A refocusing iterative optimization method based on the quad-beam mode for accurate estimation of the azimuth velocity of slow-moving targets using SAR

The effects of a dynamic auditory preview display were examined in a visual target aiming task. A moving sound stimulus aligned with a visual target was presented over various distances beyond the bounds of a visual display. Results indicated reduced error magnitudes in aimed responses to visual targets with increasing auditory preview distance. In subsequent testing, the effects of position and velocity misalignments between the sound source and the visual target were assessed. In position misalignment conditions where the sound source lagged behind the visual target, higher error magnitudes were observed. However, when the auditory display preceded the visual target, performance improved. In velocity mismatch conditions, responses toward fast moving targets improved when a relatively faster sound source was previewed but were disrupted when a slower sound source was previewed. On the contrary, responses toward slow moving targets improved when a relatively slower sound source was previewed and were disrupted when a faster sound source was previewed.

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The Moving Target Detection under Complicated Background Base on BP Neural Networks

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.706-708.1072 ◽

2013 ◽

Vol 706-708 ◽

pp. 1072-1076

Author(s):

Xiang Dong Feng

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Segmentation ◽

Experimental Verification ◽

Optimization Method ◽

Moving Target ◽

Moving Targets ◽

Detection Ability ◽

The Difference ◽

Static Background

This paper aims at utilizing BP neural network and improved BP neural network in the extraction of moving targets from the static background and the motor background. Across introduces of L_M Optimization method to improve the BP neural network, combination the difference between the consecutive frames or difference from the background for the extraction of moving targets. The paper uses one types of image segmentation improved BP neural network for extraction of moving targets from the static background. We will obtain the same moved targets with the extraction of moving targets from the static background and the motor background. After experimental verification, this method on different objects in the context of the same detection ability is better.

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Optimal Modelling of Steel Multi-Span Beams Using the Gradient-Iterative Method

Civil and Environmental Engineering Reports ◽

10.2478/ceer-2019-0048 ◽

2019 ◽

Vol 29 (4) ◽

pp. 112-127

Author(s):

Leszek Mikulski

Keyword(s):

Iterative Method ◽

Optimal Solution ◽

Optimization Method ◽

Iterative Optimization ◽

Basic Assumptions ◽

The Web ◽

Load Conditions

Abstract The article describes the gradient-iterative optimization method and outlines the method’s basic assumptions and illustrates its general use. The method’s implementation was illustrated based on a steel I-beam. The described calculation example concerns the optimization of the height of the web of a multi-span beam. The method enables finding an optimal solution with the use of simple and commonly available software. To illustrate the effectiveness of the optimization method, multiple calculations were performed for beams with various spans and various load conditions.

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