Computing the Parallelism Degree of Timed BPMN Processes

International audienceA business process is a combination of structured and related activities that aim at fulfilling a specific organizational goal for a customer or market. An important measure when developing a business process is the degree of parallelism, namely, the maximum number of tasks that are executable in parallel at any given time in a process. This measure determines the peak demand on tasks and thus can provide valuable insight on the problem of resource allocation in business processes. This paper considers timed business processes modeled in BPMN, a workflow-based graphical notation for processes, where execution times can be associated to several BPMN constructs such as tasks and flows. An encoding of timed business processes into Maude's rewriting logic system is presented, enabling the automatic computation of timed degrees of parallelism for business processes. The approach is illustrated with a simple yet realistic case study in which the degree of parallelism is used to improve the business process design with the ultimate goal of optimizing resources and, therefore, with the potential for reducing operating costs

Verification and Compliance in Collaborative Processes

Evidently, COVID-19 has changed our lives and is likely to make a lasting impact on our economic development and our industry and services. With the ongoing process of digital transformation in industry and services, Collaborative Networks (CNs) is required to be more efficient, productive, flexible, resilient and sustainable according to change of situations and related rules applied afterwards. Although the CN area is relatively young, it requires the previous research to be extended, i.e. business process management from dealing with processes within a single organization into processes across different organizations. In this paper, we review current business process verification and compliance research. Different tools approaches and limitations of them are compared. The further research issues and potential solutions of business process verification and compliance check are discussed in the context of CNs

Application of Bond Graph Modeling for Photovoltaic Module Simulation

In this paper, photovoltaic generator is represented using the bond-graph methodology. Starting from the equivalent circuit the bond graph and the block diagram of the photovoltaic generator have been derived. Upon applying bond graph elements and rules a mathematical model of the photovoltaic generator is obtained. Simulation results of this obtained model using real recorded data (irradiation and temperature) at the Renewable Energies Development Centre in Bouzaréah – Algeria are obtained using MATLAB/SMULINK software. The results have compared with datasheet of the photovoltaic generator for validation purposes

Compensation for the iron loss effect in EKF-based speed estimation of vector controlled induction motors

In vector controlled induction motor drives, the instantaneous rotor speed is measured using whether sensors or estimators. Since the basic Kalman filter is a state observer, its use in vector controlled schemes has received much attention. However, these schemes are based on the assumption that the existence of iron loss in an induction motor may be neglected. The paper shows the effect of iron loss on the extended Kalman filter performance that is designed on the basis if the ironless induction machine model. Simulation results are carried out to demonstrate this effect as well as the effectiveness of the suggested approach to minimise the speed estimation error without modifying the observer algorith

A new algorithm applied to the evaluation of self excited induction generator performance

The paper presents the application of DIRECT algorithm to analyse the performances of the Self-excited induction generator. It is used to minimize the induction generator admittance yielding the solution which consists of the magnetizing reactance and the frequency. These parameters are the keys to find out the self excitation process requirements in terms of the prime mover speed, the capacitance and the load impedance and finally the output performances such as the voltage, output power, etc. A comparison with other powerful optimization algorithms is investigated to obtain DIRECT algorithm performance

Sigmoid function approximation for ANN implementation in FPGA devices

The objective of this work is the implementation of Artificial Neural Network on a FPGA board. This implementation aim is to contribute in the hardware integration solutions in the areas such as monitoring, diagnosis, maintenance and control of power system as well as industrial processes. Since the Simulink library provided by Xilinx, has all the blocks that are necessary for the design of Artificial Neural Networks except a few functions such as sigmoid function. In this work, an approximation of the sigmoid function in polynomial form has been proposed. Then, the sigmoid function approximation has been implemented on FPGA using the Xilinx library. Tests results are satisfactor

Sensorless speed field-oriented control of induction motor tacking core loss into account

In field-oriented controlled induction motor drives, the instantaneous rotor speed is measured using whether sensors or estimators. Since the basic Kalman filter is a state observer, its use in vector controlled schemes has received much attention. However, these schemes are based on the assumption that the existence of iron loss in the induction motor may be neglected. The paper shows the effect of iron loss on the extended Kalman filter performance that is designed on the basis of the classical dq model. Original simulation results are carried out to demonstrate this effect as well as the effectiveness of the suggested approach to minimise the speed estimation error without modifying the EKF's algorithm

A fuzzy logic-based filter for the removal of spike noise from 2D electrical resistivity data

In this paper, a filter based on fuzzy logic is proposed to remove spike noise from 2 dimensional electrical resistivity data. The noise detection used in this paper is based on differentiating noisy samples from the central sample inside a moving window. These fuzzy derivatives are used by the fuzzy inference system to detect corrupted samples. To assess the performance of the proposed filter for the removal of spike noise, the root-mean squared error as well as the signal-to-noise ratio were used as an objective criterion. It has been demonstrated by synthetic and real examples that the proposed filter achieves quite good results compared to the standard median filter as well as to the very effective SD-ROM filte

A First-Order Logic Semantics for Communication-Parametric BPMN Collaborations

International audienceBPMN is suitable to model not only intra-organization workflows but also inter-organization collaborations. There has been a great effort in providing a formal semantics for BPMN, and then in building verification tools on top of this semantics. However, communication aspects are often discarded in the literature. This is an issue since BPMN has gained interest outside its original scope, e.g., for the IoT, where the configuration of communication modes plays an important role. In this paper, we propose a formal semantics for a subset of BPMN, taking into account inter-process communication and parametric verification with reference to communication modes. As opposed to transformational approaches, that map BPMN into some formal model such as transition systems or Petri nets, we give a direct formalization in First-Order Logic that is then implemented in TLA + to enable formal verification. Our approach is tool supported. The tool, as well as the TLA + theories, and experiment models are available online