Modeling and control of flatness in cold rolling mill using fuzzy petri nets

Today, having a good flatness control in steel industry is essential to ensure an overall product quality, productivity and successful processing. Flatness error, given as difference between measured strip flatness and target curve, can be minimized by modifying roll gap with various control functions. In most practical systems, knowing the definition of the model in order to have an acceptable control is essential. In this paper, a fuzzy Petri net method for modeling and control of flatness in cold rolling mill is developed. The method combines the concepts of Petri net and fuzzy control theories. It focuses on the fuzzy decision making problems of the fuzzy rule tree structures. The method is able to detect and recover possible errors that can occur in the fuzzy rule of the knowledge-based system. The method is implemented and simulated. The results show that its error is less than that of a PI conventional controller.<br /

Mapping Fuzzy Petri Net to Fuzzy Extended Markup Language

Use of model gives the knowledge and information about the phenomenon also eradicates the cost, the effort and the hazard of using the real phenomenon. Characteristics and concepts of Petri nets are in a way that makes it simple and strong to describe and study the information processing system; especially it is shown in those which are dealing with discrete, concurrent, distributed, parallel and indecisive events. Yet, due to Petri nets inability to face with systems working on obscure data and continues events, the interest to develop fundamental concept of Petri nets has been raised which is led to new style of presented model named "fuzzy Petri nets". The difference in Petri nets is in the elements that have been fuzzed. Transitions, places, signs and arcs can be fuzzed. PMNL, on the other hand as a markup language has been engaged in uttering Petri nets in previous researches. Fuzzy markup nets can model the uncertainty of concurrent scenarios different from a dynamic system by a board of parameters and use of fuzzy membership dependencies. Therefore, in order to define these uncertain data, it is vital to use a formal language to describe fuzzy Petri nets. To support this version in this thesis, a markup language will be presented stating the structure and grammar of markup language and covering fuzzy concepts in Petri nets as well. Presenting the suggested grammar accommodates the support of fuzzy develope.DOI:http://dx.doi.org/10.11591/ijece.v3i5.403

Architecting a System Model for Personalized Healthcare Delivery and Managed Individual Health Outcomes

In recent years, healthcare needs have shifted from treating acute conditions to meeting an unprecedented chronic disease burden. The healthcare delivery system has structurally evolved to address two primary features of acute care: the relatively short time period, on the order of a patient encounter, and the siloed focus on organs or organ systems, thereby operationally fragmenting and providing care by organ specialty. Much more so than acute conditions, chronic disease involves multiple health factors with complex interactions between them over a prolonged period of time necessitating a healthcare delivery model that is personalized to achieve individual health outcomes. Using the current acute-based healthcare delivery system to address and provide care to patients with chronic disease has led to significant complexity in the healthcare delivery system. This presents a formidable systems’ challenge where the state of the healthcare delivery system must be coordinated over many years or decades with the health state of each individual that seeks care for their chronic conditions. This paper architects a system model for personalized healthcare delivery and managed individual health outcomes. To ground the discussion, the work builds upon recent structural analysis of mass-customized production systems as an analogous system and then highlights the stochastic evolution of an individual’s health state as a key distinguishing feature

Optimization of management information support as a basis for organizational transformations at an enterprise

Increasing of information flows cause the necessity of optimizing their quantity, structure and distribution. In order to eliminate the disadvantages inherent in information systems of electricity delivery enterprises for the processing of internal information, a methodical approach to optimizing document circulation on the basis of modeling with the help of Petri Nets is developed. This article presents a systematic methodology for modeling document circulation flows at enterprise. The constructed model allows to form the structure of the system and the processes taking place in it, to analyze the static state of the system of document circulation and the dynamics of information flows. Also, we described mathematical model of document circulation process, where the central place is occupied by identification and removing of duplicate documents and those that are not processed at each stage of their moving. At final stage we propose to distribute information flows due to specificity of division. For this we need to define and assign information functions to divisions and formalize them considering the rules for processing documents. To determine the extent to which the processing rules of the documents actually go in the subdivision of its main for each transition we introduce the measure of its specificity for subsystem

Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets

Background and objective: In human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must be dynamically allocated in accordance with continuous-time fluctuation of psychophysiological functional status of the operator, so-called operator functional state (OFS). The behavior of the HM system is hybrid in nature due to the co-existence of discrete task-load (control) variable and continuous operator performance (system output) variable. Methods: Petri net is an effective tool for modeling discrete event systems, but for hybrid system involving discrete dynamics, generally Petri net model has to be extended. Instead of using different tools to represent continuous and discrete components of a hybrid system, this paper proposed a method of fuzzy inference Petri nets (FIPN) to represent the HM hybrid system comprising a Mamdani-type fuzzy model of OFS and a logical switching controller in a unified framework, in which the task-load level is dynamically reallocated between the operator and machine based on the model-predicted OFS. Furthermore, this paper used a multi-model approach to predict the operator performance based on three electroencephalographic (EEG) input variables (features) via the Wang-Mendel (WM) fuzzy modeling method. The membership function parameters of fuzzy OFS model for each experimental participant were optimized using artificial bee colony (ABC) evolutionary algorithm. Three performance indices, RMSE, MRE, and EPR, were computed to evaluate the overall modeling accuracy. Results: Experiment data from six participants are analyzed. The results show that the proposed method (FIPN with adaptive task allocation) yields lower breakdown rate (from 14.8% to 3.27%) and higher human performance (from 90.30% to 91.99%). Conclusion: The simulation results of the FIPN-based adaptive HM (AHM) system on six experimental participants demonstrate that the FIPN framework provides an effective way to model and regulate/optimize the OFS in HM hybrid systems composed of continuous-time OFS model and discrete-event switching controller

A new paradigm for uncertain knowledge representation by Plausible Petri nets

This paper presents a new model for Petri nets (PNs) which combines PN principles with the foundations of information theory for uncertain knowledge representation. The resulting framework has been named Plausible Petri nets (PPNs). The main feature of PPNs resides in their efficiency to jointly consider the evolution of a discrete event system together with uncertain information about the system state using states of information. The paper overviews relevant concepts of information theory and uncertainty representation, and presents an algebraic method to formally consider the evolution of uncertain state variables within the PN dynamics. To illustrate some of the real-world challenges relating to uncertainty that can be handled using a PPN, an example of an expert system is provided, demonstrating how condition monitoring data and expert opinion can be modelled