The MIRELA framework: modeling and analyzing mixed reality applications using timed automata

Mixed Reality (MR) aims to link virtual entities with the real world and has many applications such as military and medical domains [JBL+00, NFB07]. In many MR systems and more precisely in augmented scenes, one needs the application to render the virtual part accurately at the right time. To achieve this, such systems acquire data related to the real world from a set of sensors before rendering virtual entities. A suitable system architecture should minimize the delays to keep the overall system delay (also called end-to-end latency) within the requirements for real-time performance. In this context, we propose a compositional modeling framework for MR software architectures in order to specify, simulate and validate formally the time constraints of such systems. Our approach is first based on a functional decomposition of such systems into generic components. The obtained elements as well as their typical interactions give rise to generic representations in terms of timed automata. A whole system is then obtained as a composition of such defined components. To write specifications, a textual language named MIRELA (MIxed REality LAnguage) is proposed along with the corresponding compilation tools. The generated output contains timed automata in UPPAAL format for simulation and verification of time constraints. These automata may also be used to generate source code skeletons for an implementation on a MR platform. The approach is illustrated first on a small example. A realistic case study is also developed. It is modeled by several timed automata synchronizing through channels and including a large number of time constraints. Both systems have been simulated in UPPAAL and checked against the required behavioral properties.

Modeling and Vibration Suppression for Telescopic Systems of Structural Members with Clearance

Telescopic systems of structural members with clearance are found in many applications, e.g., mobile cranes, rack feeders, fork lifters, stacker cranes (see Figure 1). Operating these machines, undesirable vibrations may reduce the performance and increase safety problems. Therefore, this contribution has the aim to reduce these harmful vibrations. For a better understanding, the dynamic behaviour of these constructions is analysed. The main interest is the overlapping area of each two sections of the above described systems (see markings in Figure 1) which is investigated by measurements and by computations. A test rig is constructed to determine the dynamic behaviour by measuring fundamental vibrations and higher frequent oscillations, damping coefficients, special appearances and more. For an appropriate physical model, the governing boundary value problem is derived by applying Hamilton’s principle and a classical discretisation procedure is used to generate a coupled system of nonlinear ordinary differential equations as the corresponding truncated mathematical model. On the basis of this model, a controller concept for preventing harmful vibrations is developed.

Fuzzy Multi-Objective Linear Programming and Simulation Approach to the Development of Valid and Realistic Master Production Schedule

Master production schedule (MPS) plays an important role in an integrated production planning system. It converts the strategic planning defined in a production plan into the tactical operation execution. The MPS is also known as a tool for top management to control over manufacture resources and becomes input of the downstream planning levels such as material requirement planning (MRP) and capacity requirement planning (CRP). Hence, inappropriate decision on the MPS development may lead to infeasible execution, which ultimately causes poor delivery performance. One must ensure that the proposed MPS is valid and realistic for implementation before it is released to real manufacturing system. In practice, where production environment is stochastic in nature, the development of MPS is no longer simple task. The varying processing time, random event such as machine failure is just some of the underlying causes of uncertainty that may be hardly addressed at planning stage so that in the end the valid and realistic MPS is tough to be realized. The MPS creation problem becomes even more sophisticated as decision makers try to consider multi-objectives; minimizing inventory, maximizing customer satisfaction, and maximizing resource utilization. This study attempts to propose a methodology for MPS creation which is able to deal with those obstacles. This approach takes into account uncertainty and makes trade off among conflicting multi-objectives at the same time. It incorporates fuzzy multi-objective linear programming (FMOLP) and discrete event simulation (DES) for MPS development.

Realistic Single Cell Modeling – from Experiment to Simulation

This tutorial gives a step by step explanation of how one uses experimental data to construct a biologically realistic multicompartmental model. Special emphasis is given on the many ways that this process can be imprecise. The tutorial is intended for both experimentalists who want to get into computer modeling and for computer scientists who use abstract neural network models but are curious about biological realistic modeling. The tutorial is not dependent on the use of a specific simulation engine, but rather covers the kind of data needed for constructing a model, how they are used, and potential pitfalls in the process.