scholarly journals Oracle Recognition of Oracle Network Based on Ant Colony Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Xianjin Shi ◽  
Xiajiong Shen
Keyword(s):  
Social Networks ◽  
Ant Colony Algorithm ◽  
Recognition Rate ◽  
Recommendation Systems ◽  
Recognition Algorithm ◽  
Ant Colony ◽  
Experimental Simulation ◽  
Maximum Matching ◽  
Matching Problem ◽  
Matching Degree

Recent studies have shown that compared with traditional social networks, networks in which users socialize through interest recommendation have obvious homogeneity characteristics. Recommending topics of interest to users has become one of the main objectives of recommendation systems in such social networks, and the widespread data sparsity in such social networks has become the main problem faced by such recommendation systems. Particularly, in the oracle interest network, this problem is more difficult to solve because there are very few people who read and understand the Oracle. To address this problem, we propose an ant colony algorithm based recognition algorithm that can greatly expand the data in the oracle interest network and thus improve the efficiency of oracle interest network recommendation in this paper. Using the one-to-one correspondence between characters and translation in Oracle rubbings, the Oracle recognition problem is transformed into character matching problem, which can skip manual feature engineering experts, so as to realize efficient Oracle recognition. First, the coordinates of each character in the oracle bones are extracted. Then, the matching degree value of each oracle character corresponding to the translation of the oracle rubbings is assigned according to the coordinates. Finally, the maximum matching degree value of each character is searched using the improved ant colony algorithm, and the search result is the Chinese character corresponding to the oracle rubbings. In this paper, through experimental simulation, it is proved that this method is very effective when applied to the field of oracle recognition, and the recognition rate can approach 100% in some special oracle rubbings.

Application Research about the Chaos Ant Colony Algorithm in Emergency Logistics Distribution Routing Optimization

2011 ◽  
Vol 268-270 ◽  
pp. 1733-1738
Author(s):  
Teng Fei ◽  
Li Yi Zhang ◽  
Hong Wei Ren ◽  
Jin Zhang ◽  
Cui Wen Huang
Keyword(s):  
Quantitative Methods ◽  
Ant Colony Algorithm ◽  
Mathematic Model ◽  
Ant Colony ◽  
Experimental Simulation ◽  
Application Research ◽  
The Real ◽  
Logistics Distribution ◽  
Routing Optimization ◽  
Emergency Logistics

In this essay, the solution about emergency logistics distribution routing optimization has been analyzed by quantitative methods, and the mathematic model focusing on trying the best to shorten distribution time has been established, in which the actual situations of the pathways and the shortage of goods at each affected point have been considered in order to keep further close to the real circumstances where had suffered disaster. Using the Chaos Ant Colony Algorithm solves the mathematic model. The experimental simulation indicates that the arithmetic is feasible and effective to settle the problem about the optimization of emergency logistics distribution route.

scholarly journals Recognition Algorithm of Popular Elements of Ethnic Minority Traditional Clothing Based on PCA

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hu Juan
Keyword(s):  
Ethnic Minority ◽  
National Culture ◽  
Euclidean Distance ◽  
Recognition Rate ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Pca Method ◽  
Neural Network Algorithm ◽  
Matching Degree ◽  
Better Than

Image recognition of ethnic minority costumes is helpful for people to understand, carry forward, and inherit national culture. Taking the minority clothing image as the research object, the image enhancement and threshold segmentation are completed; the principal component features of the minority clothing image are extracted by PCA method; and the image matching degree is obtained according to the principle of minimizing the Euclidean distance. Finally, the calculation process of the PCA method is optimized by a wavelet transform algorithm to realize the recognition of popular elements of minority traditional clothing. The comparative experimental results show that the PCA + BP neural network algorithm is better than the other two recognition algorithms in recognition rate and recognition time.

scholarly journals A Novel Automated Guided Vehicle (AGV) Remote Path Planning Based on RLACA Algorithm in 5G Environment

2021 ◽  
Author(s):  
Wangwang Yu ◽  
Jun Liu ◽  
Jie Zhou
Keyword(s):  
Remote Control ◽  
Path Planning ◽  
Ant Colony Algorithm ◽  
Future Trend ◽  
Ant Colony ◽  
Training Data ◽  
Experimental Simulation ◽  
Target Point ◽  
Automated Guided Vehicle ◽  
Blind Search

Remote control and monitoring will become the future trend. High-quality automated guided vehicle (AGV) path planning through web pages or clients can reduce network data transmission capacity and server resource occupation. Many Remote path planning algorithms in AGV navigation still have blind search, path redundancy, and long calculation time. This paper proposed an RLACA algorithm based on 5G network environment through remote control of AGV. The distribution of pheromone in each iteration of the ant colony algorithm had an impact on the follow-up. RLACA algorithm changed the transfer rules and pheromone distribution of the ant colony algorithm to improve the efficiency of path search and then modify the path to reduce path redundancy. Considering that there may be unknown obstacles in the virtual environment, the path obtained by the improved ant colony algorithm is used as the training data of reinforcement learning to obtain the Q-table. During the movement, the action of each step is selected by the Q-table until the target point is reached. Through experimental simulation, it can be concluded that the enhanced ant colony algorithm can quickly obtain a reasonable and adequate path in a complex environment and effectively avoid unknown obstacles in the environment.

scholarly journals Star Recognition Based on Path Optimization in Star Sensor with Multiple Fields of View

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Di Jiang ◽  
Ke Zhang ◽  
Olivier Debeir
Keyword(s):  
Low Cost ◽  
Optimal Path ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Ant Colony ◽  
Path Optimization ◽  
Star Image ◽  
Starting Point ◽  
Feature Pattern ◽  
Small Field Of View

Star sensors make use of astronomical information in stars to determine attitude for spacecrafts by star image recognition. For low-cost star sensors with small field of view, fusion of observed images from multiple fields of view is performed and a novel recognition algorithm based on path optimization by randomly distributed ant colony is proposed. According to pheromone intensity, the ant colony can autonomously figure out a close optimal path without starting or ending point, rather than certifying a starting point first. Feature patterns extracted from the optimal path in guiding template and observed image after fusion are compared to perform star recognition. By the proposed algorithm, starting point for path optimization has no influence on the extracted feature pattern. Thus the star recognition rate is improved due to the higher stability of the extracted pattern. Simulations indicate that the algorithm improves recognition accuracy and robustness against noise for sensors with multiple fields of view.

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