• Spatial Information Research
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• v.8 no.2
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• pp.317-328
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• 2000

It is a well-known fact that groundwater is difficult to be recovered, once it is polluted. Since its damage may continue for a long time, its management is very much necessary. For groundwater pollution managemet, current groundwater quality should be analyzed and its diffusion should be estimated. Such analysis and estimation are greatly enhanced by a GIS. In order to build the GIS, groundwater information management system, various database related to groundwater should be constructed. The system can be utilized to analyze groundwater quality and to help administrative processes of groundwater management. In this study, we analyze No3N diffusion in the groundwater under the study area, a part of Jung-Gu area, by using groundwater analysis subsystem and create the 1/5,000 scale map for the diffusion prediction of groundwater pollution. Although Seoul Metropolitan Government has constructed the 1/25,000 scale hydrogeology map of Seoul area through basic groundwater survey in 1996, the survey data are not sufficient for local groundwater pollution management. The large scaled map constructed in this study is expected to be utilized for the management. The GIS softwares, Arc/Info and Arc/View, are used. MODFLOW and MT3D programs are extensively used to analyze groundwater pollution.

• Journal of KIISE
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• v.41 no.10
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• pp.792-798
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• 2014

In spatial databases, R-tree is one of the most widely used indexing structures and many variants have been proposed for its performance improvement. Among these variants, Hilbert R-tree is a representative method using Hilbert curve to process large amounts of data without high cost split techniques to construct the R-tree. This Hilbert R-tree, however, is hardly applicable to large-scale applications in practice mainly due to high pre-processing costs and slow bulk-load time. To overcome the limitations of Hilbert R-tree, we propose a novel approach for parallelizing Hilbert mapping and thus accelerating bulk-loading of Hilbert R-tree on GPU memory. Hilbert R-tree based on GPU improves bulk-loading performance by applying the inversed-cell method and exploiting parallelism for packing the R-tree structure. Our experimental results show that the proposed scheme is up to 45 times faster compared to the traditional CPU-based bulk-loading schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3984-4005
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• 2017

Image recognition has become an increasingly important topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The recognition systems rely on a key component, i.e. the low-level feature or the learned mid-level feature. The recognition performance can be potentially improved if the data distribution information is exploited using a more sophisticated way, which usually a function over hidden variable, model parameter and observed data. These methods are called generative score space. In this paper, we propose a discriminative extension for the existing generative score space methods, which exploits class label when deriving score functions for image recognition task. Specifically, we first extend the regular generative models to class conditional models over both observed variable and class label. Then, we derive the mid-level feature mapping from the extended models. At last, the derived feature mapping is embedded into a discriminative classifier for image recognition. The advantages of our proposed approach are two folds. First, the resulted methods take simple and intuitive forms which are weighted versions of existing methods, benefitting from the Bayesian inference of class label. Second, the probabilistic generative modeling allows us to exploit hidden information and is well adapt to data distribution. To validate the effectiveness of the proposed method, we cooperate our discriminative extension with three generative models for image recognition task. The experimental results validate the effectiveness of our proposed approach.

• Journal of Korea Spatial Information System Society
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• v.10 no.3
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• pp.67-78
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• 2008

With the rapid development of wireless communication and mobile equipment, many applications for location-based services have been emerging. Moving objects such as vehicles and ships change their positions over time. Moving objects have their moving path, called the trajectory, because they move continuously. To monitor the trajectory of moving objects in a large scale database system, an efficient Indexing scheme to processed queries related to trajectories is required. In this paper, we focus on the issues of minimizing the dead space of index structures. The Minimum Bounding Boxes (MBBs) of non-leaf nodes in trajectory-preserving indexing schemes have large amounts of dead space since trajectory preservation is achieved at the sacrifice of the spatial locality of trajectories. In this thesis, we propose entry relocating techniques to reduce dead space and overlaps in non-leaf nodes. we present performance studies that compare the proposed index schemes with the TB-tree and the R*-tree under a varying set of spatio-temporal queries.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.165-166
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• 2019

Big data analysis is the ability to collect, store, manage and analyze data from existing database management tools. Big data refers to large scale data that is generated in a digital environment, is large in size, has a short generation cycle, and includes not only numeric data but also text and image data. Big data is data that is difficult to manage and analyze in the conventional way. It has huge size, various types, fast generation and velocity. Therefore, companies in most industries are making efforts to create value through the application of Big data. In this study, we analyzed the meaning of keyword using Social Matrix, a big data analysis tool of Daum communications. Also, the theoretical implications are presented based on the analysis results.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.39-48
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• 2023

Because a railway bridge is designed over a long period of time and covers a large site, it involves various environmental factors and uncertainties. For this reason, design changes often occur, even if the design was thoroughly reviewed in the initial design stage. In particular, design changes of large-scale facilities, such as railway bridges, consume significant time and cost, and it is extremely inefficient to repeat all the procedures each time. In this study, a technique that can improve the efficiency of learning after design change was developed by utilizing the learning result before design change through transfer learning among deep-learning algorithms. For analysis, scenarios were created, and a database was built using a previously developed railway bridge deep-learning-based prediction system. The proposed method results in similar accuracy when learning only 1000 data points in the new domain compared with the 8000 data points used for learning in the old domain before the design change. Moreover, it was confirmed that it has a faster convergence speed.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.313-322
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• 2020

The recognition of the risk hazards is a vital step to effectively prevent accidents on a construction site. The advanced development in computer vision systems and the availability of the large visual database related to construction site made it possible to take quick action in the event of human error and disaster situations that may occur during management supervision. Therefore, it is necessary to analyze the risk factors that need to be managed at the construction site and review appropriate and effective technical methods for each risk factor. This research focuses on analyzing Occupational Safety and Health Agency (OSHA) related to risk zone identification rules that can be adopted by the image recognition technology and classify their risk factors depending on the effective technical method. Therefore, this research developed a pattern-oriented classification of OSHA rules that can employ a large scale of safety hazard recognition. This research uses joint reasoning of risk zone Identification and numeric input by utilizing a stereo camera integrated with an image detection algorithm such as (YOLOv3) and Pyramid Stereo Matching Network (PSMNet). The research result identifies risk zones and raises alarm if a target object enters this zone. It also determines numerical information of a target, which recognizes the length, spacing, and angle of the target. Applying image detection joint logic algorithms might leverage the speed and accuracy of hazard detection due to merging more than one factor to prevent accidents in the job site.

• Wind and Structures
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• v.38 no.2
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• pp.129-146
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• 2024

Aiming at the problem of non-stationary wind field simulation of downbursts, a non-stationary down-burst generation system was designed by adding a nozzle and program control valve to the inlet of the original wall jet model. The computational fluid dynamics (CFD) method was used to simulate the downburst. Firstly, the two-dimensional (2D) model was used to study the outflow situation, and the database of working conditions was formed. Then the combined superposition of working conditions was carried out to simulate the full-scale measured downburst. The three-dimensional (3D) large eddy simulation (LES) was used for further verification based on this superposition condition. Finally, the wind tunnel test is used to further verify. The results show that after the valve is opened, the wind ve-locity at low altitude increases rapidly, then stays stable, and the wind velocity at each point fluctuates. The velocity of the 2D model matches the wind velocity trend of the measured downburst well. The 3D model matches the measured downburst flow in terms of wind velocity and pulsation characteris-tics. The time-varying mean wind velocity of the wind tunnel test is in better agreement with the meas-ured time-varying mean wind velocity of the downburst. The power spectrum of fluctuating wind ve-locity at different vertical heights for the test condition also agrees well with the von Karman spectrum, and conforms to the "-5/3" law. The vertical profile of the maximum time-varying average wind veloci-ty obtained from the test shows the basic characteristics of the typical wind profile of the downburst. The effectiveness of the downburst generation system is verified.

• Journal of Yeungnam Medical Science
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• v.41 no.1
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• pp.39-44
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• 2024

Background: Medication-related osteonecrosis of the jaw (MRONJ) is a significant concern, particularly among patients taking bisphosphonates (BPs), denosumab, and selective estrogen receptor modulators (SERMs) for osteoporosis. Despite the known risks, large-scale cohort studies examining the incidence and severity of MRONJ are lacking. We aimed to ascertain the incidence and risk of MRONJ among these patients, whom we stratified by age groups, medication types, and duration of use. Methods: We utilized data from the National Health Insurance Service's sample cohort database, focusing on patients aged 40 years and above diagnosed with osteoporosis. The patients were divided into three groups: those prescribed BPs only, those prescribed SERMs only, and those prescribed both. Results: The overall incidence rate of MRONJ was 0.17%. A significantly higher incidence rate was observed among those taking osteoporosis medications, particularly among females with a relative risk of 4.99 (95% confidence interval, 3.21-7.74). The SERM group also had an incidence rate comparable to that of the BP group. Severity was assessed based on the invasiveness of the treatment methods, with 71.3% undergoing invasive treatment in the medication group. Conclusion: This study provides valuable insights into the incidence and severity of MRONJ among a large cohort of patients with osteoporosis. It underscores the need for comprehensive guidance on MRONJ risks across different medication groups and sets the stage for future research focusing on specific populations and treatment outcomes.

• The KIPS Transactions:PartD
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• v.10D no.1
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• pp.1-12
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• 2003

Recently, there have been many indexing schemes for multimedia data such as image, video data. But recent database applications, for example data mining and multimedia database, are required to support multi-user environment. In order for indexing schemes to be useful in multi-user environment, a concurrency control algorithm is required to handle it. So we propose a concurrency control algorithm that can be applied to CBF (cell-based filtering method), which uses the signature of the cell for alleviating the dimensional curse problem. In addition, we extend the SHORE storage system of Wisconsin university in order to handle high-dimensional data. This extended SHORE storage system provides conventional storage manager functions, guarantees the integrity of high-dimensional data and is flexible to the large scale of feature vectors for preventing the usage of large main memory. Finally, we implement the web-based image retrieval system by using the extended SHORE storage system. The key feature of this system is platform-independent access to the high-dimensional data as well as functionality of efficient content-based queries. Lastly. We evaluate an average response time of point query, range query and k-nearest query in terms of the number of threads.