• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.466-472
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• 2008

The paper concerns Fuzzy C-Means clustering based fuzzy neural networks (FCM-FNN) and the optimization of the network is carried out by means of hierarchal fair competition-based parallel genetic algorithm (HFCPGA). FCM-FNN is the extended architecture of Radial Basis Function Neural Network (RBFNN). FCM algorithm is used to determine centers and widths of RBFs. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM-FNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Since the performance of FCM-FNN is affected by some parameters of FCM-FNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the HFCPGA which is a kind of multipopulation-based parallel genetic algorithms(PGA) is exploited to carry out the structural optimization of FCM-FNN. Moreover the HFCPGA is taken into consideration to avoid a premature convergence related to the optimization problems. The proposed model is demonstrated with the use of two representative numerical examples.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.4 no.1
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• pp.34-44
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• 1992

A numerical model using Hybrid Element Method(HEM) is presented for the prediction of wave agitations in a harbor which are induced by the intrusion and transformation of incident short-period waves. A linear mild-slope equation including bottom friction is used as the governing equation and a partial absorbing boundary condition is used on solid boundaries. Functional derived in the present paper is based on the Chen and Mei(1974)'s concept which uses finite element net in the inner region and analytical solution of Helmholtz equation in the outer region. Final simultaneous equations are solved using the Gaussian Elimination Method. The model appears to be reasonably good from the comparison of numerical calculation with hydraulic experimental results of short-wave diffraction through a breakwater gap(Pos and Kilner, 1987). The problem of requring large computational memory could be overcome using 8-noded isoparametric elements.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.833-842
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• 2017

In this study, the design methodology of radial basis function neural networks based on incremental K-means clustering is introduced for learning and processing the big data. If there is a lot of dataset to be trained, general clustering may not learn dataset due to the lack of memory capacity. However, the on-line processing of big data could be effectively realized through the parameters operation of recursive least square estimation as well as the sequential operation of incremental clustering algorithm. Radial basis function neural networks consist of condition part, conclusion part and aggregation part. In the condition part, incremental K-means clustering algorithms is used tweights of the conclusion part are given as linear function and parameters are calculated using recursive least squareo get the center points of data and find the fitness using gaussian function as the activation function. Connection s estimation. In the aggregation part, a final output is obtained by center of gravity method. Using machine learning data, performance index are shown and compared with other models. Also, the performance of the incremental K-means clustering based-RBFNNs is carried out by using PSO. This study demonstrates that the proposed model shows the superiority of algorithmic design from the viewpoint of on-line processing for big data.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.32 no.3
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• pp.368-373
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• 1999

Topex/Poseidon sea surface heights are compared to tide gauge sea levels in the South Indian Ocean in the period of January 1993 to December 1995. A user's handbook (AVISO) for processing sea surface height data was used in this study. Topex/Poseidon sea surface heights were obtained from satellite data at the proximity of tide gauge stations. These data were reproduced by a linear interpolation with the interval of 10 days and were processed by the Gaussian filter with a 60-day window. The tide gauge sea levels were obtained in the same manner as the satellite data. The main results on RMS (Root-Mean-Square) and CORR (CORRelation coefficient) in our study were shown as follows: 1) on the characteristics between two data (in-situ and model data), the results (RMS=2.96 cm & CORR=$92\%$ in the Amsterdam plateau, and RMS=3.45 cm & CORR=$59\%$ in the Crozet plateau) of the comparison of Topex/Poseidon sea surface heights with tide gauge sea levels, which was calculated by in-situ data of obsewed station showed generally low values in RMS and high values in CORR against to the results (RMS=4.69 cm & CORR=$79\%$ in the Amsterdam plateau, and RMS= 6.29 cm & CORR= $49\%$ in the Crozet plateau) of the comparison of Topex/Poseidon sea surface heights with tide gauge sea levels, which was calculated by model data of ECMWF (European Center for Medium-range Weather Forecasting), and 2) on the characteristics between two areas (Kerguelen plateau and island), the results (RMS=3.28 cm & CORR= $54\%$ in the Kerguelen plateau) of open sea area showed low values in RMS and high values in CORR against to the results (RMS= 5.71 cm & CORR=$38\%$ in the Kerguelen island) of coast area, respectively.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.83-102
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• 1994

Some optimal designs and data analysis methods based on a Gaussian spatial linear model for computer simulation experiments are considered. For designs of computer experiments, Latin-hypercube designs and some optimal designs are combined. A two-stage computational (2-points exchange and Newton-type) algorithm for finding the optimal Latin-hypercube design is presented. The spatial prediction model which was discussed by Sacks, Welch, Mitchell and Wynn(1989) for computer experiments, is used for analysis of the simulated data. Moreover, a method of contructing sequential (optimal) Latin-hypercube designs is considered. An application of this approach to the quality improvement and optimization of the integrated circuit design via the main-effects plot and the sequential experimental strategy is presented.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.2
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• pp.153-158
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• 2017

In this paper, we take into account topology optimization problems considering spatial randomness in the material property of elastic modulus. Based on 88 lines MATLAB Code, Monte Carlo analysis has been performed for MBB(messerschmidt-$b{\ddot{o}}lkow$-blohm) model using 5,000 random sample fields which are generated by using the spectral representation scheme. The random elastic modulus is assumed to be Gaussian in the spatial domain of the structure. The variability of the volume fraction of the material, which affects the optimum topology of the given problem, is given in terms of correlation distance of the random material. When the correlation distance is small, the randomness in the topology is high and vice versa. As the correlation distance increases, the variability of the volume fraction of the material decreases, which comply with the feature of the linear static analysis. As a consequence, it is suggested that the randomness in the material property is need to be considered in the topology optimization.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.576-583
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• 2020

This paper proposes a technique to determine the spam comments on YouTube, which have recently seen tremendous growth. On YouTube, the spammers appeared to promote their channels or videos in popular videos or leave comments unrelated to the video, as it is possible to monetize through advertising. YouTube is running and operating its own spam blocking system, but still has failed to block them properly and efficiently. Therefore, we examined related studies on YouTube spam comment screening and conducted classification experiments with six different machine learning techniques (Decision tree, Logistic regression, Bernoulli Naive Bayes, Random Forest, Support vector machine with linear kernel, Support vector machine with Gaussian kernel) and ensemble model combining these techniques in the comment data from popular music videos - Psy, Katy Perry, LMFAO, Eminem and Shakira.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.554-561
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• 2013

In this paper, we introduce a modeling and a process simulator developed with the aid of pRBFNNs for activated sludge process in the sewage treatment system. Activated sludge process(ASP) of sewage treatment system facilities is a process that handles biological treatment reaction and is a very complex system with non-linear characteristics. In this paper, we carry out modeling by using essential ASP factors such as water effluent quality, the manipulated value of various pumps, and water inflow quality, and so on. Intelligent algorithms used for constructing process simulator are developed by considering multi-output polynomial radial basis function Neural Networks(pRBFNNs) as well as Fuzzy C-Means clustering and Particle Swarm Optimization. Here, the apexes of the antecedent gaussian functions of fuzzy rules are decided by C-means clustering algorithm and the apexes of the consequent part of fuzzy rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The coefficients of the consequent polynomial of fuzzy rules and performance index are considered by the Least Square Estimation and Mean Squared Error. The descriptions of developed process simulator architecture and ensuing operation method are handled.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.11 no.1
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• pp.99-108
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• 1991

Rapid changes of urban hydrologic events need new management operation rule of detention reservoir which is essential outflow control system in urban area. Therefore, this study is to develop the outflow management method of Seoul city considering the Han river flood characteristics, to analyze the inundation of detention reservoir according to variation of design storm patterns, and to examine the safety of gate due to design flood water level. From this study, new operation rule is presented. The design storm patterns are determined by instantaneous intensity method and Huff's quartile method. And the inflow hydrograph of detention reservoir is obtained by applying ILLUDAS model and RRL method. The operation rule of existing drainage pump is designed to have linear relation between storage and pumping discharge. But in this study, it is effective for preventing inundation when the operation rule of drainage pump have Gaussian function which is combined the storage of detention reservoir with its inflow according to increasing or decreasing of inflow hydrograph.