A new method of target change detection based on network in network structure

2021 ◽  
Author(s):  
Junfeng Xu ◽  
Na Yang ◽  
Dong Lin ◽  
Shilun Kang ◽  
Yilan Lou ◽  
...  
Keyword(s):  
Change Detection ◽  
Network Structure ◽  
New Method

scholarly journals Conjugated Polymer Films Having a Uniaxial Molecular Orientation and Network Structure Prepared by Electrochemical Polymerization in Liquid Crystals

Polymers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2425
Author(s):  
Jiuchao Dong ◽  
Shigeki Nimori ◽  
Hiromasa Goto
Keyword(s):  
Liquid Crystals ◽  
Network Structure ◽  
Polymer Films ◽  
Conjugated Polymer ◽  
Main Chain ◽  
Electrochemical Polymerization ◽  
Electronic Devices ◽  
New Method ◽  
Magnetic Orientation ◽  
Organic Electronic Devices

A new method for fabricating conjugated polymer films was developed using electrochemical polymerization in liquid crystals and magnetic orientation. A uniaxial main chain orientation and a crosslinked network structure were achieved with this method. By employing eight types of monomers, the influence of the crosslinking for the film was investigated. The crosslinking was found to improve the solvent resistance of the conjugated polymer films. This new method is expected to be useful in various applications, such as high-powered organic electronic devices with durability.

scholarly journals Ship Target Detection Based on Improved YOLO Network

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Hong Huang ◽  
Dechao Sun ◽  
Renfang Wang ◽  
Chun Zhu ◽  
Bangquan Liu
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Network Structure ◽  
Small Volume ◽  
Recall Rate ◽  
New Method ◽  
Convolution Operation ◽  
Time Performance ◽  
Guided Filtering ◽  
Bounding Boxes

Ship target detection is an important guarantee for the safe passage of ships on the river. However, the ship image in the river is difficult to recognize due to the factors such as clouds, buildings on the bank, and small volume. In order to improve the accuracy of ship target detection and the robustness of the system, we improve YOLOv3 network and present a new method, called Ship-YOLOv3. Firstly, we preprocess the inputting image through guided filtering and gray enhancement. Secondly, we use k-means++ clustering on the dimensions of bounding boxes to get good priors for our model. Then, we change the YOLOv3 network structure by reducing part of convolution operation and adding the jump join mechanism to decrease feature redundancy. Finally, we load the weight of PASCAL VOC dataset into the model and train it on the ship dataset. The experiment shows that the proposed method can accelerate the convergence speed of the network, compared with the existing YOLO algorithm. On the premise of ensuring real-time performance, the precision of ship identification is improved by 12.5%, and the recall rate is increased by 11.5%.

Attentional Enhancement Opposite a Peripheral Flash Revealed Using Change Blindness

2003 ◽  
Vol 14 (2) ◽  
pp. 91-99 ◽  
Author(s):  
P.U. Tse ◽  
D.L. Sheinberg ◽  
N.K. Logothetis
Keyword(s):  
Change Detection ◽  
Spatial Attention ◽  
Hot Spot ◽  
Change Blindness ◽  
Visual Fixation ◽  
Visual Area ◽  
New Method ◽  
Full Field ◽  
Task Irrelevant

We describe a new method for mapping spatial attention that reveals a pooling of attention in the hemifield opposite a peripheral flash. Our method exploits the fact that a brief full-field blank can interfere with the detection of changes in a scene that occur during the blank. Attending to the location of a change, however, can overcome this change blindness, so that changes are detected. The likelihood of detecting a new element in a scene therefore provides a measure of the occurrence of attention at that element's location. Using this measure, we mapped how attention changes in response to a task-irrelevant peripheral cue. Under conditions of visual fixation, change detection was above chance across the entire visual area tested. In addition, a “hot spot” of attention (corresponding to near-perfect change detection) elongated along the cue-fixation axis, such that performance improved not only at the cued location but also in the opposite hemifield.

scholarly journals A new method for abrupt change detection in dynamic structures

2008 ◽  
Vol 15 (4) ◽  
pp. 601-606 ◽  
Author(s):  
W. P. He ◽  
G. L. Feng ◽  
Q. Wu ◽  
S. Q. Wan ◽  
J. F. Chou
Keyword(s):  
Change Detection ◽  
Detrended Fluctuation Analysis ◽  
Abrupt Change ◽  
New Method ◽  
Fluctuation Analysis ◽  
Dynamic Structures ◽  
Detrended Fluctuation ◽  
Abrupt Change Detection

Abstract. Based on Detrended Fluctuation Analysis (DFA), we propose a new method – Moving Detrended Fluctuation Analysis (MDFA) – to detect abrupt change in dynamic structures. Application of this technique shows that this method may be of use in detecting time-instants of abrupt change in dynamic structures and we even find that the MDFA results almost do not depend on length of subseries, and are less affected by noise.

ENTRAINMENT COMPETITION IN COMPLEX NETWORKS

2010 ◽  
Vol 20 (03) ◽  
pp. 827-833
Author(s):  
I. LEYVA ◽  
I. SENDIÑA-NADAL ◽  
J. A. ALMENDRAL ◽  
J. M. BULDÚ ◽  
D. LI ◽  
...  
Keyword(s):  
Complex Networks ◽  
Network Structure ◽  
New Method ◽  
Simultaneous Presence ◽  
Modular Network ◽  
Overlapping Communities ◽  
Frequency Changes

The response of a random and modular network to the simultaneous presence of two frequencies is considered. The competition for controlling the dynamics of the network results in different behaviors, such as frequency changes or permanent synchronization frustration, which can be directly related to the network structure. From these observations, we propose a new method for detecting overlapping communities in structured networks.

GENERATING STRUCTURE SPECIFIC NETWORKS

2010 ◽  
Vol 13 (02) ◽  
pp. 239-250 ◽  
Author(s):  
N. HȦKANSSON ◽  
A. JONSSON ◽  
J. LENNARTSSON ◽  
T. LINDSTRÖM ◽  
U. WENNERGREN
Keyword(s):  
Distribution Function ◽  
Probability Distribution ◽  
Network Structure ◽  
Spectral Methods ◽  
Probability Distribution Function ◽  
New Method ◽  
Network Construction ◽  
Link Density ◽  
Spectral Networks ◽  
Fragmentation Degree

Theoretical exploration of network structure significance requires a range of different networks for comparison. Here, we present a new method to construct networks in a spatial setting that uses spectral methods in combination with a probability distribution function. Nearly all previous algorithms for network construction have assumed randomized distribution of links or a distribution dependent on the degree of the nodes. We relax those assumptions. Our algorithm is capable of creating spectral networks along a gradient from random to highly clustered or diverse networks. Number of nodes and link density are specified from start and the structure is tuned by three parameters (γ, σ, κ). The structure is measured by fragmentation, degree assortativity, clustering and group betweenness of the networks. The parameter γ regulates the aggregation in the spatial node pattern and σ and κ regulates the probability of link forming.

A new method for abrupt dynamic change detection of correlated time series

2011 ◽  
Vol 32 (10) ◽  
pp. 1604-1614 ◽  
Author(s):  
Wenping He ◽  
Guolin Feng ◽  
Qiong Wu ◽  
Tao He ◽  
Shiquan Wan ◽  
...  
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Dynamic Change ◽  
New Method
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