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11th IEEE International Conference on Teaching, Assessment and Learning for engineering, TALE 2022

作者： Wan, Han Luo, Hongzhen Zhong, Zihao Yang, Jianlei Beihang University School of Computer Science and Engineering State Key Laboratory of Virtual Reality Technology and Systems Beijing China

ISBN: (纸本)9781665491174

Due to the COVID-19 pandemic, traditional teaching has been migrated online. Different from traditional face-to-face teaching, when students learn online, the online learning platform will generate various data. And these data make it possible for us to analyze students' final academic performance. In this paper, we use structural equation modeling (SEM) to analyze the relationship between students' learning factors. It is found that students' lab scores (LS), exercise scores (LS) and participation in the discussion (PID) have a direct impact on their final programming scores (FPS). This paper also finds that students' assignment submissions have an indirect influence on their final programming scores (FPS) but have a direct effect on lab scores (LS). In addition, students' participation in the discussion (PID) has an indirect influence on assignment submissions (AS) and lab scores (LS). The research in this paper can provide instructional designers with references for instructional design. © 2022 IEEE.

关键词： Students

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Giant saturation absorption of tungsten trioxide film prepared based on the seedless layer hydrothermal method

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Chinese Physics B 2023年第3期32卷 286-290页

作者：马晓光胡芳珍陈希王艺盟郝晓剑顾敏张启明 Science and Technology on Electronic Test and Measurement Laboratory North University of ChinaTaiyuan 030051China Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Nonlinear materials have gained wide interest as saturable absorbers and pulse compression for pulsed laser applications due to their unique optical *** work investigates the third-order nonlinear phenomenon of tungsten trioxide(WO_(3))thin *** giant nonlinear absorption and nonlinear refractive index of WO_(3)thin films were characterized by Z-scan method at 800 *** experimentally observed the giant saturable absorption(SA)and nonlinear refractive index of WO_(3)thin films prepared by the seedless layer hydrothermal method,with SA coefficient being as high as-2.59×105cm·GW^(-1).The SA coefficient is at least one order of magnitude larger than those of the conventional *** nonlinear refractive index n_(2)of WO_(3)film has been observed for the first time in recent studies and the corresponding coefficient can be up to 1.793 cm^(2)·GW^(-1).The large third-order nonlinear optical(NLO)response enables WO_(3)thin films to be promising candidates for optoelectronic and photonic applications in the near-infrared domain.

关键词： tungsten trioxide Z-scan saturable absorption

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Generative Cooperative Network for Person Image Generation 6

Generative Cooperative Network for Person Image Generation

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6th IEEE International Conference on Universal Village, UV 2022

作者： Liu, Yang Wang, Shuai Wu, Yubin Sheng, Hao Xiong, Zhang Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China

ISBN: (纸本)9781665474771

Person image generation is to synthesize realistic pedestrian images that follow the same distribution as the given dataset. Previous attempts can be generally categorized into two classes: some methods use human pose information as guidance and others try to generate person images from scratch. The former is to transfer the pose of a source image to a reference pose. The generated person image have the same identity as the source image. The latter takes a random noise from latent space as input, and the real person images are only used as references for the discriminator. While pose-guided person image generation is widely studied, generating-from-scratch methods are also worth exploring because they can synthesize person image with new identity, which is a useful manner of data augmentation. These two types of generating methods have their different advantages and disadvantages, and sometimes they are complementary. In this work, the authors design a Generative Cooperative Network (GCN) to jointly train two types of GANs. The two GANs serve different purposes, and can learn from each other during the cooperative learning procedure. The proposed approach is verified on public datasets, and the results show that our GCN improves the performance of the baseline methods. Comparisons with state-of-the-art methods also prove the effectiveness of the proposed method. © 2022 IEEE.

关键词： computer vision

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TRANSFER LEARNING FOR DAY-AHEAD LOAD FORECASTING: A CASE STUDY ON EUROPEAN NATIONAL ELECTRICITY DEMAND TIME SERIES

arXiv

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arXiv 2023年

作者： Tzortzis, Alexandros-Menelaos Pelekis, Sotiris Spiliotis, Evangelos Karakolis, Evangelos Mouzakitis, Spiros Psarras, John Askounis, Dimitris Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece

Short-term load forecasting (STLF) is crucial for the daily operation of power grids. However, the non-linearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. Various forecasting approaches have been proposed for improving STLF, including neural network (NN) models which are trained using data from multiple electricity demand series that may not necessary include the target series. In the present study, we investigate the performance of this special case of STLF, called transfer learning (TL), by considering a set of 27 time series that represent the national day-ahead electricity demand of indicative European countries. We employ a popular and easy-to-implement NN model and perform a clustering analysis to identify similar patterns among the series and assist TL. In this context, two different TL approaches, with and without the clustering step, are compiled and compared against each other as well as a typical NN training setup. Our results demonstrate that TL can outperform the conventional approach, especially when clustering techniques are considered. © 2023, CC BY.

关键词： Time series

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UAV-Based 'True' and 'Pseudo' Antenna Pattern Measurements for Phased Array Radar

UAV-Based 'True' and 'Pseudo' Antenna Pattern Measurements f...

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2024 IEEE International Symposium on Phased Array systems and Technology, ARRAY 2024

作者： Burdi, Khuda Schvartzman, David Segales, Antonio R. Salazar-Cerreno, Jorge L. Fulton, Caleb Ivic, Igor Wasielewski, Daniel Palmer, Robert D. The University of Oklahoma United States School of Electrical and Computer Engineering The University of Oklahoma United States School of Meteorology The University of Oklahoma United States The University of Oklahoma United States NOAA/OAR National Severe Storms Laboratory United States

ISBN: (纸本)9798350392142

Phased Array Radars (PARs) are emerging as a cutting-edge technology for atmospheric observations, offering unprecedented capabilities in beamforming and rapid electronic beam steering. However, their complexity and the need for accurate calibration pose significant challenges. The use of Uncrewed Aerial systems (UAS) in the characterization of these systems is being shown to be a robust and cost-effective approach, but their current development to meet demanding calibration requirements involves more research efforts. This paper reconsiders the role of the PAR and UAS in the traditional full raster scan approach to UAS-based radiation pattern characterization to address the existing limitations on the time and efficiency of such measurements. Leveraging recent advancements in both PAR and UAS technologies, the study introduces the use of antenna pseudo-patterns, which shifts the measurement-heavy paradigm from the UAS onto the faster-scanning PARs. Results from simulations and field measurements using the Horus radar show promising results with the proposed method. The findings indicate that pseudo-pattern measurements can reduce the aver-age time needed for the radiation pattern characterization of a PAR from approximately 22 minutes to under 2 minutes, with room for further optimization. This offers a promising and viable solution for fast and accurate PAR characterization over many beam positions. © 2024 IEEE.

关键词： Calibration

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Evaluation of Polarimetric Calibration of the Horus Radar using Weather Returns

Evaluation of Polarimetric Calibration of the Horus Radar us...

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2024 IEEE International Symposium on Phased Array systems and Technology, ARRAY 2024

作者： Pehl, Jonah Schvartzman, David Zrnic, Dusan Ryzhkov, Alexander Herndon, Matthew Palmer, Robert D. The University of Oklahoma United States School of Meteorology The University of Oklahoma United States School of Electrical and Computer Engineering The University of Oklahoma United States National Severe Storms Laboratory NOAA/OAR United States The University of Oklahoma United States

ISBN: (纸本)9798350392142

Fully digital rotating planar polarimetric phased array radar (PAR) is one of the technologies being considered to replace the existing fleet of operational WSR-88D weather radars across the United States. This architecture benefits from both high temporal resolution, relatively low cost (compared to 4-faced PAR), and flexibility. Of importance is to achieve the improvement in update times needed for weather surveillance while maintaining or exceeding current operational polarimetric data quality. To this end, a methodology using the 'birdbath' scanning (vertical pointing) technique is presented to evaluate po-larimetric calibration of the fully-digital Horus PAR. Preliminary results indicate that the embedded copolar element patterns for the horizontal (H) and vertical (V) polarizations can be estimated using the birdbath technique. Furthermore, using the birdbath estimated patterns, it may be feasible to derive corrections for antenna-induced biases in polarimetric measurements. © 2024 IEEE.

关键词： Calibration

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A Universal Hybrid Precoding Scheme for Massive MIMO Communications

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China Communications 2022年第11期19卷 160-178页

作者： Yimeng Feng Yi Jiang Mahesh K.Varanasi Key Laboratory for Information Science of Electromagnetic Waves(MoE) School of Information Science and TechnologyFudan UniversityShanghai 200433China the Department of Electrical Computer and Energy EngineeringUniversity of ColoradoBoulderColorado 80309-0425

Based on an analog radio frequency(RF)network,hybrid precoding(HPC)for massive MIMO can achieve very high spectral efficiencies with moderate hardware cost and power *** the extensive research efforts in recent years,the practioners are still looking for HPCs that are efficient and *** this paper,we present a new method termed as the universal hybrid precoding(UHP),which is nearly optimal,computationally efficient,and applicable to various types of RF network(thus,the name universal):the components of the network can be phase shifters(with finite or infinite resolutions),switches,or their combinations;the topology of the network can be fully-connected or *** the standard UHP,we also propose a simplified version termed as sUHP to trade a negligible performance loss for much reduced computational *** analysis shows that the computational complexity of the proposed UHP/sUHP is one to two orders of magnitude lower than the state-of-theart *** results verify the(near-)optimality of the proposed UHP scheme for various forms of the analog networks.

关键词： hybrid beamforming massive MIMO phase-shifter network switch network

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Accurate, provable, and fast nonlinear tomographic reconstruction: A variational inequality approach

arXiv

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arXiv 2025年

作者： Lou, Mengqi Verchand, Kabir Aladin Fridovich-Keil, Sara Pananjady, Ashwin Georgia Institute of Technology School of Industrial and Systems Engineering United States School of Electrical and Computer Engineering United States University of Cambridge Statistical Laboratory United Kingdom Stanford University Department of Electrical Engineering United States

We consider the problem of signal reconstruction for computed tomography (CT) under a nonlinear forward model that accounts for exponential signal attenuation, a polychromatic X-ray source, general measurement noise (e.g. Poisson shot noise), and observations acquired over multiple wavelength windows. We develop a simple iterative algorithm for single-material reconstruction, which we call EXACT (EXtragradient Algorithm for Computed Tomography), based on formulating our estimate as the fixed point of a monotone variational inequality. We prove guarantees on the statistical and computational performance of EXACT under practical assumptions on the measurement process. We also consider a recently introduced variant of this model with Gaussian measurements, and present sample and iteration complexity bounds for EXACT that improve upon those of existing algorithms. We apply our EXACT algorithm to a CT phantom image recovery task and show that it often requires fewer X-ray projection exposures, lower source intensity, and less computation time to achieve similar reconstruction quality to existing methods. © 2025, CC BY.

关键词： computerized tomography

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A COMPARATIVE ASSESSMENT OF DEEP LEARNING MODELS FOR DAY-AHEAD LOAD FORECASTING: INVESTIGATING KEY ACCURACY DRIVERS

arXiv

引用

arXiv 2023年

作者： Pelekis, Sotiris Seisopoulos, Ioannis-Konstantinos Spiliotis, Evangelos Pountridis, Theodosios Karakolis, Evangelos Mouzakitis, Spiros Askounis, Dimitris Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece

Short-term load forecasting (STLF) is vital for the effective and economic operation of power grids and energy markets. However, the non-linearity and non-stationarity of electricity demand as well as its dependency on various external factors renders STLF a challenging task. To that end, several deep learning models have been proposed in the literature for STLF, reporting promising results. In order to evaluate the accuracy of said models in day-ahead forecasting settings, in this paper we focus on the national net aggregated STLF of Portugal and conduct a comparative study considering a set of indicative, well-established deep autoregressive models, namely multi-layer perceptrons (MLP), long short-term memory networks (LSTM), neural basis expansion coefficient analysis (N-BEATS), temporal convolutional networks (TCN), and temporal fusion transformers (TFT). Moreover, we identify factors that significantly affect the demand and investigate their impact on the accuracy of each model. Our results suggest that N-BEATS consistently outperforms the rest of the examined models. MLP follows, providing further evidence towards the use of feed-forward networks over relatively more sophisticated architectures. Finally, certain calendar and weather features like the hour of the day and the temperature are identified as key accuracy drivers, providing insights regarding the forecasting approach that should be used per case. © 2023, CC BY.

关键词： Convolution

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A Clutter Mitigation Technique for Phased Array Weather Radar

A Clutter Mitigation Technique for Phased Array Weather Rada...

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2024 IEEE International Symposium on Phased Array systems and Technology, ARRAY 2024

作者： Kim, Yoon-S.L. Schvartzman, David Palmer, Robert D. Yu, Tian-You Nai, Feng Curtis, Christopher The University of Oklahoma United States School of Meteorology The University of Oklahoma United States School of Electrical and Computer Engineering The University of Oklahoma United States The University of Oklahoma United States National Severe Storms Laboratory NOAA/OAR United States

ISBN: (纸本)9798350392142

Weather radar data are often contaminated by unwanted clutter from stationary and/or moving objects, complicating the accurate interpretation of weather phenomena. Traditional clutter mitigation techniques such as the Gaussian Model Adaptive Processing (GMAP) are effective against stationary clutter but struggle with moving clutter due to the variable, non-zero Doppler velocities. Phased Array Radars (PARs), with capabilities such as Digital Beamforming (DBF), offer the possibility for advanced clutter classification and mitigation by leveraging both spatial and spectral information. This paper proposes an adaptive clutter mitigation algorithm for weather radars. Initially, the algorithm employs Fourier DBF to classify clutter signals. Next, adaptive DBF is applied to the transmit beam where clutter was identified in the previous step. During this stage, artificial high power due to sidelobes and grating lobes is filtered out. Subsequently, adaptive spectral analysis is performed to filter out the clutter through the power spectrum density. This algorithm optimizes data quality while minimizing computational load by selectively performing adaptive processing only when necessary. The algorithm's effectiveness is evaluated using data from the Horus radar system, demonstrating its capability to accurately classify clutter and weather, improving Doppler velocity estimation and overall weather data quality by filtering out clutter. © 2024 IEEE.

关键词： Radar clutter

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