Development and Test of an RGB-D Camera-based Rock Detection System and Path Optimization Algorithm in an Indoor Environment

2021 ◽  
Author(s):  
Jithin Jose Mathew ◽  
Yu Zhang ◽  
Paulo Flores ◽  
Cannayen Igathinathane ◽  
Zhao Zhang
Keyword(s):  
Optimization Algorithm ◽  
Indoor Environment ◽  
Detection System ◽  
Path Optimization

Research on Enhancement Technology of Conveyor Belt Fault Images Based on Fruit Fly Optimization Algorithm

2014 ◽  
Vol 8 (1) ◽  
pp. 685-689
Author(s):  
Chunqing Ye ◽  
Changyun Miao ◽  
Xianguo Li ◽  
Yanli Yang
Keyword(s):  
Optimization Algorithm ◽  
Detection System ◽  
Fruit Fly ◽  
Conveyor Belt ◽  
Recognition Algorithm ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Wire Ropes ◽  
Fault Recognition ◽  
Steel Cord

In this research, we studied the fault recognition algorithm of steel cord conveyor belt, and obtained the wire ropes image by adopting the detection system of steel cord conveyor belt, so that the fault recognition algorithm of steel cord conveyor belt was proposed based on Fruit fly optimization algorithm. As we know that the fruit fly optimization algorithm is used for fault detection of the processing steel cord conveyor belt image and for obtaining the fault image. In the MATLAB environment, the algorithm process was designed and verified in terms of the effectiveness and accuracy. The experimental results show that with fast speed and high accuracy in detecting the fault image of steel cord conveyor belt rapidly and accurately, and in classifying scratch from fracture the proposed algorithm is suitable for the fault recognition of steel cord conveyor belt automatically.

Semi supervised inspection algorithm of automatic packaging curve based on deep learning

2021 ◽  
pp. 1-10
Author(s):  
Yong He
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Optimization Algorithm ◽  
Posterior Probability ◽  
Detection Method ◽  
Detection System ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Data Set ◽  
Packaging Process

The current automatic packaging process is complex, requires high professional knowledge, poor universality, and difficult to apply in multi-objective and complex background. In view of this problem, automatic packaging optimization algorithm has been widely paid attention to. However, the traditional automatic packaging detection accuracy is low, the practicability is poor. Therefore, a semi-supervised detection method of automatic packaging curve based on deep learning and semi-supervised learning is proposed. Deep learning is used to extract features and posterior probability to classify unlabeled data. KDD CUP99 data set was used to verify the accuracy of the algorithm. Experimental results show that this method can effectively improve the performance of automatic packaging curve semi-supervised detection system.

Comparison of GA, ACO algorithm, and PSO algorithm for path optimization on free-form surfaces using coordinate measuring machines

2021 ◽  
Author(s):  
Yueping Chen ◽  
Naiqi Shang
Keyword(s):  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Path Optimization ◽  
Inspection Time ◽  
Free Form ◽  
Coordinate Measuring Machines ◽  
Ant Colony Optimization Algorithm ◽  
Measuring Machine ◽  
Free Form Surfaces

Abstract Coordinate measuring machines (CMMs) play an important role in modern manufacturing and inspection technologies. However, the inspection process of a CMM is recognized as time-consuming work. The low efficiency of coordinate measuring machines has given rise to new inspection strategies and methods, including path optimization. This study describes the optimization of an inspection path on free-form surfaces using three different algorithms: an ant colony optimization algorithm, a genetic algorithm, and a particle swarm optimization algorithm. The optimized sequence of sampling points is obtained in MATLAB R2020b software and tested on a Leitz Reference HP Bridge Type Coordinate Measuring Machine produced by HEXAGON. This study compares the performance of the three algorithms in theoretical and practical conditions. The results demonstrate that the use of the three algorithms can result in a collision-free path being found automatically and reduce the inspection time. However, owing to the different optimization methodologies, the optimized processes and optimized times of the three algorithms, as well as the optimized paths, are different. The results indicate that the ant colony algorithm has better performance for the path optimization of free-form surfaces.

Free Surface NC Machining Tool Path Optimization Algorithm Based on the Iso-Scallop Method

2015 ◽  
Vol 799-800 ◽  
pp. 1193-1196 ◽  
Author(s):  
Shu Kun Cao ◽  
Yong Hong Deng ◽  
Kun Zhang ◽  
Shi Ping Liu ◽  
Wen Jing Meng
Keyword(s):  
Free Surface ◽  
Optimization Algorithm ◽  
Tool Path ◽  
Nc Machining ◽  
Path Optimization ◽  
Surface Boundary ◽  
Step Size ◽  
Processing Efficiency ◽  
Tool Path Optimization ◽  
Point Set

In order to solve the problem of free surface processing of tool redundancy,the tool lack problem, and the demerit of low machining efficiency, etc., based on the iso-scallop method, based on the iso-scallop method, we put forward a kind of free surface NC machining tool path optimization algorithm,make the surface boundary discrete point set, which is generated by point set ring machining path, diagonal connection and then use the path of the adjacent curve, forming cutting tool machining line.finally, the calculation of step size and line spacing in machining path based on the iso-scallop method and the process of feeding direction is optimized. Proved by the simulation process, the algorithm is feasible and can effectively avoid tool redundancy and tool lack problems,concesquently, processing efficiency improved significantly.

The Shortest Path Optimization Based on Mutation Particle Swarm Optimization Algorithm

2014 ◽  
Vol 1049-1050 ◽  
pp. 1690-1693 ◽  
Author(s):  
Juan Li
Keyword(s):  
Particle Swarm Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Path Optimization ◽  
Adaptive Mutation ◽  
Swarm Optimization ◽  
Mutation Strategy

The traditional evolutionary algorithm is cannot converge faster to solve the path optimization problems, and the path that is computed is not the shortest path, in allusion to the disadvantage of this algorithm, a mutation particle swarm optimization algorithm is proposed. The algorithm introduces the adaptive mutation strategy, and accelerated the speed to search for the global optimal solution. For seven examples experiment in standard database, the result shows that the algorithm is more efficient..

Sign in / Sign up

Export Citation Format

Share Document