• University Duisburg-Essen, Germany
  • Lecture Rooms
  • Robotic, AKS
  • Faculty of Engineering, AKS
University Duisburg-Essen, Germany1 Lecture Rooms2 Robotic, AKS3 Faculty of Engineering, AKS4

Due to the Corona-crisis, we have to announce to cancel ICMIC 2020.

We thank you for your valuble support and wish you all safe and healthy.

Distributed fault-tolerant environments monitoring systems for aircraft cabins

Rui Wang

In case unexpected environmental events happen in aircraft cabins or cargos, the flight safety and the health safety of passengers and crew members will be considerably affected since aircraft cabins, cargos and avionics compartments are located in a narrow and closed limited spaces. Disasters include cases like wires becoming aging leading to self-burning producing contaminants like CO, inhalable particles, etc. Therefore, studies on the methods of monitoring cabin environments and providing alerts timely and accurately become ultimately important. In this talk, 3D deployment of wireless sensor network in the aircraft, the environmental parameters measurement methods and models over a network system and the multi-stage hybrid data fusion decision algorithms will be presented. Some simulation verifications will also be shown and discussed. The goal of this study is to going to suggest new methods to monitor aircraft environments accurately, timely stably and reliably while decreasing the false alarm rate.

Multiagent Systems: Decentralized, Distributed and Cloud-Based Control Strategies

Magdi S. Mahmoud

An integral ingredient to the operation of industrial or engineering systems, including cooperative robotics, sensor networks, and grid computing, is its control architecture consisting of hardware and software protocols for exchanging system status and control signals. In conventional systems, this is accomplished by Supervisory Control and Data Acquisition (SCADA) systems. Current trends to control and monitor the operation of industrial or engineering systems are however moving toward the use of an automated agent technology, which is generally known as a multiagent system (MAS). A multiagent system is a combination of several agents working in collaboration pursuing assigned tasks to achieve the overall goal of the system. The MAS has become an increasingly powerful tool in developing complex systems that take advantages of agent properties and it is autonomous in that they operate without human interventions.

In this presentation, we focus control strategies pertaining to MAS. Specifically, we address three classes of strategies:

  • Distributed Strategies in which the agent (local) control is equipped with additional information from neighbors (agent-to-agent communication) to achieve
    the global motion-coordination task.

  • Decentralized Strategies in which of the agent (local) controls are independent but collaborate in harmony with the aid of a central coordinator to attain the overall cited goal.

  • Cloud-based Strategies in which the autonomous agents are required to achieve a common coordination objective by exchanging data over a shared cloud registry. The registry is accessed asynchronously by different agents, and direct inter-agent communication is not possible. 


Our main goal is illuminate the merits/demerits of the foregoing strategies as well as the potential application of each. In addition, we discuss several possible extensions for industrial multiagent control systems.


Extended Kalman Filters for Estimation Problems in Industrial Applications

Paolo Mercorelli

Kalman’s optimum linear filter has proved to be very popular. Nevertheless, very often it is not always possible to use a linear system in practical applications. Extended Kalman Filters (EKFs) are used in different fields of industrial applications to reduce the number of sensors and simultaneously to improve the quality of the signals available from the process. EKFs are numerical efficient structures applied through microcontrollers in real industrial applications. Therefore, EKF approach appears to be one of the most suitable ones for problem estimation. The strategic choice of an EKF approach can be justified if the deterministic part of the model can be considered “dominant” and thus in this context, a first order linearized approach, which the EKF is based on, can be taken into consideration for possible industrial applications. After a short overview on the fundamental aspects of the background of EKF, this presentation intends to show how EKFs can be applied as an estimator in different fields of applications such as, for instance, in Thermal Systems, Flexible Actuators, Electromagnetic Actuators, Lithium-Ion Battery as well as in Permanent Magnet Synchronous Motors and other systems. Each System is analyzed in accordance with its physical inside, its intrinsic challenges and dedicated EKFs are proposed to solve specific issues of the considered real application. Measured results related to each application are shown and discussed.


 

 

 

 Potential Topics Include but not limited to

  • System Identification
  • Data-driven Modeling and Control
  • Adaptive Control
  • Linear/Nonlinear Control Systems
  • Predictive Control
  • Optimization and Optimal Control
  • Process Modeling and Process Control
  • Cooperative Control Systems
  • Networked Control Systems
  • Intelligent Systems
  • Soft Computing Techniques
  • Signal Processing and Information Fusion
  • Fault Diagnosis and Reliable Control
  • Vibration Analysis
  • Noise Measuring and Control
  • Condition Monitoring
  • Structural Dynamics
  • Pattern Recognition
  • Machine Learning and Artificial Intelligence
  • Systems Biology and Life Systems
  • Sensor Networks and Internet of Things
  • Big Data and Cloud Computing
  • Discrete Event and Hybrid Systems
  • Fault Diagnosis and Fault Tolerance
  • Instrumentation and Control Techniques
  • Load Modeling and Forecasting
  • Mechatronics Systems
  • Model Calibration and Validation
  • System Reliability, Security and Adequacy
  • Smart Grids and Distributed Generation Systems
  • Chaos, Fractals and Applications
  • Robotic Systems
  • Modeling & Control of Renewable Energy Systems
  • Modeling & Control of Power Systems
  • Modeling of Physiological & Biomedical Systems
  • Control in Education
 

Submission and Publications

Papers should describe original and unpublished work on the above or the related topics. Instructions for electronic submission and paper template (LaTeX or Word) are available on ICMIC2020 website. All papers must STRICTLY follow the formatting instructions as provided at Template (Word Template and Latex Template). All conference papers will be published by a book or proceedings then indexed by EI Compendex. Some selected papers by an objective peer review process will be recommended as special issues to IJMIC (https://www.inderscience.com/ijmic) and IJCAT (https://www.inderscience.com/ijcat).

 

Submission Website

https://easychair.org/conferences/?conf=icmic2020

 
  • 2008 Shanghai Jiaotong University, China
  • 2010 Okayama University, Japan
  • 2011 Shanghai Jiaotong University, China
  • 2012 Huazhong Uni. of Sci and Tec, Wuhan Uni of Sci and Tec, China
  • 2013 Cairo University, Egypt
  • 2014 Swinburne Uni of Tec, Melb, Australia
  • 2015 Uni of Al Qayrawan, Sousse, Tunisia
  • 2016 Medea University, Algiers, Algeria
  • 2017 Kunming University of Science & Technology, Kunming, China
  • 2018 Guizhou University, Guizhou, China

Conference Date

20/7/2020, Monday: registration, possibly tutorial in the afternoon

21-22/7/2020, Tuesday and Wednesday: technical sessions


Important Dates

Deadline for full paper submission:
March 15, 2020

Submission and Review:
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Notification of acceptance: May 1, 2020

Submission of camera-ready: May 20, 2020

Deadline for Authors’ Registration:
June 1, 2020

General Chairs

Headquarters of International Conference on Modelling, Identification and Control
Website: http://icmic.org.uk/

ICMIC 2020

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