scholarly journals Designing an intelligent monitoring system for corn seeding by machine vision and Genetic Algorithm-optimized Back Propagation algorithm under precision positioning

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254544
Author(s):  
Jiangtao Ji ◽  
Yayuan Sang ◽  
Zhitao He ◽  
Xin Jin ◽  
Shengsheng Wang
Keyword(s):  
Genetic Algorithm ◽  
Machine Vision ◽  
Monitoring System ◽  
Detection System ◽  
Back Propagation ◽  
National Standard ◽  
Precision Positioning ◽  
Intelligent Monitoring ◽  
Average Value ◽  
Seeding Effect

Objective To realize the regulation of the position of corn seed planting in precision farming, an intelligent monitoring system is designed for corn seeding based on machine vision and the Genetic Algorithm-optimized Back Propagation (GABP) algorithm. Methods Based on the research on precision positioning seeding technology, comprehensive application of sensors, Proportional Integral Derivative (PID) controllers, and other technologies, combined with modern optimization algorithms, the online dynamic calibration controls of line spacing and plant spacing are implemented. Based on the machine vision and GABP algorithm, a test platform for the seeding effect detection system is designed to provide a reference for further precision seeding operations. GA can obtain better initial network weights and thresholds and find the optimal individual through selection, crossover, and mutation operations; that is, the optimal initial weight of the Back Propagation (BP) neural network. Field experiments verify the seeding performance of the precision corn planter and the accuracy of the seeding monitoring system. Results 1. The deviation between the average value of the six precision positioning seeding experiments of corn under the random disturbance signal and the ideal value of the distance is less than or equal to 0.5 cm; the deviation between the average value of the six precision positioning seeding experiments of corn under the sine wave disturbance signal (1 Hz) is less than or equal to 0.4 cm; the qualified rate of grain distance reaches 100%. 2. The precision control index, replay index, and missed index of the designed corn precision seeding intelligent control system have all reached the national standard. During the operation of the seeder, an alarm of the seeder leaking occurred, and the buzzer sounded and the screen displayed 100 times each; therefore, the reliability of the alarm system is 100%. Conclusion The intelligent corn seeder designed based on precision positioning seeding technology can reduce the seeding rate of the seeder and ensure the stability of the seed spacing effectively. Based on the machine vision and GABP algorithm, the seeding effect detection system can provide a reference for the further realization of precision seeding operations.

Design and Realization of CNG Intelligent Monitoring System

2013 ◽  
Vol 380-384 ◽  
pp. 761-764
Author(s):  
Zhen Hua Wang ◽  
Ge Fei Yu
Keyword(s):  
Monitoring System ◽  
Detection System ◽  
Short Message ◽  
Danger Signal ◽  
Intelligent Monitoring ◽  
Alarm Signal ◽  
Filling Station ◽  
Temperature Detection ◽  
Arm Microprocessor ◽  
Monitoring Information

One CNG remote intelligent monitoring system is designed and realized in this article. The monitoring system can receive real time monitoring information and monitor environment of CNG filling station by using GSM short message platform , terminal PC and cell phone based on ARM microprocessor, PTM100GSM module, pressure and temperature detection system, when the pressure, temperature or consistence of gas storage well is over the threshold , the monitoring system will send the alarm signal. Its proved that the monitoring system works stably and reliably and can effectively monitor fatal public danger signal.

scholarly journals Research on deception jamming of chaotic composite short-range detection system based on bispectral analysis and genetic algorithm–back propagation

2019 ◽  
Vol 15 (5) ◽  
pp. 155014771984744 ◽  
Author(s):  
Dongxu Wei ◽  
Shuning Zhang ◽  
Si Chen ◽  
Huichang Zhao ◽  
Linzhi Zhu
Keyword(s):  
Genetic Algorithm ◽  
Short Range ◽  
Detection System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Echo Signal ◽  
Deceptive Jamming ◽  
New Type ◽  
Different Characteristics ◽  
The Mathematical Model

The chaotic compound short-range detection system is a new type of short-range detection system, which has strong anti-jamming ability. However, for the deception jamming, the characteristics of the complex short-range detection system are very similar to the detection echo, which poses a serious threat to the detection system. In order to analyze and extract the different characteristics between deceptive jamming and target echo signal, so as to realize the anti-deceptive jamming of chaotic compound short-range detection system, this article analyzes and simulates the mathematical model of deceptive jamming and target echo, and analyzes the bispectral characteristics of the simulated echo and jamming signal, and a set of anti-deception jamming feature parameters has been constructed. The identification of deceptive interference is realized by genetic algorithm–back propagation neural network, and the recognition accuracy is high and the real-time performance is good.

Machine Vision-Based Intelligent Fire Fighting Robot

2010 ◽  
Vol 450 ◽  
pp. 312-315 ◽  
Author(s):  
Chao Ching Ho ◽  
Ming Chen Chen ◽  
Chih Hao Lien
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Monitoring System ◽  
Visual Servoing ◽  
Detection System ◽  
Detection Algorithm ◽  
Video Data ◽  
Fire Fighting ◽  
Fire Source ◽  
Fire Flame

Designing a visual monitoring system to detect fire flame is a complex task because a large amount of video data must be transmitted and processed in real time. In this work, an intelligent fire fighting and detection system is proposed which uses a machine vision to locate the fire flame positions and to control a mobile robot to approach the fire source. This real-time fire monitoring system uses the motion history detection algorithm to register the possible fire position in transmitted video data and then analyze the spectral, spatial and temporal characteristics of the fire regions in the image sequences. The fire detecting and fighting system is based on the visual servoing feedback framework with portable components, off-the-shelf commercial hardware, and embedded programming. Experimental results show that the proposed intelligent fire fighting system is successfully detecting the fire flame and extinguish the fire source reliably.

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.

Left ventricular hypertrophy detection system based on electrocardiogram signal

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
C.W Liu ◽  
R.H Pan ◽  
Y.L Hu
Keyword(s):  
Left Ventricular Hypertrophy ◽  
Decision Tree ◽  
Ventricular Hypertrophy ◽  
Detection System ◽  
Pearson Correlation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Left Ventricular ◽  
Heart Beat ◽  
Knowledge Engineer

Abstract Background Left ventricular hypertrophy (LVH) is associated with increased risks of cardiovascular diseases. Electrocardiography (ECG) is generally used to screen LVH in general population and electrocardiographic LVH is further confirmed by transthoracic echocardiography (Echo). Purpose We aimed to establish an ECG LVH detection system that was validated by echo LVH. Methods We collected the data of ECGs and Echo from the previous database. The voltage of R- and S-amplitude in each ECG lead were measured twice by a study assistance blinded to the study design, (artificially measured). Another knowledge engineer analyzed row signals of ECG (the algorithm). We firstly check the correlation of R- and S-amplitude between the artificially measured and the algorythm. ECG LVH is defined by the voltage criteria and Echo LVH is defined by LV mass index >115 g/m2 in men and >95 g/m2 in women. Then we use decision tree, k-means, and back propagation neural network (BPNN) with or without heart beat segmentation to establish a rapid and accurate LVH detection system. The ratio of training set to test set was 7:3. Results The study consisted of a sample size of 953 individuals (90% male) with 173 Echo LVH. The R- and S-amplitude were highly correlated between artificially measured and the algorithm R- and S-amplitude regarding that the Pearson correlation coefficient were >0.9 in each lead (the highest r of 0.997 in RV5 and the lowest r of 0.904 in aVR). Without heart beat segmentation, the accuracy of decision tree, k-means, and BPNN to predict echo LVH were 0.74, 0.73 and 0.51, respectively. With heart beat segmentation, the signal of Echo LVH expanded to 1466, and the accuracy to predict ECG LVH were obviously improved (0.92 for decision tree, 0.96 for k-means, and 0.59 for BPNN). Conclusions Our study showed that machine-learning model by BPNN had the highest accuracy than decision trees and k-means based on ECG R- and S-amplitude signal analyses. Figure 1. Three layers of the decision tree Funding Acknowledgement Type of funding source: None

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