• Journal of Korea Multimedia Society
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• v.18 no.2
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• pp.199-206
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• 2015

Since security vulnerabilities newly discovered in a popular Web browser immediately put a number of users at risk, urgent attention from developers is required to address those vulnerabilities. Analysis of characteristics in the Web browser vulnerabilities can be used to assess security risks and to determine the resources needed to develop patches quickly to handle vulnerabilities discovered. So far, being a new research area, the quantitative aspects of the Web browser vulnerabilities and risk assessments have not been fully investigated. However, due to the importance of Web browser software systems, further detailed studies are required related to the Web browser risk assessment, using rigorous analysis of actual data which can assist decision makers to maximize the returns on their security related efforts. In this paper, quantitative software vulnerability analysis has been presented for major Web browsers with respect to the Common Vulnerability Scoring System. Further, vulnerability discovery trends in the Web browsers are also investigated. The results show that, almost all the time, vulnerabilities are compromised from remote networks with no authentication required systems. It is also found that a vulnerability discovery model which was originally introduced for operating systems is also applicable to the Web browsers.

• Investigative Magnetic Resonance Imaging
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• v.25 no.3
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• pp.156-163
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• 2021

Recently, neuromelanin and nigrosome imaging techniques have been developed to evaluate the substantia nigra in Parkinson's disease. Previous studies have shown potential benefits of quantitative analysis of neuromelanin and nigrosome images in the substantia nigra, although visual assessments have been performed to evaluate structures in most studies. In this study, we investigate the potential of using deep learning based automatic region segmentation techniques for quantitative analysis of the substantia nigra. The deep convolutional neural network was trained to automatically segment substantia nigra regions on 3D nigrosome and neuromelanin sensitive MR images obtained from 30 subjects. With a 5-fold cross-validation, the mean calculated dice similarity coefficient between manual and deep learning was 0.70 ± 0.11. Although calculated dice similarity coefficients were relatively low due to empirically drawn margins, selected slices were overlapped for more than two slices of all subjects. Our results demonstrate that deep convolutional neural network-based method could provide reliable localization of substantia nigra regions on neuromelanin and nigrosome sensitive MR images.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.255-264
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• 2021

The purpose of this study is to analyze the role of customer value and customer trust as a mediator of flexibility influence on customer retention. The study applies SEM tools with WarpPLS analysis and translates customer value and customer trust as mediation. This study was organized into three parts: exploratory research, descriptive research, and explanatory research. The quantitative approach in this study uses a survey method by taking samples from the population. This is a latent variable that is measured using a research instrument in the form of a questionnaire. The survey was conducted by distributing questionnaires to respondents. This research was conducted at PT. Nusa Prima Logistics Terminal Teluk Lamong by taking primary data, in the form of assessments or perceptions from respondents, so a survey was conducted by submitting a questionnaire directly to 194 respondents. Quantitative data analysis was performed using Structural Equation Modeling (SEM) to answer the research hypothesis. Flexibility is very influential in increasing customer retention so to develop customer retention, it is necessary to increase flexibility, customer value, and customer trust. The novelty in this research lies in identifying the role and position of customer value and customer trust as mediation that affect customer retention.

• Environmental Analysis Health and Toxicology
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• v.30 no.sup
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• pp.7.1-7.10
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• 2015

Objectives For successful adoption of legislation controlling registration and assessment of chemical substances, it is important to obtain sufficient toxicological experimental evidence and other related information. It is also essential to obtain a sufficient number of predicted risk and toxicity results. Particularly, methods used in predicting toxicities of chemical substances during acquisition of required data, ultimately become an economic method for future dealings with new substances. Although the need for such methods is gradually increasing, the-required information about reliability and applicability range has not been systematically provided. Methods There are various representative environmental and human toxicity models based on quantitative structure-activity relationships (QSAR). Here, we secured the 10 representative QSAR-based prediction models and its information that can make predictions about substances that are expected to be regulated. We used models that predict and confirm usability of the information expected to be collected and submitted according to the legislation. After collecting and evaluating each predictive model and relevant data, we prepared methods quantifying the scientific validity and reliability, which are essential conditions for using predictive models. Results We calculated predicted values for the models. Furthermore, we deduced and compared adequacies of the models using the Alternative non-testing method assessed for Registration, Evaluation, Authorization, and Restriction of Chemicals Substances scoring system, and deduced the applicability domains for each model. Additionally, we calculated and compared inclusion rates of substances expected to be regulated, to confirm the applicability. Conclusions We evaluated and compared the data, adequacy, and applicability of our selected QSAR-based toxicity prediction models, and included them in a database. Based on this data, we aimed to construct a system that can be used with predicted toxicity results. Furthermore, by presenting the suitability of individual predicted results, we aimed to provide a foundation that could be used in actual assessments and regulations.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.42 no.5
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• pp.494-502
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• 2009

A quantitative model was developed in order to estimate fishery production damage due to anthropogenically induced environmental changes. The model is described in the following equation, $Y_D=\frac{{\phi}_D}{{\phi}_G}[Y_0{\cdot}(t_p-t_0)-\frac{Y_0}{{\phi}_G}(1-e^{-{\phi}_G(t_p-t_0)})]$, where, $Y_D$ is annual amount of fishery production by nuclear power plant. ${\varphi}$ D and ${\varphi}$ G are instantaneous decreasing coefficient of fishery production by nuclear power plant and instantaneous decreasing coefficient of gross fishery production, respectively. $Y_0$ is annual mean fishery production without damages. $t_p$ is the present time, and $t_0$ is the starting time of damages. The model was applied to fishing grounds near a nuclear power plant on the east coast of Korea. Since fishery production damages have become bigger with increasing emission of thermal effluents from generators activities in the power plant, this factor has also been considered as, $\delta_{D_i}=\delta_D\({\sum}\limits_{i=0}^{n}\;W_i/W_T\)$, where, $\delta_{Di}$ is the cumulative damage rate in fishery production from generators, $\delta_D$ is the total cumulative damage rate in fishery production, $W_i$ is the emission amount of thermal effluents by generator i, and n is the number of generators in the nuclear power plant. This model can be used to conduct initial estimates of fishery production damages, before more detailed assessments are undertaken.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.161-170
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• 2015

This paper presents an efficient image fusion method to be appropriate for the KOMPSAT-2 and 3 satellites. The proposed method is based on the well-established component substitution (CS) approach. The proposed method is divided into two parts: 1) The first step is to create a intensity image by the weighted-averaging operation of a multi-spectral (MS) image and 2) the second step is to produce an optimal high-frequency image using the statistical properties of the original MS and panchromatic (PAN) images. The performance of the proposed method is evaluated in both quantitative and visual analysis. Quantitative assessments are performed by using the relative global dimensional synthesis error (Spatial and Spectral ERGAS), the image quality index (Q4), and the spectral angle mapper index (SAM). The qualitative and quantitative assessment results show that the fusion performance of the proposed method is improved in both the spectral and spatial qualities when it is compared with previous CS-based fusion methods.

• Transactions of the Korean hydrogen and new energy society
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• v.34 no.5
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• pp.505-513
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• 2023

Hydrogen infrastructure is expanding. High-capacity hydrogen refueling stations offer advantages because they can refuel a variety of light and heavy-duty vehicles, and multi-port refueling technology is developing to reduce charging time for heavy-duty vehicles. In this study, we suggest directions to lower the risk by analyzing the risk factors for each process involved in the installation of a high-capacity multi-port hydrogen refueling station in Changwon city. We conducted both qualitative and quantitative risk assessments of the equipment to evaluate the station. A hazard and operability study was performed for qualitative risk assessment, and PHAST/SAFETI were used for quantitative risk assessment. Quantitative risk assessment was used to calculate the consequence analysis of the facility to ensure secure design prior to station development and to predict individual and societal risks in various scenarios. As a result, the station's risk level was determined to be as low as reasonably practicable.

• Journal of the Optical Society of Korea
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• v.17 no.2
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• pp.205-212
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• 2013

Pain is one of the quite common symptoms in clinics and many treatment methods have been applied to relieve pain. Among the treatments, high-intensity light therapy for pain has been introduced, but this therapy has not been fully supported by confirmed efficacy due to the absence of quantitative assessments and treatment feedback data in real time. In this study, the evaluation of light distribution in tissue was performed with current high-intensity light sources quantitatively using light-tissue interaction simulations. The diffuse reflectance in tissue was generated using Monte Carlo simulation that traces photons as they undergo multiple scattering and absorption within each tissue layer (skin, fat, and muscle) and within multi-layered tissue. The results showed that the highest diffuse reflectance and the deepest penetration of tissue were achieved at ${\lambda}$=830 nm when compared with other wavelengths like ${\lambda}$=650 nm, 980 nm and 1064 nm.

• Journal of Environmental Impact Assessment
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• v.13 no.1
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• pp.1-8
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• 2004

A wide spectrum of risk assessments including qualitative and quantitative approaches and the analyses of its consequence were performed for an environmentally sensitive object such as incineration facility. To find out the major risk concerns, HAZOP(Hazard and Operability) were performed. Then, the frequency of hazardous gas release scenarios was calculated. Finally consequence analyses were performed for the gas release scenarios. On the basis of analyses through evaluation, a more innovative way for making a better control system or the enhancement of operation procedure was given. The results from these analyses would act as a substantial benefits for the incineration facility operator, and giving some measured information for the neighbors and the people involved.

• Research in Mathematical Education
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• v.13 no.2
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• pp.141-150
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• 2009

This study gives opportunity for investigating how student teachers view the teaching profession and how they transfer their pedagogical knowledge into practice. The aim of the study is to investigate the teaching skills student teachers gained in the assessment of micro teaching of their peers. The participants are 30 mathematics student teachers enrolled in the teacher training program in a state university. Document analysis and semi-structured interviews are the research instruments and inferential & descriptive statistics are used for the data analysis. The findings suggest that the qualitative and quantitative peer assessments of student teachers were graded differently which results from the difference of perceptions about teaching and different conceptualizations of the teaching qualifications.