• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.157-175
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• 2012

It is difficult to evaluate the performance of process innovation of e-procurement which has large scale and complex processes. The existing evaluation methods for measuring the effects of process innovation have been mainly done with statistically quantitative methods by analyzing operational data or with qualitative methods by conducting surveys and interviews. However, these methods have some limitations to evaluate the effects because the performance evaluation of e-procurement process innovation should consider the interactions among participants who are active either directly or indirectly through the processes. This study considers the e-procurement process as a complex system and develops a simulation model based on MAS(Multi-Agent System) to evaluate the effects of e-procurement process innovation. Multi-agent based simulation allows observing interaction patterns of objects in virtual world through relationship among objects and their behavioral mechanism. Agent-based simulation is suitable especially for complex business problems. In this study, we used Netlogo Version 4.1.3 as a MAS simulation tool which was developed in Northwestern University. To do this, we developed a interaction model of agents in MAS environment. We defined process agents and task agents, and assigned their behavioral characteristics. The developed simulation model was applied to G2B system (KONEPS: Korea ON-line E-Procurement System) of Public Procurement Service (PPS) in Korea and used to evaluate the innovation effects of the G2B system. KONEPS is a successfully established e-procurement system started in the year 2002. KONEPS is a representative e-Procurement system which integrates characteristics of e-commerce into government for business procurement activities. KONEPS deserves the international recognition considering the annual transaction volume of 56 billion dollars, daily exchanges of electronic documents, users consisted of 121,000 suppliers and 37,000 public organizations, and the 4.5 billion dollars of cost saving. For the simulation, we analyzed the e-procurement of process of KONEPS into eight sub processes such as 'process 1: search products and acquisition of proposal', 'process 2 : review the methods of contracts and item features', 'process 3 : a notice of bid', 'process 4 : registration and confirmation of qualification', 'process 5 : bidding', 'process 6 : a screening test', 'process 7 : contracts', and 'process 8 : invoice and payment'. For the parameter settings of the agents behavior, we collected some data from the transactional database of PPS and some information by conducting a survey. The used data for the simulation are 'participants (government organizations, local government organizations and public institutions)', 'the number of bidding per year', 'the number of total contracts', 'the number of shopping mall transactions', 'the rate of contracts between bidding and shopping mall', 'the successful bidding ratio', and the estimated time for each process. The comparison was done for the difference of time consumption between 'before the innovation (As-was)' and 'after the innovation (As-is).' The results showed that there were productivity improvements in every eight sub processes. The decrease ratio of 'average number of task processing' was 92.7% and the decrease ratio of 'average time of task processing' was 95.4% in entire processes when we use G2B system comparing to the conventional method. Also, this study found that the process innovation effect will be enhanced if the task process related to the 'contract' can be improved. This study shows the usability and possibility of using MAS in process innovation evaluation and its modeling.

• Journal of Distribution Science
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• v.14 no.5
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• pp.81-89
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• 2016

Purpose - Prior researches have empirically focused on the effect of management consulting quality and consultant capability on entrepreneurial firms' performance. This study, however, focused on investigating the moderating role of absorptive capacity and resource capability between management consulting and entrepreneurial firms' performance. So, this study investigated the relationship among consulting quality, consultant capability, absorptive capacity, resource capability, and entrepreneurial firms' performance from the resource based view (RBV). Especially, this study focused on the mediating role of absorptive and resource capability in relational structure of entrepreneurial firms' dimensions. Research design, data, and methodology - In this study, research hypotheses and model are established by the prior researches from the fields of strategic management and entrepreneurial behavior. Concretely, H1~H4 are the relationship between consulting (consulting quality, consultant capability) and innovation (absorptive capacity, resource capability); H5 is the relationship between absorptive capacity and resource capability; and H6~H7 are the relationship between innovation (absorptive capacity, resource capability) and management performance. The data was collected 207 copies from entrepreneurial firms in South Korea. These firms were established in January 2014 and maintained by November 2015 in high-tech industry. The questionnaire was consisted of five dimensions; consulting quality, consultant capability, absorptive capacity, resource capability, and management performance. Each dimension measured multi items on a 5-point Likert scale. The hypotheses and research model are analyzed using structural equation modeling (SEM) with AMOS 22. Results - The results of this study are as follows. 1) Consulting quality significantly influenced on the absorptive capacity of entrepreneurial firms. 2) But, consultant capability did not influence on the absorptive capacity of entrepreneurial firms. 3) Consulting quality and consultant capability significantly influenced on the resource capability of entrepreneurial firms. 4) Absorptive capacity significantly influenced on the resource capability of entrepreneurial firms; 5) Absorptive capacity did not significantly influence on the management performance of entrepreneurial firms. 6) Resource capability, however, significantly influenced on the management performance of entrepreneurial firms. By these results, absorptive capacity of entrepreneurial firms had a mediating role partly among consulting quality, consultant capability, and management capability. The resource capability of entrepreneurial firms had a mediating role among consulting quality, consultant capability, and management capability, perfectly. Conclusions - According to this study, the high level of consulting quality and consultant capability may enforce the resource capability of entrepreneurial firms. It means, practically, that external knowledge is a driver for innovation, and then the innovation effects on the management performance of entrepreneurial firms. So, at the initial stage, the management consulting programs are very important to entrepreneurial firms and should be conceived as an essential element. This study may contribute to the advancement of academic in field of new start business, small business, or venture business based on resources, especially the role of absorptive capacity and resource capability between consulting programs and management performance. However, this study has some limitations. They are the measurement of consulting quality's items, cross-sectional research, and the limitation of concept and industry.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.15 no.4
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• pp.321-332
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• 2017

In order to build a general model of a high-throughput uranium electrorefining process according to the electrode configuration, numerical analysis was conducted using the COMSOL Multiphysics V5.3 electrodeposition module with Ordinary Differential Equation (ODE) interfaces. The generated model was validated by comparing a current density-potential curve according to the distance between the anode and cathode and the electrode array, using a lab-scale (1kg U/day) multi-electrode electrorefiner made by the Korea Atomic Energy Research Institute (KAERI). The operating temperature was $500^{\circ}C$ and LiCl-KCl eutectic with 3.5wt% $UCl_3$ was used for molten salt. The efficiency of the uranium electrorefining apparatus was improved by lowering the cell potential as the distance between the electrodes decreased and the anode/cathode area ratio increased. This approach will be useful for constructing database for safety design of high throughput spent nuclear fuel electrorefiners.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.5
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• pp.497-514
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• 2017

This study quantitatively analyzes the effects of emission inventory choices on the simulated particulate matter (PM) concentrations and the domestic/foreign contributions in the Seoul Metropolitan Area (SMA) with an air quality forecasting system. The forecasting system is composed of Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Community Multi-Scale Air Quality (CMAQ). Different domestic and foreign emission inventories were selectively adopted to set up four sets of emissions inputs for air quality simulations in this study. All modeling cases showed that model performance statistics satisfied the criteria levels (correlation coefficient >0.7, fractional error <50%) suggested by previous studies. Notwithstanding the apparently good model performance of total PM concentrations by all emission cases, annual average concentrations of simulated total PM concentrations varied up to $20{\mu}g/m^3$ (160%) depending on the combination of emission inventories. In detail, the difference in simulated annual average concentrations of the primary PM coarse (PMC) was up to $25.2{\mu}g/m^3$ (6.5 times) compared with other cases. Furthermore, model performance analyses on PM species showed that the difference in the simulated primary PMC led to gross model overestimation in general, which indicates that the primary PMC emissions need to be improved. The contribution analysis using model direct outputs indicated that the domestic contributions to the annual average PM concentrations in the SMA vary from 44% to 67%. To account for the uncertainty of the simulated concentration, the contribution correction factor method proposed by Bae et al. (2017) was applied, which resulted in converged contributions(from 48% to 57%). We believe this study shows that it is necessary to improve the simulated concentrations of PM components in order to enhance the accuracy of the forecasting model. It is deemed that these improvements will provide more accurate contribution results.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.2
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• pp.81-93
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• 2020

The fatigue characteristics of glass fiber reinforced plastic (GFRP) composites were studied under repeated loads using the finite element method (FEM). To realize the material characteristics of GFRP composites, Digimat, a mean-field homogenization tool, was employed. Additionally, the micro-structures and material models of GFRP composites were defined with it to predict the fatigue behavior of composites more realistically. Specifically, the fatigue characteristics of polybutylene terephthalate with short fiber fractions of 30wt% were investigated with respect to fiber orientation, stress ratio, and thickness. The injection analysis was conducted using Moldflow software to obtain the information on fiber orientations. It was mapped over FEM concerned with fatigue specimens. LS-DYNA, a typical finite element commercial software, was used in the coupled analysis of Digimat to calculate the stress amplitude of composites. FEMFAT software consisting of various numerical material models was used to predict the fatigue life. The results of coupled analysis of linear and nonlinear material models of Digimat were analyzed to identify the fatigue characteristics of GFRP composites using FEMFAT. Neuber's rule was applied to the linear material model to analyze the fatigue behavior in LCF regimen. Additionally, to evaluate the morphological and mechanical structure of GFRP composites, the coupled and fatigue analysis were conducted in terms of thickness.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.6
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• pp.349-357
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• 2019

Finite element method (FEM) is utilized in the development of products to realistically analyze and predict the mechanical behavior of materials in various fields. However, the approach based on the numerical analysis of glass fiber reinforced plastic (GFRP) composites, for which the fiber orientation and strain rate affect the mechanical properties, has proven to be challenging. The purpose of this study is to define and evaluate the mechanical properties of glass fiber reinforced plastic composites using the numerical analysis models of Digimat, a linear, nonlinear multi-scale modeling program for various composite materials such as polymers, rubber, metal, etc. In addition, the aim is to predict the behavior of realistic polymeric composites. In this regard, the tensile properties according to the fiber orientation and strain rate of polybutylene terephthalate (PBT) with short fiber weight fractions of 30wt% among various polymers were investigated using references. Information on the fiber orientation was calculated based on injection analysis using Moldflow software, and was utilized in the finite element model for tensile specimens via a mapping process. LS-Dyna, an explicit commercial finite element code, was used for coupled analysis using Digimat to study the tensile properties of composites according to the fiber orientation and strain rate of glass fibers. In addition, the drawbacks and advantages of LS-DYNA's various anisotropic material models were compared and evaluated for the analysis of glass fiber reinforced plastic composites.

• The Korean Journal of Franchise Management
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• v.10 no.2
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• pp.33-47
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• 2019

Purpose - The purpose of this study is to classify and analyze existing studies from various angles through systematic literature review of how human resources development has been researched in the domestic franchise business. These studies are intended to suggest the direction in which human resource development research should be conducted in the future in the franchise business. Research design, data, and methodology - This study is based on systematic literature review methodology. It has gone through the process of subject language setting, literature search routing, search term selection, literature selection, literature classification and literature analysis. The systematic literature review identified 59 peer-reviewed dissertations and scientific journal publications on the subject of HRD in Korea franchise business. Result - This study analyzed by research methods, research industries, research population and dependent variable using the systematic review process. The literature studied in the 2000s mainly led to research on education and training of franchise employees in beauty franchise business. In the literature studied since 2010, human resources development was mainly studied in the supervisor in the restaurant franchise business, and in the study of competence rather than education and training. According to the research methods, statistical methods were mostly relatively simple, such as t-test or one-way distribution analysis until the 2000s, and after 2010, in-depth and structural studies using multiple return analysis, structural method analysis, path analysis, multi-dimensional scale analysis, AHP, etc were conducted. When classified by study dependant, early research until the 2000s focused on the study of education and training, which is an independent variable, on the satisfaction of education programs, job satisfaction, and immersion. On the other hand, studies conducted since 2010 have produced more complex results using various medium variants, and those related to management performance and relationship performance have been mainly studied, rather than the satisfaction of the education itself. Conclusions - While the domestic franchise business is expanding in terms of quantity, such as the number of franchises and franchises, the development in terms of quality for the joint growth of franchises and franchisees is still lacking. In order for the franchisee to continue to grow with each other, the franchisee must identify and develop their current performance or expected capabilities through capacity modeling at various targets and levels.

• Korean Chemical Engineering Research
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• v.52 no.6
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• pp.826-833
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• 2014

Driven by both environmental and economic reasons, the development of small to medium scale GTL(gas-to-liquid) process for offshore applications and for utilizing other stranded or associated gas has recently been studied increasingly. Microchannel GTL reactors have been prefrered over the conventional GTL reactors for such applications, due to its compactness, and additional advantages of small heat and mass transfer distance desired for high heat transfer performance and reactor conversion. In this work, multi-microchannel reactor was simulated by using commercial CFD code, ANSYS FLUENT, to study the geometric effect of the microchannels on the heat transfer phenomena. A heat generation curve was first calculated by modeling a Fischer-Tropsch reaction in a single-microchannel reactor model using Matlab-ASPEN integration platform. The calculated heat generation curve was implemented to the CFD model. Four design variables based on the microchannel geometry namely coolant channel width, coolant channel height, coolant channel to process channel distance, and coolant channel to coolant channel distance, were selected for calculating three dependent variables namely, heat flux, maximum temperature of coolant channel, and maximum temperature of process channel. The simulation results were visualized to understand the effects of the design variables on the dependent variables. Heat flux and maximum temperature of cooling channel and process channel were found to be increasing when coolant channel width and height were decreased. Coolant channel to process channel distance was found to have no effect on the heat transfer phenomena. Finally, total heat flux was found to be increasing and maximum coolant channel temperature to be decreasing when coolant channel to coolant channel distance was decreased. Using the qualitative trend revealed from the present study, an appropriate process channel and coolant channel geometry along with the distance between the adjacent channels can be recommended for a microchannel reactor that meet a desired reactor performance on heat transfer phenomena and hence reactor conversion of a Fischer-Tropsch microchannel reactor.