1,721,071 research outputs found
To what extent can artificial neural network support nuclear medicine?
No full textArtificial Neural Networks (ANN) are computer programs that emulate the operation
of a large number of processing units that mimic the fundamental mechanisms of
the biological activity of nervous cells as well as their connections and
interactions. As a human brain, an ANN has the ability to learn from the
experience of general relations between variables and thus ANN are particularly
suitable to capture the natural complexity of medical data. Today ANN are widely
used as a tool for computer aided diagnosis. This editorial discusses to what
extent ANN can support Nuclear Medicine
Near-Minimum-Time Trajectories for Robotic Systems using Adaptive Evolutionary Algorithms
No full textProbabilistic Analysis and Verification Framework for Adaptive Flight Control
Full text linkA crucial aspect that could facilitate the applications of adaptive control systems in aerospace applications is the development of effective validation and verification procedures. Most of the existing analysis and design frameworks for adaptive controllers are based on the Lyapunov direct method. One well-known drawback of this approach is the conservatism in the estimation of the uniform ultimate boundedness region with little practical utility. To overcome this limitation, a probabilistic framework for the design of uniform ultimate boundedness regions is proposed where uncertain parameters and adaptive controls are considered as random variables. In this framework, the design is translated into a stochastic convex optimization. This brings the benefit that (probabilistic) linear matrix inequality constraints can be derived without the need of matrix majorizations resulting therefore in less conservative conditions. Although the results are probabilistic, the level of confidence in the violation of linear matrix inequality constraints can be effectively established at the design level, exploiting the recent results of the probabilistic scenario design method. The approach is here applied for the design of uniform ultimate boundedness regions with prespecified component wise error requirements for a model reference adaptive control scheme in the presence of matched and input uncertainty. The approach is validated using the short-period longitudinal dynamics of an F-16 aircraft
RF ranging based on space diversity techniques and directive antennas
No full textIn this paper, a ranging technique based on narrowband transmissions in the 2.4 GHz ISM band is discussed. Multipath mitigation techniques based on Multiple Input Multiple Output (MIMO) philosophy are considered, discussing the effect of antenna directivity on the achievable performance
Effects of antenna directivity on RF ranging when using space diversity techniques
No full textIn this paper, a ranging technique based on narrowband transmissions in the 2.4. GHz ISM band is discussed. Multipath mitigation techniques based on Multiple Input Multiple Output (MIMO) philosophy are considered, discussing the effect of antenna rotations on the achievable performance under Line of Sight conditions, and the effect of RSSI measurement uncertainty on the effectiveness of the mitigation techniques. Simulation and experimental results show that the proposed approach can effectively improve the ranging accuracy. © 2015 Elsevier Ltd
A Physical Model of the Intracranial System for the Study of the Mechanisms of the Cerebral Blood Flow Autoregulation
No full textThis paper introduces a novel physical model of the intracranial system, which was built
with the specific purpose of gaining a better insight into the fundamental mechanisms involved in the
cerebral circulation. Specifically, the phenomena of passive autoregulation of the blood flow and the variation
of the intracranial compliance as a function of the mean intracranial pressure have been investigated.
The physical model allows to go beyond state-of-the-art mathematical models that are often based on
strong assumptions or simplifications on the physical mechanisms governing the cerebral circulation.
Indeed, the physical model based on passive components was able to correctly replicate some fundamental
mechanisms of the blood flow autoregulation. In particular, it allows to highlight the role of the venous
outflow, which behaves as a Starling resistor. The physical model can be employed as a demonstrator for
educational purpose and to test the behavior of shunts for the therapy of hydrocephalus
Multi-model robust adaptive control of tall buildings
No full textModel reference adaptive control (MRAC) strategies are gaining an increasing interest by researchers working in the area of structural control mainly for their capacity of compensating for large uncertainties, faults and time varying disturbances in linear and nonlinear plants. In this paper a modification of the standard MRAC algorithm is proposed which adopts multiple reference models (M-MRAC). The motivation of the proposed scheme is that flexible buildings subjected to multiple hazards, like wind and earthquake, require different damping levels to avoid the occurrence of different limit states. A M-MRAC scheme that uses two reference models is adopted. The first reference model, with small structural damping, is active in the presence of low level excitation like moderate wind loading, while the second reference model, with a higher damping, is activated when a suitable response-dependent signal exceeds a defined threshold in the presence of severe seismic loading. Analyses on a tall building subjected to wind and earthquake show the capacity of the M-MRAC in tracking the reference system and in mitigating the structural response. Comparison with traditional MRAC highlights that the main advantage of M-MRAC is the reduction of the control force, at the expense of a slight reduction of control effectivenes
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