scholarly journals A Dynamic Context-Aware Workflow Management Scheme for Cyber-Physical Systems Based on Multi-Agent System Architecture

2021 ◽  
Vol 11 (5) ◽  
pp. 2030
Author(s):  
Fu-Shiung Hsieh

Although Cyber-Physical Systems (CPS) provides a paradigm to accommodate frequent changes in manufacturing sector, modeling and managing operations of CPS are challenging issues due to the complex interactions between entities in the system. Development of an effective context-aware workflow management system to guide the entities in the system is a critical factor to attain the potential benefits of CPS. In this paper, we will address the issue on the design of context-aware workflow management systems for CPS in IoT-enabled manufacturing environment. A CPS consists two parts, the Physical World and the Cyber World. To achieve the goal to design a context-aware information system for CPS, the Cyber World models of the entities in the system are constructed based on discrete timed Petri nets (DTPN) and a multi-agent system architecture in which each entity in the system is modeled as an agent to capture the interactions of entities in CPS. To develop context-aware workflow management systems for CPS, a Configuration/Scheduling Feasibility Problem and a Context Generation Problem in CPS are formulated. A condition for configuration/scheduling feasibility based on transformation of the Cyber World Models is established to develop an algorithm to generate contextual information to guide the operation of CPS. The proposed method is illustrated by examples. A series of experiments have been conducted to demonstrate the practicality of the proposed method in terms of computation time and response time. The results indicate that the computation time and total response time increase polynomially with respect to problem size parameters and show that the proposed method is effective in solving real problems.

2020 ◽  
Vol 5 (4) ◽  
pp. 376-387
Author(s):  
John Mbuli ◽  
Tarik Chargui ◽  
Damien Trentesaux ◽  
Abdelghani Bekrar ◽  
Thierry Dailly

2012 ◽  
Vol 6 (1) ◽  
pp. 727-731 ◽  
Author(s):  
Ya Li ◽  
Hairui Wang ◽  
Zhibin Zhang

Author(s):  
Tobias Käfer ◽  
Benjamin Jochum ◽  
Nico Aßfalg ◽  
Leonard Nürnberg

AbstractFor Read-Write Linked Data, an environment of reasoning and RESTful interaction, we investigate the use of the Guard-Stage-Milestone approach for specifying and executing user agents. We present an ontology to specify user agents. Moreover, we give operational semantics to the ontology in a rule language that allows for executing user agents on Read-Write Linked Data. We evaluate our approach formally and regarding performance. Our work shows that despite different assumptions of this environment in contrast to the traditional environment of workflow management systems, the Guard-Stage-Milestone approach can be transferred and successfully applied on the web of Read-Write Linked Data.


2021 ◽  
Vol 10 (1) ◽  
pp. 18
Author(s):  
Quentin Cabanes ◽  
Benaoumeur Senouci ◽  
Amar Ramdane-Cherif

Cyber-Physical Systems (CPSs) are a mature research technology topic that deals with Artificial Intelligence (AI) and Embedded Systems (ES). They interact with the physical world via sensors/actuators to solve problems in several applications (robotics, transportation, health, etc.). These CPSs deal with data analysis, which need powerful algorithms combined with robust hardware architectures. On one hand, Deep Learning (DL) is proposed as the main solution algorithm. On the other hand, the standard design and prototyping methodologies for ES are not adapted to modern DL-based CPS. In this paper, we investigate AI design for CPS around embedded DL. The main contribution of this work is threefold: (1) We define an embedded DL methodology based on a Multi-CPU/FPGA platform. (2) We propose a new hardware design architecture of a Neural Network Processor (NNP) for DL algorithms. The computation time of a feed forward sequence is estimated to 23 ns for each parameter. (3) We validate the proposed methodology and the DL-based NNP using a smart LIDAR application use-case. The input of our NNP is a voxel grid hardware computed from 3D point cloud. Finally, the results show that our NNP is able to process Dense Neural Network (DNN) architecture without bias.


1998 ◽  
Author(s):  
Thomas Wendler ◽  
Kirsten Meetz ◽  
Joachim Schmidt

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