165 research outputs found
Oriented Response Networks
Deep Convolution Neural Networks (DCNNs) are capable of learning
unprecedentedly effective image representations. However, their ability in
handling significant local and global image rotations remains limited. In this
paper, we propose Active Rotating Filters (ARFs) that actively rotate during
convolution and produce feature maps with location and orientation explicitly
encoded. An ARF acts as a virtual filter bank containing the filter itself and
its multiple unmaterialised rotated versions. During back-propagation, an ARF
is collectively updated using errors from all its rotated versions. DCNNs using
ARFs, referred to as Oriented Response Networks (ORNs), can produce
within-class rotation-invariant deep features while maintaining inter-class
discrimination for classification tasks. The oriented response produced by ORNs
can also be used for image and object orientation estimation tasks. Over
multiple state-of-the-art DCNN architectures, such as VGG, ResNet, and STN, we
consistently observe that replacing regular filters with the proposed ARFs
leads to significant reduction in the number of network parameters and
improvement in classification performance. We report the best results on
several commonly used benchmarks.Comment: Accepted in CVPR 2017. Source code available at http://yzhou.work/OR
An integral sliding-mode parallel control approach for general nonlinear systems via piecewise affine linear models
The fundamental problem of stabilizing a general nonaffine continuous-time
nonlinear system is investigated via piecewise affine linear models (PALMs) in
this article. A novel integral sliding-mode parallel control (ISMPC) approach
is developed, where an uncertain piecewise affine system (PWA) is constructed
to model a nonaffine continuous-time nonlinear system equivalently on a compact
region containing the origin. A piecewise sliding-mode parallel controller is
designed to globally stabilize the PALM and, consequently, to semiglobally
stabilize the original nonlinear system. The proposed scheme enjoys three
favorable features: (i) some restrictions on the system input channel are
eliminated, thus the developed method is more relaxed compared with the
published approaches; (ii) it is convenient to be used to deal with both
matched and unmatched uncertainties of the system; and (iii) the proposed
piecewise parallel controller generates smooth control signals even around the
boundaries between different subspaces, which makes the developed control
strategy more implementable and reliable. Moreover, we provide discussions
about the universality analysis of the developed control strategy for two kinds
of typical nonlinear systems. Simulation results from two numerical examples
further demonstrate the performance of the developed control approach
Stability Analysis and Stabilization of T-S Fuzzy Delta Operator Systems with Time-Varying Delay via an Input-Output Approach
The stability analysis and stabilization of Takagi-Sugeno (T-S) fuzzy delta operator systems with time-varying delay are investigated via an input-output approach. A model transformation method is employed to approximate the time-varying delay. The original system is transformed into a feedback interconnection form which has a forward subsystem with constant delays and a feedback one with uncertainties. By applying the scaled small gain (SSG) theorem to deal with this new system, and based on a Lyapunov Krasovskii functional (LKF) in delta operator domain, less conservative stability analysis and stabilization conditions are obtained. Numerical examples are provided to illustrate the advantages of the proposed method
Fuzzy-Affine-Model-Based Output Feedback Dynamic Sliding Mode Controller Design of Nonlinear Systems
H∞ model reduction for discrete-time Markovian jump systems with deficient mode information
This paper investigates the problem of H∞ model reduction for a class of discrete-time Markovian jump linear systems (MJLSs) with deficient mode information, which simultaneously involves the exactly known, partially unknown, and uncertain transition probabilities. By fully utilizing the properties of the transition probability matrices, together with the convexification of uncertain domains, a new H∞ performance analysis criterion for the underlying MJLSs is first derived, and then two approaches, namely, the convex linearisation approach and iterative approach, for the H∞ model reduction synthesis are proposed. Finally, a simulation example is provided to illustrate the effectiveness of the proposed design methods
Proof of a conjecture on the ϵ-spectral radius of trees
The ϵ-spectral radius of a connected graph is the largest eigenvalue of its eccentricity matrix. In this paper, we identify the unique n-vertex tree with diameter 4 and matching number 5 that minimizes the ϵ-spectral radius, and thus resolve a conjecture proposed in [W. Wei, S. Li, L. Zhang, Characterizing the extremal graphs with respect to the eccentricity spectral radius, and beyond, Discrete Math. 345 (2022) 112686]
Characterising User Transfer Amid Industrial Resource Variation: A Bayesian Nonparametric Approach
In a multitude of industrial fields, a key objective entails optimising
resource management whilst satisfying user requirements. Resource management by
industrial practitioners can result in a passive transfer of user loads across
resource providers, a phenomenon whose accurate characterisation is both
challenging and crucial. This research reveals the existence of user clusters,
which capture macro-level user transfer patterns amid resource variation. We
then propose CLUSTER, an interpretable hierarchical Bayesian nonparametric
model capable of automating cluster identification, and thereby predicting user
transfer in response to resource variation. Furthermore, CLUSTER facilitates
uncertainty quantification for further reliable decision-making. Our method
enables privacy protection by functioning independently of personally
identifiable information. Experiments with simulated and real-world data from
the communications industry reveal a pronounced alignment between prediction
results and empirical observations across a spectrum of resource management
scenarios. This research establishes a solid groundwork for advancing resource
management strategy development
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