• Title/Summary/Keyword: Qualitative Models

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Disambiguiation of Qualitative Reasoning with Quantitative Knowledge (정성추론에서의 모호성제거를 위한 양적지식의 활용)

  • Yoon, Wan-Chul
    • Journal of Korean Institute of Industrial Engineers
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    • v.18 no.1
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    • pp.81-89
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    • 1992
  • After much research on qualitative reasoning, the problem of ambiguities still hampers the practicality of this important AI tool. In this paper, the sources of ambiguities are examined in depth with a systems engineering point of view and possible directions to disambiguation are suggested. This includes some modeling strategies and an architecture of temporal inference for building unambiguous qualitative models of practical complexity. It is argued that knowledge of multiple levels in abstraction hierarchy must be reflected in the modeling to resolve ambiguities by introducing the designer's decisions. The inference engine must be able to integrate two different types of temporal knowledge representation to determine the partial ordering of future events. As an independent quantity management system that supports the suggested modeling approach, LIQUIDS(Linear Quantity-Information Deriving System) is described. The inference scheme can be conjoined with ordinary rule-based reasoning systems and hence generalized into many different domains.

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Classify and Quantify Cumulative Impact of Change Orders On Productivity Using ANN Models

  • Lee, Min-Jae
    • Korean Journal of Construction Engineering and Management
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    • v.6 no.5 s.27
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    • pp.69-77
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    • 2005
  • Change is inevitable and is a reality of construction projects. Most construction contracts include change clauses and allowing contractors an equitable adjustment to the contract price and duration caused by change. However, the actions of a contractor can cause a loss of productivity and furthermore can result in disruption of the whole project because of a cumulative or ripple effect. Because of its complicated nature, it becomes a complex issue to determine the cumulative impact (ripple effect) caused by single or multiple change orders. Furthermore, owners and contractors do not always agree on the adjusted contract price for the cumulative Impact of the changes. A number of studies have attempted to quantify the impact of change orders on project costs and schedule. Many of these attempted to develop regression models to quantify the loss. However, regression analysis has shortcomings in dealing with many qualitative or noisy input data. This study develops ANN models to classify and quantify the labor productivity losses that are caused by the cumulative impact of change orders. The results skew that ANN models give significantly improved performance compared to traditional statistical models.

A Study on Applying Complex System Theory to Land Warfare using EINSTein Model (EINSTein 모형을 이용한 복잡계이론의 지상전 적용에 관한 연구)

  • 이태원;강성진
    • Journal of the Korea Society for Simulation
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    • v.9 no.1
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    • pp.55-66
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    • 2000
  • This paper deals with complex system theory to describe land combat situation using EINSTein (Enhanced ISAAC Neural Simulation Tool) simulation model. Many studies have shown that existing Lanchester equations used in most wargame models does not describe changes of combat units, real land warfare situation and qualitative factors in combat. Future warfare will be a non-linear combat with various weapon system and complex combat units. EINSTein models is an agent-based simulation tool using complex system theory. We have compared and tested land combat results with Lanchester models and EINSTein models. The results have shown that EINSTein model has a possibility to apply and analyze land warfare more properly than Lanchester models.

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Development of Competitive Port Model Using the Hybrid Mechanism of System Dynamic Method and Hierarchical Fuzzy Process Method (SD법과 HFP법의 융합을 이용한 항만경쟁모델의 개발)

  • 여기태;이철영
    • Korean System Dynamics Review
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    • v.1 no.1
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    • pp.103-131
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    • 2000
  • If a system such as a port has a large boundary and complexity, and the system's substance is considered as a black box, forecast accuracy will be very low. Futhermore various components in a port exert significant influence on each other. To copy with these problem the form of structure models were introduced by using SD method. The Competitive Ports Model had several sub-systems consisting of each Unit Port models, and each Unit Port model was made by quantitative, qualitative factors and their feedback loops. The fact that all components of one port have influence on the components of the other ports should be taken into account to construct Competitive Port Models. However, with the current approach that is impossible, and in this paper therefore, models were simplified by HFP adapted to integrate level variables of unit port models. Although many studies on modelling of port competitive situation have been conducted, both theoretical frame and methodology are still very weak. In this study, a new algorithm called ESD(Extensional System Dynamics) for the evaluation of port competition was presented, and applied to simulate port systems in northeast asia.

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Soil Fertility Evaluation by Application of Geographic Information System for Tobacco Fields (지리정보시스템을 활용한 연초재배 토양의 비옥도 평가)

  • 석영선;홍순달;안정호
    • Journal of the Korean Society of Tobacco Science
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    • v.21 no.1
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    • pp.36-48
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    • 1999
  • Field test was conducted in Chungbuk province to evaluate the soil fertility using landscape and soil attributes by application of geographic information system(GIS) in 48 tobacco fields during 2 years(1996 ; 23 fields, 1997 ; 25 fields). The soil fertility factors and fertilizer effects were estimated by twenty five independent variables including 13 chemical properties and 12 GIS databases. Twenty five independent variables were classified by two groups, 15 quantitative indexes and 10 qualitative indexes and were analyzed by multiple linear regression (MLR) of SAS, REG and GLM models. The estimation model for evaluation of soil fertility and fertilizer effect was made by giving the estimate coefficient for each quantitative index and for each group of qualitative index significantly selected by MLR. Estimation for soil fertility factors and fertilizer effects by independent variables was better by MLR than single regression showing gradually improvement by adding chemical properties, quantitative indexes and qualitative indexes of GIS. Consequently, it is assumed that this approach by MLR with quantitative and qualitative indexes was available as an evaluation model of soil fertility and recommendation of optimum fertilization for tobacco field.

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A Multilevel Model Integration for Collaborative Decision Making (협동적 의사결정을 위한 다단계 모형 통합)

  • 권오병;이건창
    • Journal of the Korean Operations Research and Management Science Society
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    • v.23 no.2
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    • pp.103-129
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    • 1998
  • Corporate level decision making with multiple decision makers in a consistent way is essential in Decision Support System. However, since the decision makers have different interests and knowledge, the models used by them are also different in their level of abstraction. This makes decision makers waste a lot of efforts for an integrated decision making. The purpose of this paper is to propose an integration mechanism so that collaborative decision making models may be used synthetically in multi-abstraction level. Models are classified as multimedia model, mathematical model, qualitative model, causal & directional model, causal model, directional model and relationship model according to the level of abstraction. The proposed integration mechanism consists of model interpretation phase. model transformation phase, and model integration phase. Specifically, the model transformation Phase is divided into (1) model tightening mode which gather information to make a model transformed into upper level model, and (2) model relaxing mode which makes lower level model. In the model integration phase, models of same level are to be integrated schematically. An illustrative M&A-decision example is given to show the possibility of the methodology.

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Bankruptcy Prediction Modeling Using Qualitative Information Based on Big Data Analytics (빅데이터 기반의 정성 정보를 활용한 부도 예측 모형 구축)

  • Jo, Nam-ok;Shin, Kyung-shik
    • Journal of Intelligence and Information Systems
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    • v.22 no.2
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    • pp.33-56
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    • 2016
  • Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

Korean Immigrant Women's Meanings of Breast, Breast Cancer, and Breast Cancer Screenings

  • Suh Eun-Young Eunice
    • Journal of Korean Academy of Nursing
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    • v.36 no.4
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    • pp.604-611
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    • 2006
  • Purpose. Koreans are one of the fastest growing Asian populations in the U.S. since 1960s. In Korean immigrant women (KIW), breast cancer was reported as the most frequently diagnosed cancer. However, their screening rates for breast cancer are lower than national guidelines; it is assumed that underlying cultural schemas of breast, breast cancer, and its screening modalities exist and need to be studied. This study was aimed to investigate cultural meanings of breast, breast cancer, and breast cancer screenings in KIW. Methods. Using cultural models theory from cognitive anthropology, naturalistic qualitative methodology was utilized. Three focus group interviews with fifteen KIW were conducted. Thematic analysis with constant comparison technique was performed eliciting units of meaning, categories, and themes. Results. The cultural schema of the meaning of breast is 'mother who is breast-feeding her baby,' with two themes of 'balance in size,' and 'shyness.' Regarding breast cancer, three themes, i.e., 'indifference,' 'fear,' and 'uncertainty' are emerged. 'Lack of information about screening modalities' is the overarching schema with reference to breast cancer screenings. Conclusions. The findings of this study demonstrate unique cultural models of KIW related to breast cancer and its screenings, which are critical to understand and penetrate their barriers to breast cancer screening.

3D Quantitative and Qualitative Structure-Activity Relationships of the δ -Opioid Receptor Antagonists

  • Chun, Sun;Lee, Jee-Young;Ro, Seong-Gu;Jeong, Ki-Woong;Kim, Yang-Mee;Yoon, Chang-Ju
    • Bulletin of the Korean Chemical Society
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    • v.29 no.3
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    • pp.656-662
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    • 2008
  • Antagonists of the d -opioid receptor are effective in overcoming resistance against analgesic drugs such as morphine. To identify novel antagonists of the d -opioid receptor that display high potency and low resistance, we performed 3D-QSAR analysis using chemical feature-based pharmacophore models. Chemical features for d -opioid receptor antagonists were generated using quantitative (Catalyst/HypoGen) and qualitative (Catalyst/HipHop) approaches. For HypoGen analysis, we collected 16 peptide and 16 non-peptide antagonists as the training set. The best-fit pharmacophore hypotheses of the two antagonist models comprised identical features, including a hydrophobic aromatic (HAR), a hydrophobic (HY), and a positive ionizable (PI) function. The training set of the HipHop model was constructed with three launched opioid drugs. The best hypothesis from HipHop included four features: an HAR, an HY, a hydrogen bond donor (HBD), and a PI function. Based on these results, we confirm that HY, HAR and PI features are essential for effective antagonism of the d -opioid receptor, and determine the appropriate pharmacophore to design such antagonists.

Development of Accident Scenario Models for the Risk Assessment of Railway Casualty Accidents (철도 사상사고 위험도 평가를 위한 사고 시나리오 모델 개발에 관한 연구)

  • Park, Chan-Woo;Wang, Jong-Bae;Cho, Yun-ok
    • Journal of the Korean Society of Safety
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    • v.24 no.3
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    • pp.79-87
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    • 2009
  • The objective of this study is to develop accident scenario models for the risk assessment of railway casualty accidents. To develop these scenario models, hazardous events and hazardous factors were identified by gathering various accident reports and information. Then, the accident scenario models were built up. Each accident scenario model consists of an occurrence scenario model and a progress scenario model. The occurrence scenario refers to the occurrence process of the event before the hazardous event. The progress scenario means the progress process of the event after the hazardous event. To manage a large amount of accident/incident data and scenarios, a railway accident analysis information system was developed using railway accident scenario models. To test the feasibility of the developed scenario models, more than 800 domestic railway casualty accidents that occurred in 2004 and 2005 were investigated and quantitative and qualitative analyses were performed using the developed information system.