Effects of Platform Screen Doors on Sound Fields in Underground Stations

This chapter investigates the acoustic effects of platform screen doors (PSDs) in underground stations using computer simulation and scale model testing. The dimensions of underground stations with island and side platforms were determined based on a field survey. Ray-tracing-based computer models and 1/25 scaled-down physical models of these underground stations were used to simulate their sound field characteristics. In the experiments, five types of PSDs were tested: mobile closed full-height (MCFH), mobile open full-height (MOFH), mobile half-height (MHH), fixed half-height (FHH) and fixed barrier (FB) doors. Four acoustic parameters, namely, speech intelligibility, sound pressure level, reverberation time and the inter-aural cross-correlation coefficient were used to understand the sound field characteristics from the sound source of public address announcements. It was found that speech intelligibility and the sound pressure level were increased by most types of PSDs apart from the MCFH. The MOFH showed the highest levels of speech intelligibility and spatial diffusivity. In addition, the noise reduction effects of PSDs for train noise were discussed. PSDs on side platforms showed higher noise reduction performances than PSDs on island platforms. The specific noise reduction levels for the MOFH type were 4.3 dB on island platforms and 5.0 dB on side platforms

Two-dimensional heterogeneous photonic bandedge laser

We proposed and realized a two-dimensional (2D) photonic bandedge laser surrounded by the photonic bandgap. The heterogeneous photonic crystal structure consists of two triangular lattices of the same lattice constant with different air hole radii. The photonic crystal laser was realized by room-temperature optical pumping of air-bridge slabs of InGaAsP quantum wells emitting at 1.55 micrometer. The lasing mode was identified from its spectral positions and polarization directions. A low threshold incident pump power of 0.24mW was achieved. The measured characteristics of the photonic crystal lasers closely agree with the results of real space and Fourier space calculations based on the finite-difference time-domain method.Comment: 14 pages, 4 figure

Clustering of Nodes in Layered-Tree Topology for Wireless Sensor Networks

Wireless sensor network is composed of a large number of sensor nodes of limited energy resource. The node clustering approach can improve the scalability and lifetime of wireless sensor network. In this paper we propose a novel node clustering protocol based on layered-tree topology for self-organizing distributed wireless sensor networks. It decides optimal number of clusters by employing a new approach for setting threshold value, including the probability of optimum number of cluster-heads and residual energy of the nodes. We also introduce a new scheme for layered-tree construction in each cluster. As a result, the proposed scheme can significantly improve the energy efficiency of the network and increase its lifetime. Computer simulation shows that the proposed scheme effectively reduces and balances the energy consumption of the nodes, and thus significantly extends the network lifetime compared to the existing schemes

Feature Selection for Very Short-Term Heavy Rainfall Prediction Using Evolutionary Computation

We developed a method to predict heavy rainfall in South Korea with a lead time of one to six hours. We modified the AWS data for the recent four years to perform efficient prediction, through normalizing them to numeric values between 0 and 1 and undersampling them by adjusting the sampling sizes of no-heavy-rain to be equal to the size of heavy-rain. Evolutionary algorithms were used to select important features. Discriminant functions, such as support vector machine (SVM), k-nearest neighbors algorithm (k-NN), and variant k-NN (k-VNN), were adopted in discriminant analysis. We divided our modified AWS data into three parts: the training set, ranging from 2007 to 2008, the validation set, 2009, and the test set, 2010. The validation set was used to select an important subset from input features. The main features selected were precipitation sensing and accumulated precipitation for 24 hours. In comparative SVM tests using evolutionary algorithms, the results showed that genetic algorithm was considerably superior to differential evolution. The equitable treatment score of SVM with polynomial kernel was the highest among our experiments on average. k-VNN outperformed k-NN, but it was dominated by SVM with polynomial kernel