A conflict is an event in which two or more aircraft experience a loss of minimum separation. In this paper, we formulate the problem of solving conflicts arising among several aircraft moving in a shared airspace as ...A conflict is an event in which two or more aircraft experience a loss of minimum separation. In this paper, we formulate the problem of solving conflicts arising among several aircraft moving in a shared airspace as a Constraint Satisfaction Problem (CSP). The constraint satisfaction problem being NP-complete, the algorithms developed to solve it have been of two types: non-systematic and systematic search methods. In this paper, we have considered a breakout algorithm as an example of non-systematic search methods and a backtracking procedure that maintains Arc Consistency (MAC) as an example of systematic search methods. The performance of these algorithms was compared experimentally and the Breakout algorithm is shown to be clearly superior.展开更多
The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is th...The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is the c-means method. The proposed method is introduced in order to perform a cognitive program which is assigned to be implemented on a parallel and distributed machine based on mobile agents. The main idea of the proposed algorithm is to execute the c-means classification procedure by the Mobile Classification Agents (Team Workers) on different nodes on their data at the same time and provide the results to their Mobile Host Agent (Team Leader) which computes the global results and orchestrates the classification until the convergence condition is achieved and the output segmented images will be provided from the Mobile Classification Agents. The data in our case are the big data MRI image of size (m × n) which is splitted into (m × n) elementary images one per mobile classification agent to perform the classification procedure. The experimental results show that the use of the distributed architecture improves significantly the big data segmentation efficiency.展开更多
文摘A conflict is an event in which two or more aircraft experience a loss of minimum separation. In this paper, we formulate the problem of solving conflicts arising among several aircraft moving in a shared airspace as a Constraint Satisfaction Problem (CSP). The constraint satisfaction problem being NP-complete, the algorithms developed to solve it have been of two types: non-systematic and systematic search methods. In this paper, we have considered a breakout algorithm as an example of non-systematic search methods and a backtracking procedure that maintains Arc Consistency (MAC) as an example of systematic search methods. The performance of these algorithms was compared experimentally and the Breakout algorithm is shown to be clearly superior.
文摘The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is the c-means method. The proposed method is introduced in order to perform a cognitive program which is assigned to be implemented on a parallel and distributed machine based on mobile agents. The main idea of the proposed algorithm is to execute the c-means classification procedure by the Mobile Classification Agents (Team Workers) on different nodes on their data at the same time and provide the results to their Mobile Host Agent (Team Leader) which computes the global results and orchestrates the classification until the convergence condition is achieved and the output segmented images will be provided from the Mobile Classification Agents. The data in our case are the big data MRI image of size (m × n) which is splitted into (m × n) elementary images one per mobile classification agent to perform the classification procedure. The experimental results show that the use of the distributed architecture improves significantly the big data segmentation efficiency.
