Fuzzy Penetration Control for TIG Weld Based on Wavelet-Fractal

2013 ◽  
Vol 722 ◽  
pp. 545-549
Author(s):  
Li Qin Zhang ◽  
Li Ling Zhang ◽  
Le Hui Huang
Keyword(s):  
Closed Loop ◽  
Welding Process ◽  
Detection Algorithm ◽  
Image Feature ◽  
Fuzzy Pid ◽  
Image Detection ◽  
Controlling System ◽  
Welding Automation ◽  
Welding Image ◽  
Poor Stability

Image detection was the important step of Welding automation. In view of the welding image feature of strong noise and poor stability, conventional detect method can not get the clear welding process image, so a fuzzy detection algorithm of welding image based on wavelet and fractal denoising was presented. The fuzzy detection algorithm is used to process welding image and extract molten-pools edge; and then fuzzy PID controlling theory are combined to form a whole image processing and closed-loop penetration controlling system. The experimental results indicated that the controlling system has the good anti-interference ability in welding process and therefore ensure the stabilization of welding formation quality.

scholarly journals Image target detection algorithm compression and pruning based on neural network

2021 ◽  
Vol 18 (2) ◽  
pp. 499-516
Author(s):  
Yan Sun ◽  
Zheping Yan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Network Model ◽  
Detection Algorithm ◽  
Image Feature ◽  
Propagation Mode ◽  
Learning Technology ◽  
Image Detection ◽  
Detection Algorithms

The main purpose of target detection is to identify and locate targets from still images or video sequences. It is one of the key tasks in the field of computer vision. With the continuous breakthrough of deep machine learning technology, especially the convolutional neural network model shows strong Ability to extract image feature in the field of digital image processing. Although the model research of target detection based on convolutional neural network is developing rapidly, but there are still some problems in practical applications. For example, a large number of parameters requires high storage and computational costs in detected model. Therefore, this paper optimizes and compresses some algorithms by using early image detection algorithms and image detection algorithms based on convolutional neural networks. After training and learning, there will appear forward propagation mode in the application of CNN network model, providing the model for image feature extraction, integration processing and feature mapping. The use of back propagation makes the CNN network model have the ability to optimize learning and compressed algorithm. Then research discuss the Faster-RCNN algorithm and the YOLO algorithm. Aiming at the problem of the candidate frame is not significant which extracted in the Faster- RCNN algorithm, a target detection model based on the Significant area recommendation network is proposed. The weight of the feature map is calculated by the model, which enhances the saliency of the feature and reduces the background interference. Experiments show that the image detection algorithm based on compressed neural network image has certain feasibility.

scholarly journals Spectroscopic closed loop control of penetration depth in laser beam welding process

2012 ◽  
Author(s):  
Teresa Sibillano ◽  
Antonio Ancona ◽  
Domenico Rizzi ◽  
Francesco Mezzapesa ◽  
Ali Riza Konuk ◽  
...  
Keyword(s):  
Laser Beam ◽  
Penetration Depth ◽  
Closed Loop ◽  
Laser Beam Welding ◽  
Welding Process ◽  
Closed Loop Control ◽  
Loop Control

scholarly journals Development of Wireless Sensor Device for Machine English Oral Pronunciation Noise Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yaning Zhu
Keyword(s):  
Closed Loop ◽  
Detection Algorithm ◽  
Training System ◽  
Circuit Board ◽  
Wireless Sensor ◽  
Noise Detection ◽  
Traditional System ◽  
Spoken English ◽  
Loop Design ◽  
Detection Technology

There is often noise in spoken machine English, which affects the accuracy of pronunciation. Therefore, how to accurately detect the noise in machine English spoken language and give standard spoken pronunciation is very important and meaningful. The traditional machine-oriented spoken English speech noise detection technology is limited to the improvement of software algorithm, mainly including speech enhancement technology and speech endpoint detection technology. Based on this, this paper will develop a wireless sensor network based on machine English oral pronunciation noise based on air and nonair conduction, reasonably design and configure air sensors, and nonair conduction sensors to deal with machine English oral pronunciation noise, so as to improve the naturalness and intelligibility of machine English speech. At the hardware level, this paper mainly optimizes the AD sampling, sensor matching layout, and internal hardware circuit board layout of the two types of sensors, so as to solve the compatibility problem between them and further reduce the hardware power consumption. In order to further verify or evaluate the performance of the machine spoken English speech noise detection sensor designed in this paper, a machine spoken English training system based on Android platform is designed. Compared with the traditional system, the training system can improve the intelligence of machine oriented oral English noise detection algorithm, so as to continuously improve the accuracy of system detection. The machine English pronunciation is adjusted and corrected by combining the data sensed by the sensor, so as to form a closed-loop design. The experimental results show that the wireless sensor sample proposed in this paper has obvious advantages in detecting the accuracy of machine English oral pronunciation, and its good closed-loop system is helpful to further improve the accuracy of machine English oral pronunciation.

Design on Test Bench of Safety Belt Emergency Locking Property

2014 ◽  
Vol 490-491 ◽  
pp. 640-643
Author(s):  
Chun Yan Tian ◽  
Mei Yang ◽  
Zhi Huan Lan ◽  
Cheng Da Ning ◽  
Yi Guo Ji
Keyword(s):  
Mechanical System ◽  
Pid Control ◽  
Control Algorithm ◽  
Test Bench ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Control Center ◽  
Safety Belt ◽  
Controlling System

The mechanical system and controlling system of test bench of safety belt emergency locking property are designed. Safety belt emergency locking property of a moving cars rider can be simulated by changing direct currents acceleration, so multifarious cars safety belt emergency locking property can be examined by the equipment which is developed by using engineering computer as control center,PARKER1505 and VC6.0.Fuzzy PID control algorithm is adopted to decrease the influence and enhance the systems robust.

Design of Temperature Controlling System for CCD Based on DSP

2013 ◽  
Vol 798-799 ◽  
pp. 557-560
Author(s):  
Ming Xia Xiao ◽  
Xing Ma
Keyword(s):  
Temperature Control ◽  
Dark Current ◽  
Control Device ◽  
Fuzzy Pid ◽  
Temperature Drift ◽  
Important Index ◽  
Error Range ◽  
Controlling System ◽  
Increasing Temperature ◽  
Temperature Control Device

CCD dark current is an important index of CCD properties effect, which emerges temperature drift phenomenon with the increasing temperatures. Because of the increasing temperature, CCD noise will increase exponentially. A temperature control device is designed based on DSP and fuzzy PID theory, which is composed of core control chip DSP320F28335, temperature control chip Thermal Electronic Cooler and temperature collecting chip DS18B20. Experiments show that the system can collect temperature timely and adjust temperature effectively. At the same time the system reach predetermined temperature in 2 minutes and error range is about 0.1°C.

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