1,538 research outputs found
A distributed networked approach for fault detection of large-scale systems
Networked systems present some key new challenges in the development of fault diagnosis architectures. This paper proposes a novel distributed networked fault detection methodology for large-scale interconnected systems. The proposed formulation incorporates a synchronization methodology with a filtering approach in order to reduce the effect of measurement noise and time delays on the fault detection performance. The proposed approach allows the monitoring of multi-rate systems, where asynchronous and delayed measurements are available. This is achieved through the development of a virtual sensor scheme with a model-based re-synchronization algorithm and a delay compensation strategy for distributed fault diagnostic units. The monitoring architecture exploits an adaptive approximator with learning capabilities for handling uncertainties in the interconnection dynamics. A consensus-based estimator with timevarying weights is introduced, for improving fault detectability in the case of variables shared among more than one subsystem. Furthermore, time-varying threshold functions are designed to prevent false-positive alarms. Analytical fault detectability sufficient conditions are derived and extensive simulation results are presented to illustrate the effectiveness of the distributed fault detection technique
ECA: High Dimensional Elliptical Component Analysis in non-Gaussian Distributions
We present a robust alternative to principal component analysis (PCA) ---
called elliptical component analysis (ECA) --- for analyzing high dimensional,
elliptically distributed data. ECA estimates the eigenspace of the covariance
matrix of the elliptical data. To cope with heavy-tailed elliptical
distributions, a multivariate rank statistic is exploited. At the model-level,
we consider two settings: either that the leading eigenvectors of the
covariance matrix are non-sparse or that they are sparse. Methodologically, we
propose ECA procedures for both non-sparse and sparse settings. Theoretically,
we provide both non-asymptotic and asymptotic analyses quantifying the
theoretical performances of ECA. In the non-sparse setting, we show that ECA's
performance is highly related to the effective rank of the covariance matrix.
In the sparse setting, the results are twofold: (i) We show that the sparse ECA
estimator based on a combinatoric program attains the optimal rate of
convergence; (ii) Based on some recent developments in estimating sparse
leading eigenvectors, we show that a computationally efficient sparse ECA
estimator attains the optimal rate of convergence under a suboptimal scaling.Comment: to appear in JASA (T&M
Millimeter-wave Evolution for 5G Cellular Networks
Triggered by the explosion of mobile traffic, 5G (5th Generation) cellular
network requires evolution to increase the system rate 1000 times higher than
the current systems in 10 years. Motivated by this common problem, there are
several studies to integrate mm-wave access into current cellular networks as
multi-band heterogeneous networks to exploit the ultra-wideband aspect of the
mm-wave band. The authors of this paper have proposed comprehensive
architecture of cellular networks with mm-wave access, where mm-wave small cell
basestations and a conventional macro basestation are connected to
Centralized-RAN (C-RAN) to effectively operate the system by enabling power
efficient seamless handover as well as centralized resource control including
dynamic cell structuring to match the limited coverage of mm-wave access with
high traffic user locations via user-plane/control-plane splitting. In this
paper, to prove the effectiveness of the proposed 5G cellular networks with
mm-wave access, system level simulation is conducted by introducing an expected
future traffic model, a measurement based mm-wave propagation model, and a
centralized cell association algorithm by exploiting the C-RAN architecture.
The numerical results show the effectiveness of the proposed network to realize
1000 times higher system rate than the current network in 10 years which is not
achieved by the small cells using commonly considered 3.5 GHz band.
Furthermore, the paper also gives latest status of mm-wave devices and
regulations to show the feasibility of using mm-wave in the 5G systems.Comment: 17 pages, 12 figures, accepted to be published in IEICE Transactions
on Communications. (Mar. 2015
Workload-Aware Scheduling using Markov Decision Process for Infrastructure-Assisted Learning-Based Multi-UAV Surveillance Networks
In modern networking research, infrastructure-assisted unmanned autonomous
vehicles (UAVs) are actively considered for real-time learning-based
surveillance and aerial data-delivery under unexpected 3D free mobility and
coordination. In this system model, it is essential to consider the power
limitation in UAVs and autonomous object recognition (for abnormal behavior
detection) deep learning performance in infrastructure/towers. To overcome the
power limitation of UAVs, this paper proposes a novel aerial scheduling
algorithm between multi-UAVs and multi-towers where the towers conduct wireless
power transfer toward UAVs. In addition, to take care of the high-performance
learning model training in towers, we also propose a data delivery scheme which
makes UAVs deliver the training data to the towers fairly to prevent problems
due to data imbalance (e.g., huge computation overhead caused by larger data
delivery or overfitting from less data delivery). Therefore, this paper
proposes a novel workload-aware scheduling algorithm between multi-towers and
multi-UAVs for joint power-charging from towers to their associated UAVs and
training data delivery from UAVs to their associated towers. To compute the
workload-aware optimal scheduling decisions in each unit time, our solution
approach for the given scheduling problem is designed based on Markov decision
process (MDP) to deal with (i) time-varying low-complexity computation and (ii)
pseudo-polynomial optimality. As shown in performance evaluation results, our
proposed algorithm ensures (i) sufficient times for resource exchanges between
towers and UAVs, (ii) the most even and uniform data collection during the
processes compared to the other algorithms, and (iii) the performance of all
towers convergence to optimal levels.Comment: 15 pages, 10 figure
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