Doo Kie Kim

Modified Probabilistic Neural Network Considering Heterogeneous Probabilistic Density Functions in the Design of Breakwater

In this study, a modified probabilistic neural network approach is proposed. The global probability density function (PDF) of variables is reflected by summing the heterogeneous local PDFs automatically determined in the individual standard derivation of each variable. The proposed modified probabilistic neural network (MPNN) is applied to predict the stability number of armor bøcks of break waters using the experiental data of van der Meer, and the estimated results of the MPNN are compared with those of conventional probabilistic neural network. The MPNN shows improved results in predicting the stability number of armor blocks of breakwaters and in providing the promising reliability for stability numbers estimated by using the individual standard deviation in a variable.

The Wind Flow Characteristics Measuring Simulations of Soundproof Tunnels on Expressways

Soundproof tunnels and soundproof walls constructed on expressways are designed to prevent noise for the nearby surrounding residential areas. These soundproof walls and tunnels feature excellent noise prevention for residential areas nearby, but they hamper the dispersion of air pollutants generated, thus promoting the creation of heat islands during summer and cold islands during winter.The computational fluid dynamics (CFD) analysis method was used to investigate the wind flow around soundproof tunnels. The wind angle and the size of the wind velocity were determined using data from weather stations near soundproof tunnels. The CFD analysis results of the soundproof tunnels on expressways revealed that the wind velocity decreased by 30–60% following the installation of soundproof tunnels.

Capability enhancement of probabilistic neural network for the design of breakwater armor blocks

In this study, the capability of probabilistic neural network (PNN) is enhanced. The proposed PNN is capable of reflecting the global probability density function (PDF) by summing the heterogeneous local PDF automatically determined in the individual standard deviation of variables. The present PNN is applied to predict the stability number of armor blocks of breakwaters using the experimental data of van der Meer, and the estimated results of PNN are compared with those of empirical formula and previous artificial neural network (ANN) model. The PNN showed better results in predicting the stability number of armor blocks of breakwaters and provided the promising probabilistic viewpoints by using the individual standard deviation in a variable.