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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Christoph Sachs Fabian Stamer Jan Allgeier Duleepa Thrimawithana Martin Neuburger Faculty of Mobility and Technology Esslingen University Germany Institute for System Dynamics University of Stuttgart Germany Robert Bosch GmbH Germany Dept. of Electrical Computer and Software Engineering The University of Auckland New Zealand

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

The transition to electric transportation demands efficient and cost-effective powertrains. Optimizing energy use is crucial for extending range and reducing expenses. However, comparing inverter and motor efficiency based on inverter topologies is challenging due to biased methodologies that favor certain designs over others. This document introduces a novel optimization-based approach for enhancing partial load efficiency and minimizing chip area of single and dual traction inverters, indicating potential energy savings and cost reduction. Recent publications of both industry [1], [2] and academia [3] underscore the importance of these design goals achieved by either novel inverter topologies or enhanced control methods. Two promising topologies with the inherent capability of partial load optimization are evaluated regarding chip area and system efficiency to find the most suitable concept for future electric vehicle power trains.

关键词： MOSFET Costs Silicon carbide Modulation Mechanical power transmission Motors Electric vehicles Inverters Topology Optimization

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Distributed Voltage Regulation of Distribution Network Based on Partition Control

Distributed Voltage Regulation of Distribution Network Based...

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Cyber-Energy Systems and Intelligent Energy (ICCSIE), International Conference on

作者： Wang Jine Chen Hui Zhao Liying Zhang Guangxin School of Electrical Information Changchun University of Architecture and Civil Engineering Changchun City School of Computer Technology and Engineering Changchun Institute of Technology Changchun City School of Information and Engineering College of Science & Technology Ningbo University Ningbo City

ISBN: (数字)9798350373318

ISBN: (纸本)9798350373325

As China's social economy progresses, the amount of equipment in distribution networks continues to grow, leading to increasingly complex system operations. In this environment, traditional centralized control methods consume excessive computing resources and fail to achieve the processing speeds required by modern intelligent distribution networks. To address these challenges, this article introduces the concept of electrical distance to segment the distribution network and proposes voltage regulation algorithms based on the alternating direction multiplier method and dual ascent method for efficient distributed voltage regulation.

关键词： Reactive power Network topology Electricity Process control Distribution networks Power grids Partitioning algorithms Topology Voltage control Convergence

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VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantification

arXiv

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arXiv 2024年

作者： Conti, Paolo Kneifl, Jonas Manzoni, Andrea Frangi, Attilio Fehr, Jörg Brunton, Steven L. Kutz, J. Nathan MOX Department of Mathematics Politecnico di Milano Milan Italy Institute of Engineering and Computational Mechanics University of Stuttgart Stuttgart Germany Department of Civil Engineering Politecnico di Milano Milan Italy Department of Mechanical Engineering University of Washington Seattle United States Department of Applied Mathematics and Electrical and Computer Engineering University of Washington Seattle United States

The simulation of many complex phenomena in engineering and science requires solving expensive, high-dimensional systems of partial differential equations (PDEs). To circumvent this, reduced-order models (ROMs) have been developed to speed up computations. However, when governing equations are unknown or partially known, or when access to full order solvers is restricted, typically ROMs lack interpretability and reliability of the predicted solutions. In this work we present a data-driven, non-intrusive framework for building ROMs where the latent variables and dynamics are identified in an interpretable manner and uncertainty is quantified. Starting from a limited amount of high-dimensional, noisy data the proposed framework constructs an efficient ROM by leveraging variational autoencoders for dimensionality reduction along with a newly introduced, variational version of sparse identification of nonlinear dynamics (SINDy), which we refer to as Variational Identification of Nonlinear Dynamics (VINDy). In detail, the method consists of Variational Encoding of Noisy Inputs (VENI) to identify the distribution of reduced coordinates. Simultaneously, we learn the distribution of the coefficients of a pre-determined set of candidate functions by VINDy. Once trained offline, the identified model can be queried for new parameter instances and/or new initial conditions to compute the corresponding full-time solutions. The probabilistic setup enables uncertainty quantification as the online testing consists of Variational Inference naturally providing Certainty Intervals (VICI). In this work we showcase the effectiveness of the newly proposed VINDy method in identifying interpretable and accurate dynamical system for the Rössler system with different noise intensities and sources. Then the performance of the overall method – named VENI, VINDy, VICI – is tested on PDE benchmarks including structural mechanics and fluid dynamics. Copyright © 2024, The Authors. All rights reser

关键词： Dynamics

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Numerical Feature Preprocessing Method for Daily Solar Radiation Pattern Classification 15th

Numerical Feature Preprocessing Method for Daily Solar Radia...

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15th International Conference on computer Science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Li, You Wang, Yafei Zheng, Ying Qian, Hui Zhou, Weisheng Dual-Carbon Research Center Hangzhou City University Hangzhou310015 China Asia-Japan Research Institute Ritsumeikan University Ibaraki567-8570 Japan School of Civil Engineering and Architecture Zhejiang University of Science and Technology Hangzhou310023 China Department of Computer Science and Information Technologies Frostburg State University FrostburgMD21532 United States College of Policy Science Ritsumeikan University Ibaraki567-8570 Japan

ISBN: (纸本)9789819724468

One challenging issue in daily solar radiation classification involves addressing the geometric differences in daily solar radiation patterns caused by geographic location and seasonal influences. A standardization method based on matrix transformation has been proposed to eliminate the daily geometric variations of solar radiation using classification for daily patterns of solar radiation. This method was tested using measurements taken at the Tokyo monitoring station from 2000 to 2021. Through the implementation of the proposed classification algorithm, this paper discerned twelve categories of daily clearness index distributions. The suggested classification framework has the potential to assist in the development of solar radiation transformation systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Solar irradiance

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Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model

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International Journal of Sediment Research 2022年第3期37卷 383-398页

作者： Rana Muhammad Adnan Zaher Mundher Yaseen Salim Heddam Shamsuddin Shahid Aboalghasem Sadeghi-Niaraki Ozgur Kisi State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State University New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Faculty of Science Agronomy Department Hydraulics Division University School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia (UTM) Geoinformation Tech.Center of Excellence Faculty of Geomatics Engineering K.N.Toosi University of Technology Department of Computer Science and Engineering Sejong University Civil Engineering Department Ilia State University

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm（ANFIS-FCM-PSOGSA） in improving the estimation accuracy of river suspended sediment loads（SSLs） is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization（ANFIS-FCM-PSO）, ANFIS-FCM, and sediment rating curve（SRC） models. Various input combinations involving lagged river flow（Q） and suspended sediment（S） values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error（RMSE）, mean absolute error（MAE）, Nash-Sutcliffe Efficiency（NSE）, and coefficient of determination（R2） and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO（or ANFIS-FCM） models by 8.14%（1.72%）, 14.7%（5.71%）, 12.5%（2.27%）, and 25.6%（1.86%）,in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.

关键词： Suspended sediment load Adaptive neuro-fuzzy system Particle swarm optimization Gravitational search algorithm

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Chiral materials meet perovskite solar cells

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Green Carbon 2024年第4期2卷 405-406页

作者： Tie Liu Chongwen Li Ying Li Michael Saliba Hui-Ming Cheng Zaiwei Wang Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Electrical and Computer Engineering University of TorontoToronto M5S 1A4Canada Institute for Photovoltaics(IPV) University of StuttgartPfaffenwaldring 47Stuttgart D-70569Germany Faculty of Materials Science and Energy Engineering Shenzhen University of Advanced TechnologyShenzhen 518055China

Perovskite solar cells(PSCs)have attracted significant interest in the photovoltaic field because of their remarkable efficiency,low cost,and straightforward fabrication process[1].However,the long-term stability of PSCs remains a *** major source of instability is the low mechanical reliability of heterointerfaces[2].Specifically,a thermal expansion coefficient mismatch between adjacent functional layers and inevitable temperature variations during day/night cycles can lead to interfacial sliding and void formation.

关键词： perovskite straight interfacial

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ANALYSIS OF CONCRETE CRACKS AND FATIGUE IN SMART CITIES USING YOLOV3

ANALYSIS OF CONCRETE CRACKS AND FATIGUE IN SMART CITIES USIN...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Archana, U. Sharma, Sarvdaman Singh, Sitesh Kumar Sureshkumar, R. Senthilkumar, P. Harish Babu, T. Department of Computer Science and Design R.M.K. Engineering College Chennai India Department of Civil Engineering University Institute of Engineering and Technology University of Jammu Jammu & Kashmir India Department of Civil Engineering Wollega University Oromia Nekemte Ethiopia Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai India KL School of Architecture Koneru Lakshmaiah Education Foundation Greenfield Vaddeswaram Andhra Pradesh Guntur India Department of Electrical and Electronics Engineering Sethu Institute of Technology Tamilnadu India

ISBN: (纸本)9798350305708

This study focuses on the application of the YOLOv3 (You Only Look Once version 3) object detection algorithm for the analysis of concrete cracks and fatigue within Smart Cities. Concrete infrastructure plays a crucial role in urban environments, and its structural integrity is paramount for ensuring public safety and sustainable development. The proposed approach utilizes YOLOv3 to identify and localize instances of concrete cracks and signs of fatigue in images and videos captured from various urban settings. By leveraging deep learning techniques, this research aims to enhance the efficiency and accuracy of monitoring and maintaining concrete structures, contributing to the overall resilience and longevity of Smart Cities. The findings of this study present valuable insights into the potential of utilizing advanced computer vision methods for proactive infrastructure management in the urban landscape. © 2023 IEEE.

关键词： Concrete cracks Fatigue analysis Infrastructure monitoring Smart Cities YOLOv3

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A Toolbox of Feminist Wonder: Theories and methods that can make a difference 26

A Toolbox of Feminist Wonder: Theories and methods that can ...

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26th ACM Conference on computer-Supported Cooperative Work and Social Computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

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How Mature is Requirements engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions

arXiv

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arXiv 2024年

作者： Umm-E-Habiba Haug, Markus Bogner, Justus Wagner, Stefan Institute of Software Engineering University of Stuttgart Stuttgart70569 Germany Department of Software Engineering University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands TUM School of Communication Information and Technology Technical University of Munich Heilbronn Germany

Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in requirements engineering for artificial intelligence (RE4AI), e.g., the difficulty in specifying and validating requirements for AI or considering new quality requirements due to emerging ethical implications. It is currently unclear if existing RE methods are sufficient or if new ones are needed to address these challenges. Therefore, our goal is to provide a comprehensive overview of RE4AI to researchers and practitioners. What has been achieved so far, i.e., what practices are available, and what research gaps and challenges still need to be addressed? To achieve this, we conducted a systematic mapping study combining query string search and extensive snowballing. The extracted data was aggregated, and results were synthesized using thematic analysis. Our selection process led to the inclusion of 126 primary studies. Existing RE4AI research focuses mainly on requirements analysis and elicitation, with most practices applied in these areas. Furthermore, we identified requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges, along with a few others. Additionally, we proposed seven potential research directions to address these challenges. Practitioners can use our results to identify and select suitable RE methods for working on their AI-based systems, while researchers can build on the identified gaps and research directions to push the field forward. ∗ © 2024, CC BY.

关键词： Requirements engineering

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CONSTRUCTION PROGRESS MONITORING IN SMART CITIES USING DEEP ALEXNET

CONSTRUCTION PROGRESS MONITORING IN SMART CITIES USING DEEP ...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Rajavel, A. Karkuzhali, S. Sharma, Sarvdaman Singh, Sitesh Kumar Senthilkumar, P. Ida, S. Jhansi Electronics and Instrumentation Engineering Kamaraj College of Engineering and Technology Virudhnagar India Department of Electrical and Electronics Engineering R.M.D. Engineering College Chennai India Department of Civil Engineering University Institute of Engineering and Technology University of Jammu Jammu & Kashmir India Department of Civil Engineering Wollega University Nekemte Oromia Ethiopia KL School of Architecture Koneru Lakshmaiah Education Foundation Vaddeswaram Greenfield AP Guntur India Department of Computer Science and Business Systems R.M.K Engineering College India

ISBN: (纸本)9798350305708

Construction progress monitoring plays a crucial role in ensuring the timely and efficient completion of infrastructure projects within smart cities. This paper proposes a novel approach utilizing the Deep AlexNet architecture for construction progress assessment in smart cities. The Deep AlexNet model is trained on a diverse dataset of images captured from construction sites, enabling it to accurately analyze the visual cues indicative of construction advancement. By leveraging deep learning techniques, our system extracts and processes features from images, enabling automated and real-time progress evaluation. The proposed method contributes to enhancing construction management practices by providing stakeholders with a reliable tool to monitor and manage construction projects efficiently. Experimental results demonstrate the effectiveness and robustness of the Deep AlexNet-based approach in accurately assessing construction progress, thereby facilitating informed decision-making throughout the construction lifecycle. © 2023 IEEE.

关键词： Construction progress monitoring Deep AlexNet Deep learning Infrastructure projects Smart cities

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