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arXiv 2022年

作者： Yang, Xinquan Li, Xuguang Li, Xuechen Wu, Peixi Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China The School of Dentistry Shenzhen University Shenzhen China

Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. However, such design heavily relies on the subjective experiences of dentists. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root. Convolutional stem and decoder are designed to coarsely extract image features before the operation of patch embedding and integrate multi-level feature maps for robust prediction, respectively. As both long-range relationship and local features are involved, our approach can better represent global information and achieves better location performance. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed ImplantFormer achieves superior performance than existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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A Global Asymptotically Stable Nonlinear Robust Bias Observer for MEMS Gyros

TechRxiv

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TechRxiv 2022年

作者： Tang, Ning Chang, Jieling Wang, Lingling Fu, Li Neusypin, Konstantin A. Selezneva, Maria S. Peng, Linping The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Faculty of Computer Science and Control Systems Bauman Moscow State Technical University Moscow105005 Russia Experimental Teaching Demonstration Center of Mechanical and Control Engineering Beihang University Beijing100191 China The School of Mathematics and System Sciences Beihang University Beijing100191 China

In practical applications, commercial-grade Micro-Electro-Mechanical system (MEMS) gyros tend to exhibit dynamics sensitivities which are resistant to modeling by linear drift. To address the problem, a new nonlinear robust bias observer (NRBO) is proposed in this paper. Unlike the existing attitude and gyro bias nonlinear observers, we propose a dynamics-sensitive gyro bias decomposition method to tackle unknown nonlinear dynamic models of MEMS gyro bias. We expose the potential advantages of the proposed method: the global asymptotic stability of the NBRO and the robustness to the bias instability of MEMS gyros can be guaranteed through a rational design of the attitude-angular rate nonlinear dynamic coupling (AARNDC) term and the attitude-linear coupling (ALC) term. In addition, the attitude estimation with bias correction is given in the framework of NBRO. The experiment of a cable-parallel robot demonstrate the robustness to the bias instability and the accuracy improvement of MEMS gyros. © 2022, CC BY.

关键词： Gyroscopes

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Positioning Using Visible Light Communications: A Perspective Arcs Approach

arXiv

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arXiv 2022年

作者： Zhu, Zhiyu Guo, Caili Bao, Rongzhen Chen, Mingzhe Saad, Walid Yang, Yang The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical Engineering Princeton University Princeton NJ08544 United States Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24060 United States

Visible light positioning (VLP) is an accurate indoor positioning technology that uses luminaires as transmitters. In particular, circular luminaires are a common source type for VLP, that are typically treated only as point sources for positioning, while ignoring their geometry characteristics. In this paper, the arc feature of the circular luminaire and the coordinate information obtained via visible light communication (VLC) are jointly used for positioning, and a novel perspective arcs approach is proposed for VLC-enabled indoor positioning. The proposed approach does not rely on any inertial measurement unit and has no tilted angle limitaion at the user. First, a VLC assisted perspective circle and arc algorithm (V-PCA) is proposed for a scenario in which a complete luminaire and an incomplete one can be captured by the user. Based on plane and solid geometry theory, the relationship between the luminaire and the user is exploited to estimate the orientation and the coordinate of the luminaire in the camera coordinate system. Then, the pose and location of the user in the world coordinate system are obtained by single-view geometry theory. Considering the cases in which parts of VLC links are blocked, an anti-occlusion VLC assisted perspective arcs algorithm (OA-V-PA) is proposed. In OA-V-PA, an approximation method is developed to estimate the projection of the luminaire's center on the image and, then, to calculate the pose and location of the user. Simulation results show that the proposed indoor positioning algorithm can achieve a 90th percentile positioning accuracy of around 10 cm. Moreover, an experimental prototype is implemented to verify the feasibility. In the established prototype, a fused image processing method is proposed to simultaneously obtain the VLC information and the geometric information. Experimental results in the established prototype show that the average positioning accuracy is less than 5 cm for different tilted angles of the user.

关键词： Visible light communication

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Mrf-Former: A Multi-Scale Resolution Fusion Approach for Addressing Scale Discrepancies in Remote Sensing Image Change Detection

SSRN

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SSRN 2024年

作者： Yang, Hualin Ouyang, Jiarui Cheng, Junhao Zhang, Wen Li, Hui Li, Xiying Chen, Calvin Yu-Chian Artificial Intelligence Medical Research Center School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518107 China - Preferred Program Peking University Shenzhen Graduate School Guangdong Shenzhen518055 China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Guangdong Shenzhen518055 China Department of Medical Research China Medical University Hospital Taichung40447 Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung41354 Taiwan Guangdong Provincial Key Laboratory of Intelligent Transportation System Sun Yat-sen University Guangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong Shenzhen518107 China Institute of Artificial Intelligence Guangzhou University Guangzhou China

Dealing with resolution variations in dual-temporal remote sensing images is challenging due to differences in sensor capabilities, altitudes, footprints, resampling, and data fusion. Conventional methods for different-resolution change detection struggle to effectively address these challenges, primarily due to insufficient consideration of global and local feature interactions. In this paper, we propose a novel change detection model that leverages the power of multi-scale resolution fusion(MRF). By incorporating the MRF-Block, our approach extracts hierarchical multi-scale features from the input data, enabling effective capture of contextual information at various granularities. This facilitates improved handling of resolution disparities and promotes interactions between global and local features. Experimental evaluations conducted on diverse datasets, demonstrate the effectiveness of our proposed model in achieving accurate change detection in dual-temporal remote sensing imagery, even in scenarios characterized by varying resolutions. Code is available on-line at https://***/yanghl1914/MRF-Former. © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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Convolutional Neural Network Based Career Recommender system for Pakistani engineering Students

Convolutional Neural Network Based Career Recommender System...

引用

International Conference on Innovative Computing (ICIC)

作者： Takreem Saeed Muhammad Sufian Mubashir Ali Attique Ur Rehman Department of Computer Engineering College of Electrical & Mechanical Engineering (CEME) National University of Sciences & Technology (NUST) Islamabad Pakistan Department of Software Engineering Lahore Garrison University (LGU) Lahore Pakistan School of System and Technology University of Management and Technology (UMT) Lahore Pakistan

ISBN: (数字)9781665400916

ISBN: (纸本)9781665400923

In recent years, Recommender systems are utilized in a variety of areas. One reason behind why we want a recommender system in current society is that an individual has many alternatives to use because of the pervasiveness of the Internet. A recommender system seeks to estimate and predict user content preference. Old recommender systems used State-of-the-art recommender algorithms like content based filtering to predict ratings. Career Recommender system provides engineering candidates the best possible available jobs relevant to their skills, qualification, etc. Four to six major engineering disciplines are covered in this recommender system. The proposed approach is tested using a career recommendation dataset which is collected from many students of different disciplines of various universities. A deep NLP based CNN model is used to predict the best jobs with maximum precision. 512 hidden layers are used to increase the performance of this system. Career recommendation takes care of the users and saves their cost and time spending on traditional job searching methods. Comparative study demonstrates that the proposed methodology of prediction of the best jobs achieves better results with an accuracy of 84% when matched with content based filtering technique where 81% accuracy is gained for content based career recommender system.

关键词： Costs engineering profession Filtering Neural networks Natural languages Predictive models Prediction algorithms

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Quantification and comparison of prediction uncertainty associated with different practices of crop modeling

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Agricultural and Forest Meteorology 2025年 371卷

作者： Gao, Yujing Wallach, Daniel Gu, Baojing Tang, Liang Lin, Tao Chang, Xini Hasegawa, Toshi Asseng, Senthold Kahveci, Tamer Hoogenboom, Gerrit College of Environmental and Resource Sciences Zhejiang University Hangzhou China Agricultural & Biological Engineering Department University of Florida Gainesville United States College of Biosystems Engineering and Food Science Zhejiang University Hangzhou China Institute of Crop Science and Resource Conservation University of Bonn Germany National Engineering and Technology Center for Information Agriculture Key Laboratory for Crop System Analysis and Decision Making Ministry of Agriculture Jiangsu Key Laboratory for Information Agriculture Jiangsu Collaborative Innovation Center for Modern Crop Production Nanjing Agricultural University Jiangsu Nanjing China National Agriculture and Food Research Organization Ibaraki Tsukuba Japan Technical University of Munich Department of Life Science Engineering Freising Germany Department of Computer and Information Science and Engineering University of Florida Gainesville United States

Crop models are widely used as decision support tools in agriculture and natural resource management. However, the current practices in crop modeling and evaluation vary significantly, making it challenging to compare uncertainties across different models and studies. This study aims to quantify and compare the uncertainties associated with four different crop modeling practices using a standardized evaluation framework. The four modeling practices are: 1) using a single model, considering only model bias for uncertainty;2) using a single model, accounting for both model bias and parameter uncertainty;3) employing a multi-model ensemble to account for model bias, parameter uncertainty, and structural uncertainties;and 4) using a multi-model ensemble to consider uncertainties induced by model bias, parameters, structures, and inputs. We developed a framework that integrates Markov Chain Monte Carlo (MCMC) and Bayesian Model Averaging (BMA) to consistently quantify prediction uncertainty across these practices. The framework was applied to an Asian rice dataset. The results revealed that common model evaluation approach (Practice 1) tends to underestimate the uncertainty of model predictions. Relying on a single process-based model presents a substantial risk in critical decision-making situations. In contrast, the BMA ensemble predictor (e-BMA) demonstrated higher reliability, making it a preferable choice for future decision support. Our Bayesian framework provides a more robust and adaptable approach for project-specific decision-making, with promising applications in digital agriculture. © 2025 Elsevier B.V.

关键词： Bayesian model averaging Crop model Model structure uncertainty Multi-model ensemble Parameter uncertainty Uncertainty quantification

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China in the eyes of news media:a case study under COVID-19 epidemic

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Frontiers of Information Technology & Electronic engineering 2021年第11期22卷 1443-1457页

作者： Hong HUANG Zhexue CHEN Xuanhua SHI Chenxu WANG Zepeng HE Hai JIN Mingxin ZHANG Zongya LI National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan430074China Services Computing Technology and System Lab Huazhong University of Science and TechnologyWuhan430074China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Journalism and Information Communication Huazhong University of Science and TechnologyWuhan430074China

As one of the early COVID-19 epidemic outbreak areas,China attracted the global news media’s attention at the beginning of *** the epidemic period,Chinese people united and actively fought against the ***,in the eyes of the international public,the situation reported about China is not *** better understand how the international public portrays China,especially during the epidemic,we present a case study with big data *** aim to answer three questions:(1)What has the international media focused on during the COVID-19 epidemic period?(2)What is the media’s tone when they report China?(3)What is the media’s attitude when talking about China?In detail,we crawled more than 280000 pieces of news from 57 mainstream media agencies in 22 countries and made some interesting *** example,international media paid more attention to Chinese livelihood during the COVID-19 epidemic *** March and April,“progress of Chinese vaccines,”“specific drugs and treatments,”and“virus outbreak in U.S.”became the media’s most common *** terms of news attitude,Cuba,Malaysia,and Venezuela had a positive attitude toward China,while France,Canada,and the United Kingdom had a negative *** study can help understand China’s image in the eyes of the international media and provide a sound basis for image analysis.

关键词： Country image COVID-19 epidemic Topic mining Entity Tone of news Emotion

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Stability Optimization and Verification Based on SPTG of Constant Distance Control Strategy in Train-Train Communication Train Control system

Stability Optimization and Verification Based on SPTG of Con...

引用

第33届中国控制与决策会议

作者： Wanli Lu Jidong Lv Haixia Dong Hongjie Liu Ming Chai Shuai Su Xiwang Guo National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University State Key Lab of Rail Traffic Control Safety Beijing Jiaotong Universal Computer and Communication Engineering College Liaoning Shihua University

Next Generation Train Control system(NGTC) based on train-train communication can efficiently realize train tracking control through constant distance *** to ensure the stability of the platoon with the control strategy is very crucial since the leader train action is random in the platoon *** on Stochastic Priced Timed Game(SPTG),we propose a train-train communication control strategy modeling and verification ***,according to the control strategy's requirements,a platoon control model including a leader train and follower trains are established and the stability are verified by SPTG ***,using time expansion as the cost function,the optimal operation strategy of the platoon is obtained through combination of the cooperative game of the platoon's SPTG automata model and ***,the stability optimization objection of the platoon is simulated in single-train operation,two-train operation and multi-train operation tracking *** with the random operation of the platoon,the result shows that this method can make the platoon stability error smaller.

关键词： Train-Train communication Train Control system SPTG Stability Modeling and Verification

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iGniter: Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud

arXiv

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arXiv 2022年

作者： Xu, Fei Xu, Jianian Chen, Jiabin Chen, Li Shang, Ruitao Zhou, Zhi Liu, Fangming The Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University 3663 N. Zhongshan Road Shanghai200062 China The School of Computing and Informatics University of Louisiana at Lafayette 301 East Lewis Street LafayetteLA70504 United States The Guangdong Key Laboratory of Big Data Analysis and Processing School of Computer Science and Engineering Sun Yat-sen University 132 E. Waihuan Road Guangzhou510006 China The National Engineering Research Center for Big Data Technology and System The Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology 1037 Luoyu Road Wuhan430074 China

GPUs are essential to accelerating the latency-sensitive deep neural network (DNN) inference workloads in cloud datacenters. To fully utilize GPU resources, spatial sharing of GPUs among co-located DNN inference workloads becomes increasingly compelling. However, GPU sharing inevitably brings severe performance interference among co-located inference workloads, as motivated by an empirical measurement study of DNN inference on EC2 GPU instances. While existing works on guaranteeing inference performance service level objectives (SLOs) focus on either temporal sharing of GPUs or reactive GPU resource scaling and inference migration techniques, how to proactively mitigate such severe performance interference has received comparatively little attention. In this paper, we propose iGniter, an interference-aware GPU resource provisioning framework for cost-efficiently achieving predictable DNN inference in the cloud. iGniter is comprised of two key components: (1) a lightweight DNN inference performance model, which leverages the system and workload metrics that are practically accessible to capture the performance interference;(2) A cost-efficient GPU resource provisioning strategy that jointly optimizes the GPU resource allocation and adaptive batching based on our inference performance model, with the aim of achieving predictable performance of DNN inference workloads. We implement a prototype of iGniter based on the NVIDIA Triton inference server hosted on EC2 GPU instances. Extensive prototype experiments on four representative DNN models and datasets demonstrate that iGniter can guarantee the performance SLOs of DNN inference workloads with practically acceptable runtime overhead, while saving the monetary cost by up to 25% in comparison to the state-of-the-art GPU resource provisioning strategies. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach

arXiv

引用

arXiv 2024年

作者： Kang, Jiawen Zhong, Yue Xu, Minrui Nie, Jiangtian Wen, Jinbo Du, Hongyang Ye, Dongdong Huang, Xumin Niyato, Dusit Xie, Shengli China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of Education Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China The School of Computer Science and Engineering Nanyang Technological University Singapore Singapore The Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

The synergy between Unmanned Aerial Vehicles (UAVs) and metaverses is giving rise to an emerging paradigm named UAV metaverses, which create a unified ecosystem that blends physical and virtual spaces, transforming drone interaction and virtual exploration. UAV Twins (UTs), as the digital twins of UAVs that revolutionize UAV applications by making them more immersive, realistic, and informative, are deployed and updated on ground base stations, e.g., RoadSide Units (RSUs), to offer metaverse services for UAV Metaverse Users (UMUs). Due to the dynamic mobility of UAVs and limited communication coverages of RSUs, it is essential to perform real-time UT migration to ensure seamless immersive experiences for UMUs. However, selecting appropriate RSUs and optimizing the required bandwidth is challenging for achieving reliable and efficient UT migration. To address the challenges, we propose a tiny machine learning-based Stackelberg game framework based on pruning techniques for efficient UT migration in UAV metaverses. Specifically, we formulate a multi-leader multi-follower Stackelberg model considering a new immersion metric of UMUs in the utilities of UAVs. Then, we design a Tiny Multi-Agent Deep Reinforcement Learning (Tiny MADRL) algorithm to obtain the tiny networks representing the optimal game solution. Specifically, the actor-critic network leverages the pruning techniques to reduce the number of network parameters and achieve model size and computation reduction, allowing for efficient implementation of Tiny MADRL. Numerical results demonstrate that our proposed schemes have better performance than traditional schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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