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Real-time road occupancy and traffic measurements using unmanned aerial vehicle and fundamental traffic flow diagrams

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Personal and Ubiquitous Computing 2023年第5期27卷 1669-1680页

作者： Ahmed, Afzal Outay, Fatima Farooq, Muhammad Umer Saeed, Sehrish Adnan, Muhammad Ismail, Muhammad Ali Qadir, Adnan Department of Urban & Infrastructure Engineering NED University of Engineering & Technology Karachi Pakistan Zayed University Dubai United Arab Emirates Department of Computer Science and Information Technology NED University of Engineering & Technology Karachi Pakistan Exascale Open Data Analytics Lab NED University of Engineering & Technology Karachi Pakistan Hasselt University Diepenbeek Belgium

The technologies and sensors developed for standard traffic streams often fail to accurately measure the heterogeneous traffic with no lane discipline. This research proposes an efficient framework to measure traffic flow parameters in real-time using an unmanned aerial vehicle (UAV). The proposed framework in this research estimates road area occupancy from the images extracted from the video recorded using a UAV by applying the image segmentation technique. The road area occupancy is converted into traffic density using an occupancy-density model developed for the local heterogeneous traffic. A speed-density fundamental diagram is developed, which estimates the average speed corresponding to the measured traffic density. The fundamental relation between flow rate, average speed, and density is applied to compute traffic flow rate using the estimated average speed and density. The proposed framework is computationally less demanding in comparison with other computer vision and artificial intelligence–based techniques. The proposed framework accurately measured road area occupancy with an average accuracy of 97.6%. Traffic density was measured with an average accuracy of 88.8%. Similarly, the average speed and flow rate were measured with an average accuracy of 85.4% and 84.2%, respectively. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： Image segmentation

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Dynamic Locomotion of Quadruped Robot in Sloping Terrain 6

Dynamic Locomotion of Quadruped Robot in Sloping Terrain

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6th International Symposium on Autonomous Systems, ISAS 2023

作者： Chen, Donglin Cong, Dehong Song, Xiangman Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Northeastern University Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China

ISBN: (纸本)9798350316155

In order to improve the locomotion capability of quadruped robot in the sloping terrain and enhance whose adaptability to the environment, this paper proposes a method to realize dynamic locomotion of quadruped robot in sloping terrain completely based on model predictive control (MPC) and proprioception. According to the foot contact signal and the robot body posture information measured by inertial measurement unit (IMU), quadruped robot actively changes whose body posture to adapt to different sloping terrains. This paper builds quadruped robot virtual prototype and test terrain in Webots simulation environment. The experimental results show that with this method, the roll angle fluctuation range of the quadruped robot is less than 0.05rad and the yaw angle fluctuation range is not more than 0.003rad. At the same time, when the angle of slope changes, the terrain adaptation time of the quadruped robot is less than 1.8s. The above results show that quadruped robot has excellent movement ability in sloping terrain and environmental adaptability under the condition of ensuring stability with the method proposed in this paper. © 2023 IEEE.

关键词： Model predictive control

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Image Fusion in Remote Sensing: An Overview and Meta Analysis

arXiv

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arXiv 2024年

作者： Albanwan, Hessah Qin, Rongjun Tang, Yang The Geospatial Data Analytics Lab Department of Civil Environment and Geodetic Engineering The Ohio State University Columbus United States The Geospatial Data Analytics Lab Department of Civil Environment and Geodetic Engineering The Department of Electrical and Computer Engineering Translational Data Analytics Institute The Ohio state University Columbus United States

Image fusion in Remote Sensing (RS) has been a consistent demand due to its ability to turn raw images of different resolutions, sources, and modalities into accurate, complete, and spatio-temporally coherent images. It greatly facilitates downstream applications such as pan-sharpening, change detection, land-cover classification, etc. Yet, image fusion solutions are highly disparate to various remote sensing problems and thus are often narrowly defined in existing reviews as topical applications, such as pan-sharpening, and spatial-temporal image fusion. Considering that image fusion can be theoretically applied to any gridded data through pixel-level operations, in this paper, we expanded its scope by comprehensively surveying relevant works with a simple taxonomy: 1) many-to-one image fusion;2) many-to-many image fusion. This simple taxonomy defines image fusion as a mapping problem that turns either a single or a set of images into another single or set of images, depending on the desired coherence, e.g., spectral, spatial/resolution coherence, etc. We show that this simple taxonomy, despite the significant modality difference it covers, can be presented by a conceptually easy framework. In addition, we provide a meta-analysis to review the major papers studying the various types of image fusion and their applications over the years (from the 1980s to date), covering 5,926 peer-reviewed papers. Finally, we discuss the main benefits and emerging challenges to provide open research directions and potential future works. Copyright © 2024, The Authors. All rights reserved.

关键词： Image fusion

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Melanoma Detection with Transfer Learning 3

Melanoma Detection with Transfer Learning

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Vishnu, Pragadeshwar Krishvadana, S. Rani, C. Khoodeeram, Rajeev Bouridane, Ahmed Rajesh Kumar, M. Vellore Institute of Technology School of Electronics Engineering Vellore India Université Des Mascareignes Department of Emerging Technologies Mauritius University of Sharjah Centre for Data Analytics Sharjah United Arab Emirates

ISBN: (纸本)9798350372120

The abnormal growth of skin cells that are exposed to the sun is identified as skin cancer. Even though skin cancer is curable, late diagnosis and improper treatment can lead to severe effects and death. Melanoma has been identified as the deadliest type of skin cancer and it is responsible for 85 \% of skin cancer deaths. Even though research on melanoma identification is prominent, still it has not been perfected. Automated Melanoma recognition is a challenging task due to various artifacts such as low-contrast dermoscopic images, and large and similar varieties of other skin cancers. In this study, we take on this challenge using neural networks. We implement an approach in which two trained neural networks are combined to give a higher accuracy output. With the lack of training data for classification, we employ data augmentation techniques. The proposed Model makes use of two types of learning approaches by combining their weights and predicting the output. The combined or ensemble model categorizes the output into 6 different categories with an accuracy of up to 89 \%. The approach is simple to implement and surpasses other avant-garde techniques in terms of accuracy. © 2024 IEEE.

关键词： Diseases

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Enhancing Visual Fidelity in Unpaired Night-to-Day Image Translation through a Perceptual Quality Focused Training Objective 7

Enhancing Visual Fidelity in Unpaired Night-to-Day Image Tra...

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7th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI 2023

作者： Lakmal, H.K.I.S. Dissanayake, Maheshi B. Aramvith, Supavadee University of Peradeniya Faculty of Engineering Dept. of Electrical and Electronic Engineering Peradeniya Sri Lanka Chulalongkorn University Multimedia Data Analytics and Processing Research Unit Dept. of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350319262

Nighttime driving poses visibility challenges, but image translation methods can help by transforming night images into day-like scenes. The Cycle-GAN is a versatile unpaired image translation model which can easily be adept to night-to-day image translation tasks. However, it generates unnatural and unrealistic outcomes in these cases. This study is focused on addressing this with a novel training strategy for the Cycle-GAN, employing a tailored training objective that incorporates perceptual quality optimization. This training objective aims to boost the naturalness and perceptual quality of the generated images by preserving high-level image features. The optimization process involves minimizing Euclidean distances between synthesized and target image feature maps, which are derived from the pre-trained VGG19 network. Experimental findings attest to the effectiveness of this method, revealing noteworthy improvements of 8% in Inception Score, 2% in NIQE Score, and 13% in BRISQUE Score. © 2023 IEEE.

关键词： Image enhancement

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RespoBot: Chatbot used for the prediction of diseases using Machine Learning and Deep Learning with respect to Covid-19 2

RespoBot: Chatbot used for the prediction of diseases using ...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Sreedevi, B. Rosewelt, L. Antony Ramesh, Neela Sri Sai Ram Institute of Technology Computer Science and Engineering Chennai India Sri Sai Ram Institute of Technology Big Data Analytics Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

Resembling man's evolution over time the life threatening diseases have also evolved with him a disease is basically a disorder in structure or functioning of an organism's body. There are a several kinds of diseases but the most dangerous ones are the infectious diseases. Infectious diseases occur when a foreign body such as bacteria, fungi, and virus enter the body and attack the immune system. That being the smaller problem of infectious diseases as the main problem it's a communicable disease that spreads when a uninfected person comes in contact with an infected person they are highly likely to contract the same. The prediction is performed using Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Stochastic Gradient Descent SGD), Gradient Boosting (GD), Decision Trees (DT), Naive Bayes Classifier (NBC) and Voting Classifier (VC) ensemble technique. After the prediction a comparative analysis in terms of accuracy is performed. A Chatbot using Natural Learning Processing on a Neural Network is employed. The user is asked to upload a Chest X-ray if in possession as a second step confirmation for Tuberculosis, Pneumonia, Covid-19 and Healthy lung. In terms of highest accuracy for Covid-19 the GBC, VC, 56 tops the charts and for other diseases also GBC, VC, ANN top the charts. © 2023 IEEE.

关键词： Diseases

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Predictive Insight of Global Warming-A Sustainable Control System 7

Predictive Insight of Global Warming-A Sustainable Control S...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Roy, Soumit Mahmudi, Hafizullah Das, Rik Dewan, Jaya H. Thepade, Sudeep D. Analytics Presales and Solution PracticeLead Jade Global Chicago United States Data Management and Analytics Kamyab Its Kabul Afghanistan Association for Computing Machinery Ranchi India Pimpri Chinchwad College of Engineering Department of Information Technology Pune India Pimpri Chinchwad College of Engineering Computer Engineering Department Pune India

ISBN: (纸本)9798350304268

Global warming is one of the major issues for decades. Implementation of integrated solution to get sustainable control system has significant challenges. Acceptance of the key precautions and maintenance of sustainability became a bottleneck. The consequence of human social activities increases the consumption of fossil fuels as power resources which indirectly increased the concentration of greenhouse gases and water vapor, in the environment. This causes the earth's average surface temperature to increase. Water vapor is responsible for two-thirds of this issue;however, C02 is proven to be the key factor of global warming. The global temperature could have increased to 3.8 o C in case C02 concentration was doubled. The trend and association between rising temperatures and C02 emissions are shown in this work, along with an explanation of the many methods behind ideal mitigation. One of the goals of this research is to identify the issue area and integrate the climatic data. This will assist in obtaining a dependable control system capable of addressing global warming concerns. This paper outlines how data engineering and analytics may be used to regulate global warming and provides an architecture for the Green Warming Model (GWD). Therefore, the primary contribution of this study effort is environment preservation using data engineering and machine learning (ML). © 2023 IEEE.

关键词： data engineering Global warming Green House IPA SARIMAX

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Advances in intelligent computing approaches for solving problems related to photonic crystal fibers: Advances in Intelligent..: D. Yang et al.

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Optical and Quantum Electronics 2025年第1期57卷 1-31页

作者： Yang, Dan Zhao, Yuyu Liu, Hong Li, Yijin Tang, Chang Li, Shijun Lingye, Zhenglin College of Information Science & Engineering Northeastern University Shenyang110819 Thailand National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 Thailand

Photonic crystal fiber (PCF) has shown a promising application in various fields of modern optics due to its excellent transmission characteristics. However, conventional numerical solution methods have limitations such as high computational effort and low efficiency in designing PCF structures and characterizing pulse propagation in PCFs. With the advancement of artificial intelligence technology, various types of intelligent computing approaches, especially intelligent algorithm and machine learning algorithm (IML), provide new perspectives to solve the above difficulties. Therefore, an increasing number of researchers are committed to using IML to break through the limitations of conventional methods and accordingly open up a new interdisciplinary research direction. This paper begins with a brief overview of common optical properties and typical algorithms. Subsequently, it reviews specific areas where PCF and IML are combined, including the prediction and optimization of optical properties, the design of polarization devices, fiber lasers and amplifiers, as well as the control and prediction of Supercontinuum generation. Finally, this paper offers insights into the future developments and challenges in this emerging interdisciplinary field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Supercontinuum generation

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New Results on Interval Type-3 Fuzzy Control for Nonlinear Time-Delay Systems Using Convex Relaxation Technique

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3630-3641页

作者： Harikaran, B. Harshavarthini, S. Lee, Sangmoon Sakthivel, R. Sathiyaraj, T. School of Advanced Sciences Department of Mathematics Chennai600127 India Kyungpook National University School of Electronic and Electrical Engineering Daegu41566 Korea Republic of Bharathiar University Department of Applied Mathematics Coimbatore641046 India Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur56000 Malaysia

In this article, the interval type-3 fuzzy-based state feedback control is proposed for the stabilization problem of interval type-3 fuzzy systems (IT3FSs) subject to time-varying delay. Specifically, to improve the model accuracy and control capabilities, we proposed a nonparallel distributed compensation-based controller approach, be precise, the system and controller adopts the distinct membership functions. Moreover, the stability analysis of IT3FSs in the state space form is studied newly in this article. Besides the primary membership function that exists for interval type-2 fuzzy systems (IT2FSs), the IT3FSs possess the secondary membership function to handle the overall uncertainty which enacts the superiority of the proposed work. Notably, the adequate stability criteria are derived in the form of linear matrix inequality (LMI) using the Lyapunov stability theorem. Additionally, the convex relaxation technique is used to strengthen the design flexibility and achieve the faster convergence rate, which converts an optimization problem with a vector variable to a convex program with a matrix variable, via a lifting technique. A noteworthy aspect is that the application of the convex relaxation technique in the context of IT3FSs is proposed for the first time in literature. Finally, the comparative results between IT2FSs and IT3FSs are shown via two illustrative examples, including the inverted-pendulum model, to underscore the efficacy and practical implementations of the developed control methodology. © 2013 IEEE.

关键词： Membership functions

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A Review on AI Algorithms for Energy Management in E-Mobility Services 7

A Review on AI Algorithms for Energy Management in E-Mobilit...

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7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023

作者： Yan, Sen Shah, Maqsood Hussain Li, Ji O'Connor, Noel Liu, Mingming The School of Electronic Engineering SFI Insight Centre for Data Analytics Dublin City University Ireland The Nanjing University of Aeronautics and Astronautics China The Department of Mechanical Engineering University of Birmingham United Kingdom

ISBN: (纸本)9798350340488

E-mobility, or electric mobility, has emerged as a pivotal solution to address pressing environmental and sustainability concerns in the transportation sector. The depletion of fossil fuels, escalating greenhouse gas emissions, and the imperative to combat climate change underscore the significance of transitioning to electric vehicles (EVs). This paper seeks to explore the potential of artificial intelligence (AI) in addressing various challenges related to effective energy management in e-mobility systems (EMS). These challenges encompass critical factors such as range anxiety, charge rate optimization, and the longevity of energy storage in EVs. By analyzing existing literature, we delve into the role that AI can play in tackling these challenges and enabling efficient energy management in EMS. Our objectives are twofold: to provide an overview of the current state-of-the-art in this research domain and propose effective avenues for future investigations. Through this analysis, we aim to contribute to the advancement of sustainable and efficient e-mobility solutions, shaping a greener and more sustainable future for transportation. © 2023 IEEE.

关键词： Artificial Intelligent Electric Mobility Energy Consumption Estimation Energy Management Machine Learning

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