This paper aims to tackle an important uncertainty which extremely affects seismic performance of wharf structures in earthquake events. According to previous studies performed for structures on land, it is shown that structures on land are highly susceptible to unknown orientation of earthquakes called as the Directional uncertainty. However, for marine structures, especially pile supported wharves, research efforts are rare to assess the effect of Directional uncertainty of earthquakes on structural responses. Therefore, to show this effect on seismic performance of pile supported wharves, fragility analysis is performed based on methodology suggested by Pacific Earthquake Engineering Research Center (PEER) for the modeled pile supported wharf located in Maah-shahr port as a case study. As the first phase of this methodology, nonlinear static pushover analyses are performed for randomly chosen incident angles in order to quantitatively measure damage states suggested by marine design code. After damage states are obtained, IDA analyses are conducted in the selected incident angles to obtain nonlinear structural responses which are supposed to be used for fragility analysis as inputs. Finally, once fragility curves have been developed according to the last phase of PEER methodology, the more vulnerable direction(s) of wharf from those incident angles is represented.

A novel method of parametric active contours with geometric shape prior is presented in this paper. The main idea of the method consists in minimizing an energy function that includes additional information on a shape reference called a prototype (mean shape). Shape prior introduced through a similarity measurement between evolving contour and Procrustes mean shape of desired object. This similarity measurement is Procrustes distance between these two contours that is invariant with respect to similarity transformations (translation, scaling, and rotation). Procrustes mean shape is extracted from a set of sample shapes. We apply k-mean clustering to sample shapes for overcoming non Gaussian distribution of them e.g., multiple views of a 3D object. In this case the final energy function is invariant with respect to changing viewpoint of 3D object. This extra shape knowledge enhances the model robustness to noise, occlusion and complex background. We also use gradient direction information in addition to its magnitude for more robustness to complex background and noise. We obtain promising results for 3D object tracking and segmentation showing the robustness of our method against noise, complex background, similarity transformations, occlusion, and changing viewpoint of 3D object.

The purpose of this paper is to characterize the concept of monotonicity according to a direction related to a set of n random variables in terms of its associated n-copula. We start establishing relationships in the bivariate and trivariate cases, which will help to understand the extension to the multivariate case. Several examples are provided.

A general overview of the scheduling’s literature of some researches shows that among various factors, the priority rules and also the structure of projects are two main factors that can be affected on the performance of multiDirectional scheduling schemes. In addition, a variation on the number of directors in scheduling schemes (e.g., single Directional, bi-Directional, and tri-Directional scheduling scheme) produces different makespans. However, the question of when to move from the single direction to the multiDirectional scheduling schemes remained open. In this paper, based on analytical and also empirical results, we show that when availability and distributions of resource measures such as the number of peaks and number of overflows or the average amount of overflows are increased, higher Directional scheduling schemes can be produced smaller makespans. Hence, in the light of these resource measures, the multiDirectional scheduling schemes can be reduced the dependency of the solution’s quality on the priority rules used.

Let V and W be two real vector spaces and let ~ be a relation on both V and W. A linear function T : V ® W is said to be a linear preserver (respectively strong linear preserver) of ~ if Tx ~ Ty whenever x ~ y (respectively Tx ~ Ty if and only if x ~ y). In this paper we characterize all linear functions T : Mn,m ® Mn, k which preserve or strongly preserve multivariate and Directional majorization.