• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.93-103
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• 2021

Despite the efforts of financial authorities in conducting the direct management and supervision of collection agents and bond-collecting guideline, the illegal and unfair collection of debts still exist. To effectively prevent such illegal and unfair debt collection activities, we need a method for strengthening the monitoring of illegal collection activities even with little manpower using technologies such as unstructured data machine learning. In this study, we propose a classification model for illegal debt collection that combine machine learning such as Support Vector Machine (SVM) with a rule-based technique that obtains the collection transcript of loan companies and converts them into text data to identify illegal activities. Moreover, the study also compares how accurate identification was made in accordance with the machine learning algorithm. The study shows that a case of using the combination of the rule-based illegal rules and machine learning for classification has higher accuracy than the classification model of the previous study that applied only machine learning. This study is the first attempt to classify illegalities by combining rule-based illegal detection rules with machine learning. If further research will be conducted to improve the model's completeness, it will greatly contribute in preventing consumer damage from illegal debt collection activities.

• Journal of the Korean Society of Industry Convergence
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• v.6 no.1
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• pp.37-42
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• 2003

In this paper a self-organizing fuzzy system with rule pruning is proposed. A conventional self-organizing fuzzy system having only rule generation has a drawback in generating many slightly different rules from the existing rules which results in increased computation time and slowly learning. The proposed self-organizing fuzzy system generates fuzzy rules based on input-output data and prunes redundant rules which are caused by parameter training. The proposed system has a simple structure but performs almost equivalent function to the conventional self-organizing fuzzy system. Also, this system has better learning speed than the conventional system. Simulation results on several numerical examples demonstrate the performance of the proposed system.

• Journal of Engineering Education Research
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• v.26 no.2
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• pp.23-31
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• 2023

The purpose of this study is to explore the effectiveness of VR-based industrial safety education compared with conventional methods. For the study, three types of safety learning contents(VR-based learning, rule-based learning, and case-based learning) were developed and implemented with three college students groups. The results show that VR-based learning was effective in sustaining learning outcomes compared to other two conventional contents groups. In addition, participants perceived VR-based safety learning is attractive that facilitates their learning motivation and usefulness.

• Journal of The Korean Association of Information Education
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• v.14 no.3
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• pp.291-300
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• 2010

Group-based learning is known to be an effective means to improve scholastic achievement in online learning. Therefore, there are some previous researches for the group-based learning. A lot of previous researches define factors for grouping from the characteristics of classes, teacher's decision and students' preferences and then generate a group based on the defined factors. However, many algorithms proposed by previous researches depend on a specific class and is not a general approach since there exist several differences in terms of the need of courses, learners, and teachers. Moreover it is hard to find a automatic system for group generation. This paper proposes a grouping system which automatically generate a learner group according to characteristics of various classes. the proposed system automatically generates a learner group by using basic information for a class or additional factors inputted from a user. The proposed system defines a set of rules for learner grouping which enables automatic selection of a learner grouping algorithm tailored to the characteristics of a given class. This rule based approach allows the proposed system to accommodate various learner grouping algorithms for a later use. Also we show the usability of our system by serviceability evaluation.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.1072-1077
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• 1992

In ths paper a new active assembly algorithm for chamferless precision parts mating, is considered. The successful assembly task requires an extremely high position accuracy and a good knowledge of mating parts. However, conventional assembly mehtod alone makes it difficult to achieve satisfactory assembly performance because of the complexity and the uncertainties of the process and its environments such as imperfect knowledge of the parts being assembled as well as the limitation of the devices performing the assebled as well as the limitation of the devices performing the assembly. To cope with these problems, a self-learning rule-based assembly algorithm is proposed by intergaring fuzzy set theory and neural network. In this algortihm, fuzzy set theory copes with the complexity and the uncertainties of the assembly process, while neural network enhances the assembly schemen so as to learn fuzzy rules form experience and adapt to changes in environment of uncertainty and imprecision. The performance of the proposed assembly algorithm is evaluated through a series of experiments. The results show that the self-learning fuzzy assembly scheme can be effecitively applied to chamferless precision parts mating.

• Journal of the Korean Geographical Society
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• v.43 no.5
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• pp.761-774
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• 2008

Integration of GIS data and human expert knowledge into digital image processing has long been acknowledged as a necessity to improve remote sensing image analysis. We propose inductive machine learning algorithm for GIS data integration and rule-based classification method for land cover classification. Proposed method is tested with a land cover classification of a Landsat ETM+ multispectral image and GIS data layers including elevation, aspect, slope, distance to water bodies, distance to road network, and population density. Decision trees and production rules for land cover classification are generated by C5.0 inductive machine learning algorithm with 350 stratified random point samples. Production rules are used for land cover classification integrated with unsupervised ISODATA classification. Result shows that GIS data layers such as elevation, distance to water bodies and population density can be effectively integrated for rule-based image classification. Intuitive production rules generated by inductive machine learning are easy to understand. Proposed method demonstrates how various GIS data layers can be integrated with remotely sensed imagery in a framework of knowledge base construction to improve land cover classification.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.183-192
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• 2017

k-nearest neighbor (K-NN) is a well-known classification algorithm, being feature space-based on nearest-neighbor training examples in machine learning. However, K-NN, as we know, is a lazy learning method. Therefore, if a K-NN-based system very much depends on a huge amount of history data to achieve an accurate prediction result for a particular task, it gradually faces a processing-time performance-degradation problem. We have noticed that many researchers usually contemplate only classification accuracy. But estimation speed also plays an essential role in real-time prediction systems. To compensate for this weakness, this paper proposes correlation coefficient-based clustering (CCC) aimed at upgrading the performance of K-NN by leveraging processing-time speed and plurality rule-based density (PRD) to improve estimation accuracy. For experiments, we used real datasets (on breast cancer, breast tissue, heart, and the iris) from the University of California, Irvine (UCI) machine learning repository. Moreover, real traffic data collected from Ojana Junction, Route 58, Okinawa, Japan, was also utilized to lay bare the efficiency of this method. By using these datasets, we proved better processing-time performance with the new approach by comparing it with classical K-NN. Besides, via experiments on real-world datasets, we compared the prediction accuracy of our approach with density peaks clustering based on K-NN and principal component analysis (DPC-KNN-PCA).

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.127-136
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• 2005

In this paper, a new architecture and comprehensive design methodology of genetically optimized Rule-based Fuzzy Polynomial Neural Networks(gRFPNN) are introduced and a series of numeric experiments are carried out. The architecture of the resulting gRFPNN results from asynergistic usage of the hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the gRFPNN. The consequence part of the gRFPNN is designed using PNNs. At the premise part of the gRFPNN, FNN exploits fuzzy set based approach designed by using space partitioning in terms of individual variables and comes in two fuzzy inference forms: simplified and linear. As the consequence part of the gRFPNN, the development of the genetically optimized PNN dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gRFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed gRFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.3
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• pp.560-574
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• 2011

In this paper, we propose a hybrid method of Machine Learning (ML) algorithm and a rule-based algorithm to implement a lightweight Named Entity (NE) extraction system for Korean SMS text. NE extraction from Korean SMS text is a challenging theme due to the resource limitation on a mobile phone, corruptions in input text, need for extension to include personal information stored in a mobile phone, and sparsity of training data. The proposed hybrid method retaining the advantages of statistical ML and rule-based algorithms provides fully-automated procedures for the combination of ML approaches and their correction rules using a threshold-based soft decision function. The proposed method is applied to Korean SMS texts to extract person's names as well as location names which are key information in personal appointment management system. Our proposed system achieved 80.53% in F-measure in this domain, superior to those of the conventional ML approaches.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.6
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• pp.62-70
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• 2003

This paper deals with a approximation and tracking control of hydraulic servo system using a real time recurrent neural networks (RTRN) with 2-dimensional iterative learning rule. And it was driven that 2-dimensional iterative learning rule in discrete time. In order to control the trajectory of position, two RTRN with same network architecture were used. Simulation results show that two RTRN using 2-D learning algorithm is able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two same RTRN was very effective to control trajectory tracking of electro-hydraulic servo system.