Communication Modeling and Mobile Object Monitoring by Using Colored Petri Nets

Summary. Monitoring moving objects and analyzing their statuses are the best opportunities currently offered by mobile technology. The methods and software for wireless systems allow the exchange of many possible data formats (e.g., text, visual or audio communication) and provide information about the state of the object’s geographical coordinates in real time. The necessary information is received from the sensors and mobile device’s contextual information. Information is sent to remote servers whenever applicable, and, after some calculation, more accurate data is obtained. Software to identify different situations has been designed and implemented. The software and therefore the identification of the situations of technical equipment can send data, warnings or reminders to a given situation. Colored Petri nets (CPN) allowed to more precisely model complex situations of scenarios and to capture the information any time, anywhere provided in advance of a moving object. Mobile devices detect the necessary data via the external or internal physical environment through sensors. The mobile device components are interacting with internal or external physical environment and have the sensor detectors’ parameters. Such information is stored into data-warehouses in which the knowledge discovery is made by CPN models, which represent rules of analysis

2010),The Reinforcement Framework of a Decision Support System for the Localization and Monitoring

This paper analyses the possibilities of the integration of different technological and knowledge representation techniques for the development of reinforcement frameworks for the remote control of multiple agents such as wheelchair-type robots. Some technological solutions are discussed regarding the recognition of localization of moving objects by using mobile technologies. Large-scale multi-dimensional recognitions of emotional diagnoses of disabled persons often generate large amounts of multidimensional data with complex recognition mechanisms, based on the integration of different knowledge representation techniques and complex inference models. The problem is to reveal the main components of a diagnosis as well as to construct flexible decision making models. Sensors can help to record primary data for monitoring objects; however the recognition of abnormal situations, the clustering of emotional stages and resolutions for certain types of diagnoses is an oncoming issue for bio-robot constructors. The prediction criteria of the diagnosis of the emotional situation of disabled persons are described using knowledge based models of neural networks. The research results present the development of a multi-layered framework architecture with the integration of artificial agents and support components for diagnosis recognition and control, or further actions, by using mobile technologies. The method of fuzzy neural network control of the speed of wheelchair-type robots working in real time by providing movement support for disabled individuals is presented. The fuzzy reasoning by using fuzzy logical Petri nets is described in order to define the physiological state of disabled individuals through recognizing their emotions during their different activities. Some new possibilities of the recognition of moving object location are introduced in the system