scholarly journals Position Measurement Based on Fisheye Imaging

Proceedings ◽  
2019 ◽  
Vol 15 (1) ◽  
pp. 38
Author(s):  
Xianjing Li ◽  
Kun Li ◽  
Yanwen Chen ◽  
ZhongHao Li ◽  
Yan Han
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Imaging System ◽  
Large Angle ◽  
Image Features ◽  
Position Measurement ◽  
Fisheye Lens ◽  
Position Information ◽  
Object Distance ◽  
Nonlinear Imaging

For the omnidirectional measurement, the collected images of large-angle fisheye lens need to be corrected and spliced before next procedure, which is complicated and inaccurate. In this paper, a direct position measurement method based on fisheye imaging is proposed for large-angle imaging without any image correcting and splicing. A nonlinear imaging system of fisheye lens is used to acquire the sequence images based on its distortion model, and the critical distortion features of the sequence images are extracted, which contains the position information. And a BP neural network is trained with the extracted image features of previous standard experimental dataset. Finally, the trained BP neural network is employed to measure the object’s distance. Experimental results demonstrate show that the proposed method achieves simple close-object distance measurement with high robustness and a measurement error of ±0.5cm. The proposed method overcomes the shortcomings of conventional measurement methods and expands the fisheye applications filed for omnidirectional measurement.

Auto Focusing Method of Imaging System of Digital PCR Instrument Based on BP Neural Network

2019 ◽  
Vol 33 (12) ◽  
pp. 1954031 ◽  
Author(s):  
Shanxiong Chen ◽  
Xueqing Xie ◽  
Fangyuan Zheng ◽  
Sheng Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Good Accuracy ◽  
Imaging System ◽  
Digital Pcr ◽  
Imaging Device ◽  
Focusing Effect ◽  
Electronic Imaging ◽  
Som Neural Network ◽  
Auto Focusing

The digital PCR instrument is a digital instrument for amplifying specific DNA fragments. The problem studied in this paper is the autofocus problem of its electronic imaging device. Based on the analysis of existing SOM neural network autofocus scheme, we propose an improved scheme-BP neural network for autofocus. It directly takes the SOM input and the actual focus position as the input and output of the BP neural network, which eliminates the process of prior classification and then corresponding to the focus matrix in the original SOM scheme, saving time. The experimental results show that the traditional autofocus method has good focusing effect, but the speed is slow, and the universality of the BP neural network autofocus scheme is not good enough, but within a good accuracy range, the speed is faster. Compared to traditional focusing methods, the autofocus scheme designed in this paper successfully achieves faster focusing speed for biochips.

Image Segmentation of Eggplants in Growing Environment Based on Improved BP Neural Network

2010 ◽  
Vol 40-41 ◽  
pp. 599-603
Author(s):  
Jian Song
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Bp Neural Network ◽  
Image Features ◽  
The Neural Network ◽  
Training Samples ◽  
Neural Network Algorithm ◽  
Growing Environment ◽  
Improved Bp Algorithm ◽  
Gray Values

Aim at the complex background of eggplant image in the growing environment, a image segmentation method based on BP neural network was put forward. The EXG gray values of 3×3 neighborhood pixels were obtained as image features through by analyzing the eggplant image. 30 eggplant images were taken as training samples and results of manual segmentation images by Photoshop were regarded as teacher signals. The improved BP algorithm was used to train the parameter of the neural network. The effective parameter was achieved after 120 times of training. The result of this experiment showed that the eggplant fruit could be preferably segmented from the background by using BP neural network algorithm and it could totally meet the demands of the picking robots after further processing by way of combining mathematics morphology with median filtering.

Application of the Improved BP Neural Network in Optimization Design of Control System of Simple Pendulum Surface Plate

2013 ◽  
Vol 718-720 ◽  
pp. 1682-1686
Author(s):  
Hong Zhou Zhang ◽  
Wei Wang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Optimization Design ◽  
Large Angle ◽  
Plate Motion ◽  
Plate Movement ◽  
High Control ◽  
The Stability ◽  
Surface Plate

According to the characteristics of large angle of pendulum movement, in order to ensure the stability of the fixed plate motion pendulum, to analysis quantitatively the relation of oscillation angle and plate movement, it designed an automatic flat control system with STC89C52RC as the core of control chip, collected the information of oscillation angle by the sensor of angle,controls the angle of surface plate by advanced four phase stepper motor,uses the improved BP neural network to real-time correction in the software design part, the control system has high control precision, the stability and speedability of control system of surface plate were improved by measuring .

scholarly journals A state-of-art method for solar irradiance forecast via using fisheye lens

2020 ◽  
Author(s):  
Lei Chen ◽  
Yangluxi Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Solar Irradiance ◽  
Weather Condition ◽  
Neutral Network ◽  
Fisheye Lens ◽  
Network Method ◽  
Forecast Function ◽  
State Of Art ◽  
Irradiance Forecast

Abstract The purpose of this investigation is to enable the solar irradiance forecast function implementing a common camera devise instead of specialized instrument thereby serve for other researches. Development of various simulated tools requires higher accuracy surrounding weather condition data. Previous studies mainly focus on the improvement of precision for professional monitor equipment i.e. total sky imager, which is limited to the scope of users. In this research, a fisheye lens graph is rectified following a particular algorithm based on the image forming principle. Moreover, solar irradiance prediction adopts the advanced BP neutral network method being proved to be valid. Final results indicate that after rectifying the special perspective images under fisheye direction, colour threshold configuration could remarkably recognize the cloud image. The conclusion shows that common camera fisheye lens coupled with BP neural network successfully predict the solar irradiance.

scholarly journals Fast recognition method of moving video images based on BP neural networks

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 1024-1032 ◽  
Author(s):  
Yu Shao ◽  
Deden Witarsyah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Energy Consumption ◽  
Fault Tolerance ◽  
Real Time ◽  
Image Recognition ◽  
Bp Neural Network ◽  
Image Features ◽  
Video Image ◽  
Error Signal

Abstract At present, the accuracy of real-time moving video image recognition methods are poor. Also energy consumption is high and fault tolerance is not ideal. Consequently this paper proposes a method of moving video image recognition based on BP neural networks. The moving video image is divided into two parts: the key content and the background by binary gray image. By collecting training cubes. The D-SFA algorithm is used to extract moving video image features and to construct feature representation. The image features are extracted by collecting training cubes. The BP neural network is constructed to get the error function. The error signal is returned continuously along the original path. By modifying the weights of neurons in each layer, the weights propagate to the input layer step by step, and then propagates forward. The two processes are repeated to minimize the error signal. The result of image feature extraction is regarded as the input of BP neural network, and the result of moving video image recognition is output. And fault tolerance in real-time is better than the current method. Also the recognition energy consumption is low, and our method is more practical.

scholarly journals Position Soft-Sensing of Direct-Driven Hydraulic System Based on Back Propagation Neural Network

Actuators ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 322
Author(s):  
Shuzhong Zhang ◽  
Tianyi Chen ◽  
Tatiana Minav ◽  
Xuepeng Cao ◽  
Angeng Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Position Estimation ◽  
Soft Sensing ◽  
Position Information ◽  
Single Output ◽  
Batch Simulation ◽  
Sensing Model

Automated operations are widely used in harsh environments, in which position information is essential. Although sensors can be equipped to obtain high-accuracy position information, they are quite expensive and unsuitable for harsh environment applications. Therefore, a position soft-sensing model based on a back propagation (BP) neural network is proposed for direct-driven hydraulics (DDH) to protect against harsh environmental conditions. The proposed model obtains a position by integrating velocity computed from the BP neural network, which trains the nonlinear relationship between multi-input (speed of the electric motor and pressures in two chambers of the cylinder) and single-output (the cylinder’s velocity). First, the model of a standalone crane with DDH was established and verified by experiment. Second, the data from batch simulation with the verified model was used for training and testing the BP neural network in the soft-sensing model. Finally, position estimation with a typical cycle was performed using the created position soft-sensing model. Compared with the experimental data, the maximum soft-sensing position error was about 7 mm, and the error rate was within ±2.5%. Furthermore, position estimations were carried out with the proposed soft-sensing model under differing working conditions and the errors were within 4 mm, but the periodically cumulative error was observed. Hence, a reference point is proposed to minimize the accumulative error, for example, a point at the middle of the cylinder. Therefore, the work can be applied to acquire position information to facilitate automated operation of machines equipped with DDH.

Digitalization system of ancient architecture decoration art based on neural network and image features

2020 ◽  
pp. 1-12
Author(s):  
Wu Xin ◽  
Qiu Daping
Keyword(s):  
Neural Network ◽  
Construction Industry ◽  
Three Dimensional ◽  
Performance Testing ◽  
Image Features ◽  
Three Dimensional Model ◽  
Performance Effect ◽  
Data Process ◽  
And Performance ◽  
Construction Mode

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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