IEEE Symposium on Computational Intelligence for Engineering

Solutions (IEEE CIES)

http://ieeessci2020.org/symposiums/cies.html

Part of 2020 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE

1-4 December 2020, online this year, ieeessci2020.org

Deadline is August 7, there will be no extensions

Developments in Engineering are characterized by a growing

complexity, which is balanced by an extensive utilization of

computational resources. This complexity is not only a feature of

engineering systems, processes and products, it is primarily a key

attribute of the respective algorithms for analysis, control and

decision-making to develop those engineering solutions. To cope

with complexity in this broad spectrum of demands, Computational

Intelligence is implemented increasingly in virtually all

engineering disciplines. This emerging approach provides a basis

for developments of a new quality.

This Symposium is focused on the utilization of Computational

Intelligence in this context in the entire field of engineering.

Examples concern the control of processes of various kinds and for

various purposes, monitoring with sensors, smart sensing, system

identification, decision-support and assistance systems,

visualization methods, prediction schemes, the solution of

classification problems, response surface approximations, the

formulation of surrogate models, etc. The engineering application

fields may comprise, for example, bioengineering with prostheses

design and control, civil and mechanical engineering processes,

systems and structures concerned with vehicles, aircraft or

bridges, industrial and systems engineering with design and control

of power systems, electrical and computer engineering with

developments in robotics, etc. All kinds of approaches from the

field of Computational Intelligence are welcome.

As a part of the Symposium special attention is paid to sustainable

engineering solutions to address current and future challenges of

environmental changes and uncertainty. This includes developments

dealing with climate change, environmental processes, disaster

warning and management, infrastructure security, life cycle

analysis and design, etc. Events, disasters and issues under

consideration may be natural such as earthquakes or tsunamis,

man-made such as human failure or terrorist attacks, or a

combination thereof including secondary effects such as failures in

nuclear power plants,which may be critical for systems, the

environment and the society. Developments which include a

comprehensive consideration of uncertainty and techniques of

reliable computing are explicitly invited. These may involve

probabilistic including Bayesian approaches,interval methods, fuzzy

methods, imprecise probabilities and further concepts. In this

context robust design is of particular interest with all its facets

as a basic concept to develop sustainable engineering solutions.

Topics

•Complex engineering systems, structures and processes

•Intelligent analysis, control and decision-making

•Management and processing of uncertainties

•Problem solution in uncertain and noisy environments

•Reliable computing

•Sustainable solutions

•Infrastructure security

•Climate change

•Environmental processes

•Disaster warning and management

•Lifecycle analysis and design

•Automotive systems

•Monitoring

•Smart sensing

•System identification

•Decision-support and assistance systems

•Visualization methods

•Prediction schemes

•Classification methods, cluster analysis

•Response surface approximations and surrogate models

•Sensitivity analysis

•Robust design, reliability-based design, performance-based design

•Risk analysis, hazard analysis, risk and hazard mitigation

•Optimization methods, evolutionary concepts

•Probabilistic and statistical methods

•Simulation methods, Monte-Carlo and quasi Monte-Carlo

•Bayesian approaches / networks

•Artificial Neural Networks

•Imprecise probabilities

•Evidence theory

•p-box approach

•Fuzzy probability theory

•Interval methods

•Fuzzy methods

•Convex modeling

•Information gap theory

Symposium Chairs

Michael Beer

beer@irz.uni-hannover.de

Leibniz University Hannover, Germany

Vladik Kreinovich

vladik@utep.edu

University of Texas at El Paso, USA

Rudolf Kruse

rudolf.kruse@ovgu.de

Otto-von-Guericke University, Germany