Automatic Procedure Following Evaluation Using Petri Net-Based Workflows

At present, SOILD-STATE Fermentation (SSF) is mainly controlled by artificial experience, and the product quality and yield are not stable. Accurately predicting the quality and yield of SSF is of great significance for improving human food security and supply. In this paper, we propose an Intelligent Utility Prediction (IUP) scheme for SSF in 5G Industrial Internet of Things (IoT), including parameter collection and utility prediction of SSF process. This IUP scheme is based on the environmental perception and intelligent learning algorithms of the 5G Industrial IoT. We build a workflow model based on rewritable petri net to verify the correctness of the system model function and process. In addition, we design a utility prediction model for SSF based on the Generative Adversarial Networks (GAN) and Fully Connected Neural Network (FCNN). We design a GAN with constraint of mean square error (MSE-GAN) to solve the problem of few-shot learning of SSF, and then combine with the FCNN to realize the utility prediction (usually use the alcohol) of SSF. Based on the production of liquor in laboratory, the experiments show that the proposed method is more accurate than the other prediction methods in the utility prediction of SSF, and provide the basis for the numerical analysis of the proportion of preconfigured raw materials and the appropriate setting of cellar temperature.

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“…In this case, it is considered that consistency in the graph of transitions is ensured if simultaneous transitions along any two or more arcs emanating from one vertex are prohibited in it [10,11].…”

Section: The Matrix Algorithmmentioning

confidence: 99%

Analysis of the Correctness of the Model of Biomedical Experiment Based on the Transition Graph by Means of a Matrix Algebra

Kruzhkov¹,

Nasyrov²,

Mulayanov³

2019

Proceedings of the 21st International Workshop on Computer Science and Information Technologies (CSIT 2019)

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The article deals with the problems of formal description of methods of organization of biomedical experiment. The need for a formal description of the experimental scheme is postulated. One of the ways of formal representation of the biomedical experiment based on the transition graph, which is one of the variants of the description of the finite state machine, is considered. The description revealed that the main characteristics of this representation are the correctness and adequacy of the representation in the form of a graph of transitions. The problem of description of nested sequences of actions corresponding to the beginning and end of the use of different components of the experiment is considered. An example of such use is the serial and parallel activation of experimental equipment. In this case, the correct sequences correspond to the implemented experiments, and the wrong ones correspond to the unrealizable ones. In this regard, there is a need to verify such feasibility. The necessity of checking the correctness of the graph as the main condition for the feasibility of the experiment is substantiated. One of the approaches to the analysis of the correctness of such a graph on the basis of matrix calculations is presented. The method of constructing the transition graph matrix is considered. The basic transformations necessary for checking the correctness of the transition graph of the biomedical experiment are given.

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Automatic Procedure Following Evaluation Using Petri Net-Based Workflows

Cited by 9 publications

References 25 publications

DLUP: A Deep Learning Utility Prediction Scheme for Solid-State Fermentation Services in IIoT

DLUP: A Deep Learning Utility Prediction Scheme for Solid-State Fermentation Services in IIoT

IUP: An Intelligent Utility Prediction Scheme for Solid-State Fermentation in 5G IoT

Analysis of the Correctness of the Model of Biomedical Experiment Based on the Transition Graph by Means of a Matrix Algebra

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