• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.146-148
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• 2005

In this paper, we propose a genetically optimized identification of information granulation(IG)-based fuzzy model. To optimally design the IG-based fuzzy model we exploit a hybrid identification through genetic alrogithms(GAs) and Hard C-Means (HCM) clustering. An initial structure of fuzzy model is identified by determining the number of input, the seleced input variables, the number of membership function, and the conclusion inference type by means of GAs. Granulation of information data with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial paramters of fuzzy model such as the initial apexes of the membership functions and the initial values of polyminial functions being used in the premise and consequence part of the fuzzy rules. And the inital parameters are tuned effectively with the aid of the genetic algorithms and the least square method. And also, we exploite consecutive identification of fuzzy model in case of identification of structure and parameters. Numerical example is included to evaluate the performance of the proposed model.

• 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.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.273-275
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• 2006

In this paper, we introduce a new design of fuzzy neural networks using input-output data information of target system. The proposed fuzzy neural networks is constructed by input-output data information and used the center of data distance by HCM clustering to obtain the characteristics of data. A membership function is defined by HCM clustering and is applied input-output dat included each rule to conclusion polynomial functions. We use triangular membership functions and simplified fuzzy inference, linear fuzzy inference, and modified quadratic fuzzy inference in conclusion. In the networks learning, back propagation algorithm of network is used to update the parameters of the network. The proposed model is evaluated with benchmark data.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.552-554
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• 2003

The second harmonic component is commonly used for blocking differential relay in power transformers. However, it is difficult to distinguish between inrush and internal winding fault with differential current protective relaying. This paper proposed a new method using Nuro-Fuzzy System based on HCM(Hard C-Means). The proposed system is more objective and systematic than existing model. The data used in input are 3-phase primary voltage and fundamental harmonic of differential current. Various states of transformer are simulated using BCTRAN and HYSDAT of EMTP. As a result of the application of algorithm in various cases, the exact discrimination between internal winding fault and inrush is performed.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.654-656
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• 1998

This paper presents an optimal identification method of nonlinear and complex system that is based on fuzzy-neural network(FNN). The FNN used simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM Algorithm to find initial parameters of membership function. And then to obtain optimal parameters, we use the genetic algorithm. Genetic algorithm is a random search algorithm which can find the global optimum without converging to local optimum. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance of the FNN, we use the time series data for 9as furnace and the sewage treatment process.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.83-86
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• 2015

We present observational results characterizing molecular outflows from very low-mass objects in ${\rho}$ Ophiuchi and Taurus. Our results provide us with important implications that clarify the formation process of very low-mass objects.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.339-349
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• 2001

This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity(distribution of I/O data), we show that it is available and effective to design an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• International Journal of Highway Engineering
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• v.17 no.2
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• pp.123-134
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• 2015

PURPOSES : The key point of a multimodal LOS (level-of-service) evaluation system is that all of the modes are mutually associated to determine each mode's LOS. For example, the LOS of the bicycle mode is measured based on not only bicycle volumes, but also automobile volumes. However, the Korea Highway Capacity Manual (KHCM) still focuses on the automobile mode in evaluating the LOS of the roads. Additionally, the KHCM's LOS of the other modes, except for the automobile, is not consistent with actual road conditions. The KHCM, therefore, needs to develop and introduce a multimodal LOS system in order to evaluate the service conditions more accurately. METHODS: As a preliminary step to the introduction of multimodal LOS research, in this study the current problem of the KHCM's LOS system through a close review and comparison with other HCMs (highway capacity manuals) was identified. Secondly, a field survey and investigation of the urban streets to apply the HCM's multimodal LOS system was conducted. Finally, a comparison analysis of the results of the HCM and KHCM LOS was performed. RESULTS: In the study, it was found that the results of the LOS for the automobile mode did not show a significant difference between the HCM and KHCM. However, the LOS of the bicycle and pedestrian mode tended to be worse in the multimodal LOS system, which results from considering the effects of the automobile mode. Moreover, it was found that many cases have the potential to improve the overall LOS conditions, while reducing the automobile capacity. CONCLUSIONS: With the introduction of the multimodal LOS system, road diet and complete streets can be easily applied to ans actual road improvement project. Ultimately, the multimodal LOS system should be introduced into the KHCM, which can then be applied to traffic impact studies and other road improvement projects for more accurate evaluations.

• Korean Journal of Pharmacognosy
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• v.49 no.2
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• pp.182-187
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• 2018

Many plant derived phytochemicals have been considered as the main therapeutic strategy against Alzheimer's disease (AD). AD is a progressive neurodegenerative disorder, and the most predominant cause of dementia in the elderly. Cholinergic deficit, senile plaque/${\beta}$-amyloid ($A{\beta}$) peptide deposition and oxidative stress have been identified as three main pathogenic pathways which contribute to the progression of AD. We screened many different plant species for their effective use in both modern and traditional system of medicines. In this study, we tested that MeOH extract of the stem bark of Hopea chinensis (Merr.) Hand.-Mazz. (HCM) affects on the processing of Amyloid precursor portein (APP) from the APPswe over-expressing Neuro2a cell line. We showed that HCM reduced the secretion level of $A{\beta}42$ and $A{\beta}40$ in a dose dependent manner. We found that HCM increased over 1.5 folds of the secretion level of $sAPP{\alpha}$, a metabolite of ${\alpha}$-secretase. Furthermore, we found that HCM inhibited acetylcholinesterase activity in vitro. We suggest that the stem bark of Hopea chinensis may be a useful source to develop a therapeutics for AD.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.6
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• pp.57-65
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• 2015

Manufacturing optimization problem is to find the optimal solution under satisfying various and complicated constraints with the design variables of nonlinear types. To achieve the objective, this paper proposes a hybrid approach. The proposed hybrid approach is consist of genetic algorithm(GA), cuckoo search(CS) and hill climbing method(HCM). First, the GA is used for global search. Secondly, the CS is adapted to overcome the weakness of GA search. Lastly, the HCM is applied to search precisely the convergence space after the GA and CS search. In experimental comparison, various types of manufacturing optimization problems are used for comparing the efficiency between the proposed hybrid approach and other conventional competing approaches using various measures of performance. The experimental result shows that the proposed hybrid approach outperforms the other conventional competing approaches.