检索结果-内蒙古大学图书馆

Resource Allocation for Edge Computing-Based Blockchain: A Game Theoretic Approach

学校读者我要写书评

暂无评论

Resource Allocation for Edge Computing-Based Blockchain: A G...

IEEE International Conference on Communications Workshops, ICC

作者： Wenlong Guo Zheng Chang Xijuan Guo Dushnatha Nalin K. Jayakody Tapani Ristaniemi College of Information Science and Engineering Yanshan University Qinhuangdao China University of Jyvaskyla Faculty of Information Technology Jyvaskyla Finland School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk Russia

ISBN: (数字)9781728174402

ISBN: (纸本)9781728174419

Blockchain has been progressively applied to various Internet of Things (IoT) platforms. As the efficiency of the blockchain depends on its computing capability, how to make sure the acquisition of the computational resources and participation of the devices would be the driving force. In this work, an edge computing-based blockchain network is considered, where the edge service provider (ESP) offers computational resources for the miners. The focus is to investigate an efficient incentive mechanism for the miners to purchase the computational resources. Accordingly, a two-stage Stackelberg game is formulated between the miners and ESP. By exploring the Stackelberg equilibrium of the optimal mining strategy under two different mining schemes. The aim is to find the optimal incentive for the ESP and miners to choose auto-fit strategies. Through theoretical analysis and numerical simulation, we demonstrate the effectiveness of the proposed scheme on encouraging devices to participate the Blockchain.

关键词： Games Internet of Things Pricing Standards Computational modeling Radio frequency

Automation of Predictive Maintenance: An Experimental Framework for Aircraft Landing Gear

学校读者我要写书评

暂无评论

Engineering Reports

Engineering Reports 2025年第6期7卷

作者： Izaak Stanton Kamran Munir Ahsan Ikram Murad El-Bakry Centre for Machine Vision Bristol Robotics Laboratory (BRL) University of the West of England (UWE) Bristol UK Computer Science Research Centre (CSRC) School of Computing and Creative Technologies (SCC) College of Arts Technology and Environment (CATE) University of the West of England (UWE) Bristol UK Airbus Operations Ltd. Filton Bristol UK

Terabytes of data are recorded per flight by modern aircraft, providing a goldmine for predictive maintenance modeling, however, the required domain knowledge to build ML tools limits the number developed by airline manufacturers each year. Automated machine learning (AutoML) libraries can simplify model development, providing features such as automated preprocessing, model selection, and hyperparameter tuning to improve the efficiency and accessibility of the development workflow. This research presents an experimental analysis comparing industry-selected machine learning models and a hand-picked selection of automated machine-learning tools. The selected models were evaluated against real and synthetic time series datasets for different Airbus landing gear components across six datasets. The traditional and automated models obtained comparable MAE and F1 scores on regression and classification problems, accordingly, demonstrating the effectiveness of their use in this field. Based on these findings, a robust framework is proposed to utilize automated ML to optimize predictive maintenance tool development. This research is a stepping stone towards greater use of automation for predictive maintenance and presents insights into the field and AutoML. By integrating greater automation, AutoML can exploit more of the available data and deskill the development process to enable non-data scientists to produce health monitoring models for a more diverse pool of aircraft components.

关键词： aircraft maintenance automated machine learning machine learning predictive maintenance

A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2018年第6期5卷 1113-1120页

作者： Fangfang Zhao Cuiqiao Chen Wei He Shuzhi Sam Ge School of Computer Science and Engineering and Center for RoboticsUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Automation and Electrical Engineering University of Science and Technology of BeijingBeijing 100083China Social Robotics Laboratory Interactive Digital Media Instituteand Department of Electrical and Computer EngineeringNational University of SingaporeSingapore 117576Singapore Singapore 117576 Singapore

This paper explores multiple model adaptive estimation(MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter— multiple model adaptive estimation unscented Kalman filter(MMAE-UKF) rather than conventional Kalman filter methods,like the extended Kalman filter(EKF) and the unscented Kalman filter(UKF). UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters,which the improved filtering method can overcome. Meanwhile,this algorithm is used for integrated navigation system of strapdown inertial navigation system(SINS) and celestial navigation system(CNS) by a ballistic missile's motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.

关键词： Celestial navigation integrated navigation multiple model adaptive estimation unscented Kalman filter （MMAEUKF） strap-down inertial navigation

Advancing Early Detection of Virus Yellows: Developing a Hybrid Convolutional Neural Network for Automatic Aphid Counting in Sugar Beet Fields

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Gao, Xumin Xue, Wenxin Lennox, Callum Stevens, Mark Gao, Junfeng School of Computer Science University of Lincoln LincolnLN6 7TS United Kingdom Tobacco Research Institute Chinese Academy of Agricultural Sciences Qingdao266101 China British Beet Research Organisation Norwich Research Park Innovation Centre Colney Lane NorwichNR4 7GJ United Kingdom Lincoln Agri-Robotics Centre Lincoln Institute for Agri-Food Technology University of Lincoln LincolnLN2 2LG United Kingdom University of Lincoln LincolnLN6 7TS United Kingdom

Aphids are efficient vectors to transmit virus yellows in sugar beet fields. Timely monitoring and control of their populations are thus critical to prevent the large-scale outbreak of virus yellows. However, the manual counting of aphids, which is the most common practice, is labor-intensive and time-consuming. Additionally, two of the biggest challenges in aphid counting are that aphids are small objects and their density distributions are varied in different areas of the field. To address these challenges, we proposed a hybrid automatic aphid counting network architecture which integrates the detection network and the density map estimation network. When the distribution density of aphids is low, it utilizes an improved Yolov5 to count aphids. Conversely, when the distribution density of aphids is high, its witches to CSRNet to count aphids. To the best of our knowledge, this is the first framework integrating the detection network and the density map estimation network for counting tasks. Through comparison experiments of counting aphids, it verified that our proposed approach outperforms all other methods in counting aphids. It achieved the lowest MAE and RMSE values for both the standard and high-density aphid datasets: 2.93 and 4.01 (standard), and 34.19 and 38.66 (high-density), respectively. Moreover, the AP of the improved Yolov5 is 5% higher than that of the original Yolov5. Especially for extremely small aphids and densely distributed aphids, the detection performance of the improved Yolov5 is significantly better than the original Yolov5. This work provides an effective early warning for the virus yellows risk caused by aphids in sugar beet fields, offering protection for sugar beet growth and ensuring sugar beet yield. The datasets and project code are released at: https://***/JunfengGaolab/Counting-Aphids. Copyright © 2023, The Authors. All rights reserved.

关键词： Viruses

Sleepposenet: Multi-View multi-task learning for sleep postural transition recognition using UWB

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Piriyajitakonkij, Maytus Warin, Patchanon Lakhan, Payongkit Leelaarporn, Pitsharponrn Pianpanit, Theerasarn Niparnan, Nattee Mukhopadhyay, Subhas Chandra Wilaiprasitporn, Theerawit Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand Nakhon Nayok Thailand Computer Engineering Department Chulalongkorn University Bangkok Thailand School of Engineering Macquarie University SydneyNSW2109 Australia

Recognizing the movements during sleep is crucial for monitoring of patients with sleep disorders. However, the utilization of the deep learning approaches ultra-wideband (UWB)-based indoor applications for the classification of human sleeping postures has not been explored widely. This study investigates the performance of the off-the-shelf single antenna UWB in a novel application of sleep postural transition (SPT) recognition. The proposed Multi-View Multi-Task Learning, entitled SleepPoseNet or SPN, with time series data augmentation aims to classify four standard SPTs. SPN exhibits an ability to capture both time and frequency features, including the movement and direction of sleeping positions. Three cost functions for multi-views, multi-tasks, and classification are proposed to optimize SPN. The data recorded from 26 volunteers displayed that SPN with mean accuracy of 79.7±0.5% significantly outperformed the mean accuracy of 73.5 ± 0.7% obtained from deep convolution neural network (DCNN) in a recent state-of-the-art work on human activity recognition using UWB. Apart from UWB system, SPN with the data augmentation can ultimately be adopted to learn and classify time series data in various applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

SAHRTA: A Supervisory-Based Adaptive Human-Robot Teaming Architecture

学校读者我要写书评

暂无评论

SAHRTA: A Supervisory-Based Adaptive Human-Robot Teaming Arc...

IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)

作者： Jamison Heard Julian Fortune Julie A. Adams Rochester Institute of Technology Rochester NY School of Electrical Engineering and Computer Science Oregon State University Corvallis OR Collaborative Robotics and Intelligent Systems Institute Oregon State University Corvallis OR

ISBN: (数字)9781728160016

ISBN: (纸本)9781728160023

Supervisory-based human-robot teams are deployed in various dynamic and extreme environments (e.g., space exploration). Achieving high task performance in such environments is critical, as a mistake may lead to significant monetary loss or human injury. Task performance may be augmented by adapting the supervisory interface's interactions or autonomy levels based on the human supervisor's workload level, as workload is related to task performance. Typical adaptive systems rely solely on the human's overall or cognitive workload state to select what adaptation strategy to implement; however, overall workload encompasses many dimensions (i.e., cognitive, physical, visual, auditory, and speech) called workload components. Selecting an appropriate adaptation strategy based on a complete human workload state (rather than a single workload dimension) may allow for more impactful adaptations that ensure high task performance. A Supervisory-Based Adaptive Human-Robot Teaming Architecture (SAHRTA) that selects an appropriate level of autonomy or system interaction based on a complete realtime multi-dimensional workload estimate and predicted future task performance is introduced. SAHRTA was shown to improve overall task performance in a physically expanded version of the NASA Multi-Attribute Task Battery.

关键词： Task analysis Adaptive systems NASA Monitoring Fuels Atmospheric measurements Particle measurements

Learning Context-Based Non-local Entropy Modeling for Image Compression

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Timofte, Radu Zhang, David Zuo, Wangmeng Zhang, Kai Li, Mu School of Science and Engineering Chinese University of Hong Kong ShenzhenGuangdong518172 China Computer Vision Laboratory ETH Zurich Zurich Switzerland School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Shenzhen Research Institute of Big Data Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

The entropy of the codes usually serves as the rate loss in the recent learned lossy image compression methods. Precise estimation of the probabilistic distribution of the codes plays a vital role in the performance. However, existing deep learning based entropy modeling methods generally assume the latent codes are statistically independent or depend on some side information or local context, which fails to take the global similarity within the context into account and thus hinder the accurate entropy estimation. To address this issue, we propose a non-local operation for context modeling by employing the global similarity within the context. Specifically, we first introduce the proxy similarity functions and spatial masks to handle the missing reference problem in context modeling. Then, we combine the local and the global context via a non-local attention block and employ it in masked convolutional networks for entropy modeling. The entropy model is further adopted as the rate loss in a joint rate-distortion optimization to guide the training of the analysis transform and the synthesis transform network in transforming coding framework. Considering that the width of the transforms is essential in training low distortion models, we finally produce an U-Net block in the transforms to increase the width with manageable memory consumption and time complexity. Experiments on Kodak and Tecnick datasets demonstrate the superiority of the proposed context-based non-local attention block in entropy modeling and the U-Net block in low distortion compression against the existing image compression standards and recent deep image compression models. Copyright © 2020, The Authors. All rights reserved.

关键词： Image compression

Machine learning assisted predictive urban digital twin for intelligent monitoring of air quality index for smart city environment

学校读者我要写书评

暂无评论

Environmental Modelling & Software 2025年 192卷

作者： Khazina Naveed Tariq Umer Aamer Bilal Asghar Muhammad Aslam Krzysztof Ejsmont Ahmed Sayed Mohammed Metwally Kien Nguyen Thanh Department of Computer Science Bahria University Lahore Campus 54000 Pakistan Department of Computer Science COMSATS University Islamabad Lahore Campus Lahore 54000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Lahore Campus Lahore 54000 Pakistan Department of Chemical Engineering COMSATS University Islamabad Lahore Campus Lahore 54000 Pakistan Faculty of Mechanical and Industrial Engineering Warsaw University of Technology 02-524 Warsaw Poland Department of Mathematics College of Science King Saud University Riyadh 11451 Saudi Arabia Faculty of Engineering School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Australia

Environmental factors such as urban air pollutants have detrimental effects on human health. In this research a digital twin (DT) based innovative strategy is presented for accurately forecasting Air Quality Index (AQI) in smart city environment. The historic data of Delhi city is collected, and six different deep learning algorithms are implemented to forecast AQI. The 3D model of the smart city is developed in the Blender, and its urban DT is developed in Microsoft Azure. The InfluxDB database is used for storage and retrieval of time-series data. The experimental results show that the CNN-1D-2 layer model outperforms all other algorithms with MAPE of 0.01231, MSLE of 0.00036, R 2 score reaching 0.99951, and model accuracy of 97.950647. The 3D urban DT model highlights the polluted areas with different colors based on AQI thresholds and DT Grafana dashboard displays the graphical values of AQI and different pollutants along with their trends.

关键词： Air quality index Smart city Digital twin Machine learning Artificial neural networks

Robot Learning from Randomized Simulations: A Review

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Muratore, Fabio Ramos, Fabio Turk, Greg Yu, Wenhao Gienger, Michael Peters, Jan Intelligent Autonomous Systems Group Technical University of Darmstadt Germany Honda Research Institute Europe Offenbach Am Main Germany School of Computer Science University of Sydney Australia NVIDIA SeattleWA United States Georgia Institute of Technology AtlantaGA United States Robotics at Google Mountain ViewCA United States

The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data. Unfortunately, it is prohibitively expensive to generate such data sets on a physical platform. Therefore, state-of-the-art approaches learn in simulation where data generation is fast as well as inexpensive and subsequently transfer the knowledge to the real robot (sim-to-real). Despite becoming increasingly realistic, all simulators are by construction based on models, hence inevitably imperfect. This raises the question of how simulators can be modified to facilitate learning robot control policies and overcome the mismatch between simulation and reality, often called the 'reality gap'. We provide a comprehensive review of sim-to-real research for robotics, focusing on a technique named 'domain randomization' which is a method for learning from randomized simulations. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning