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Simulation Systems

simulations_systemsBuilding advanced simulation systems to help you visualise, viagra sales test and achieve your ideal solutions and by putting in place specific optimisation techniques to work along with your system’s processes to ensure your satisfaction with the results.

Beyond virtual reality we offer Augmented Reality systems to give experts such as surgeons a better sensory perception of patient’s body during an operation resulting in less risky and more efficient surgical operations.

These systems could also be used in conjunction with other medical equipment such as an x-ray machines or magnetic resonance imaging to instantly give the doctors the information they need to make a medical diagnosis or decision.

Computer simulations are exquisite tools for modelling and analysing the true performance of the evolving production systems.

To overcome the inflexible and limited issues of traditional simulation techniques, we offer our customers intelligent knowledge based modelling, and intelligent computer simulation environment, for flexible manufacturing systems, information systems, process plants, just in time and aerospace manufacturing systems.

Intelligent simulation environment would allow the designers of manufacturing systems to predict and provide the means to control the relevant disturbances to an acceptable degree of completeness, automatically enabling you to foresee the behaviour of the systems in normal and increased production situations and so guiding your system to a smoother as well as more efficient performance.

An intelligent simulation environment is a large knowledge integration system consisting of several symbolic reasoning systems (LISP, PROLOG, etc.) and numerical simulation software. It is a framework for integration of numerical simulation, expert system and artificial intelligence techniques which is based on machine learning and improvement mechanisms and is applied as means of extracting rules to be used in expert systems. It is flexible, integrated and knowledge based framework capable of learning, correcting itself and adapting to necessary changes. It is a goal oriented simulation environment for the purpose of optimisation and searches for the best solutions by referring to a desired target. On application we organise the integration of an integrated database and modelling, a rule-based (goal-oriented) performance, parametric and flexible structures.

Integrated simulation (IS) modelling (ISM)

Integrated modelling environment is an essential aspect of integrated simulation, composed of an integrated database and integrated modelling. The former requires an integrated rather than the stationary approach of modelling, therefore the hidden points and bottlenecks are reduced. Additionally all the system requirements, limitations, constraints, inputs, interrelationships (with other systems) and feedback are taken into account and modelled.
Integrated database (i.e. a key element in developing an intelligent knowledge based software engineering) is complementary to integrated simulation modelling, concerned with comprehensive and robust data collection analysis, capable of revealing system status based on maintenance and operation activities.
The component failure, operation and service intervals of complex production systems may not be easily predicted due to their stochastic and ambiguous nature.
The main problems would be that the engineers prefer to deal with absolutes than probabilities and they do not collect enough data to make statistical estimates of important parameters such as process times, failure rates, etc., therefore the data analysis would show biased results that may or may not foresee the true nature of production systems. Hence the simulation results may not reflect the actual performance.

Therefore we offer our customers the service to analyse the stochastic nature and collect the necessary and sufficient data to assess their non transient point estimates.

The integrated database in context of an Integrated modelling environment would address the above issues. It will contain robust and meaningful sets of data and acts as a knowledge base for the software engineering.

For Intelligent simulations, each system being atypical, the problem solving approach of each would be based on Systems Uniqueness Philosophy, requiring an approach that dwells on its own contextual requirements.

By conceptual models we address the states, conditions and operations, modelling system’s tolerances, capacity requirements and its interactions with other systems resulting in an intelligent simulation model which is flexible so that various alternatives and strategies can be retrieved, incorporating management and economics factors to drive towards the best solutions.

An integrated simulation model is a set of versatile, flexible (i.e. its module defines system’s tolerances, capacity requirements and limitations) and parametric computer programs. It is composed of an integrated database that by learning process (rule based learning module) leads to the best answer or a set of optimum solutions. By learning from the past it creates new rules that if matched with system’s expectations, it would then be chosen as the best answer.

With a none isolated approach of simulation modelling, this involves globally none stationary (local) methods of synthesis, locating hidden points and deficiencies. It considers all endogenous and exogenous factors.

To complete an integrated simulation modelling, we establish a more holistic simulation approach. The distinct features are integration of flexible structure, rule based module, integrated database and modelling and parametric simulation modelling.


  • It searches for the best solution or a set of optimum answers by a rule-based mechanism.
  • An intelligent simulation model considers actual system’s limitations and constraints. Thus the system would be simulated without much confusion in a shorter time.
  • The results of an intelligent simulation model are practical and may be implemented.
  • It is an integrated approach that considers all endogenous and exogenous factors affecting system being studied.

Other Applications

Reinforcement learning is an approach for an autonomous agent learning to choose optimal actions to achieve its goals by sensing and acting in its environment, giving feedback to the agent from a simulated environment. The agent receives a reinforcement feedback from the environment along with the current state. The goal for the agent would be to create an optimal action selection policy to maximise the reward.

Solutions Delivered


    • VR sim. System, Training and surgical application (Phase one).
    • Integrated air traffic sim. together with Airport Management system.

Mathematical Modelling

    • Modelling of probe/surface-contact, field systems.
    • Communication systems (Reverberation Modelling)
    • Tomography and Inversion, deep earth.
    • Mapping geothermal dynamic flow patterns

Stochastic Modelling

    • Population dynamics
    • Epidemics.
    • Reliability of equipments subject to failure

Finite elements

    • Stress strain and heat transfer (new high strength materials)

System identification

    • Noise filtration.


  • Optimization Methods for Traffic Engineering, new method for solving traffic problems
  • Chaotic systems, Dynamic systems analysis.
  • Energy optimisation for wind farm site.
  • Fault analysis prediction (early warning for structural faults in dams).