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arXiv 2025年

作者： Wu, Zijie Wang, Yaonan Mo, Yang Zhu, Qing Xie, He Wu, Haotian Feng, Mingtao Mian, Ajmal College of Electrical and Information Engineering Hunan University Changsha410082 China School of Computer Science and Technology Xidian University Xi’an710071 China Department of Computer Science and Software Engineering The University of Western Australia Crawley PerthWA6009 Australia

Point cloud registration is an essential step for free-form blade reconstruction in industrial measurement. Nonetheless, measuring defects of the 3D acquisition system unavoidably result in noisy and incomplete point cloud data, which renders efficient and accurate registration challenging. In this paper, we propose a novel global registration method that is based on the minimum potential energy (MPE) method to address these problems. The basic strategy is that the objective function is defined as the minimum potential energy optimization function of the physical registration system. The function distributes more weight to the majority of inlier points and less weight to the noise and outliers, which essentially reduces the influence of perturbations in the mathematical formulation. We decompose the solution into a globally optimal approximation procedure and a fine registration process with the trimmed iterative closest point algorithm to boost convergence. The approximation procedure consists of two main steps. First, according to the construction of the force traction operator, we can simply compute the position of the potential energy minimum. Second, to find the MPE point, we propose a new theory that employs two flags to observe the status of the registration procedure. We demonstrate the performance of the proposed algorithm on four types of blades. The proposed method outperforms the other global methods in terms of both accuracy and noise resistance. Copyright © 2025, The Authors. All rights reserved.

关键词： Turbomachine blades

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Dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in smart cities

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Digital Communications and Networks 2021年第4期7卷 492-504页

作者： Nyothiri Aung Weidong Zhang Kashif Sultan Sahraoui Dhelim Yibo Ai National Center for Materials Service Safely University of Science and Technology BeijingBeijing100083China Department of Software Engineering Bahria University Islamabad CampusIslamabadPakistan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China

The integration of the Internet of Vehicles(IoV)in future smart cities could help solve many traffic-related challenges,such as reducing traffic congestion and traffic *** congestion pricing and electric vehicle charging policies have been introduced in recent ***,the majority of these schemes emphasize penalizing the vehicles that opt to take the congested roads or charge in the crowded charging station and do not reward the vehicles that cooperate with the traffic management *** this paper,we propose a novel dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in an urban smart city *** proposed system rewards the drivers that opt to take alternative congested-free ways and congested-free charging *** propose a token management system that serves as a virtual currency,where the vehicles earn these tokens if they take alternative non-congested ways and charging stations and use the tokens to pay for the charging *** proposed system is designed for Vehicular Ad-hoc Networks(VANETs)in the context of a smart city environment without the need to set up any expensive toll collection *** large-scale traffic simulation in different smart city scenarios,it is proved that the system can reduce the traffic congestion and the total charging time at the charging stations.

关键词： IoV EV VANET Smart city Congestion pricing Congestion avoidance EV charging Traffic optimization

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Fully Distributed Shape Sensing of a Flexible Surgical Needle Using Optical Frequency Domain Reflectometry for Prostate Interventions

Fully Distributed Shape Sensing of a Flexible Surgical Needl...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jacynthe Francoeur Dimitri Lezcano Yernar Zhetpissov Raman Kashyap Iulian Iordachita Samuel Kadoury Department of Electrical Engineering Polytechnique Montréal Montréal QC Canada Department of Mechanical Engineering Johns Hopkins University Baltimore MD USA Departments of Electrical Engineering and Physics Engineering Polytechnique Montréal Montréal QC Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal QC Canada

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In minimally invasive procedures such as biopsies and prostate cancer brachytherapy, accurate needle placement remains challenging due to limitations in current tracking methods related to interference, reliability, resolution or image contrast. This often leads to frequent needle adjustments and reinsertions. To address these shortcomings, we introduce an optimized needle shape-sensing method using a fully distributed grating-based sensor. The proposed method uses simple trigonometric and geometric modeling of the fiber using optical frequency domain reflectometry (OFDR), without requiring prior knowledge of tissue properties or needle deflection shape and amplitude. Our optimization process includes a reproducible calibration process and a novel tip curvature compensation method. We validate our approach through experiments in artificial isotropic and inhomogeneous animal tissues, establishing ground truth using 3D stereo vision and cone beam computed tomography (CBCT) acquisitions, respectively. Our results yield an average RMSE ranging from 0.58 ± 0.21 mm to 0.66 ± 0.20 mm depending on the chosen spatial resolution, achieving the submillimeter accuracy required for interventional procedures.

关键词： Optical fiber sensors Accuracy Three-dimensional displays Shape Reflectometry Needles Robot sensing systems

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Multi-Level Feature-Guided Network for Few-shot Medical Image Segmentation

Multi-Level Feature-Guided Network for Few-shot Medical Imag...

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International Conference on computer Supported Cooperative Work in Design

作者： Yue Shen Wanshu Fan Zhongbin Han Dongsheng Zhou National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian China Department of Surgery Affiliated Zhongshan Hospital of Dalian University Dalian China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Few-shot medical image segmentation is a prominent area of collaborative medical technology research to support healthcare and homecare, but often grapples with challenges, such as the problem of local information loss in most of the current prototype-based methods. To mitigate this concern, we propose a novel Multi-Level Feature-Guided Network (MLFGNet) for few-shot medical image segmentation, which aims to enhance segmentation performance by extracting richer information from multi-level features. Specifically, A Multi-Feature Processing Module is proposed that utilizes hybrid attention mechanisms working together to efficiently learn more useful features. Furthermore, we design a Prediction Aggregation Module to aggregate multiple segmentation prediction maps by assigning appropriate weights, thereby effectively improving segmentation accuracy. Extensive experiments prove that our method exhibits favorable performance with respect to the state-of-the-art methods on two public datasets, including Abdominal-CT and Abdominal-MRI datasets.

关键词： Image segmentation Accuracy Attention mechanisms Federated learning Aggregates Collaboration Medical services

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Variable Time Step Reinforcement Learning for Robotic Applications

arXiv

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arXiv 2024年

作者： Wang, Dong Beltrame, Giovanni The Department of Computer and Software Engineering Polytechnique Montréal MontréalQCH3T1J4 Canada

Traditional reinforcement learning (RL) generates discrete control policies, assigning one action per cycle. These policies are usually implemented as in a fixed-frequency control loop. This rigidity presents challenges as optimal control frequency is task-dependent;suboptimal frequencies increase computational demands and reduce exploration efficiency. Variable Time Step Reinforcement Learning (VTS-RL) addresses these issues with adaptive control frequencies, executing actions only when necessary, thus reducing computational load and extending the action space to include action durations. In this paper we introduce the Multi-Objective Soft Elastic Actor-Critic (MOSEAC) method to perform VTS-RL, validating it through theoretical analysis and experimentation in simulation and on real robots. Results show faster convergence, better training results, and reduced energy consumption with respect to other variable- or fixed-frequency approaches. © 2024, CC BY.

关键词： Reinforcement learning

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Arabic Offense Text Detection in Social Networks using Collaborative Machine Learning

Arabic Offense Text Detection in Social Networks using Colla...

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International Symposium on Advanced Electrical and Communication Technologies (ISAECT)

作者： Maria Alabdulrahman Lara Alotaibi Ghazanfar Latif Jaafar Alghazo Software Engineering Department Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Computer Science Prince Mohammad bin Fahd University Al-Khobar Saudi Arabia Software Engineering and Information Technology Management Departments University of Minnesota Crookston MN USA

ISBN: (数字)9798331529987

ISBN: (纸本)9798331529994

In an era of abundant social media use and digital communication, the classification of Arabic social media content and user comments into relevant categories poses a significant challenge. The content in social network platforms often encompasses a wide range of topics, sentiments, and societal concerns, making it essential to develop effective methods for discerning and categorizing these expressions. In this research, a new dataset for Arabic offensive text is built along with the methodology proposed for classifying Arabic content into five categories collected from different social networking sites. Leveraging a diverse array of text representation techniques, including TF-IDF, Bag of Words (BoW), custom embeddings, and pre-trained embeddings from models such as Arabert and Mbert, a comprehensive evaluation is conducted based, shedding light on the optimal approach for tackling this complex problem. The findings reveal that the TF-IDF and BoW methods stand out with an impressive 89% accuracy, emphasizing their robustness in addressing the nuances of Arabic tweet classification across these sensitive topics. This study offers valuable insights into the challenges inherent in categorizing Arabic social media content, emphasizing the significance of the TF-IDF approach as an effective tool for this task.

关键词： Accuracy Social networking (online) Text categorization Text detection Collaboration Machine learning Digital communication Robustness Communications technology

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Public Health Emergencies: Health Data and Public Health Surveillance

Public Health Emergencies: Health Data and Public Health Sur...

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Digital Health (ICDH), IEEE International Conference on

作者： Mohammad Nazayer Samaneh Madanian Hamidreza Rasouli Panah Dave Parry Department of Computer Science and Software Engineering AUT Auckland New Zealand School of IT Murdoch University Perth Australia

After COVID-19, the healthcare system’s ineffectiveness in managing pandemics or public health emergencies is significantly highlighted. Based on the increase in the frequency of pandemics, our objective in this research is to define and propose an integrated health system to support healthcare preparedness for future public health emergencies. This system can support managers and authorities in healthcare and disaster management, through data collection, sharing, and analysis, which would ultimately enhance the effectiveness of managing an outbreak before becoming a pandemic.

关键词：

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Deep Reinforcement Learning for Bandit Arm Localization

Deep Reinforcement Learning for Bandit Arm Localization

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Du, Wenbin Jin, Huaqing Yu, Chao Yin, Guosheng Shenzhen University College of Computer Science and Software Engineering Shenzhen China University of California Department of Radiology and Biomedical Imaging San FranciscoCA United States Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China University of Hong Kong Department of Statistics and Actuarial Science Hong Kong

ISBN: (纸本)9781665480451

In the multi-armed bandit (MAB) framework, we investigate the problem of learning the means of distributions that are associated with a finite n umber o f a rms under a monotonic constraint. Different from the traditional MAB, our problem involves a parameter constraint and a limited trial budget (i.e., the number of arm pulls is small). However, the number of training samples can be as large as possible through (infinite) simulations, while each training sample is of limited size. This situation arises when some additional information is provided before the trial starts and each arm pull (or testing) could be of extraordinary cost. For example, in cancer dose-finding clinical trials, higher toxicity probabilities are typically associated with higher dose levels (i.e., the monotonic dose-toxicity constraint), and the loss due to the drug's toxicity, side-effects or death of patients can be enormous. We formulate this problem in the reinforcement learning (RL) paradigm, which is referred to as a bandit arm localization problem. We propose a novel approach in a double deep Q-learning framework, which is integrated with a state-of-the-art statistical model to preserve the parameter constraint and develop a more effective learning strategy. The double deep Q-learning model can be trained with a large (can be as large as infinite) number of simulated trials, which is the first time to cast dose finding in the RL framework. We evaluate the performance of our approach through extensive simulation studies in realistic settings of phase I clinical trials. The proposed double deep Q-learning is shown to outperform the baseline methods in cancer dose-finding trials. © 2022 IEEE.

关键词： Toxicity

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Application of an Agent-Based Post-Earthquake Evacuation Simulation to Enhance Early-Stage Design of Non-Structural and Architectural Layout

SSRN

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SSRN 2024年

作者： Hassanpour, Sajjad González, Vicente A. Zou, Yang Liu, Jiamou Cabrera-Guerrero, Guillermo Department of Civil and Environmental Engineering The University of Auckland Auckland New Zealand Department of Civil and Environmental Engineering The University of Alberta Edmonton Canada Department of Electrical Computer and Software Engineering Escuela de Ingeniería Inforḿatica Pontificia Universidad Catolica de Valparaíso Chile

In the indoor design process, architects make crucial decisions regarding architectural layout and the selection of non-structural elements. However, there is a lack of comprehensive consideration for human evacuation behavior, specifically in the event of earthquake evacuation, during the design process. This research aims to bridge this gap by presenting the application of Agent-Based Building Earthquake Evacuation Simulation (AB2E2S). The paper assesses a novel post-earthquake evacuation simulation prototype, which integrates an agent-based simulation technique with probabilistic earthquake damage assessment. The proposed model is applied to the case of the engineering building at The University of Auckland, to evaluate the impact of earthquake intensity, design, and behavioral variables on Safe Evacuation Time and number of casualties. Overall, this research demonstrates the potential of the AB2E2S prototype to inform architects and designers about the effective selection of non-structural elements and architectural layout scenarios for post-earthquake evacuation during the schematic design process. © 2024, The Authors. All rights reserved.

关键词： Earthquakes

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Evaluation of Deep Convolutional Neural Network-Based Tattoo Detection

Evaluation of Deep Convolutional Neural Network-Based Tattoo...

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Inteligencia Ambiental, Ingeniería de software y Salud Electrónica y Móvil (AmITIC), V Congreso Internacional en

作者： E. Jiménez-Delgado C. Quesada-López A Méndez-Porras A Lara-Petitdemange J Alfaro-Velasco Graduate Program in Computer Science and Informatics University of Costa Rica Computer Engineering Department Technological Institute of Costa Rica School of Computer Science and Informatics University of Costa Rica

ISBN: (数字)9798350364538

ISBN: (纸本)9798350364545

In this study, two deep learning models for automatic tattoo detection were analyzed; a modified Convolutional Neural Network (CNN) and pre-trained ResNet-50 model. In order to achieve this, ResNet-50 uses transfer learning with fine-tuning. The purpose of this study was to evaluate the accuracy, precision, recall, F1-score, and computational efficiency of the system being considered. To augment the dataset included 1000 photos that were equally divided between those showing tattoos and those that did not show tattoos. A k-fold cross-validation approach was employed in training and testing the models. Although custom CNNs are effective, utilizing pre-trained ones like ResNet-50 can offer even better outcomes. Specifically, ResNet-50 attained a higher accuracy (0.86 compared to 0.79), precision (0.85 versus 0.78), recall (0.91 against 0.86), and F1-score (0.91 vis-a-vis 0.86) as compared to custom CNNs. In selecting these models for examination, two main motivations were considered. The first motivation is to see whether transfer learning with a pre-trained ResNet-50 model does well when compared with a customized CNN designed specifically for tattoo detection. Secondly,the intent of this study is to know what advantages can be derived from each approach and their demerits too. Furthermore, it seeks to determine if transfer learning can provide an alternative in contrast to the common CNN techniques with regards to precision and computational efficiency. In this research, two models will be evaluated in order to answer the question of what is better for tattoo detection: transfer learning or designing custom architectures.

关键词： Training Analytical models Accuracy Computational modeling Transfer learning computer architecture Real-time systems Computational efficiency Convolutional neural networks Tuning

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