• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.05a
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• pp.230-231
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• 2019

This study is a development on condition based maintenance(CBM) technology which is a core item of future autonomous ships. It is developing to design & installation of condition monitoring system and acquisition & processing of data from ongoing ships for fault prediction & prognosis of engine in operation. The ultimate goal of this study is to develop a predicts and decision support software for marine engine faults. To do this, the FMEA and fault tree analysis of the main engine should be accompanied by the analysis of classification of system, identification of the components, the type of faults, and the cause and phenomenon of the failure. Finally, the CBM system solution software could predict and diagnose the failure of main engine through integrated analysis for bid-data of ongoing ships and engineering knowledge. Through this study, it is possible to pro-actively cope with abnormal signals of engine and to manage efficiently, and as a result, expected that marine accident and ship operation loss during navigation will be prevented in advance.

• Journal of Korean Society of Forest Science
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• v.111 no.2
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• pp.302-310
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• 2022

Here we aimed to classify the major coniferous tree species (Pinus densiflora, Pinus koraiensis, and Larix kaempferi) by tree measurement information and machine learning algorithms to establish an efficient forest management plan. We used national forest monitoring information amassed over nine years for the measurement information of trees, and random forest (RF), XGBoost (XGB), and light GBM (LGBM) as machine learning algorithms. We compared and evaluated the accuracy of the algorithm through performance evaluation using the accuracy, precision, recall, and F1 score of the algorithm. The RF algorithm had the highest performance evaluation score for all tree species, and highest scores for Pinus densiflora, with an accuracy of about 65%, a precision of about 72%, a recall of about 60%, and an F1 score of about 66%. The classification accuracy for the dominant trees was higher than about 80% in the crown classes, but that of the co-dominant trees, the intermediate trees, and the overtopper trees was evaluated as low. We consider that the results of this study can be used as reference data for decision-making in the selection of thinning trees for forest management.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.351-361
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• 2021

The seriousness of fire is rising because fire causes enormous damage to property and human life. Therefore, this study aims to predict various risk factors affecting fire by fire type. The predictive analysis of fire factors was carried out targeting Gyeonggi-do, which has the highest number of fires in the country. For the analysis, using machine learning methods SVM (Support Vector Machine), RF (Random Forest), GBRT (Gradient Boosted Regression Tree) the accuracy of each model was presented with a high fit model through MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error), and based on this, predictive analysis of fire factors in Gyeonggi-do was conducted. In addition, using machine learning methods such as SVM (Support Vector Machine), RF (Random Forest), and GBRT (Gradient Boosted Regression Tree), the accuracy of each model was presented with a high-fit model through MAE and RMSE. Predictive analysis of occurrence factors was achieved. Based on this, as a result of comparative analysis of three machine learning methods, the RF method showed a MAE = 1.765 and RMSE = 1.876, as well as the MAE and RMSE verification and test data were very similar with a difference between MAE = 0.046 and RMSE = 0.04 showing the best predictive results. The results of this study are expected to be used as useful data for fire safety management allowing decision makers to identify the sequence of dangers related to the factors affecting the occurrence of fire.

• 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.

• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.85-100
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• 2012

The development of technology causes a lot of change. Many researchers have insisted that the proliferation of cyber space changes the physical space. Their insistences have been accumulated into three aspects. Firstly, the proliferation of cyber space brings out the concentration in the physical space, secondly the decentralization and lastly both at the same time. In Korea, the concentration of plastic surgery clinics has taken place in Kangnam-gu area at similar period of the Internet proliferation. In this research, we execute empirical study of whether the concentration of plastic surgery in specific areas correlates with the proliferation of cyber space or not. In order to do this, we verified homogeneity of plastic surgery websites between Kangnam-gu and Non-Kangnam-gu areas. Also, we used three statistical and data-mining techniques which are Multi-discriminant analysis, Decision tree analysis and artificial neural network analysis. As a result, there was homogeneity between two different area plastic surgery clinics websites, but there was not big heterogeneity as well. Therefore, in this case of concentration of plastic surgery in Korea, the proliferation of cyber space restrictively correlates with the concentration of physical space.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1869-1886
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• 2018

This paper proposes a MIDI- and audio-based music genre classification method for Korean traditional music. There are many traditional instruments in Korea, and most of the traditional songs played using the instruments have similar patterns and rhythms. Although music information processing such as music genre classification and audio melody extraction have been studied, most studies have focused on pop, jazz, rock, and other universal genres. There are few studies on Korean traditional music because of the lack of datasets. This paper analyzes raw audio and MIDI phrases in Korean traditional music, performed using Korean traditional musical instruments. The classified samples and MIDI, based on our classification system, will be used to construct a database or to implement our Kontakt-based instrument library. Thus, we can construct a management system for a Korean traditional music library using this classification system. Appropriate feature sets for raw audio and MIDI phrases are proposed and the classification results-based on machine learning algorithms such as support vector machine, multi-layer perception, decision tree, and random forest-are outlined in this paper.

• Journal of the Korea Convergence Society
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• v.8 no.2
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• pp.27-33
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• 2017

To efficiently provide cloud computing services to users over the Internet, IT resources must be configured in the data center based on virtualization and distributed computing technology. This paper focuses specifically on the problem that new training data can be added at any time in a wide range of fields, and new attributes can be added to training data at any time. In such a case, rule generated by the training data with the former attribute set can not be used. Moreover, the rule can not be combined with the new data set(with the newly added attributes). This paper proposes further development of the new inference engine that can handle the above case naturally. Rule generated from former data set can be combined with the new data set to form the refined rule.

• Journal of the Korea Society of Computer and Information
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• v.23 no.8
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• pp.9-16
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• 2018

In this paper, a kidnapping detection scheme in which human pose estimation is used to classify accurately between kidnapping cases and normal ones is proposed. To estimate human poses from input video, human's 10 joint information is extracted by OpenPose library. In addition to the features which are used in the previous study to represent the size change rates and the regularities of human activities, the human pose estimation features which are computed from the location of detected human's joints are used as the features to distinguish kidnapping situations from the normal accompanying ones. A frame-based kidnapping detection scheme is generated according to the selection of J48 decision tree model from the comparison of several representative classification models. When a video has more frames of kidnapping situation than the threshold ratio after two people meet in the video, the proposed scheme detects and notifies the occurrence of kidnapping event. To check the feasibility of the proposed scheme, the detection accuracy of our newly proposed scheme is compared with that of the previous scheme. According to the experiment results, the proposed scheme could detect kidnapping situations more 4.73% correctly than the previous scheme.