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An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation. � 2009 Elsevier Ireland Ltd. All rights reserved.

In this paper a newly applied approach to model the effects of high-rise blocking buildings near a base station (BS) in cellular communications networks is presented. Although recently accurate deterministic propagation prediction methods have become popular to account for these propagation effects, many mobile operators still employ rather simple and quick semi-deterministic propagation models, which use averaged geo-location information instead of actual building shapes in their calculations. In this work important insights from purely deterministic propagation theories are adopted and employed in a popular semi-deterministic model. After performing measurements, the difference in the prediction error for shadow and no-shadow areas is used to quantify the effect of the blocking object. It is found that statistical geo-location information can be used to predict the amount of blocking in a deterministic way, after application of a simple transformation algorithm. Subsequently, correction factors are proposed in order to adapt the single knife-edge diffraction model to improve the attenuation predictions and prevent the overestimation. The derived factors are based on the degree of urbanity of the environment. Multiplying the predictions of the diffraction model with the environment-based correction factors indeed significantly improves the predictions for the sectors that were studied.

In this paper, a new method is presented to model the given reflectance data obtained from a "passive one-port physical device", as a lossless two port consists of lumped and distributed elements. Basis of the new method rests on the interpolation of the given data as a realizable bounded-real (BR) reflection function in two variables. The desired circuit model is then obtained as the result of the synthesis of this function in two kinds of elements; namely, distributed and lumped circuit elements. An algorithm is presented to generate the circuit model step by step and an example is also included to exhibit the utilization of the new modeling algorithm. Publisher's Version

The advance in interactive/cooperative robotics and integration of robots into populated environments puts emphasis on robot-robot and robot-environment (incl. human) interactions. Sensing and sensory data interpretation play ever increasing role because sensing becomes the primary way of collecting information about the environment. In this application domain the environment is unstructured, which results in demanding data interpretation and control algorithms. Similarly, the control and decision-making algorithms inevitably become more complex in order to be able to cope with the unstructured and dynamic environment. Consequently a sophisticated evaluation/test environment is required, which provides full control of the circumstances, reproducibility and flexible mix of real and virtual components. The paper presents a multi-agent based distributed HIL simulation environment (MARS), which assures scalable real-time performance and introduces an extension with high fidelity sensor simulation functionality.

The advance in interactive/cooperative robotics and integration of robots into populated environments puts emphasis on robot-robot and robot-environment (incl. human) interactions. Sensing and sensory data interpretation play ever increasing role because sensing becomes the primary way of collecting information about the environment. In this application domain the environment is unstructured, which results in demanding data interpretation and control algorithms. Similarly, the control and decision-making algorithms inevitably become more complex in order to be able to cope with the unstructured and dynamic environment. Consequently a sophisticated evaluation/test environment is required, which provides full control of the circumstances, reproducibility and flexible mix of real and virtual components. The paper presents a multi-agent based distributed HIL simulation environment (MARS), which assures scalable real-time performance and introduces an extension with high fidelity sensor simulation functionality.

Vital to the effectiveness of simulation-based design is having a system model of known quality. Previous research introduced an algorithm called AVASIM for assessing model validity systematically and quantitatively. AVASIM assess the accuracy and validity of a model based on a specific input and set of system parameters. The purpose of this paper is to present a AVASIM-based methodology that defines a range of validity of a model with respect to input and system parameter variations Two illustrative examples are presented to explore the feasibility of the proposed procedure. The first example analyzes a linearized version of a nonlinear transient vehicle-handling model. This model's accuracy and validity are evaluated with respect to variation of two system parameters, resulting in a two-dimensional range of validity. Then a complex nonlinear hydrogen fuel cell model is linearized in order to investigate its accuracy and validity with respect to two input parameters. This again results in a two-dimensional range of validity, but with respect to input rather than system parameters. The results agree well with what is expected for the various models based on knowledge of the effects of linearization on model accuracy. The proposed algorithm for assessing model range of validity is a promising tool for determining model quality and thus potentially useful for simulation-based design. Copyright © 2004 by ASME. 73 835 843 835-843

This paper presents the tools for design and validation of pre-crash systems: the software tool PRE-crash SCenario ANalyzer (PRESCAN) and the VEhicle-Hardware-In-the-Loop (VEHIL) facility. PRESCAN allows to investigate a pre-crash scenario in simulation. This scenario can then be compared with tests performed in the VEHIL facility for validation of the real sensor and controller of the pre-crash system. Using PRESCAN and VEHIL the development process and more specifically the validation of intelligent vehicles can be carried out safer, cheaper, and more reliable.

Scheduling in flexible assembly systems is a complicated phenomenon owing to the large variability in the operating parameters. In this paper, coloured Petri nets are shown to be useful in modelling, simulating and scheduling flexible assembly systems. Case studies from published literature are chosen to show its superiority over some of the heuristic and analytical tools. In addition, the tool is also shown to model and analyse systems with some realistic constraints.