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arXiv 2022年

作者： Kim, Serena Y. Ganesan, Koushik Soderman, Crystal O'Rourke, Raven College of Engineering Design and Computing University of Colorado Denver 1200 Larimer St. DenverCO80204 United States School of Public Affairs University of Colorado Denver 1380 Lawrence St. DenverCO80204 United States Computer Science University of Colorado Boulder 1111 Engineering Or. BoulderCO80309 United States Physics University of Colorado Boulder 2000 Colorado Ave. BoulderCO80309 United States Computer Science University of Colorado Denver 1380 Lawrence St. Suite 3034 DenverCO80204 United States RadiaSoft 6525 Gunpark Dr Suite 370-411 BoulderCO80301 United States

This paper presents a comprehensive analysis of the social and environmental determinants of solar photovoltaic (PV) deployment rates in Colorado, USA. Using 652,795 satellite imagery and computer vision frameworks based on a convolutional neural network, we estimated the proportion of households with solar PV systems and the roof areas covered by solar panels. At the census block group level, 7% of Coloradan households have a rooftop PV system, and 2.5% of roof areas in Colorado are covered by solar panels as of 2021. Our machine learning models predict solar PV deployment based on 43 natural and social characteristics of neighborhoods. Using four algorithms (Random Forest, CATBoost, LightGBM, XGBoost), we find that the share of Democratic party votes, hail risks, strong wind risks, median home value, and solar PV permitting timelines are the most important predictors of solar PV count per household. In addition to the size of the houses, PV-to-roof area ratio is highly dependent on solar PV permitting timelines, proportion of renters and multifamily housing, and winter weather risks. We also find racial and ethnic disparities in rooftop solar deployment. The average marginal effects of median household income on solar deployment are lower in communities with a greater proportion of African American and Hispanic residents and are higher in communities with a greater proportion of White and Asian residents. In the ongoing energy transition, knowing the key predictors of solar deployment can better inform business and policy decision making for more efficient and equitable grid infrastructure investment and distributed energy resource management. © 2022, CC BY-NC-ND.

关键词： Roofs

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Urban Trip Generation Forecasting Based on Gradient Boosting Algorithm

Urban Trip Generation Forecasting Based on Gradient Boosting...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Zhishuai Li Gang Xiong Yu Zhang Meng Zheng Xisong Dong Yisheng Lv State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Municipal Institute of City Planning and Design Beijing China

The four-step transportation model plays an important role in urban planning. The quality of the first phase, i.e. trip generation, determines the performance of the global course. The majority of trip generation forecasting models highly rely on mathematical derivation and have many predictor variables during the prediction, which leads to low accuracy of results and requires laboriously hand-crafted design of input vectors. This paper is the first to introduce the gradient boosting decision tree (GBDT) algorithm for trip generation prediction, and harmonizes such a powerful machine learning method with traditional urban planning requirements to achieve better prediction performance. Unlike the commonly used linear regression method, GBDT can automatically perform feature selection and model the non-linear relationships between input and output variables. Experimental results on real-world residential travel census in Beijing prove that the GBDT model significantly outperforms the baseline and can forecast the trip generation more accurately.

关键词： Machine learning algorithms Urban planning Transportation Predictive models Prediction algorithms Feature extraction Boosting

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Revealing Electron Spill-Out in Plasmonic Nanostructures Using Particle Simulation

Revealing Electron Spill-Out in Plasmonic Nanostructures Usi...

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IEEE International Conference on Plasma Science (ICOPS)

作者： Hue Thi Bich Do Wen Jun Ding Xiao Xiong Ching Eng Png Lin Wu Jeremy Zhen Jie Lim Ricky Ang Zackaria Mahfoud Michel Bosman NUS Graduate School of Integrative Sciences and Engineering National University of Singapore Singapore Singapore National University of Singapore Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research (A*STAR)SingaporeSingapore138632 Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore Singapore SUTD-MIT International Design Center & Science and Math Singapore University of Technology and Design Singapore Singapore Institute of Materials Research and Engineering Agency for Science Technology and Research (A*STAR) Singapore Singapore

ISBN: (纸本)9781728153087

Classical macroscopic electromagnetic descriptions fail to accurately predict the optical response of plasmonic nanostructures approaching electronic length scales due to the omission of nonlocality and free electron spill-out. Alternatively, microscopic first-principles approaches are accurate, yet too computationally intensive for multiscale problems. Starting from a microscopic viewpoint but more computationally efficient, particle-in-cell (PIC) simulation is probably an appropriate mesoscopic treatment for such problems. Here, we leverage on the inherent soft boundary conditions of PIC to study the spill-out effects of surface electrons in mesoscopic-scale metal nanostructures, specifically Na nanowires [1]. The relationship between the surface plasmon resonance frequency and the radius of the nanowire is investigated, exhibiting a 1/R redshift, which agrees well with the self-consistent hydrodynamic method [2] that includes spill-out of electron density. The developed methodology can be extended to model noble metals by including inter-band transitions, di-metallic nanoplasmonic structures, electron spill-out and charge transport between nearly touching nanoparticles.

关键词： Nanoparticles Integrated optics Optical microscopy Metals Resonant frequency Nanowires Plasmas

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A note on community-detection (Kemeny) based testing for COVID-19

arXiv

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arXiv 2020年

作者： Yilmaz, Serife Dudkina, Ekaterina Bin, Michelangelo Crisostomi, Emanuele Ferraro, Pietro Murray-Smith, Roderick Parisini, Thomas Stone, Lewi Shorten, Robert Dyson School of Design Engineering Imperial College London London United Kingdom Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Department of Energy Systems Territory and Constructions Engineering University of Pisa Pisa Italy School of Computing Science University of Glasgow Glasgow United Kingdom George S. Wise Faculty of Life Sciences Tel Aviv University Israel

The Kemeny constant of a graph can be used to identify and analyse bridges between communities in a graph. Testing, tracking and tracing abilities have been identified as pivotal in helping countries to safely reopen activities after the first wave of the COVID-19 virus. Tracing techniques aim at reconstructing past history of contacts, but can face practical limits in an exponential growth of either testing or overly conservative quarantining. We show how this application of graph theory can be conveniently used to efficiently intercept new virus outbreaks, when they are still in their early stage. Simulations provide promising results in early identification and blocking of possible ‘super-spreader’ links that transmit disease between different communities. Copyright © 2020, The Authors. All rights reserved.

关键词： Viruses

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Ethical Aspects of Computational Modelling in Science, Decision Support and Communication 15th

Ethical Aspects of Computational Modelling in Science, Decis...

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15th International Workshop on Artificial Life and Evolutionary Computation, WIVACE 2021

作者： Füchslin, Rudolf Marcel Ambühl, Jacques Faggian, Alessia Fellermann, Harold M. Flumini, Dandolo Geller, Armando Hanczyc, Martin M. Klinkert, Andreas Krütli, Pius Matuttis, Hans-Georg Ott, Thomas Scheidegger, Stephan Schmid, Gary Bruno Smieszek, Timo Schneider, Johannes J. Steiner, Albert Weyland, Mathias S. Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Zurich Technikumstrasse 9 Winterthur8400 Switzerland European Centre for Living Technology Ca’ Bottacin Dorsoduro 3911 Calle Crosera Venice30123 Italy World Bank Group Geneva Switzerland University of Trento Trento38123 Italy School of Computing Newcastle University Science Square 1 Newcastle Upon TyneNE4 5TG United Kingdom GmbH Fraumünsterstrasse 11 Zurich8001 Switzerland Institute of Data Analysis and Process Design Zurich University of Applied Sciences Rosenstrasse 3 Winterthur8400 Switzerland Transdisciplinarity Lab ETH Zurich Zurich8092 Switzerland Department of Mechanical Engineering and Intelligent Systems The University of Electrocommunications Tokyo182-8585 Japan Institute of Computational Life Sciences Zurich University of Applied Sciences Schloss 1 Wädenswil8820 Switzerland Swiss NeuroCreations GmbH Hambergersteig 25 Zurich8008 Switzerland Muesmattweg 70 Allschwil4123 Switzerland

ISBN: (纸本)9783031239281

The development of data science, the increase of computational power, the availability of the internet infrastructure for data exchange and the urgency for an understanding of complex systems require a responsible and ethical use of computational models in science, communication and decision-making. Starting with a discussion of the width of different purposes of computational models, we first investigate the process of model construction as an interplay of theory and experimentation. We emphasise the different aspects of the tension between model variables and experimentally measurable observables. The resolution of this tension is a prerequisite for the responsible use of models and an instrumental part of using models in the scientific processes. We then discuss the impact of models and the responsibility that results from the fact that models support and may also guide experimentation. Further, we investigate the difference between computational modelling in an interdisciplinary science project and computational models as tools in transdisciplinary decision support. We regard the communication of model structures and modelling results as essential;however, this communication cannot happen in a technical manner, but model structures and modelling results must be translated into a "narrative." We discuss the role of concepts from disciplines such as literary theory, communication science, and cultural studies and the potential gains that a broader approach can obtain. Considering concepts from the liberal arts, we conclude that there is, besides the responsibility of the model author, also a responsibility of the user/reader of the modelling results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Modal Analysis of Optical Waveguide using a Novel Symplectic Compact FDTD Schemes

Modal Analysis of Optical Waveguide using a Novel Symplectic...

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International Conference on Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz Applications (NEMO)

作者： Xiaojing Kuang Kong Meng Qi Qi Xin Zeng Zhixiang Huang Mingshen Chen The Anhui Province key laboratory of simulation & design for electronic information system Hefei Normal University Hefei China Digital circuit design east china institute of photo-electronic Suzhou China Key laboratory of intelligent & computing & signal processing Anhui University Hefei China School of Electronic Engineering Huainan Normal University Huainan China

ISBN: (数字)9781728169668

ISBN: (纸本)9781728169675

For modal analysis of longitudinally invariant optical devices, the compact-finite-difference time-domain is an efficient algorithm for alleviating the large amount of storage space and computing time under the 3D complex structure simulation. However, this algorithm needs to scale down the time step to mitigate the numerical dispersion errors while simulating optical waveguide working at high frequency. Here, a novel high-order symplectic compact FDTD scheme is proposed, which is attributed to the theory combination of the fast-spatial scheme and stable temporal scheme. The research results not only verify the effectiveness by numerical experiment on a planar dielectric waveguide with complex cross section, but also provide efficient theoretical guidance for the design and optimization of optical devices at the nanometer scale.

关键词： Electromagnetic waveguides Optical waveguide theory Adaptive optics Numerical models Time-domain analysis Optical waveguides Finite difference methods

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Emotion recognition in conversations with transfer learning from generative conversation modeling

arXiv

引用

arXiv 2019年

作者： Hazarika, Devamanyu Poria, Soujanya Zimmermann, Roger Mihalcea, Rada School of Computing National University of Singapore Computer Science & Engineering University of Michigan United States Information Systems Technology and Design Singapore University of Technology and Design

Recognizing emotions in conversations is a challenging task due to the presence of contextual dependencies governed by self- and inter-personal influences. Recent approaches have focused on modeling these dependencies primarily via supervised learning. However, purely supervised strategies demand large amounts of annotated data, which is lacking in most of the available corpora in this task. To tackle this challenge, we look at transfer learning approaches as a viable alternative. Given the large amount of available conversational data, we investigate whether generative conversational models can be leveraged to transfer affective knowledge for the target task of detecting emotions in context. We propose an approach, TL-ERC, where we first train a neural dialogue generation model and then perform parameter transfer to initiate our target emotion classifier. Apart from the traditional pre-trained sentence encoders, we also incorporate parameter transfer from the recurrent components that model inter-sentence context across the whole conversation. Based on this idea, we perform several experiments across multiple datasets and find improvement in performance and robustness against limited training data. TL-ERC also achieves better validation performances in significantly fewer epochs. Overall, we infer that knowledge acquired from dialogue generators can indeed help recognize emotions in conversations. Copyright © 2019, The Authors. All rights reserved.

关键词： Speech recognition

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Spectrum learning-aided reconfigurable intelligent surfaces for 'green' 6G networks

arXiv

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arXiv 2021年

作者： Yang, Bo Cao, Xuelin Huang, Chongwen Guan, Yong Liang Yuen, Chau Di Renzo, Marco Niyato, Dusit Debbah, Mérouane Hanzo, Lajos Engineering Product Development Pillar Singapore University of Technology and Design Singapore487372 Singapore College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center Zhejiang University Haining314400 China Hangzhou310027 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes 3 Rue Joliot-Curie Gif-sur-Yvette91192 France School of Computer Science and Engineering Nanyang Technological University Singapore Lagrange Mathematical and Computing Research Center Paris75007 France Université Paris-Saclay Centrale-Supélec Gif-sur-Yvette91190 France School of Electronics and Computer Science University of Southampton United Kingdom

In the sixth-generation (6G) era, emerging largescale computing based applications (for example processing enormous amounts of images in real-time in autonomous driving) tend to lead to excessive energy consumption for the end users, whose devices are usually energy-constrained. In this context, energy-efficiency becomes a critical challenge to be solved for harnessing these promising applications to realize 'green' 6G networks. As a remedy, reconfigurable intelligent surfaces (RIS) have been proposed for improving the energy efficiency by beneficially reconfiguring the wireless propagation environment. In conventional RIS solutions, however, the received signal-tointerference- plus-noise ratio (SINR) sometimes may even become degraded. This is because the signals impinging upon an RIS are typically contaminated by interfering signals which are usually dynamic and unknown. To address this issue, 'learning' the properties of the surrounding spectral environment is a promising solution, motivating the convergence of artificial intelligence and spectrum sensing, termed here as spectrum learning (SL). Inspired by this, we develop an SL-aided RIS framework for intelligently exploiting the inherent characteristics of the radio frequency (RF) spectrum for green 6G networks. Given the proposed framework, the RIS controller becomes capable of intelligently 'think-and-decide' whether to reflect or not the incident signals. Therefore, the received SINR can be improved by dynamically configuring the binary ON-OFF status of the RIS elements. The energy-efficiency benefits attained are validated with the aid of a specific case study. Finally, we conclude with a list of promising future research directions. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

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A Novel Solution of Underdetermined Equations for DGTD based on Prior knowledge

A Novel Solution of Underdetermined Equations for DGTD based...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Qi Qi Xinyuan Cao Mingsheng Chen Zhixiang Huang Yi Liu Xianliang Wu Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei Anhui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University Hefei China School of Electronic Engineering Huainan Normal University Huainan China

ISBN: (数字)9781728157337

ISBN: (纸本)9781728157344

To accelerate solving transient electromagnetic problems by discontinuous Galerkin time domain (DGTD) method, a solution of underdetermined equations is established based on prior knowledge. In the proposed method, nodal basis function and a leapfrog scheme are used to Maxwell curl equations for spatial and time discretization. Electromagnetic fields in each element of computation domain are updated individually by applying conventional DGTD in initial time. When the electromagnetic wave covers the domain, the fields of all elements are updated as a whole. The underdetermined equations are established by randomly extracting rows from global mass matrix. Taking the results of a few previous time steps as the prior knowledge, a sparse transform is constructed. Then the underdetermined system of equations can be solved by recovery algorithms. Meanwhile,in order to maintain the advantage of low computational complexity, a restart mechanism is presented. The validity of the proposed method is verified by numerical experiments.

关键词： Microwave technology Millimeter wave technology Transforms Sparse matrices Electromagnetic fields Transient analysis Time-domain analysis

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Adaptive Handover Decision Inspired By Biological Mechanism in Vehicle Ad-hoc Networks

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Computers, Materials & Continua 2019年第9期61卷 1117-1128页

作者： Xuting Duan Jingyi Wei Daxin Tian Jianshan Zhou Haiying Xia Xin Li Kunxian Zheng Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems&SafetyControl Beijing100191China School of Transportation Science and Engineering Beihang UniversityBeijing100191China Department of Engineering and Design University of SussexBrightonBN19RHUK Research Institute of Highway Ministry Transport Beijing100088China Department of Computer Science and Technology Tsinghua UniversityHaidian DistrictBeijing100088China

In vehicle ad-hoc networks(VANETs),the proliferation of wireless communication will give rise to the heterogeneous access environment where network selection becomes *** by the self-adaptive paradigm of cellular attractors,this paper regards an individual communication as a cell,so that we can apply the revised attractor selection model to induce each connected *** at improving the Quality of Service(QoS),we presented the bio-inspired handover decision-making *** addition,we employ the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)for any vehicle to choose an access *** paper proposes a novel framework where the bio-inspired mechanism is combined with *** a dynamic and random mobility environment,our method achieves the coordination of performance of heterogeneous networks by guaranteeing the efficient utilization and fair distribution of network resources in a global *** experimental results confirm that the proposed method performs better when compared with conventional schemes.

关键词： Revised attraction selection model TOPSIS VANETs QoS

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