• Journal of applied mathematics & informatics
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• v.40 no.1_2
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• pp.289-303
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• 2022

This article examines the multidimensional index extraction method of the disability social security system based on data mining. While creating the data warehouse of the social security system for the disabled, we need to know the elements of the social security indicators for the disabled. In this context, a clustering algorithm was used to extract the indicators of the social security system for the disabled by investigating the historical dimension of social security for the disabled. The simulation results show that the index extraction method has high coverage, sensitivity and reliability. In this paper, a multidimensional extraction method is introduced to extract the indicators of the social security system for the disabled based on data mining. The simulation experiments show that the method presented in this paper is more reliable, and the indicators of social security system for the disabled extracted are more effective in practical application.

• Smart Structures and Systems
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• v.14 no.1
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• pp.55-70
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• 2014

The paper presents a multi-objective optimization strategy for a multi-type sensor placement for Structural Health Monitoring (SHM) of long span bridges. The problem is formulated for simultaneous placement of strain sensors and accelerometers (heterogeneous network) based on application demands for SHM system. Modal Identification (MI) and Accurate Mode Shape Expansion (AMSE) were chosen as the application demands for SHM. The optimization problem is solved through the use of integer Genetic Algorithm (GA) to maximize a common metric to ensure adequate MI and AMSE. The performance of the joint optimization problem solved by GA is compared with other established methods for homogenous sensor placement. The results indicate that the use of a multi-type sensor system can improve the quality of SHM. It has also been demonstrated that use of GA improves the overall quality of the sensor placement compared to other methods for optimization of sensor placement.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.47-50
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• 2000

In this paper, an optimal identification method using Multi-FNN(Fuzzy-Neural Network) is proposed for model ins of nonlinear complex system. In order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM clustering algorithm which carry out the input-output data preprocessing function and Genetic Algorithm which carry out optimization of model. The proposed Multi-FNN is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. HCM clustering method which carry out the data preprocessing function for system modeling, is utilized to determine the structure of Multi-FNN by means of the divisions of input-output space. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model, To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.183-188
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• 2005

Although it is widely used to find an optimum setting of manufacturing process parameters in a variety of engineering fields, the Taguchi method has a difficulty in dealing with multi-response situations in which several response variables should be considered at the same time. For example, electrode wear, surface roughness, and material removal rate are important process response variables in an electrical discharge machining (EDM) process. A simultaneous optimization should be accomplished. Many researches from various disciplines have been conducted for such multi-response optimizations. One of them is a fuzzy logic approach presented by Lin et al. [1]. They showed that two response characteristics are converted into a single performance index based upon fuzzy logic. However, it is pointed out that information regarding relative importance of response variables is not considered in that method. In order to overcome this problem, a desirability function can be adopted, which frequently appears in the statistical literature. In this paper, we propose a novel approach for the multi-response optimization by incorporating fuzzy logic into desirability function. The present method is illustrated by an EDM data of Lin and Lin [2].

• Proceedings of the KIPE Conference
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• 2002.11a
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• pp.171-174
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• 2002

This paper deals with a novel method in order to analyze the harmonic characteristics in the multi-level inverter. Generally, the magnitude of harmonic components is different according to the carrier PWM techniques, modulation Index(Mi), and the level of multi-level inverter The previous papers analyzed the harmonic characteristics from the viewpoint of the space vector. Hence, the calculation of the harmonic vector becomes difficult and complex in 4-level or more than S-level. However, the proposed method of this paper reduced an amount of calculation and simplified the process of calculation by using the relationship between reference voltage and output phase voltage to load neutral. This paper analyzed the harmonic and it is applied to the multi-carrier PWM techniques in 5- level and other-level of cascaded inverter system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.479-480
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• 2009

• 한국IT서비스학회:학술대회논문집
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• 2003.11a
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• pp.543-549
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• 2003

다속성 위험평가는 위협과 보안요구사항의 집합을 순위화해서 계량적으로 위험을 평가하는 유용한 체계를 제공해 준다. 본 논문의 목적은 위험을 파악해서 순위화 하는 과정을 다속성 위험평가에 의해서 분석하는 이론과 사례를 제시하는 것이다.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Proceedings of the Korea Database Society Conference
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• 1997.10a
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• pp.472-482
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• 1997

Multi-dimensional data play a crucial role in various fields, as like computer graphics, geographical information system, and multimedia applications. Indexing method fur multi-dimensional data Is a very Important factor in overall system performance. What is proposed in this paper is a new dynamic access method for spatial objects called HL-CIF(Hierarchically Layered Caltech Intermediate Form) tree which requires small amount of storage space and facilitates efficient query processing. HL-CIF tree is a combination of hierarchical management of spatial objects and CIF tree in which spatial objects and sub-regions are associated with representative points. HL-CIF tree adopts "centroid" of spatial objects as the representative point. By reflecting objects′sizes and positions in its structure, HL-CIF tree guarantees the high spatial locality of objects grouped in a sub-region rendering query processing more efficient.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.103-106
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• 1998

본 연구는 연구·개발 프로젝트의 평가를 위하여 연구·개발 프로젝트의 다-속성(Multi-attributes)을 고려하한 평가 모델의 연구이다. 이를 위하여 우선 평가구조를 구축하고 각 속성별 평가를 종합하기 위한 종합성과도(Total Preference Index)로 단일 측정치로 평가 할 수 있도록 종합하기 위한 적절한 효용함수를 도입하여 사용하였다. 이러한 평가 과정을 다-속성 의사결정 모델(Multi-attribute Utility Model)로 통합하였으며 연구·개발프로젝트의 특성을 고려하여 각 연구실의 책임자(Laboratory Directors)의 평가체계를 개발하여 본 평가모델에 포함하였다. 본 평가모델의 시험 적용을 위하여 특정 연구소에 시험적용하고 그 결과를 보였으며, 부분적으로 보완 연구될 경우 일반적인 프로젝트의 평가모델로 활용될 수 있으리라 생각된다.