A Semi-Analytical Method to Calculate the Consolidation Problem of the Soilbag Filled with Mud Soil Buried with PVD

2014 ◽  
Vol 638-640 ◽  
pp. 345-349 ◽  
Author(s):  
Ben Niu ◽  
Chao Jie Zhang ◽  
Xiu Liang Chen ◽  
Heng Yu Wang
Keyword(s):  
Analytical Method ◽  
Lab Experiment ◽  
Dimensional Solution ◽  
Test Base ◽  
Body Model ◽  
Low Dimension ◽  
Rectangular Area ◽  
Degree Of Consolidation ◽  
Low Dimensional

A semi-analytical method to calculate the degree of consolidation of the soilbag filled with mud soil with PVD built in is proposed. First a unit-body model is founded to obtain the low-dimensional solution. Through the successive iterations of the low-dimension solution, the solution considering the situation when the physical or geometry parameters of the model vary is finally obtained. The solution of the rectangular area is obtained by summing solutions of two square area with coefficients set. A lab experiment was done in Liubao test base of Zhejiang institute of hydraulics and estuary to research the consolidation performance of soilbag filled with mud soil buried with PVD. The semi-analytical method and FEM are applied to calculate the lab experiment case. From the comparison among the lab experiment, the semi-analytical method and FEM, it shows that the semi-analytical method calculates a good result of the degree of consolidation of the lab experiment.

On the Observed Complexity of Chaotic Stellar Pulsation

1993 ◽  
Vol 134 ◽  
pp. 141-143
Author(s):  
Z. Kolláth
Keyword(s):  
Theoretical Calculations ◽  
Period Doubling ◽  
Variable Stars ◽  
Light Variation ◽  
Light Curves ◽  
Striking Similarity ◽  
Stochastic Perturbations ◽  
Stellar Pulsation ◽  
Low Dimension ◽  
Low Dimensional

The existence of some variable stars producing very complicated light curves is well known. Theoretical calculations suggest that the irregular behaviour of the pulsation models in the RV Tauri and W Virginis regime is low dimensional is the result of period-doubling or tangent bifurcations (see e.g. Buchler and Kovács 1987, Kovács and Buchler 1988, Tanaka and Takeuti 1988).The light variation of the RV Tauri star R Scuti covering 150 years was analyzed by Kolláth (1990). A striking similarity was found between the reconstructed attractor of the Rössler model and that of the light variation of R Scuti. This result confirms the theoretical prediction of the existence of chaos, but a discrepancy still exists between the theory and observation. We could not find evidence for low dimension (D = 2– 3). The analyses of other stars also show rather erratic behaviour (e.g. Cannizzo and Goodings 1988, Cannizzo et al. 1990). A possible answer for this discrepancy is the treatment of stochastic perturbations by convection (Perdang 1991).

scholarly journals Reactive Power Optimization of Large-Scale Power Systems: A Transfer Bees Optimizer Application

Processes ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 321 ◽  
Author(s):  
Huazhen Cao ◽  
Tao Yu ◽  
Xiaoshun Zhang ◽  
Bo Yang ◽  
Yaxiong Wu
Keyword(s):  
Power Systems ◽  
Large Scale ◽  
Reactive Power ◽  
Power Optimization ◽  
Solution Space ◽  
Reactive Power Optimization ◽  
Dimensional Solution ◽  
Q Learning ◽  
State Action ◽  
Low Dimensional

A novel transfer bees optimizer for reactive power optimization in a high-power system was developed in this paper. Q-learning was adopted to construct the learning mode of bees, improving the intelligence of bees through task division and cooperation. Behavior transfer was introduced, and prior knowledge of the source task was used to process the new task according to its similarity to the source task, so as to accelerate the convergence of the transfer bees optimizer. Moreover, the solution space was decomposed into multiple low-dimensional solution spaces via associated state-action chains. The transfer bees optimizer performance of reactive power optimization was assessed, while simulation results showed that the convergence of the proposed algorithm was more stable and faster, and the algorithm was about 4 to 68 times faster than the traditional artificial intelligence algorithms.

scholarly journals Strategy oriented to strengthen the college work of teachers for the benefit of the consolidation of Academic Corps

2020 ◽  
pp. 1-5
Author(s):  
María Blanca Palomares-Ruiz ◽  
Arturo Torres-Bugdud ◽  
María Isabel Dimas-Rangel ◽  
Cesar Sordia-Salinas
Keyword(s):  
Analytical Method ◽  
Educational Program ◽  
Academic Staff ◽  
Educational Programs ◽  
Full Time ◽  
International Level ◽  
Joint Work ◽  
Degree Of Consolidation ◽  
College Work ◽  
The Impact

The present work aims to show a strategy aimed at strengthening the academic staff through the creation of collegiate work groups which in the Mexican context are called Academic Corps (CA); the foregoing based on its strategic planning, collegiate work, relevance of the members of the CA, as well as the impact of its Lines of Generation and Application of Knowledge (LGAC) in the Educational Programs of the Faculty of Mechanical and Electrical Engineering, for which which proposes an academic-administrative structure that favors institutional achievements through the elevation of its indicators, where in the first instance a detailed analytical method of how many full-time professors participate in CA and the description of their LGAC, historical of academic corps by educational program and in what degree of consolidation they are found, in conclusion, different strategies were implemented promoting the increase of consolidated and consolidating academic corps , and their dissemination through means of recognized prestige at national and international level, combined to the degree of empowerment of its members, evidence of joint work and integration ration of thematic networks of collaboration.

ZnO Nanorods by a Simple Two Step Process

2013 ◽  
Vol 678 ◽  
pp. 223-226
Author(s):  
W. Bhagath Singh ◽  
Aleyamma Alexander ◽  
C.X. Joana May ◽  
Pricilla Mary ◽  
K. Thiyagarajan ◽  
...  
Keyword(s):  
Zno Nanorods ◽  
Morphological Properties ◽  
X Ray Diffraction ◽  
Ir Detectors ◽  
Step Process ◽  
One Dimensional ◽  
Low Dimension ◽  
Nanostructured Metal Oxides ◽  
Low Dimensional ◽  
Low Dimensional Materials

Low-dimension materials such as nanobelts, nanowires and nanorods are being investigated for their superior properties and numerous applications. Among them, one-dimensional semiconductor ZnO, representing one of the most important low dimensional materials, finds its applications in many different fields such as sensors, solar cells, IR detectors, microelectronics, etc. Synthesis of nanostructures without any catalytic template, or using the self-catalytic behavior of the material would be of interest. In this work, ZnO nanorods have been synthesized by simple two step process without using any catalyst. This method provides an easy way to produce nanostructured metal oxides under normal conditions. The prepared samples were characterized by studying their structural, optical and morphological properties using X-Ray Diffraction, Photoluminescence and Scanning Electron Microscopy. The diameter of the prepared nanorods were around 20-30 nm¬. The room temperature Photoluminescence spectra of the ZnO nanorods shows a broad visible emission around 450–530 nm.

Parsimonious Seismic Tomography with Poisson Voronoi Projections: Methodology and Validation

2019 ◽  
Vol 91 (1) ◽  
pp. 343-355 ◽  
Author(s):  
Hongjian Fang ◽  
Robert D. van der Hilst ◽  
Maarten V. de Hoop ◽  
Konik Kothari ◽  
Sidharth Gupta ◽  
...  
Keyword(s):  
High Dimension ◽  
Seismic Tomography ◽  
Plate Boundary ◽  
Model Space ◽  
Boundary Region ◽  
Normal Equation ◽  
Dimensional Solution ◽  
Voronoi Cells ◽  
Low Dimension ◽  
Conventional Methods

Abstract Ill‐posed seismic inverse problems are often solved using Tikhonov‐type regularization, that is, incorporation of damping and smoothing to obtain stable results. This typically results in overly smooth models, poor amplitude resolution, and a difficult choice between plausible models. Recognizing that the average of parameters can be better constrained than individual parameters, we propose a seismic tomography method that stabilizes the inverse problem by projecting the original high‐dimension model space onto random low‐dimension subspaces and then infers the high‐dimensional solution from combinations of such subspaces. The subspaces are formed by functions constant in Poisson Voronoi cells, which can be viewed as the mean of parameters near a certain location. The low‐dimensional problems are better constrained, and image reconstruction of the subspaces does not require explicit regularization. Moreover, the low‐dimension subspaces can be recovered by subsets of the whole dataset, which increases efficiency and offers opportunities to mitigate uneven sampling of the model space. The final (high‐dimension) model is then obtained from the low‐dimension images in different subspaces either by solving another normal equation or simply by averaging the low‐dimension images. Importantly, model uncertainty can be obtained directly from images in different subspaces. Synthetic tests show that our method outperforms conventional methods both in terms of geometry and amplitude recovery. The application to southern California plate boundary region also validates the robustness of our method by imaging geologically consistent features as well as strong along‐strike variations of San Jacinto fault that are not clearly seen using conventional methods.

scholarly journals Enhancing hierarchical surrogate-assisted evolutionary algorithm for high-dimensional expensive optimization via random projection

2021 ◽  
Author(s):  
Xiaodong Ren ◽  
Daofu Guo ◽  
Zhigang Ren ◽  
Yongsheng Liang ◽  
An Chen
Keyword(s):  
Optimization Problems ◽  
Solution Space ◽  
Random Projection ◽  
Surrogate Models ◽  
High Dimensional ◽  
Local Models ◽  
Dimensional Solution ◽  
Training Samples ◽  
Low Dimensional ◽  
Expensive Optimization

AbstractBy remarkably reducing real fitness evaluations, surrogate-assisted evolutionary algorithms (SAEAs), especially hierarchical SAEAs, have been shown to be effective in solving computationally expensive optimization problems. The success of hierarchical SAEAs mainly profits from the potential benefit of their global surrogate models known as “blessing of uncertainty” and the high accuracy of local models. However, their performance leaves room for improvement on high-dimensional problems since now it is still challenging to build accurate enough local models due to the huge solution space. Directing against this issue, this study proposes a new hierarchical SAEA by training local surrogate models with the help of the random projection technique. Instead of executing training in the original high-dimensional solution space, the new algorithm first randomly projects training samples onto a set of low-dimensional subspaces, then trains a surrogate model in each subspace, and finally achieves evaluations of candidate solutions by averaging the resulting models. Experimental results on seven benchmark functions of 100 and 200 dimensions demonstrate that random projection can significantly improve the accuracy of local surrogate models and the new proposed hierarchical SAEA possesses an obvious edge over state-of-the-art SAEAs.

scholarly journals Learning physics-based models from data: perspectives from inverse problems and model reduction

Acta Numerica ◽  
2021 ◽  
Vol 30 ◽  
pp. 445-554
Author(s):  
Omar Ghattas ◽  
Karen Willcox
Keyword(s):  
Inverse Problems ◽  
Model Reduction ◽  
Large Scale ◽  
Dimensional Subspace ◽  
Dimensional Solution ◽  
Scale Models ◽  
Recent Developments ◽  
Engineered Systems ◽  
Low Dimensional ◽  
Solution Manifold

This article addresses the inference of physics models from data, from the perspectives of inverse problems and model reduction. These fields develop formulations that integrate data into physics-based models while exploiting the fact that many mathematical models of natural and engineered systems exhibit an intrinsically low-dimensional solution manifold. In inverse problems, we seek to infer uncertain components of the inputs from observations of the outputs, while in model reduction we seek low-dimensional models that explicitly capture the salient features of the input–output map through approximation in a low-dimensional subspace. In both cases, the result is a predictive model that reflects data-driven learning yet deeply embeds the underlying physics, and thus can be used for design, control and decision-making, often with quantified uncertainties. We highlight recent developments in scalable and efficient algorithms for inverse problems and model reduction governed by large-scale models in the form of partial differential equations. Several illustrative applications to large-scale complex problems across different domains of science and engineering are provided.

scholarly journals Geometries arising from trilinear forms on low-dimensional vector spaces

2019 ◽  
Vol 19 (2) ◽  
pp. 269-290 ◽  
Author(s):  
Ilaria Cardinali ◽  
Luca Giuzzi
Keyword(s):  
Vector Space ◽  
Vector Spaces ◽  
Dimensional Vector ◽  
Low Dimension ◽  
Low Dimensional ◽  
Point Line

Abstract Let 𝓖k(V) be the k-Grassmannian of a vector space V with dim V = n. Given a hyperplane H of 𝓖k(V), we define in [3] a point-line subgeometry of PG(V) called the geometry of poles of H. In the present paper, exploiting the classification of alternating trilinear forms in low dimension, we characterize the possible geometries of poles arising for k = 3 and n ≤ 7 and propose some new constructions. We also extend a result of [6] regarding the existence of line spreads of PG(5, 𝕂) arising from hyperplanes of 𝓖3(V).

LEARNING BY CRITERION OPTIMIZATION ON A UNITARY UNIMODULAR MATRIX GROUP

2008 ◽  
Vol 18 (02) ◽  
pp. 87-103 ◽  
Author(s):  
SIMONE FIORI
Keyword(s):  
Optimization Problems ◽  
Learning Theories ◽  
Neural Systems ◽  
Matrix Group ◽  
Unimodular Group ◽  
Unimodular Matrix ◽  
Low Dimension ◽  
Low Dimensional ◽  
Complex Valued ◽  
Special Case

The present manuscript aims at illustrating fundamental challenges and solutions arising in the design of learning theories by optimization on manifolds in the context of complex-valued neural systems. The special case of a unitary unimodular group of matrices is dealt with. The unitary unimodular group under analysis is a low dimensional and easy-to-handle matrix group. Notwithstanding, it exhibits a rich geometrical structure and gives rise to interesting speculations about methods to solve optimization problems on manifolds. Also, its low dimension allows us to treat most of the quantities involved in computation in closed form as well as to render them in graphical format. Some numerical experiments are presented and discussed within the paper, which deal with complex-valued independent component analysis.

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