysis has not been successful due to limited algorithmic structure and operational performance. To meet these challenges, this paper suggests four algorithms explored robust search techniques: an extensive iterative search technique; a conventional solution based on the Tornqvist algorithm; genetic algorithm; and simulated annealing technique. The solution performance of these algorithms is compared on a set of visibility location problems and the experiment results demonstrate the useful feasibility. Finally, this paper presents the potential applicability of the new spatial search techniques for GIS visibility analysis by which the new search algorithms are of particular useful for tackling extensive visibility optimization problems as the next GIS analysis tool.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.3
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• pp.84-95
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• 2005

This paper presents solution heuristics to solving optimal multiple-viewpoint location problems that are based on topographic features. The visibility problem is to maximise the viewshed area for a set of viewpoints on digital elevation models (DEM). For this analysis, five areas are selected, and fundamental topographic features (peak, pass, and pit) are extracted from the DEMs of the study areas. To solve the visibility problem, at first, solution approaches based on the characteristics of the topographic features are explored, and then, a benchmark test is undertaken that solution performances of the solution methods, such as computing times, and visible area sizes, are compared with the performances of traditional spatial heuristics. The feasibility of the solution methods, then, are discussed with the benchmark test results. From the analysis, this paper can conclude that fundamental topographic features based solution methods suggest a new sight of visibility analysis approach which did not discuss in traditional algorithmic approaches. Finally, further research avenues are suggested such as exploring more sophisticated selection process of topographic features related to visibility analysis, exploiting systematic methods to extract topographic features, and robust spatial analytical techniques and optimization techniques that enable to use the topographic features effectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.1996-2011
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• 2021

To understand a trend is to explore the intricate process of how something or a particular situation is constantly changing or developing in a certain direction. This exploration is about observing and describing an unknown field of knowledge, not testing theories or models with a preconceived hypothesis. The purpose is to gain knowledge we did not expect and to recognize the associations among the elements that were suspected or not. This generally requires examining a massive amount of data to find information that could be transformed into meaningful knowledge. That is, looking through the lens of big-data analytics with an inductive reasoning approach will help expand our understanding of the complex nature of a trend. The current study explored the trend of well-being in South Korea using big-data analytic techniques to discover hidden search patterns, associative rules, and keyword signals. Thereafter, a theory was developed based on inductive reasoning - namely the hook, upward push, and downward pull to elucidate a holistic picture of how big-data implications alongside social phenomena may have influenced the well-being trend.

• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.347-359
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• 2010

Ortho-photos provide valuable spatial and spectral information for various Geographic Information System (GIS) and mapping applications. The absence of relief displacement and the uniform scale in ortho-photos enable interested users to measure distances, compute areas, derive geographic locations, and quantify changes. Differential rectification has traditionally been used for ortho-photo generation. However, differential rectification produces serious problems (in the form of ghost images) when dealing with large scale imagery over urban areas. To avoid these artifacts, true ortho-photo generation techniques have been devised to remove ghost images through visibility analysis and occlusion detection. So far, the Z-buffer method has been one of the most popular methods for true ortho-photo generation. However, it is quite sensitive to the relationship between the cell size of the Digital Surface Model (DSM) and the Ground Sampling Distance (GSD) of the imaging sensor. Another critical issue of true ortho-photo generation using high resolution satellite imagery is the scan line search. In other words, the perspective center corresponding to each ground point should be identified since we are dealing with a line camera. This paper introduces alternative methodology for true ortho-photo generation that circumvents the drawbacks of the Z-buffer technique and the existing scan line search methods. The experiments using real data are carried out while comparing the performance of the proposed and the existing methods through qualitative and quantitative evaluations and computational efficiency. The experimental analysis proved that the proposed method provided the best success ratio of the occlusion detection and had reasonable processing time compared to all other true ortho-photo generation methods tested in this paper.

• Convergence Security Journal
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• v.21 no.3
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• pp.13-23
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• 2021

The attacker's techniques and tools are becoming intelligent and sophisticated. Existing Anti-Virus cannot prevent security accident. So the security threats on the endpoint should also be considered. Recently, EDR security solutions to protect endpoints have emerged, but they focus on visibility. There is still a lack of detection and responsiveness. In this paper, we use real-world EDR event logs to aggregate knowledge-based MITRE ATT&CK and autoencoder-based anomaly detection techniques to detect anomalies in order to screen effective analysis and analysis targets from a security manager perspective. After that, detected anomaly attack signs show the security manager an alarm along with log information and can be connected to legacy systems. The experiment detected EDR event logs for 5 days, and verified them with hybrid analysis search. Therefore, it is expected to produce results on when, which IPs and processes is suspected based on the EDR event log and create a secure endpoint environment through measures on the suspicious IP/Process.