• 한국컴퓨터정보학회논문지
• /
• 제26권4호
• /
• pp.93-103
• /
• 2021

금융당국의 채권추심 가이드라인, 추심업자에 대한 직접적인 관리 감독 수행 등의 노력에도 불구하고 채무자에 대한 불법, 부당한 채권 추심은 지속되고 있다. 이러한 불법, 부당한 채권추심행위를 효과적으로 예방하기 위해서는 비정형데이터 기계학습 등 기술을 활용하여 적은 인력으로도 불법 추심행위에 대한 점검 등에 대한 모니터링을 강화 할 수 있는 방법이 필요하다. 본 연구에서는 대부업체의 추심 녹취 파일을 입수하여 이를 텍스트 데이터로 변환하고 위법, 위규 행위를 판별하는 규칙기반 검출과 SVM(Support Vector Machine) 등 기계학습을 결합한 불법채권추심 분류 모델을 제안하고 기계학습 알고리즘에 따라 얼마나 정확한 식별을 하였는지를 비교해 보았다. 본 연구는 규칙기반 불법 검출과 기계학습을 결합하여 분류에 활용할 경우 기존에 연구된 기계학습만을 적용한 분류모델 보다 정확도가 우수하다는 것을 보여 주었다. 본 연구는 규칙기반 불법검출과 기계학습을 결합하여 불법여부를 분류한 최초의 시도이며 후행연구를 진행하여 모델의 완성도를 높인다면 불법채권 추심행위에 대한 소비자 피해 예방에 크게 기여할 수 있을 것이다.

• 한국산업융합학회 논문집
• /
• 제6권1호
• /
• pp.37-42
• /
• 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.

• 공학교육연구
• /
• 제26권2호
• /
• pp.23-31
• /
• 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.

• 정보교육학회논문지
• /
• 제14권3호
• /
• pp.291-300
• /
• 2010

협동 학습은 학습자들을 소그룹으로 나누어 상호 협력하여 학습하게 함으로써 학습자의 학업 성취감을 향상시키는 것을 그 목적으로 한다. 그러므로 효과적인 소그룹 생성을 위한 몇몇 기존 연구들이 존재하며, 대부분 연구에서는 교과목, 교수자, 그리고 학습자의 정보로부터 변인 요소들을 추출하고 이를 기반으로 소그룹을 생성한다. 그러나 아직까지 많은 연구들은 특정 교과목에 의존적인 그룹 생성 방법을 제안하고 있을 뿐 다양한 교과목을 대상으로 그룹을 생성하기위한 방법을 제안한 연구는 많지 않다. 더욱이 그룹 생성을 위해 자동화된 시스템을 제안한 연구는 찾아보기 힘들다. 본 논문에서는 다양한 교과목 상황에 따라 그에 맞는 소그룹을 자동으로 생성하는 시스템을 제안한다. 제안된 시스템은 교과목의 기본 정보만을 입력받아 자동으로 그룹을 생성하거나 사용자가 필요하다고 판단되면 추가로 변인 요소를 입력받아 자동으로 소그룹을 생성한다. 본 논문에서는 다양한 변인 요소를 반영하기 위하여 규칙(Rule)을 정의하고 규칙을 기반으로 소그룹을 생성하는 방법을 제안한다. 또한 본 논문은 제안한 그룹 생성 시스템의 사용성을 평가하여 다양한 교과목이 존재하는 대학교육을 대상으로 실제 응용에서 활용 가능함을 보인다.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
• /
• pp.1072-1077
• /
• 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.

• 대한지리학회지
• /
• 제43권5호
• /
• pp.761-774
• /
• 2008

원격탐사에서 위성 영상의 디지털 처리 기술이 발달하면서 GIS 자료와 지식 기반 전문가 시스템과의 통합에 대한 관심이 증가하고 있다. 본 연구에서는 위성영상을 토지피복 분류하는 과정에서 GIS 자료를 통합하기 위하여 기계 학습 기법과 규칙 기반 분류 기법을 적용하였다. 사례 지역을 대상으로 Landsat ETM+ 영상과 고도, 경사, 향, 수역과의 거리, 도로와의 거리, 인구밀도 등의 GIS 자료를 함께 활용하였다. C5.0 추론 기계 학습 알고리듬을 이용하여 350개의 표본점으로부터 결정 트리와 분류 규칙을 생성하였다. 본 연구에서 도출된 규칙을 이용하여 분류한 결과, 고독 수역과의 거리, 인구밀도 등의 GIS 자료가 규칙 기반 분류에 효과적인 것으로 나타났다. 본 연구에서 제안한 기계 학습과 지식 기반 분류 기법을 이용하면 다양한 GIS 자료들을 통합하여 위성영상을 보다 효과적으로 분류할 수 있다.

• IEIE Transactions on Smart Processing and Computing
• /
• 제6권3호
• /
• pp.183-192
• /
• 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).

• 제어로봇시스템학회논문지
• /
• 제11권2호
• /
• pp.127-136
• /
• 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)
• /
• 제5권3호
• /
• pp.560-574
• /
• 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.

• 한국정밀공학회지
• /
• 제20권6호
• /
• pp.62-70
• /
• 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.