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检索条件"任意字段=International Conference on Simulation and Modeling Methodologies, Technologies and Applications"

共 3923 条记录，以下是571-580 订阅

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Neural Network Modelling, Testing, and simulation Study of an Autonomous Vehicle Using the Udacity Simulator

Neural Network Modelling, Testing, and Simulation Study of a...

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Intelligent technologies (CONIT), international conference on

作者： Vinit Sinha Amal Barman Student of Department of Electrical Engineering Indian Institute of Engineering Science and Technology Shibpur India Faculty of Department of Electrical Engineering Indian Institute of Engineering Science and Technology Shibpur India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Self-driven vehicles, also known as autonomous vehicles, have become an innovative technology with the capacity to completely change the transportation sector. This report offers an in-depth study and implementation of self-driving vehicles. Furthermore, the research aims to evaluate the performance and accuracy of the neural network model using CPU and GPU computational power. It seeks to identify and analyse the difficulties encountered while training and testing the neural network model. Also, some different kinds of image processing are discussed in this paper. This includes exploring the key components of self-driving vehicles, such as perception systems, decision-making algorithms, and control systems. Also, the study aims to provide a brief overview of the current state of research, advancements, and applications related to object detection algorithms and self-driving vehicles. It highlights advancements in self-driving vehicle technologies. Lastly, at the end of this report, the workings of a self-driven vehicle are represented within the two different environments, or tracks.

关键词： Training Accuracy Computational modeling Neural networks Graphics processing units Transportation Predictive models

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Exploring User Interactions in AR/VR Interfaces: A simulation-Based Study

Exploring User Interactions in AR/VR Interfaces: A Simulatio...

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Electrical, Computer and Energy technologies (ICECET), international conference on

作者： Sahar Zandi Gregory A. Luhan Dept. of Architecture Texas A&M University College Station TX USA

Virtual Reality (VR) and Augmented Reality (AR) technologies have gained significant attention in various domains. User interactions and error rates in VR/AR environments are crucial for improving system performance and user experience. In this study, a simulated dataset of 20,000 data points representing interactions of 1,000 users with four actions: “Select,” “Drag,” “Resize,” and “Rotate” is presented. Likelihood analysis is employed to explore error rates and their correlations with user-related factors such as action duration, fatigue, task success, preference, and learning rate. The findings deepen the understanding of error rates in VR/AR environments and provide insights into optimizing user experiences. The methodology of using simulated data and likelihood analysis can serve as a foundation for future studies investigating error modeling and prediction in real-world VR/AR applications.

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Advanced Parkinson's Disease Classification Using Hybrid Model: SVM and Random Forest

Advanced Parkinson's Disease Classification Using Hybrid Mod...

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Intelligent Data Communication technologies and Internet of Things (IDCIoT), international conference on

作者： Archana S H Rajeshkannan M Anish M J Pasupuleti Sandeep Artificial Intelligence and Data Science Saveetha Engineering College Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Parkinson's disease (PD) is a progressive and chronic neurodegenerative condition that significantly impairs motor and cognitive functions, posing a considerable burden on global healthcare systems. Early and precise diagnosis is vital to improving patient outcomes, yet traditional diagnostic techniques often encounter limitations due to overlapping symptoms with other neurological disorders and the subtle nature of early PD indicators. This research introduces a machine learning-driven approach that employs a hybrid ensemble model integrating Support Vector Machine (SVM) and Random Forest classifiers to enhance diagnostic accuracy by analyzing vocal and acoustic features. To address dataset imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied, enabling the model to identify meaningful patterns within underrepresented data. Principal Component Analysis (PCA) is utilized to reduce dimensionality, preserving critical data variance while minimizing computational complexity and mitigating overfitting. Hyper-parameter tuning further optimizes each classifier, bolstering the model's accuracy and robustness. Comparative analyses demonstrate that this hybrid ensemble model effectively distinguishes PD patients from healthy individuals, showcasing its potential as a reliable predictive tool in clinical applications. This study high-lights the promise of advanced machine learning methodologies in supporting early PD detection, offering clinicians a valuable resource for timely intervention and improved patient care.

关键词： Support vector machines Analytical models Accuracy Parkinson's disease Computational modeling Data models Robustness Random forests Tuning Principal component analysis

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modeling and optimization of supply sensitivity for a time-domain temperature sensor 16

Modeling and optimization of supply sensitivity for a time-d...

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2021 international conference on Synthesis, modeling, Analysis and simulation Methods and applications to Circuit Design, SMACD 2021 and 16th conference on PhD Research in Microelectronics and Electronics, PRIME 2021

作者： Tan, Jun Ilmenau Germany

ISBN: (纸本)9783800755899

This paper presents a methodology for modeling and optimizing the supply sensitivity for a time-domain temperature sensor. Many modern System-on-Chip (SoC) designs integrate multiple functional blocks, such as sensors, power management units (PMU), and wired/wireless communication interfaces. Interference on the supply line between such blocks becomes critical, since it has a significant impact on sensor performance. Many state-of-the-art designs utilize supply sensitivity as a sensor parameter to describe the immunity to supply interference. Therefore, modeling and analysis of this parameter to optimize sensor performance is needed. This paper begins with the modeling of the common time-domain temperature sensor, while an instance of the model is precisely created with extracted parameters. Additionally, verification is performed by comparing the model with a transistor-level design. Finally, the optimization methodology for supply sensitivity improvement is discussed. The results show that the DC supply sensitivity is significantly reduced by a factor of 41. With the proposed methodologies, a further AC supply sensitivity can be analyzed and optimized, so that sensor performance in SoC designs can be improved. © VDE VERLAG GMBH.

关键词： Temperature sensors

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An Object-oriented Approach to Ontological Engineering of Complex Cyber-physical systems Based on Meta-associative Graphs

An Object-oriented Approach to Ontological Engineering of Co...

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international conference on Information technologies in Engineering Education (Inforino)

作者： A.E. Misnik Information Technology Software Inter-State Educational Institution of Higher Education ‘Belarusian-Russian University” Mogilev Belarus

ISBN: (数字)9798350371369

ISBN: (纸本)9798350371376

This article delves into the pivotal role of cyberphysical systems in the fabric of modern digital and automated environments, serving as a cornerstone of the Industrial Internet of Things and the Industry 4.0 revolution. These systems represent a synthesis of computational algorithms and physical processes, resulting in intelligent, autonomous systems equipped for self-correction and adaptation. The ability of cyber-physical systems to monitor environmental changes, adapt to the physical world, and manage complex tasks marks a significant leap in automating production and ensuring safety in intelligent transportation, among other applications. At the heart of developing and deploying cyber-physical systems is the ontological approach, which employs structured frameworks to delineate the interactions between cyber and physical elements. Ontologies, by categorizing and organizing knowledge, are vital for modeling relationships within cyber-physical systems, enhancing interoperability and communication across various domains. The quasi-hierarchical structure of cyber-physical systems ontologies, where a child node may link to multiple parents, introduces a nuanced representation of complex system interdependencies, surpassing the capabilities of traditional hierarchical models. Ontological engineering for cyber-physical systems merges object-oriented and structural analysis methodologies, focusing on key entity classes and their interrelations to dictate system evolution and behavior. The object-oriented approach divides into structuring data and knowledge, organizing system data, and developing user interfaces that support cyber-physical systems business processes, culminating in a coherent, functional system model. The article posits meta-associative graphs as a superior ontological engineering tool for cyber-physical systems compared to the Web Ontology Language (OWL) and frames, particularly due to their flexibility in representing quasi-hierarchical structures an

关键词： Object oriented modeling OWL Semantics Cyber-physical systems Ontologies User interfaces Software

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Stability and Causality Enforced Macromodeling Technique for Coaxial-Microstrip Transitions

Stability and Causality Enforced Macromodeling Technique for...

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IEEE international conference on Electronics, Computing and Communication technologies (CONECCT)

作者： Priya R. Sanjika Devi R. V. Parul Mathur Dhanesh G. Kurup Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

This paper presents an effective technique to develop the macromodel of a coaxial-to-microstrip transition (CMT) for X-band applications. CMTs generally manifest as discontinuous structures in the assembly and processing phases of RF and microwave circuit design, especially in systems that employ printed circuit boards. Macro models are necessary to address the parasitic coupling effects in CMT that occur at high frequencies. The proposed approach employs fast relaxed vector fitting (FRVF) to model the CMT as a stable state-space model. The developed model effectively simplifies the simulation process of systems that incorporate CMT which enables a reduction in computational complexity while maintaining system accuracy and stability. The developed macromodel using the proposed approach was found to be stable and causal with an RMSE fitting error of ≈ 10 −4 for an order of approximation of 18.

关键词： Computational modeling Space technology Fitting Printed circuits Microwave theory and techniques Stability analysis Vectors

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Performance Analysis of Different Graphs in Graph-Based Remaining Useful Life Prediction Model

Performance Analysis of Different Graphs in Graph-Based Rema...

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Annual IEEE Systems conference

作者： Abrar Mahi Al Rashid Anwar Hosen Burhan Khan Institute for Intelligent Systems Research and Innovation Deakin University Geelong Australia

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Condition-based maintenance has progressed substantially through the integration of IoT and advanced sensing technologies. Prognostics and Health Management (PHM) has emerged as a pivotal strategy for enhancing reliability and minimizing downtime by facilitating early fault detection and optimizing maintenance planning. Predicting the Remaining Useful Life (RUL) is a vital component of prognostics and health management for machinery. It effectively prevents catastrophic failures and reduces the costs associated with unplanned maintenance. Numerous Deep Learning (DL) methodologies have been applied for predicting RUL owing to the versatility of their architectures. This paper investigates the use of Graph Neural Networks (GNNs) for RUL estimation. GNNs are adept at modeling complex relationships within equipment data by representing components and their connections as nodes and edges in a graph, thereby capturing intricate dependencies and operational patterns. Building on recent advancements, this study conducts a comparative analysis of various methods for constructing adjacency matrices used in GNNs, crucial for accurate RUL estimation. The study leverages the Graph Attention Model (GAT) to assess the effectiveness of different graph structures in predicting RUL, especially under conditions of machinery degradation. Our findings emphasize the importance of edge quality and quantity in determining the performance of GAT models. This paper concludes with an in-depth discussion on the impact of various graph construction methods on RUL estimation, providing insights for future research and practical applications in industrial settings.

关键词： Degradation Computational modeling Estimation Predictive models Graph neural networks Maintenance Sensors Prognostics and health management Machinery Load modeling

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Enhancement of Quality of Service in Mobile Ad-Hoc Networks using Hybrid Load Balanced Simulator: Annealing Simulated 11

Enhancement of Quality of Service in Mobile Ad-Hoc Networks ...

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11th international conference on System modeling and Advancement in Research Trends, SMART 2022

作者： Kumar, Anil Shukla, R.K. Shukla, R.S. Pal, Shelendra Invertis University Depatment of Computer Applications Bareilly India Invertis University Department of Applied Sciences and Humanities Bareilly India College of Computing and Informatics Electronic University Department of Computer Science Saudi Arabia Tmu U.P. Moradabad India

ISBN: (纸本)9781665487320

Wireless topologies can change rapidly and unexpectedly. Hybrid networks are connected by gateways, which act as entry points for all traffic. Some gateways may be subject to high traffic loads and therefore may be overloaded. To compare the performance of the proposed work, various parameters including end-to-end latency, power consumption, and throughput are considered. Load Balancing Hybrid Simulator (LBHS) is an algorithm used in this paper to distribute traffic load among multiple gateways to solve load balancing problems in MANET. It combines a Hybridge Annealing simulation (HAS) network technology with Decentralized Random Search (DRS). A network that supports QoS in MANET. The Dominant Minimal Set (DMS) problem is solved using AS. The DSS heuristic algorithm provides an algorithm with a simple structure and a high degree of exploration. In addition to faster convergence than other algorithms, it also implements an axiomatic algorithm to ensure the diversity of agents and avoid falling into local optimum. The test results show that the proposed Hybridge Annealing Simulated Decentralized Random Search (HAS-DRS) procedure performs better than the dominated connection set (DCS). © 2022 IEEE.

关键词： Simulated annealing

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modeling and Designing Subsystem of Fiber-Optic Sensors for Diagnostics and Control of Mechanical Structures Health in Differential System

Modeling and Designing Subsystem of Fiber-Optic Sensors for ...

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international conference on Computer Science and Engineering (UBMK)

作者： Aliya Kalizhanova Murat Kunelbayev Ainur Kozbakova Zhalau Aitkulov Feruza Malikova Zulfiya Zhilkishbayeva Almaty University of Power Engineering and Telecommunications named after Gumarbek Daukeyev Institute of Information and Computational Technologies CS MSHE RK Almaty Kazakhstan Al-Farabi Kazakh National University Institute of Information and Computational Technologies CS MSHE RK Almaty Kazakhstan Almaty Technological University Institute of Information and Computational Technologies CS MSHE RK Almaty Kazakhstan Academic of Logistic and Transport. Kazakh National Women's Teacher Training University Institute of Information and Computational Technologies CS MSHE RK Almaty Kazakhstan Almaty Technological University Almaty Kazakhstan

The article examines the modeling and design of a subsystem of fiber-optic sensors for the diagnosis and monitoring of the operability of mechanical structures in a differential system. In this study, the definition of the problem was developed by a mathematical model of the FBG using the transfer matrix method and using this method, a simulation model for the sensor was solved. The transfer matrix method makes it possible to assign spectral characteristics of optical components based on the theory of coupled modes and the matrix description of an electromagnetic wave passing through an optical fiber. The simulation is performed by solving the equations of coupled modes in MatLab (Simulink) using the transfer matrix method, which provides the basis for the relationship between the controlled modes. The method can take into account refractive index profiles of arbitrary shape and be implemented in a tool called MatLab (Simulink). As a result of the study, optimization was carried out, where light is shed on the effectiveness of the proposed scheme for analyzing deformation measurements, obtaining a linear response. It's inventory-friendly, fast, and open-source. The main contribution of the study is the developed simulation model of the subsystem for fiber Bragg sensors, which shows that the power of the side lobes is completely minimized in the Gaussian reflection spectra of FBG, and the results of MatLab (Simulink) are in excellent agreement with the results of the optical lattice. MatLab (Simulink) has a wide range of applications in modeling various optical fibers for various fields such as visualization, communication, material processing and remote sensing.

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RECENT HYDRODYNAMIC modeling ENHANCEMENTS IN OPENFAST 4

RECENT HYDRODYNAMIC MODELING ENHANCEMENTS IN OPENFAST

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ASME 4th international Offshore Wind Technical conference (IOWTC)

作者： Wang, Lu Jonkman, Jason Hayman, Greg Platt, Andy Jonkman, Bonnie Robertson, Amy Natl Renewable Energy Lab Golden CO 80401 USA Hayman Consulting LLC Boulder CO USA Envis Energy USA Ltd Boulder CO USA

ISBN: (纸本)9780791886618

To improve the hydrodynamic modeling capabilities of the offshore wind design and modeling tool OpenFAST, a restructuring of the HydroDyn hydrodynamics module was undertaken with several new features implemented. The generation of the wave field is now separated from HydroDyn into a new module called SeaState. Unlike previous versions of HydroDyn, which precomputed the wave kinematics at the undisplaced positions of the hydrodynamic nodes, the new SeaState module computes the complete time history of the wave field at the vertices of a user-defined wave grid. During the simulation, the wave kinematics at any point within the grid can be efficiently interpolated, allowing the strip-theory wave loads to be evaluated based on the wave kinematics at the displaced structure position. A phase correction was also implemented for the potential-flow wave-exciting loads to account for large structure displacements. Several wave-stretching methods were implemented for the strip-theory solution along with a wave-load redistribution method that ensures smooth variation of the nodal loads as the nodes of a discrete hydrodynamic mesh enter and exit the water. The load smoothing is included to avoid the excitation of unphysical high- frequency structural vibration during hydroelastic simulations. The MacCamy-Fuchs diffraction correction for the strip-theory solution and the capability to insert a constrained NewWave into extreme stochastic sea states with directional spreading were also added. The present article documents the formulations of the new improvements to HydroDyn with example applications and numerical results.

关键词： OpenFAST HydroDyn nonlinear wave stretching constrained wave MacCamy-Fuchs instantaneous position

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