Predicting Hairiness for Ring and Rotor Spun Yarns and Analyzing the Impact of Fiber Properties

1997 ◽  
Vol 67 (9) ◽  
pp. 694-698 ◽  
Author(s):  
Reiyao Zhu ◽  
M. Dean Ethridge
Keyword(s):  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Fiber Properties ◽  
Fiber Property ◽  
Spun Yarns ◽  
Neural Network Algorithm ◽  
Yarn Hairiness ◽  
Rotor Yarn ◽  
Optimum Model ◽  
The Impact

Models for predicting ring or rotor yarn hairiness are built using a back-propagation neural network algorithm. These models are based on fiber property input measured by three different systems, hvi, afis, and fmt. We compare the prediction results from the different models, which reveal that yarn hairiness measurements from hvi data are superior to other models. The optimum model is based on the availability of all three measurement systems. We also study the impact of each fiber property on yarn hairiness. The dominant effect is fiber length. Each of the remaining properties has a different degree of impact on ring or rotor yarn hairiness.

Classroom Lighting Energy-Saving Control System Based on Machine Vision Technology

2018 ◽  
pp. 143-149 ◽  
Author(s):  
Ruijie CHENG
Keyword(s):  
Control System ◽  
Machine Vision ◽  
Energy Saving ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lighting Control ◽  
Lighting Energy ◽  
Neural Network Algorithm ◽  
Energy Saving Control ◽  
Model Algorithm

In order to further improve the energy efficiency of classroom lighting, a classroom lighting energy saving control system based on machine vision technology is proposed. Firstly, according to the characteristics of machine vision design technology, a quantum image storage model algorithm is proposed, and the Back Propagation neural network algorithm is used to analyze the technology, and a multi­feedback model for energy­saving control of classroom lighting is constructed. Finally, the algorithm and lighting model are simulated. The test results show that the design of this paper can achieve the optimization of the classroom lighting control system, different number of signals can comprehensively control the light and dark degree of the classroom lights, reduce the waste of resources of classroom lighting, and achieve the purpose of energy saving and emission reduction. Technology is worth further popularizing in practice.

A STUDY OF DIGITAL WATERMARKING RECOGNITION USING ORTHOGONAL CODE SEQUENCES WITH A BACK-PROPAGATION NEURAL NETWORK

2013 ◽  
Vol 37 (3) ◽  
pp. 459-465
Author(s):  
Chih-Ta Yen ◽  
Ing-Jr Ding ◽  
Zong-Wei Lai
Keyword(s):  
Neural Network ◽  
Digital Watermarking ◽  
Median Filter ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Noise Interference ◽  
Low Pass ◽  
Neural Network Algorithm

Digital watermarking is an encryption technology commonly used to protect intellectual property and copyright. In this study, we restored watermarks that had already been affected by noise interference, used the Walsh–Hadamard codes as the watermark identification codes, and applied salt-and-pepper noise and Gaussian noise to destroy watermarks. First method, we used a low-pass filter and median filter to remove noise interferences. The second one, we used a back-propagation neural network algorithm to suppress noises. We removed nearly all noise and recovered the originally embedded watermarks of Walsh–Hadmard codes.

Research and Realization of Hardware Back-Propagation Neural Network Based on FPGA

2013 ◽  
Vol 333-335 ◽  
pp. 2469-2474
Author(s):  
Fei Guo ◽  
Xiao Luo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Variable Structure ◽  
Maximum Error ◽  
Back Propagation Neural Network ◽  
Neural Network Modeling ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Timing Simulation

In order to meet the requirements of real-time and embedded of industrial field, a reconfigurable Back-Propagation neural network based on FPGA has been implemented on Xilinx's Spartan-3E (XC3S250E) chip which has 250000 gate. First the optimal network structure and weights were gotten by a variable structure of BP neural network algorithm. Then an improved hardware approaching method of excitation function was put forward, and the maximum error was 1.58% by simulation and comparative analysis on the error. Finally hardware co-imitation and timing simulation was token based on a reasonable choice of data accuracy, and then the hardware BP neural network algorithm was been downloaded and implemented on FPGA. This method has better accuracy and speed, it is an effective method of BP neural network modeling based on hardware, and lays the foundation for the hardware realization of other neural network and embedded image processing.

Automatic Detection and Classification of Diabetes Using Artificial Intelligence

2021 ◽  
Vol 8 (1) ◽  
pp. 01-05
Author(s):  
V. Nithyalakshmi ◽  
Dr.R. Sivakumar ◽  
Dr.A. Sivaramakrishnan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Diabetes Diagnosis ◽  
Artificial Intelligence Technique ◽  
Classification Rate ◽  
Neural Network Algorithm ◽  
Health Complications

Diabetes is characterized as a chronic disease that may cause many health complications. Artificial intelligence techniques are adopted diagnose diabetes more accurately. This paper presents an artificial intelligence technique for diabetes diagnosis. Efficacy of the technique is evaluated using diabetes database. Experimental results show that the back propagation neural network algorithm yields the highest classification rate compared to k-nearest neighbourhood classifier. Additionally, the back propagation neural network provides error with the highest area under curve of 90 %.

scholarly journals A Data Classification Algorithm of Internet of Things Based on Neural Network

2017 ◽  
Vol 13 (09) ◽  
pp. 28 ◽  
Author(s):  
Zhenjun Li
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Back Propagation ◽  
Data Classification ◽  
Back Propagation Neural Network ◽  
Multidimensional Data ◽  
Back Propagation Algorithm ◽  
Huge Data ◽  
Neural Network Algorithm ◽  
Working Principle

<p style="margin: 1em 0px;"><span lang="EN-US"><span style="font-family: 宋体; font-size: medium;">To alleviate the pressure of data size, data transmission and data processing in the huge data dimension of the Internet of things., data classification is realized based on back propagation (BP) neural network algorithm. The working principle is deduced in detail. For the shortcomings of slow convergence and easy to fall into the local minimum, the combination of variable learning and momentum factors is used to improve the traditional back propagation algorithm. The results show that the optimized algorithm improves the convergence speed of the network to a certain extent. Therefore, it is concluded that the back propagation neural network has higher classification success rate when classifying multidimensional data in Internet of things.</span></span></p>

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