• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• Advances in Computational Design
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• v.3 no.4
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• pp.367-384
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• 2018

The consideration of constructability issues at the design stage can lead to improved construction performance with smooth project delivery and savings in time and money. Empirical studies demonstrate the value obtained by integrating construction knowledge with the building design process, and its benefits for owners, contractors and designers. However, it is still a challenge to implement the concept into current design practice. There is a need for a decision support tool to aid designers in reviewing their design constructability, deploying current technological tools, such as BIM. Such tools are beneficial at the conceptual design stage when there is a room to improve the design significantly with less incurred cost. This research investigates how current process- and object-oriented models can be used to assess design constructability. It proposes a BIM-based model using embedded information within the design environment to conduct the assessment. The modelling framework is demonstrated in four key parts; namely, the conceptual design model, the constructability assessment model, the assessment process model and the decision-making phase. Each is associated with a set of components and functions that contribute towards the targeted constructability assessment outcomes. The proposed framework is the first to combine a numerical assessment system and a rule-based system, allowing for both quantitative and qualitative approaches. The modelling framework and its implementation through a prototype are described in this paper. It is believed that this framework is the first to enable users to transfer their construction knowledge and experience directly into a design platform linked to BIM models. The assessment criteria can be customised by the users who can reflect their own constructability preferences into various specialised profiles that can be added to the constructability assessment model. It also allows for the integration of the assessment process with the design phase, facilitating the optimisation of constructability performance from the early design stage.

• Journal of the Korea Society of Computer and Information
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• v.3 no.2
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• pp.189-199
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• 1998

This study presents a FZOGP(fuzzified zero-one goal programming) model and a DHP (Delphic Hierarchy Process) that can be used to help information systems(IS) managers decides which IS projects should be selected. Delphic method is conducted prior to AHP so that not only can the objectives to be considered in analysis be determined, but the opinions of all decision makers can also be incorporated in problem formulation. While the DHP provides an ideal ranking process for the selection of IS Projects, it does not consider real constraints that exists in decision making process. Then this study intends to show how the DHP can be used to establish a priority structure for use within a FZOGP model. The advantages of FZOGP model are as follows: the imprecise aspiration level for each objective can be considered in FZOGP model. And, the common features between the new FZOGP and the GP models are that the objective functions in both models are minimized and the structure of their formulations are the same.

• Journal of Korean Society of Transportation
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• v.22 no.6
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• pp.175-196
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• 2004

This study focuses on improving the performance of freeway incident detection by introducing some new measures to reduce false alarms in developing a new incident detection model. The model consists of the 5 major components through which a series of decision makings in determining the given traffic flow condition are made. The decision making process was designed such that the causes of traffic congestions can be accurately classified into several types including incidents and bottlenecks according to their unique characteristics. The model performance was tested and found to be compatible with that of the existing well-recognized models in terms of the detection rate and detection time. It should noted that the model produced much less false alarms than most of the existing models. The study results prove that the initial objective of the study was satisfied as it was an experimental trial to improve the false alarm rate for the incident detection model to be more pactically usable for traffic management purposes.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.636-650
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• 2020

A modeling system that can consider the overall water environment and be used to integrate hydrology, water quality, and aquatic ecosystem on a watershed scale is essential to support decision-making in integrated water resources management (IWRM). In adapting imported models for evaluating the unique water environment in Korea, a platform perspective is becoming increasingly important. In this study, a modeling platform is defined as an ecosystem that continuously grows and provides sustainable values through voluntary participation- and interaction-of all stakeholders- not only experts related to model development, but also model users and decision-makers. We assessed the conceptual values provided by the IWRM modeling platform in terms of openness, transparency, scalability, and sustainability. I We also reviewed the technical aspects of functional and spatial integrations in terms of socio-economic factors and user-centered multi-scale climate-forecast information. Based on those conceptual and technical aspects, we evaluated potential modeling platforms such as Source, FREEWAT, Object Modeling System (OMS), OpenMI, Community Surface-Dynamics Modeling System (CSDMS), and HydroShare. Among them, CSDMS most closely approached the values suggested in model development and offered a basic standard for easy integration of existing models using different program languages. HydroShare showed potential for sharing modeling results with the transparency expected by model user-s. Therefore, we believe that can be used as a reference in development of a modeling platform appropriate for managing the unique integrated water environment in Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1377-1393
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• 2023

Social media is used for various purposes including entertainment, communication, information search, and voicing their thoughts and concerns about a service, product, or issue. The social media data can be used for information mining and getting insights from it. The World Health Organization has listed COVID-19 as a global epidemic since 2020. People from every aspect of life as well as the entire health system have been severely impacted by this pandemic. Even now, after almost three years of the pandemic declaration, the fear caused by the COVID-19 virus leading to higher depression, stress, and anxiety levels has not been fully overcome. This has also triggered numerous kinds of discussions covering various aspects of the pandemic on the social media platforms. Among these aspects is the part focused on vaccines developed by different countries, their features and the advantages and disadvantages associated with each vaccine. Social media users often share their thoughts about vaccinations and vaccines. This data can be used to determine the popularity levels of vaccines, which can provide the producers with some insight for future decision making about their product. In this article, we used Twitter data for the vaccine popularity detection. We gathered data by scraping tweets about various vaccines from different countries. After that, various machine learning and deep learning models, i.e., naive bayes, decision tree, support vector machines, k-nearest neighbor, and deep neural network are used for sentiment analysis to determine the popularity of each vaccine. The results of experiments show that the proposed deep neural network model outperforms the other models by achieving 97.87% accuracy.

• Journal of Korean Society of Rural Planning
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• v.9 no.4 s.21
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• pp.43-51
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• 2003

This study aims to integrate the simulation models for rural settlement planning (SimRusep) in the district level (Myon) area of rural counties. The SimRusep, which has two modules of key villages selection and spatial planning for the selected villages, consists of four sub-models such as the spatial location-allocation model of center villages (SLAMCV), the potential centrality evaluation model (PCEM), the land use planning model (LUPM), and the 3-dimensional spatial planning modeller (3DSPLAM). Basically, map data of the integrated system which can be operated on the UNIX environment is inputted and treated using GIS (ARC/INFO) and then its village planning results is graphically presented on the AutoCAD. In order to verify the practical applicabilities of the SimRusep, an administrative area, Ucheon-myun, HoengSung-gun, KangWon-do, was selected as a case study area. It was well operated in the strategic application trials considering application of each sub-model in the study area. The operation results of the SimRusep showed the possibilities of realtime simulation from the selection of key village to its final stereoscopic presentation of planned results. Alternative village plan proposals can be swiftly drafted, which means very practical support for decision making process and public participation.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.11
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• pp.4913-4916
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• 2016

Objective: As a source of information, medical data can feature hidden relationships. However, the high volume of datasets and complexity of decision-making in medicine introduce difficulties for analysis and interpretation and processing steps may be needed before the data can be used by clinicians in their work. This study focused on the use of Bayesian models with different numbers of nodes to aid clinicians in breast cancer risk estimation. Methods: Bayesian networks (BNs) with a retrospectively collected dataset including mammographic details, risk factor exposure, and clinical findings was assessed for prediction of the probability of breast cancer in individual patients. Area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were used to evaluate discriminative performance. Result: A network incorporating selected features performed better (AUC = 0.94) than that incorporating all the features (AUC = 0.93). The results revealed no significant difference among 3 models regarding performance indices at the 5% significance level. Conclusion: BNs could effectively discriminate malignant from benign abnormalities and accurately predict the risk of breast cancer in individuals. Moreover, the overall performance of the 9-node BN was better, and due to the lower number of nodes it might be more readily be applied in clinical settings.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.3
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• pp.109-125
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• 1996

Biases and errors in the human being's reasoning process have been studied continuously by the researchers, especially psychlogists and social scientists. These bias phenomenon is classified on the basis of the origin, i. e. motivation and cognition. Furthermore the necessity of research on the bias in the management and management information system areas in increased more and more recently, which have their academic backgrounds in the psychology and social science. The biased information stream is transformed into the systematic error due to the motivation and cognitive bias of human-being, then its resulting phenomena are as follows; 1. the availability of salient information 2. preconceived ideas or theories about peoples and event 3. anchoring and perseverence phenomena. In order to reduce the information errors, Satty suggested the Analytic Hierarchy Process (AHP) that is the subject of this paper and that is widely used for evaluation of complex decision making alternatives. THerefore this paper studies AHP's effects and its limitations in applying to the management area. Thus this paper compared the performances of the 3 models : 1 the traditional additive regression model. 2 regression model using the factor score, and 3 the regression model with AHP. As a result, 3 models produce the different outcomes.

• Journal of Korean Academy of Nursing Administration
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• v.23 no.2
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• pp.111-117
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• 2017

Getting evidence in to practice tends to focus on strategies, theories and studies that aim to close the gap between research knowledge and clinical practice. The evidence to practice gap is more about systems than individual clinician decision making. The absence of evidence for administration and management in the organization of healthcare is persistent. Teaching nurses and providing evidence as the solution to evidence-based healthcare is no longer axiomatic. Previous studies have concluded that unit level strategies integrate multi-professional teams with organizational needs and priorities. This 'best fit' approach that characterizes how healthcare is structured and delivered. The published literature shows that increased readiness for change is aligned with integrated approaches informed by conceptual models. The Joanna Briggs Collaboration is the largest global collaboration to integrate evidence within a theory informed model that brings together academic centres, hospitals and health systems for evidence synthesis, transfer and implementation. The best approaches to implementation are tailored to local culture and context, benchmark against international evidence, combine a theory informed model and stakeholder perspectives to improve the structure and processes of health care policy and practice.