A cooperative multi-robot system (CMRS) modeling method called fuzzy timed agent based Petri nets (FTAPN) is proposed in this paper, which has been extended from fuzzy timed object-oriented Petri net (FTOPN). The prop...A cooperative multi-robot system (CMRS) modeling method called fuzzy timed agent based Petri nets (FTAPN) is proposed in this paper, which has been extended from fuzzy timed object-oriented Petri net (FTOPN). The proposed FTAPN can be used to model and illustrate both the structural and dynamic aspects of CMRS, which is a typical multi-agent system (MAS). At the same time, supervised learning is supported in FTAPN. As a special type of high-level object, agent is introduced into FTAPN, which is used as a common modeling object in its model. The proposed FTAPN can not only be used to model CMRS and represent system aging effect, but also be refined into the object-oriented implementation easily. At the same time, it can also be regarded as a conceptual and practical artificial intelligence (AI) tool for multi-agent systems (MAS) into the mainstream practice of the software development.展开更多
This paper describes an identification system for Chinese Materia Medicas （CMMs） in microscopic powder images. The imaging processing of the microscopic powder image is very complex because of the low contrast, blur...This paper describes an identification system for Chinese Materia Medicas （CMMs） in microscopic powder images. The imaging processing of the microscopic powder image is very complex because of the low contrast, blurry boundaries, overlapping objects, and messy background. Therefore, the object detection must segment the significant microscopic structures from the complex image. The objects are detected in these images using an adaptable interactive method. After identifying the significant microscopic structures, the system identifies 14 features belonging to three main characteristics. These features form a 14-dimensional vector that represents the microscopic structures. The multi-dimensional vector is then analyzed using a feature assignment algorithm that picks the most notable features to construct a decision tree with thresholds. The identification system consists of a coarse classifier based on the decision tree and a fine classifier using similarity measurements to rank the possible results. Tests on 528 images from 24 different kinds of microscopic structures show the system effectiveness and applicability.展开更多
文摘A cooperative multi-robot system (CMRS) modeling method called fuzzy timed agent based Petri nets (FTAPN) is proposed in this paper, which has been extended from fuzzy timed object-oriented Petri net (FTOPN). The proposed FTAPN can be used to model and illustrate both the structural and dynamic aspects of CMRS, which is a typical multi-agent system (MAS). At the same time, supervised learning is supported in FTAPN. As a special type of high-level object, agent is introduced into FTAPN, which is used as a common modeling object in its model. The proposed FTAPN can not only be used to model CMRS and represent system aging effect, but also be refined into the object-oriented implementation easily. At the same time, it can also be regarded as a conceptual and practical artificial intelligence (AI) tool for multi-agent systems (MAS) into the mainstream practice of the software development.
文摘This paper describes an identification system for Chinese Materia Medicas （CMMs） in microscopic powder images. The imaging processing of the microscopic powder image is very complex because of the low contrast, blurry boundaries, overlapping objects, and messy background. Therefore, the object detection must segment the significant microscopic structures from the complex image. The objects are detected in these images using an adaptable interactive method. After identifying the significant microscopic structures, the system identifies 14 features belonging to three main characteristics. These features form a 14-dimensional vector that represents the microscopic structures. The multi-dimensional vector is then analyzed using a feature assignment algorithm that picks the most notable features to construct a decision tree with thresholds. The identification system consists of a coarse classifier based on the decision tree and a fine classifier using similarity measurements to rank the possible results. Tests on 528 images from 24 different kinds of microscopic structures show the system effectiveness and applicability.
