• Composites Research
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• v.32 no.5
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• pp.265-269
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• 2019

The direct spinning of carbon nanotube (CNT) fibers is a promising method in the high performance composite materials. However, most of the reported CNT arrays do not have spinning properties because of their limited synthesis conditions. In this study, we investigate the properties of spinnable CNT arrays, which is closely related to the morphology of CNT array. The array morphology controlled by controlling the conditions of catalyst, carbon source, etc. By additional carbon source of ethylene and changing the composition of the catalyst, the waviness of the CNT array can be remarkably reduced, which leads to improve of the spinning properties. The synthesized CNT arrays were well aligned along c-axis and the synthesis conditions of the spinning array could be derived.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.466-474
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• 2023

In recent years, the government has strongly promoted multi-purpose utilization of paddy field. However, poor drainage causes waterlogging stress in upland crops, requiring subsurface drainage technology, resulting in high installation and management costs. To address this issue, a low-cost and high-efficiency technique was developed that utilizes wasted coir substrates which have characteristics of high porosity and good drainage, for upland crop cultivation in paddy fields. Soybeans were grown in both paddy soil and wasted coir slab with two planting densities (80×20 cm and 60×20 cm). The results showed that the coir substrates had better performance than the paddy soil in terms of soil physical and chemical properties and the growth and yield of upland crops are improved. The treatments using wasted coir substrate showed a 41.4% increase in yield and a 21.3% increase in protein content compared to PS treatment. Our findings demonstrate that recycling waste coir substrates to grow upland crops is a positive cultivation strategy to solve some drainage problems in paddy fields. This approach offers a sustainable solution for upland crop production while also addressing the issue of waste management in agriculture.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.273-282
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• 2016

This study analyzed the origin and status of the registration system of water leisure craft to suggest an improvement plan for the ocean leisure industry. To identify the transition of the registration system, related water leisure craft, registration procedures and revised acts from "water leisure safety act", which was established in the early 2000 and enacted since then were examined. To understand the problems raised, the management of the registration system and related other systems were analyzed. The results were as follows. First, the registration system was designed just to check the number of registrations of water leisure craft so it does not provide various and useful information that administrators and registrants want. Second, it is impossible to utilize and reconstruct the information regarding water leisure craft. In addition, the current registration system does not have a data sharing system with the Ministry of Government Administration and Home Affairs so self-governing administrations are not connected to each other or to other electronic government systems. To resolve these problems, the construction of a new registration system is required. The new registration system of water leisure craft should facilitate not only processing registration files and administration work, but also multiple electronic governmental services that provide the water leisure users with useful information and encourage them to learn about their craft management and so on.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.195-211
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• 2014

Cloud computing services provide IT resources as services on demand. This is considered a key concept, which will lead a shift from an ownership-based paradigm to a new pay-for-use paradigm, which can reduce the fixed cost for IT resources, and improve flexibility and scalability. As IT services, cloud services have evolved from early similar computing concepts such as network computing, utility computing, server-based computing, and grid computing. So research into cloud computing is highly related to and combined with various relevant computing research areas. To seek promising research issues and topics in cloud computing, it is necessary to understand the research trends in cloud computing more comprehensively. In this study, we collect bibliographic information and citation information for cloud computing related research papers published in major international journals from 1994 to 2012, and analyzes macroscopic trends and network changes to citation relationships among papers and the co-occurrence relationships of key words by utilizing social network analysis measures. Through the analysis, we can identify the relationships and connections among research topics in cloud computing related areas, and highlight new potential research topics. In addition, we visualize dynamic changes of research topics relating to cloud computing using a proposed cloud computing "research trend map." A research trend map visualizes positions of research topics in two-dimensional space. Frequencies of key words (X-axis) and the rates of increase in the degree centrality of key words (Y-axis) are used as the two dimensions of the research trend map. Based on the values of the two dimensions, the two dimensional space of a research map is divided into four areas: maturation, growth, promising, and decline. An area with high keyword frequency, but low rates of increase of degree centrality is defined as a mature technology area; the area where both keyword frequency and the increase rate of degree centrality are high is defined as a growth technology area; the area where the keyword frequency is low, but the rate of increase in the degree centrality is high is defined as a promising technology area; and the area where both keyword frequency and the rate of degree centrality are low is defined as a declining technology area. Based on this method, cloud computing research trend maps make it possible to easily grasp the main research trends in cloud computing, and to explain the evolution of research topics. According to the results of an analysis of citation relationships, research papers on security, distributed processing, and optical networking for cloud computing are on the top based on the page-rank measure. From the analysis of key words in research papers, cloud computing and grid computing showed high centrality in 2009, and key words dealing with main elemental technologies such as data outsourcing, error detection methods, and infrastructure construction showed high centrality in 2010~2011. In 2012, security, virtualization, and resource management showed high centrality. Moreover, it was found that the interest in the technical issues of cloud computing increases gradually. From annual cloud computing research trend maps, it was verified that security is located in the promising area, virtualization has moved from the promising area to the growth area, and grid computing and distributed system has moved to the declining area. The study results indicate that distributed systems and grid computing received a lot of attention as similar computing paradigms in the early stage of cloud computing research. The early stage of cloud computing was a period focused on understanding and investigating cloud computing as an emergent technology, linking to relevant established computing concepts. After the early stage, security and virtualization technologies became main issues in cloud computing, which is reflected in the movement of security and virtualization technologies from the promising area to the growth area in the cloud computing research trend maps. Moreover, this study revealed that current research in cloud computing has rapidly transferred from a focus on technical issues to for a focus on application issues, such as SLAs (Service Level Agreements).

• Korean Chemical Engineering Research
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• v.49 no.6
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• pp.857-864
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• 2011

Oxy-fuel combustion with many advantages such as high combustion efficiency, low flue gas flow rate and low NOx emission has emerged as a promising CCS technology for coal combustion facilities. In this study, the effects of the direct sulfation reaction on $SO_2$ removal efficiency were evaluated in a drop tube furnace under typical oxy-fuel combustion conditions represented by high concentrations of $CO_2$ and $SO_2$ formed by gas recirculation to control furnace combustion temperature. The effects of the operating parameters including the reaction temperature, $CO_2$ concentration, $SO_2$ concentration, Ca/S ratio and humidity on $SO_2$ removal efficiency were investigated experimentally. $SO_2$ removal efficiency increased with reaction temperature up to 1,200 due to promoted calcination of limestone reagent particles. And $SO_2$ removal efficiency increased with $SO_2$ concentrations and the humidity of the bulk gas. The increase of $SO_2$ removal efficiency with $CO_2$ concentrations showed that $SO_2$ removal by limestone was mainly done by the direct sulfation reaction under oxy-fuel combustion conditions. From the impact assessment of operation parameters, it was shown that these parameters have an effects on the desulfurization reaction by the order of the Ca/S ratio > residence time > $O_2$ concentration > reaction temperature > $SO_2$ concentration > $CO_2$ concentration > water vapor. The semi-empirical model equation for to evaluate the effect of the operating parameters on the performance of in-furnace desulfurization for oxy-fuel combustion was established.

• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.27-41
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• 2007

Nowadays the context-aware systems have been regarded as a promising opportunity to create differentiated e-marketplaces. Context-aware system aims to provide personalized services by understanding the user's current situation which is automatically acquired from the context data. This aim naturally leads us to a motivation to evaluate to what extent a system is context-aware. Even though lots of endeavors have stated about the level of context-aware system, a structured evaluation has been so far very rare. Hence, the purpose of this paper is to propose a two-phased methodology for assessing context-aware systems. In the first phase, we perform a requisite analysis to discriminate a context-aware system from general or context-based systems. Once an information system is recognized as context-aware system, then level of collaboration, mobility and embeddedness is derived to determine the level of context-aware system in the second phase. To do so, device collaboration diagram (DCD) is proposed to visualize the system architecture. Moreover, readiness and level of system are Jointly considered in the phase to provide a development strategy for each context-aware system development project. To show the feasibility of the idea proposed in this paper, legacy context-aware systems are actually analyzed and evaluated.

• Information Systems Review
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• v.24 no.1
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• pp.105-123
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• 2022

Thanks to the remarkable success of Artificial Intelligence (A.I.) techniques, a new possibility for its application on the real-world problem has begun. One of the prominent applications is the bankruptcy prediction model as it is often used as a basic knowledge base for credit scoring models in the financial industry. As a result, there has been extensive research on how to improve the prediction accuracy of the model. However, despite its impressive performance, it is difficult to implement machine learning (ML)-based models due to its intrinsic trait of obscurity, especially when the field requires or values an explanation about the result obtained by the model. The financial domain is one of the areas where explanation matters to stakeholders such as domain experts and customers. In this paper, we propose a novel approach to incorporate financial domain knowledge into local rule generation to provide explanations for the bankruptcy prediction model at instance level. The result shows the proposed method successfully selects and classifies the extracted rules based on the feasibility and information they convey to the users.

• The Korean Journal of Air & Space Law and Policy
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• v.30 no.2
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• pp.3-36
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• 2015

As of end of 2014, the population engaging in aviation activities for leisure has reached approximately 13 million, where approximately 356 cases involve a general aircraft, 200 cases involve light aircraft, and 636 cases involve an ULM. The industry for leisure has become a very promising industry in line with rapidly rising living standards which are expected to further increase in the future. The demand for such services is expected to increase over time. The purpose of this paper is to review the development and site selection of airfields in anticipation of these developments in the industry. While the government also has experience in the review of airfield location and candidate sites, it is not the government that carries out the actual construction. As such, the feasibility of the site needs to be verified in terms of actual construction. This study identified factors for Site Selection of factors through a review of related documents and existing research reports. A questionnaire was also used to collect the views of experts in the field, which was then analyzed. The Research model was confirmed in the layered form for an AHP analysis. The factors for Site Selection were identified as the technical / operational factors and economic / political elements for a two-stage configuration. The third step consisted of technical and operational elements. The final step is was constructed a total of 11 elements (weather, surface conditions, obstacle limitation surface, airspace conditions, operating procedures, noise problems, environmental issues, availability of facilities, construction and investment costs, contribution to the local economy, accessibility, demand / the proximity of demand). The surveys are conducted for more than 10 General and light aircraft pilots, professionals, and instructor. The analysis results showed a higher level in the technical / operating elements (73.2%) in the first step, while the next step sawa higher level of the operational elements (30.9%) than the other. The factors for Site Selection were any particular elements did not appear high, the weather conditions (17.5%), noise problems (19.8%), the proximity of demand (6%), accessibility (5.7%), environmental issues (11.1%), availability of facilities (8%), airspace conditions (7.9%), obstacle limitation surface (12%), construction and investment costs (4.2%) and to operating procedures (4.9%), contribution to the local economy (3.8%).

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.67-88
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• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.