scholarly journals Computer image processing and neural network technology for boiler thermal energy diagnosis

2020 ◽  
Vol 24 (5 Part B) ◽  
pp. 3059-3068
Author(s):  
Qinghong Wu
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Thermal Energy ◽  
Processing System ◽  
Heat Energy ◽  
Image Feature ◽  
The Neural Network ◽  
Computer Image Processing ◽  
On Line ◽  
Parameter Values

The paper uses the flame image processing technology to diagnose the furnace flame combustion achieve the measurement of boiler heat energy. The paper obtains the combustion image of the flame image processing system, and extracts the flame image characteristics of the boiler thermal energy diagnosis, constructs the neural network model of the boiler thermal energy diagnosis, and trains and tests the extracted flame image feature parameter values as the input of the neural network. A rough diagnosis of the boiler?s thermal energy is obtained while predicting the state of combustion. According to the research results, a boiler thermal energy diagnosis system was designed and tested on the boiler of 200 MW unit. The experimental results confirmed the applicability of the system, which can realize on-line monitoring of boiler heat energy and evaluate the combustion situation.

scholarly journals Computer image processing and neural network technology for thermal energy diagnosis of boiler plants

2020 ◽  
Vol 24 (5 Part B) ◽  
pp. 3221-3228
Author(s):  
Junzhen Zhang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Network Model ◽  
Thermal Energy ◽  
Neural Network Model ◽  
Computer Image ◽  
Network Technology ◽  
Flame Combustion ◽  
Computer Image Processing ◽  
Neural Network Technology

Objective: The computer image processing and neural network technology are applied to diagnose the thermal energy of boiler plants, i. e., the flame combustion diagnosis, to verify their effectiveness and superiority. Methods: First, the YD-NQ type endoscopic high temperature video acquisition system is used to collect the images of flame combustion. Second, the images are pre-processed by the gray-scale method and the median filtering method. Then the flame combustion parameter features are extracted. The neural network algorithm is improved, and the boiler combustion model based on the improved neural network algorithm is established. Therefore, the combustion decision base is obtained. Finally, the improved neural network model is compared with the traditional neural network model and the 5-4 model to verify its validity. Results: The experiments have found that the improved neural network model is superior to the traditional neural network model. Meanwhile, its accuracy rate and confidence are relatively higher than those of the traditional model. In addition, a single sample also consumes shorter running time, which is 0.0075 seconds. Comparing with the 5-4 model, the improved neural network model has certain advantages, i. e., its accuracy rate and confidence are relatively higher, which are, respectively 91.28% and 96.69%, however, a single sample consumes longer running time than the 5-4 model. Conclusion: The experimental research has found that the application of computer image processing and neural network technology to the thermal energy diagnosis of boiler plants can effectively determine the stability of flame combustion, timely understand the state of flame combustion, and thus diagnose the thermal energy. Therefore, they have values for applications.

scholarly journals Convolutional neural network in the images colorization problem

2019 ◽  
pp. 340-353
Author(s):  
M V Bulygin ◽  
M M Gayanova ◽  
A M Vulfin ◽  
A D Kirillova ◽  
R Ch Gayanov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Processing System ◽  
Black And White ◽  
Convolutional Networks ◽  
Advantages And Disadvantages ◽  
The Neural Network ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Object of the research are modern structures and architectures of neural networks for image processing. Goal of the work is improving the existing image processing algorithms based on the extraction and compression of features using neural networks using the colorization of black and white images as an example. The subject of the work is the algorithms of neural network image processing using heterogeneous convolutional networks in the colorization problem. The analysis of image processing algorithms with the help of neural networks is carried out, the structure of the neural network processing system for image colorization is developed, colorization algorithms are developed and implemented. To analyze the proposed algorithms, a computational experiment was conducted and conclusions were drawn about the advantages and disadvantages of each of the algorithms.

Self-Organizing Neural Network Application to Drill Wear Classification

1994 ◽  
Vol 116 (2) ◽  
pp. 233-238 ◽  
Author(s):  
E. Govekar ◽  
I. Grabec
Keyword(s):  
Neural Network ◽  
Power Spectra ◽  
Processing System ◽  
Drilling Process ◽  
Feed Force ◽  
The Neural Network ◽  
On Line ◽  
Characteristic Features ◽  
Self Organizing

The article describes an application of a simulated neural network to drill wear classification from cutting force signals generated by the drilling process. As the input to the neural network, a multicomponent vector composed of a sensory part and a descriptive part is used. The components of the sensory part represent characteristic features of the cutting momentum and the feed force power spectra, while the descriptive part encodes the corresponding drill wear class. During adaptation, the self-organizing neural network is used to form a set of prototype vectors representing an empirical model of the observed drilling process. The model is used in the analysis mode of the system for an on-line classification of the drill wear from the cutting forces. The performance of the developed information processing system is experimentally demonstrated by classification of drill wear during machining on a steel workpiece.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Estimation of Weld Pool Sizes in GMA Welding Process Using Neural Networks

1993 ◽  
Vol 207 (1) ◽  
pp. 15-26 ◽  
Author(s):  
T G Lim ◽  
H S Cho
Keyword(s):  
Neural Network ◽  
Weld Pool ◽  
Gma Welding ◽  
Quality Monitoring ◽  
Design Parameters ◽  
Geometrical Parameters ◽  
The Neural Network ◽  
On Line ◽  
Welding Processes ◽  
Pool Sizes

In gas metal arc (GMA) welding processes, the geometrical shape and size of the weld pool are utilized to assess the integrity of the weld quality. Monitoring of these geometrical parameters for on-line process control as well as for on-line quality evaluation, however, is an extremely difficult problem. The paper describes the design of a neural network estimator to estimate weld pool sizes for on-line use in quality monitoring and control. The neural network estimator is designed to estimate the weld pool sizes from surface temperatures measured at various points on the top surface of the weldment. The main task of the neural network is to realize the mapping characteristics from the point temperatures to the weld pool sizes through training. The chosen design parameters of the neural network estimator, such as the number of hidden layers and the number of nodes in a layer, are based on an estimation error analysis. A series of bead-on-plate welding experiments were performed to assess the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can estimate the weld pool sizes with satisfactory accuracy.

Computer Image Processing System Networks

1974 ◽  
Author(s):  
Herbert H. Hopf ◽  
Murray Shapiro ◽  
Marvin King
Keyword(s):  
Image Processing ◽  
Computer Image ◽  
Processing System ◽  
Image Processing System ◽  
Computer Image Processing

A Study of PID Control System Based on BP Neural Network

2011 ◽  
Vol 328-330 ◽  
pp. 1908-1911
Author(s):  
Wei Liu ◽  
Jian Jun Cai ◽  
Xi Pin Fan
Keyword(s):  
Neural Network ◽  
Control System ◽  
Conjugate Gradient ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Steepest Descent ◽  
The Neural Network ◽  
On Line ◽  
New Type

To deal with the defects of the steepest descent in slowly converging and easily immerging in partialm in imum,this paper proposes a new type of PID control system based on the BP neural network, which is a combination of the neural network and the PID strategy. It has the merits of both neural network and PID controller. Moreover, Fletcher-Reeves conjugate gradient in controller can make the training of network faster and can eliminate the disadvantages of steepest descent in BP algorithm. The parameters of the neural network PID controller are modified on line by the improved conjugate gradient. The programming steps under MATLAB are finally described. Simulation result shows that the controller is effective.

Neural Network-Based Speed Control of A Two-Mass-Model System

1999 ◽  
Vol 3 (5) ◽  
pp. 427-430 ◽  
Author(s):  
Rached Dhaouadi ◽  
◽  
Khaled Nouri
Keyword(s):  
Neural Network ◽  
Inverse Dynamics ◽  
Mass Model ◽  
Neural Network Learning ◽  
Inverse Control ◽  
Network Learning ◽  
The Neural Network ◽  
Control Scheme ◽  
On Line ◽  
Neural Network Structure

We present an application of artificial neural networks to the problem of controlling the speed of an elastic drive system. We derive a neural network structure to simulate the inverse dynamics of the system, then implement the direct inverse control scheme in a closed loop. The neural network learning is done on-line to adaptively control the speed to follow a stepwise changing reference. The experimental results with a two-mass-model analog board confirm the effectiveness of the proposed neurocontrol scheme.

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