scholarly journals Fault Recovery Path Analysis of a Software Dynamic Image Based on a Fuzzy Control Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 897
Author(s):  
Tuqian Zhang
Keyword(s):  
Feature Extraction ◽  
Fuzzy Control ◽  
Spectral Characteristic ◽  
Noise Reduction ◽  
Control Algorithm ◽  
Embedded Software ◽  
High Order ◽  
Fault Recovery ◽  
Dynamic Image ◽  
Fuzzy Control Algorithm

In order to improve the ability of software dynamic image fault detection, a software dynamic image fault recovery path detection algorithm based on a fuzzy control algorithm is proposed. A software dynamic image fault signal model of a software dynamic image fault was constructed by adopting an embedded feature extraction and a fuzzy control algorithm, and the dynamic image fault signal of the embedded software under the multi-load was subjected to frequency spectrum decomposition and blind source separation. The method comprises the following steps: (1) carrying out noise reduction processing on a software dynamic image fault signal by adopting a multi-dimensional wavelet decomposition method, (2) carrying out wavelet entropy feature extraction on the software dynamic image fault signal of the noise reduction output, and (3) combining the wavelet structural feature recombination method to carry out the recombination of the software dynamic image fault feature. The high-order spectral characteristic of the software dynamic image fault signal was extracted and the high-order spectral characteristic of the extracted embedded software dynamic image fault recovery path was automatically matched, and the automatic identification and detection of the fault part of the software dynamic image was realized.

Space robot motion path planning based on fuzzy control algorithm

2021 ◽  
pp. 1-8
Author(s):  
Baoyu Shi ◽  
Hongtao Wu
Keyword(s):  
Path Planning ◽  
Fuzzy Control ◽  
Obstacle Avoidance ◽  
Control Algorithm ◽  
Space Robot ◽  
Robot Motion ◽  
Robot Path Planning ◽  
Behavior Based ◽  
Fuzzy Control Algorithm ◽  
Robot Path

Path planning technology is one of the core technologies of intelligent space robot. Combining image recognition technology and artificial intelligence learning algorithm for robot path planning in unknown space environment has become one of the hot research issues. The purpose of this paper is to propose a spatial robot path planning method based on improved fuzzy control, aiming at the shortcomings of path planning in the current industrial space robot motion control process, and based on fuzzy control algorithm. This paper proposes a fuzzy obstacle avoidance method with speed feedback based on the original advantages of the fuzzy algorithm, which improves the obstacle avoidance performance of space robot under continuous obstacles. At the same time, we integrated the improved fuzzy obstacle avoidance strategy into the behavior-based control technology, making the avoidance become an independent behavioral unit. Divide the path planning into a series of relatively independent behaviors such as fuzzy obstacle avoidance, cruise, trend target, and deadlock by the behavior-based method. According to the interaction information between the space robot and the environment, each behavior acquires the dominance of the robot through the competition mechanism, making the robot complete the specific behavior at a certain moment, and finally realize the path planning. Furthermore, to improve the overall fault tolerance of the space, robot we introduced an elegant downgrade strategy, so that the robot can successfully complete the established task in the case of control command deterioration or failure of important information, avoiding the overall performance deterioration effectively. Therefore, through the simulation experiment of the virtual environment platform, MobotSim concluded that the improved algorithm has high efficiency, high security, and the planned path is more in line with the actual situation, and the method proposed in this paper can make the space robot successfully reach the target position and optimize the spatial path, it also has good robustness and effectiveness.

Development of Dedicated Controller for HVAC

2012 ◽  
Vol 430-432 ◽  
pp. 1472-1476
Author(s):  
Jin Ming Yang ◽  
Yi Lin
Keyword(s):  
Fuzzy Control ◽  
Control Algorithm ◽  
Control Strategies ◽  
Control Software ◽  
Interface Circuits ◽  
Pid Algorithm ◽  
Hardware Interface ◽  
Hvac Control ◽  
Fuzzy Control Algorithm

This article describes the development of a dedicated controller for HVAC control, and introduces the hardware interface circuits about some main chip on controller. In addition, the article also explains composition and principle about control software applied to the controller, further more points out that the fuzzy control algorithm is more reasonable than the PID algorithm for most HVAC control and dedicated control strategies play an important role for HVAC control.

Profile Measurement of a Curved Workpiece by the Fuzzy Control of Sensor Positioning

1999 ◽  
Author(s):  
Masatake Shiraishi ◽  
Gongjun Yang
Keyword(s):  
Fuzzy Control ◽  
Control Algorithm ◽  
Experimental Results ◽  
Measuring System ◽  
Displacement Sensor ◽  
Profile Measurement ◽  
Laser Displacement Sensor ◽  
Inclination Angles ◽  
Fuzzy Control Algorithm ◽  
Sensor Positioning

Abstract A laser displacement sensor which has a resolution of 0.5 μm was used to determine the measurement of a curved workpiece profile in turning. This sensor is attached to a specially designed stage and is operated by three motors which are controlled by a fuzzy control algorithm. The experimental results show that the measuring system can be applied to workpieces having inclination angles of up to around 45°. The proposed measuring system has a practical measuring accuracy to within ten micrometers.

scholarly journals Fault Location Measurement of Sensor Nodes based on Fuzzy Control Algorithm

2021 ◽  
Author(s):  
Nuo Yu
Keyword(s):  
Fuzzy Control ◽  
Measurement Method ◽  
Control Algorithm ◽  
Fault Location ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Cluster Member ◽  
Cluster Heads ◽  
Fuzzy Control Algorithm ◽  
Location Measurement

Abstract Aiming at the problems of the traditional fault location measurement method for sensor nodes, such as more energy consumption and longer measurement time, a fault location measurement method for sensor nodes based on fuzzy control algorithm is designed and proposed. First of all, the fuzzy control algorithm is analyzed; then the clustering based on cluster head diagnosis is carried out for the network, that is, the nodes that meet the cluster head conditions and are set as normal cluster heads are selected as cluster heads. Finally, combined with the fuzzy control algorithm, the fault location of each cluster member node is measured directly by cluster head nodes. The simulation results show that the proposed method has good performance.

scholarly journals Fuzzy Control and Connected Region Marking Algorithm-Based SEM Nanomanipulation

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Dongjie Li ◽  
Weibin Rong ◽  
Lining Sun ◽  
Bo You ◽  
Yu Zou ◽  
...  
Keyword(s):  
Fuzzy Control ◽  
Control Algorithm ◽  
Force Feedback ◽  
Connected Region ◽  
Feedback Information ◽  
Sem Image ◽  
Position Data ◽  
Update Rate ◽  
Fuzzy Control Algorithm ◽  
Slave Mode

The interactive nanomanipulation platform is established based on fuzzy control and connected region marking (CRM) algorithm in SEM. The 3D virtual nanomanipulation model is developed to make up the insufficiency of the 2D SEM image information, which provides the operator with depth and real-time visual feedback information to guide the manipulation. The haptic device Omega3 is used as the master to control the 3D motion of the nanopositioner in master-slave mode and offer the force sensing to the operator controlled with fuzzy control algorithm. Aiming at sensing of force feedback during the nanomanipulation, the collision detection method of the virtual nanomanipulation model and the force rending model are studied to realize the force feedback of nanomanipulation. The CRM algorithm is introduced to process the SEM image which provides effective position data of the objects for updating the virtual environment (VE), and relevant issues such as calibration and update rate of VE are also discussed. Finally, the performance of the platform is validated by the ZnO nanowire manipulation experiments.

Sign in / Sign up

Export Citation Format

Share Document