39,274 research outputs found
Evolutionary L∞ identification and model reduction for robust control
An evolutionary approach for modern robust control oriented system identification and model reduction in the frequency domain is proposed. The technique provides both an optimized nominal model and a 'worst-case' additive or multiplicative uncertainty bounding function which is compatible with robust control design methodologies. In addition, the evolutionary approach is applicable to both continuous- and discrete-time systems without the need for linear parametrization or a confined problem domain for deterministic convex optimization. The proposed method is validated against a laboratory multiple-input multiple-output (MIMO) test rig and benchmark problems, which show a higher fitting accuracy and provides a tighter L�¢���� error bound than existing methods in the literature do
Estimating Signals with Finite Rate of Innovation from Noisy Samples: A Stochastic Algorithm
As an example of the recently-introduced concept of rate of innovation,
signals that are linear combinations of a finite number of Diracs per unit time
can be acquired by linear filtering followed by uniform sampling. However, in
reality, samples are rarely noiseless. In this paper, we introduce a novel
stochastic algorithm to reconstruct a signal with finite rate of innovation
from its noisy samples. Even though variants of this problem has been
approached previously, satisfactory solutions are only available for certain
classes of sampling kernels, for example kernels which satisfy the Strang-Fix
condition. In this paper, we consider the infinite-support Gaussian kernel,
which does not satisfy the Strang-Fix condition. Other classes of kernels can
be employed. Our algorithm is based on Gibbs sampling, a Markov chain Monte
Carlo (MCMC) method. Extensive numerical simulations demonstrate the accuracy
and robustness of our algorithm.Comment: Submitted to IEEE Transactions on Signal Processin
Bulk superconductivity in Bi4O4S3 revealed by specific heat measurement
Specific heat experiments on a well-characterized polycrystalline sample of
the BiS2 based superconductor Bi4O4S3 revealed that it shows a crear specific
heat anomaly at about Tc = 4.4 K, consistent with Tc from the resistivity and
dc susceptibility. This observation indicates the superconductivity of Bi4O4S3
to be bulk in nature
Detection of zeptojoule microwave pulses using electrothermal feedback in proximity-induced Josephson junctions
We experimentally investigate and utilize electrothermal feedback in a
microwave nanobolometer based on a normal-metal
(\mbox{Au}_{x}\mbox{Pd}_{1-x}) nanowire with proximity-induced
superconductivity. The feedback couples the temperature and the electrical
degrees of freedom in the nanowire, which both absorbs the incoming microwave
radiation, and transduces the temperature change into a radio-frequency
electrical signal. We tune the feedback in situ and access both positive and
negative feedback regimes with rich nonlinear dynamics. In particular, strong
positive feedback leads to the emergence of two metastable electron temperature
states in the millikelvin range. We use these states for efficient threshold
detection of coherent 8.4 GHz microwave pulses containing approximately 200
photons on average, corresponding to 1.1 \mbox{ zJ} \approx 7.0 \mbox{ meV}
of energy
Optimal Attack against Cyber-Physical Control Systems with Reactive Attack Mitigation
This paper studies the performance and resilience of a cyber-physical control
system (CPCS) with attack detection and reactive attack mitigation. It
addresses the problem of deriving an optimal sequence of false data injection
attacks that maximizes the state estimation error of the system. The results
provide basic understanding about the limit of the attack impact. The design of
the optimal attack is based on a Markov decision process (MDP) formulation,
which is solved efficiently using the value iteration method. Using the
proposed framework, we quantify the effect of false positives and
mis-detections on the system performance, which can help the joint design of
the attack detection and mitigation. To demonstrate the use of the proposed
framework in a real-world CPCS, we consider the voltage control system of power
grids, and run extensive simulations using PowerWorld, a high-fidelity power
system simulator, to validate our analysis. The results show that by carefully
designing the attack sequence using our proposed approach, the attacker can
cause a large deviation of the bus voltages from the desired setpoint. Further,
the results verify the optimality of the derived attack sequence and show that,
to cause maximum impact, the attacker must carefully craft his attack to strike
a balance between the attack magnitude and stealthiness, due to the
simultaneous presence of attack detection and mitigation
Modeling and Detecting False Data Injection Attacks against Railway Traction Power Systems
Modern urban railways extensively use computerized sensing and control
technologies to achieve safe, reliable, and well-timed operations. However, the
use of these technologies may provide a convenient leverage to cyber-attackers
who have bypassed the air gaps and aim at causing safety incidents and service
disruptions. In this paper, we study false data injection (FDI) attacks against
railways' traction power systems (TPSes). Specifically, we analyze two types of
FDI attacks on the train-borne voltage, current, and position sensor
measurements - which we call efficiency attack and safety attack -- that (i)
maximize the system's total power consumption and (ii) mislead trains' local
voltages to exceed given safety-critical thresholds, respectively. To
counteract, we develop a global attack detection (GAD) system that serializes a
bad data detector and a novel secondary attack detector designed based on
unique TPS characteristics. With intact position data of trains, our detection
system can effectively detect the FDI attacks on trains' voltage and current
measurements even if the attacker has full and accurate knowledge of the TPS,
attack detection, and real-time system state. In particular, the GAD system
features an adaptive mechanism that ensures low false positive and negative
rates in detecting the attacks under noisy system measurements. Extensive
simulations driven by realistic running profiles of trains verify that a TPS
setup is vulnerable to the FDI attacks, but these attacks can be detected
effectively by the proposed GAD while ensuring a low false positive rate.Comment: IEEE/IFIP DSN-2016 and ACM Trans. on Cyber-Physical System
Fireball/Blastwave Model and Soft Gamma-ray Repeaters
Soft gamma-ray repeaters are at determined distances and their positions are
known accurately. If observed, afterglows from their soft gamma-ray bursts will
provide important clues to the study of the so called "classical gamma-ray
bursts". On applying the popular fireball/blastwave model of classical
gamma-ray bursts to soft gamma-ray repeaters, it is found that their X-ray and
optical afterglows are detectable. Monitoring of the three repeaters is
solicited.Comment: Already published in 1998 in "Chinese Physics Letters", replaced with
the published version. See astro-ph/0502452 for a more detailed versio
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