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Light trapping enhanced broadband photodetection and imaging based on MoSe_(2)/pyramid Si vdW heterojunction

Nano Research 2023年第7期16卷 10552-10558页

作者： Shaoqin Pan Shuo-En Wu Jinjin Hei Zhiwen Zhou Longhui Zeng Yakun Xing Pei Lin Zhifeng Shi Yongtao Tian Xinjian Li Di Wu School of Physics and Microelectronics Key Laboratory of Material PhysicsZhengzhou UniversityZhengzhou 450052China Department of Electrical and Computer Engineering University of California San DiegoLa JollaCA 92093USA Department of Electronic Engineering Chinese University of Hong KongHong Kong 999077China

Two-dimensional(2D)layered materials have been considered promising candidates for next-generation ***,the performance of 2D photodetectors still has much room for improvement due to weak light absorption of planar 2D materials and lack of high-quality heterojunction preparation ***,2D materials integrating with mature bulk semiconductors are a promising pathway to overcome this limitation and promote the practical application on *** this work,we present the patterned assembly of MoSe_(2)/pyramid Si mixed-dimensional van der Waals(vdW)heterojunction arrays for broadband photodetection and *** from the light trapping effect induced enhanced optical absorption and high-quality vdW heterojunction,the photodetector demonstrates a wide spectral response range from 265 to 1550 nm,large responsivity up to 0.67 A·W^(-1),high specific detectivity of 1.84×10^(13)Jones,and ultrafast response time of 0.34/5.6μs at 0 ***,the photodetector array exhibits outstanding broadband image sensing *** study offers a novel development route for high-performance and broadband photodetector array by MoSe_(2)/pyramid Si mixed-dimensional heterojunction.

关键词： two-dimensional(2D)MoSe_(2)layers light trapping van der Waals(vdW)heterojunction broadband photodetector selfpowered imaging

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Forecasting Electricity Demand in Greece: A Functional Data Approach in High Dimensional Hourly Time Series

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SN computer Science 2024年第5期5卷 566页

作者： Varelas, George Tzimas, Giannis Alefragis, Panayiotis Data and Media Laboratory Department of Electrical and Computer Engineering University of Peloponnese Patras Greece

This paper presents a novel approach to forecasting electricity demand in Greece by employing functional data analysis to analyze power consumption. Unlike traditional methods that predict a single future point, this approach forecasts a curve representing daily power consumption. This functional object enables electricity providers to better tailor their pricing and supply strategies in the upcoming day's megawatt hours (MWh) market. To achieve this, the ARIMA algorithm supplemented by functional principal components is utilized for one-step-ahead forecasting. The analysis also incorporates a functional regression using a functional linear model for functional responses, identifying patterns that link one day's consumption to the preceding day. The results demonstrate that the FDA-based method enhances the accuracy of short-term demand forecasts, outperforming classical time series algorithms and neural networks. By providing a more dynamic and comprehensive model of electricity demand, this approach offers significant implications for energy policy and management practices. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electricity demand forecasting FDA FPCA High dimensional time series

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A survey on cross-user federated recommendation

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Science China(Information Sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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Multimodal Price Prediction

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Annals of Data Science 2023年第3期10卷 619-635页

作者： Zehtab-Salmasi, Aidin Feizi-Derakhshi, Ali-Reza Nikzad-Khasmakhi, Narjes Asgari-Chenaghlu, Meysam Nabipour, Saeideh Computerized Intelligence Systems Laboratory Department of Computer Engineering University of Tabriz Tabriz Iran Department Computer and Electrical Engineering University of Mohaghegh Ardabili Ardabil Iran

Price prediction is one of the examples related to forecasting tasks and is a project based on data science. Price prediction analyzes data and predicts the cost of new products. The goal of this research is to achieve an arrangement to predict the price of a cellphone based on its specifications. So, five deep learning models are proposed to predict the price range of a cellphone, one unimodal and four multimodal approaches. The multimodal methods predict the prices based on the graphical and non-graphical features of cellphones that have an important effect on their valorizations. Also, to evaluate the efficiency of the proposed methods, a cellphone dataset has been gathered from GSMArena. The experimental results show 88.3% F1-score, which confirms that multimodal learning leads to more accurate predictions than state-of-the-art techniques. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Price prediction Multimodal learning Convolutional neural network Inception Classification Prediction Multimodal Price Deep learning Data science Mobile phone

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Enhanced CNN Security based on Adversarial FGSM Attack Learning: Medical Image Classification 20

Enhanced CNN Security based on Adversarial FGSM Attack Learn...

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20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

作者： Khriji, Lazhar Messaoud, Seifeddine Bouaafia, Soulef Ammari, Ahmed Chiheb Machhout, Mohsen Sultan Qaboos University Electrical & Computer Engineering Department Oman Monastir University Laboratory of Electronics & Microelectronics Monastir Tunisia

ISBN: (纸本)9798350332568

Convolutional Neural Networks (CNNs) have grown in popularity for clinical image processing applications like as Covid and cancer detection. A new study, however, shows that hostile attacks with modest, unnoticeable disruptions can damage deep healthcare learning systems. This creates safety issues about using these technologies in healthcare situations. In this study, we will look at the approaches used to fight adversarial attacks on medical imaging. Next, we intend to investigate the resilience of pre-trained CNN architectures, as well as LeNet5 and MobileNetV1 models against Fast Gradient Sign Method (FGSM) attacks in a medical healthcare application-based chest X-ray dataset. We discover that pre-trained CNN models are much more sensitive to antagonistic assaults than other models, due to key feature discrepancies between them and regular models. Finally, we propose to improve the CNN' models security by investigating adversarial training. According to the numerical results, models with lower computational complexity and restricted layers are much more safe against malicious attacks than bigger models which are commonly utilized in medical healthcare systems. © 2023 IEEE.

关键词： Convolutional neural networks

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Evaluating Dibenzyltoluene as a Dielectric Liquid for Encapsulation in High Voltage, High Power Density U(WBG) Power Electronics Modules

Evaluating Dibenzyltoluene as a Dielectric Liquid for Encaps...

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2024 IEEE International Conference on Green Energy and Smart Systems, GESS 2024

作者： Adhikari, Pujan Arafat, Easir Ghassemi, Mona Laboratory Department of Electrical and Computer Engineering RichardsonTX United States

ISBN: (纸本)9798350379617

The integration of (ultra) wide bandgap (UWBG) semiconductor technologies, such as silicon carbide and diamond, is crucial for the advancement of zero-emission vehicles (ZEVs), all-electric aircraft (AEA), and renewable energy systems. These technologies operate at higher temperatures, frequencies, and voltages, necessitating the development of novel materials that can endure these demanding conditions. Specifically, the polymeric encapsulation materials currently in use, such as silicone gel and epoxy resin, must be replaced with materials possessing superior properties in every aspect. This paper evaluates the suitability of dibenzyltoluene (DBT), a dielectric liquid, by assessing its thermal conductivity, viscosity, and dielectric breakdown strength under AC voltage. The effects of increased temperature on these properties are examined. Compared to room temperature, DBT exhibits minimal decline in both breakdown strength and thermal conductivity, which are crucial characteristics for insulating media in power modules. When these properties are compared to those of silicone gel, the superior performance of DBT becomes evident. This paper also briefly highlights the challenges to be considered when selecting a dielectric liquid, using the issues related to DBT as a reference. © 2024 IEEE.

关键词： Wide band gap semiconductors

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A Comparative Study on Partial Discharge and Dielectric Strength of MMEI Flat Samples under AC and DC Conditions at Atmospheric Pressure

A Comparative Study on Partial Discharge and Dielectric Stre...

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2024 IEEE International Conference on Green Energy and Smart Systems, GESS 2024

作者： Chowdhury, Saikat Saha, Anoy Rahman, Md Asifur Ghassemi, Mona Laboratory Department of Electrical and Computer Engineering RichardsonTX United States

ISBN: (纸本)9798350379617

The shift towards electrifying transportation systems has become a pivotal strategy in achieving net-zero emissions, driving extensive adoption across the transportation sector. Consequently, the aviation industry is transitioning to all-electric aircraft (AEA). For AEA, an electric power system (EPS) that delivers high power while maintaining low system mass is crucial. Cables are vital components in this future EPS, and one way to reduce their weight is by operating at higher voltages. However, designing insulation systems for higher voltages poses challenges due to increased partial discharge (PD) activity in harsh aviation environments characterized by extremely low pressures and moisture levels. To address these challenges, multilayer multifunction electrical insulation (MMEI) systems have been developed, offering higher breakdown strength compared to single-layer insulation systems. Our previous study proposed some novel MMEI designs for medium voltage direct current (MVDC) power cables capable of operating at low pressures. In this paper, the PD characteristics of these designed MMEI systems are analyzed using the OMICRON MPD 800 under both AC and DC conditions on flat samples. Additionally, the dielectric strength for AC and DC is tested and compared using the Weibull distribution method. © 2024 IEEE.

关键词： Partial discharges

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Influence of Rise Time on the Aging of Electric Machine Windings

Influence of Rise Time on the Aging of Electric Machine Wind...

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2024 IEEE Conference on electrical Insulation and Dielectric Phenomena, CEIDP 2024

作者： Arafat, Easir Rahman, Md Asifur Islam, Farzana Ghassemi, Mona Laboratory Department of Electrical and Computer Engineering RichardsonTX United States

ISBN: (纸本)9798350366679

This study examines how fast rise times, which are common in modern power electronics and drive systems, affect the aging of electric machine windings. It focuses on how to ensure these windings can last longer and work reliably in electrical systems. A twisted pair magnet wire with insulation commonly used in wound machines was used to get experimental data to understand how different voltage waveforms can influence endurance testing of motor insulation systems powered by inverters. Unlike past studies that looked at comparatively slower rise times and fewer repetitions, this research specifically addresses the challenges posed by next-generation wide bandgap (WBG)-based conversion systems. These systems operate at very high speeds, up to 100 kV/μs, and switch frequencies up to 500 kHz, where both frequency and rise time are crucial factors affecting insulation aging over time. © 2024 IEEE.

关键词： Machine windings

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Impact of High-Frequency Repetitive Bipolar Square Wave Voltages on the Lifetime of Turn-to-Turn Insulation

Impact of High-Frequency Repetitive Bipolar Square Wave Volt...

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2024 IEEE Conference on electrical Insulation and Dielectric Phenomena, CEIDP 2024

作者： Rahman, Md Asifur Arafat, Easir Islam, Farzana Ghassemi, Mona Laboratory Department of Electrical and Computer Engineering RichardsonTX United States

ISBN: (纸本)9798350366679

Emerging power electronic drives and converters are planned to operate at high voltages with high switching frequencies (up to a few hundred kHz), which will affect the reliability of insulation systems of windings of electrical machines. To evaluate the insulation quality under such high-frequency pulses, lifetime test is a prominent way. For simplification of test procedure, turn-to-turn insulation samples are approved by IEC 60034-18-42. In past studies, the impact of switching frequencies has been investigated under lower frequencies (up to 20 kHz), which cannot address the challenges of next-generation wide bandgap (WBG)-based power converters. In this study, the lifetimes of turn-to-turn insulations under high switching frequencies (50 kHz) are tested at four different rise times (50 ns, 100 ns, 150 ns, 200 ns), and the impact of switching frequency on the lifetime of the insulation is analyzed. © 2024 IEEE.

关键词： Switching frequency

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A Neuromorphic Spiking Neural Network Using Time-to-First-Spike Coding Scheme and Analog Computing in Low-Leakage 8T SRAM

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IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2024年第5期32卷 848-859页

作者： Chen, Chao-Yu Dai, Yan-Siou Hong, Hao-Chiao Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Novatech Microelectronics Corporation Technology Innovation Department Hsinchu300092 Taiwan

This article demonstrates the first functional neuromorphic spiking neural network (SNN) that processes the time-to-first-spike (TTFS) encoded analog spiking signals with the second-order leaky integrate-and-fire (SOLIF) neuron model to achieve superior biological plausibility. An 8-kb SRAM macro is used to implement the synapses of the neurons to enable analog computing in memory (ACIM) operation and produce current-type dendrite signals of the neurons. A novel low-leakage 8T (LL8T) SRAM cell is proposed for implementing the SRAM macro to reduce the read leakage currents on the read bitlines (RBLs) when performing ACIM. Each neuron's soma is implemented with low-power analog circuits to realize the SOLIF model for processing the dendrite signals and generating the final analog output spikes. No data converters are required in our design by virtue of analog computing's nature. A test chip implementing the complete output layer of the proposed SNN was fabricated in 90-nm CMOS. The active area is 553.4 118.6 {m}{2}. The measurement results show that our SNN implementation achieves an average inference latency of 196 ns and an inference accuracy of 81.4%. It consumes 242 {W} with an energy efficiency of 4.74 pJ/inference/neuron. © 1993-2012 IEEE.

关键词： Neural networks

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