Lossless Compression of 3D Grid-Based Model Based on Octree

Method Comparison of Model Based Tomography and Grid Based Tomography to refine interval velocity

INDONESIAN JOURNAL OF APPLIED PHYSICS ◽

10.13057/ijap.v4i01.1170 ◽

2016 ◽

Vol 4 (01) ◽

pp. 63

Author(s):

Yuninggar Dwi Nugroho ◽

Sudarmaji S

Keyword(s):

Method Comparison ◽

Velocity Model ◽

Seismic Section ◽

Initial Interval ◽

Depth Migration ◽

Interval Velocity ◽

Model Based ◽

Time Migration ◽

Tomography Method ◽

Grid Based

The input data for pre stack time migration and pre stack depth migration is velocity model. The exact velocity model can provide maximum result in seismic section. The best seismic section can minimize possibility of errors during interpretation. Model based and grid based tomography are used to refine the interval velocity model. The interval velocity will be used as input in the pre stack depth migration. Initial interval velocity is obtained from RMS velocity using Dix formula. This velocity will be refined by global depth tomography method. The global depth tomography method is divided into model based and grid based tomography. Velocity analysis is performed along the horizon (depth model). Residual depth move out is obtained from picking velocity. It is used as input in tomography method. The flat gather is obtained at tenth iteration. The interval velocity that is obtained from tenth iteration has the small errors. Tomography method can provide maximum result on velocity refinement. That is shown by the result that the pre stack depth migration is much better than using initial interval velocity. The pull up effect can be corrected by tomography method.

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TB-NET: A Two-Branch Neural Network for Direction of Arrival Estimation under Model Imperfections

Electronics ◽

10.3390/electronics11020220 ◽

2022 ◽

Vol 11 (2) ◽

pp. 220

Author(s):

Liyu Lin ◽

Chaoran She ◽

Yun Chen ◽

Ziyu Guo ◽

Xiaoyang Zeng

Keyword(s):

Neural Network ◽

Deep Learning ◽

Direction Of Arrival ◽

Doa Estimation ◽

Cross Entropy ◽

Data Driven ◽

Estimation Accuracy ◽

Model Based ◽

Classification And Regression ◽

Grid Based

For direction of arrival (DoA) estimation, the data-driven deep-learning method has an advantage over the model-based methods since it is more robust against model imperfections. Conventionally, networks are based singly on regression or classification and may lead to unstable training and limited resolution. Alternatively, this paper proposes a two-branch neural network (TB-Net) that combines classification and regression in parallel. The grid-based classification branch is optimized by binary cross-entropy (BCE) loss and provides a mask that indicates the existence of the DoAs at predefined grids. The regression branch refines the DoA estimates by predicting the deviations from the grids. At the output layer, the outputs of the two branches are combined to obtain final DoA estimates. To achieve a lightweight model, only convolutional layers are used in the proposed TB-Net. The simulation results demonstrated that compared with the model-based and existing deep-learning methods, the proposed method can achieve higher DoA estimation accuracy in the presence of model imperfections and only has a size of 1.8 MB.

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Structural Vulnerability Evaluation Model Based on Improved Electrical Betweenness

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.291-294.2169 ◽

2013 ◽

Vol 291-294 ◽

pp. 2169-2172

Author(s):

Liang HUA Zheng ◽

Tao Lin ◽

Chun Gui Xie ◽

Xing Yuan Fan ◽

Lia Xia Cai ◽

...

Keyword(s):

Power System ◽

Weak Link ◽

Evaluation Model ◽

Cascading Failures ◽

Strengthening Effect ◽

Structural Vulnerability ◽

Model Based ◽

Vulnerability Evaluation ◽

Accelerated Failure ◽

Grid Based

Several blackouts happened in a worldwide area, it exposures the vulnerability of continual increment of power system scale. The weak link has a strengthening effect on the spread of the accelerated failure, and leads to the occurrence of cascading failures, it's a serious threat to the security and stability of the grid. Based on the existing researches, the article proposes a new structure vulnerability evaluation model based on improved electrical betweenness, and verifies the rationality and effectiveness of this model by the analysis of the IEEE-39 node system.

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Representation, abstraction, and simple-minded sophisticates

Behavioral and Brain Sciences ◽

10.1017/s0140525x19002942 ◽

2020 ◽

Vol 43 ◽

Author(s):

Peter Dayan

Keyword(s):

Decision Theory ◽

Predictive Coding ◽

Bayesian Decision Theory ◽

Bayesian Decision ◽

Model Based ◽

Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

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Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

Microscopy and Microanalysis ◽

10.1017/s1431927600028968 ◽

2001 ◽

Vol 7 (S2) ◽

pp. 578-579

Author(s):

David W. Knowles ◽

Sophie A. Lelièvre ◽

Carlos Ortiz de Solόrzano ◽

Stephen J. Lockett ◽

Mina J. Bissell ◽

...

Keyword(s):

Image Analysis ◽

Mammary Epithelial Cells ◽

Nuclear Organization ◽

Critical Role ◽

Quantitative Model ◽

Mammary Epithelial ◽

Cell Phenotype ◽

Proliferating Cells ◽

Mitotic Apparatus ◽

Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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