检索结果-内蒙古大学图书馆

American Control Conference (ACC)

作者： Xiangyuan Zhang Bin Hu Tamer Başar Department of Electrical and Computer Engineering and Coordinated Science Laboratory University of Illinois at Urbana-Champaign Urbana IL USA

We develop the first end-to-end sample complexity of model-free policy gradient (PG) methods in discrete-time infinite-horizon Kalman filtering. Specifically, we introduce the receding-horizon policy gradient (RHPG-KF) framework and demonstrate sample complexity for RHPG-KF in learning a stabilizing filter that is ϵ-close to the optimal Kalman filter. Notably, the proposed RHPG-KF framework does not require the system to be open-loop stable nor assume any prior knowledge of a stabilizing filter. Our results shed light on applying model-free PG methods to control a linear dynamical system where the state measurements could be corrupted by statistical noises and other (possibly adversarial) disturbances.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Optimal Design of RDARS-aided Multi-user systems with Low-resolution DACs

Optimal Design of RDARS-aided Multi-user Systems with Low-re...

引用

IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Jintao Wang Chengzhi Ma Shaodan Ma Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC) University of Macau Macao SAR China

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

Recently, a flexible reconfigurable architecture called reconfigurable distributed antenna and reflecting surface (RDARS), which maintains the advantages of distributed antennas while reducing hardware costs, has been proposed. In this paper, we investigate the appealing benefits of the RDARS’s architecture by considering an RDARS-aided downlink communication system under the limited resolution of digital-to-analog converters (DACs) and phase shifts. The dynamic selection of the reflection/connection modes at the RDARS and the reflective phase coefficients are optimized to maximize the communication rate. To tackle the mix-integer optimization problem, an inexact block coordinate descent (BCD) algorithm is then proposed. Numerical simulations demonstrate great superiority for the proposed RDARS-aided architecture as compared to the conventional benchmarks and show potential benefits in compensating for the limited resolution of hardware.

关键词： Wireless communication Heuristic algorithms Transmitting antennas Signal processing algorithms Benchmark testing Reflector antennas Downlink Hardware Surface treatment Antenna arrays

来源：评论

学校读者我要写书评

暂无评论

Near-End Listening Enhancement Using a Noise-Robust Linear Time-Invariant Filter

Near-End Listening Enhancement Using a Noise-Robust Linear T...

引用

International Workshop on Acoustic Signal Enhancement (IWAENC)

作者： Filippo Villani Wai-Yip Chan Zheng-Hua Tan Jan Østergaard Jesper Jensen Department of Electronic Systems Aalborg University Aalborg Denmark Department of Electrical and Computer Engineering Queen’s University Kingston Canada Oticon A/S Smørum Denmark

ISBN: (数字)9798350361858

ISBN: (纸本)9798350361865

In environments with competing sound sources, speech intelligibility can be significantly compromised. This paper addresses the near-end listening enhancement (NELE) problem, i.e., the problem of processing an available clean speech signal in order to maximize its intelligibility when it is subsequently presented to a human listener in an adverse acoustic situation. We propose a time-invariant and low-complexity NELE algorithm that maximizes an approximation of the Speech Intelligibility Index by redistributing speech energy across frequency bands. Unlike existing algorithms, the proposed algorithm incorporates a mechanism that allows it to distinguish between temporally fluctuating and non-fluctuating noise maskers by using only long-term speech and noise statistics. Simulation results show that the proposed method outperforms baseline algorithms, whether time-invariant or time-varying, in a wide range of noise conditions.

关键词： Time-frequency analysis Working environment noise Simulation Noise Nonlinear filters Speech enhancement Filtering algorithms Approximation algorithms Acoustics Indexes

来源：评论

学校读者我要写书评

暂无评论

Enhancing Human-Robot Cohesion through HAT Methods: A Crowd-Avoidance Model for Safety Aware Navigation

Enhancing Human-Robot Cohesion through HAT Methods: A Crowd-...

引用

IEEE International Conference on Human-Machine systems

作者： Timothy Sellers Tingjun Lei Chaomin Luo Lantao Liu Daniel W. Carruth Department of Electrical and Computer Engineering Mississippi State University Mississippi State MS USA Department of Intelligent Systems Engineering Indiana University Bloomington IN USA Center for Advanced Vehicular Systems Mississippi State University MS USA

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

In the world of autonomous robots, achieving complete accuracy remains a challenge, underscoring the importance of human intervention, especially regarding safety. In Human-Autonomy Teaming (HAT), ensuring safe and effective human-robot cooperation in dynamic indoor settings is crucial. This paper introduces a framework designed to address this accuracy shortfall, enhancing safety and robotic interactions in these environments. Central to our approach is a hybrid graph system that melds the Generalized Voronoi Diagram (GVD) with spatio-temporal graphs, harmonizing human feedback, environmental elements, and key waypoints. An essential component is the improved Node Selection Algorithm (iNSA), which utilizes the revised Grey Wolf Optimization (rGWO) for enhanced adaptability and performance. Human insights are instrumental, from supplying initial environmental data and determining key waypoints to stepping in during unexpected challenges or dynamic changes in the environment. Extensive simulation and comparison tests confirm the reliability and effectiveness of our model, highlighting its unique advantages in the realm of HAT.

关键词： Adaptation models Accuracy Navigation Robot kinematics Heuristic algorithms Instruments Path planning

来源：评论

学校读者我要写书评

暂无评论

Geometric Parameterized Macromodel of Uniaxial Capacitance Accelerometer Based on Modal Superposition 16

Geometric Parameterized Macromodel of Uniaxial Capacitance A...

引用

16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument engineering, APEIE 2023

作者： Gridchin, Aleksandr V. Koleda, Aleksei N. Barbin, Evgenii S. Nesterenko, Tamara G. Kolchuzhin, Vladimir A. Department of Semiconductor Devices and Microelectronics Novosibirsk State Technical University Novosibirsk Russia Department of Electronic Engineering National Research Tomsk Polytechnic University Tomsk Russia Laboratory of Intelligent Computer Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Advanced Cellular Group Qorvo Munich GmbH Munich Germany

ISBN: (纸本)9798350330885

The paper presents a piecewise geometric parameterization method and a parametric analysis of uniaxial capacitance accelerometer using a reduced model formulated on the modal superposition method. The extraction of parameters for the macromodel such as modal frequencies, modal masses, modal stiffnesses, strain energy, and capacitances are described. The reduced model consists of the two modes: torsion mode, and transversal mode, three electrodes in the electrostatic domain, and eight master nodes in the geometrical domain located at the edges of the plate. The generation of the discrete macromodels is executed using commercial software. The thickness of the plate has been selected as a design parameter. Linear piecewise interpolation is used for macromodel parameterization which have been done inside VHDL-AMS code. The accelerometer's dynamic response simulated using of parametrized macromodel considering the mechanical contact phenomena and the nonlinear electrostatic interaction are presented and discussed. The developed approach can be applied for fast system level simulations in view of the variation of the geometric parameters. © 2023 IEEE.

关键词： Capacitance

来源：评论

学校读者我要写书评

暂无评论

Soft Switched Hybrid Energy Storage Module for Power and Energy Management of a DC Bus

Soft Switched Hybrid Energy Storage Module for Power and Ene...

引用

Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Ilya Zeltser Michael Evzelman Daniel Beniaminson Mor M. Peretz Electronics Department Rafael Advanced Defense Systems Ltd. Haifa Israel Department of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

A hybrid energy storage module based around two strings consistent of battery cells as an energy optimized string, and super-capacitors as a power surge optimized string, is presented. The energy transfer between the two strings is carried out using a series resonant link, that connects one battery to two super capacitors. The rest of the cells are equalized by series resonant balancers that shuttle the energy along each of the strings. The serial resonant link between the strings results in faster energy transfer, operates more efficiently than previous options, and reduces component count compared to the latest available solutions. Hybrid energy storage connection to a common DC bus options are outlined, and three setups that provide added value for dual string architecture are shown. Dual voltage source management methods are summarized, along with their advantages and down sides. The system is analyzed analytically, evaluated in simulation, and verified by an experimental prototype. The simulation and experimental results are in good agreement with theoretical predictions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Based Multivariate Classification of Cardiac Autonomic Neuropathy Via Imbalanced ECG Recordings

Machine Learning-Based Multivariate Classification of Cardia...

引用

Advances in Science and engineering Technology International Conferences (ASET)

作者： Muaz Al Radi Abdelfatah Ahmad Abdulrahman Ahmad Abderrahmene Boudiaf Nouf Alkaabi Maher Malouf Herbert Jelinek Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE Department of Industrial And Systems Engineering Khalifa University Abu Dhabi UAE Department of Biomedical Engineering Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350344134

ISBN: (纸本)9798350344141

Cardiac Autonomic Neuropathy (CAN) is a frequent complication of diabetes that presents a diagnostic challenge in clinical settings. Ewing tests, comprising five assessments of blood pressure and heart rate, have traditionally been used to categorize the severity of CAN. This paper investigates the application of a machine learning (ML) approach that leverages patient demographic information and electrocardiogram (ECG) recordings to automatically classify CAN dysfunction without any of the Ewing tests. Additionally, the ML model being proposed addresses the challenges posed by imbalanced datasets both at the data level, through the utilization of synthetic minority over-sampling technique (SMOTE), and at the algorithmic level, by employing the focal loss function. The effectiveness of the proposed techniques is reflected in the ROC curve achieving an area under curve (AUC) value of 92%. Moreover, the model is able to produce 80% accurate prediction of the development of CAN. However, for enhancing system's classification performance, two Ewing tests are supported as additional features. A higher performance is achieved as indicated by accuracy and F1-score values of 88% and 88%, respectively.

关键词： Measurement Accuracy Prevention and mitigation Machine learning Artificial neural networks Electrocardiography Predictive models Prediction algorithms Recording Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

ONLINE ML-BASED JOINT CHANNEL ESTIMATION AND MIMO DECODING FOR DYNAMIC CHANNELS

arXiv

引用

arXiv 2024年

作者： Fernando, Luiz Teixeira, Moreira Luiz, Vinicius Henrique Soares, Jonathan Aguiar Mayer, Kayol Soares Arantes, Dalton Soares Digital Communications Laboratory – ComLab Department of Communications School of Electrical and Computer Engineering University of Campinas – Unicamp SP Campinas13083-852 Brazil

This paper presents an online method for joint channel estimation and decoding in massive MIMO-OFDM systems using complex-valued neural networks (CVNNs). The study evaluates the performance of various CVNNs, such as the complex-valued feedforward neural network (CVFNN), split-complex feedforward neural network (SCFNN), complex radial basis function (C-RBF), fully-complex radial basis function (FC-RBF) and phase-transmittance radial basis function (PT-RBF), in realistic 5G communication scenarios. Results demonstrate improvements in mean squared error (MSE), convergence, and bit error rate (BER) accuracy. The C-RBF and PT-RBF architectures show the most promising outcomes, suggesting that RBF-based CVNNs provide a reliable and efficient solution for complex and noisy communication environments. These findings have potential implications for applying advanced neural network techniques in next-generation wireless systems. © 2024, CC BY.

关键词： Radial basis function networks

来源：评论

学校读者我要写书评

暂无评论

Insights into Heterogenous Sensor Fusion and Passive RF Feature Extraction for xAI

Insights into Heterogenous Sensor Fusion and Passive RF Feat...

引用

IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Asad Vakil Robert Ewing Erik Blasch Jia Li Department of Electrical and Computer Engineering Oakland University Rochester MI Air Force Research Laboratory Dayton OH Oakland University Rochester MI

ISBN: (数字)9798350367621

ISBN: (纸本)9798350367638

Deep learning-based models have made significant contributions to many fields in recent years but lack robust explainability in their decision making and interpretability in their inference processes. While deep learning is capable of processing information that traditional methods might struggle with, such as nonlinear data, the lack of results explainability can be detrimental to the credibility in such models. Hence, there is a need to enhance the level of explainability, which can come from multimodal analysis. In this study, we implement a Convolutional Neural Network (CNN) for the fusion of Passive RF (P-RF) and Electro-Optical (EO) data to gain insights into how P-RF data can be utilized for target detection. The P-RF data first undergoes feature extraction via Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), Wigner-Villle Distribution (WVD) and Constant-Q Gabor Transform (CQT). In previous experiments using the ESCAPE dataset, the multimodal design training was incentivized to utilize the P-RF data with I/Q histogram as the feature by purposefully restricting the available EO data. Prior experimentation with both Greedy Algorithms and Saliency Maps indicated that the fusion of P-RF and EO data still heavily focuses on the EO data, if possible, only relying on the P-RF data if it was necessary to detect the target. While P-RF has seen more use in vehicle detection for both autonomous driving and drone applications in recent years, its impact on sensor fusion based decision making is still under investigation. By expanding on the available P-RF data, this paper compares different features of P-RF data and their impact in the fusion using diverse counterfactual explanations (DiCE), as well as potentially increasing the reliability of the P-RF data for target detection.

关键词： Radio frequency Training Continuous wavelet transforms Vehicle detection Decision making Object detection Sensor fusion

来源：评论

学校读者我要写书评

暂无评论

Representative Period Selection for Robust Capacity Expansion Planning in Low-carbon Grids

Representative Period Selection for Robust Capacity Expansio...

引用

IEEE PES Transmission and Distribution Conference and Exposition

作者： Osten Anderson Nanpeng Yu Konstantinos Oikonomou Patrick Maloney Di Wu Department of Electrical and Computer Engineering University of California Riverside Riverside CA System Evaluation Group Pacific Northwest National Laboratory Richland WA

ISBN: (数字)9798350316377

ISBN: (纸本)9798350316384

Amid the urgency to decarbonize power systems, while mitigating extreme weather events, capacity expansion models can play a vital role in reliably planning the expansion of power systems and facilitating the integration of renewable energy (RE) sources. Optimizing capacity expansion generally involves selecting surrogate representative days from forecasts of load and the generation profiles of variable RE resources. To properly select those representative days, we propose a novel input-based clustering approach that utilizes three unique operational characteristics: load shedding, renewable curtailment, and transmission congestion. The proposed method allows for more robust and cost-effective capacity planning. The method is validated using a capacity expansion model and a production cost model aligned with California Independent System Operator (CAISO)’s decarbonization goals, and results in significant cost reduction and substantial decreases in load shedding.

关键词： Renewable energy sources Costs Weather forecasting Load shedding Production Low-carbon economy Planning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：