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28th Annual Conference of the International Group for Lean Construction 2020, IGLC 2020

作者： Wickramasekara, Amila N. Gonzalez, Vicente A. Sullivan, Michael O. Walker, Cameron G. Poshdar, Mani Ying, Fei Department of Civil and Environmental Engineering The University of Auckland Auckland New Zealand Department of Civil and Environmental Engineering Founder and Research Lead Smart Digital Lab The University of Auckland 20 Symonds Street Auckland New Zealand Department of Engineering Sciences The University of Auckland Auckland New Zealand Department of Built Environment Engineering Auckland University of Technology Auckland New Zealand School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand

Much attention has been paid to computer Simulation and Modelling (CSM) in the research circles of construction over the last sixty years. However, it has not been widely adopted within the construction industry. In this paper, we explore theoretically the potential of the integration between CSM, the Last Planner System (LPS) and Building Information Modelling (BIM) to accelerate the uptake of CSM in the construction industry. To do so, a review of the literature to identify the synergies between CSM, LPS and BIM was undertaken, where it was found that little research has been done associated with their integration and linkages as a whole. We found that an explicit integration of these three domains has the potential to enhance and accelerate the adoption of CSM in construction as it provides the opportunity to engage construction project stakeholders towards CSM, focusing on domain-specific characteristics and recent advancements of construction management. Based on our review, we propose five research questions to guide the development of a domain-specific construction simulation framework to be considered in future research. © 2020 IGLC 28 - 28th Annual Conference of the International Group for Lean Construction 2020. All rights reserved.

关键词： Integration

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A Comparative Study of Control Methods for Grid Side Converters in PMSG-Based Wind Energy Conversion systems

A Comparative Study of Control Methods for Grid Side Convert...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Mohammad Sadegh Eslahi Sadegh Vaez-Zadeh Alireza Jabbarnejad Advanced Motion System Research Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Advanced Motion System Research Lab and the Center of Excellence on Applied Electromagnetic Systems School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (数字)9781728156354

ISBN: (纸本)9781728156361

Wind energy conversion systems are interfaced with the power grids through power electronic converters. The main duty of grid side converters is to deliver the power to the grid, while fulfilling the grid code requirements. In this paper, the grid codes for the integration of wind power to the grids are presented first. Then the principles of three main control methods for the grid side converters, i.e. vector control, direct power control, and combined control, for permanent magnet synchronous generators are reviewed. In addition, their capabilities and weaknesses in meeting grid codes are investigated. Finally, a comparison of the control performances in fulfilling grid code requirements are presented by extensive simulation results.

关键词： Reactive power Wind turbines Control systems Couplings Wind power generation Power quality Wind energy

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Exploring the Role of Diversity in Example Selection for In-Context Learning

arXiv

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arXiv 2025年

作者： Kapuriya, Janak Kaushik, Manit Ganguly, Debasis Bhatia, Sumit Data Science Institute University of Galway Galway Ireland Computer Science and Engineering IIIT Delhi New Delhi India School of Computing University of Glasgow Glasgow United Kingdom Media and Data Science Research Lab Adobe Systems Noida India

In-Context Learning (ICL) has gained prominence due to its ability to perform tasks without requiring extensive training data and its robustness to noisy labels. A typical ICL workflow involves selecting localized examples relevant to a given input using sparse or dense embedding-based similarity functions. However, relying solely on similarity-based selection may introduce topical biases in the retrieved contexts, potentially leading to suboptimal downstream performance. We posit that reranking the retrieved context to enhance topical diversity can improve downstream task performance. To achieve this, we leverage maximum marginal relevance (MMR) which balances topical similarity with inter-example diversity. Our experimental results demonstrate that diversifying the selected examples leads to consistent improvements in downstream performance across various context sizes and similarity functions. The implementation of our approach is made available at https://***/janak11111/Diverse-ICL. © 2025, CC BY.

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Low-Velocity-Favored Transition Radiation

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Physical Review Letters 2023年第11期131卷 113002-113002页

作者： Jialin Chen Ruoxi Chen Fuyang Tay Zheng Gong Hao Hu Yi Yang Xinyan Zhang Chan Wang Ido Kaminer Hongsheng Chen Baile Zhang Xiao Lin Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center College of Information Science & Electronic Engineering Zhejiang University Hangzhou 310027 China International Joint Innovation Center the Electromagnetics Academy at Zhejiang University Zhejiang University Haining 314400 China Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa 32000 Israel Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas 77005 USA School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Singapore Department of Physics University of Hong Kong Hong Kong 999077 China Key Lab of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University Zhejiang University Jinhua 321099 China Shaoxing Institute of Zhejiang University Zhejiang University Shaoxing 312000 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Centre for Disruptive Photonic Technologies Nanyang Technological University Singapore 637371 Singapore

When a charged particle penetrates through an optical interface, photon emissions emerge—a phenomenon known as transition radiation. Being paramount to fundamental physics, transition radiation has enabled many applications from high-energy particle identification to novel light sources. A rule of thumb in transition radiation is that the radiation intensity generally decreases with the decrease of particle velocity v; as a result, low-energy particles are not favored in practice. Here, we find that there exist situations where transition radiation from particles with extremely low velocities (e.g., v/c<10−3) exhibits comparable intensity as that from high-energy particles (e.g., v/c=0.999), where c is the light speed in free space. The comparable radiation intensity implies an extremely high photon extraction efficiency from low-energy particles, up to 8 orders of magnitude larger than that from high-energy particles. This exotic phenomenon of low-velocity-favored transition radiation originates from the interference of the excited Ferrell-Berreman modes in an ultrathin epsilon-near-zero slab. Our findings may provide a promising route toward the design of integrated light sources based on low-energy electrons and specialized detectors for beyond-standard-model particles.

关键词： Atom or molecule scattering from surfaces Atomic & molecular collisions Cherenkov radiation Crystal optics Electron beams & optics Light-matter interaction Optical sources & detectors Optical transient phenomena Plasmonics

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Spatially adaptive pyramid feature fusion for scale-aware crowd counting

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Pattern Recognition 2025年 168卷

作者： Shenjian Gong Zhaoliang Yao Wangmeng Zuo Jian Yang PongChi Yuen Shanshan Zhang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China Hikvision Research Institute China School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China Department of Computer Science Hong Kong Baptist University Hong Kong

Crowd counting is a challenging task to predict the number of persons in crowded scenes. The major challenge comes from density/scale variation in one single image, which results in a significant performance decrease for most methods. In this work, we aim to explore how to solve the scale variation problem with assistance from the segmentation task. We first simultaneously predict a coarse density map, along with multi-scale segmentation masks, which provide strong priors of background and head regions with different scales; then we propose a Spatially Adaptive Pyramid (SAP) feature fusion block to fully integrate the coarse density map and multi-scale segmentation masks; in SAP, a feature pyramid and corresponding attention pyramid are obtained for multi-scale head regions, and then cross-scale correlations are computed to handle the scale variation problem. These high-dimensional cross-scale correlation features are then used to refine the density map, based on which the final counting numbers are obtained. Experiments show that our proposed SAP block significantly outperforms previous fusion methods, and obtains consistent improvements on various backbones. Compared to previous state-of-the-art methods, our method is better at handling scale variation, surpassing previous methods on the challenging ShanghaiTech A, UCF-QNRF and NWPU datasets.

关键词： Image segmentation

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Corrigendum to “Multi population-based chaotic differential evolution for multi-modal and multi-objective optimization problems” Appl. Soft Comput. 132 (2023) 109909

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Applied Soft Computing 2025年 169卷

作者： Hafiz Tayyab Rauf Jiechao Gao Ahmad Almadhor Ali Haider Yu-Dong Zhang Fadi Al-Turjman Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom Department of Computer Science University of Virginia USA Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakaka 72388 Saudi Arabia Department of Computer Science University of Gujrat Gujrat Pakistan School of Informatics University of Leicester Leicester LE1 7RH UK Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin 10 Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Mersin 10 Turkey

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Convergence of Artificial Intelligence and the Internet of Things 1

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丛书名： Internet of Things

2020年

作者： George Mastorakis Constandinos X. Mavromoustakis Jordi Mongay Batalla Evangelos Pallis

ISBN: (数字)9783030449070

ISBN: (纸本)9783030449063;9783030449094

The book is intended for researchers, practitioners, engineers and scientists involved in the design and development of protocols and AI applications for IoT-related devices. As the book covers a wide range of mobile applications and scenarios where IoT technologies can be applied, it also offers an essential introduction to the field.

关键词： Information systems Applications (incl. Internet) Communications engineering, Networks Artificial Intelligence Computational Intelligence

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Modern deep learning in bioinformatics

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Journal of Molecular Cell Biology 2020年第11期12卷 823-827页

作者： Haoyang Li Shuye Tian Yu Li Qiming Fang Renbo Tan Yijie Pan Chao Huang Ying Xu Xin Gao Cancer Systems Biology Center The China-Japan Union HospitalJilin UniversityChangchun 130033China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Biology Southern University of Science and TechnologyShenzhen 518055China Computational Bioscience Research Center(CBRC) Computer Electrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Ningbo Institute of Information Technology Application Chinese Academy of SciencesNingbo 315040China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensGA 30602USA

Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A number of comprehensive reviews have been published on such applications,ranging from high-level reviews with future perspectives to those mainly serving as tutorials.

关键词： has such she

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Lessons Learnt from Designing a smart Clothing Telehealth System for Hospital Use 32

Lessons Learnt from Designing a Smart Clothing Telehealth Sy...

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32nd Australian computer-Human Interaction Conference, OzCHI 2020

作者： Aggarwal, Deepti Hoang, Thuong Ploderer, Bernd Vetere, Frank Khot, Rohit Ashok Bradford, Mark Hafp Research Lab Rmit University Australia School of Information Technology Deakin University Australia School of Electrical Engineering and Computer Science Queensland University of Technology Australia Interaction Design Lab School of Computing and Information Systems The University of Melbourne Australia Dept. of Anaesthesia and Pain Management Royal Children's Hospital Australia

ISBN: (纸本)9781450389754

In this paper, we describe the design journey of a smart clothing system, SoPhy from the research laboratory to finally being evaluated in the hospital setting. SoPhy is a smart socks-based system, designed to make physiotherapy video consultations effective for assessing lower limb issues. SoPhy is the result of a 3-year journey of development, study and refinement of a research prototype done in collaboration with a physiotherapist. Drawing on this journey, we present seven lessons that emphasize on the importance of fostering strong collaborations with clinicians to move beyond laboratory studies and reach the target health setting. We provide contextual narratives on how we designed a comfortable smart clothing for patients;how we created an intuitive mapping of sensor data for clinicians;how we integrated hospital practice in the system design, and how we managed the ethics clearance for the field evaluation. We hope that these lessons are useful for HCI and health researchers who aim to innovate the field of health but feel restricted with the little knowledge of design and electronics. © 2020 ACM.

关键词： research laboratories

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RACE: Reinforced cooperative autonomous vehicle collision avoidance

arXiv

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arXiv 2020年

作者： Yuan, Yali Tasik, Robert Adhatarao, Sripriya Srikant Yuan, Yachao Liu, Zheli Fu, Xiaoming Computer Networking Group Institute of Computer Science University of Goettingen Goettingen37077 Germany Smart Mobility Research Group Institute of Information Systems University of Goettingen Goettingen37075 Germany college of computer and control engineering Nankai University Tianjin China

—With the rapid development of autonomous driving, collision avoidance has attracted attention from both academia and industry. Many collision avoidance strategies have emerged in recent years, but the dynamic and complex nature of driving environment poses a challenge to develop robust collision avoidance algorithms. Therefore, in this paper, we propose a decentralized framework named RACE: Reinforced Cooperative Autonomous Vehicle Collision AvoidancE. Leveraging a hierarchical architecture we develop an algorithm named Co-DDPG to efficiently train autonomous vehicles. Through a security abiding channel, the autonomous vehicles distribute their driving policies. We use the relative distances obtained by the opponent sensors to build the VANET instead of locations, which ensures the vehicle’s location privacy. With a leader-follower architecture and parameter distribution, RACE accelerates the learning of optimal policies and efficiently utilizes the remaining resources. We implement the RACE framework in the widely used TORCS simulator and conduct various experiments to measure the performance of RACE. Evaluations show that RACE quickly learns optimal driving policies and effectively avoids collisions. Moreover, RACE also scales smoothly with varying number of participating vehicles. We further compared RACE with existing autonomous driving systems and show that RACE outperforms them by experiencing 65% less collisions in the training process and exhibits improved performance under varying vehicle density. Copyright © 2020, The Authors. All rights reserved.

关键词： Autonomous vehicles

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