• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.6376-6384
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• 2015

In practice of urban redevelopment promotion projects, since the decision-making is dependent on the verbal agreements between the participants, planning changes are frequently occur. A master plan can be utilized as an effective feedback and adjusting means for decisions. It is a difficult to establish a master plan quickly and effectively to consider the large number of information. Effective decision support system that can storage and retrieval information items systematically and efficiently necessary when planning master plan is required. In this study, a data-warehouse prototype that supports the master planning at early stage was suggested. A metadata index database was developed by identifying information items of base survey results and master plan cases. Also user interface for information searching was presented. The applicability of the prototype was evaluated by case application. It was found that the prototype allows effective searching of desired information through meta data.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.873-881
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• 2019

There are limitations to the manager's investigation of all damage sites and establishment of management plan in terms of manpower and cost. Community mapping can be used to overcome these problems with the information. However, it is difficult to make decisions when multiple information are registered in multiple areas of damage. Because community mapping information are registered only with pictures and simple contents, it is so difficult for the manager to clearly understand the site situation. This study suggests a methodology to support decision-making processes during disaster management through system integration between the community mapping and the drone mapping. By applying the proposed method, decision makers can make a timely judgment effectively on the damage situation. It is expected that the proposed method will save time, manpower, and cost in the recovery phase.

• The Journal of Korean Medicine
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• v.40 no.1
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• pp.46-62
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• 2019

Objectives: This study was made by Chung Yeon Korean Medicine Hospital in order to perform appropriate East-West integrative medicine. The purpose of this manual is to support decision-making and communication in the implementation of the East-West cooperative treatment of vascular dementia. Methods: In order to carry out this study, it is based on search terms such as 'vascular dementia', 'acupuncture', 'herbal medicine', 'integrative medicine', 'chinese traditional medicine', and 'cognitive function' in databases such as MEDLINE, EMBASE, OASIS and CNKI We collected references. The drafting proceeded with the collaboration of two specialists of the Korean medicine, and the disagreement on the basis of the quotation was determined through a two person agreement. After, The draft was reviewed by a western medical doctor(rehabilitation specialist). Then, The opinions of the entire medical staff of the committee were reflected in the draft and finalized the agreement. Results: Through this study, manuals for diagnosis, treatment, and other considerations in the process of applying East-West integrative medicine to vascular dementia were derived. Conclusions: This study has significance in that it provides manual information about the decision structure, treatment contents, role distribution, etc. of East-West integrative medicine within the medical institution that conducts the vascular dementia consultation. In order for this study to function as a generalized medical guideline, it is necessary to improve the research methodology and carry out professional consensus procedures.

• 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.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.700-706
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• 2009

Bridges are vital components of any road network which demand crucial and timely decision-making for Maintenance, Repair and Rehabilitation (MR&R) activities. Bridge Management Systems (BMSs) as a decision support system (DSS), have been developed since the early 1990's to assist in the management of a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for obtaining reliable future structural performances. To alleviate this problem, the verified Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings. This is achieved through establishing the correlation between known condition ratings and such non-bridge factors as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to obtain the bridge condition ratings of the missing years. With the help of these generated datasets, the currently available bridge deterioration model can be utilized to more reliably forecast future bridge conditions. In this paper, the prediction accuracy based on 4 and 9 BPM-generated historical condition ratings as input data are compared, using deterministic and stochastic bridge deterioration models. The comparison outcomes indicate that the prediction error decreases as more historical condition ratings obtained. This implies that the BPM can be utilised to generate unavailable historical data, which is crucial for bridge deterioration models to achieve more accurate prediction results. Nevertheless, there are considerable limitations in the existing bridge deterioration models. Thus, further research is essential to improve the prediction accuracy of bridge deterioration models.

• 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.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.463-481
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• 2020

Cutter suction dredgers (CSDs) are widely used in various dredging constructions such as channel excavation, wharf construction, and reef construction. During a CSD construction, the main operation is to control the swing speed of cutter to keep the slurry concentration in a proper range. However, the slurry concentration cannot be monitored in real-time, i.e., there is a "time-lag effect" in the log of slurry concentration, making it difficult for operators to make the optimal decision on controlling. Concerning this issue, a solution scheme that using real-time monitored indicators to predict current slurry concentration is proposed in this research. The characteristics of the CSD monitoring data are first studied, and a set of preprocessing methods are presented. Then we put forward the concept of "index class" to select the important indices. Finally, an ensemble learning algorithm is set up to fit the relationship between the slurry concentration and the indices of the index classes. In the experiment, log data over seven days of a practical dredging construction is collected. For comparison, the Deep Neural Network (DNN), Long Short Time Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and the Bayesian Ridge algorithm are tried. The results show that our method has the best performance with an R² of 0.886 and a mean square error (MSE) of 5.538. This research provides an effective way for real-time predicting the slurry concentration of CSDs and can help to improve the stationarity and production efficiency of dredging construction.

• Journal of the military operations research society of Korea
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• v.34 no.3
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• pp.67-77
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• 2008

In the 21st century, it is highly required to develop a better security management system to prevent many severe accidents occurred in the sea because it has been growing both size and importance for industries and global business which are heavily related to the sea. In this paper, we proposed a new theoretical criteria for three core decisions to make for SAR(search and rescue). These are three decisions for search scope, search assets, and search duration. We first brought up several issues and problems of current SAR system, and then studies all related factors of these three decisions for SAR operations. This paper provides a theoretical foundation of SAR operations by applying a theoretical approach and reasonable standards.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.1
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• pp.149-157
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• 2006

The purpose of this study is to search for location potential in Busan metropolitan city and to support decision-making in land use policy. As basis work for the analysis of the location potential, we build rank-map database by using the 11 index. And then by using rank-map, we carried out the location potential ($P_i$) analysis. As a result, we found that many commercial use located in Rank 1 to 2. Also, Rank 4-7 must be made an un-commercial use in assignment of land use zone. These data can be effectively used for land use plan in Busan metropolitan city as the basis data.

• Journal of Information Technology Services
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• v.14 no.1
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• pp.237-249
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• 2015

The volume of unstructured text data generated by various social media has been increasing rapidly; therefore, use of text mining to support decision making has also been increasing. Especially, issue Clustering-determining a new relation with various issues through clustering-has gained attention from many researchers. However, traditional issue clustering methods can only be performed based on the co-occurrence frequency of issue keywords in many documents. Therefore, an association between issues that have a low co-occurrence frequency cannot be discovered using traditional issue clustering methods, even if those issues are strongly related in other perspectives. Therefore, issue clustering that fits each of criteria needs to be performed by the perspective of analysis and the purpose of use. In this study, a multi-dimensional issue clustering is proposed to overcome the limitation of traditional issue clustering. We assert, specifically in this study, that issue clustering should be performed for a particular purpose. We analyze the results of applying our methodology to two specific perspectives on issue clustering, (i) consumers' interests, and (ii) related R&D terms.