• The Journal of Asian Finance, Economics and Business
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• 제5권3호
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• pp.185-193
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• 2018

This paper aims to explore the risk governance framework and socially viable solutions, attempting to provide guidance for the decision making process. The key idea of this study start with overcoming the limitations of IRGC risk governance framework, which mainly focuses on a comprehensive framework for risk governance. This article has employed SWOT analysis as a methodology, which is a strategic planning technique used to help identifying the strengths, weaknesses, opportunities, and threats related to business competition or risk management. In this paper, socially viable solutions as an alternative plan place emphasis on the adoption of concern assessment through a concerns table. It is also proposed that scoping has to get introduced, with SWOT analysis in the process. The results of this paper support that multiple stakeholders have to participate in the process of identifying and framing risk and communicating with each other, considering the context. It should be noted that communities can become involved and take important parts in decision making process in various ways. It is recommended that engaging stakeholders to both risk assessment and risk management is material to dealing with risk in a socially viable way. It also implies that the community-based disaster management should be better prepared for the decision making process in socially viable solutions.

• 산업경영시스템학회지
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• 제38권1호
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• pp.90-100
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• 2015

This study is conducted to suggest ways to mitigate the mismatch phenomenon between job seekers who want to find right company for themselves and companies looking for appropriate new employees. For this purpose, this study improves the company assessment framework reflecting job seekers interests by using fuzzy analytic hierarchy process. The improved evaluation framework is a three-level hierarchical structure, where there are 4 groups at the top level, 12 factors at the intermediate level and 36 indexes at the bottom level. For the empirical analysis of the applicants preferences based on the improved model, a survey for F-AHP analysis is carried out to university students and then priorities of components in the evaluation model are calculated. Moreover, the differences of priority of the company assessment framework are analyzed for different genders, college years, and major divisions. The results show that job seekers' most concerning factors are wages, stability, working environments, and labor deal, which are ranked highly in this order and the differences in preferences for each type of job seekers (genders, college years, and major divisions) are obvious. The results also show that the male prefers wages to environment, on the other hand female does working environment to wages.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.103-109
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• 2018

This paper presents an idea and framework to automate a full cycle business process management and re-engineering by integrating traditional business process management systems, process mining, data mining, machine learning, and simulation. We build our framework on the cloud-based platform such that various data sources can be incorporated. We design our systems to be extensible so that not only beneficial for practitioners of BPM, but also for researchers. Our framework can be used as a test bed for researchers without the complication of system integration. The automation of redesigning phase and selecting a baseline process model for deployment are the two main contributions of this study. In the redesigning phase, we deal with both the analysis of the existing process model and what-if analysis on how to improve the process at the same time, Additionally, improving a business process can be applied in a case by case basis that needs a lot of trial and error and huge data. In selecting the baseline process model, we need to compare many probable routes of business execution and calculate the most efficient one in respect to production cost and execution time. We also discuss the challenges and limitation of the framework, including the systems adoptability, technical difficulties and human factors.

• 한국경영공학회지
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• 제23권4호
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• pp.165-177
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• 2018

Smart technology has been utilized in various fields of all kinds of industries. Manufacturing industry has built its smart technology environment appropriate for its manufacturing fields in order to strengthen its manufacturing performance and competitiveness. The advance of smart technology for manufacturing industry needs to efficiently produce products, and response customer's demands and services in a global industrial environment. The smart technology capability of manufacturing fields is very crucial for the innovative production and efficient operation activities, and for efficient advancement of the manufacturing performance. We have necessitated a scientific and objective method that can gauge a smart technology ability in order to manage and strengthen the smart technology ability of manufacturing fields. This research provides a comprehensive framework that can rationally gauge the smart technology capability of manufacturing fields for effectively managing and advancing their smart technology capabilities. In this research, we especially develop a structural framework that can gauge the smart technology capability for a smart factory of manufacturing fields, with verifying by reliability analysis and factor analysis based on previous literature. This study presents a 13-item framework that can measure the smart technology capability for a smart factory of manufacturing fields in a smart technology perspective.

• International Journal of Aeronautical and Space Sciences
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• 제18권1호
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• pp.70-81
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• 2017

Various components of an engine nozzle are modeled as flexible multi-body components that are operated under high temperature and pressure. In this paper, in order to predict complex behavior of an engine nozzle, thermo-fluid-flexible multi-body dynamics coupled analysis framework was developed. Temperature and pressure on the nozzle wall were obtained by the steady-state flow analysis for a two-dimensional nozzle. The pressure and temperature-dependent material properties were delivered to the flexible multi-body dynamics analysis. Then the deflection and strain distribution for a nozzle configuration was obtained. Heat conduction and thermal analyses were done using MSC.NASTRAN. The present framework was validated for a simple nozzle configuration by using a one-way coupled analysis. A two-way coupled analysis was also performed for the simple nozzle with an arbitrary joint clearance, and an asymmetric flow was observed. Finally, the total strain result for a realistic nozzle configuration was obtained using the one-way and two-way coupled analyses.

• 한국인공지능학회지
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• 제11권2호
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• pp.19-27
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• 2023

In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.

• 한국항공우주학회지
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• 제36권10호
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• pp.947-953
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• 2008

다분야 통합 최적설계(MDO) 프레임워크는 항공우주시스템의 설계에 고려해야 할 다양한 설계 분야의 통합적이고 동시적인 해석 및 설계 최적화를 위한 통합 환경으로 해석자원 및 최적화자원은 물론 CAD 툴과 DBMS 또한 통합해야하며 사용자편의환경을 제공해야한다. 또한 설계하고자 하는 대상 및 개발환경에 따라 프레임워크의 구축방법은 달라질 수 있다. 본 논문에서는 개발환경에 따라 단일 PC기반 프레임워크와 PLinda기반 프레임워크, 그리고 웹서비스 기반 프레임워크로 분류하여 이들을 비교 분석하였다.

• Journal of information and communication convergence engineering
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• 제13권4호
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• pp.270-274
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• 2015

In this paper, we propose an improved framework to analyze an N-ocular imaging system under fixed constrained resources such as the number of image sensors, the pixel size of image sensors, the distance between adjacent image sensors, the focal length of image sensors, and field of view of image sensors. This proposed framework takes into consideration, for the first time, the defocusing effect of the imaging lenses according to the object distance. Based on the proposed framework, the N-ocular imaging system such as integral imaging is analyzed in terms of depth resolution using two-point-source resolution analysis. By taking into consideration the defocusing effect of the imaging lenses using ray projection model, it is shown that an improved depth resolution can be obtained near the central depth plane as the number of cameras increases. To validate the proposed framework, Monte Carlo simulations are carried out and the results are analyzed.

• 정보처리학회논문지C
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• 제13C권3호
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• pp.295-302
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• 2006

침입탐지 시스템은 실시간으로 공격행위에 대하여 다량의 경보를 기록한다. 이들 경보 중에는 실제 공격 경보뿐만 아니라 공격으로 잘못 탐지하여 발생된 오 경보들도 있다. 오 경보는 침입탐지 시스템의 효율성을 저하시키는 주요요인이 되므로, 이 논문에서는 오경보 분석을 위한 프레임워크를 제안한다. 또한 지속적으로 증가하는 오 경보를 분석하기 위해 점진적 데이터 마이닝 기법을 적용한다. 제안한 오경보 분석 프레임워크는 GUI, DB Manager, Alert Preprocessor, False Alarm Analyzer로 구성되어 있다. 우리는 실험을 통해 증가하는 오경보를 분석하고, 분석된 오경보 규칙을 침입탐지 시스템에 적용하여 오 경보가 감소됨을 확인하였다.

• Computers and Concrete
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• 제32권3호
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• pp.313-326
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• 2023

The seismic safety of the shear wall structure can be assessed through seismic fragility analysis, which requires high computational costs in estimating seismic demands. Accordingly, machine learning methods have been applied to such fragility analyses in recent years to reduce the numerical analysis cost, but it still remains a challenging task. Therefore, this study uses the ensemble machine learning method to present an improved framework for developing a more accurate seismic demand model than the existing ones. To this end, a rank-based selection method that enables determining an excellent model among several single machine learning models is presented. In addition, an index that can evaluate the degree of overfitting/underfitting of each model for the selection of an excellent single model is suggested. Furthermore, based on the selected single machine learning model, we propose a method to derive a more accurate ensemble model based on the bagging method. As a result, the seismic demand model for which the proposed framework is applied shows about 3-17% better prediction performance than the existing single machine learning models. Finally, the seismic fragility obtained from the proposed framework shows better accuracy than the existing fragility methods.