A mathematical model of compaction in sedimentary basins is presented and analyzed. Compaction occurs when accumulating sediments compact under their own weight, expelling pore water in the process. If sedimentation is rapid or the permeability is low, then high pore pressures can result, a phenomenon which is of importance in oil drilling operations. Here we show that one-dimensional compaction can be described in its simplest form by a nonlinear diffusion equation, controlled principally by a dimensionless parameter $\lambda$, which is the ratio of the hydraulic conductivity to the sedimentation rate. Large $\lambda$ corresponds to very permeable sediments, or slow sedimentation, a situation which we term "fast compaction," since the rapid pore water expulsion allows the pore water pressure to equilibrate to a hydrostatic value. On the other hand, small $\lambda$ corresponds to "slow compaction," and the pore pressure is in excess above the hydrostatic value and more nearly equal to the overburden value. We provide analytic and numerical results for both large and small $\lambda$, using also the assumption that the permeability is a strong function of porosity. In particular, we can derive Athy's law (that porosity decreases exponentially with depth) when $\lambda\gg 1$.
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No abstract is provided for this article.
Business optimization is becoming increasingly important because all business activities aim to maximize the profit and performance of products and services, under limited resources and appropriate constraints. Recent developments in support vector machine and metaheuristics show many advantages of these techniques. In particular, particle swarm optimization is now widely used in solving tough optimization problems. In this paper, we use a combination of a recently developed Accelerated PSO and a nonlinear support vector machine to form a framework for solving business optimization problems. We first apply the proposed APSO-SVM to production optimization, and then use it for income prediction and project scheduling. We also carry out some parametric studies and discuss the advantages of the proposed metaheuristic SVM.
No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
Precise marketing is one of the trends for the service industry, it means nowadays, many consumers are keen on personalized as well as diversified consuming experience.When it comes to the tourism industry, there are a lot of examples of precise marketing, like wenyi tourist destinations have gained popularity on social media in recent years, many tourist destinations and enterprises try to attract target guest groups with the label of wenyi image, but there are few papers studying on the target consumer groups of this kind of destinations.In this paper, the research team captured the network text from Zhihu, a popular ChineseQ&A platform, and analysed the self-portrait of the target group of the wenyi destination systematically.The coding results reveal that there are many outstanding characteristics of wenyi qingnian in both behavior(237) and psychology(193), providing a reference for relative tourist destinations' precise marketing strategies which are aimed at their target customer group.
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Filtering multi-dimensional images such as color images, color videos, multispectral images and magnetic resonance images is challenging in terms of both effectiveness and efficiency. Leveraging the nonlocal self-similarity (NLSS) characteristic of images and sparse representation in the transform domain, the block-matching and 3D filtering (BM3D) based methods show powerful denoising performance. Recently, numerous new approaches with different regularization terms, transforms and advanced deep neural network (DNN) architectures are proposed to improve denoising quality. In this paper, we extensively compare over 60 methods on both synthetic and real-world datasets. We also introduce a new color image and video dataset for benchmarking, and our evaluations are performed from four different perspectives including quantitative metrics, visual effects, human ratings and computational cost. Comprehensive experiments demonstrate: (i) the effectiveness and efficiency of the BM3D family for various denoising tasks, (ii) a simple matrix-based algorithm could produce similar results compared with its tensor counterparts, and (iii) several DNN models trained with synthetic Gaussian noise show state-of-the-art performance on real-world color image and video datasets. Despite the progress in recent years, we discuss shortcomings and possible extensions of existing techniques. Datasets and codes for evaluation are made publicly available at https://github.com/ZhaomingKong/Denoising-Comparison.
Swarm intelligence is a very powerful technique appropriate to optimization. In this paper, we present a new swarm intelligence algorithm, which is based on the bat algorithm. Bat algorithm has been hybridized with differential evolution strategies. This hybridization showed very promising results on standard benchmark functions and also significantly improved the original bat algorithm.
No abstract is provided for this article.
No abstract is provided for this article.
In this paper, we intend to formulate a new metaheuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Levy flight behaviour of some birds and fruit flies. We validate the proposed algorithm against test functions and then compare its performance with those of genetic algorithms and particle swarm optimization. Finally, we discuss the implication of the results and suggestion for further research.
The Maximum Likelihood Estimator (MLE) serves an important role in statistics and machine learning. In this article, for i.i.d. variables, we obtain constant-specified and sharp concentration inequalities and oracle inequalities for the MLE only under exponential moment conditions. Furthermore, in a robust setting, the sub-Gaussian type oracle inequalities of the log-truncated maximum likelihood estimator are derived under the second-moment condition.
