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IEEE International Conference on Image Processing

作者： Sadaf Khademi Anastasia Oikonomou Konstantinos Plataniotis Arash Mohammadi Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Medical Imaging Sunnybrook Health Sciences Centre Toronto Canada Department of Electrical and Computer Engineering University of Toronto Toronto Canada

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Drawing inspiration from the primate brain’s intriguing evidence accumulation process, and guided by models from cognitive psychology and neuroscience, the paper introduces the NYCTALE framework, a neuro-inspired and evidence accumulation-based Transformer architecture. The proposed neuro-inspired NYCTALE offers a novel pathway in the domain of Personalized Medicine (PM) for lung cancer diagnosis. In nature, Nyctales are small owls known for their nocturnal behavior, hunting primarily during the darkness of night. The NYCTALE operates in a similarly vigilant manner, i.e., processing data in an evidence-based fashion and making predictions dynamically/adaptively. Distinct from conventional Computed Tomography (CT)-based Deep Learning (DL) models, the NYCTALE performs predictions only when sufficient amount of evidence is accumulated. In other words, instead of processing all or a pre-defined subset of CT slices, for each person, slices are provided one at a time. The NYCTALE framework then computes an evidence vector associated with contribution of each new CT image. A decision is made once the total accumulated evidence surpasses a specific threshold. Preliminary experimental analyses conducted using a challenging in-house dataset comprising 114 subjects. The results are noteworthy, suggesting that NYCTALE outperforms the benchmark accuracy even with approximately $60 \%$ less training data on this demanding and small dataset.

关键词： Analytical models Adaptation models Accuracy Computed tomography Computational modeling Lung Lung cancer

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A Multi-stage Precision Landing Method for Autonomous eVTOL Based on Multi-marker Joint Localization

A Multi-stage Precision Landing Method for Autonomous eVTOL ...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Xiang, Senwei Ye, Minxiang Zhu, Shiqiang Gu, Jason Xie, Anhuan Men, Zehua Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou310000 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

Electric vertical takeoff and landing aircraft (eVTOL) has drawn more and more attention from home and abroad in recent years. It is believed autonomous eVTOL will create a new era of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM). To autonomous eVTOL, precision landing is an extremely critical operation for it directly affects flight safety. In this paper, we analyze the special issues an eVTOL will encounter when it lands in the UAM applications. A multi-stage precision landing method based on multi-marker joint localization is proposed accordingly. Our method contains three key elements: a compatible vertiport with multiple visual markers;an accurate, fast detection and localization algorithm for the vertiport and a multi-stage landing strategy. We implement our method on an eVTOL prototype named ZJ-Copter developed by Zhejiang Lab. A series of real-world experiments have been conducted to validate the effectiveness and accuracy of the proposed method. Experiment results show that our method works well in real-world scenarios for autonomous eVTOL without GPS signal during landing process. The positioning accuracy is less than 0.1m (altitude © 2022 IEEE.

关键词： Aircraft landing

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A Study of Different Machine Learning Approaches for Relation Discovery Task

A Study of Different Machine Learning Approaches for Relatio...

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2025 International Conference on Energy, Power, Environment, Control and Computing, ICEPECC 2025

作者： AL-Aswadi, Fatima N. Za’al Alma’aitah, Wafa Chan, Huah Yong Gan, Keng Hoon Iqbal, Zahid Al-Dhamari, Ahlam Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Faculty of Computer Science and Engineering Hodeidah University P.O. Box 3114 Hodeidah Yemen Department of Intelligence Systems Faculty of Artificial Intelligence Al-Balqa Applied University Salt19117 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Gelugor11800 Malaysia Department of Computer Science Air University Kharian Pakistan Department of Computer Science University of Gujrat Gujrat Pakistan Department of Electrical and Computer Engineering Curtin University Malaysia Sarawak Miri98009 Malaysia

ISBN: (纸本)9781837243235

Relation extraction and classification, collectively referred to as relation discovery, are fundamental tasks in the ontology construction process. In recent years, numerous studies have dedicated their efforts to devising hybrid approaches, supporting inclusive innovation in computational linguistics and ontology engineering. These approaches merge traditional methods with Machine Learning (ML) or Deep Learning (DL) techniques to tackle relation discovery tasks. This research examines the applicability and efficacy of four distinct models— Support Vector Machine (SVM), Naive Bayes (NB), k-Nearest Neighbors (KNN), and Convolutional Neural Network (CNN)—in relation extraction and classification. Two different datasets, ACL RD-TEC 2.0 and ACLRelAcS, are utilized, alongside two distinct text representation methodologies, BOW and TF-IDF-BOW. An in-depth analysis of the results, including the advantages and limitations of each model, is presented in the results and discussion sections. © The Institution of engineering & Technology 2025.

关键词： Deep learning

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A Data-Informed Analysis of Scalable Supervision for Safety in Autonomous Vehicle Fleets

A Data-Informed Analysis of Scalable Supervision for Safety ...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Cameron Hickert Zhongxia Yan Cathy Wu Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Autonomous driving is a highly anticipated approach toward eliminating roadway fatalities. At the same time, the bar for safety is both high and costly to verify. This work considers the role of remotely-located human operators supervising a fleet of autonomous vehicles (AVs) for safety. Such a ‘scalable supervision’ concept was previously proposed to bridge the gap between still-maturing autonomy technology and the pressure to begin commercial offerings of autonomous driving. The present article proposes DISCES, a framework for Data-Informed Safety-Critical Event Simulation, to investigate the practicality of this concept from a dynamic network loading standpoint. With a focus on the safety-critical context of AVs merging into mixed-autonomy traffic, vehicular arrival processes at 1,097 highway merge points are modeled using microscopic traffic reconstruction with historical data from interstates across three California counties. Combined with a queuing theoretic model, these results characterize the dynamic supervision requirements and thereby scalability of the teleoperation approach. Across all scenarios we find reductions in operator requirements greater than 99% as compared to in-vehicle supervisors for the time period analyzed. The work also demonstrates two methods for reducing these empirical supervision requirements: (i) the use of cooperative connected AVs — which are shown to produce an average 3.67 orders-of-magnitude system reliability improvement across the scenarios studied — and (ii) aggregation across larger regions.

关键词： Road transportation Scalability System performance Predictive models Safety Reliability Vehicle dynamics Autonomous vehicles Queueing analysis Load modeling

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Autonomous Multiple-Trolley Collection System with Nonholonomic Robots: Design, Control, and Implementation

arXiv

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arXiv 2024年

作者： Xie, Peijia Xia, Bingyi Hu, Anjun Zhao, Ziqi Meng, Lingxiao Sun, Zhirui Gao, Xuheng Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Jiaxing Research Institute Southern University of Science and Technology Jiaxing China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada

The intricate and multi-stage task in dynamic public spaces like luggage trolley collection in airports presents both a promising opportunity and an ongoing challenge for automated service robots. Previous research has primarily focused on handling a single trolley or individual functional components, creating a gap in providing cost-effective and efficient solutions for practical scenarios. In this paper, we propose a mobile manipulation robot incorporated with an autonomy framework for the collection and transportation of multiple trolleys that can significantly enhance operational efficiency. We address the key challenges in the trolley collection problem through the novel design of the mechanical system and the vision-based control strategy. We design a lightweight manipulator and docking mechanism, optimized for the sequential stacking and transportation of multiple trolleys. Additionally, based on the Control Lyapunov Function and Control Barrier Function, we propose a novel vision-based control with the online Quadratic Programming which significantly improves the accuracy and efficiency of the collection process. The practical application of our system is demonstrated in real-world scenarios, where it successfully executes multiple-trolley collection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Quadratic programming

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Generating Realistic Sound with Prosthetic Hand: A Reinforcement Learning Approach

Generating Realistic Sound with Prosthetic Hand: A Reinforce...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Taemoon Jeong Sankalp Yamsani Jooyoung Hong Kyungseo Park Joohyung Kim Sungjoon Choi Department of Artificial Intelligence Korea University Seoul Korea Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Champaign IL USA Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology (DG-IST) Daegu South Korea

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

In this study, we tackle the complex task of enabling prosthetic hands to accurately reproduce sounds, a crucial aspect for distinguishing between different materials through auditory feedback. Sound identification, such as discerning a drywall tap from that on a brick wall, significantly enhances the functionality and user experience of prosthetic devices. However, achieving this level of auditory feedback in prosthetic hands poses considerable challenges. We utilize reinforcement learning (RL) techniques to train prosthetic hands in emulating human-like sound characteristics, focusing on key auditory signals like amplitude and onset timing. Our approach integrates a detailed analysis of these sound attributes to direct the prosthetic hand’s movements for the sound generation that mimics natural human actions. We developed a tailored reward function incorporating amplitude, onset strength, and timing criteria to ensure the prosthetic hand’s movements align closely with the intended human-like sound output.

关键词： MIMICs Focusing Reinforcement learning User experience Timing Object recognition engineering in medicine and biology Prosthetic hand

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Total Harmonics Distortion Prediction at the Point of Common Coupling of industrial load with the grid using Artificial Neural Network

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Energy and AI 2023年第4期14卷 294-303页

作者： Emenike Ugwuagbo Adeola Balogun Biplob Ray Adnan Anwar Chikodili Ugwuishiwu Centre for Space Transport and Propulsion (CSTP)Epe1 LASU Epe driveLagos106101Nigeria Department of Electrical&Electronics Engineering University of LagosAkoka101017LagosNigeria Centre for Intelligent Systems(CIS) School of Engineering and TechnologyCQ UniversityNorman Gardens4701RockhamptonAustralia Strategic Centre for Cyber Security Research Institute(CSRI) School of Information TechnologyDeakin UniversityGeelong3216MelbourneAustralia Department of Computer Science University of NigeriaNsukka410001EnuguNigeria

Power quality challenges have generated a lot of disputes between utilities,customers,network operators,and equipment manufacturers around the world as regards the share of responsibility for power quality solutions,this results in different levels of financial and technical losses for both the network operators and the *** of the major consequences of the operation of heavy-duty factories globally is the corruption of power quality at the point of common coupling(PCC).In order to quantify the harmonics contribution at the PCC by industrial consumers,this paper presents three-phase total harmonics distortion of current(THDi)prediction model at the *** proposed artificial neural network(ANN)models use a multilayer perceptron neural network(MLPN)to predict three-phase total harmonic *** input parameter used in the models is easily measured with basic power *** model was trained with input parameters captured at 33 kV and 132 kV voltage levels using power quality meters at five(5)different steel manufacturing ***(8)different models were designed,trained,validated,and tested with different combinations of input parameters,number of hidden layers,and number of neurons in the hidden *** results show that the model with two hidden layers which uses four major power parameters(Current,apparent power,reactive and active power)as input parameters in the training model had the best performance with a 95.5%coefficient of correlation between the measured THDi and the predicted THDi.

关键词： Total harmonics distortion(THD) Power quality(PQ) Artificial neural network(ANN)

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Methods for Class-Imbalanced Learning with Support Vector Machines: A Review and an Empirical Evaluation

arXiv

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arXiv 2024年

作者： Rezvani, Salim Pourpanah, Farhad Lim, Chee Peng Jonathan Wu, Q.M. Department of Computer Science Toronto Metropolitan University TorontoON Canada Department of Electrical and Computer Engineering Queen's University KingstonON Canada Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC Australia

This paper presents a review on methods for class-imbalanced learning with the Support Vector Machine (SVM) and its variants. We first explain the structure of SVM and its variants and discuss their inefficiency in learning with class-imbalanced data sets. We introduce a hierarchical categorization of SVM-based models with respect to class-imbalanced learning. Specifically, we categorize SVM-based models into re-sampling, algorithmic, and fusion methods, and discuss the principles of the representative models in each category. In addition, we conduct a series of empirical evaluations to compare the performances of various representative SVM-based models in each category using benchmark imbalanced data sets, ranging from low to high imbalanced ratios. Our findings reveal that while algorithmic methods are less time-consuming owing to no data pre-processing requirements, fusion methods, which combine both re-sampling and algorithmic approaches, generally perform the best, but with a higher computational load. A discussion on research gaps and future research directions is provided. © 2024, CC BY-NC-SA.

关键词： Support vector machines

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Equilibrium-Independent Control of Continuous-Time Nonlinear systems via the LPV Framework - Extended Version

arXiv

引用

arXiv 2023年

作者： Koelewijn, Patrick J.W. Weiland, Siep Tóth, Roland The Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands The Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In this paper, we consider the analysis and control of continuous-time nonlinear systems to ensure universal shifted stability and performance, i.e., stability and performance w.r.t. each forced equilibrium point of the system. This "equilibrium-free" concept is especially beneficial for control problems that require the tracking of setpoints and rejection of persistent disturbances, such as input loads. In this paper, we show how the velocity form, i.e., the time-differentiated dynamics of the system, plays a crucial role in characterizing these properties and how the analysis of it can be solved by the application of Linear Parameter-Varying (LPV) methods in a computationally efficient manner. Furthermore, by leveraging the properties of the velocity form and the LPV framework, a novel controller synthesis method is presented which ensures closed-loop universal shifted stability and performance. The proposed controller design is verified in a simulation study and also experimentally on a real system. Additionally, we compare the proposed method to a standard LPV control design, demonstrating the improved stability and performance guarantees of the new approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Controllers

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Kaiser and Cumulative Proportion Principal Component Analysis for Temperature Compensation of Vibration in Reinforced Concrete Bridge 14

Kaiser and Cumulative Proportion Principal Component Analysi...

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14th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2022

作者： Concepcion, Ronnie Cruz, Febus Reidj G. Carpio, Joy Bandala, Argel Relano, R-Jay De Leon, Joseph Aristotle Genevie Janairo, Adrian Guillermo, Marielet Galupino, Joenel Genoguin, Marvin Jade James Loresco, Pocholo De la Salle University Department of Manufacturing Engineering and Management Manila Philippines Mapua University School of Electrical Electronics and Computer Engineering Manila Philippines University of California Systems Engineering Department BerkeleyCA United States De la Salle University Department of Electronics and Computer Engineering Manila Philippines De la Salle University Department of Civil Engineering Manila Philippines Eastern Visayas State University Department of Civil Engineering Tacloban City Philippines FEU Institute of Technology Electrical and Electronics Engineering Department Manila Philippines

ISBN: (纸本)9781665464932

Structural health monitoring (SHM) was developed to provide diagnosis of the state of civil structures, such as bridge and building, throughout their lifespan. The superstructure degrades over time mainly because of the continuous effect of environmental factors including temperature, wind, humidity, and traffic loading. Consequently, this study is concerned in reducing the masking effect of environmental factors, specifically on temperature. Principal component analysis (PCA), a supervised machine learning algorithm, was employed as the embedded statistical treatment for multidimensional reduction of feature matrix data to eliminate the temperature effect. Kaiser's criterion eliminated data variance of almost 20% that may result to poor data reconstruction. Cumulative proportion (CP) criterion eliminated data variance around 4%, which is a better choice for deciding the number of principal components to eliminate. Thus, the proposed experimental study addressed successful temperature compensation from reinforced concrete bridge vibration data by using PCA and CP criterion. © 2022 IEEE.

关键词： Structural health monitoring

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