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The establishing of an accurate and reliable dam deformation prediction model is an important content of dam safety evaluation. To this end, a dam deformation prediction method based on support vector machine (SVM) with optimal parameters selected by improved grey wolf optimization algorithm (IGWO) is proposed by introducing the crossover and mutation operators of the differential evolution algorithm into the GWO algorithm. The initial population is enriched by the differential evolution algorithm, an IGWO is proposed, and the penalty factor and kernel function of SVM are optimized by the IGWO algorithm, then a dam deformation prediction model based on the SVM-IGWO algorithm is established. The results will be compared with those of the SVM and SVM-GWO models through the measured data of Xiluodu super high arch dam. The research shows that the proposed SVM-IGWO model can effectively improve the accuracy of dam deformation prediction.

Multimode optical fiber is widely used because of its large core size, strong light-gathering ability, and remote on-line sensing ability in various environments. Due to the mode coupling and polarization loss, the signal curve obtained has wide full width at half-maximum (FWHM) and small peak value. Therefore, efficient methods to calculate the resonant wavelength are required. This paper presents a method to process the surface plasmon resonance curve (SPR) based on the Fourier series fitting. The calculated resonant wavelength is obtained with one-dimensional extremum-search method. Experiment results show the proposed method can accurately calculate the resonant wavelength of high concentration solution. Additionally, the method has higher sensitivity and is unaffected by the light source fluctuation. This method has potential value in processing the SPR signal of the multimode optical fiber.

We study faster algorithms for producing the minimum degree ordering used to speed up Gaussian elimination. This ordering is based on viewing the non-zero elements of a symmetric positive definite matrix as edges of an undirected graph, and aims at reducing the additional non-zeros (fill) in the matrix by repeatedly removing the vertex of minimum degree. It is one of the most widely used primitives for pre-processing sparse matrices in scientific computing. Our result is in part motivated by the observation that sub-quadratic time algorithms for finding min-degree orderings are unlikely, assuming the strong exponential time hypothesis (SETH). This provides justification for the lack of provably efficient algorithms for generating such orderings, and leads us to study speedups via degree-restricted algorithms as well as approximations. Our two main results are: (1) an algorithm that produces a min-degree ordering whose maximum degree is bounded by $\Delta$ in $O(m \Delta \log^3{n})$ time, and (2) an algorithm that finds an $(1 + \epsilon)$-approximate marginal min-degree ordering in $O(m \log^{5}n \epsilon^{-2})$ time. Both of our algorithms rely on a host of randomization tools related to the $\ell_0$-estimator by [Cohen `97]. A key technical issue for the final nearly-linear time algorithm are the dependencies of the vertex removed on the randomness in the data structures. To address this, we provide a method for generating a pseudo-deterministic access sequence, which then allows the incorporation of data structures that only work under the oblivious adversary model., Comment: 57 pages