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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Vidyullatha, Pellakuri Unhelkar, Bhuvan Chakrabarti, Prasun Ghantasala, G.S. Pradeep Shaik, Amjan Pyla, Meghana Department of Information Systems and Decision Sciences Muma College of Business University of South Florida South Florida United States Department of CSE Koneru Lakshmaiah Educationn Foundation Vaddeswaram AP Guntur India Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering St.Peters Engineering College TS Maisammaguda India

ISBN: (纸本)9798350383867

Generative Adversarial Networks (GANs) have the ability to produce realistic images from random noise vectors and has attracted a lot of attention in recent years. The textual input is discontinuous and requires efficient semantic information capture, extending GANs to create images from text descriptions has special obstacles. In this research, a unique GAN-based text-to-image creation method that makes use of conditional GAN architectures and text embeddings. The generator neural network in our suggested model converts text embeddings into appropriate image representations, whereas the discriminator network separates real images from those produced by the generator. This paper shows the strategies like multi-modal fusion mechanisms and attention mechanisms in the network architecture to help textual descriptions and visual information match. This method uses the benchmark datasets and show that it can produce realistic and varied visuals that correspond with textual descriptions. Both qualitative and quantitative assessments demonstrate our model's capacity to grasp minute details and subtle semantic nuances found in the input text. In addition, we perform user studies to evaluate the fidelity and perceived quality of the generated images relative to ground truth images. Based on our experimental findings, it appears that the suggested framework for text-to-pixel image production provides both textual relevance and image quality. This paper illuminates how our methodology may be utilized in different settings, including virtual universes, PC supported plan, and the production of imaginative substance. We likewise accentuate future bearings and potential for profound learning and normal language handling procedures to further develop text- to-picture creating models' abilities. © 2024 IEEE.

关键词： Generative adversarial networks

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Supremal and Robust Control against Actuator Jamming Attacks in Cyber-Physical systems

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第9期9卷 1-13页

作者： Duo, Wenli Wang, Shouguang Zhou, MengChu You, Dan Wisniewski, Remigiusz Bazydlo, Grzegorz Albeshri, Aiiad Institute of Systems Engineering Macau University of Science and Technology Macau China School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Institute of Control and Computation Engineering University of Zielona Góra Zielona Góra Poland Department of Computer Science King Abdulaziz University Jeddah Saudi Arabia

Cyber-physical systems, such as unmanned aerial vehicles and connected and autonomous vehicles, are vulnerable to cyber attacks, which can cause significant damage to society. This paper examines the attack issue in cyber-physical systems within the framework of discrete event systems. Specifically, we consider a scenario where a malicious intruder injects a jamming signal into an actuator channel. It disrupts the transmission of control commands and prevents an actuator from receiving them. This is termed an actuator jamming attack. In the paper, we first analyze the closed-loop system behavior under such an attack. An attack structure is constructed to illustrate how an intruder exploits a jamming attack to drive a system into unsafe states. Then, we study the supervisory control problem for a system exposed to such an attack. The problem is reduced to a basic supervisory control one in discrete event systems by introducing the concept of dynamically controllable language. A solution to this problem is explored, where we establish an existence condition for a supremal and robust supervisor that is capable of defending against actuator jamming attacks, and design an algorithm to derive it. Finally, the effectiveness of our method is illustrated by an intelligent automated guided vehicle system. IEEE

关键词： Discrete event simulation

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Indirect Vector Control of Linear Induction Motors Using Space Vector Pulse Width Modulation

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computers, Materials & Continua 2023年第3期74卷 6263-6287页

作者： Arjmand Khaliq Syed Abdul Rahman Kashif Fahad Ahmad Muhammad Anwar Qaisar Shaheen Rizwan Akhtar Muhammad Arif Shah Abdelzahir Abdelmaboud Electrical Engineering Department University of Engineering and TechnologyLahore54700Pakistan Department of Basic Sciences Deanship of Common First YearJouf UniversitySakakaAljouf72341Saudi Arabia Department of Information Science Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan Sub CampusPakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia

Vector control schemes have recently been used to drive linear induction motors(LIM)in high-performance *** trend promotes the development of precise and efficient control schemes for individual *** research aims to present a novel framework for speed and thrust force control of LIM using space vector pulse width modulation(SVPWM)*** framework under consideration is developed in four *** begin,MATLAB Simulink was used to develop a detailed mathematical and electromechanical *** research presents a modified SVPWM inverter control *** tuning the proportional-integral(PI)controller with a transfer function,optimized values for the PI controller are *** the subsystems mentioned above are integrated to create a robust simulation of the LIM’s precise speed and thrust force control *** reference speed values were chosen to evaluate the performance of the respective system,and the developed system’s response was verified using various data *** the low-speed range,a reference value of 10m/s is used,while a reference value of 100 m/s is used for the high-speed *** speed output response indicates that themotor reached reference speed in amatter of seconds,as the delay time is between 8 and 10 *** maximum amplitude of thrust achieved is less than 400N,demonstrating the controller’s capability to control a high-speed LIM with minimal thrust *** to the controlled speed range,the developed system is highly recommended for low-speed and high-speed and heavy-duty traction applications.

关键词： Space vector pulse width modulation linear induction motor proportional-integral controller indirect vector control electromechanical dynamic modeling

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Research on Polarization Theory Based on Target Polarization Projection 2

Research on Polarization Theory Based on Target Polarization...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： He, Yaomin Yang, Jian Yang, Yimin Yin, Junjun Liu, Chao Academy of Military Sciences Institute of Systems Engineering Beijing China Tsinghua University Department of Electronic Engineering Beijing China University of Science and Technology School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798331515669

Aiming at the information processing in classical polarization theory, such as polarization decomposition, optimal polarization, polarization filtering, polarization detection, and polarization classification, et al, they are of great significance which supported a large number of polarization applications. Although the theory is relatively rich, there are also some deficiencies, i.e., overlapping theories and inconsistent concepts, which have inconvenient effects on the systematic research of polarization theory. In this regard, this paper proposes the concept and mathematical model of "target polarization projection", in order to form two polarization basic theories with target polarization decomposition. It can achieve a series of polarization information processing such as polarization filtering, polarization detection, and polarization classification, thus providing a clear polarization theory framework and laying a theoretical foundation for the application of polarimetric SAR. © 2024 IEEE.

关键词： Wiener filtering

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Teamwork Optimization with Deep Learning Based Fall Detection for IoT-Enabled Smart Healthcare System

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computers, Materials & Continua 2023年第4期75卷 1353-1369页

作者： Sarah B.Basahel Saleh Bajaba Mohammad Yamin Sachi Nandan Mohanty E.Laxmi Lydia Department of Management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddahSaudi Arabia Department of business Administration Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia School of Computer Science&Engineering(SCOPE) VIT-AP UniversityAmaravatiAndhra PradeshIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajam 532127India

The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart *** services could be enhanced by incorporating key techniques like AI and *** convergence of AI and IoT provides distinct opportunities in the medical *** is regarded as a primary cause of death or post-traumatic complication for the ageing ***,earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary ***,the emergence of IoT,AI,smartphones,wearables,and so on making it possible to design fall detection(FD)systems for smart home *** article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled Smart Healthcare systems(TWODLFDSHS).The TWODL-FDSHS technique’s goal is to detect fall events for a smart healthcare ***,the presented TWODL-FDSHS technique exploits IoT devices for the data collection ***,the TWODLFDSHS technique applies the TWO with Capsule Network(CapsNet)model for feature *** last,a deep random vector functional link network(DRVFLN)with an Adam optimizer is exploited for fall event detection.A wide range of simulations took place to exhibit the enhanced performance of the presentedTWODL-FDSHS *** experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30%on the URFD dataset.

关键词： Internet of things smart healthcare deep learning team work optimizer capsnet fall detection

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Prediction of room temperature in Trombe solar wall systems using machine learning algorithms

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Energy Storage and Saving 2024年第4期3卷 243-249页

作者： Seyed Hossein Hashemi Zahra Besharati Seyed Abdolrasoul Hashemi Seyed Ali Hashemi Aziz Babapoor Petroleum Systems Engineering Faculty of Engineering and Applied ScienceUniversity of ReginaReginaSK S4S 0A2Canada Department of Chemical Engineering University of Mohaghegh ArdabiliArdabil 13131-56199Iran Department of Chemistry School of ScienceAlzahra UniversityTehran ***Iran Computer Engineering Gachsaran Oil and Gas Exploitation CompanyGachsaran ***Iran Architectural Engineering Shiraz Islamic Azad UniversityShiraz ***Iran

A Trombe wall-heating system is used to absorb solar energy to heat *** parameters affect the system performance for optimal *** study evaluated the performance of four machine learning algorithms—linear regression,k-nearest neighbors,random forest,and decision tree—for predicting the room temperature in a Trombe wall *** accuracy of the algorithms was assessed using R^(2)and root mean squared error(RMSE)*** results demonstrated that the k-nearest neighbors and random forest algorithms exhibited superior performance,with R^(2)and RMSE values of 1 and *** contrast,linear regression and decision tree showed weaker *** findings highlight the potential of advanced machine learning algorithms for accurate room temperature prediction in Trombe wall systems,enabling informed design decisions to enhance energy efficiency.

关键词： Trombe wall Solar energy Thermal storage wall Machine learning algorithms

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Smart-City-based Data Fusion Algorithm for Internet of Things

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computers, Materials & Continua 2022年第11期73卷 2407-2421页

作者： Jawad Khan Muhammad Amir Khan N.Z.Jhanjhi Mamoona Humayun Abdullah Alourani Department of Computer Science IIC University of TechnologyPhnom PenhCambodia Department of Computer Science COMSATS University IslamabadAbbottabad Campus22060Pakistan School of Computer Science and Engineering SCETaylor’s UniversitySubang JayaSelangorMalaysia Department of Information Systems College of Computer and Information SciencesJouf Universityal-JoufKSA Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Increasingly,Wireless Sensor Networks(WSNs)are contributing enormous amounts of *** the recent deployments of wireless sensor networks in Smart City infrastructures,significant volumes of data have been produced every day in several domains ranging from the environment to the healthcare system to *** wireless sensor nodes,a Smart City environment may now be shown for the benefit of *** Smart City delivers intelligent infrastructure and a stimulating environment to citizens of the Smart Society,including the elderly and ***,Quality of Service(QoS)and poor data performance are common problems in WSNs,caused by the data fusion method,where a small amount of bad data can significantly impact the total fusion *** our proposed research,a WSN multisensor data fusion technique employing fuzzy logic for event *** the new proposed Algorithm,sensor nodes will collect less repeated data,and redundant data will be used to increase the data’s overall *** network’s fusion delay problem is investigated,and a minimum fusion delay approach is provided based on the nodes’fusion waiting *** proposed algorithm performs well in fusion,according to the results of the *** a result of these discoveries,It is concluded that the algorithm describe here is effective and dependable instrument with a wide range of applications.

关键词： Internet of things data fusion wireless sensor network fuzzy inference system

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Enhance Planning with Physics-informed Safety Controller for End-to-end Autonomous Driving

Enhance Planning with Physics-informed Safety Controller for...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Zhou, Hang Liu, Haichao Lu, Hongliang Ma, Jun Ji, Yiding Robotics and Autonomous Systems Thrust Systems Hub Guangzhou China School of Engineering The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering SAR Hong Kong Hong Kong Intelligent Transportation Thrust Systems Hub Guangzhou China

ISBN: (纸本)9781665481090

Recent years have seen a growing research interest in applications of Deep Neural Networks (DNN) on autonomous vehicle technology. The trend started with perception and prediction a few years ago and it is gradually being applied to motion planning tasks. Despite the performance of networks improve over time, DNN planners inherit the natural drawbacks of Deep Learning. Learning-based planners have limitations in achieving perfect accuracy on the training dataset and network performance can be affected by out-of-distribution problem. In this paper, we propose FusionAssurance, a novel trajectory-based end-to-end driving fusion framework which combines physics-informed control for safety assurance. By incorporating Potential Field into Model Predictive Control, FusionAssurance is capable of navigating through scenarios that are not included in the training dataset and scenarios where neural network fail to generalize. The effectiveness of the approach is demonstrated by extensive experiments under various scenarios on the CARLA benchmark. © 2024 IEEE.

关键词： Deep neural networks

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Improved Ingredients-based Recipe Recommendation Software using Machine Learning 11

Improved Ingredients-based Recipe Recommendation Software us...

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11th IEEE International Conference on Intelligent Computing and Information systems, ICICIS 2023

作者： Ashraf, Mariam Tarek, Khadija Fathy, Naglaa Mahmoud, Mariam Emad, Rana Khaled, Khadija Amr, Noran Ain Shams University Faculty of Computer and Information Science Software Engineering Department Cairo Egypt Ain Shams University Faculty of Computer and Information Science Information Systems Department Cairo Egypt

ISBN: (纸本)9798350322101

In this paper we propose an improved recipe recommendation system that employs image recognition of food ingredients. The system is currently a mobile application that performs image recognition on uploaded or camera-captured images and recommends recipes containing the recognized ingredients. We used the ResNet-V2 architecture to build a convolutional neural network model for image recognition, which was able to identify 33 different food ingredients with an accuracy rate of 89%. The recommendation system uses the identified ingredient labels, as well as user preferences and restrictions, to display a list of recipes containing the identified ingredients. This feature allows users to discover new and exciting recipes based on the ingredients they currently have at home, without having to worry about dietary restrictions or other preferences. Overall, our system provides a convenient and personalized way for users to discover and prepare delicious meals based on their unique needs and preferences. © 2023 IEEE.

关键词： Image recognition

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Deep Learning-based Cluster Analysis for Healthcare Data Classification

Deep Learning-based Cluster Analysis for Healthcare Data Cla...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Gayathri, M. Kavitha, V. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai Kattankulathur India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India

ISBN: (纸本)9798350348910

The use of cutting-edge technology in the medical field results in the production of massive volumes of data on a daily basis. Various categories of information are applied in the domain of healthcare, including clinic information, medical record data, and genetic information. In addition, real-time monitoring in the medical industry produces enormous volumes of data, and properly interpreting such enormous amounts of data is a significant problem. The evaluation of medical information becomes more required so that suitable drugs may be supplied and issues can be avoided by taking appropriate measures depending on the history of the patient. Automation makes data analysis more effective, but performance might deteriorate when there are problems with data integrity, diversity, or consistency. Automation makes data analysis more effective. In order to handle the management of massive amounts of data, many models that are powered by neural networks have been developed;still researchers are currently attempting to develop a superior model that is more accurate. Hence, fuzzy c denotes the process of clustering, whereas generative adversarial networks are used in this study to clusters and classify medical data, aiming to achieve the maximum degree of efficiency in classification. The experiment will use both the benchmark lung cancer sample and the arrhythmias sample. The proposed model achieves a maximum accuracy of 97.3% for dataset 1 and 98.2% for dataset 2, outperforming other methods such as support vector machine, decision tree, and random forest algorithms. © 2023 IEEE.

关键词： Classification (of information)

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