• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2140-2149
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• 2013

Managing uncertainty or discontinuity in an innovation is still a challenge to most companies. For sustainable corporate survival over the long term, one of the problems caused by discontinuous innovation is the innovator's dilemma. In specific, the dynamics between discontinuous innovation and incumbents inspires the interestof researchers and managers. This paper employs catastrophe theory as a theoretical basis to explain the driving force of new discontinuous change. In other words, we extract the control variables overcoming innovation dilemma by interpreting the dynamics of corporate strategy for discontinuous innovation from the perspective of catastrophe theory. First, we define four types of discontinuity such as technology discontinuity, product discontinuity, business discontinuity, and consumer preference discontinuity. Second, we analyze the dynamics of the competition between companies by interpreting the cases of discontinuous innovation. This analyzing process enables us to identify the control variable which can, in advance, respond to the discontinuous situation.

• Wind and Structures
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• v.23 no.5
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• pp.449-464
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• 2016

Catastrophe models appraise the natural risk of the built-infrastructure simulating the interaction of its exposure and vulnerability with a hazard. Because of unique configurations and reduced number, mid/high-rise buildings present singular challenges to the assessment of their damage vulnerability. This paper presents a novel approach to estimate the vulnerability of mid/high-rise buildings (MHB) which is used in the Florida Public Hurricane Loss Model, a catastrophe model developed for the state of Florida. The MHB vulnerability approach considers the wind pressure hazard exerted over the building's height as well as accompanying rain. The approach assesses separately the damages caused by wind, debris impact, and water intrusion on building models discretized into typical apartment units. Hurricane-induced water intrusion is predicted combining the estimates of impinging rain with breach and pre-existing building defect size estimates. Damage is aggregated apartment-by-apartment and story-by-story, and accounts for vertical water propagation. The approach enables the vulnerability modeling of regular and complex building geometries in the Florida exposure and elsewhere.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.05a
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• pp.125-126
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• 2019

This study aims to recognize damage indicators of typhoon and to develop damage function's indicators, using information derived from the actual loss of typhoon Maemi. As typhoons engender significant financial damage all over the world, governments and insurance companies, local or global, develop hurricane risk assessment models and use it in quantifying, avoiding, mitigating, or transferring the risks. For the reason, it is crucial to understand the importance of the risk assessment model for typhoons, and the importance of reflecting local vulnerabilities for more advanced evaluation. Although much previous research on the economic losses associated with natural disasters has identified the risk indicators that are indispensable, more comprehensive research addressing the relationship between vulnerability and economic loss are still called for. Hence this study utilizes and analyzes the actual loss record of the typhoon Maemi provided by insurance companies to fill such gaps. In this study, natural disaster indicators and basic building information indicators are used in order to generate the vulnerability functions; and the results and indicators suggest a practical approach to create the vulnerability functions for insurance companies and administrative tasks, while reflecting the financial loss and local vulnerability of the actual buildings.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.390-399
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• 2022

Cyber security and resilience are phrases that describe safeguards of ICTs (information and communication technologies) from cyber-attacks or mitigations of cyber event impacts. The sole purpose of Risk models are detections, analyses, and handling by considering all relevant perceptions of risks. The current research effort has resulted in the development of a new paradigm for safeguarding services offered online which can be utilized by both service providers and users. customers. However, rather of relying on detailed studies, this approach emphasizes task selection and execution that leads to successful risk treatment outcomes. Modelling intelligent CSGs (Cyber Security Games) using MLTs (machine learning techniques) was the focus of this research. By limiting mission risk, CSGs maximize ability of systems to operate unhindered in cyber environments. The suggested framework's main components are the Threat and Risk models. These models are tailored to meet the special characteristics of online services as well as the cyberspace environment. A risk management procedure is included in the framework. Risk scores are computed by combining probabilities of successful attacks with findings of impact models that predict cyber catastrophe consequences. To assess successful attacks, models emulating defense against threats can be used in topologies. CSGs consider widespread interconnectivity of cyber systems which forces defending all multi-step attack paths. In contrast, attackers just need one of the paths to succeed. CSGs are game-theoretic methods for identifying defense measures and reducing risks for systems and probe for maximum cyber risks using game formulations (MiniMax). To detect the impacts, the attacker player creates an attack tree for each state of the game using a modified Extreme Gradient Boosting Decision Tree (that sees numerous compromises ahead). Based on the findings, the proposed model has a high level of security for the web sources used in the experiment.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.291-311
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• 2007

With a series of unexpected huge losses in the financial markets around the world recently, especially in the insurance market with extreme loss cases such as catastrophes, there is an increasing demand for risk management for extreme loss exposures due to high unpredictability of those risks. For extreme risk management, to make a maximum use of the information concerning the tail part of a loss distribution, EVT(Extreme Value Theory) modelling nay be the best to analyze extreme values. The Extreme Value Theory is widely used in practice and, especially in financal markets, EVT modelling is getting popular to analyBe the effects of extreme risks. This study is to review the significance of the Extreme Value Theory in risk management and, focusing on analyzing insurer's risk capital, extreme risk is measured using the real fire loss data and insurer's specific amount of risk capital is figured out to buffer the extreme risk.

• Journal of the Korean earth science society
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• v.34 no.6
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• pp.524-535
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• 2013

Catastrophe risk models require the damage functions of each vulnerable item in inventory to estimate volcanic ash losses. The damage functions are used to represent the relation between damage factors and damage and also widely used in engineering and natural hazard studies to calculate the vulnerability. In most cases, damage functions are constructed as fragility or vulnerability curves, and researchers are confused by the similarities between them particularly when they perform interdisciplinary research. Thus, we aim to explain the similarities and differences between fragility and vulnerability curves and their relationship by providing case studies to construct them. In addition, we suggest a simple method to construct the damage functions between damage ratio and volcanic ash thickness using limited damage data. This study comes from the fact that damage functions are generally constructed using damage data. However, there is no available volcanic ash damage data in Korea, and not even enough volcanic disaster data to construct damage functions in the world, compared to other hazards. Using the method suggested in the study and the limited damage data from Japan and New Zealand, we construct Weibull-type functions or linear functions dependent of available data to calculate volcanic ash loss estimation, which we think need to be corrected to make it more suitable for inventory characteristics and environmental conditions in Korea.

• Journal of the Korea Institute of Building Construction
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• v.19 no.6
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• pp.529-538
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• 2019

Typhoons can cause significant financial damage worldwide. For this reason, states, local governments and insurance companies attempt to quantify and mitigate the financial risks related to these natural disasters by developing a typhoon risk assessment model. As such, the importance of typhoon risk assessment models is increasing, and it is also important to reflect local vulnerabilities to enable sophisticated assessments. Although a practical study of economic losses associated with natural disasters has identified essential risk indicators, comprehensive studies covering the correlation between vulnerability and economic loss are still needed. The purpose of this study is to identify typhoon damage indicators and to develop evaluation indicators for typhoon damage prediction functions, utilizing the loses from Typhoon Maemi as data. This study analyzes actual loss records of Typhoon Maemi provided by local insurance companies to prepare for a scenario of maximum losses. To create a vulnerability function, the authors used the wind speed and distance from the coast and the total value of property, construction type, floors, and underground floor indicators. The results and metrics of this study provide practical guidelines for government agencies and insurance companies in developing vulnerability functions that reflect the actual financial losses and regional vulnerabilities of buildings.

• Economic and Environmental Geology
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• v.47 no.3
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• pp.205-217
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• 2014

As Mt. Backdu is expected to erupt, the social and economic impacts of the eruption on the Korean peninsula as well as on the world become a research topic of interest. If the volcano erupts, South Korea can be directly impacted by volcanic ash, which will bring out secondary damages in various ways. Given that the direct damage is a basis to estimate indirect and secondary damages, this paper was to review a method to estimate direct damages, called catastrophe risk models, and estimate the direct damages of available eruption scenarios of Mt. Baekdu. Based on the results, the damages by volcanic ash will occur mostly around Gangwon province if the Mt. Backdu erupts. Thus the inventory lists and their damage functions of Gangwon provinces were collected. In particular agricultural and forestry products were surveyed based on the land use. Direct damages were estimated using volcanic ash distribution of eruption scenarios, inventory information and their damage functions. In result, a scenario in winter caused the damage of 299.8 billion KRW (20.4% of total agricultural production in 2010) and 28.9 billion KRW (9.0% of total forestry production in 2010) in agriculture and forestry, respectively. The damages in agriculture was larger, and it is due to the damage functions which show the agricultural products are more vulnerable to volcanic ash than forestry products. Also the agricultural production (1,471.7 billion KRW in 2010) are more than 4.5 times the forestry production (322.3 billion KRW in 2010) in Gangwon province. Inje and Gangnung had the most damages in the scenario in winter. Inje had the most damage due to the thick ash deposit (8.5 mm in average) despite the low production. On the other hand, Goseong had a low damage compared to the ash thickness larger than 20mm, owing to the low production. The direct damage estimated through this process can be used to estimate indirect damages.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.171-182
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• 2022

The safe use of a structure requires it to be maintained in an undamaged state. Thus, a typical factor that determines the safety of a structure is a crack in it. In addition, cracks are caused by various reasons, damage the structure in various ways, and exist in different shapes. Making matters worse, if these cracks are unattended, the risk of structural failure increases and proceeds to a catastrophe. Hence, recently, methods of checking structural damage using deep learning and computer vision technology have been introduced. These methods usually have the premise that there should be a large amount of training image data. However, the amount of training image data is always insufficient. Particularly, this insufficiency negatively affects the performance of deep learning crack detection algorithms. Hence, in this study, a method of augmenting crack image data based on the image translation technique was developed. In particular, this method obtained the crack image data for training a deep learning neural network model by transforming a specific case of a asphalt crack image into a concrete crack image or vice versa . Eventually, this method expected that a robust crack detection algorithm could be developed by increasing the diversity of its training data.