Dynamic compressed sensing (DCS) techniques have been applied to enhance the performance of channel estimation (CE) in underwater acoustic (UWA) communications. Existing DCS-CE schemes are mainly applicable to slow-va...
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ISBN:
(数字)9798350362077
ISBN:
(纸本)9798350362084
Dynamic compressed sensing (DCS) techniques have been applied to enhance the performance of channel estimation (CE) in underwater acoustic (UWA) communications. Existing DCS-CE schemes are mainly applicable to slow-varying channels and solutions working under rapid channel conditions are highly desirable. In this paper, tracking of time-varying sparse UWA channel is formulated as a
$\ell_{p}$
-norm regularized recursive least square (RLS) problem, which is then solved via a proximal gradient (PG) algorithm. The resulting CE scheme is named the dynamic PG (DPG) CE method. Experimental results verified the advantage of the proposed DPG CE scheme over existing sparse CE schemes.
This work proposed a new forecasting approach for predictive maintenance in industrial settings, combining standard segmentation approaches like Symbolic Aggregate approximation (SAX) and Piecewise Aggregate Approxima...
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ISBN:
(数字)9798350365887
ISBN:
(纸本)9798350365894
This work proposed a new forecasting approach for predictive maintenance in industrial settings, combining standard segmentation approaches like Symbolic Aggregate approximation (SAX) and Piecewise Aggregate approximation (PAA) with LSTM (Long-Short Time Memory). The work aims to construct a robust forecasting mechanism to estimate maintenance requirements in advance properly. We first demonstrated the results of the proposed approach for synthetically generated data and extended the results with real industrial vibration data. The algorithm's performance is assessed using real-world industry data from steel production furnaces, where timely maintenance is critical for increasing operating efficiency and reducing downtime. Experimental results show that using SAX and LSTM for forecasting industrial time series data achieves high accuracy rates (90.2 %) in a reasonable computational time.
To suppress noise and extract weak features in pulsed eddy current response signals, in this study, we employ the unscented Kalman filter (UKF) algorithm in conjunction with the radial basis function (RBF) neural netw...
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ISBN:
(数字)9798350360325
ISBN:
(纸本)9798350360332
To suppress noise and extract weak features in pulsed eddy current response signals, in this study, we employ the unscented Kalman filter (UKF) algorithm in conjunction with the radial basis function (RBF) neural network to establish the predictive and observational equations for the pulsed eddy current response signal. The RBF neural network's strong nonlinear capabilities are leveraged, while the UKF algorithm is integrated to mitigate noise and extract the signal. To assess the efficacy of this approach, we utilize COMSOL simulation data for validation purposes. Our findings demonstrate that the proposed algorithm effectively reduces noise, particularly for signals with a signal-to-noise ratio of 30 dB.
Multiplication is a frequent computation in many algorithms, classical and quantum. This paper targets the im-plementation of quantum integer multiplication. Quantum array multipliers take inspiration from classical a...
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ISBN:
(数字)9798331541378
ISBN:
(纸本)9798331541385
Multiplication is a frequent computation in many algorithms, classical and quantum. This paper targets the im-plementation of quantum integer multiplication. Quantum array multipliers take inspiration from classical array multipliers, with the result of reduced circuit depth. They take advantage of the quantum phase domain, through rotations controlled by the mul-tiplier's qubits. This work further explores this implementation by applying approximate rotations. Although this approach can have an impact on the accuracy of the result, the reduction in depth can result in better outcomes when noise is involved.
With the proliferation of densely deployed access points (APs), competition among multiple APs has led to increased occurrences of co-channel interference and access conflicts between APs. The IEEE 802.11 working grou...
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ISBN:
(数字)9798350387414
ISBN:
(纸本)9798350387421
With the proliferation of densely deployed access points (APs), competition among multiple APs has led to increased occurrences of co-channel interference and access conflicts between APs. The IEEE 802.11 working group has proposed multi-AP coordination schemes in the next-generation Wi-Fi standard to enhance Wi-Fi performance in multi-AP scenarios. We primarily investigate the coordinated spatial reuse (CSR) mechanism for multiple APs. In the CSR group, a set of APs utilize the same channel for data transmission within a single transmission opportunity (TXOP), during which power control for individual devices is necessary to mitigate interference between APs. We propose a Federated Learning Double Deep Q-Network (FL-DDQN) algorithm for power allocation among multiple users in the CSR environment. The simulation results indicate that the proposed algorithm can improve throughput performance by approximately 11.06% to 100.51% and reduce the latency by approximately 61.34% to 86.71 % compared to several baseline schemes.
Piecewise linear neural networks (PLNNs) are proven universal approximators for continuous functions on the compact domain. For multiple PLNNs (mPLNNs) differing from each other in suffering different approximation er...
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ISBN:
(数字)9798350358513
ISBN:
(纸本)9798350358520
Piecewise linear neural networks (PLNNs) are proven universal approximators for continuous functions on the compact domain. For multiple PLNNs (mPLNNs) differing from each other in suffering different approximation errors (AEs) when approximating the same continuous function, activating all the PLNNs to approximate the continuous function, and then, picking up the minimum AE (MAE) from all the AEs seems to be a practical way to arrive at such MAE. Activating PLNNs has to consume energy, and more activated PLNNs provide more AEs to consider, which can maximize the probability of guaranteeing the MAE, while also needing to harvest more energy. Therefore, how to make the optimal tradeoff between energy harvested and approximation accuracy for mPLNNs arises as an interesting issue. To address this problem, this paper first deduces the objective function, with the accumulative probability of obtaining the MAE as the objective and the accumulative energy harvested as the constraint, then proposes an algorithm for mPLNNs to enjoy the maximum probability of achieving the MAE within the acceptable level of accumulative energy harvested. Experiments verify its performance.
This study presents a comparative study between the Finite Control Set Model Predictive Control (FCS-MPC) and the multi-loop control strategy based on Proportional Resonant (PR) Controller for a three-phase four-leg i...
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ISBN:
(数字)9798350371154
ISBN:
(纸本)9798350371161
This study presents a comparative study between the Finite Control Set Model Predictive Control (FCS-MPC) and the multi-loop control strategy based on Proportional Resonant (PR) Controller for a three-phase four-leg inverter to generate various grid disturbances under various load conditions. It is shown that MPC has better transient response in all cases of grid disturbances. Regarding the steady-state performances, the output voltage has a lower THD when using MPC, especially for inductive loads, while the relative error on the output voltage is relatively the same for both control schemes.
Unmanned aerial vehicle (UAV) can provide flexi-ble on-demand services for maritime communications while the vessels sail away from the terrestrial base stations. Considering the mobility of both the multiple UAVs and...
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ISBN:
(数字)9798350304053
ISBN:
(纸本)9798350304060
Unmanned aerial vehicle (UAV) can provide flexi-ble on-demand services for maritime communications while the vessels sail away from the terrestrial base stations. Considering the mobility of both the multiple UAVs and users, this paper presents an effective maritime communication scheduling strategy, which maximizes the sum rate by jointly optimizing the trajectory and the transmission power. To optimize trajectory overlapping, we particularly adopt the Fuzzy C-means (FCM) algorithm to cluster the UAVs. We address the original non-convex scheduling problem into three solvable sub-problems with successive convex approximation scheme. Furthermore, we present a joint alternative optimization to obtain the optimal trajectories and transmission power of all UAVs. Simulation results demonstrate that the proposed scheme can provide effective communication for users in terms of transmission rate.
Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algorithms have been obtained for several ...
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ISBN:
(数字)9798331516741
ISBN:
(纸本)9798331516758
Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algorithms have been obtained for several fundamental problems in this area. However, a significant limitation of this approach is that it requires full knowledge of the underlying probability distributions. Can we still get good (approximation) algorithms if these distributions are unknown, and the algorithm needs to learn them through repeated interactions? In this paper, we resolve this question for a large class of “monotone” stochastic problems, by providing a generic online learning algorithm with
$\sqrt{T\log T}$
regret relative to the best approximation algorithm (under known distributions). Importantly, our online algorithm works in a semi-bandit setting, where in each period, the algorithm only observes samples from the random variables that were actually probed. Our frame-work applies to several fundamental problems in stochastic optimization such as prophet inequality, Pandora's box, stochastic knapsack, stochastic matchings and stochastic submodular optimization.
The high degree of uncertainty in the operation of new power systems with large-scale access to new energy sources has increased the difficulty of clearing spot transactions in the power market, and the computational ...
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ISBN:
(数字)9798350377798
ISBN:
(纸本)9798350377804
The high degree of uncertainty in the operation of new power systems with large-scale access to new energy sources has increased the difficulty of clearing spot transactions in the power market, and the computational volume and complexity of model solving have become higher and higher. The rapid development of quantum computers provides a new idea for the future power market spot trading model solution. In this paper, we propose a model for day-ahead clearing in the electricity spot market using a quantum approximation optimization algorithm. The original day-ahead security constrained unit combination model of the electricity spot market is decoupled into a discrete-variable model and a continuous-variable model by the alternating direction multiplier method. The discrete-variable model is solved by the quantum approximation optimization algorithm, and the continuous-variable model is solved by the solver, and the start-stop state solutions of the units are obtained by constant alternating iterations. Based on the optimized unit start-stop solution, the winning power and node marginal tariff of the unit are solved by the security constrained unit combination model. Simulation examples based on the IEEE 39 -node system are performed to verify the validity of the proposed model.
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