We propose a cavity quantum electrodynamic system consisting of a five-level atom coupled to a single mode of the cavity electromagnetic field. The study is focused on the regime of strong coupling between the cavity ...
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We propose a cavity quantum electrodynamic system consisting of a five-level atom coupled to a single mode of the cavity electromagnetic field. The study is focused on the regime of strong coupling between the cavity and atom. Pump laser fields and cavity fields connect the split energy levels of the atom. Instead of the well-known two-level Dicke model obtained by adiabatic elimination of the high-energy levels, we consider the pump lasers' detunings to the atomic transitions to be very small such that we can examine the influence of the higher-energy states. We have studied the effect of an external coherent drive and incoherent pumping on these higher-energy levels and observed the enhancement of intracavity photon numbers due to quantum coherence effects. The amplification of intracavity photons is achieved even without a population inversion. However, the effect of the coherent and incoherent drive is negligible for very large detunings when the higher-energy states are adiabatically eliminated. At zero and small detunings, the system reaches the steady state at an earlier instant of time for higher incoherent pumping. We find an almost agreeable steady-state behavior of the system's exact full quantum dynamics model and its semiclassical approximation. Our model tries to accurately simulate the open system by considering the cavity decay, spontaneous decay, and dephasing of the system.
We demonstrate the improvement of theradar-based human activity recognition using the combination of four datadomains: time-frequency, time-range, range-Doppler and, for the first time,time-angle domain. Six different...
We demonstrate the improvement of theradar-based human activity recognition using the combination of four datadomains: time-frequency, time-range, range-Doppler and, for the first time,time-angle domain. Six different activities are observed from nine subjectsusing frequency-modulated continuous-wave millimeter-wave radar. Each domainoffers additional information to the classification process. The classificationresults of four deep convolutional neural networks are then combined using theJoint Probability Mass Function method to achieve a combined classificationaccuracy of 100%. The proposed system also demonstrates similar performance inrecognizing activities from participants not involved in training the *** the best of our knowledge, this is the first work that demonstrates theutilization of four data domains to address the radar-based human activityrecognition problem.
In this paper, we study the abnormal behaviors detection and the corresponding data poisoning attacks in digital twin (DT)-based networks. We first analyze the abnormal behaviors existing in the DT-based networks, inc...
In this paper, we study the abnormal behaviors detection and the corresponding data poisoning attacks in digital twin (DT)-based networks. We first analyze the abnormal behaviors existing in the DT-based networks, including environment anomalies, hardware and software faults, and network attacks. Specially, we design a machine learning (ML)-based anomaly detector to identify network attacks. Furthermore, due to the strong dependency of ML models on training data, in which the outputs of the trained ML models can be affected by the poisoned samples. We design a data poisoning attack scheme against the proposed ML-based anomaly detector, in which attackers can effectively compromise the output of anomaly detectors. Extensive experimental results adopting three commonly used ML-based models demonstrate that the attack can compromise these detectors with over 80% probability.
This study proposes a noninvasive method to determine the axial load in continuous welded rails using numeric models and field test data. A general finite element model under varying boundary conditions and axial stre...
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We consider the problem of distributed non-Bayesian learning (or hypothesis testing) where a group of agents interacts over a peer-to-peer network to identify the true state of the world from a finite set of hypothese...
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ISBN:
(数字)9781665467612
ISBN:
(纸本)9781665467629
We consider the problem of distributed non-Bayesian learning (or hypothesis testing) where a group of agents interacts over a peer-to-peer network to identify the true state of the world from a finite set of hypotheses, based on a series of stochastic signals that each agent receives. Prior work on this problem has provided distributed algorithms that guarantee asymptotic learning of the true state, with corresponding efforts to improve the rate of learning. In this paper, we first argue that one can readily modify existing asymptotic learning algorithms to enable learning in finite time, effectively yielding arbitrarily large (asymptotic) rates. Furthermore, we show that such finite-time learning can be achieved via a simple algorithm which only requires the agents to exchange a binary vector (of length equal to the number of possible hypotheses) with their neighbors at each time-step. Finally, we show that if the agents know the diameter of the network, our algorithm can be further modified to allow all agents to learn the true state and stop transmitting to their neighbors after a finite number of time-steps.
A meta optics design tool is implemented and shown to automatically optimize the topology of three-dimensional meta-atoms to be used in optical applications. The tool can optimize for bandwidth, fabrication limitation...
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We establish an operational risk assessment approach for integrated power and gas systems (IPGS) considering varying hydrogen concentrations with high penetration of wind. First, the operational availability of wind g...
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We establish an operational risk assessment approach for integrated power and gas systems (IPGS) considering varying hydrogen concentrations with high penetration of wind. First, the operational availability of wind generation is formulated considering the stochastic process of wind is developed. Then, an operational risk mitigation scheme (ORMS) is devised to minimize both load shedding and gas security violation. The varying gas composition on the physical properties of IPGS is characterized to fully reflect the impact of alternative gas on the IPGS operation. Moreover, several risk indices, improved from traditional gas security indices, are proposed to evaluate gas security under various uncertainties. An analytical risk evaluation method is used for better computation efficiency. Finally, a test IPGS is utilized to demonstrate the proposed approach. We can find that the proposed operational risk evaluation approach is able to evaluate the weak spot of the IPGS when distributed injections of hydrogen are considered, and the trend of the risk evolution can also be obtained during the operation.
Determining the best shortest path between locations in intelligent transportation systems is crucial but challenging. Traditional approaches, which assume fixed travel times, fall short of accurately reflecting dynam...
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In this paper, a structural study of the inductive power transfer system charging a Li-ion battery is presented. Instead of representing the battery by a constant resistor, an actual model of the battery is included. ...
In this paper, a structural study of the inductive power transfer system charging a Li-ion battery is presented. Instead of representing the battery by a constant resistor, an actual model of the battery is included. The inclusion of the battery reveals that the dynamics of the system is dominated by the battery parameters. It is shown that the system is both completely controllable and observable. For frequency control operation of the inductive power transfer system, it is shown that the states associated with the inductive power transfer are the most controllable and observable states whilst those associated with the battery are the least controllable and observable states. Based on these findings, a PID controller is designed to control the charging of the battery in both constant current and constant voltage modes. Simulation and experimental results of the designed controller are presented.
Predictive maintenance is crucial for optimizing the performance of photovoltaic (PV) systems by proactively addressing potential issues before they escalate. Even though many fault diagnosis methods have been propose...
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ISBN:
(数字)9798350375923
ISBN:
(纸本)9798350375930
Predictive maintenance is crucial for optimizing the performance of photovoltaic (PV) systems by proactively addressing potential issues before they escalate. Even though many fault diagnosis methods have been proposed, a major challenge remains the lack of accurate predictive maintenance routines for utility-scale PV systems. The scope of this work is to address this fundamental challenge by proposing a data-driven predictive maintenance alerting routine (that leverages digital twin technologies and decision rules) for detecting and predicting PV system faults. The results demonstrated that the developed predictive maintenance routine yielded a sensitivity of 93.9% for PV utility-scale failure detection over a yearly test period. The findings of the predictive maintenance alerting model proved its capability to anticipate faults up to 10 days in advance, achieving a sensitivity of 83.3%. Finally, the predictive maintenance alerting system can be used by operation and maintenance teams for scheduling corrective actions and optimizing field activities.
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