5,088 research outputs found
Stability analysis of event-triggered anytime control with multiple control laws
To deal with time-varying processor availability and lossy communication
channels in embedded and networked control systems, one can employ an
event-triggered sequence-based anytime control (E-SAC) algorithm. The main idea
of E-SAC is, when computing resources and measurements are available, to
compute a sequence of tentative control inputs and store them in a buffer for
potential future use. State-dependent Random-time Drift (SRD) approach is often
used to analyse and establish stability properties of such E-SAC algorithms.
However, using SRD, the analysis quickly becomes combinatoric and hence
difficult to extend to more sophisticated E-SAC. In this technical note, we
develop a general model and a new stability analysis for E-SAC based on Markov
jump systems. Using the new stability analysis, stochastic stability conditions
of existing E-SAC are also recovered. In addition, the proposed technique
systematically extends to a more sophisticated E-SAC scheme for which, until
now, no analytical expression had been obtained.Comment: Accepted for publication in IEEE Transactions on Automatic Contro
On "the authentic damping mechanism" of the phonon damping model
Some general features of the phonon damping model are presented. It is
concluded that the fits performed within this model have no physical content
A path planning control for a vessel dynamic positioning system based on robust adaptive fuzzy strategy
The thrusters and propulsion propellers systems, as well as the operating situations, are all well-known nonlinearities which are caused less accuracy of the dynamic positioning system (DPS) of vessels in the path planning control process. In this study, to enhance the robust performance of the DPS, we proposed a robust adaptive fuzzy control model to reduce the effect of uncertainty problems and disturbances on the DPS. Firstly, the adaptive fuzzy controller with adaptive law is designed to adjust the membership function of the fuzzy controller to minimize the error in path planning control of the vessel. Secondly, the H∞ performance of robust tracking is proved by the Lyapunov theory. Moreover, compared to the other controller, a simulation experiment comprising two case studies confirmed the efficiency of the approach. Finally, the results showed that the proposed controller reaches control quality, performance and stability
BPLight-CNN: A Photonics-based Backpropagation Accelerator for Deep Learning
Training deep learning networks involves continuous weight updates across the
various layers of the deep network while using a backpropagation algorithm
(BP). This results in expensive computation overheads during training.
Consequently, most deep learning accelerators today employ pre-trained weights
and focus only on improving the design of the inference phase. The recent trend
is to build a complete deep learning accelerator by incorporating the training
module. Such efforts require an ultra-fast chip architecture for executing the
BP algorithm. In this article, we propose a novel photonics-based
backpropagation accelerator for high performance deep learning training. We
present the design for a convolutional neural network, BPLight-CNN, which
incorporates the silicon photonics-based backpropagation accelerator.
BPLight-CNN is a first-of-its-kind photonic and memristor-based CNN
architecture for end-to-end training and prediction. We evaluate BPLight-CNN
using a photonic CAD framework (IPKISS) on deep learning benchmark models
including LeNet and VGG-Net. The proposed design achieves (i) at least 34x
speedup, 34x improvement in computational efficiency, and 38.5x energy savings,
during training; and (ii) 29x speedup, 31x improvement in computational
efficiency, and 38.7x improvement in energy savings, during inference compared
to the state-of-the-art designs. All these comparisons are done at a 16-bit
resolution; and BPLight-CNN achieves these improvements at a cost of
approximately 6% lower accuracy compared to the state-of-the-art
measures on compact Riemannian -manifolds
We construct the measure on an arbitrary 3-dimensional compact
Riemannian manifold without boundary as an invariant probability measure of a
singular stochastic partial differential equation. Proving the nontriviality
and the covariance under Riemannian isometries of that measure gives for the
first time a non-perturbative, non-topological interacting Euclidean quantum
field theory on curved spaces in dimension 3. This answers a longstanding open
problem of constructive quantum field theory on curved 3 dimensional
backgrounds. To control analytically several Feynman diagrams appearing in the
construction of a number of random fields, we introduce a novel approach of
renormalization using microlocal and harmonic analysis. This allows to obtain a
renormalized equation which involves some universal constants independent of
the manifold. We also define a new vectorial Cole-Hopf transform which allows
to deal with the vectorial model where is now a bundle valued
random field. In a companion paper, we develop in a self-contained way all the
tools from paradifferential and microlocal analysis that we use to build in our
manifold setting a number of analytic and probabilistic objects.Comment: references added, Section 6.2 adde
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