• Journal of the Society of Naval Architects of Korea
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• v.59 no.6
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• pp.362-371
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• 2022

As considerable interests in noise emission from the ships have been increased, International Maritime Organization (IMO) standardized the Underwater Radiated Noise (URN) measurement process of commercial ships in deep seas by enacting the related ISO standard ISO 17208-1 and classification societies responded with the enactment or revision of corresponding notations. According to this trend, a new hybrid underwater sound propagation model based on underwater sound propagation theories was developed and its accuracy on analysis was verified through the result comparison with the results of other generally used models. Using the verified model, each URN propagation characteristics adjusted by the correction methods proposed in the ISO standard and class notations were analyzed and compared in two assumed URN measurement cases. The results showed that the effects of transmission loss corrections in the circumstances with less bottom reflections generally similar but they had rather large differences in the model analysis results with bottom-reflection-dominant conditions. It was concluded that the deep consideration of effective bottom-reflection-correction method should be made in future revisions of ISO standard and class notations.

• 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.