634 research outputs found
Cooperative H-infinity Estimation for Large-Scale Interconnected Linear Systems
In this paper, a synthesis method for distributed estimation is presented,
which is suitable for dealing with large-scale interconnected linear systems
with disturbance. The main feature of the proposed method is that local
estimators only estimate a reduced set of state variables and their complexity
does not increase with the size of the system. Nevertheless, the local
estimators are able to deal with lack of local detectability. Moreover, the
estimators guarantee H-infinity-performance of the estimates with respect to
model and measurement disturbances.Comment: Short version published in Proc. American Control Conference (ACC),
pp.2119-2124. Chicago, IL, 201
Quantum Phase Recognition via Quantum Kernel Methods
The application of quantum computation to accelerate machine learning
algorithms is one of the most promising areas of research in quantum
algorithms. In this paper, we explore the power of quantum learning algorithms
in solving an important class of Quantum Phase Recognition (QPR) problems,
which are crucially important in understanding many-particle quantum systems.
We prove that, under widely believed complexity theory assumptions, there
exists a wide range of QPR problems that cannot be efficiently solved by
classical learning algorithms with classical resources. Whereas using a quantum
computer, we prove the efficiency and robustness of quantum kernel methods in
solving QPR problems through Linear order parameter Observables. We numerically
benchmark our algorithm for a variety of problems, including recognizing
symmetry-protected topological phases and symmetry-broken phases. Our results
highlight the capability of quantum machine learning in predicting such quantum
phase transitions in many-particle systems
An improved CTGAN for data processing method of imbalanced disk failure
To address the problem of insufficient failure data generated by disks and
the imbalance between the number of normal and failure data. The existing
Conditional Tabular Generative Adversarial Networks (CTGAN) deep learning
methods have been proven to be effective in solving imbalance disk failure
data. But CTGAN cannot learn the internal information of disk failure data very
well. In this paper, a fault diagnosis method based on improved CTGAN, a
classifier for specific category discrimination is added and a discriminator
generate adversarial network based on residual network is proposed. We named it
Residual Conditional Tabular Generative Adversarial Networks (RCTGAN). Firstly,
to enhance the stability of system a residual network is utilized. RCTGAN uses
a small amount of real failure data to synthesize fake fault data; Then, the
synthesized data is mixed with the real data to balance the amount of normal
and failure data; Finally, four classifier (multilayer perceptron, support
vector machine, decision tree, random forest) models are trained using the
balanced data set, and the performance of the models is evaluated using G-mean.
The experimental results show that the data synthesized by the RCTGAN can
further improve the fault diagnosis accuracy of the classifier
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