Speed Up or Slow Down? Toward a Domestic Dependency Network Model

2020 ◽  
Vol 2020 (1) ◽  
pp. 20005
Author(s):  
Heechun Kim ◽  
Jie Wu ◽  
Douglas A Schuler ◽  
Robert E. Hoskisson ◽  
Sung Hun Chung
Keyword(s):  
Network Model ◽  
Speed Up ◽  
Dependency Network

An Improved Functional Dependency Network Model for SoS Operability Analysis

2014 ◽  
Vol 602-605 ◽  
pp. 3355-3358
Author(s):  
Wang Xun Zhang ◽  
Yue Wang ◽  
Qun Li
Keyword(s):  
Network Analysis ◽  
Network Model ◽  
System Of Systems ◽  
Functional Dependency ◽  
Dependency Network ◽  
Comparative Results ◽  
Complex Dependency ◽  
Exact Definition

There are often complex dependency relationships between systems in a system of systems (SoS), which present great challenges for SoS operability analysis. Functional Dependency Network Analysis offers the capability to evaluate the effect of both topology and of the possible degraded functioning of one or more systems on the operability of each node in the SoS. But there are some insufficiencies in the method, so here we give exact definition to self-effectiveness, which includes absolute self-effectiveness and relative self-effectiveness. Then according to the new definitions we show the detail calculation equations. Finally we we take the application of Guariniello and DeLaurentis’s five-node aerospace network for example, and compare the results with theirs. Comparative results show that their results are larger than the exact outcome, and our version give the authentic outcomes.

scholarly journals Remote Sensing Image Target Detection: Improvement of the YOLOv3 Model with Auxiliary Networks

2021 ◽  
Vol 13 (19) ◽  
pp. 3908
Author(s):  
Zhenfang Qu ◽  
Fuzhen Zhu ◽  
Chengxiao Qi
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Network Model ◽  
Feature Fusion ◽  
Remote Sensing Image ◽  
Frame Rate ◽  
Main Research ◽  
Speed Up ◽  
Main Components ◽  
Detection Speed

Remote sensing image target detection is widely used for both civil and military purposes. However, two factors need to be considered for remote sensing image target detection: real-time and accuracy for detecting targets that occupy few pixels. Considering the two above issues, the main research objective of this paper is to improve the performance of the YOLO algorithm in remote sensing image target detection. The reason is that the YOLO models can guarantee both detection speed and accuracy. More specifically, the YOLOv3 model with an auxiliary network is further improved in this paper. Our model improvement consists of four main components. Firstly, an image blocking module is used to feed fixed size images to the YOLOv3 network; secondly, to speed up the training of YOLOv3, DIoU is used, which can speed up the convergence and increase the training speed; thirdly, the Convolutional Block Attention Module (CBAM) is used to connect the auxiliary network to the backbone network, making it easier for the network to notice specific features so that some key information is not easily lost during the training of the network; and finally, the adaptive feature fusion (ASFF) method is applied to our network model with the aim of improving the detection speed by reducing the inference overhead. The experiments on the DOTA dataset were conducted to validate the effectiveness of our model on the DOTA dataset. Our model can achieve satisfactory detection performance on remote sensing images, and our model performs significantly better than the unimproved YOLOv3 model with an auxiliary network. The experimental results show that the mAP of the optimised network model is 5.36% higher than that of the original YOLOv3 model with the auxiliary network, and the detection frame rate was also increased by 3.07 FPS.

Topology Learning Solved by Extended Objects: A Neural Network Model

1994 ◽  
Vol 6 (3) ◽  
pp. 441-458 ◽  
Author(s):  
Csaba Szepesvári ◽  
László Balázs ◽  
András Lőrincz
Keyword(s):  
Neural Network ◽  
Topological Space ◽  
Network Model ◽  
Neural Network Model ◽  
Hebbian Learning ◽  
Receptive Fields ◽  
Self Organized ◽  
Topographical Map ◽  
Speed Up ◽  
Local Filters

It is shown that local, extended objects of a metrical topological space shape the receptive fields of competitive neurons to local filters. Self-organized topology learning is then solved with the help of Hebbian learning together with extended objects that provide unique information about neighborhood relations. A topographical map is deduced and is used to speed up further adaptation in a changing environment with the help of Kohonen-type learning that teaches the neighbors of winning neurons as well.

Research on a New Convolutional Neural Network Model Combined With Random Edges Adding

2021 ◽  
Vol 12 (1) ◽  
pp. 67-76
Author(s):  
Jin Zhang ◽  
Sen Tian ◽  
XuanYu Shu ◽  
Sheng Chen ◽  
LingYu Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Small World ◽  
Cross Layer ◽  
Data Set ◽  
Speed Up ◽  
Model Recognition ◽  
And Training

It is always a hot and difficult point to improve the accuracy of the convolutional neural network model and speed up its convergence. Based on the idea of the small world network, a random edge adding algorithm is proposed to improve the performance of the convolutional neural network model. This algorithm takes the convolutional neural network model as a benchmark and randomizes backwards and cross layer connections with probability p to form a new convolutional neural network model. The proposed idea can optimize the cross-layer connectivity by changing the topological structure of the convolutional neural network and provide a new idea for the improvement of the model. The simulation results based on Fashion-MINST and cifar10 data set show that the model recognition accuracy and training convergence speed are greatly improved by random edge adding reconstructed models with a probability of p = 0.1.

Scanning x-ray fluorescence microscopy and principal component analysis

1992 ◽  
Vol 50 (2) ◽  
pp. 1752-1753
Author(s):  
Brian Cross
Keyword(s):  
Principal Component Analysis ◽  
Fluorescence Microscopy ◽  
Spatial Resolution ◽  
High Speed ◽  
Image Acquisition ◽  
Principal Component ◽  
Fluorescence Microscope ◽  
Acquisition Rate ◽  
X Ray ◽  
Speed Up

A relatively new entry, in the field of microscopy, is the Scanning X-Ray Fluorescence Microscope (SXRFM). Using this type of instrument (e.g. Kevex Omicron X-ray Microprobe), one can obtain multiple elemental x-ray images, from the analysis of materials which show heterogeneity. The SXRFM obtains images by collimating an x-ray beam (e.g. 100 μm diameter), and then scanning the sample with a high-speed x-y stage. To speed up the image acquisition, data is acquired "on-the-fly" by slew-scanning the stage along the x-axis, like a TV or SEM scan. To reduce the overhead from "fly-back," the images can be acquired by bi-directional scanning of the x-axis. This results in very little overhead with the re-positioning of the sample stage. The image acquisition rate is dominated by the x-ray acquisition rate. Therefore, the total x-ray image acquisition rate, using the SXRFM, is very comparable to an SEM. Although the x-ray spatial resolution of the SXRFM is worse than an SEM (say 100 vs. 2 μm), there are several other advantages.

Direct measurement of electron-diffraction-pattern intensities using an energy loss spectrometer

1989 ◽  
Vol 47 ◽  
pp. 668-669
Author(s):  
A. G. Jackson ◽  
M. Rowe
Keyword(s):  
Thermal Effects ◽  
Dynamic Range ◽  
Scattering Amplitude ◽  
Dynamical Theory ◽  
Site Symmetry ◽  
Proof Of Concept ◽  
Parallel Acquisition ◽  
Kinematic Approximation ◽  
Speed Up ◽  
Structure Calculations

Diffraction intensities from intermetallic compounds are, in the kinematic approximation, proportional to the scattering amplitude from the element doing the scattering. More detailed calculations have shown that site symmetry and occupation by various atom species also affects the intensity in a diffracted beam. [1] Hence, by measuring the intensities of beams, or their ratios, the occupancy can be estimated. Measurement of the intensity values also allows structure calculations to be made to determine the spatial distribution of the potentials doing the scattering. Thermal effects are also present as a background contribution. Inelastic effects such as loss or absorption/excitation complicate the intensity behavior, and dynamical theory is required to estimate the intensity value.The dynamic range of currents in diffracted beams can be 104or 105:1. Hence, detection of such information requires a means for collecting the intensity over a signal-to-noise range beyond that obtainable with a single film plate, which has a S/N of about 103:1. Although such a collection system is not available currently, a simple system consisting of instrumentation on an existing STEM can be used as a proof of concept which has a S/N of about 255:1, limited by the 8 bit pixel attributes used in the electronics. Use of 24 bit pixel attributes would easily allowthe desired noise range to be attained in the processing instrumentation. The S/N of the scintillator used by the photoelectron sensor is about 106 to 1, well beyond the S/N goal. The trade-off that must be made is the time for acquiring the signal, since the pattern can be obtained in seconds using film plates, compared to 10 to 20 minutes for a pattern to be acquired using the digital scan. Parallel acquisition would, of course, speed up this process immensely.

A Network Model for Optimizing two Echelon Multi-Item Repair Level Decisions

1991 ◽  
Vol 8 (1) ◽  
pp. 77-90
Author(s):  
W. Steven Demmy ◽  
Lawrence Briskin
Keyword(s):  
Network Model
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