• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.4
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• pp.151-158
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• 2014

In this paper, we introduce an inference system using hard scatter partition method and model the nonlinear process. To do this, we use the hard scatter partition method that partition the input space in the scatter form with the value of the membership degree of 0 or 1. The proposed method is implemented by C-Means clustering algorithm. and is used for the initial center values by means of binary split. by applying the LBG algorithm to compensate for shortcomings in the sensitive initial center value. Hard-scatter-partitioned input space forms the rules in the rule-based system modeling. The premise parameters of the rules are determined by membership matrix by means of C-Means clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the coefficient parameters of each rule are determined by the standard least-squares method. The data widely used in nonlinear process is used to model the nonlinear process and evaluate the characteristics of nonlinear process.

• KIEE International Transaction on Systems and Control
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• v.12D no.1
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• pp.17-26
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• 2002

In this paper, a new design methodology of the fuzzy controller is presented. The performance of the fuzzy controller is sensitive to the variety of scaling factors. The design procedure is based on evolutionary computing (more specifically, a genetic algorithm) and estimation algorithm to adjust and estimate scaling factors respectively. The tuning of the soiling factors of the fuzzy controller is essential to the entire optimization process. And then we estimate scaling factors of the fuzzy controller by means of two types of estimation algorithms such as HCM (Hard C-Means) and Neuro-Fuzzy model[7]. The validity and effectiveness of the proposed estimation algorithm for the fuzzy controller are demonstrated by the inverted pendulum system.

• International journal of advanced smart convergence
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• v.1 no.2
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• pp.30-33
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• 2012

This paper propose a new design of fuzzy neural networks based on hard partition to generate the rules of the networks. For this we use hard c-means (HCM) clustering algorithm. The premise part of the rules of the proposed networks is realized with the aid of the hard partition of input space generated by HCM clustering algorithm. The consequence part of the rule is represented by polynomial functions. And the coefficients of the polynomial functions are learned by BP algorithm. The number of the hard partition of input space equals the number of clusters and the individual partitioned spaces indicate the rules of the networks. Due to these characteristics, we may alleviate the problem of the curse of dimensionality. The proposed networks are evaluated with the use of numerical experimentation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.253-258
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• 2005

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informal speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality. To identify the structure of fuzzy rules we use genetic algorithms (GAs). Granulation of information with the aid of Hard C-Means (HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method (LSM). The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.25-29
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• 2005

Microarry 기술의 발전은 유전자의 기능과 상호 관련성 그리고 특성을 파악 가능하게 하였으며, 이를 위한 다양한 분석 기법들이 소개되고 있다. 본 연구에서 소개하는 fuzzy clustering 기법은 genome 영역의 expression 분석에 가장 널리 사용되는 기법중 비지도학습(unsupervized) 분석 기법이다. Fuzzy clustering 기법을 효모(yeast) expression 데이터를 이용하여 분류하여 hard k-means와 비교 하였다.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.3 no.3
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• pp.206-214
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• 1991

Flow field in a hard disk drive has been predicted numerically. Theoretical model was constructed based on a commercially available hard disk drive with 40 Mega byte capacity. Since the gap between disk tip and shroud is not homogeneous in real hard disk drive, three kinds of gap size have been tested as computational model. The discussion has been made on the circumferential velocity, radial velocity, and pressure fields. As a result, the average shear stress on the disk surface was reduced as the gap size decreased. This means that the shroud should be designed compactly to reduce power consumption of the spindle motor.

• The korean journal of orthodontics
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• v.23 no.4 s.43
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• pp.707-724
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• 1993

The purpose of the study is to estimate hard and soft tissue changes after orthognathic surgery for the correction of the mandibular prognathism and to describe interrelationship and ratios of soft and hard tissue changes. The presurgical and postsurgical lateral cephalograms of 31 treated patients(17 males and 14 females) was used ; these patients had received combined orthodontic-surgical treatment by means of a bilateral sagittal split ramus osteotomy. Their ages ranged from 16 to 31 years and mean age was 21.4 years. A computerized cephalometric appraisal was developed and used to analyse linear and angular changes of skeletal and soft tissue profile. The statistical elaboration of the data was made by means of $SPSS/PC^+$. The results of the study were as follows : 1. The correlations of soft and hard tissue horizontal changes were significantly high and the ratios were $97\%$ at LI, $107\%$ at ILS, and $93\%$ at Pog'. 2. The correlations of vertical changes at Stm, LI and horizontal changes at Pog were high$(26\%)$ and at the other areas were not statistically high. 3. The correlations of soft ad hard tissue vertical changes were not significantly high in all areas except Gn' $(30\%)$ and Me' $(56\%)$. 4. The soft tissue thickness was significantly decreased in upper lip and increased in lower lip, and the amount of changes after surgery was reversely correlated with initial thickness. 5. The facial convexity was increased and relative protrusion of upper lip was increased and that of lower lip was decreased. 6. The upper to lower facial height(Gl-Sn/Sn-Me') was increased and upper to lower jaw height(Sn-Stms/Stmi-Me') was increased.

• Elastomers and Composites
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• v.27 no.3
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• pp.161-173
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• 1992

A series of polyurethane block copolymers based on hydroxyterminated poly(dimethyl siloxane), poly(propylene glycol) and poly(tetramethylene glycol) soft segments of molecular weights 1,809, 2,000 and 2,000, respectively, were synthesized. The hard segments consisted of 4,4'-diphenylmethane diisocyanate and 1,4-butanediol as the chain extender. Samples with different molar ratios were prepared. We tried to synthesize poly(dimethyl siloxane)-based polyurethane(PDMS-PU) containing a hard block as major fraction and a soft block as minor fraction for preparing toughened rigid systems. After a study of the pure PDMS-PU, poly(propylene glycol)-based polyurethane(PPG-PU) and poly(tetramethylene glycol)-based polyurethane(PTMG-PU), (mixed polyol)-based block copolymers and blends between PDMS-PU, PPG-PU and PTMG-PU were prepared, and characterized by means of differential scanning calorimetry, tensile testing and scanning electron microscopy. In (mixed polyol)-based PU and in lower hard segment content blends, macro-phase separation was shown, but blends with higher hard segment contents showed significant reduction in amounts of phase separation.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.177-182
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• 2022

Technology is progressing with every passing day and the enormous usage of electricity is becoming a necessity. One of the techniques to enjoy the assistances in a smart home is the efficiency to manage the electric energy. When electric energy is managed in an appropriate way, it drastically saves sufficient power even to be spent during hard time as when hit by natural calamities. To accomplish this, prediction of energy consumption plays a very important role. This proposed prediction model Coherent Weighted K-Means Clustering ARIMA (CWKMCA) enhances the weighted k-means clustering technique by adding weights to the cluster points. Forecasting is done using the ARIMA model based on the centroid of the clusters produced. The dataset for this proposed work is taken from the Pecan Project in Texas, USA. The level of accuracy of this model is compared with the traditional ARIMA model and the Weighted K-Means Clustering ARIMA Model. When predicting,errors such as RMSE, MAPE, AIC and AICC are analysed, the results of this suggested work reveal lower values than the ARIMA and Weighted K-Means Clustering ARIMA models. This model also has a greater loglikelihood, demonstrating that this model outperforms the ARIMA model for time series forecasting.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.1
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• pp.1-16
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• 1998

The zone clustering problem arising from several area such as deciding the optimal location of ambient measuring stations is to devide the 2-dimensional area into several sub areas in which included individual zone shows simimlar properties. In general, the optimal solution of this problem is very hard to obtain. Therefore, instead of finding an optimal solution, the generation of near optimal solution within the limited time is more meaningful. In this study, the combination of a genetic algorithm and the modified k-means method is used to obtain the near optimal solution. To exploit the genetic algorithm effectively, a representation of chromsomes and appropriate genetic operators are proposed. The k-means method which is originally devised to solve the object clustering problem is modified to improve the solutions obtained from the genetic algorithm. The experiment shows that the proposed method generates the near optimal solution efficiently.