Fuzzy Earthwork Dynamic Allocation and Optimization for Construction of High Concrete Face Rockfill Dam

Due to the complexity of earthwork allocation system for the construction of high concrete face rockfill dam,traditional allocation and planning are not able to function properly in the construction process with strong randomness.In this paper,the working mechanism of earthwork dynamic allocation system is analyzed comprehensively and a solution to fuzzy earthwork dynamic allocation is proposed on the basis of uncertain factors in the earthwork allocation of a hydropower project.Under the premise of actual situation and the experience of the construction site,an all-coefficient-fuzzy linear programming mathematical model with fuzzy parameters and constraints for earthwork allocation is established according to the structure unit weighted ranking criteria.In this way,the deficiency of certain allocation model can be overcome.The application results indicate that the proposed method is more rational compared with traditional earthwork allocation.

Ranking with Adaptive Neighbors

Retrieving the most similar objects in a large-scale database for a given query is a fundamental building block in many application domains, ranging from web searches, visual, cross media, to document retrievals. Stateof-the-art approaches have mainly focused on capturing the underlying geometry of the data manifolds. Graphbased approaches, in particular, define various diffusion processes on weighted data graphs. Despite success,these approaches rely on fixed-weight graphs, making ranking sensitive to the input affinity matrix. In this study,we propose a new ranking algorithm that simultaneously learns the data affinity matrix and the ranking scores.The proposed optimization formulation assigns adaptive neighbors to each point in the data based on the local connectivity, and the smoothness constraint assigns similar ranking scores to similar data points. We develop a novel and efficient algorithm to solve the optimization problem. Evaluations using synthetic and real datasets suggest that the proposed algorithm can outperform the existing methods.

Improved polyreference time domain method for modal identification using local or global noise removal techniques

Modal identification involves estimating the modal parameters, such as modal frequencies, damping ratios, and mode shapes, of a structural system from measured data. Under the condition that noisy impulse response signals associated with multiple input and output locations have been measured, the primary objective of this study is to apply the local or global noise removal technique for improving the modal identification based on the polyreference time domain (PTD) method. While the traditional PTD method improves modal parameter estimation by over-specifying the computational model order to absorb noise, this paper proposes an approach using the actual system order as the computational model order and rejecting much noise prior to performing modal parameter estimation algorithms. Two noise removal approaches are investigated: a "local" approach which removes noise from one signal at a time, and a "global" approach which removes the noise of multiple measured signals simultaneously. The numerical investigation in this article is based on experimental measurements from two test setups: a cantilever beam with 3 inputs and 10 outputs, and a hanged plate with 4 inputs and 32 outputs. This paper demonstrates that the proposed noise-rejection method outperforms the traditional noise-absorption PTD method in several crucial aspects.