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SSRN 2023年

作者： Zheng, Bo Xue Cai, Zhi Wen Zhao, Pei Dong Xu, Xiao Yang Chan, Tak Shing Yu, Peng Department of Mathematics University of Oslo Oslo0851 Norway Guangdong Provincial Key Laboratory of Turbulence Research and Applications Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China China Ship Scientific Research Center Wuxi214082 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Naval Architecture and Ocean Engineering Dalian University of Technology Dalian116024 China School of Computer Science and Technology Xi’an University of Science and Technology Xian710054 China Center for Complex Flows and Soft Matter Research Southern University of Science and Technology Shenzhen518055 China

Weakly compressible Smoothed Particle Hydrodynamics (SPH) is known to suffer from numerical pressure noise, which can significantly undermine simulation stability and accuracy. In response, this study introduces a novel density dissipation scheme aimed at suppressing such unphysical noise. The proposed scheme overcomes the limitations of conventional density dissipation schemes, which are ineffective at fluid interfaces in multiphase flow simulations. As a result, the novel scheme is generally applicable to the entire computational domain, encompassing both single-phase flow and multiphase flow. This advancement is achieved through two key components. Firstly, the traditional density dissipation is replaced with a density increment dissipation approach that enables dissipation to cross interfaces separating different fluids. Secondly, based on dissipation volume conservation, we employ a dissipation volume correction factor (VCF) to stabilize simulations characterized by large density ratios. We demonstrate the accuracy, stability, and robustness of our method through four three-dimensional benchmarks: sloshing under external excitations, the rising of single and double bubbles, Rayleigh-Taylor instability, and Kelvin-Helmholtz instability. Additionally, this study reveals the relationship between SPH utilizing density dissipation and the approximate Riemann solver. © 2023, The Authors. All rights reserved.

关键词： Multiphase flow

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YOLO-LLTS: Real-Time Low-Light Traffic Sign Detection via Prior-Guided Enhancement and Multi-Branch Feature Interaction

arXiv

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arXiv 2025年

作者： Lin, Ziyu Wu, Yunfan Ma, Yuhang Chen, Junzhou Zhang, Ronghui Wu, Jiaming Yin, Guodong Lin, Liang Guangdong Key Laboratory of Intelligent Transportation System School of intelligent systems engineering Sun Yat-sen University Guangzhou510275 China Department of Architecture and Civil Engineering Chalmers University of Technology Sven Hultins gata 6 GothenburgSE-412 96 Sweden School of Mechanical Engineering Southeast University Nanjing211189 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China

Detecting traffic signs effectively under low-light conditions remains a significant challenge. To address this issue, we propose YOLO-LLTS, an end-to-end real-time traffic sign detection algorithm specifically designed for low-light environments. Firstly, we introduce the High-Resolution Feature Map for Small Object Detection (HRFM-TOD) module to address indistinct small-object features in low-light scenarios. By leveraging high-resolution feature maps, HRFM-TOD effectively mitigates the feature dilution problem encountered in conventional PANet frameworks, thereby enhancing both detection accuracy and inference speed. Secondly, we develop the Multi-branch Feature Interaction Attention (MFIA) module, which facilitates deep feature interaction across multiple receptive fields in both channel and spatial dimensions, significantly improving the model’s information extraction capabilities. Finally, we propose the Prior-Guided Enhancement Module (PGFE) to tackle common image quality challenges in low-light environments, such as noise, low contrast, and blurriness. This module employs prior knowledge to enrich image details and enhance visibility, substantially boosting detection performance. To support this research, we construct a novel dataset, the Chinese Nighttime Traffic Sign Sample Set (CNTSSS), covering diverse nighttime scenarios, including urban, highway, and rural environments under varying weather conditions. Experimental evaluations demonstrate that YOLO-LLTS achieves state-of-the-art performance, outperforming the previous best methods by 2.7% mAP50 and 1.6% mAP50:95 on TT100K-night, 1.3% mAP50 and 1.9% mAP50:95 on CNTSSS, and achieving superior results on the CCTSDB2021 dataset. Moreover, deployment experiments on edge devices confirm the real-time applicability and effectiveness of our proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Traffic signs

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Robotic additive construction of bar structures: Unified sequence and motion planning

arXiv

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arXiv 2021年

作者： Huang, Yijiang Garrett, Caelan R. Ting, Ian Parascho, Stefana Mueller, Caitlin T. Building Technology Program Department of Architecture Massachusetts Institute of Technology CambridgeMA02139 United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Princeton University CREATE Laboratory School of Architecture PrincetonNJ08544 United States

Additive robotic construction of building-scale discrete bar structures, such as trusses and space frames, is increasingly attractive due to the potential improvements in efficiency, safety, and design possibilities. However, programming complex robots, such as manipulators with seven degrees of freedom, to successfully complete construction tasks can be tedious, challenging, or impossible for a human to do manually. Namely, the structure must be constructed in a sequence that preserves structural properties, such as stiffness, at each step. At the same time, this sequence must allow for the robot to precisely manipulate elements within the in-progress structure while respecting geometric constraints that, for example, ensure the robot does not collide with what it has built. In this work, we present an automated and newly generalized planning approach for jointly finding a construction sequence and robot motion plan for additive construction that satisfies these requirements. Our approach can be applied in a variety of additive construction processes, and we demonstrate it specifically on spatial extrusion and discrete bar assembly in this paper. We demonstrate the effectiveness of our approach on several simulated and real-world extrusion and assembly tasks, including a human-scale physical prototype, for which our algorithm is deployed for the first time to plan the assembly of a complicated double tangent bar system design. © 2021, CC BY.

关键词： Additives

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Terahertz time-domain spectroscopy of boron nitride nanotube-reinforced PMMA composites

引用

Polymer Testing 2025年 147卷

作者： Zhai, Min Gou, Feilin Ke, Changhong He, Wenlong Zhai, Cong Locquet, Alexandre Citrin, D.S. College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen518060 China Department of Mechanical Engineering State University of New York at Binghamton BinghamtonNY13902 United States School of Transportation and Civil Engineering and Architecture Foshan University Guangdong Foshan528000 China School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Georgia Tech-CNRS IRL2958 Georgia Tech-Europe 2 Rue Marconi Lorraine Metz57070 France

Polymethyl methacrylate (PMMA) reinforced with boron nitride nanotubes (BNNT) at weight fill factors fw of 0.2 and 0.5 wt % are characterized by terahertz time-domain spectroscopy (THz-TDS). It is known that the introduction of BNNTs in polymer composites can provide thermal conduction paths and significantly reduce interface thermal resistance. Despite the low values of fw, the presence of the BNNTs has a significant effect on the optical constants, either due to the intrinsic contribution of the BNNTs or due to extrinsic effects such as induced strain in the PMMA matrix. Negligible THz birefringence observed in the polarization-resolved THz transmission measurements indicates a high degree of isotropy of the BNNT dispersed in the PMMA. Assuming an intrinsic effect and the validity of modified Maxwell–Garnett theory regardless of the inclusion shape, the frequency-dependent optical constants of BNNTs are extracted. The refractive index of BNNTs varies roughly linearly from n(ν=500GHz)≈1.8 to n(ν=1.5THz)≈1.75, while the absorption coefficient increases from α(ν=500GHz)≈10 cm−1 to α(ν=1.5THz)≈45 cm−1. The extracted THz dielectric properties of the BNNTs trend toward reported values for bulk h-BNs. © 2025 The Authors

关键词： Terahertz spectroscopy

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Joint high-resolution feature learning and vessel-shape aware convolutions for efficient vessel segmentation

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computers in Biology and Medicine 2025年 191卷 109982-109982页

作者： Zhang, Xiang Zhu, Qiang Hu, Tao Guo, Song Bian, Genqing Dong, Wei Hong, Rao Lin, Xia Ling Wu, Peng Zhou, Meili Yan, Qingsen Mohi-ud-din, Ghulam Ai, Chen Li, Zhou College of Information and Control Engineering Xi'an University of Architecture and Technology Xi'an China Shaanxi Provincial Key Lab of Bigdata of Energy and Intelligence Processing School of Physics and Electronic Information Yanan University Yanan China School of Computer Science and Engineering Northwestern Polytechnical University Xi'an China School of Software Nanchang University Nanchang China School of Cyber Security Zhejiang University Hangzhou China Department of Basic Education and Research Jiangxi Police College Nanchang China Northwestern Polytechnical University China

Clear imagery of retinal vessels is one of the critical shreds of evidence in specific disease diagnosis and evaluation, including sophisticated hierarchical topology and plentiful-and-intensive capillaries. In this work, we propose a new topology- and shape-aware model named Multi-branch Vessel-shaped Convolution Network (MVCN) to adaptively learn high-resolution representations from retinal vessel imagery and thereby capture high-quality topology and shape information thereon. Two steps are involved in our pipeline. The former step is proposed as Multiple High-resolution Ensemble Module (MHEM) to enhance high-resolution characteristics of retinal vessel imagery via fusing scale-invariant hierarchical topology thereof. The latter is a novel vessel-shaped convolution that captures the retinal vessel topology to emerge from unrelated fundus structures. Moreover, our MVCN of separating such topology from the fundus is a dynamical multiple sub-label generation via using epistemic uncertainty, instead of manually separating raw labels to distinguish definitive and uncertain vessels. Compared to other existing methods, our method achieves the most advanced AUC values of 98.31%, 98.80%, 98.83%, and 98.65%, and the most advanced ACC of 95.83%, 96.82%, 97.09%,and 96.66% in DRIVE, CHASE_DB1, STARE, and HRF datasets. We also employ correctness, completeness, and quality metrics to evaluate skeletal similarity. Our method's evaluation metrics have doubled compared to previous methods, thereby demonstrating the effectiveness thereof. © 2025 Elsevier Ltd

关键词： Ophthalmology

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A Smart Capacity Enhancement and Estimation Model for Hybrid Buildings by using Light Weight Deep Learning Model

A Smart Capacity Enhancement and Estimation Model for Hybrid...

引用

Disruptive Technologies (ICDT), International Conference on

作者： Senthil Kumar Nageswara Rao Atyam Khadar Nawas K Shashank Awasthi M. Kotteeswaran K. S. Thirunavukkarasu KL School of Architecture Koneru Lakshmanan Education Foundation Guntur Andhra Pradesh India Electrical and Electronics Department CMR Institute of Technology Bengaluru Karnataka India School of Computing Science and Engineering Vellore Institute of Technolog Chennai Tamil Nadu India Department of Computer Science and Engineering GL Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh India School of Management and Commerce Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India School of Computing Science Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India

Generally, for the buildings constructed in our areas, the conventional foundation known as 'shallow', i.e. from the ground level downwards, is set up in three dimensions such as length, width and depth. Foundations are not the same for all types of structures. The size and structure of the foundation varies depending on the nature of the prevailing soil, ground water level of the plot, type of building, and total load of the structure. In this paper an innovation estimation model was proposed based on light weigth deep learning technique. The proposed model introduces capacity estimation and energy efficiency plans and designs. These are all relating to commercial buildings, craft facilities and mass housing projects to enable design, construction. It also maintains the buildings under minimum energy consumption without stressing the operation of buildings. The health and comfort of the occupants are establishing compliance standards and ensure the maximum standards for energy efficiency in the plans or designs of commercial buildings. The proposed model also focuses the capacity estimation and energy efficiency programs beyond maximum standards.

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A Feasibility Study on Indoor Localization and Multi-person Tracking Using Sparsely Distributed Camera Network with Edge Computing

arXiv

引用

arXiv 2023年

作者： Kwon, Hyeokhyen Hegde, Chaitra Kiarashi, Yashar Madala, Venkata Siva Krishna Singh, Ratan Nakum, ArjunSinh Tweedy, Robert Tonetto, Leandro Miletto Zimring, Craig M. Doiron, Matthew Rodriguez, Amy D. Levey, Allan I. Clifford, Gari D. The Department of Biomedical Informatics School of Medicine Emory University AtlantaGA30322 United States The School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States The School of Industrial Design College of Design Georgia Institute of Technology AtlantaGA30332 United States The School of Architecture College of Design Georgia Institute of Technology AtlantaGA30332 United States The Department of Neurology School of Medicine Emory University AtlantaGA30322 United States The Department of Biomedical Informatics Emory University School of Medicine AtlantaGA30322 United States The Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA30322 United States

Camera-based activity monitoring systems are becoming an attractive solution for smart building applications with the advances in computer vision and edge computing technologies. In this paper, we present a feasibility study and systematic analysis of a camera-based indoor localization and multi-person tracking system implemented on edge computing devices within a large indoor space. To this end, we deployed an end-to-end edge computing pipeline that utilizes multiple cameras to achieve localization, body orientation estimation and tracking of multiple individuals within a large therapeutic space spanning 1700m2, all while maintaining a strong focus on preserving privacy. Our pipeline consists of 39 edge computing camera systems equipped with Tensor Processing Units (TPUs) placed in the indoor space’s ceiling. To ensure the privacy of individuals, a real-time multi-person pose estimation algorithm runs on the TPU of the computing camera system. This algorithm extracts poses and bounding boxes, which are utilized for indoor localization, body orientation estimation, and multi-person tracking 1. Our pipeline demonstrated an average localization error of 1.41 meters, a multiple-object tracking accuracy score of 88.6%, and a mean absolute body orientation error of 29°. These results shows that localization and tracking of individuals in a large indoor space is feasible even with the privacy constrains. © 2023, CC BY-NC-SA.

关键词： computer vision

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Wideband Gap-Waveguide Phase Shifter Based on a Glide-Symmetric Ridge

TechRxiv

引用

TechRxiv 2021年

作者： Palomares-Caballero, Ángel Megías, Carlos Molero, Carlos Alex-Amor, Antonio Padilla, Pablo Granada18071 Spain The Department of Computer Architecture and Technology CITIC ETSIIT University of Granada Granada18071 Spain The Information Technologies Department Universidad CEU San Pablo Madrid28003 Spain

This letter presents a gap-waveguide phase shifter based on ridged unit cells with glide-symmetric configuration. The proposed unit cell design provides a higher stable phase shift compared to a conventional ridged unit cell whose ridge height and waveguide width are tuned to achieve a stable phase shift. Through the insertion of glide-symmetric holes with semicircle base in the ridged waveguide, a stable phase shift in a wide frequency range is achieved. Depending on the radii of the holes, the operating frequency range of the phase shifter can be tuned. A 90o phase shifter in millimeter-wave range is designed and manufactured to validate the analysis. The experimental results reveal a 85o± 5o phase shift from 33 GHz to 43 GHz (26.3% bandwidth). In this bandwidth, the reflection coefficient is below -10 dB with a maximum insertion loss of 0.7 dB. © 2021, CC BY-NC-SA.

关键词： Phase shifters

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Recommendation System Towards Residential Energy Saving Based on Anomaly Detection

Recommendation System Towards Residential Energy Saving Base...

引用

International Conference on Signal Processing and Information Security (ICSPIS)

作者： Shadi Atalla Yassine Himeur Wathiq Mansoor Abbes Amira Fodil Fadli Abigail Copiaco Shahab Saquib Sohail College of Engineering and IT University of Dubai Dubai UAE Department of Computer Science University of Sharjah Sharjah UAE Dept. of Architecture & Urban Planning Qatar University Doha 2713 Qatar School of Engineering Sciences & Technology Jamia Hamdard New Delhi 110062 India

ISBN: (纸本)9781665492669

This paper presents a recommender system to promote energy consumption reduction behaviors in residential buildings. The system exploits data stream processing methods jointly with machine learning algorithms on real-time residential data. Specifically, the data stream includes disaggregated power consumption, context, and weather conditions data. Internally the system converts time-series data streams into discrete ordered data points, which serve as inputs for training ML models. This method is used to predict power consumption anomalies. Gradually, the system helps to shape its users’ activities into more energy-efficient ones. The experimental evaluation on real and simulated datasets demonstrates the promising performance of the proposed method, primarily when the K-neighbors neighbors’ algorithm is used to classify the features extracted with interleaving current with the previous data points. The performance assessment of the machine learning algorithms shows the suitability of our implementation for Edge and Fog platforms in terms of accuracy, latency, and model size.

关键词： Training Power demand Machine learning algorithms Shape Signal processing algorithms Signal processing Feature extraction

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TensorKMC: Kinetic Monte Carlo Simulation of 50 Trillion Atoms Driven by Deep Learning on a New Generation of Sunway Supercomputer

TensorKMC: Kinetic Monte Carlo Simulation of 50 Trillion Ato...

引用

Supercomputing Conference

作者： Honghui Shang Xin Chen Xingyu Gao Rongfen Lin Lifang Wang Fang Li Qian Xiao Lei Xu Qiang Sun Leilei Zhu Fei Wang Yunquan Zhang Haifeng Song SKL of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Applied Physics and Computational Mathematics Beijing China Department of Computer Science and Technology Tsinghua University Wuxin China National Supercomputer Center in Wuxi Wuxi China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

The atomic kinetic Monte Carlo method plays an important role in multi-scale physical simulations because it bridges the micro and macro worlds. However, its accuracy is limited by empirical potentials. We therefore propose herein a triple-encoding algorithm and vacancy-cache mechanism to efficiently integrate ab initio neural network potentials (NNPs) with AKMC and implement them in our TensorKMC codes. We port our program to SW26010-pro and innovate a fast feature operator and a big fusion operator for the NNPs for fully utilizing the powerful heterogeneous computing units of the new-generation Sunway supercomputer. We further optimize memory usage. With these improvements, TensorKMC can simulate up to 54 trillions of atoms and achieve excellent strong and weak scaling performance up to 27,456,000 cores.

关键词： Technological innovation Monte Carlo methods Codes Quantum chemistry High performance computing Optimization methods Artificial neural networks

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