• Industrial Engineering and Management Systems
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• v.14 no.4
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• pp.392-400
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• 2015

Monitoring of plasma etch processes for fault detection is one of the hallmark procedures in semiconductor manufacturing. Optical emission spectroscopy (OES) has been considered as a gold standard for modeling plasma etching processes for on-line diagnosis and monitoring. However, statistical quantitative methods for processing the OES data are still lacking. There is an urgent need for a statistical quantitative method to deal with high-dimensional OES data for improving the quality of etched wafers. Therefore, we propose a robust relevance vector machine (RRVM) for regression with statistical quantitative features for modeling etch rate and uniformity in plasma etch processes by using OES data. For effectively dealing with the OES data complexity, we identify seven statistical features for extraction from raw OES data by reducing the data dimensionality. The experimental results demonstrate that the proposed approach is more suitable for high-accuracy monitoring of plasma etch responses obtained from OES.

• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.633-642
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• 2005

Ubiquitous computing system is about networked processors, which is constructed with one or more computers interconnected by the networks. However, traditional security solution lacks a Proactive maintenance technique because of its focusing on developing the qualitative detection and countermeasure after attack. Thus, in this paper, we propose a quantitative assessment modeling technique, by which the general infrastructure can be improved and the attacks on a specific infrastructure be detected and protected. First of all, we develop the definition of survivality and modeling technique for quantitative assessment modeling with the static information on the system random information, and attack-type modeling. in addition, the survivality analysis on TCP-SYN attack and code-Red worm attack is performed for validating the proposed technique.

• Journal of the Korea Institute of Military Science and Technology
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• v.8 no.4 s.23
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• pp.91-101
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• 2005

In this paper, a technique for the quantitative analysis of the noise jamming effect is proposed. This technique based upon the mathematical modeling for noise jammers and the probability theory for random processes analyses the jamming effect by means of the modeling of the relationship among jammer, radar variables and radar detection probability under noise jamming environment. Computer simulation results show that the proposed technique not only makes the quantitative analysis of the jamming effect possible, but also provides the basis for quantitative analysis of the electronic warfare environment.

• Toxicological Research
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• v.34 no.4
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• pp.303-310
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• 2018

The methods of applied genetic toxicology are changing from qualitative hazard identification to quantitative risk assessment. Recently, quantitative analysis with point of departure (PoD) metrics and benchmark dose (BMD) modeling have been applied to in vitro genotoxicity data. Two software packages are commonly used for BMD analysis. In previous studies, we performed quantitative dose-response analysis by using the PROAST software to quantitatively evaluate the mutagenicity of four piperidine nitroxides with various substituent groups on the 4-position of the piperidine ring and six cigarette whole smoke solutions (WSSs) prepared by bubbling machine-generated whole smoke. In the present study, we reanalyzed the obtained genotoxicity data by using the EPA's BMD software (BMDS) to evaluate the inter-platform quantitative agreement of the estimates of genotoxic potency. We calculated the BMDs for 10%, 50%, and 100% (i.e., a two-fold increase), and 200% increases over the concurrent vehicle controls to achieve better discrimination of the dose-responses, along with their BMDLs (the lower 95% confidence interval of the BMD) and BMDUs (the upper 95% confidence interval of the BMD). The BMD values and rankings estimated in this study by using the EPA's BMDS were reasonably similar to those calculated in our previous studies by using PROAST. These results indicated that both software packages were suitable for dose-response analysis using the mouse lymphoma assay and that the BMD modeling results from these software packages produced comparable rank orders of the mutagenic potency.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.605-618
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• 2018

Risk management has been a crucial part of the daily operations of the financial industry over the past two decades. Value at Risk (VaR), a quantitative measure introduced by JP Morgan in 1995, is the most popular and simplest quantitative measure of risk. VaR has been widely applied to the risk evaluation over all types of financial activities, including portfolio management and asset allocation. This paper uses the implementations of multivariate GARCH models and copula methods to illustrate the performance of a one-day-ahead VaR prediction modeling process for high-dimensional portfolios. Many factors, such as the interaction among included assets, are included in the modeling process. Additionally, empirical data analyses and backtesting results are demonstrated through a rolling analysis, which help capture the instability of parameter estimates. We find that our way of modeling is relatively robust and flexible.

• Food Science and Biotechnology
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• v.18 no.2
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• pp.279-293
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• 2009

Quantitative risk assessments are related to implementing hazard analysis and critical control points (HACCP) by its potential involvement in identifying critical control points (CCPs), validating critical limits at a CCP, enabling rational designs of new processes, and products to meet required level of safety, and evaluating processing operations for verification procedures. The quantitative risk assessment is becoming a standard research tool which provides useful predictions and analyses on microbial risks and, thus, a valuable aid in implementing a HACCP system. This paper provides a review of microbial modeling in quantitative risk assessments, which can be applied to HACCP systems.

• Journal of KIISE:Software and Applications
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• v.33 no.1
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• pp.58-68
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• 2006

Requirements are very important to model software architecture. Requirements are divided into functional and quality requirements. Functional requirements are pinpointed subsystems and components. Quality requirements affect the structure of architecture. Thus requirements are essential to understand clearly in order to design software architecture. This paper focuses on a quantitative approach to requirements analysis for modeling architectures. In our proposal, functional requirements are quantified through calculating each priority of components. Quality requirements are quantified through calculating the correlation degree between components and quality attributes. The proposed method is implemented by DRAMA (Domain Requirements Analysis for Modeling Architectures), which fully supports our approach and are developed in Java environments. Our proposal is validated to apply some industrial examples.

• Transactions of Materials Processing
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• v.21 no.6
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• pp.366-371
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• 2012

Atomistic simulations have become useful tools for exploring new insights in materials science, but the length and time scale that can be handled with atomistic simulations are seriously limiting their practical applications. In order to make meaningful quantitative predictions, atomistic simulations are necessarily combined with higher-scale modeling. The present research is thus concerned with the development of a multi-scale model and its application to the prediction of the mechanical properties of body-centered cubic(BCC) iron with an emphasis on the coupling of atomistic molecular dynamics with meso-scale discrete dislocation dynamics modeling. In order to achieve predictive multi-scale simulations, it is necessary to properly incorporate atomistic details into the meso-scale approach. This challenge is handled with the proposed hierarchical information passing strategy from atomistic to meso-scale by obtaining material properties and dislocation mobility. Finally, this fundamental and physics-based meso-scale approach is employed for quantitative predictions of the mechanical response of single crystal iron.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.46-54
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• 2017

'Consulting', which is the main research topic of the knowledge service industry, is a field of study that is essential for the growth and development of companies and proliferation to specialized fields. However, it is difficult to grasp the current status of international research related to consulting, mainly on which topics are being studied, and what are the latest research topics. The purpose of this study is to analyze the research trends of academic research related to 'consulting' by applying quantitative analysis such as topic modeling and statistic analysis. In this study, we collected statistical data related to consulting in the Scopus DB of Elsevier, which is a representative academic database, and conducted a quantitative analysis on 15,888 documents. We scientifically analyzed the research trends related to consulting based on the bibliographic data of academic research published all over the world. Specifically, the trends of the number of articles published in the major countries including Korea, the author key word trend, and the research topic trend were compared by country and year. This study is significant in that it presents the result of quantitative analysis based on bibliographic data in the academic DB in order to scientifically analyze the trend of academic research related to consulting. Especially, it is meaningful that the traditional frequency-based quantitative bibliographic analysis method and the text mining (topic modeling) technique are used together and analyzed. The results of this study can be used as a tool to guide the direction of research in consulting field. It is expected that it will help to predict the promising field, changes and trends of consulting industry related research through the trend analysis.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.33-36
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• 2000

Recently, in the shipping companies have been employing prudently in order to prevent from sea accidents occurred by human factors. Also the students of merchant marine universities are choosing prudently the shipping companies when taking a job. But many qualitative and quantitative factors are considered in decision making for the employment. FSM(Fuzzy Structural Modeling) has been widely used in modeling the system composed of such qualitative and quantitative factor. In this paper, a case study is discussed for the analysis of the consciousness of the employment of shipping companies using FSM. Also this paper proposed the planes for educating and recruitment guiding the student in maritime university.