• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.877-884
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• 1994

In the power system state estimation, the J(x)-index test and normalized residuals ${\gamma}$S1NT have been the presence of bad measurements and identify their location. But, these methods require the complete re-estimation of system states whenever bad data is identified. This paper presents back-propagation neural network medel using autoregressive filter for identification of bad measurements. The performances of neural network method are compared with those of conventional mehtods and simulation results show the geed performance in the bad data identification based on the neural network under sample power system.

• Structural Engineering and Mechanics
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• v.10 no.1
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• pp.37-51
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• 2000

Identification of damage location based on modal measurement is an important problem in structural health monitoring. The damage index method that attempts to evaluate the changes in modal strain energy distribution has been found to be effective under certain circumstances. In this paper two damage index methods using bending strain energy and shear strain energy have been evaluated for numerous cases at different locations and degrees of damage. The objective is to evaluate the feasibility of the damage index method to localize the damage on large span concrete bridge. Finite element models were used as the test structures. Finally this method was used to predict the damage location in an actual structure, using the results of a modal survey from a large concrete bridge.

• The Journal of Internal Korean Medicine
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• v.30 no.3
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• pp.550-557
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• 2009

Objectives : The purpose of this study was to investigate the possibility of Bi-Su as a pattern identification (PI) index in stroke patients. Methods : The subjects were 424 hospitalized stroke patients within 1 month from onset and diagnosed with the same PI subtypes (dampness & phlegm, qi deficiency, fire & heat, eum deficiency, and blood stasis) by agreement of two clinical experts. Bi-Su type is a kind of body shape (Bi : fat, Su : lean). Bi-Su type and degree (Bi-Su score) were decided by clinical expert. Body mass index (BMI) and waist-hip ratio (WHR) were used as an obesity index. Correlation analysis between Bi-Su score and obesity index (Spearman) and variance analysis for Bi-Su score, BMI, and WHR among PI subtypes (ANOVA) and sex were carried out. Results : While there was partial correlation between Bi-Su type and BMI($r^2$=0.634, p<0.001), the distribution of the BMI group based on the Bi-Su group showed the broadest range. The Bi-Su score in the dampness & phlegm group was higher than in the other groups (p<0.001). BMI in the dampness & phlegm groups was also higher but the BMI differences among PI subtypes was low (p=0.002). The Bi-Su score in the dampness & phlegm group was similar in both sexes, although the hand score in the eum deficiency group was the lowest, especially in males. Conclusions : Although BMI is not an objective enough tool for evaluating Bi-Su type, Bi-Su type is more appropriate than BMI as PI index. Therefore Bi-Su type could be used as one of the PI indices for dampness & phlegm or eum deficiency group in stroke patients.

• Convergence Security Journal
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• v.19 no.5
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• pp.75-84
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• 2019

Recently, the value of video as an important data of medical information technology is increasing due to the feature of rich clinical information. On the other hand, video is also required to be de-identified as a medical image, but the existing methods are mainly specialized in the stereotyped data and still images, which makes it difficult to apply the existing methods to the video data. In this paper, we propose an automated system to index candidate elements of personal identification information on a frame basis to solve this problem. The proposed system performs indexing process using text and person detection after preprocessing by scene segmentation and color knowledge based method. The generated index information is provided as metadata according to the purpose of use. In order to verify the effectiveness of the proposed system, the indexing speed was measured using prototype implementation and real surgical video. As a result, the work speed was more than twice as fast as the playing time of the input video, and it was confirmed that the decision making was possible through the case of the production of surgical education contents.

• The Korean Journal of Archival Studies
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• no.70
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• pp.41-83
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• 2021

After the revision of the Enforcement Decree of the Public Records Act, the archives created a management standard table for data set records management and performed management and control. Therefore, in this study, the data set record identification procedure and evaluation index were developed for systematic data set record management of archives. By applying this, a management standard table was prepared after identifying the records of 8 datasets in kr's electronic procurement system, and the evaluation was carried out according to the evaluation index, and the retention period, transfer, and collection were determined. It is hoped that this case study will be of practical use to the archives at a time when concrete examples of procedures for the management of dataset records are lacking.

• Journal of the Korean Society for information Management
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• v.25 no.2
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• pp.199-221
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• 2008

The purpose of this study is to investigate the characteristics of indexes by human and machine, and differences between them in terms of term identification in a fulltext environment. A back-of-book index and two indexes produced by two term identifiers (LinkIt and Termer) as pseudo-indexing systems for a whole body of a monograph are examined. In the investigation, the traditional contrast between manual and automatic indexing is confirmed in fulltext environment, manual index is for browsing and human use, and automatic index is for searching and machine use. The border between them, however, becomes vague. Some considerations for the use of the term identifiers for browsing and for searching are discussed, and further research for the use of the term identifier is suggested.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.101-110
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• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.12 s.117
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• pp.1192-1200
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• 2006

Viscoelastic damping materials are widely used to reduce noise and vibration because of its low cost and easy implementation, for examples, on the body structure of passenger cars, air planes, electric appliances and ships. To design the damped structures, the material property such as elastic modulus and loss factor is essential information. The four-parameter fractional derivative model well describes the dynamic characteristics of the viscoelastic damping materials with respect to both frequency and temperature. However, the identification procedure of the four-parameter is very time-consuming one. In this study a new identification procedure of the four-parameters is proposed by using an FE model and a gradient-based numerical search algorithm. The identification procedure goes two sequential steps to make measured frequency response functions(FRF) coincident with simulated FRFs: the first one is a peak alignment step and the second one is an amplitude adjustment step. A numerical example shows that the proposed method is useful in identifying the viscoelastic material parameters of fractional derivative model.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.789-799
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• 1999

In this paper, the optimization of fuzzy inference systems is proposed for fuzzy model of nonlinear systems. A fuzzy model needs to be identified and optimized by means of the definite and systematic methods, because a fuzzy model is primarily acquired by expert's experience. The proposed rule-based fuzzy model implements system structure and parameter identification using the HCM(Hard C-mean) clustering method, genetic algorithms and fuzzy inference method. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. in this paper, nonlinear systems are expressed using the identification of structure such as input variables and the division of fuzzy input subspaces, and the identification of parameters of a fuzzy model. To identify premise parameters of fuzzy model, the genetic algorithms is used and the standard least square method with the gaussian elimination method is utilized for the identification of optimum consequence parameters of fuzzy model. Also, the performance index with weighting factor is proposed to achieve a balance between the performance results of fuzzy model produced for the training and testing data set, and it leads to enhance approximation and predictive performance of fuzzy system. Time series data for gas furnace and sewage treatment process are used to evaluate the performance of the proposed model.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.1235-1242
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• 2006

Viscoelastic damping materials are widely used to reduce noise and vibration because of its low cost and easy implementation, for examples, on the body structure of passenger cars, air planes, electric appliances and ships. To design the damped structures, the material property such as elastic modulus and loss factor is essential information. The four-parameter fractional derivative model well describes the nonlinear dynamic characteristics of the viscoelastic damping materials with respect to both frequency and temperature with fewer parameters than conventional spring-dashpot models. However the identification procedure of the four-parameter is very time-consuming one. An efficient identification procedure of the four-parameters is proposed by using an FE model and a gradient-based numerical search algorithm. The identification procedure goes two sequential steps to make measured FRFs coincident with simulated FRFs: the first one is a peak alignment step and the second one is an amplitude adjustment. A numerical example shows that the proposed method is efficient and robust in identifying the viscoelastic material parameters of fractional derivative model.