Formal apparatus for measurement of lightweight protocols

Lightweight protocols are an important topic in the area of computer communications. With the proliferation of security services not only ordinary communication protocols, but also cryptographic protocols, i.e. security services, have become a subject of research into possible appropriate lightweight solutions. At first glance it may seem surprising, but the evidence suggests that there is a permanent need for lightweight protocols. And this need is ever increasing, due to the gap between desktop (and other ordinary computing devices) and mobile wireless devices that have inherently limited resources. However, the notion of lightweight protocol has not been formally addressed in the literature, which is the purpose of this paper. A formal model that can be used to evaluate lightweight properties of protocols is presented and the appropriate metrics are introduced. Despite the fact that the model and the metrics target weak processing devices, they can be deployed for ordinary computing environments and may present a methodology for evaluation of lightweight cryptographic protocols in standardization processes.     

On-line learning and modulation of periodic movements with nonlinear dynamical systems

The paper presents a two-layered system for (1) learning and encoding a periodic signal without any knowledge on its frequency and waveform, and (2) modulating the learned periodic trajectory in response to external events. The system is used to learn periodic tasks on a humanoid HOAP-2 robot. The first layer of the system is a dynamical system responsible for extracting the fundamental frequency of the input signal, based on adaptive frequency oscillators. The second layer is a dynamical system responsible for learning of the waveform based on a built-in learning algorithm. By combining the two dynamical systems into one system we can rapidly teach new trajectories to robots without any knowledge of the frequency of the demonstration signal. The system extracts and learns only one period of the demonstration signal. Furthermore, the trajectories are robust to perturbations and can be modulated to cope with a dynamic environment. The system is computationally inexpensive, works on-line for any periodic signal, requires no additional signal processing to determine the frequency of the input signal and can be applied in parallel to multiple dimensions. Additionally, it can adapt to changes in frequency and shape, e.g. to non-stationary signals, such as hand-generated signals and human demonstrations.

Gaussian process approach for modelling of nonlinear systems

Parametric modelling principals such as neural networks, fuzzy models and multiple model techniques have been proposed for modelling of nonlinear systems. Research effort has focused on issues such as the selection of the structure, constructive learning techniques, computational issues, the curse of dimensionality, off-equilibrium behaviour, etc. To reduce these problems, the use of non-parametrical modelling approaches have been proposed. This paper introduces the Gaussian process (GP) prior approach for the modelling of nonlinear dynamic systems. The relationship between the GP model and the radial basis function neural network is explained. Issues such as selection of the dimension of the input space and the computation load are also discussed. The GP modelling technique is demonstrated on an example of the nonlinear hydraulic positioning system.     

Online fuzzy identification for an intelligent controller based on a simple platform

The paper presents the identification issues of the self-tuning nonlinear controller ASPECT (Advanced control algorithmS for ProgrammablE logiC conTrollers). The controller is implemented on a simple PLC platform with an extra mathematical coprocessor, but is intended for the advanced control of complex processes. The model of the controlled plant is obtained by means of experimental modelling. A special batch-wise algorithm that is based on the Takagi–Sugeno model and uses “fuzzy instrumental variables” technique is described in the paper. Many robustness problems of the classical adaptive approaches can be circumvented to some extent by the proposed batch-wise approach combined with a supervisory mechanism. The paper also includes some experimental results on the hydraulic pilot plant and some simulation case studies.     

On decidability of LTL model checking for process rewrite systems

We establish a decidability boundary of the model checking problem for infinite-state systems defined by Process Rewrite Systems (PRS) or weakly extended Process Rewrite Systems (wPRS), and properties described by basic fragments of action-based Linear Temporal Logic (LTL) with both future and past operators. It is known that the problem for general LTL properties is decidable for Petri nets and for pushdown processes, while it is undecidable for PA processes.We show that the problem is decidable for wPRS if we consider properties defined by LTL formulae with only modalities strict eventually, strict always, and their past counterparts. Moreover, we show that the problem remains undecidable for PA processes even with respect to the LTL fragment with the only modality until or the fragment with modalities next and infinitely often.     

The simple model of cell prestress maintained by cell incompressibility

Living cells are reinforced by polymer fibers (the so-called cytoskeleton) which are responsible for their mechanical behaviour. There are many evidences that these fibres are prestressed without an external load. To include this prestress into mechanical models of living tissues is not an easy task. We propose an approach in which the intracellular prestress is maintained by the incompressibility of cells. A simple illustrative structure is studied in order to determine the dependence of stiffness on the level of prestress. Some macroscopic models of living tissues with prestressed cells are formulated. The results show a clear dependence of the macroscopic mechanical response on the level of prestress at microscale. The model exhibits some features of living cells (prestress-induced stiffening, strain hardening).     

Temporomandibular joint and its two-dimensional and three-dimensional modelling

Detailed knowledge about the function and morphology of temporomandibular joint are necessary for clinical applications and for analyses of the function of temporomandibular joint prosthesis. Movements of temporomandibular joint are biomechanically sophisticated and are up-to-date not clear. Therefore, the aim of the paper is to give the suitable mathematical approach for analyses of temporomandibular joint. In the paper the analysis of the temporomandibular joint, loaded by traction and compression forces, is presented and shortly discussed. The obtained results, based on two- and three-dimensional mathematical models, represent an introductory work for further studies of biomechanical aspects of temporomandibular joint and of its artificial replacements. The models are based on the theory of contact problems in linear elasticity. For the numerical solutions of the investigated problems the FEM approaches are used and the used algorithm is based on an active-set method for quadratic programming.     

NPV-based decision support in multi-objective design using evolutionary algorithms

Optimum design problems are frequently formulated using a single excellence criterion (minimum mass or similar) with evolutionary algorithms engaged as decision-support tools. Alternatively, multi-objective formulations are used with a posteriori decision-making amongst the Pareto candidate solutions. The former typically introduces excessive simplification in the decision space and subjectivity, the latter leads to extensive numerical effort and postpones the compromise decision-making. In both cases, engineering excellence metrics such as minimum mass can be misleading in terms of performance of the respective design in the given operational environment. This paper presents an alternative approach to conceptual design where a compound objective function based on the Net Present Value (NPV) and Internal Rate of Return (IRR) aggregate performance metrics is developed. This formulation models the integral value delivered by the candidate designs over their respective life-cycles by applying value-based NPV discounting to all objectives. It can be incorporated as an a priori compromise and consequently viewed as a weighted sum of individual objectives corresponding to their economically faithful representation over the entire operational life-time of the designs. The multi-objective design optimization is consequently expanded from purely engineering terms to coupled engineering–financial decision support.     

Hybrid explicit model predictive control of a nonlinear process approximated with a piecewise affine model

The recently developed methods of explicit (multi-parametric) model predictive control (e-MPC) for hybrid systems provide an interesting opportunity for solving a class of nonlinear control problems. With this approach, the nonlinear process is approximated by a piecewise affine (PWA) hybrid model containing a set of local linear dynamics. Compared to linear-model-based MPC, a performance improvement is expected with the reduction of the plant-to-model mismatch; however at a cost of controller computation complexity. In order to reduce the computational load, so that desired horizon lengths may be used, we present an efficient sub-optimal solution. The feasibility of the approach for the application was evaluated in an experimental case study, where an output feedback, offset-free-tracking hybrid e-MPC controller was considered as a replacement for a PID-controller-based scheme for the control of the pressure in a wire-annealing machine.