• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.349-352
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• 2017

Since the early 2000s, many information security professionals have sought to measure the effectiveness of information security investments. Such efforts have devised a number of ways to calculate the return in ROSI (Return On Security Investment) including the Gordon & Loeb method for calculating cyber damage. However, due to the characteristics of information security structure, the lack of relate information sharing, and many qualitative factors are included, the damage calculation is inaccurate.. This study reviews related studies, analyzes the Gordon & Loeb method and the Shin-Jin method, which are considered to be the most efficient of the existing methods, and designs improved methods.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.52-66
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• 2017

This study aims at designing standard metadata that can be incorporated for advanced utilization of damage prediction maps, and thereby constructing the standard meta-information management prototype system on the basis of the proposed design. Based on the ISO/TC 211 19115 international standard, which is considered as the most widely used standard (as per the results of a domestic and foreign metadata standard survey), the designing process for the standard metadata was established and the metadata was categorized into nine classes. Additionally, based on the output of the standard metadata design process, a standard meta-information management prototype system, capable of checking and downloading meta-property information, was constructed using the JAVASCRIPT language. By incorporating the obtained results, it is possible to maintain the quality of the constructed damage prediction map by establishing a standardized damage prediction map database. Furthermore, disaster response can be actuated through the provision and management of data for effective operation of the proposed damage prediction system.

• Computational Structural Engineering
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• v.10 no.4
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• pp.303-313
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• 1997

Though many techniques for the damage assessment of structures have been studied recently, most of them can be only applied to simple structures. Therefore, practical damage assessment techniques that evaluate the damage location and the damage state for large structures need to be developed. In this study, a damage assessment technique using a neural network is developed, in which the bilevel damage assessment procedure is proposed to evaluate the damage of a large structure from the limited monitoring data. The procedure is as follows ; first, for the rational selection of damage critical members, the members that affect the probability of failure or unusual structural behavior are selected by sensitivity analysis. Secondly, the monitoring points and the number of sensors that are sensitive to the damage severity of the selected members are also selected through the sensitivity analysis with a proposed sensitivity measurement format. The validity and applicability of the developed technique are demonstrated by various examples, and it has been shown that the practical information on the damage state of the selected critical members can be assessed even though the limited monitoring data have been used.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.561-578
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• 2010

This study proposes a multi-level damage localization strategy to achieve an effective damage detection system for civil infrastructure systems based on wireless sensors. The proposed system is designed for use of distributed computation in a wireless sensor network (WSN). Modal identification is achieved using the frequency-domain decomposition (FDD) method and the peak-picking technique. The ASH (angle-between-string-and-horizon) and AS (axial strain) flexibility-based methods are employed for identifying and localizing damage. Fundamentally, the multi-level damage localization strategy does not activate all of the sensor nodes in the network at once. Instead, relatively few sensors are used to perform coarse-grained damage localization; if damage is detected, only those sensors in the potentially damaged regions are incrementally added to the network to perform finer-grained damage localization. In this way, many nodes are able to remain asleep for part or all of the multi-level interrogations, and thus the total energy cost is reduced considerably. In addition, a novel distributed computing strategy is also proposed to reduce the energy consumed in a sensor node, which distributes modal identification and damage detection tasks across a WSN and only allows small amount of useful intermediate results to be transmitted wirelessly. Computations are first performed on each leaf node independently, and the aggregated information is transmitted to one cluster head in each cluster. A second stage of computations are performed on each cluster head, and the identified operational deflection shapes and natural frequencies are transmitted to the base station of the WSN. The damage indicators are extracted at the base station. The proposed strategy yields a WSN-based SHM system which can effectively and automatically identify and localize damage, and is efficient in energy usage. The proposed strategy is validated using two illustrative numerical simulations and experimental validation is performed using a cantilevered beam.

• Journal of Ocean Engineering and Technology
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• v.8 no.1
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• pp.144-153
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• 1994

An algorithm to locate and estimate severity of damage in jacket-type offshore structures for which modal responses are availabit' for very few vibrational modes is presented. First, a theory of damage locaization and severity estimation(which yields information on the location and severity of damage directly from changes in mode shapes) is formulated. Next, the feasibility the damage detection algorithm is demonstrated by using a numerical example of an offshore jacket platform for which only three vibration modes are measured. Form the material presented here, two major results are observed. First, all damage locations in the offshore jacket platform are correctly predicted. Next, predicted damage is relatively correctly estimated.

• Smart Structures and Systems
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• v.5 no.1
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• pp.43-54
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• 2009

This study introduces a novel approach for locating damage in a structure using wireless sensor system with local level computational capability to alleviate data traffic load on the centralized computation. Smart wireless sensor systems, capable of iterative damage-searching, mimic an optimization process in a decentralized way. The proposed algorithm tries to detect damage in a structure by monitoring abnormal increases in strain measurements from a group of wireless sensors. Initially, this clustering technique provides a reasonably effective sensor placement within a structure. Sensor clustering also assigns a certain number of master sensors in each cluster so that they can constantly monitor the structural health of a structure. By adopting a voting system, a group of wireless sensors iteratively forages for a damage location as they can be activated as needed. Since all of the damage searching process occurs within a small group of wireless sensors, no global control or data traffic to a central system is required. Numerical simulation demonstrates that the newly developed searching algorithm implemented on wireless sensors successfully localizes stiffness damage in a plate through the local level reconfigurable function of smart sensors.

• Journal of KIBIM
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• v.10 no.2
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• pp.21-28
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• 2020

As the number of aging bridges increases, more studies are being conducted on developing effective and reliable methods for the assessment and maintenance of bridges. With the advancement in new sensing systems and data learning techniques through AI technology, there is growing interests in how to evaluate bridges using these advanced techniques. This paper presents a CNN(Convolution Neural Network) deep learning based technique for evaluating the damage existence and for estimating the damage location in PSC bridges using static strain data. Simulation studies were conducted to investigate the proposed method with error analysis. Damage was simulated as the reduction in the stiffness of a finite element. A data learning model was constructed by applying the CNN technique as a type of deep learning. The damage status and its location were estimated using data set built through simulation. It was assumed that the strain gauges were installed in a regular interval under the PSC bridge girders. In order to increase the accuracy in evaluating damage, the squared error between the intact and measured strains are computed and applied for training the data model. Considering the damage occurring near the supports, the results of error analysis were compared according to whether strain data near the supports were included.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.81-87
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• 2016

It is important to analyze the exact damage information for fast recovery when natural disasters cause damage on river-side facilities such as dams, bridges, embankments etc. In this study, we shows the method to effectively damage analysis plan using UAV(Unmanned aerial vehicle) images and accuracy assessment of it. The UAV images are captured on area near the river-side facilities and the core methodology for damage analysis are image matching and change detection algorithm. The result(point cloud) from image matching is to construct 3-dimensional data using by 2-dimensional images, it extracts damage areas by comparing the height values on same area with reference data. The results are tested absolute locational precision compared by post-processed aerial LiDAR data named reference data. The assessment analysis test shows our matching results 10-20 centimeter level precision if external orientation parameters are very accurate. This study shows suggested method is very useful for damage analysis in a large size structure like river-side facilities. But the complexity building can't apply this method, it need to the other method for damage analysis.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.149-160
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• 2024

Severe casualties and property damage are occurring due to urban floods caused by extreme rainfall. However, there is a lack of research on preparedness, appropriate estimation of flood damages, assessment of losses, and compensation. Particularly, the flood damage estimation methods used in the USA and Japan show significant differences from the domestic situation, highlighting the need for methods tailored to the Korean context. This study addresses these issues by developing an optimized flood damage estimation technique based on the building characteristics. Utilizing the flood prediction solution developed by the Korea Institute of Science and Technology Information (KISTI), we have established an optimal flood damage estimation technology. We introduced a methodology for flood damage estimation by incorporating vulnerability curves based on the inventory of structures and apply this technique to real-life cases. The results show that our approach yields more realistic outcomes compared to the flood damage estimation methods employed in the USA and Japan. This research can be practically applied to procedures for flood damage in urban basement residences, and it is expected to contribute to establishing appropriate response procedures in cases of public grievances.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.3
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• pp.49-58
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• 2016

Applications of drone in various fields have been increasing in recent years. Drone has great potential for forest management. Therefore this paper is using drone for forest damage areas. Forest damage areas is divided into caused by anthropogenic and occurs naturally, the possibility of disasters, such as slope sliding, slope failures and landslides, sediment runoff exists. Therefore, this research was to utilize the drone photogrammetry to perform the damage analysis of forest damage areas. Geometrical treatment processing results in Drone Photogrammetry, the plane position error RMSE was ${\pm}0.034m$, the elevation error RMSE was ${\pm}0.017m$. The plane position error of orthophoto RMSE was ${\pm}0.083m$, the elevation error of digital elevation model RMSE was ${\pm}0.085m$. In addition, It was possible to current state analysis of damage in forest damage areas of airborne LiDAR data of before forest damage and drone photogrammetry data of after forest damage. and application of drone photogrammetry for production base data for restoration and design in forest damage areas.