Aspects about bouncing of plough caused by random

In this paper the author deals aspects about the vertical motion (named bouncing) of a tractor with plough mounted on the rear frame, during displacement over the random excitation surface of the agricultural land. Final results of the simulation process, performed on the model of tractor-plough with 3 degree of freedoms, show the difference between digging depth function as velocity motion and longitudinal profile of the terrain. Thus, the deviation of the plough depth from the reference depth is evaluated.

Outlier Detection Using Minimum Vector Variance Algorithm with Depth Function and Mahalanobis Distance

Outliers are observations where the point of observation deviates from the data pattern. The existence of outliers in the data can cause irregularities in the results of data analysis. One solution to this problem is to detect outliers using a statistical approach. The statistical approach method used in this study is the Minimum Vector Variance (MVV) algorithm which has robust characteristics for outliers. The purpose of this research is to detect outliers using the MVV algorithm by changing the data sorting criteria using the Robust Depth Mahalanobis to produce maximum detection. The results obtained from this study are that RDMMVV is superior to the observed value in showing the outliers and the location of the outliers in the data plot compared to DMMVV and MMVV.

CRATER DETECTION USING TEXTURE FEATURE AND RANDOM PROJECTION DEPTH FUNCTION

In this paper, a novel automatic crater detection algorithm (CDA) based on traditional texture feature and random projection depth function has been proposed. By using traditional texture feature, mathematical morphology is used to identify crater initially. To further reduce the false detection rate, random projection depth function is used. For this purpose, firstly, gray level co-occurrence matrix and a novel grade level co-occurrence matrix are both used to further obtain the texture features of these candidate craters. Secondly, based on the above collected features, random projection depth function is used to refine the crater candidate detection results. LRO Narrow Angle Camera (NAC) mosaic images (1 m/pixel) and Wide-angle Camera (WAC) mosaic images (100 m/pixel) are used to test the accuracy of proposed method. The experimental results indicate our proposed method is robust to detect craters located in different terrains.

Gravity inversion with depth normalization

<p>Actually the most common method of gravity data interpretation is a manual fitting method. In this case, the density model is divided into many polygons with constant density and each polygon is editing manually by interpreter. This approach has two main disadvantages:</p><p>- significant amount of time is needed to build a high-quality density model;</p><p>- if density isn’t constant within anomalous object or a layer, object must be divided into many blocks, which requires additional time, and editing the model during the interpretation process becomes more complicated.</p><p> To solve these problems, we can use methods of automatic fitting of the density model (inversion). At the same time, it is convenient to divide the model into many identical cells with constant density (grid). In this case, solving the inverse problem of gravity is reduced to solving a system of linear algebraic equations. To solve the system of equations, it is necessary to construct a loss function, which includes terms responsible for the difference between the observed gravitational field and the theoretical field, as well as for the difference between the model and a priori data (regularizer). Further, the problem is solved using iterative gradient optimization methods (gradient descent method, Newton's method and etc.).</p><p>However, in this case, the problem arises – final fitted model differs from the initial by contrasting near-surface layer due to the greater influence of the near-surface cells on the loss function, and the deep sources of gravity field anomalies are not included in inversion. Such models can be used in the processing of gravity data (source-based continuation, filtering), but are useless in solving of geological problems.</p><p>To take into account the influence of the deep cells of the model, the following solution is proposed: multiplying the gradient of the loss function by a normalization depth function that increases with depth. For example, such a function can be a quadratic function (its choice is conditioned by the fact that the gravity is inversely proportional to the square of the distance).</p><p>The use of inversion with a normalizing depth function allows solving the following problems:</p><p>- taking into account both surface and deep sources of gravity anomalies;</p><p>- solving the problem of taking into account the density gradient within the layers (since the layer is divided into many cells, the densities of which can be differen);</p><p>- reliably determine singular points of anomalous objects;</p><p>- significantly reduce the time of  the density model fitting.</p>

Wave-Induced Seabed Response around a Dumbbell Cofferdam in Non-Homogeneous Anisotropic Seabed

Cofferdams are frequently used to assist in the construction of offshore structures that are built on a natural non-homogeneous anisotropic seabed. In this study, a three-dimensional (3D) integrated numerical model consisting of a wave submodel and seabed submodel was adopted to investigate the wave–structure–seabed interaction. Reynolds-Averaged Navier–Stokes (RANS) equations were employed to simulate the wave-induced fluid motion and Biot’s poroelastic theory was adopted to control the wave-induced seabed response. The present model was validated with available laboratory experimental data and previous analytical results. The hydrodynamic process and seabed response around the dumbbell cofferdam are discussed in detail, with particular attention paid to the influence of the depth functions of the permeability K i and shear modulus G j . Numerical results indicate that to avoid the misestimation of the liquefaction depth, a steady-state analysis should be carried out prior to the transient seabed response analysis to first determine the equilibrium state caused by seabed consolidation. The depth function G j markedly affects the vertical distribution of the pore pressure and the seabed liquefaction around the dumbbell cofferdam. The depth function K i has a mild effect on the vertical distribution of the pore pressure within a coarse sand seabed, with the influence concentrated in the range defined by 0.1 times the seabed thickness above and below the embedded depth. The depth function K i has little effect on seabed liquefaction. In addition, the traditional assumption that treats the seabed parameters as constants may result in the overestimation of the seabed liquefaction depth and the liquefaction area around the cofferdam will be miscalculated if consolidation is not considered. Moreover, parametric studies reveal that the shear modulus at the seabed surface G z 0 has a significant influence on the vertical distribution of the pore pressure. However, the effect of the permeability at the seabed surface K z 0 on the vertical distribution of the pore pressure is mainly concentrated on the seabed above the embedded depth in front and to the side of the cofferdam. Furthermore, the amplitude of pore pressure decreases as Poisson’s ratio μ s increases.