• Journal of the Society of Naval Architects of Korea
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• v.39 no.3
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• pp.75-80
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• 2002

In these days of severe struggle for existence, the world has changed a great deal to global and digital oriented period. The enterprises try to introduce new management and production system to adapt such a change. But, if the only new technologies are applied to an enterprise without definite analysis about manufacturing, failure fellows as a logical consequence. Hence, enterprise must analyze manufacturing system definitely and needs new methodologies to mitigate risk. This study suggests that the new approach, which is systems approach for process improvement, is organized to systems analysis, systems diagnosis, and systems verification. Systems analysis analyzes manufacturing systems with object-oriented methodology-UML(Unified Modeling language) from a point of product, process, and resource view. Systems diagnosis identifies the constraints to optimize the system through scientific management or TOC(Theory of constraints). Systems verification shows the solution with virtual manufacturing technique applied to the core problem which emerged from systems diagnosis. This research shows the artifacts to improve the productivity with the above methodology applied to forming shop. UML provides the definite tool for analysis and re-usability to adapt itself to environment easily. The logical tree of TOC represents logical tool to optimize the forming shop. Discrete event simulator-QUEST suggests the tool for making a decision to verify the optimized forming shop.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.91-97
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• 2021

This paper uses a data extension technique originally designed for the rule refinement problem to handling incomplete data. This technique is characterized in that each event can have a weight indicating importance, and each variable can be expressed as a probability value. Since the key problem in this paper is to find the probability that is closest to the missing value and replace the missing value with the probability, three different algorithms are used to find the probability for the missing value and then store it in this data structure format. And, after learning to classify each information area with the SVM classification algorithm for evaluation of each probability structure, it compares with the original information and measures how much they match each other. The three algorithms for the imputation probability of the missing value use the same data structure, but have different characteristics in the approach method, so it is expected that it can be used for various purposes depending on the application field.

• Steel and Composite Structures
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• v.51 no.6
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• pp.679-695
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• 2024

This paper presents a novel technique that combines machine learning (ML) with moth-flame optimization (MFO) methods to predict the axial compressive strength (ACS) of concrete filled double skin steel tubes (CFDST) columns. The proposed model is trained and tested with a dataset containing 125 tests of the CFDST column subjected to compressive loading. Five ML models, including extreme gradient boosting (XGBoost), gradient tree boosting (GBT), categorical gradient boosting (CAT), support vector machines (SVM), and decision tree (DT) algorithms, are utilized in this work. The MFO algorithm is applied to find optimal hyperparameters of these ML models and to determine the most effective model in predicting the ACS of CFDST columns. Predictive results given by some performance metrics reveal that the MFO-CAT model provides superior accuracy compared to other considered models. The accuracy of the MFO-CAT model is validated by comparing its predictive results with existing design codes and formulae. Moreover, the significance and contribution of each feature in the dataset are examined by employing the SHapley Additive exPlanations (SHAP) method. A comprehensive uncertainty quantification on probabilistic characteristics of the ACS of CFDST columns is conducted for the first time to examine the models' responses to variations of input variables in the stochastic environments. Finally, a web-based application is developed to predict ACS of the CFDST column, enabling rapid practical utilization without requesting any programing or machine learning expertise.

• Management & Information Systems Review
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• v.32 no.1
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• pp.43-62
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• 2013

In order to know the characteristics of high visit web sites that many people have visited, 37 high visit websites of plastic surgery were compared to 69 benchmark sites of same industry. We selected 36 web site attributes that can be measured objectively from existing studies and composed the data set of 36 attributes multiplied by 106 websites. For analysis, Multiple Discriminant Analysis(MDA) and Decision Tree Technique are conducted for searching what attributes divide two group definitely. The result of this study shows the dividing attributes fall into 3 categories like 'Community', 'Mobile', 'Up to date'. Thus, we are able to conclude that high visit plastic surgery web sites are community centric site but not contents centric, response a change to mobile environment rapidly and are maintained with tide up to date. The methodology employed in this study provides an efficient way of improving satisfaction of visitors of plastic surgery website.

• Journal of radiological science and technology
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• v.28 no.1
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• pp.19-24
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• 2005

The purpose of this study was to design and implement a useful annotation-based Radiological image retrieval system to accurately determine on education and image information for Radiological technologists. For better retrieval performance based on large image databases, we presented an indexing technique that integrated $B^+-tree$ proposed by Bayer for indexing simple attributes and inverted file structure for text medical keywords acquired from additional description information about Radiological images. In our results, we implemented proposed retrieval system with Delphi under Windows XP environment. End users, Radiological technologists, are able to store simple attributes information such as doctor name, operator name, body parts, disease and so on, additional text-based description information, and Radiological image itself as well as to retrieve wanted results by using simple attributes and text keywords from large image databases by graphic user interface. Consequently proposed system can be used for effective clinical decision on Radiological image, reduction of education time by organizing the knowledge, and well organized education in the clinical fields. In addition, It can be expected to develop as decision support system by constructing web-based integrated imaging system included general image and special contrast image for the future.

• Journal of information and communication convergence engineering
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• v.5 no.2
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• pp.140-143
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• 2007

Fingerprint Recognition System is made up of Off-line treatment and On-line treatment; the one is registering all the information of there trieving features which are retrieved in the digitalized fingerprint getting out of the analog fingerprint through the fingerprint acquisition device and the other is the treatment making the decision whether the users are approved to be accessed to the system or not with matching them with the fingerprint features which are retrieved and database from the input fingerprint when the users are approaching the system to use. In matching between On-line and Off-line treatment, the most important thing is which features we are going to use as the standard. Therefore, we have been using "Delta" and "Core" as this standard until now, but there might have been some deficits not to exist in every person when we set them up as the standards. In order to handle the users who do not have those features, we are still using the matching method which enables us to make up of the spanning tree or the triangulation with the relations of the spanned feature. However, there are some overheads of the time on these methods and it is not sure whether they make the correct matching or not. In this paper, introduces a new data structure, called Union and Division, representing binary fingerprint image. Minutiae detecting procedure using Union and Division takes, on the average, 32% of the consuming time taken by a minutiae detecting procedure without using Union and Division.

• The Journal of Society for e-Business Studies
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• v.13 no.3
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• pp.51-66
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• 2008

The Business Process Management System(BPMS) has received more attentions as companies increasingly realize the importance of business processes. However, traditional BPMS has focused mainly on correct modeling and exact automation of process flow, and paid little attention to the achievement of final goals of improving process efficiency and innovating processes. BPMS usually generates enormous amounts of log data during and after execution of processes, where numerous meaningful rules and patterns are hidden. In the present study we employ the data mining technique to find out useful knowledge from the complicated process log data. A data model and a system framework for process mining are provided to help understand the existing BPMS. Experiments with the simulated data demonstrate the effectiveness of the model and the framework.

• The Transactions of the Korea Information Processing Society
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• v.5 no.4
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• pp.942-950
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• 1998

Extraction of knowledge, especially in the form of rules, from raw data is very important in data mining, the aim of which is to help users who feel the lack of knowledge in spite of the abundance of data. Logic minimization tools are ones which derive optimized knowledge given ON set and DC set. First, the parallel expansion scheme of logic minimization is extracted and used to obtain intial knowledge to get final rules, which are successfully applicable to real world data. The prototype system based on this new approach has been experimented with real world data to show that it is as practical as conventional long studied decision tree methods like C4.5 system.

• The Journal of Information Systems
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• v.27 no.3
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• pp.63-80
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• 2018

Purpose The purpose of this study is to build a machine learning-based customer classification model to promote customer expansion effect of the free sample promotion. Specifically, the proposed model classifies potential target customers who are expected to purchase the products included in the free sample promotion after receiving the free samples. Design/methodology/approach This study proposes to build a customer classification model for determining customers suitable for providing free samples by using various machine learning techniques such as logistic regression, multiple discriminant analysis, case-based reasoning, decision tree, artificial neural network, and support vector machine. To validate the usefulness of the proposed model, we apply it to a real-world free sample-based target marketing case of a Korean major cosmetic retail company. Findings Experimental results show that a machine learning-based customer classification model presents satisfactory accuracy ranging from 70% to 75%. In particular, support vector machine is found to be the most effective machine learning technique for free sample-based target marketing model. Our study sheds a light on customer relationship management strategies using free sample promotions.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.467-476
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• 2011

We developed the hybrid model coupled with predictive model and business rule model for administration of injury by utilizing medical data of the National Health Insurance in Korea. We performed decision tree analysis using data mining methodology and used SAS Enterprise Miner 4.1. We also investigated under several business rule for benefits (expense paid by insurer) and claims of injury in National Health Insurance Corporation. We can see that the proposed hybrid model provides a quite efficient plausible results.