• Asian Pacific Journal of Cancer Prevention
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• v.17 no.8
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• pp.3759-3762
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• 2016

Background: Breast cancer risk assessment is a helpful method for estimating development of breast cancer at the population level. Materials and Methods: In this cross-sectional study, participants consisted of a group of 3,847 volunteers ($mean{\pm}SD$ age: $463{\pm}7.59$ years) in a convenience sample of women referred to health centers affiliated to Tehran University of Medical Sciences in Tehran, Iran. The risk of breast cancer was estimated by applying the National Cancer Institute's online version of the Gail Risk Assessment Tool. Results: Some 24.9% of women reported having one first-degree female relative with breast cancer, with 8.05% of them having two or more first-degree relatives with breast cancer. The mean five-year risk of breast cancer for all participants was $1.61{\pm}0.73%$, and 9.36% of them had a five-year risk of breast cancer >1.66%. The mean lifetime risk of breast cancer was $11.7{\pm}3.91%$. Conclusions: The Gail model is useful for assessing probability of breast cancer in Iranian women. Based on the their breast cancer risk, women may decide to accept further screening services.

• Communications for Statistical Applications and Methods
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• v.22 no.4
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• pp.389-399
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• 2015

It is important not to overcalculate sample sizes for clinical trials due to economic, ethical, and scientific reasons. Kang and Kim (2014) investigated the accuracy of a well-known sample size calculation formula based on the approximate power for continuous endpoints in equivalence trials, which has been widely used for Development of Biosimilar Products. They concluded that this formula is overly conservative and that sample size should be calculated based on an exact power. This paper extends these results to binary endpoints for three popular metrics: the risk difference, the log of the relative risk, and the log of the odds ratio. We conclude that the sample size formulae based on the approximate power for binary endpoints in equivalence trials are overly conservative. In many cases, sample sizes to achieve 80% power based on approximate powers have 90% exact power. We propose that sample size should be computed numerically based on the exact power.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.476-483
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• 2021

In order to maintain the safety of the airspace with ever increasing traffic volume, it is necessary to thoroughly analyze the collision risk with the current data. In this study, collision risk analysis was conducted using Detect and Avoid (DAA) Well-Clear (DWC) metrics, risk induces developed for the DAA systems of unmanned aerial vehicles. All flights in year 2019 that flew within the Incheon Flight Information Region (FIR) boundary were analyzed using the recorded Automatic Dependent Surveillance-Broadcast(ADS-B) data. High risk regions as well as trends by airports and seasons were identified. The results indicate that the risk is higher around the congested area near Incheon International Airport and Gimpo International Airport. Seasonally, the risk was highest in August that coincides with the Summer vacation period. The result will be useful for the baseline data for various aviation safety enhancement activities such as revision of routes and procedures and training of the field specialists.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.921-932
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• 2015

We proposed a new scoring system on software vulnerabilities, which calculates quantitatively the severity of software vulnerabilities. The proposed scoring system consists of metrics for vulnerability severity and scoring equations; the metrics are designed to measure the severity of a software vulnerability considering the prevalence of the vulnerability, the risk level of the vulnerability, the domestic market share of the software and the frequency of the software. We applied the proposed scoring system to domestically reported software vulnerabilities, and discussed the effectiveness of the scoring system, comparing it with CVSS and CWSS. We also suggested the prospective utilization areas of the proposed scoring system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1717-1732
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• 2015

The widespread use of location-based services (LBSs), which allows untrusted service provider to collect large number of user request records, leads to serious privacy concerns. In response to these issues, a number of LBS privacy protection mechanisms (LPPMs) have been recently proposed. However, the evaluation of these LPPMs usually disregards the background knowledge that the adversary may possess about users' contextual information, which runs the risk of wrongly evaluating users' query privacy. In this paper, we address these issues by proposing a generic formal quantification framework,which comprehensively contemplate the various elements that influence the query privacy of users and explicitly states the knowledge that an adversary might have in the context of query privacy. Moreover, a way to model the adversary's attack on query privacy is proposed, which allows us to show the insufficiency of the existing query privacy metrics, e.g., k-anonymity. Thus we propose two new metrics: entropy anonymity and mutual information anonymity. Lastly, we run a set of experiments on datasets generated by network based generator of moving objects proposed by Thomas Brinkhoff. The results show the effectiveness and efficient of our framework to measure the LPPM.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.171-171
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• 2020

Decision making strategies should consider both adaptiveness and robustness in order to deal with two main characteristics of climate change: non-stationarity and deep uncertainty. Especially, robust strategies are different from traditional optimal strategies in the sense that they are satisfactory over a wider range of uncertainty and may act as a key when confronting climate change. In this study, a new framework named Robust Stochastic Dynamic Programming (R-SDP) is proposed, which couples previously developed robust optimization (RO) into the objective function and constraint of SDP. Two main approaches of RO, feasibility robustness and solution robustness, are considered in the optimization algorithm and consequently, three models to be tested are developed: conventional-SDP (CSDP), R-SDP-Feasibility (RSDP-F), and R-SDP-Solution (RSDP-S). The developed models were used to derive optimal monthly release rules in a single reservoir, and multiple simulations of the derived monthly policy under inflow scenarios with varying mean and standard deviations are undergone. Simulation results were then evaluated with a wide range of evaluation metrics from reliability, resiliency, vulnerability to additional robustness measures. Evaluation results were finally visualized with advanced visualization tools that are used in multi-objective robust decision making (MORDM) framework. As a result, RSDP-F and RSDP-S models yielded more risk averse, or conservative, results than the CSDP model, and a trade-off relationship between traditional and robustness metrics was discovered.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.323-334
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• 2015

he current VaR model based on the J.P. Morgan's RiskMetrics structurally can not reflect the future economic situation. In this study, we propose a One-factor model resulting from the Wiener stochastic process decomposed into a systematic risk factor and an idiosyncratic risk factor. Therefore, we are able to perform a preemptive risk management by means of reflecting the predicted common risk factors in the model. Stocks in the portfolio are satisfied with the independence to each other because the common factors are fixed by the predicted value. Therefore, we can easily determine the investment in each stock to minimize the variance of the portfolio. In addition, the portfolio VaR is decomposed into the sum of the individual VaR. So we can effectively implement the constitution of the portfolio to meet the target maximum losses.

• Earthquakes and Structures
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• v.14 no.6
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• pp.577-587
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• 2018

The quantitative assessment of the seismic collapse risk of a structure requires the usage of an optimal intensity measure (IM) which can adequately characterise the severity of the ground motion. Research suggests that the average spectral acceleration ($Sa_{avg}$) may be an efficient and sufficient alternate IM as compared to the more traditional first mode spectral acceleration, $Sa(T_1)$, particularly during seismic collapse risk estimation. This study primarily presents a comparative evaluation of the sufficiency of the average spectral acceleration with respect to ground motion duration, and secondarily assesses the impact of ground motion duration on collapse risk estimation. By assembling a suite of 100 historical ground motions, incremental dynamic analysis of 60 different inelastic single-degree-of-freedom (SDF) oscillators with varying periods and ductility capacities were analysed, and collapse risk estimates obtained. Linear regression models are used to comparatively quantify the sufficiency of $Sa_{avg}$ and $Sa(T_1)$ using four significant duration metrics. Results suggests that an improved sufficiency may exist for $Sa_{avg}$ when the period of the SDF system increases, particularly beyond 0.5, as compare to $Sa(T_1)$. In reference to the ground motion duration measures, results indicated that the sufficiency of $Sa_{avg}$ is more sensitive to significant duration definitions that consider almost the full wave train of an accelerogram ($SD_{a5-95}$ and $SD_{v5-95}$). In order to obtain a reduced variability of the collapse risk estimate, the 5-95% significant duration metric defined using the Arias integral ($SD_{a5-95}$) should be used for seismic collapse risk estimation in conjunction with $Sa_{avg}$.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.65-77
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• 2021

COVID-19 has recently impacted the world with the large numbers of infected and deaths. The development of effective COVID-19 vaccine has not been successful. Hence, people have a high concern on the infection of this disease. The infection information from the governmantal public organizations are mainly based on simple summary statistics. Consequently, it is hard to assess the infection risks of individual person and the current location of the person. In this paper, we present a machine learning-based software system that analyzes COVID-19 infection risks and guidelines for safe activities.This paper proposes a suite of risk factors regarding COVID-19 infection and deaths and methods to quantitatively measure the individual and group risks using the proposed metrics. The proposed system utilizes a clustering algorithms and various software approaches that reflect the information and features of inviduals and their geograpical locations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1373-1378
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• 2017

This paper presents a basic ECU(Electronic Control Unit) hardware development procedure for the functional safety of in-vehicle network systems. We consider complete hardware redundancy as a safety mechanism for in-vehicle communication network under the assumption of the wired network failure such as disconnection of a CAN bus. An ESC (Electronic Stability Control) system is selected as an item and the required ASIL(Automotive Safety Integrity Level) for this item is assigned by performing the HARA(Hazard Analysis and Risk Assessment). The basic hardware architecture of the ESC system is designed with a microcontroller, passive components, and communication transceivers. The required ASIL for ESC system is shown to be satisfied with the designed safety mechanism by calculation of hardware architecture metrics such as the SPFM(Single Point Fault Metric) and the LFM(Latent Fault Metric).