• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.254-265
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• 2021

Purpose: Inspection and diagnosis on the performance and safety through domestic port facilities have been conducted for over 20 years. However, the long-term development strategies and directions for facility renewal and performance improvement using the diagnosis history and results are not working in realistically. In particular, in the case of port structures with a long service life, there are many problems in terms of safety and functionality due to increasing of the large-sized ships, of port use frequency, and the effects of natural disasters due to climate change. Method: In this study, the maintenance history data of the gravity type quay in element level were collected, defined as big data, and a predictive approximation model was derived to estimate the pattern of deterioration and aging of the facility of project level based on the data. In particular, we compared and proposed models suitable for the use of big data by examining the validity of the state-based deterioration pattern and deterioration approximation model generated through machine learning algorithms of GP and SGP techniques. Result: As a result of reviewing the suitability of the proposed technique, it was considered that the RMSE and R² in GP technique were 0.9854 and 0.0721, and the SGP technique was 0.7246 and 0.2518. Conclusion: This research through machine learning techniques is expected to play an important role in decision-making on investment in port facilities in the future if port facility data collection is continuously performed in the future.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.1
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• pp.89-100
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• 2006

Recently the dramatical development of domestic spatial information technology and the successful construction of Korea NGIS(Nation Geographic Information System) have been the foundation of the scientific national territory utilization and management. However, there still exists the original problems to construct the thematic maps for supporting the various administrative services because administrative officials tend to depend on paper maps and inventories to generate spatial information, process, upgrade and manage. In this situation, there is a greater need to develop GIS system for the effective construction of various thematic maps. In this study, the satellite image based high accurate e-thematic construction and management system was developed to support the forest administrative service such as generating user based forest thematic maps, modifying them, analyzing and outputting through GIS, GPS and satellite images. For the case study, the previous forest paper map of Jeju Island was converted in format of raster and vector data using satellite images to maintain more exact location information so that this system helps to manage domestic spatial information scientifically and effectively within shorter time then support the standard for domestic spatial information. Moreover, this system plays the role of DSS(Decision Supporting System) for forest administrative affairs by integrating the attribute data, managing the GPS data and linking the multimedia data. For this, the additional main objective of this study was acquired powerful GIS component, which is called as e-mapping component, so that it could be regarded as enabling interoperability and reusability within this application. For the future works, the essential element idea and technology in this study could be applied very usefully to other official works such as constructing thematic maps and supporting the desired affairs.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.71-76
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• 2004

The recent ERP and CRM is mostly focused on the conventional function performances. However, the recent business environment has brought the change in market due to the rapid progress of internet and e-commerce. It is mostly becoming e-business and spreading out as development of the relationship with other cooperating companies, the rapid progress of the relationship with customers, and intensification competitive power through the development of business progress in the organization. CRM(custom relationship management) is a kind of the marketing progress which forms, manages, and intensifies the relationship between the customers and companies to manage the acquired customers and increase the worth of customers for the company. It needs the system base which analyzes the information of customers since it functions on the basis of various information about customers and is linked to the business category such as producing, marketing, and decision making. Since ERP is extending its function to SCM, CRM, and SEM(strategic Enterprise Management), the 21 century s ERP develop as the strategy tool of e-business and, as the mediation for this, will subdivide the functions of CRM effectively by the analogic study of data. Also, to accomplish classification work of the file which in existing becomes accomplished with possibility work with an automatic movement with the user will be able to accomplish a more efficiently work the agent which in order leads the machine studying law, it is one thing with system feature.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.2 s.40
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• pp.87-93
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• 2007

Over the years, Advanced Public Transportation System (APTS) has been implemented to manage and operate public transportation system. With the expanding mass spatio-temporal data such as comprehensive spatial information of each individual passenger and public transportation vehicle, it has been required to consolidate and analyze these multiple data sets from various sources. This paper demonstrates how GIS is utilized for the consolidation of massive transit related spatio-temporal information. And it presents effective applications to improve transit planning process and support transit related decision-making activities. GIS based system is used to combine multi-agents' data in the areas of transit operation and individual transit ride and transfer management. Due to the unique comprehensiveness and the level of detail of the data provide by the Seoul Transit system, this GIS based information consolidation is the first in its class. Based on the integrated database, this paper describes the effective and efficient GIS based analysis deployed in a transportation system planning process. The data integration systems and analytic models developed in this paper can be transferred and applied by any municipal governments provided that the appropriate data is available.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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• pp.535-542
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• 2013

Since case-based reasoning(CBR) has many advantages, it has been used for supporting decision making in various areas including medical checkup, production planning, customer classification, and so on. However, there are several factors to be set by heuristics when designing effective CBR systems. Among these factors, this study addresses the issue of selecting appropriate neighbors in case retrieval step. As the criterion for selecting appropriate neighbors, conventional studies have used the preset number of neighbors to combine(i.e. k of k-nearest neighbor), or the relative portion of the maximum similarity. However, this study proposes to use the absolute similarity threshold varying from 0 to 1, as the criterion for selecting appropriate neighbors to combine. In this case, too small similarity threshold value may make the model rarely produce the solution. To avoid this, we propose to adopt the coverage, which implies the ratio of the cases in which solutions are produced over the total number of the training cases, and to set it as the constraint when optimizing the similarity threshold. To validate the usefulness of the proposed model, we applied it to a real-world target marketing case of an online shopping mall in Korea. As a result, we found that the proposed model might significantly improve the performance of CBR.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.6
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• pp.649-658
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• 2009

The recent emergence of the paradigm of new urban planning for building intelligent urban spaces, such as U-City and U-Eco City, of which the concept of ubiquitous technology is applied, requires high quality three-dimensional spatial information of the urban area. The aim of this study is to build a multi-dimensional spatio-visual information system that includes the solution for visualization, spatial information search, analysis, and evaluation by integrating various types of 3D-modeled spatial information concerning the large urban-size area based on the latest GIS application technology. The range of this study is the integration, visualization, and utilization of spatial information with the goal of building 3D virtual urban environment of high-quality and high-resolution by increasing the utilization of the systematic urban facilities in order to fully reflect the actual user's needs, using the aerial LiDAR data as the plan to overcome the limitations of the existing 3D urban modeling. By reproducing the virtual urban environment the most similar to the actual world through the mash-up of satellite images and aerial photos on the standard format of spatial information constituted of properties and signs, the system will be built with many analysis and utilization functions that support the view and sunlight analysis, various administrative tasks, as well as the decision making process of the city.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.356-366
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• 2021

The high-tech industry, a leader in the national economy, has a larger investment cost compared to general buildings, a shorter construction period, and requires continuous investment. Therefore, accurate construction cost prediction and quick decision-making are important factors for efficient cost and process management. Overseas, the construction information classification system has been standardized since 1980 and has been continuously developed, improving construction productivity by systematically collecting and utilizing project life cycle information. At domestic construction sites, attempts have been made to standardize the classification system of construction information, but it is difficult to achieve continuous standardization and systematization due to the absence of a standardization body and differences in cost and process management methods for each construction company. Particular, in the case of the high-tech industry, the standardization and systematization level of the construction information classification system for high-tech facility construction is very low due to problems such as large scale, numerous types of work, complex construction and security. Therefore, the purpose of this study is to construct a construction information classification system suitable for high-tech facility construction through collection, classification, and analysis of related project data constructed in Korea. Based on the WBS (Work Breakdown Structure) and CBS (Cost Breakdown Structure) classified and analyzed through this study, a code system through hierarchical classification was proposed, and the cost model of buildings by linking WBS and CBS was three-dimensionalized and the utilized method was presented. Through this, an information classification system based on inter-relationships can be developed beyond the one-way tree structure, which is a general construction information classification system, and effects such as shortening of construction period and cost reduction will be maximized.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.339-346
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• 2022

Recently, the resource depletion and climate change problem caused by the massive usage of fossil fuels for electric power generation has become a critical issue worldwide. According to this issue, interest in renewable energy resources that can replace fossil fuels is increasing. Especially, photovoltaic power has gaining much attention because there is no risk of resource exhaustion compared to other energy resources and there are low restrictions on installation of photovoltaic system. In order to use the power generated by the photovoltaic system efficiently, a more accurate photovoltaic power forecasting model is required. So far, even though many machine learning and deep learning-based photovoltaic power forecasting models have been proposed, they showed limited success in terms of interpretability. Deep learning-based forecasting models have the disadvantage of being difficult to explain how the forecasting results are derived. To solve this problem, many studies are being conducted on explainable artificial intelligence technique. The reliability of the model can be secured if it is possible to interpret how the model derives the results. Also, the model can be improved to increase the forecasting accuracy based on the analysis results. Therefore, in this paper, we propose an explainable photovoltaic power forecasting scheme based on BiLSTM (Bidirectional Long Short-Term Memory) and SHAP (SHapley Additive exPlanations).

• Journal of Digital Convergence
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• v.13 no.3
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• pp.155-165
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• 2015

Bankruptcy prediction has been one of the important research topics in finance since 1960s. In Korea, it has gotten attention from researchers since IMF crisis in 1998. This study aims at proposing a novel model for better bankruptcy prediction by converging three techniques - support vector machine(SVM), fuzzy theory, and genetic algorithm(GA). Our convergence model is basically based on SVM, a classification algorithm enables to predict accurately and to avoid overfitting. It also incorporates fuzzy theory to extend the dimensions of the input variables, and GA to optimize the controlling parameters and feature subset selection. To validate the usefulness of the proposed model, we applied it to H Bank's non-external auditing companies' data. We also experimented six comparative models to validate the superiority of the proposed model. As a result, our model was found to show the best prediction accuracy among the models. Our study is expected to contribute to the relevant literature and practitioners on bankruptcy prediction.