A novel spatial interpolation method based on integrated radial basis function artificial neural networks （IRBFANNs） is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at un- sampled sites in a mountain region. The IRBFANNs hybridize the advantages of the artificial neural networks and the neural networks integration approach. Three experimental projects under different sampling densities are carried out to study the performance of the proposed IRBFANNs-based interpolation method. This novel method is compared with six peer spatial interpolation methods based on the root mean square error and visual evaluation of the distribution maps of Mn elements. The experimental results show that the proposed method performs better in accuracy and stability. Moreover, the proposed method can provide more details in the spatial distribution maps than the compared interpolation methods in the cases of sparse sampling density.
Ultrasound simulation for carotid arteries is helpful to the performance assessments of vessel wall detection and signal processing methods by using ultrasound techniques. An ul- trasound simulation method of carotid artery wall with a three-membrane structure is proposed in present study. According to the ultrasound speckle distributions varying with the shapes and densities of scatterer distributions, as well as the statistic results of the clinical images, the parameters of distributions, densities and intensities of scatterers for different kinds of tissues in the carotid artery phantoms are determined. Each region is acoustically characterized using FIELD II software to produce the radio frequency echo signals, from which ultrasound images are derived. The results based on 30 simulations show that the echo distributions of the intimae, mediae, adventitias and blood are consistent with the clinical ones. Moreover, compared with the results from the central frequency of 8 MHz, the mean measurements for thicknesses of the intima, media and adventitia membranes, as well as the lumen diameter from the simulation images based on 12 MHz are the same as the preset ones, and the maximum relative errors are the 4.01%, 1.25%, 0.04% and 0.15%, respectively. The simulation under this condition is more realistic.
HU XiaoZHANG YufengGAO LianCAI GuanghuiJIA ZhiguoZHANG KexinDENG Li
To solve the shortest path planning problems on grid-based map efficiently,a novel heuristic path planning approach based on an intelligent swarm optimization method called Multivariant Optimization Algorithm( MOA) and a modified indirect encoding scheme are proposed. In MOA,the solution space is iteratively searched through global exploration and local exploitation by intelligent searching individuals,who are named as atoms. MOA is employed to locate the shortest path through iterations of global path planning and local path refinements in the proposed path planning approach. In each iteration,a group of global atoms are employed to perform the global path planning aiming at finding some candidate paths rapidly and then a group of local atoms are allotted to each candidate path for refinement. Further,the traditional indirect encoding scheme is modified to reduce the possibility of constructing an infeasible path from an array. Comparative experiments against two other frequently use intelligent optimization approaches: Genetic Algorithm( GA) and Particle Swarm Optimization( PSO) are conducted on benchmark test problems of varying complexity to evaluate the performance of MOA. The results demonstrate that MOA outperforms GA and PSO in terms of optimality indicated by the length of the located path.
