• 제목/요약/키워드: Hybrid risk management model

검색결과 12건 처리시간 0.023초

여러 가지 Inductive 방법에 대한 통합모델 개발과 그 실증적 유효성에 대한 연구 (The Development of Hybrid Model and Empirical Study for the Several Inductive Approaches)

  • 김광용
    • 한국경영과학회지
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    • 제23권3호
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    • pp.185-207
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    • 1998
  • This research investigates computer generated hybrid second-order model of two numerically based approaches to risk classification : discriminant analysis and neural networks. The hybrid second-order models are derived by rule induction using the ID3 and tested in the several different kinds of data. This new hybrid approach is designed to combine the high prediction accuracy and robustness of DA or NN with perspicuity of ID3. The hybrid model also eliminates the problem of contradictory inputs of ID3. After doing empirical test for the validity of hybrid model using small and medium companies' bankrupt data, hybrid model shows high perspicuity, high prediction accuracy for bankrupt, and simplicity for rules. The hybrid model also shows high performance regardless the type of data such as numeric data, non-numeric data, and combined data.

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Identification and risk management related to construction projects

  • Boughaba, Amina;Bouabaz, Mohamed
    • Advances in Computational Design
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    • 제5권4호
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    • pp.445-465
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    • 2020
  • This paper presents a study conducted with the aim of developing a model of tendering based on a technique of artificial intelligence by managing and controlling the factors of success or failure of construction projects through the evaluation of the process of invitation to tender. Aiming to solve this problem, analysis of the current environment based on SWOT (Strengths, Weaknesses, Opportunities, and Threats) is first carried out. Analysis was evaluated through a case study of the construction projects in Algeria, to bring about the internal and external factors which affect the process of invitation to tender related to the construction projects. This paper aims to develop a mean to identify threats-opportunities and strength-weaknesses related to the environment of various national construction projects, leading to the decision on whether to continue the project or not. Following a SWOT analysis, novel artificial intelligence models in forecasting the project status are proposed. The basic principal consists in interconnecting the different factors to model this phenomenon. An artificial neural network model is first proposed, followed by a model based on fuzzy logic. A third model resulting from the combination of the two previous ones is developed as a hybrid model. A simulation study is carried out to assess performance of the three models showing that the hybrid model is better suited in forecasting the construction project status than RNN (recurrent neural network) and FL (fuzzy logic) models.

하이브리드 모형을 이용한 고위험 임부 간호의 개념 분석 (A concept analysis of high-risk pregnant nursing: Using hybrid model)

  • 채미영;김현진
    • 디지털융복합연구
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    • 제19권9호
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    • pp.423-433
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    • 2021
  • 본 연구의 목적은 고위험 임부 간호의 개념 정의 및 분석을 하기 위함이다. 이 연구는 Schwartz - Barcott & Hesook Suzie Kim의 하이브리드 모형을 사용하여 분석하였다. 하이브리드 모델을 사용하여 주요 속성과 지표를 식별하고, 현장 실사 고위험 임부 병실에서 고위험 임부를 직접 간호를 수행한 5년 이상 수행한 간호사 10을 면담하여 자료를 수집하였다. 그 결과 3가지 차원, 5가지 속성, 37개의 지표로 도출되었다. 본 연구로 고위험 임부 간호의 개념 분석은 고위험 임부 간호에 대한 지침을 제공할 수 있으며 이론적 초석을 마련할 수 있다.

수소경제로의 이행을 위한 안전관리 정책 연구 (A Study on Safety Policies for a Transition to a Hydrogen Economy)

  • 전대천
    • 한국수소및신에너지학회논문집
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    • 제25권2호
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    • pp.161-172
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    • 2014
  • Hydrogen, which can be produced from abundant and widely distributed renewable energy resources, seems to be a promising candidate for solving the concerns for improving energy security, urban air pollution, and reducing greenhouse gas emissions. The two primary motivating factors for hydrogen economy are fossil fuel supply limitations and concerns about global warming. But the safety issues associated with hydrogen economy need to be investigated and fully understood before being considered as a future energy source. Limited operating experience with hydrogen energy systems in consumer environments is recognised as a significant barrier to the implementation of hydrogen economy. To prevent unnecessary restrictions on emerging codes, standards and local regulations, safety policies based on real hazards should be developed. This article studies briefly the direct impact-distances from hazard events such as hydrogen release and jet fire, and damage levels from hydrogen gas explosion in a confined space. Based on the direct impact-distances indicated in the accident scenarios and consumer environments in Korea, the safety policies, which are related to hydrogen filling station, hydrogen fuel cell car, portable fuel cell, domestic fuel cells, and hydrogen town, are suggested to implement hydrogen economy. To apply the safety policies and overcome the disadvantages of prescriptive risk management, which is setting guidance in great detail to management well known risk but is not covering unidentified risk, hybrid risk management model is also proposed.

Two dimensional reduction technique of Support Vector Machines for Bankruptcy Prediction

  • Ahn, Hyun-Chul;Kim, Kyoung-Jae;Lee, Ki-Chun
    • 한국경영정보학회:학술대회논문집
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    • 한국경영정보학회 2007년도 International Conference
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    • pp.608-613
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    • 2007
  • Prediction of corporate bankruptcies has long been an important topic and has been studied extensively in the finance and management literature because it is an essential basis for the risk management of financial institutions. Recently, support vector machines (SVMs) are becoming popular as a tool for bankruptcy prediction because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. In addition, they don't require huge training samples and have little possibility of overfitting. However. in order to Use SVM, a user should determine several factors such as the parameters ofa kernel function, appropriate feature subset, and proper instance subset by heuristics, which hinders accurate prediction results when using SVM In this study, we propose a novel hybrid SVM classifier with simultaneous optimization of feature subsets, instance subsets, and kernel parameters. This study introduces genetic algorithms (GAs) to optimize the feature selection, instance selection, and kernel parameters simultaneously. Our study applies the proposed model to the real-world case for bankruptcy prediction. Experimental results show that the prediction accuracy of conventional SVM may be improved significantly by using our model.

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식생가뭄반응지수 (VegDRI)를 활용한 위성영상 기반 가뭄 평가 (Satellite-based Hybrid Drought Assessment using Vegetation Drought Response Index in South Korea (VegDRI-SKorea))

  • 남원호;;;장민원;홍석영
    • 한국농공학회논문집
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    • 제57권4호
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    • pp.1-9
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    • 2015
  • The development of drought index that provides detailed-spatial-resolution drought information is essential for improving drought planning and preparedness. The objective of this study was to develop the concept of using satellite-based hybrid drought index called the Vegetation Drought Response Index in South Korea (VegDRI-SKorea) that could improve spatial resolution for monitoring local and regional drought. The VegDRI-SKorea was developed using the Classification And Regression Trees (CART) algorithm based on remote sensing data such as Normalized Difference Vegetation Index (NDVI) from MODIS satellite images, climate drought indices such as Self Calibrating Palmer Drought Severity Index (SC-PDSI) and Standardized Precipitation Index (SPI), and the biophysical data such as land cover, eco region, and soil available water capacity. A case study has been done for the 2012 drought to evaluate the VegDRI-SKorea model for South Korea. The VegDRI-SKorea represented the drought areas from the end of May and to the severe drought at the end of June. Results show that the integration of satellite imageries and various associated data allows us to get improved both spatially and temporally drought information using a data mining technique and get better understanding of drought condition. In addition, VegDRI-SKorea is expected to contribute to monitor the current drought condition for evaluating local and regional drought risk assessment and assisting drought-related decision making.

도로비탈면 상시계측 실측치와 드론 사진측량에 의한 3D 모델값의 정확도 비교분석 (Accuracy Analysis for Slope Movement Characterization by comparing the Data from Real-time Measurement Device and 3D Model Value with Drone based Photogrammetry)

  • 조한광;장기태;홍성진;홍구표;김상환;권세호
    • 한국지리정보학회지
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    • 제23권4호
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    • pp.234-252
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    • 2020
  • 계측기기만을 이용한 현장 상황대응의 재래적 방식에서 벗어나 온라인 '첨단기술(Hi-Technology)'과 오프라인의 '직관적 경험(Hi-Experience)'을 융합한 하이브리드(Hybrid) 재해관리 기법의 유효성을 검증하였다. 이를 위해 대상 현장에 매설된 상시 계측기 GNSS(RTK) 5대를 지상기준점(Ground Control Point, GCP)으로 사용하였다. 또한, 인근 지점에 크기 불변 특징점(Scale Invariant Feature Transform, SIFT) 4곳을 추출하여 검사점(Control Point, CP)으로 활용하였다. 이를 통해 현장 실측치와 드론기반 3차원 측정 결과치와의 정확도를 각 좌표값의 차이의 평균제곱근오차(Root Mean Square Error)를 이용하여 분석하였다. 결과적으로 드론에 의해 획득된 3차원 수치 모델을 정밀하게 후처리 분석함으로써 피사체의 모든 지형지물이 변위추적의 객체로 활용할 수 있음을 확인할 수 있었다. 포인트 클라우드(Point cloud) 기반의 3-D 수치 영상은 현장 그대로의 모습을 초실감, 고정도 가시화 함으로서 직관적인 경험에 공감할 수 있는 친화적인 솔루션을 제공하며, 단순 신호처리 기반의 계측기기 하드웨어 중심의 재해관리를 탈피해 인명피해/예산 절감 등 비탈면 유지관리에 최적의 플랫폼을 제공할 수 있을 것으로 판단된다. 특히, 특정 위치에 설치된 특정지점(Pin-point) 센서에 의존한 국지적인 정보의 한계를 뛰어넘어 기술생산 중심에서 재난관리의 중심으로 신속하게 전환될 수 있는 매개체가 될 것으로 기대한다.

하이브리드 인공신경망 모형을 이용한 부도 유형 예측 (Bankruptcy Type Prediction Using A Hybrid Artificial Neural Networks Model)

  • 조남옥;김현정;신경식
    • 지능정보연구
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    • 제21권3호
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    • pp.79-99
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    • 2015
  • 부도 예측은 회계와 재무 분야에서 꾸준히 연구되고 있는 분야이다. 초기에는 주로 다중판별분석(multiple discriminant analysis)와 로짓 분석(logit analysis)과 같은 통계적 방법을 이용하였으나, 1990년대 이후에는 경영 분야의 분류 문제를 위해 많은 연구자들이 인공신경망(back-propagation neural network), 사계기반추론(case-based reasoning), 서포트 벡터 머신(support vector machine) 등과 같은 인공지능을 통한 접근법을 이용하여 통계적 방법보다 분류 성과 측면에서 우수함을 입증해왔다. 기존의 기업의 부도에 관한 연구에서 많은 연구자들이 재무비율을 이용하여 부도 예측 모형을 구축하는 것에 초점을 맞추어왔다. 부도예측에 관한 연구가 꾸준히 진행되고 있는 반면, 부도의 세부적인 유형을 예측하여 제시하는 것에 대한 연구는 미흡한 실정이었다. 따라서 본 연구에서는 수익성, 안정성, 활동성 지표를 중심으로 국내 비외감 건설업 기업들의 부도 여부뿐만 아니라 부도의 세부적인 유형까지 예측 가능한 모형을 개발하고자 한다. 본 연구에서는 부도 유형을 예측하기 위해 두 개의 인공신경망 모형을 결합한 하이브리드 접근법을 제안하였다. 첫 번째 인공신경망 모형은 부도예측을 위한 역전파 인공신경망을 이용한 모형이며, 두 번째 인공신경망 모형은 부도 데이터를 몇 개의 유형으로 분류하는 자기조직화지도(self-organizing map)을 이용한 모형이다. 실험 결과를 통해 정의된 5개의 부도 유형인 심각한 부도(severe bankruptcy), 안정성 부족(lack of stability), 활동성 부족(lack of activity), 수익성 부족(lack of profitability), 회생 가능한 부도(recoverable bankruptcy)는 재무 비율에 따라 유형별로 상이한 특성을 갖는 것을 확인할 수 있었다. 본 연구 결과를 통해 신용 평가 분야의 연구자와 실무자들이 기업의 부도의 유형에 대한 유용한 정보를 얻을 것으로 기대한다.

조직의 정보 니즈와 ERP 기능과의 불일치 및 그 대응책에 대한 이해: 조직 메모리 이론을 바탕으로 (Understanding the Mismatch between ERP and Organizational Information Needs and Its Responses: A Study based on Organizational Memory Theory)

  • 정승렬;배억호
    • Asia pacific journal of information systems
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    • 제22권2호
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    • pp.21-38
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    • 2012
  • Until recently, successful implementation of ERP systems has been a popular topic among ERP researchers, who have attempted to identify its various contributing factors. None of these efforts, however, explicitly recognize the need to identify disparities that can exist between organizational information requirements and ERP systems. Since ERP systems are in fact "packages" -that is, software programs developed by independent software vendors for sale to organizations that use them-they are designed to meet the general needs of numerous organizations, rather than the unique needs of a particular organization, as is the case with custom-developed software. By adopting standard packages, organizations can substantially reduce many of the potential implementation risks commonly associated with custom-developed software. However, it is also true that the nature of the package itself could be a risk factor as the features and functions of the ERP systems may not completely comply with a particular organization's informational requirements. In this study, based on the organizational memory mismatch perspective that was derived from organizational memory theory and cognitive dissonance theory, we define the nature of disparities, which we call "mismatches," and propose that the mismatch between organizational information requirements and ERP systems is one of the primary determinants in the successful implementation of ERP systems. Furthermore, we suggest that customization efforts as a coping strategy for mismatches can play a significant role in increasing the possibilities of success. In order to examine the contention we propose in this study, we employed a survey-based field study of ERP project team members, resulting in a total of 77 responses. The results of this study show that, as anticipated from the organizational memory mismatch perspective, the mismatch between organizational information requirements and ERP systems makes a significantly negative impact on the implementation success of ERP systems. This finding confirms our hypothesis that the more mismatch there is, the more difficult successful ERP implementation is, and thus requires more attention to be drawn to mismatch as a major failure source in ERP implementation. This study also found that as a coping strategy on mismatch, the effects of customization are significant. In other words, utilizing the appropriate customization method could lead to the implementation success of ERP systems. This is somewhat interesting because it runs counter to the argument of some literature and ERP vendors that minimized customization (or even the lack thereof) is required for successful ERP implementation. In many ERP projects, there is a tendency among ERP developers to adopt default ERP functions without any customization, adhering to the slogan of "the introduction of best practices." However, this study asserts that we cannot expect successful implementation if we don't attempt to customize ERP systems when mismatches exist. For a more detailed analysis, we identified three types of mismatches-Non-ERP, Non-Procedure, and Hybrid. Among these, only Non-ERP mismatches (a situation in which ERP systems cannot support the existing information needs that are currently fulfilled) were found to have a direct influence on the implementation of ERP systems. Neither Non-Procedure nor Hybrid mismatches were found to have significant impact in the ERP context. These findings provide meaningful insights since they could serve as the basis for discussing how the ERP implementation process should be defined and what activities should be included in the implementation process. They show that ERP developers may not want to include organizational (or business processes) changes in the implementation process, suggesting that doing so could lead to failed implementation. And in fact, this suggestion eventually turned out to be true when we found that the application of process customization led to higher possibilities of failure. From these discussions, we are convinced that Non-ERP is the only type of mismatch we need to focus on during the implementation process, implying that organizational changes must be made before, rather than during, the implementation process. Finally, this study found that among the various customization approaches, bolt-on development methods in particular seemed to have significantly positive effects. Interestingly again, this finding is not in the same line of thought as that of the vendors in the ERP industry. The vendors' recommendations are to apply as many best practices as possible, thereby resulting in the minimization of customization and utilization of bolt-on development methods. They particularly advise against changing the source code and rather recommend employing, when necessary, the method of programming additional software code using the computer language of the vendor. As previously stated, however, our study found active customization, especially bolt-on development methods, to have positive effects on ERP, and found source code changes in particular to have the most significant effects. Moreover, our study found programming additional software to be ineffective, suggesting there is much difference between ERP developers and vendors in viewpoints and strategies toward ERP customization. In summary, mismatches are inherent in the ERP implementation context and play an important role in determining its success. Considering the significance of mismatches, this study proposes a new model for successful ERP implementation, developed from the organizational memory mismatch perspective, and provides many insights by empirically confirming the model's usefulness.

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재무예측을 위한 Support Vector Machine의 최적화 (Optimization of Support Vector Machines for Financial Forecasting)

  • 김경재;안현철
    • 지능정보연구
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    • 제17권4호
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    • pp.241-254
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    • 2011
  • Support vector machines(SVM)은 비교적 최근에 등장한 데이터마이닝 기법이지만, 재무, CRM 등의 경영학 분야에서 많이 연구되고 있다. SVM은 인공신경망과 필적할 만큼의 예측 정확도를 보이는 사례가 많았지만, 암상자로 불리는 인공신경망 모형에 비해 구축된 예측모형의 구조를 이해하기 쉽고, 인공신경망에 비해 과도적합의 가능성이 적어서 적은 수의 데이터에서도 적용 가능하다는 장점을 가지고 있다. 하지만, 일반적인 SVM을 이용하려면, 인공신경망과 마찬가지로 여러 가지 설계요소들을 설계자가 선택하여야 하기 때문에 임의성이 높고, 국부 최적해에 수렴할 가능성도 크다. 또한, 많은 수의 데이터가 존재하는 경우에는 데이터를 분석하고 이용하는데 시간이 소요되고, 종종 잡음이 심한 데이터가 포함된 경우에는 기대하는 수준의 예측성과를 얻지 못할 가능성이 있다. 본 연구에서는 일반적인 SVM의 장점을 그대로 유지하면서, 전술한 두 가지 단점을 보완한 새로운 SVM 모형을 제안한다. 본 연구에서 제안하는 모형은 사례선택기법을 일반적인 SVM에 융합한 것으로 대용량의 데이터에서 예측에 불필요한 데이터를 선별적으로 제거하여 예측의 정확도와 속도를 제고할 수 있는 방법이다. 본 연구에서는 잡음이 많고 예측이 어려운 것으로 알려진 재무 데이터를 활용하여 제안 모형의 유용성을 확인하였다.