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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Dias, Joaquim N. Melo, Gabriel A. Department of Aerospace Engineering Auburn University AuburnAL United States Computer Science Division Instituto Tecnológico de Aeronáutica SP São José dos Campos Brazil

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Gas turbine modeling exhibits nonlinearities in both steady-state and transient operations, with additional complexity introduced by varying ambient conditions, making it difficult to obtain accurate gas turbine models. In the present work, we examine classical system identification methods and deep learning approaches for modeling of an advanced geared turbofan engine using high-fidelity simulation data across multiple flight conditions. First, we implement a Hammerstein model through conventional nonlinear system identification, deriving static gains from quasi-steady states and estimating dynamic parameters from throttle inputs. The second approach was based on black-box modeling using neural networks. A number of different deep learning architectures were tested, namely feed-forward layers, long-short term memory architecture, and neural differential architecture. Model performance is quantified through prediction accuracy of five engine parameters (fuel flow, thrust, turbine temperature, shaft rotational speeds N1 and N2) using only altitude, Mach number and throttle as inputs across distinct flight conditions. The results demonstrate that the Neural Differential architecture significantly outperformed both classical and other neural approaches, reducing the standard deviations of residuals by 82% up to 92%, when compared with the conventional system identification method, across all engine parameters. This work advances the understanding of classical and deep learning methodologies for gas turbine modeling in flight simulation applications. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Gas turbines

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Wireless Semantic Communication Based on Probability Distribution: An Initial Work

Wireless Semantic Communication Based on Probability Distrib...

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IEEE Conference on Wireless Communications and Networking

作者： Qingxiang Luo Yashuang Guo Aoran Zheng Zhitong Ni F. Richard Yu Victor C.M. Leung School of Computer Science and Technology Beijing Jiaotong University Big Data Technologies Centre University of Technology Sydney Department of Systems and Computer Engineering Carleton University Department of Electrical and Computer Engineering The University of British Columbia

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In the paper, we consider the general semantic transmission in wireless networks based on probability distribution. Firstly, we extract a multidimensional semantic probability distribution function, independent of any a specific wireless channel model, by using the variational inference technique. Secondly, we propose a new semantic similarity metric for measuring the difference between the received semantics and the expected semantics based on Kullback-Leibler divergence. Then, we formulate the semantic transmission problem as an optimization problem of transmission symbol adjustment with the aim to maximize the semantic similarity. Finally, we develop an optimal semantic transformation and transmission (STT) algorithm to obtain the optimal transmission symbol adjustment decision. This decision makes the closed-form expression of semantic transmission symbol available, which can realize lossless semantic transmission with energy constraint. Simulation results verify the effectiveness and robustness of the proposed STT algorithm.

关键词： Measurement Wireless networks Simulation Semantics Symbols Semantic communication Propagation losses Probability distribution Robustness Optimization

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Effect of inclined magnetic field, non-uniform heat source on hybrid EG-MoS2-SiO2 radiative nanofluid flow with viscous and Joule dissipation over convectively heated elongating surface

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Multiscale and Multidisciplinary Modeling, Experiments and Design 2025年第6期8卷 1-22页

作者： Bompally, Radha Krishna Rao Gurijala, Rajitha Reddy, R. Archana Thumma, Thirupathi Department of Mathematics School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Sumathi Reddy Institute of Technology for Women Warangal506371 India Department of Computer Science and Engineering Vardhaman College of Engineering Kacharam Shamshabad Telangana Hyderabad501218 India

The interaction of non-uniform heat sources and viscous dissipation effects introduces intriguing complexities in studying hybrid nanofluid dynamics over an elongating surface, a topic of significant interest for thermal engineering applications. Such phenomena hold the potential for advancing heat transfer efficiency in cutting-edge technologies like cooling systems, energy storage, and material processing, where precise thermal regulation is paramount. Thus, the current research investigates the impact of an inclined magnetic field and a non-uniform heat source on the flow dynamics of radiative EG-MoS2-SiO2, considering the impacts of Joule heating and viscous dissipation over a convectively heated elongating surface. The governing equations, representing the intricate dynamics of hybrid nanofluid stream, are formulated and solved by employing the Runge–Kutta method integrated with the Shooting technique to address missing initial conditions. Numerical outcomes are rigorously validated against established literature, showcasing remarkable alignment. Key parameters such as nanoparticle concentration, magnetic field inclination angle, non-uniform heat source intensity, Eckert number, thermal Biot number, suction velocity, and linear radiative heat flux are analyzed to elucidate their effects on flow characteristics near the stretching sheet. Graphical representations of the friction factor and local Nusselt number are provided to illustrate their dependence on these parameters. The findings reveal that magnetic field inclination and uniform suction significantly influence thermal regulation and fluid momentum. A nanoparticle concentration of 40% enhances the thermal boundary layer thickness, driven by increased thermal Biot number, viscous dissipation, and radiative heat flux near the sheet surface. The comparative analysis underscores that hybrid nanofluids display superior momentum and thermal boundary layer behavior compared to traditional nanofluids, highlightin

关键词： Runge Kutta methods

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Effective Edge-Cloud Interplay for NFV-Based Optical Metro-Access Networks Supporting IoT Services

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China Communications 2025年第4期22卷 117-128页

作者： Jin Feiming Zhang Yukun Li Hanxue Amr Tolba Zhang Tiantian Science and Technology Department Chongqing University of Posts and TelecommunicationsChongqing 400065China School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Department of Computer Science Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia

In optical metro-access networks,Access Points(APs)and Data Centers(DCs)are located on the fiber *** the cloud-centric solution,a large number of Internet of Things(IoT)data pose an enormous burden on DCs,so the Virtual Machines(VMs)cannot be successfully launched due to the server *** addition,transferring the data from the AP to the remote DC may cause an undesirable delivery *** this end,we propose a promising solution considering the interplay between the cloud DC and edge *** specifically,bringing the partial capability of computing in APs close to things can reduce the pressure of DCs while guaranteeing the expected Quality of Service(QoS).In this work,when the cloud DC resource becomes limited,especially for delay sensitive but not computing-dependent IoT applications,we degrade their VMs and migrate them to edge APs instead of the remote *** avoid excessive VM degradation and computing offloading,we derive appropriate VM degradation coefficients based on classic microeconomic *** results demonstrate that our algorithms improve the service providers'utility with the ratio from 34%to 89%over traditional cloud-centric solutions.

关键词： computing offloading edge-cloud interplay network function virtualization optical metroaccess network service degradability

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A Robust Protocol Design to Perform Data Communication over Wireless Sensor Networks with Enhanced Privacy Preserving Principles

A Robust Protocol Design to Perform Data Communication over ...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Bandela Sathyavani Rakesh Kumar Yadav John Shiny J Revathi. R Ashish Kumar Bulakh Shilpa Jain Department of EEE School of Engineering SR University Warangal Telangana India Amity School of Engineering and Technology Amity University Patna Bihar Velammal College Of Engineering And Technology Madurai Department of Computer Science-Information Technology PSGR Krishnammal College for Women Coimbatore Department of Computer science and Engineering CDGI Indore Department of Computer Science and Engineering IES College of Technology Bhopal

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

the growth of wireless sensor networks has transformed all automation and industrial systems. Nonetheless, guaranteeing safe and privacy-preserving connectivity in sensor networks continues to be a difficult task. Current authentication and routing techniques frequently jeopardize privacy, entail significant design intricacy, also vulnerable to malware assaults. To mitigate these restrictions, we present an innovative secure protocol, the Asymmetric Cryptography-based Modified Cuckoo Search Optimization Protocol (MCuSO) for wireless sensor networks. Our proposed technique impacts asymmetric credentials with belief supervision for the privacy preservation of WSN data. This method attains an equilibrium in the middle of security and efficiency. Through comprehensive simulations utilizing the MATLAB platform, we illustrate that MCuSO attains an average network throughput of 94% and a network residual energy of 89%, in comparison to leading other algorithms. Moreover, the protocol efficiently reduces malware attacks. These findings underscore MCuSO as a viable outcome of resource limited WSN devices, providing an advantageous equilibrium of information security, data integrity and data efficacy.

关键词： Wireless sensor networks Data privacy Protocols Heuristic algorithms Authentication Throughput Routing Malware Cryptography Optimization

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Improved Gated Recurrent Unit Based Network Traffic Anomaly Detection

Improved Gated Recurrent Unit Based Network Traffic Anomaly ...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Layth Hussein Jasim P Nagarathna Yada Sunitha V Lekha R. Padmavathy Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering CVR College of Engineering Hyderabad India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering Technology Mamallapuram India Department of Electronics and Communication Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The detection of unpredicted events or rare patterns in traffic data plays an important role in intelligence traffic management, that offers alerts of incidents. To address the problem of less accuracy in industrial network traffic anomaly detection, in this manuscript developed the Improved Gated Recurrent Unit (GRU) for multi-type anomaly detection. The pre-processing included label encoding and non-string numerical process. The Synthetic Minority Over-Sampling Technique with Edited Nearest Neighbors (SMOTE-ENN) technique is utilized for balancing the data by oversampling and under sampling. The improved GRU is assigned for extracting linear, non-linear and temporal features from network traffic data. These features are given to classifier developed based on Softmax for obtaining high performance detection on traffic attacks. The efficiency is evaluated based on UNSW-B15 dataset with the detection accuracy of 0.97 for DoS attacks, 0.96 for Fuzzers attacks and 0.94 for Worms attacks while compared to existing algorithms like conventional GRU for network traffic anomaly detection.

关键词： Integrated circuits Accuracy Telecommunication traffic Traffic control Feature extraction Encoding Real-time systems Noise measurement Grippers Anomaly detection

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Cardiac Vascular Disease Prediction Using Stacked Ensemble Algorithm for Type-2 Diabetes Mellitus 4

Cardiac Vascular Disease Prediction Using Stacked Ensemble A...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Selvarathi, C. Manikandan, T. Madhu Mitha, G. Sharmila, S. Thanasingh, Sengolrajan Pandiaraja, P. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai600 062 India Department of Ece Tamilnadu Thiruchengode637205 India Vivekanandha College of Engineering for Women Department of Electrical and Electronics Engineering Tamilnadu Thiruchengode637205 India Vivekanandha College of Engineering for Women Department of Ece Tamilnadu Thiruchengode637205 India Department of Electrical and Electronics and Engineering Tamilnadu Trichy621 215 India

ISBN: (纸本)9798331523923

Cardiac vascular disease (CVD) is a most serious disease associated with highest humanity and complaint rates in the global population. The risk of developing CVD consciously increases in patients having long-term diabetes difficulties. Consequently, it is necessary to predict the association between CVD and type-2 Diabetes Mellitus (T2DM) for effective decision making to initiate appropriate treatment of T2DM patients. First, the proposed research work to examine the purpose of machine learning (ML) techniques to predict the risk of death rate in CVD patients with T2DM. Then the proposed method is mainly focusing to predict the survival rate of regularity of CVD patient's incidence with T2DM. However, the dataset is unbalanced and handling such unbalanced dataset is one of the major challenges addressed using cost sensitive learning algorithms for reducing the misclassification rate. This proposed method stacked ensemble with cost-sensitive learning that combines the prediction results of the three base classifiers to predict the survival rate of patients with cardiovascular disease and T2DM. The proposed stacked ensemble algorithm uses a 10 fold cross validation method with network search optimization to tune the hyper-parameter of the meta classifier model. The proposed algorithm is used to measure the performance of dataset being evaluated by splitting the data into 80% training and 20% testing. The analysis of outcomes displays that the survival rate of heart failure patients is better predicted using the cost-sensitive stacked ensemble learning technique to achieves precision of 93% and higher accuracy. The unbalanced class and cost sensitive learning may help to address the class imbalance issue. The incorrect weight assignment may lead to overfitting to the minority class while underperforming on the majority class. © 2025 IEEE.

关键词： Patient treatment

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Analyzing the Effectiveness of ML and DL in Combatting Personal Condition Misinformation on Social Media

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第1期52卷 258-267页

作者： Babu, Jaladanki Ravindra Venkataiah, Vaadaala Narayana, Tirumalasetti Lakshmi Rao, Chegudi Ranga Raj, Ch. Deva Sairam, Rallabandi C.H. S. N. P. Anusha, B. Electronics and Communication Engineering Department Prasad V Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Computer Science and Engineering Department CMR College of Engineering & Technology Telangana Hyderabad India Electrical and Electronics Engineering Department Aditya University Andhra Pradesh Surampalem India Electrical and Electronics Engineering Department R. V. R. & J. C. College of Engineering Andhra Pradesh Guntur India Mechanical Engineering Department R. V. R. & J. C. College of Engineering Andhra Pradesh Guntur India Department R.V. R. & J. C. College of Engineering Andhra Pradesh Guntur India Electronics and Communication Engineering Department Andhra University Andhra Pradesh Visakhapatnam India

The widespread broadcast of fake health news offers a huge challenge that puts the health and well-being of the general population in peril. This is especially true in a time when there is an excess of information available. In order to develop automated systems that are capable of spotting fraudulent health-related information, researchers have applied artificial intelligence methodologies such as machine learning (ML) and deep learning (DL). These approaches have been utilized in order to construct these systems. In order to address the problem that is now being faced, this is being done. The objective of this research is to give an analysis of the method, performance measurements, and problems that are linked with machine learning (ML) and deep learning (DL) systems for the purpose of recognizing fake health news. This analysis is presented in the course of this research. We study the many ways that are applied in the process of feature engineering, that of model architectural design, and that of evaluation metrics throughout the machine learning (ML) and deep learning (DL) paradigms. These approaches are utilized in the process of feature engineering. Additionally, we evaluate the significance of our findings for the development of the efficiency of systems that are meant to identify false health news and offer prospective areas for additional research in this vitally important sector. Lastly, we conclude that our findings have the potential to improve the effectiveness of these systems. © (2025), (International Association of Engineers). All rights reserved.

关键词： Adversarial machine learning

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Collaborative Deadline-sensitive Multi-task Offloading in Vehicular-Cloud Networks 17

Collaborative Deadline-sensitive Multi-task Offloading in Ve...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Kumar, Prasanna Sushma, S.A. Chandrasekaran, K. Addya, Sourav Kanti National Institute of Technology Karnataka Cloud and Smart System Services Lab Department of Computer Science and Engineering Surathkal India Siddaganga Institute of Technology Cloud and Smart System Services Lab Tumakuru India

ISBN: (纸本)9798331531195

With the growing technological advancements in the Internet and advanced functionalities in vehicular networks, it becomes crucial to execute tasks quickly and efficiently. However, the limited onboard computational capacity and vehicle mobility make it challenging to accomplish latency-sensitive tasks efficiently. Task offloading provides a promising solution to overcome these challenges. Cloud data centers provide efficient solutions, but returning the results to the vehicles takes longer due to the large physical distance. Leveraging edge servers to execute latency-sensitive tasks provides a fast, interactive response and less transmission cost. However, in a dynamic network, vehicles will be in constant motion with varying speeds, resulting in frequent handoffs from one base station to another. Our proposed work aims to select the optimal nodes to perform binary offloading with minimum cost using the collaborative vehicular network. We use a greedy-based offloading approach to address these challenges and achieve better quality-of-service and quality-of-experience in a dynamic environment to minimize costs, delay reduction ratio, and satisfaction ratio. The proposed work outperforms the baseline by 60.44%, 53.43% in reducing total system cost, delay reduction ratio, and 36% improvement in the satisfaction ratio compared to baseline algorithms. © 2025 IEEE.

关键词： Quality of service

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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

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International Journal of Advanced computer science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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