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检索条件"主题词=approximation Algorithms"

共 32107 条记录，以下是4661-4670 订阅

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Estimation of Time-Varying Underwater Acoustic Channel Via Dynamic Proximal Gradient Algorithm

Estimation of Time-Varying Underwater Acoustic Channel Via D...

引用

OCEANS

作者： Yunfeng Hu Jun Tao Key Laboratory of Underwater Acoustic Signal Processing of the Ministry of Education School of Information Science and Engineering Southeast University Nanjing China Pengcheng Lab Shenzhen China

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.

关键词： Acoustic communication (telecommunication) Heuristic algorithms Oceans Channel estimation Estimation approximation algorithms Sensors

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On Symbolic Prediction of Time Series for Predictive Maintenance Based on SAX-LSTM

On Symbolic Prediction of Time Series for Predictive Mainten...

引用

International Conference on Computer Science and Engineering (UBMK)

作者： Aykut Güler Tuǧçe Ballı E. Fatih Yetkin Management Information Systems Kadir Has University Istanbul Türkiye

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.

关键词： Industries Accuracy Aggregates Time series analysis approximation algorithms Prediction algorithms Maintenance Forecasting Long short term memory Predictive maintenance

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An Improved UKF Algorithm Based on RBF Neural Network for PEC Response Signal Extraction

An Improved UKF Algorithm Based on RBF Neural Network for PE...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Bin Luo Yibing Shi Aihua Tao Zhipeng Li Wei Zhang School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Welltech Research and Design Institute of China Oilfield Services Co

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.

关键词： Neural networks Noise reduction Geoscience and remote sensing Interference Prediction algorithms approximation algorithms Feature extraction

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Approximate Quantum Array Multiplier

Approximate Quantum Array Multiplier

引用

Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Aden Crimmins Sonia Lopez Alarcon KGCOE Department of Computer Engineering Rochester Institute of Technology Rochester New York

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.

关键词： Hands Accuracy Quadrature amplitude modulation Quantum entanglement Noise Qubit Logic gates approximation algorithms Probability distribution Noise measurement

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Research on Next-Generation Wi-Fi Spatial Reuse Power Control Based on Federated Reinforcement Learning

Research on Next-Generation Wi-Fi Spatial Reuse Power Contro...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Jing Wang Xuming Fang Key Laboratory of Information Coding and Transmission Southwest Jiaotong University Chengdu China

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.

关键词： Performance evaluation Simulation Power control Reinforcement learning Throughput approximation algorithms Delays

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Activation Control of Multiple Piecewise Linear Neural Networks

Activation Control of Multiple Piecewise Linear Neural Netwo...

引用

IEEE International Conference on Automation Science and Engineering (CASE)

作者： Chen Hou National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China APKU-Changsha Institute for Computing and Digital Economy Changsha China

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.

关键词： Energy consumption Accuracy Computer aided software engineering Automation Neurons approximation error approximation algorithms Linear programming Biological neural networks

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Finite Control Set MPC versus multi-loop PR control for a three-phase four-leg inverter as grid disturbances generator: a Comparative Analysis

Finite Control Set MPC versus multi-loop PR control for a th...

引用

International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Katria Kamiantako Nzimbakani Angelo Kuti Lusala Johan Gyselinck Faculté Polytechnique Université de Kinshasa Kinshasa Rep. Dem.Congo BEAMS Department Université Libre de Bruxelles Brussels Belgium

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.

关键词： Transient response Switching loss Prediction algorithms approximation algorithms Inverters Vectors Steady-state

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Multi-UAV Assisted Maritime Communication Scheduling Strategy

Multi-UAV Assisted Maritime Communication Scheduling Strateg...

引用

IEEE International Conference on Communications Workshops, ICC

作者： Yuanxue Xin Jingyao Wang Wenqi Zhao Jun Zhang Pengfei Shi Jiangsu Key Laboratory of Power Transmission & Distribution Equipment Technology Hohai University Changzhou China College of Information Science and Engineering Hohai University Changzhou China School of Information Science and Engineering Southeast University Nanjing China Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing China College of Artifical Intelligence and Automation Hohai University Changzhou China

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.

关键词： Simulation Conferences Clustering algorithms approximation algorithms Autonomous aerial vehicles Throughput Scheduling

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Semi-Bandit Learning for Monotone Stochastic Optimization*

Semi-Bandit Learning for Monotone Stochastic Optimization*

引用

Annual IEEE Symposium on Foundations of Computer Science

作者： Arpit Agarwal Rohan Ghuge Viswanath Nagarajan Computer Science & Engineering Indian Institute of Technology Bombay Mumbai India H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta USA Industrial and Operations Engineering University of Michigan Ann Arbor USA

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.

关键词： Computer science Uncertainty Stochastic processes approximation algorithms Probability distribution Random variables Optimization

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QAOA-based Day-ahead Clearing Model for Electricity Spot Market

QAOA-based Day-ahead Clearing Model for Electricity Spot Mar...

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Energy, Power and Grid (ICEPG), International Conference on

作者： Dong Pan Na Yang Bao Wang Jianxiong Jia Zhuangzhuang Cao Dabo Zhang State Grid State Grid Anhui Electric Power Co. Ltd. Economic Research Institute Hefei China Anhui Province Key Laboratory of Renewable Energy Utilization and Energy Saving Hefei University of Technology Hefei China

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.

关键词： Analytical models Accuracy Electricity Computational modeling approximation algorithms Electricity supply industry Convex functions Power markets Security Optimization

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