Using a Macroscopic Fundamental Diagram (MFD) to implement partition control is effective in improving mobility in heterogeneous city networks. As one of the most complex issues in partition control, accurate sub-regi...
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Using a Macroscopic Fundamental Diagram (MFD) to implement partition control is effective in improving mobility in heterogeneous city networks. As one of the most complex issues in partition control, accurate sub-region partition is critical for control effectiveness. Current partition methods focus on the link density and precondition of existing MFD and disregard the factors that influence MFD distribution. To overcome this drawback, this study uses the characteristic value of the link and the intersection connected to the link as the analysis object and proposes an MFD sub-region partitioning method for large-scale networks. Firstly, the influences of road state parameters on MFD distribution are classified into traffic flow parameters, network physical properties, network operation mechanisms and emergencies. Simulation experiments are conducted to determine the degree to which these classifications affect MFD distribution. Secondly, a partition method combined with the link density and influence parameters of MFD is developed. The method is used for a preliminarily division of a road network through Minimum Spanning Tree (MST) and depth partition by the normalisedcut (Ncut) algorithm. Finally, a case study is conducted in an actual city centre network, and results show that the developed method is superior to the single method based simply on link density.
Optical coherence tomography is an immersive technique for depth analysis of retinal layers. Automatic choroid layer segmentation is a challenging task because of the low contrast inputs. Existing methodologies carrie...
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Optical coherence tomography is an immersive technique for depth analysis of retinal layers. Automatic choroid layer segmentation is a challenging task because of the low contrast inputs. Existing methodologies carried choroid layer segmentation manually or semi-automatically. The authors proposed automated choroid layer segmentation based on normalised cut algorithm, which aims at extracting the global impression of images and treats the segmentation as a graph partitioning problem. Due to the structure complexity of retinal and choroid layers, the authors employed a series of pre-processing to make the cut more deterministic and accurate. The proposed method divided the image into several patches and ran the normalised cut algorithm on every patch separately. The aim was to avoid insignificant vertical cuts and focus on horizontal cutting. After processing every patch, the authors acquired a global cut on the original image by combining all the patches. Later the authors measured the choroidal thickness which is highly helpful in the diagnosis of several retinal diseases. The results were computed on a total of 525 images of 21 real patients. Experimental results showed that the mean relative error rate of the proposed method was around 0.4 when compared with the manual segmentation performed by the experts.
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