Ant Colony Algorithm’s Application of Textile Monitoring Image Recognition

2011 ◽  
Vol 328-330 ◽  
pp. 1701-1704
Author(s):  
Yang Lie Fu ◽  
Shu Qian Chen ◽  
Li Hong Zhang
Keyword(s):  
Artificial Intelligence ◽  
Global Optimization ◽  
Glass Fiber ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Default Rule ◽  
Rule Based ◽  
Intelligent Search ◽  
Textile Machine ◽  
Surveillance Method

We can use video surveillance method to detection the weft in Glass fiber textile machine, avoids glass fiber weft bristling by contact weft detection sensor, Glass fiber dust damages to human health. Using Ant Colony algorithm of intelligent search, global optimization, robust, positive feedback, distributed computing, easy combination with other methods and other characteristics, resolve image segmentation, extraction monitor weft system in regional conditions, then use the default rule-based artificial intelligence reasoning in the region separately.

Ant Colony Algorithm Application In Glass Fiber Textile Machine Parameter Tuning

2012 ◽  
Vol 462 ◽  
pp. 71-76 ◽  
Author(s):  
Li Hong Zhang ◽  
Shu Qian Chen ◽  
Gui Zhi Bai
Keyword(s):  
Glass Fiber ◽  
Pid Control ◽  
Ant Colony Algorithm ◽  
Control Process ◽  
Parameter Tuning ◽  
Integral Function ◽  
Parameters Optimization ◽  
Ant Colony ◽  
Control Parameters ◽  
Optimization Strategy

In glass fiber textile process, non-axis volume cloth drive motor with glass fabric volume increases, increasing the pressure on the drive shaft, moreover, because of cloth non-axis volume makes the pressure in the process of change is evident, that causes the motor load changing constantly, the traditional PID control system controller cannot timely tracking response. In order to solve the problem which the control parameters optimizes, improves the system performance, proposed a new Ant colony algorithm PID parameters optimization strategy, this solution can combine characteristics that Ant colony algorithm can fast find the most superior parameter solution stably and PID can precise adjustment. In the control process, taken the PID parameters as a colony of ants, used to control the absolute error integral function as the optimization objective, dynamically adjust the PID control parameters in the control process, so as to realize the PID parameters on-line tuning.

scholarly journals A Rule-Based Model for Bankruptcy Prediction Based on an Improved Genetic Ant Colony Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yudong Zhang ◽  
Shuihua Wang ◽  
Genlin Ji
Keyword(s):  
Ant Colony Algorithm ◽  
Model Simulation ◽  
Bankruptcy Prediction ◽  
Ant Colony ◽  
Misclassification Error ◽  
Net Income ◽  
Forward Selection ◽  
Rule Based ◽  
Chaotic Operator ◽  
Retained Earnings

In this paper, we proposed a hybrid system to predict corporate bankruptcy. The whole procedure consists of the following four stages: first, sequential forward selection was used to extract the most important features; second, a rule-based model was chosen to fit the given dataset since it can present physical meaning; third, a genetic ant colony algorithm (GACA) was introduced; the fitness scaling strategy and the chaotic operator were incorporated with GACA, forming a new algorithm—fitness-scaling chaotic GACA (FSCGACA), which was used to seek the optimal parameters of the rule-based model; and finally, the stratifiedK-fold cross-validation technique was used to enhance the generalization of the model. Simulation experiments of 1000 corporations’ data collected from 2006 to 2009 demonstrated that the proposed model was effective. It selected the 5 most important factors as “net income to stock broker’s equality,” “quick ratio,” “retained earnings to total assets,” “stockholders’ equity to total assets,” and “financial expenses to sales.” The total misclassification error of the proposed FSCGACA was only 7.9%, exceeding the results of genetic algorithm (GA), ant colony algorithm (ACA), and GACA. The average computation time of the model is 2.02 s.

Parallel Ant System Based on OpenMP

2013 ◽  
Vol 765-767 ◽  
pp. 658-661
Author(s):  
Yan Zhang ◽  
Hui Ling Wang ◽  
Xu Li ◽  
Yong Hua Zhang ◽  
Hao Wang
Keyword(s):  
Global Optimization ◽  
Local Search ◽  
Parallel Programming ◽  
Ant Colony Algorithm ◽  
Global Search ◽  
Ant Colony ◽  
Ant System ◽  
Ant Colonies ◽  
Computing Power

To overcome the limitation of precocity and stagnation in classical ant colony algorithm, this article presents a Parallel Ant System Based on OpenMP. The ant colony is divided into three children ant colonies according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm. By Open Multi-Processing parallel programming idea, the parallel and cooperating optimization of children ant colonies was obtained. It organically combines local search and global search, makes full use of computing power of multi-core CPU, and improves the efficiency significantly. Contrastive experiments show that the algorithm has a better capability of global optimization than traditional ant colony algorithm.

Simulation of English intelligent system based on CA-IAFSA algorithm and artificial intelligence

2021 ◽  
pp. 1-11
Author(s):  
Xinyu Wei
Keyword(s):  
Artificial Intelligence ◽  
Information Search ◽  
Ant Colony Algorithm ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Transition Probability ◽  
Ant Colony ◽  
Feedback Strategies ◽  
Planning Algorithm ◽  
Update Strategy

The traditional English teaching mode mostly relies on rote memorization of textbooks, but it lacks the training of oral expression skills and lacks intelligent guidance for students. Taking machine learning algorithm as the system algorithm, this paper combines the CA-IAFSA algorithm to construct an English intelligent system based on artificial intelligence. The system uses image recognition technology, introduces population pheromone and tribal pheromone, and adopts multiple ant colony planning and dual pheromone feedback strategies. Moreover, this paper improves the heuristic information search strategy, pheromone update strategy, and state transition probability of the basic ant colony algorithm. In addition, this paper proposes the MACDPA path planning algorithm to realize the intelligent analysis of English textbook images. Finally, after constructing the model, this paper conducts research and analysis on the performance of the model and uses controlled experimental methods and mathematical statistics to analyze the data. The research results show that the model constructed in this paper performs well in assisted teaching and intelligent translation and meets the expected requirements.

scholarly journals Computer Aided Drug Design Based on Artificial Intelligence Algorithm

2021 ◽  
Vol 2066 (1) ◽  
pp. 012012
Author(s):  
Wenye Yu ◽  
Zhenyu Chen
Keyword(s):  
Artificial Intelligence ◽  
Molecular Docking ◽  
Drug Design ◽  
Success Rate ◽  
Ant Colony Algorithm ◽  
Protein Docking ◽  
Ant Colony ◽  
Intelligence Algorithm ◽  
Computer Aided ◽  
Artificial Intelligence Algorithm

Abstract The problems such as high cost and long development time in drug design and development have an important impact on its development, which makes many scholars devote themselves to looking for the auxiliary model of drug design. With the rapid development of computer technology, computer-aided drug molecular research model is more and more mature. This paper aims to study the computer-aided drug system based on artificial intelligence algorithm, so that researchers can speed up the process and reduce the cost when searching for specific protein molecules. In this paper, the principle of complementary matching in the docking process of target molecules and ligands, which is commonly used in drug design, is described, and the functional expression mode and various docking methods of molecular docking are studied. Finally, the research hotspots of molecular docking technology are analyzed, including scoring function, search strategy and flexible protein docking. Ant colony algorithm is introduced into molecular docking platform as a variant of conformation search algorithm, and a new plants algorithm is developed. Finally, the implementation of plants algorithm is analyzed in detail, and the optimized plants system and gold system based on genetic algorithm are simulated, and the relevant experimental data are counted. The simulation results show that the new drug design method based on ant colony algorithm has advantages in docking success rate, docking speed and docking accuracy. The success rate of plants is higher than that of gold, and the docking time is only 1/6 of that of gold.

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