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Coherent and incoherent laser pump on a five-level atom in a strongly coupled cavity-QED system

Physical Review A 2022年第1期105卷 013701-013701页

作者： Asha Devi Sarath D. Gunapala Malin Premaratne Advanced Computing and Simulation Laboratory Department of Electrical and Computer Systems Engineering Monash University Clayton Victoria 3800 Australia Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 USA

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.

关键词： Atoms, ions, & molecules in cavities Cavity quantum electrodynamics Nanophotonics

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Genetic Algorithm Optimizer for Volumetric Metasurface Elements 6

Genetic Algorithm Optimizer for Volumetric Metasurface Eleme...

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6th Annual IEEE Research and Applications of Photonics in Defense Conference, RAPID 2023

作者： Martinez, M.F. Bustamante, E. Touma, Jimmy E. Rumpf, R.C. University of Texas at El Paso Department of Electrical and Computer Engineering 500 W. University Ave. El PasoTX79938 United States Air Force Research Laboratory/Munitions Optoelectronics and Photonics Group Eglin AFB United States

ISBN: (纸本)9781665423526

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 limitations, field of view, and device height simultaneously for fully three-dimensional meta-atoms with realistic features. © 2023 IEEE.

关键词： Genetic algorithms

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Operational Risk for Integrated Power and Gas systems Considering Varying Hydrogen Concentrations With High Penetration of Wind

Operational Risk for Integrated Power and Gas Systems Consid...

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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： Sheng Wang Hongxun Hui Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart City University of Macau Macao China

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.

关键词： Hydrogen Wind turbines Security Wind speed Renewable energy sources Risk management Load shedding

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Data-driven Predictive Maintenance Alerting Routine for utility-scale Photovoltaic systems

Data-driven Predictive Maintenance Alerting Routine for util...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Demetris Marangis Andreas Livera George Makrides George E. Georgiou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus

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.

关键词： Photovoltaic systems Fault diagnosis Schedules Sensitivity Accuracy Fault detection Predictive models Digital twins Maintenance Predictive maintenance

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Data Poisoning Attack Against Anomaly Detectors in Digital Twin-Based Networks

Data Poisoning Attack Against Anomaly Detectors in Digital T...

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IEEE International Conference on Communications (ICC)

作者： Shaofeng Li Wen Wu Yan Meng Jiachun Li Haojin Zhu Xuemin Sherman Shen Frontier Research Center Peng Cheng Laboratory Shenzhen China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

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.

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Facial Recognition Attendance Tracking: An Intelligent Monitoring Approach

Facial Recognition Attendance Tracking: An Intelligent Monit...

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IEEE International Conference on Electro-Information Technology

作者： Syed Tauseeq Hussain Mario Antonio Nogueira Mansur Carvalho Vikrant Kishor Rathod Srinivasa Mani Sankar Reddy Karri Xinrui Yu Mikhail Gromov Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

This paper presents an Intelligent Monitoring System that utilizes the Internet of Things (IoT) and Artificial Intelligence (AI) technologies to automate the class attendance process reliably and efficiently. Conventional approaches for attendance tracking have been laborious and have consumed a significant amount of time, with errors and inconsistencies being a common occurrence. In contrast, the Intelligent Monitoring System combines object detection & recognition AI models, wireless communication, and cloud monitoring to generate reliable attendance data that can be used for various purposes, such as tracking student-by-student attendance data and monitoring overall attendance statistics. The system comprises an ID reader that uses radio frequency tags, a facial recognition system that uses a camera and AI algorithms, and a cloud monitoring system for attendance statistics. The proposed system is designed to overcome the challenges of traditional attendance-taking processes and provide a solution that is accurate, reliable, and efficient.

关键词： Wireless communication Cloud computing Face recognition Scalability Authentication Reliability engineering Security

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A Comprehensive Structural Study and Frequency Control of Inductive Power Transfer System

A Comprehensive Structural Study and Frequency Control of In...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Kuseso Onai Olorunfemi Ojo Ezekiel Arogunjo Department of Electrical and Computer Engineering Laboratory for Electric Machine and Power Electronics Tennessee Technological University Cookeville TN USA

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.

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Integrating Deep Learning and Ant Colony Optimization for Improved Shortest Route Problem Solving 5

Integrating Deep Learning and Ant Colony Optimization for Im...

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5th International Conference on Big-data Service and Intelligent Computation, BDSIC 2023

作者： Turky, Ayad Hussain, Abir Bettayeb, Anissa M. Serhani, Mohamed Adel Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering College of Engineering University of Sharjah Sharjah United Arab Emirates Information Technology Center University of Sharjah Sharjah United Arab Emirates Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798400708923

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 dynamic factors such as weather, time, and day. This paper introduces a novel two-stage approach that integrates deep learning and ant colony optimization to address this limitation. Firstly, deep learning is employed to predict travel times, incorporating weather conditions as input for improved accuracy. Secondly, an ant colony optimization algorithm is utilized to identify the shortest route based on the predicted travel times. We evaluated the proposed approach using the New York City taxi benchmark dataset and observed superior performance compared to current state-of-the-art algorithms. © 2023 ACM.

关键词： Travel time

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Peer-to-Peer Non-Bayesian Learning in Finite Time with a Finite Amount of Communication

Peer-to-Peer Non-Bayesian Learning in Finite Time with a Fin...

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IEEE Conference on Decision and Control

作者： Shreyas Sundaram Aritra Mitra Elmore Family School of Electrical and Computer Engineering Purdue University Department of Electrical and Systems Engineering University of Pennsylvania

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.

关键词： Peer-to-peer computing Distributed algorithms Testing

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Implementation of a Robust Chirplet Signal Decomposition Algorithm on FPGA-SoC Platform for Ultrasonic Flaw Detection

Implementation of a Robust Chirplet Signal Decomposition Alg...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Tianyang Fang Austin Fite Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

This research presents a robust chirplet decomposition algorithm designed for hardware implementation, specifically aimed at enhancing ultrasonic nondestructive testing and imaging. The study focuses on developing an algorithm and an FPGA hardware module that enable real-time chirplet decomposition, emphasizing increased processing speed while maintaining echo accuracy. A novel Maximum Likelihood Estimation (MLE)-based parameter estimation algorithm was developed to improve chirplet parameter estimation, enhancing both accuracy and convergence speed. This high-performance platform enables real-time ultrasonic imaging for flaw detection in steel specimens, demonstrating significant improvements in defect detection and characterization. The resultant platform has been tested against four alternative platforms to evaluate its effectiveness in enhancing execution times. Remarkably, when compared to similar embedded platforms, this speed-optimized platform achieves a 150-fold increase in processing speed, surpassing the next fastest embedded platform, the Teensy 4.0, which includes a Cortex-M7 processor operating at 600MHz. Additionally, when compared to an AMD Ryzen 7 3700X, this platform operates 6.6 times faster.

关键词： Parameter estimation Accuracy Ultrasonic imaging Chirp Signal processing algorithms Transforms Hardware Acoustics Real-time systems Steel

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