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Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

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computer Modeling in engineering & Sciences 2024年第3期138卷 2103-2127页

作者： Othman S.Al-Heety Zahriladha Zakaria Ahmed Abu-Khadrah Mahamod Ismail Sarmad Nozad Mahmood Mohammed Mudhafar Shakir Hussein Alsariera Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka(UTeM)Durian TunggalMalaysia College of Computing&Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Electrical Electronic and System EngineeringUniversiti Kebangsaan MalaysiaUKM BangiSelangorMalaysia Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityKirkukIraq Faculty of Engineering and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Batu PahatMalaysia 6Research Center DepartmentThe University of MashreqBaghdadIraq

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the *** this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things *** framework integrates Kalman filtering and *** smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction *** traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction *** evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art ***,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

关键词： Q-learning intelligent transportation system(ITS) traffic control vehicular communication kalman filtering smart city Internet of Things

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IPRPAS: A Dataset of Physical Adversarial Samples for Assessing Object Detection in Intelligent Vehicles

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IEEE Internet of Things Journal 2025年第12期12卷 21255-21263页

作者： Safarzadehvahed, Mahdieh Zulkernine, Mohammad Araujo, Paulo Ricardo Marques de Givigi, Sidney Queen’s University School of Computing KingstonONK7L 3N6 Canada Queen’s University Department of Electrical and Computer Engineering KingstonONK7L 3N6 Canada

Physical adversarial samples (PASs) can deceive object detection models (ODMs). Research on the real-world impacts of these samples on ODMs in intelligent vehicles and securing them requires a publicly available benchmark dataset. In this article, we leverage our autonomous driving platform, equipped with a high-resolution video camera and a light detection and ranging, to create a novel and challenging benchmark for securing 2-D and multisensor ODMs. We present images and point clouds of resembling PASs (IPRPASs), a collection of 3409 annotated paired images and point clouds of printed, displayed, or projected resembling PASs (RPASs) in both indoor and outdoor scenarios. IPRPAS includes various common driving objects, such as cars, traffic signs, traffic lights, and persons, under different weather conditions, positions, distances, lighting conditions, and viewing angles. Our experimental evaluation demonstrates that IPRPAS samples deceive state-of-the-art ODMs. Our benchmark is available online at: https://***/***?persistentId=doi:10.5683/SP3/O8DLSU. This dataset and the described experiments provide a benchmark for future research on the effectiveness of RPASs and defenses against RPASs in real-world environments. © 2014 IEEE.

关键词： Image annotation

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A Bias-Free Revenue-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning 33

A Bias-Free Revenue-Maximizing Bidding Strategy for Data Con...

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33rd International Joint Conference on Artificial Intelligence, IJCAI 2024

作者： Tang, Xiaoli Yu, Han Li, Zengxiang Li, Xiaoxiao College of Computing and Data Science Nanyang Technological University Singapore ENN Group Beijing China Department of Electrical and Computer Engineering The University of British Columbia Canada

ISBN: (纸本)9781956792041

Auction-based Federated Learning (AFL) is a burgeoning research area. However, existing bidding strategies for AFL data consumers (DCs) primarily focus on maximizing expected accumulated utility, disregarding the more complex goal of revenue maximization. They also only consider winning bids, leading to biased estimates by overlooking information from losing bids. To address these issues, we propose a Bias-free Revenue-maximizing Federated bidding strategy for DCs in AFL (BR-FEDBIDDER). Our theoretical exploration of the relationships between Return on Investment (ROI), bid costs, and utility, and their impact on overall revenue underscores the complexity of maximizing revenue solely by prioritizing ROI enhancement. Leveraging these insights, BR-FEDBIDDER optimizes bid costs with any given ROI constraint. In addition, we incorporate an auxiliary task of winning probability estimation into the framework to achieve bias-free learning by leveraging bid records from historical bid requests, including both winning and losing ones. Extensive experiments on six widely used benchmark datasets show that BR-FEDBIDDER outperforms eight state-of-the-art methods, surpassing the best-performing baseline by 5.66%, 6.08% and 2.44% in terms of the total revenue, ROI, and test accuracy of the resulting FL models, respectively. © 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.

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Al0.87Ga0.13N/Al0.64Ga0.36N HFET with fT >17 GHz and Vbr > 360 v 82

Al0.87Ga0.13N/Al0.64Ga0.36N HFET with fT >17 GHz and Vbr > 3...

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82nd Device Research Conference, DRC 2024

作者： Chen, Jiahao Stephenson, Kenneth Mamun, Md Abdullah Wang, Zehuan Seshadri, Parthasarathy Khan, Asif Gupta, Chirag University of Wisconsin-Madison Department of Electrical and Computer Engineering MadisonWI53705 United States University of South Carolina College of Engineering and Computing ColumbiaSC29208 United States

ISBN: (纸本)9798350373738

Ultra-wide bandgap (AlN, diamond, Ga 2 O 3) have attracted significant research interest for high power and high frequency applications. Al rich (> 60%) AlGaN channel heterojunction field effect transistor (HFET) is a promising candidate for high power RF devices due to the large bandgap, high breakdown field and compatible saturation velocity with GaN. Consequently, Al rich (>60%) AlGaN channel transistors have been explored [ 1 - 8 ]. The AlGaN channel (Al > 60%) transistors have demonstrated good unity gain frequency (f T) performance (~40 GHz) and decent breakdown voltage (~45 V) [6]. However, short channel effects in Al rich (Al > 60%) transistors due to a reduced gate length between 80nm-150nm limited the high breakdown voltage performance demonstration with >10 GHz f T for Al rich (Al > 60%) AlGaN channels [6 , 7]. In this work, we demonstrate high frequency (fT > 17 GHz) and high breakdown voltage (>360 V, >150 V/μm) simultaneously for 64% AlGaN channel transistors. © 2024 IEEE.

关键词： Electric breakdown

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Transfer Learning-Based Pneumonia Detection: A Comparative Evaluation of Machine Learning Models

Transfer Learning-Based Pneumonia Detection: A Comparative E...

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2023 IEEE Technology and engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023

作者： Anitha, G. Priya, S.Baghavathi Laxmikant, Kumar Pattanaik, Balachandra Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Wallaga University School of Electrical and Computer Engineering Ethiopia Africa Nekemte Ethiopia

ISBN: (纸本)9798350384659

Pneumonia, a widespread respiratory infection, poses significant health risks and requires timely detection for effective treatment. This study employs transfer learning for pattern identification and predictive modeling for classification in pneumonia detection using chest radiographs. Pre-trained deep learning models are utilized to extract informative features, which are then classified using various machine learning algorithms. A comprehensive evaluation is conducted on a benchmark dataset to assess the performance of the approach. The results demonstrate the effectiveness of transfer learning-based feature extraction combined with machine learning classification in accurate pneumonia detection. The findings contribute to improving diagnostic accuracy and have the potential to enhance healthcare decision-making processes. © 2023 IEEE.

关键词： Health risks

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Development of an online condition monitoring based system for the partial demagnetization fault diagnosis of SPM-type BLDC motor

Energy Conversion and Economics

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Energy Conversion and Economics 2022年第2期3卷 72-84页

作者： Usman, Adil Rajpurohit, Bharat Singh School of Computing and Electrical Engineering Indian Institute of Technology-Mandi Mandi India Electrical and Computer Engineering Department University of California Santa CruzCA United States

This study investigates partial demagnetization faults arising from stator interturn faults in a surface-mounted permanent-magnet-type brushless direct current motor. Because of rotor demagnetization, the fault severity increases significantly owing to an increase in the stator phase current and temperature. The effect of such a fault is reflected in machine parameters such as the motor back-EMF and radial magnetic flux, which are used to analyse the characteristics of faults. A mathematical model of a machine under possible fault conditions is developed using the finite element method and advanced hybrid model approaches. Experimental investigations are conducted to validate the proposed methodology. Subsequently, the machine parameters used for fault diagnosis are employed to develop an online expert-based system that can detect, classify and estimate the percent increase in the values of the parameters to determine the fault severity of the machine under fault conditions. It is discovered that the proposed approach is suitable for industrial and commercial applications in electric vehicles, where the machine's state-of-health estimation is crucial for avoiding major faults that may result in its failure. © 2022 The Authors. Energy Conversion and Economics published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology and the State Grid of China Economic & Technological Research Institute Co Ltd.

关键词： Failure analysis

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Review on AI Assistant Systems for Programming Language Learning in Learning Environments 8

Review on AI Assistant Systems for Programming Language Lear...

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8th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI 2024

作者： Senanayake, Savithree Karunanayaka, Kasun Ekanayake, K.V.J.P. University of Colombo School of Computing University of Colombo Colombo Sri Lanka The Open University of Sri Lanka Department of Electrical and Computer Engineering Nugegoda Sri Lanka

ISBN: (纸本)9798331509170

Artificial intelligence (AI) has made it possible to improve student engagement in the learning environment in many ways, including fusing the adaptive, individualized support of AI assistants with the motivating elements of gamification. The study examined the range of AI-powered platforms and tools that include gamification components, including AI-powered code generation aids, adaptive feedback mechanisms, and intelligent tutoring systems. These technologies meet the varied needs of students, which improves retention and comprehension, in addition to making learning more engaging and participatory. The paper also emphasizes how an AI-enabled gamified environment may facilitate group learning and help students gain a deeper comprehension of challenging programming concepts. However, it also discusses the difficulties and moral issues surrounding the application of AI in education such as loneliness, less student interaction, low motivation, etc, highlighting the necessity of cautious application and ongoing assessment. The review's conclusions indicate that gamified learning environments with AI capabilities have great potential to transform programming education and give students a more effective, customized, and interesting learning experience. © 2024 IEEE.

关键词： Adversarial machine learning

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A Surrogate-Assisted Evolutionary Algorithm for Expensive Dynamic Multimodal Optimzation 13

A Surrogate-Assisted Evolutionary Algorithm for Expensive Dy...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wu, Xunfeng Liu, Songbai Ji, Junkai Ma, Lijia Leung, Victor C. M. Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350308365

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases. © 2024 IEEE.

关键词： Digital elevation model

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Dates Fruit Recognition: From Classical Fusion to Deep Learning

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computer Systems Science & engineering 2022年第1期40卷 151-166页

作者： Khaled Marji Alresheedi Suliman Aladhadh Rehan Ullah Khan Ali Mustafa Qamar Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

There are over 200 different varieties of dates fruit in the ***,every single type has some very specific features that differ from the *** recent years,sorting,separating,and arranging in automated industries,in fruits businesses,and more specifically in dates businesses have inspired many research *** this regard,this paper focuses on the detection and recognition of dates using computer vision and machine *** experimental setup is based on the classical machine learning approach and the deep learning approach for nine classes of dates *** machine learning includes the Bayesian network,Support Vector Machine,Random Forest,and Multi-Layer Perceptron(MLP),while the Convolutional Neural Network is used for the deep learning *** feature set includes Color Layout features,Fuzzy Color and Texture Histogram,Gabor filtering,and the Pyramid Histogram of the Oriented *** fusion of various features is also extensively explored in this *** MLP achieves the highest detection performance with an F-measure of ***,deep learning shows better accuracy than the classical machine learning *** fact,deep learning got 2%more accurate results as compared to the MLP and the Random *** also show that classical machine learning could give increased classification performance which could get close to that provided by deep learning through the use of optimized tuning and a good feature set.

关键词： features Deep separating

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Energy-Efficient and Latency-Aware Task Offloading for Industrial Cloud-Edge Systems with Heterogeneous CPUs and GPUs

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IEEE Internet of Things Journal 2025年第13期12卷 25757-25772页

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Buyya, Rajkumar Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States Lab School of Computing and Information Systems MelbourneVIC3010 Australia

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption. © 2025 IEEE.

关键词： Mobile edge computing

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