Bowtie filters are used to modulate an incoming x-ray beam as a function of the angle of the x-ray to balance the photon flux on a detector array. Because of their key roles in radiation dose reduction and multi-energy imaging, bowtie filters have attracted a major attention in modern X-ray computed tomography (CT). However, few researches are concerned on the effects of the structure and materials for the bowtie filter in the Cone Beam CT (CBCT). In this study, the influence of bowtie filters’ structure and materials on X-ray photons distribution are analyzed using Monte Carlo (MC) simulations by MCNP5 code. In the current model, the phantom was radiated by virtual X-ray source (its’ energy spectrum calculated by SpekCalc program) filtered using bowtie, then all photons were collected through array photoncounting detectors. In the process above, two bowtie filters’ parameters which include center thickness (B), edge thickness (controlled by A), changed respectively. Two kinds of situation are simulated: 1) A=0.036, B=1, 2, 3, 4, 5, 6mm and the material is aluminum; 2) A=0.016, 0.036, 0.056, 0.076, 0.096, B=2mm and the material is aluminum. All the X-ray photons' distribution are measured through MCNP. The results show that reduction in center thickness and edge thickness can reduce the number of background photons in CBCT. Our preliminary research shows that structure parameters of bowtie filter can influence X-ray photons, furthermore, radiation dose distribution, which provide some evidences in design of bowtie filter for reducing radiation dose in CBCT.
Bonding layers, serving as the strain transmission mediums, may bring undesirable effects to the sensing properties of fiber Bragg grating (FBG) strain sensors during their fatigue process. To analyze the strain sensitivity and the reflected spectrum of FBG strain sensors in different fatigue stages of bonding layers, their strain sensitivities were derived according to strain transfer models. Resorting to T-matrix formalism, the affected reflected spectra were stimulated. Theoretical analysis results show that there is a gradual decline in the strain sensitivity during the stage of fatigue crack initiation, and that significant distortions emerge in the reflected spectrum during the stage of fatigue crack propagation. In addition, a cyclic loading fatigue test was conducted and the phenomenon observed in the test showed a good agreement with the theoretical prediction.
An Improved Support Vector Machines was proposed which starts with a small set and then sequentially expands to
include feature space informative data points into the set. These feature space informative data points will be identified
by solving a small least squares problem. The approach provides a mechanism to determine the set size automatically
and dynamically and the set generated by this method will be more representative than the one by purely random
selection. All advantages of SVM for dealing with nonlinear classification problem are retained.
To improve the performance of data clustering, this study proposes a novel clustering method called ABCA (ACO Based
Clustering Algorithm). The presented method is based on heuristic concept and using Ant Colony Optimization
algorithm (ACO) to obtain global search. The main advantage of these algorithms lies in the fact that no additional
information, such as an initial partitioning of the data or the number of clusters, is needed. Since the proposed method is
very efficiently, thus it can perform data clustering very quickly.