Enhanced moth flame optimization based Supervision Kernel Entropy Component Analysis for high‐speed encrypted transmission model

2021 ◽  
Author(s):  
Babu M ◽  
G A Sathish Kumar
Keyword(s):  
High Speed ◽  
Component Analysis ◽  
Transmission Model ◽  
Entropy Component

scholarly journals A High Performance Target Tracing Transmission Model Oriented to Lifecycle Maximization

2016 ◽  
Vol 2016 ◽  
pp. 1-11
Author(s):  
Zhong-Nan Zhao ◽  
Pei-Li Qiao ◽  
Jian Wang
Keyword(s):  
High Speed ◽  
High Performance ◽  
Network Performance ◽  
Transmission Rate ◽  
Relay Node ◽  
High Rate ◽  
Transmission Model ◽  
Limited Resources ◽  
Speed Sensor ◽  
Rate Transmission

For the high speed sensor networks applications such as Internet of Things, multimedia transmission, the realization of high-rate transmission under limited resources has become a problem to be solved. A high speed transmission and energy optimization model oriented to lifecycle maximization is proposed in this paper. Based on information-directed mechanism, the energy threshold set and the relay node distance selection will be done in the process of target tracing, as a result, retaining a balance between transmission rate and energy consumption. Meanwhile, multiagent coevolution is adopted to achieve the maximum of network lifecycle. Comparing with the relevant methods, indexes for network such as hops, throughput, and number of active nodes, standard deviation of remaining energy, and the network lifecycle are considered, and the simulated experiments show that the proposed method will promote the transmission rate effectively, prolong the network lifecycle, and improve network performance as a whole.

Class-information–incorporated kernel entropy component analysis with application to bearing fault diagnosis

2020 ◽  
pp. 107754632093203
Author(s):  
Hongdi Zhou ◽  
Fei Zhong ◽  
Tielin Shi ◽  
Wuxing Lai ◽  
Jian Duan ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Hebbian Learning ◽  
Component Analysis ◽  
Operating Conditions ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Class Information ◽  
Entropy Component

Rolling bearings are present ubiquitously in industrial fields; timely fault diagnosis is of crucial significance in avoiding serious catastrophe. The extraction of ideal fault feature is a challenging task in vibration-based bearing fault detection. In this article, a novel method called class-information–incorporated kernel entropy component analysis is proposed for bearing fault diagnosis. The method is developed based on the Hebbian learning theory of neural network and the kernel entropy component analysis which attempts to compress the most Renyi quadratic entropy of input dataset after dimension reduction and presents a good performance for nonlinear feature extraction. Class-information–incorporated kernel entropy component analysis can take advantage of the label information of training samples to guide dimensional reduction and still follow the same simple mathematical formulation as kernel entropy component analysis. The high-dimensional feature dataset including time-domain, frequency-domain, and time–frequency domain characteristic parameters is first derived from the vibration signals. Then, the intrinsic geometric features are extracted by class-information–incorporated kernel entropy component analysis, and a classification strategy based on fusion information is applied to recognize different operating conditions of bearings. The experimental results demonstrated the feasibility and effectiveness of the proposed method.

Double Helical Synchronous Belt Transmission Design

2014 ◽  
Vol 800-801 ◽  
pp. 672-677
Author(s):  
Jian Hua Guo ◽  
Hong Yuan Jiang ◽  
Yi Zhen Wu ◽  
Wen Ya Chu ◽  
Qing Xin Meng
Keyword(s):  
High Speed ◽  
Contact Point ◽  
Three Dimensional ◽  
Transmission Error ◽  
Transmission Model ◽  
Adjacent Area ◽  
Speed Test ◽  
Finite Element Software ◽  
Timing Belt ◽  
Belt Transmission

The meshing impact noise caused by the gradually engagement between double helical synchronous belt and the pulley was reduced due to its spiral angle effect. Therefore, double helical synchronous belt transmission receives much concern with its excellent characteristics of de-noising, low transmission error and high carrying capacity. The profiles of synchronous belt and belt pulley were studied based on conjugate-curvature high degree contact meshing theory under the circumstance that the pitch of belt and belt pulley are identical. The higher contact strength of the belt teeth and a smaller clearance in the contact point adjacent area were ensured with Hertz contact theory as the synchronous belt is in contact with pulley. And then a conjugated arc tooth profile with two-step contact and three-step adjacent gap infinitesimal was proposed based on the simple easy to processing method, which was adopted as main parameters for double synchronous belt and pulley’s normal teeth profile. The three-dimensional transmission model was built and the static nonlinear contact analysis was done with finite element software ANSYS. Finally, the noise experiment was conducted on the high speed test bench to compare the noise reduction effect between double helical synchronous belt and straight tooth timing belt with the identical end face profile. The simulation and experiment result show that the double helical synchronous belt transmission can reduce noise level by 11dB approximately compared with straight tooth timing belt transmission.

RECOGNITION OF SMILING FACES USING NEURAL NETWORKS AND SPCA

2004 ◽  
Vol 04 (02) ◽  
pp. 153-163 ◽  
Author(s):  
MIYOKO NAKANO ◽  
FUMIKO YASUKATA ◽  
MINORU FUKUMI
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
High Speed ◽  
Principal Component ◽  
Component Analysis ◽  
Research Field ◽  
A Value ◽  
Large Matrix ◽  
The Difference ◽  
The Cost

Research on "man-machine interface" has increased in many fields of engineering and its application to facial expressions recognition is expected. The eigenface method by using the principal component analysis (PCA) is popular in this research field. However, it is not easy to compute eigenvectors with a large matrix if the cost of calculation when applying it for time-varying processing is taken into consideration. In this paper, in order to achieve high-speed PCA, the simple principal component analysis (SPCA) is applied to compress the dimensionality of portions that constitute a face. A value of cos θ is calculated using an eigenvector by SPCA as well as a gray-scale image vector of each picture pattern. By using neural networks (NNs), the difference in the value of cos θ between the true and the false (plastic) smiles is clarified and the true smile is discriminated. Finally, in order to show the effectiveness of the proposed face classification method for true or false smiles, computer simulations are done with real images. Furthermore, an experiment using the self-organisation map (SOM) is also conducted as a comparison.

scholarly journals Kernel Entropy Component Analysis for Remote Sensing Image Clustering

2012 ◽  
Vol 9 (2) ◽  
pp. 312-316 ◽  
Author(s):  
Luis Gomez-Chova ◽  
Robert Jenssen ◽  
Gustavo Camps-Valls
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Component Analysis ◽  
Image Clustering ◽  
Entropy Component
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