• Smart Media Journal
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• v.9 no.1
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• pp.75-83
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• 2020

Compensation services that are indispensable during large-scale public utilities projects have been gradually increasing with the recent increase in construction, but there are no systematic compensation services due to the complicated procedures and manual work. For this reason, various problems such as construction period delays due to various complaints, corruption in compensation work, and impossible to trace the history of compensation data in the past are emerging. In this paper, in order to solve this problem, in-depth interviews and questionnaires were conducted to find out the problems of each compensation status. Based on this, 3 core technologies and 10 technical needs based on ICT were selected to improve the compensation work by deriving STEEP analysis and Issue Tree. The three core technologies are big data-based decision-making and prediction technology, advanced measurement technology, and open cloud-based compensation platform technology. In order to introduce the derived technologies to the institutions in charge of compensation, the possibility of technology diffusion by project operators was suggested based on the results of the current status of informatization by institution. Based on the core technology derived from this paper, it is necessary to make a prototype that can be advanced in compensation work and apply it to each institution and analyze the effect.

• Korean Journal of Construction Engineering and Management
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• v.7 no.5
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• pp.128-137
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• 2006

This paper is to develop curtain wall process life-cycle system in high-rise buildings in order to establish effective cooperation communication channels among the diverse constituents. This paper is to provide a base toward a curtain wall life-cycle management system to support decision making and the effective flow in light of information and materials. The four objectives of the research are 1) the analysis of the current curtain wall life-cycle process, 2) the analysis and development of the curtain wall design process As-Is model, and 3) the Muda analysis of the design process As-Is model and the suggestion of the improvements, 4) the development of curtain wall design To-Be model and comparative analysis of the improvement in terms of value streams. This research indicates the wastes decrease (or the values increase) from 6.7% up to 100% in different decision criteria through the improvement by the comparative analysis between the As-ls and To-Be curtain wall design process. This research suggests the potential improvement by VSM and a curtain wall life-cycle management system in curtain wall construction for diverse constituents be significant.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.116-126
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• 2021

Ground Component Command (GCC) has been developing operational planning and execution systems to implement "Decisive Integrated Operations", which is the concept of ground operations execution, and achieved remarkable results. In particular, "Simultaneous Offense-Defense Integrated Operations" is developed mainly to neutralize enemies in deep areas and develop favorable conditions for the allies early by simultaneously attacking and defending from the beginning of the war. On the other hand, it is limited to providing scientific and reasonable support for the commander's decision-making process because analyzing the effects of the deep operation with existing M&S systems is impossible. This study developed a model for analyzing the effects of deep operations that can be used in the KJCCS. Previous research was conducted on the effects of surveillance, physical strike, and non-physical strike, which are components of deep operations to find the characteristics and limitations and suggest a research direction. A methodology for analyzing the effects of deep operations reflecting the interactions of components using data was then developed by the GCC, and input data for each field was calculated through combat experiments and a literature review. Finally, the Deep operations Effect CAlculating Model(DECAM) was developed and distributed to the GCC and Corps battle staff during the ROK-US Combined Exercise. Through this study, the effectiveness of the methodology and the developed model were confirmed and contribute to the development of the GCC and Corps' abilities to perform deep operations.

• Information Systems Review
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• v.25 no.4
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• pp.27-45
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• 2023

The importance of information security has grown alongside the development of information and communication technology. However, companies struggle to select suitable countermeasures within their limited budgets. Sönmez and Kılıç (2021) proposed a model using AHP and mixed integer programming to determine the optimal investment combination for mitigating information security breaches. However, their model had limitations: 1) a lack of objective measurement for countermeasure efficacy against security threats, 2) unrealistic scenarios where risk reduction surpassed pre-investment levels, and 3) cost duplication when using a single countermeasure for multiple threats. This paper enhances the model by objectively quantifying countermeasure efficacy using the beta probability distribution. It also resolves unrealistic scenarios and the issue of duplicating investments for a single countermeasure. An empirical analysis was conducted on domestic SMEs to determine investment budgets and risk levels. The improved model outperformed Sönmez and Kılıç's (2021) optimization model. By employing the proposed effectiveness measurement approach, difficulty to evaluate countermeasures can be quantified. Utilizing the improved optimization model allows for deriving an optimal investment portfolio for each countermeasure within a fixed budget, considering information security costs, quantities, and effectiveness. This aids in securing the information security budget and effectively addressing information security threats.

• Journal of Hospice and Palliative Care
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• v.16 no.2
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• pp.90-97
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• 2013

Purpose: Ten years have passed since the Korean government announced its plan to institutionally support hospice and palliative care in 2002. In line with that, this study aims to suggest future directions for Korea's hospice and palliative care policy. Methods: We conducted a survey on people's perception and acceptance of well-dying. Data were collected from 1,000 participants aged 19~69 years between June 1 and June 11, 2012 via computer-assisted telephone interviews. Results: The most important factor for well-dying was placing no burden of care on others (36.7%) and the second most important factor was staying with their family and loved ones (19.1%). Among nine suggestions of policy support for well-dying, the most popular was the promotion of voluntary care sharing (88.3%), followed by the palliative care training support for healthcare providers (83.7%) and the support for palliative care facilities instead of funeral halls (81.7%). The idea of formulating a five-year national plan for end-of-life care drew strong support (91%). According to the survey, the plan should be implemented by the central government (47.5%), the National Assembly (20.2%) or civic groups (10%). Conclusion: This study demonstrated the public consensus and their consistent direction toward policy support for well-dying. Results of this study may serve as a foundation for the establishment of policy support for people's well-dying and palliative care at the national-level.

• 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 Korean Society of Rural Planning
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• v.20 no.2
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• pp.71-80
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• 2014

This study intended for Cheongryongsan Community Garden in Gwanak-gu, one of the demonstration places for 'Community Garden' Project recently implemented by the Seoul Government. This study had two major purposes: investigating the current status of the management and usage and identifying its characteristics; investigating users' awareness to consider the construction and operation directions of sustainable community gardens. This study was conducted based on several surveys such as an investigation into the spatial configuration and the management system of parks through a field study, a use survey through a questionnaire survey for vegetable garden users and an awareness survey about the construction direction of gardens direction preferred by users through the analytical hierarchy process (AHP). As a result of a questionnaire survey for vegetable garden users, the usage status was summarized as follows: Considering the common trends in the 2012 and the 2013 user survey, women used Cheongryongsan Vegetable Garden more than men. Over fifties used it most of all users. Users were mostly neighborhood residents. They used to visit there three to five times a week and stayed for about 30 minutes to one hour. Users differently responded to the question related to the order of priority for the use of the garden in the 2012 and the 2013 survey. They had increasingly used it for individuals' production activities more than social exchanges. As a result of making an AHP analysis for general park users, vegetable garden users there were clear differences in the targets which each subject put emphasis on in relation to the construction and operation of vegetable gardens. General park users recognized a vegetable garden as a park where park functions and the functions of the vegetable garden coexisted. On the other hand, vegetable garden users viewed it as a space where they attached importance to the functions of the vegetable garden like an allotment. Last, this study contemplated subjects related to the construction and operation of vegetable gardens which had to be considered in the future. Vegetable gardens tended to be biased as personal hobby places. It was viewed that the main reason was insufficient support activities for vegetable garden education and exchange programs originally planned when vegetable gardens had been constructed. Vegetable garden users recognized vegetable gardens as places for individuals' farming activities like allotments. For the desirable operation of vegetable gardens, it would be necessary to give priority to the park management before the production activities in individuals' vegetable gardens. The important role of the government would be to build the base through the provision of education and opportunities so that a local resident organization could actively participate in the management of a vegetable garden after a vegetable garden was constructed. It would be necessary to make a use survey and an awareness survey for users conducted in this study on a regular basis because the surveys could be important basic data in the decision-making process for the sustainable operations of the vegetable garden.

• Journal of the Economic Geographical Society of Korea
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• v.4 no.1
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• pp.61-76
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• 2001

The purposes of this research are to examine the theoretical background and industrial policy issues with regard to building a Innovation System for encouraging industrial competitiveness and fostering regional industry in Korea. Knowledge has become the driving force of economic growth and the primary source of competitiveness in the world market. So since 1990s, Innovation Systems have been put emphasis on as new industrial development strategy in a knowledge-based economy. It can be understood that Innovation System is composed of National Innovation System(NIS) and Regional Innovation System(RIS) and interrelated the concept of clusters and networks, which are contribute to industry development throughout boosting innovation. As for the Korean industrial policy, when the former centralized policy decision making process became decentralized through the implementation of local autonomy, the role of local or state government in relation to regional industrial promotion intensified. But with the impotance of for fostering strategic industry in the region. new industrial policy issues in Korea are needed as follows; $\circled1$ Building a market-oriented support system for industrial cluster through providing the resource of innovation. $\circled2$ Establishing agency for regional industrial development. $\circled3$ Making a evolutionary vision for broader region including 2 or 3 province, $\circled4$ Fostering strategic industry which is selected in term of specialization and potential of the region. The RIS model for industry development is outlined in this paper but policy initiatives for building a RIS have to be extracted from further case studies.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.3
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• pp.164-178
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• 2009

This study was aimed to make GIS oiling thematic maps and analyze temporal oiling variation patterns for two months after 'Hebei Spirit' oil spill accident in December 7, 2007 using GIS and oiling status surveyed data. As a basic work for making of oiling thematic maps, geometric corrections were performed with IKONOS images using ground control points data. These corrected images were used to make detailed coastline from digital charts, and then spatial unit of coastline were defined using classified coastline types. And to know the representative parameters which reflect oiling situation, relationship between oiling status parameters extracted from four times oiling assessment reports and total petroleum hydrocarbons (TPHs) data (December 2007 and January 2008) monitored by Korea Ocean Research and Development Institute (KORDI). Using these representative oiling status parameters pollution value were calculated, and they were keyed into spatial unit of coastline as attributive value. GIS oiling status thematic maps made with coastline containing pollution value showed the initial two month's situations somewhat well. Also, to analyze temporal variation pattern of coastline types, about 13.4km length coastline around Malripo beach was defined as detailed study area where is the common spatial zone surveyed oiling status by each organizations. Based on this study results, it could be possible to provide oiling status maps quickly and to support decision making for oiling control action and scientific pollution monitoring.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.830-860
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• 2022

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.