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Time-dependent performance analysis of the 802.11p-based platooning communications under disturbance

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arXiv 2020年

作者： Wu, Qiong Ge, Hongmei Fan, Pingyi Wang, Jiangzhou Fan, Qiang Li, Zhengquan School of Internet of Things Engineering Jiangnan University Wuxi214122 China State key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China School of Engineering and Digital Arts University of Kent CanterburyCT2 7NT United Kingdom Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Platooning is a critical technology to realize autonomous driving. Each vehicle in platoons adopts the IEEE 802.11p standard to exchange information through communications to maintain the string stability of platoons. However, one vehicle in platoons inevitably suffers from a disturbance resulting from the leader vehicle acceleration/deceleration, wind gust and uncertainties in a platoon control system, i.e., aerodynamics drag and rolling resistance moment etc. Disturbances acting on one vehicle may inevitably affect the following vehicles and cause that the spacing error is propagated or even amplified downstream along the platoon, i.e., platoon string instability. In this case, the connectivity among vehicles is dynamic, resulting in the performance of 802.11p in terms of packet delay and packet delivery ratio being time-varying. The effect of the string instability would be further deteriorated once the time-varying performance of 802.11p cannot satisfy the basic communication requirement. Unlike the existing works which only analyze the steady performance of 802.11p in vehicular networks, we will focus on the impact of disturbance and construct models to analyze the time-dependent performance of 802.11p-based platooning communications. The effectiveness of the models is validated through simulation results. Moreover, the time-dependent performance of 802.11p Copyright © 2020, The Authors. All rights reserved.

关键词： Military applications

A Doubly-Masked-Ballot Multi-questions Voting Scheme with Distributed Trust

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A Doubly-Masked-Ballot Multi-questions Voting Scheme with Di...

Computing Conference, 2019

作者： Szabo, Robert Telcs, Andras Department of Telecommunications and Media Informatics Budapest University of Technology and Economics Budapest Hungary Department of Computational Sciences Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Department of Quantitative Methods Faculty of Business and Economics University of Pannonia Veszprem Hungary

ISBN: (纸本)9783030228675

We propose an electronic voting scheme for multiple questions voting based on masked ballots, homomorph counting, publicly verifiable secret sharing based tallying and public communication channels. In our scheme all on-line authority functions (authentication, candidate slate check, counting and tallying) are distributed among the active voters. Off-line authorities only prepare and distribute election secrets. We analyze our scheme for different adversary attacks and show that the maximum that the strongest adversary can achieve is a random attack. © 2019, Springer Nature Switzerland AG.

关键词： Security of data

Non-parametric priors for generative adversarial networks

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arXiv 2019年

作者： Singh, Rajhans Turaga, Pavan Jayasuriya, Suren Garg, Ravi Braun, Martin W. School of Electrical Computer and Energy Engineering Arizona State University TempeAZ United States School of Arts Media and Engineering Arizona State University TempeAZ United States Intel Corporation ChandlerAZ United States

The advent of generative adversarial networks (GAN) has enabled new capabilities in synthesis, interpolation, and data augmentation heretofore considered very challenging. However, one of the common assumptions in most GAN architectures is the assumption of simple parametric latent-space distributions. While easy to implement, a simple latent-space distribution can be problematic for uses such as interpolation. This is due to distributional mismatches when samples are interpolated in the latent space. We present a straightforward formalization of this problem;using basic results from probability theory and offthe-shelf-optimization tools, we develop ways to arrive at appropriate non-parametric priors. The obtained prior exhibits unusual qualitative properties in terms of its shape, and quantitative benefits in terms of lower divergence with its mid-point distribution. We demonstrate that our designed prior helps improve image generation along any Euclidean straight line during interpolation, both qualitatively and quantitatively, without any additional training or architectural modifications. The proposed formulation is quite flexible, paving the way to impose newer constraints on the latentspace statistics. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

Dispensed transformer network for unsupervised domain adaptation

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arXiv 2021年

作者： Li, Yunxiang Li, Jingxiong Dan, Ruilong Wang, Shuai Jin, Kai Zeng, Guodong Wang, Jun Pan, Xiangji Zhang, Qianni Zhou, Huiyu Jin, Qun Wang, Li Wang, Yaqi College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Artificial Intelligence and Biomedical Image Analysis Lab Westlake University Hangzhou China School of Mechanical Electrical and Information Engineering Shandong University Weihai China Department of Ophthalmology Hospital of Zhejiang University Hangzhou China sitem Center for Translational Medicine and Biomedical Entrepreneurship University of Bern Bern Switzerland School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Computing and Mathematical Sciences University of Leicester United Kingdom Department of Human Informatics and Cognitive Sciences Faculty of Human Sciences Waseda University Tokyo Japan Department of Radiology and Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill United States College of Media Engineering Communication University of Zhejiang Hangzhou China

Accurate segmentation is a crucial step in medical image analysis and applying supervised machine learning to segment the organs or lesions has been substantiated effective. However, it is costly to perform data annotation that provides ground truth labels for training the supervised algorithms, and the high variance of data that comes from different domains tends to severely degrade system performance over cross-site or cross-modality datasets. To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper. Our novel DTNet contains three modules. First, a dispensed residual transformer block is designed, which realizes global attention by dispensed interleaving operation and deals with the excessive computational cost and GPU memory usage of the Transformer. Second, a multi-scale consistency regularization is proposed to alleviate the loss of details in the low-resolution output for better feature alignment. Finally, a feature ranking discriminator is introduced to automatically assign different weights to domain-gap features to lessen the feature distribution distance, reducing the performance shift of two domains. The proposed method is evaluated on large fluorescein angiography (FA) retinal nonperfusion (RNP) cross-site dataset with 676 images and a wide used cross-modality dataset from the MM-WHS challenge. Extensive results demonstrate that our proposed network achieves the best performance in comparison with several state-of-the-art techniques. © 2021, CC BY.

关键词： Supervised learning

A critical review on decade progress and future prospects of P3HT:PCBM bulk heterojunction solar cells

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Solar Energy 2025年 297卷

作者： Balraj, Babu Raja, M. Prabhu, T.G. Vignesh Balaji, M. Chandrasekaran, J. Nagarajan, Senthil Kumar Bharathi, Mohanbabu Lee, Sejoon Sivakumar, Chandrasekar Department of Physics Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Physics Vivekanandha College of Arts and Science for Women Tamil Nadu Namakkal637205 India Department of Physics Sri Ramakrishna Mission Vidyalaya College of Arts and Science Tamil Nadu Coimbatore641 020 India Department of Electrical & Computer Engineering National University of Singapore Singapore117608 Singapore Postgraduate and Research Department of Physics Nanotechnology Lab Kongunadu Arts and Science College Tamilnadu Coimbatore641029 India Centre for Research Impact & Outcome Chitkara University Rajpura 140401 Punjab India Department of Semiconductor Science Dongguk University-Seoul Seoul04620 Korea Republic of Department of Physics National Chung Hsing University Taichung City40227 Taiwan

In recent years, Π-conjugated polymer:fullerene-based bulk heterojunction (BHJ) solar cells have received significant attention since combining a narrow P3HT donor and a high electron affinity PCBM acceptor blend could help achieve high efficiencies. In order to compete with other organic solar cell materials, further optimization of P3HT:PCBM-based BHJ has to be done to improve the device's efficiency. This review traces the historical development of bulk heterojunction, architecture, and its PV characterizations, presents the various work functions of the active layer, different charge transport interface layers and electrodes of P3HT:PCBM-based BHJ solar cells, and P3HT:PCBM-based inverted and tandem device structures. Besides, the PCE performance of different material combinations is listed better to understand the P3HT:PCBM-based BHJ solar cells. This review concludes by discussing the optimization of different PV parameters and fabrication technologies of the BHJ. © 2025 International Solar Energy Society

关键词： Buffer layers Bulk heterojunction solar cells P3HT:PCBM blend Processing parameters PV characteristics

Role of element-specific damping in ultrafast, helicity-independent, all-optical switching dynamics in amorphous (Gd,Tb)Co thin films

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Physical Review B 2021年第2期103卷 024438-024438页

作者： Alejandro Ceballos Akshay Pattabi Amal El-Ghazaly Sergiu Ruta Christian P. Simon Richard F. L. Evans Thomas Ostler Roy W. Chantrell Ellis Kennedy Mary Scott Jeffrey Bokor Frances Hellman Department of Materials Science and Engineering University of California Berkeley Berkeley California 94720 USA Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley California 94720 USA Department of Physics University of York Heslington York YO10 5DD United Kingdom Department of Physics University of California Berkeley Berkeley California 94720 USA Materials and Engineering Research Institute Sheffield Hallam University Howard Street Sheffield S1 1WB United Kingdom Faculty of Science Technology and Arts Sheffield Hallam University Howard Street Sheffield S1 1WB United Kingdom National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley California 94720 USA

Ultrafast control of the magnetization in ps timescales by fs laser pulses offers an attractive avenue for applications such as fast magnetic devices for logic and memory. However, ultrafast helicity-independent all-optical switching (HI-AOS) of the magnetization has thus far only been observed in Gd-based, ferrimagnetic amorphous (a-) rare earth-transition metal (a-RE-TM) systems, and a comprehensive understanding of the reversal mechanism remains elusive. Here, we report HI-AOS in ferrimagnetic a−Gd22−xTbxCo78 thin films, from x=0 to 18, and elucidate the role of Gd in HI-AOS in a-RE-TM alloys and multilayers. Increasing Tb content results in increasing perpendicular magnetic anisotropy and coercivity, without modifying magnetization density, and slower remagnetization rates and higher critical fluences for switching but still shows picosecond HI-AOS. Simulations of the atomistic spin dynamics based on the two-temperature model reproduce these results qualitatively and predict that the lower damping on the RE sublattice arising from the small spin-orbit coupling of Gd (with L=0) is instrumental for the faster dynamics and lower critical fluences of the Gd-rich alloys. Annealing a−Gd10Tb12Co78 leads to slower dynamics which we argue is due to an increase in damping. These simulations strongly indicate that accounting for element-specific damping is crucial in understanding HI-AOS phenomena. The results suggest that engineering the element-specific damping of materials can open up new classes of materials that exhibit low-energy, ultrafast HI-AOS.

关键词： Ferrimagnetism First-principles calculations Magnetism Magnetization dynamics Magnetization switching Magneto-optical effect Ultrafast magnetization dynamics

Human Activity Recognition Using LSTM-RNN Deep Neural Network Architecture

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arXiv 2019年

作者： Pienaar, Schalk Wilhelm Malekian, Reza Department of Electrical Electronic and Computer Engineering University of Pretoria Pretoria0002 South Africa Department of Computer Science and Media Technology Malmö University Malmö20506 Sweden Malmö University Malmö20506 Sweden

Using raw sensor data to model and train networks for Human Activity Recognition can be used in many different applications, from fitness tracking to safety monitoring applications. These models can be easily extended to be trained with different data sources for increased accuracies or an extension of classifications for different prediction classes. This paper goes into the discussion on the available dataset provided by WISDM and the unique features of each class for the different axes. Furthermore, the design of a Long Short Term Memory (LSTM) architecture model is outlined for the application of human activity recognition. An accuracy of above 94% and a loss of less than 30% has been reached in the first 500 epochs of training. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

Deciphering Domestic Energy Demand through Household Electricity and Gas Consumption

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Energy and Built Environment

Energy and Built Environment 2025年

作者： Sari Khdhaer Mukhlif Ahmed K. Nassar Omer Al-Dulaimi Hassan Falah Fakhruldeen Talib Munshid Hanoon Feryal Ibrahim Jabbar Doaa H. Khalaf Sameer Algburi Technical College of Engineering Al-Bayan University Baghdad 10011 Iraq. Policy Planning and Development Program Department of International Affairs College of Arts and Sciences Qatar University Doha 2713 Qatar. Electrical Technical College Al-Farahidi University Baghdad 10001 Iraq. Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Baghdad 10011 Iraq Mazaya University College Nasiriyah 64001 Iraq Medical Physics Department College of Sciences Al-Mustaqbal University Babil 51001 Iraq. Design Department Al-Turath University College Baghdad 10011 Iraq College of Engineering Al-Kitab University Kirkuk 36015 Iraq

This study investigates residential electricity and gas demand in Iraq using smart meter data from 15,000 households between 2019 and 2023. The primary objective is to analyze temporal energy consumption patterns, focusing on the impact of environmental, behavioral, and household-specific factors. The results show that electricity consumption peaks during the afternoon, particularly between 12:00 and 14:00, with average usage reaching 2.3 to 2.6 kWh, while gas consumption increases in the winter months, especially in the early morning hours, driven by heating and cooking activities. The analysis of weekend versus weekday consumption reveals a 6.4% increase in electricity and a 3.1% rise in gas usage on weekends, indicating shifts in behavioral energy usage patterns. The study finds that predicted values closely match observed data, with a deviation of only 5% for electricity and 7% for gas, showcasing high model accuracy. The concept of "variability" is clarified as fluctuations in demand across different times of the day, which were reduced in households with efficient appliances, as identified from household energy audits linked to smart meters. The dataset also provides insights into the use of energy-efficient appliances, collected from utility-linked surveys and integrated household registration data. The findings offer valuable insights into demand-side energy management and are particularly relevant to urban Iraqi households but may also inform demand forecasting in similar regions with comparable climates and energy use behaviors.

关键词： Household energy consumption Electricity and gas demand patterns Energy clustering analysis Predictive energy modeling

Deep Learning Based Techniques to Enhance the Performance of Microgrids: A Review

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Deep Learning Based Techniques to Enhance the Performance of...

Frontiers of Information technology (FIT)

作者： Sheraz Aslam Herodotos Herodotou Nasir Ayub Syed Muhammad Mohsin Department of Electrical Engineering Cyprus University of Technology Limassol Cyprus Department of Computer Science COMSATS University Islamabad Islamabad Pakistan Science and Technology Federal Urdu University of Arts Islamabad Pakistan

ISBN: (数字)9781728166254

ISBN: (纸本)9781728166261

In the last few years, carbon emissions and energy demand have increased dramatically around the globe due to a surge in population and energy-consuming devices. The integration of renewable energy resources (RERs) in a power supply system provides an efficient solution in terms of low energy cost with lower carbon emissions. However, renewable sources like solar panels have irregular nature of power generation because of their dependence on weather conditions, such as solar radiation, humidity, and temperature. Therefore, to tackle this intermittent nature of solar energy, power prediction is necessary for efficient energy management. Deep learning and machine learning-based methods have frequently been implemented for energy forecasting in the literature. The current work summarizes the state-of-theart deep learning-based methods that are proposed to forecast the solar power for proper energy management. We also explain the methodologies of solar energy forecasting along with their outcomes. At the end, future challenges and opportunities are uncovered in the application of deep and machine learning in this area.

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