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检索条件"任意字段=International Conference on Computer Modeling and Simulation"

共 35215 条记录，以下是151-160 订阅

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Optimizing Wireless Sensor Network Connectivity Using Math modeling Techniques 5th

Optimizing Wireless Sensor Network Connectivity Using Math M...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Gobi, N. Bhatnagar, Gunjan Bhavana, A. Mishra, Awakash Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Wi-Fi sensor networks (WSNs) are extensively deployed in various programs and have an increasing significance within the net of things (Iota). However, their connectivity remains a chief challenge, especially in large-scale networks, as it's far crucial for reliable communication and green statistics aggregation. To address this project, this paper presents a unique method to optimize the connectivity of WSNs through utilizing mathematical modeling techniques. Mainly, nonlinear optimization methods are hired to acquire a surest deployment sample for a given community configuration that maximizes the connectivity metric. The proposed method is proven to provide better connectivity outcomes than conventional solutions and is analyzed each theoretically and empirically with extensive simulations. simulation outcomes additionally show that the method is robust to various node deployment techniques. Furthermore, it is evaluated using exceptional styles of environments and a huge variety of community configurations. The offered outcomes demonstrate the versatility and scalability of the proposed optimization method in optimizing the connectivity of WSNs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensor nodes

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17th international conference on the simulation of Adaptive Behavior, SAB 2024

17th International Conference on the Simulation of Adaptive ...

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17th international conference on the simulation of Adaptive Behavior, SAB 2024

ISBN: (纸本)9783031715327

The proceedings contain 26 papers. The special focus in this conference is on simulation of Adaptive Behavior. The topics include: Vector-Based Navigation Inspired by Directional Place Cells;a Behavior-Based Model of Foraging Nectarivorous Echolocating Bats;benefit of Varying Navigation Strategies in Robot Teams;no-brainer: Morphological Computation Driven Adaptive Behavior in Soft Robots;cuttleBot: Emulating Cuttlefish Behavior and Intelligence in a Novel Robot Design;the Emergence of a Complex Representation of Touch Through Interaction with a Robot;analyzing Multi-robot Leader-Follower Formations in Obstacle-Laden Environments;spatio-Temporal Dynamics of Social Contagion in Bio-inspired Interaction Networks;behavioural Contagion in Human and Artificial Multi-agent Systems: A Computational modeling Approach;transient Milling Dynamics in Collective Motion with Visual Occlusions;extended Swarming with Embodied Neural Computation for Human Control over Swarms;bio-Inspired Agent-Based Model for Collective Shepherding;DaNCES: A Framework for Data-inspired Agent-Based Models of Collective Escape;the Role of Energy Constraints on the Evolution of Predictive Behavior;influence of the Costs of Acquisition of Private and Social Information on Animal Dispersal;integrated Information in Genetically Evolved Braitenberg Vehicles;neural Chaotic Dynamics for Adaptive Exploration Control of an Autonomous Flying Robot;non-instructed Motor Skill Learning in Monkeys: Insights from Deep Reinforcement Learning Models;memory-Feedback Controllers for Lifelong Sensorimotor Learning in Humanoid Robots;extracting Principles of Exploration Strategies with a Complex Ecological Task;the Cost of Behavioral Flexibility: Reversal Learning Driven by a Spiking Neural Network;"Value" Emerges from Imperfect Memory;the Role of Theory of Mind in Finding Predator-Prey Nash Equilibria;nonverbal Immediacy Analysis in Education: A Multimodal Computational Model.

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System Identification for Non-destructive Assessment of Structures: Lessons Learned 2nd

System Identification for Non-destructive Assessment of Stru...

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2nd international conference on Information Technologies and Their Applications

作者： Kasimzade, Azer A. Nematli, Emin Mahmudov, Eldaniz Huseynli, Nicat Azerbaijan Univ Architecture & Construct Baku Azerbaijan Ondokuz Mayis Univ Dept Civil Engn Samsun Turkiye

ISBN: (纸本)9783031734168;9783031734175

For development and application of the System Identification in Dynamic Design Solution and Health Monitoring of Civil Engineering Structures, based on author's awarded project (MF-046), related facility has been established at the Department of Civil Engineering, Ondokuz Mayis University, Turkey in 2002-2003 years. And it was the first in this field. FE model updating was performed through an optimization procedure that starts from a sensitivity analysis to find the most influential model parameters on the modal properties of the system. The updated model was used as a benchmark to define the properties of a model, with which an assessment of the vulnerability of the structure. The presented research offers an overview of monitoring systems, alongside highlighting major findings concerning the dynamic response properties of monitored structures, with a particular focus on: -finite element calibration of reinforce concrete and steel structures;steel structures with cable elements;steel structures retrofitted with CFRP;possibility using micro tremor data on ground level for ambient excitation in shaking tables;aliasing (bound checking) problems for extracting modal and physical parameters;and others like this.

关键词： Systems Identification Finite Element modeling Calibration Kalman Filter Gain Monte-Carlo simulation

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Digital Twin Technology in University Data Centers: A Model for Operational Efficiency and Sustainability 33

Digital Twin Technology in University Data Centers: A Model ...

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33rd Euromicro international conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Vaccaro, Viviana Tagliabue, Lavinia Chiara Polytechnic University of Milan Milan Italy University of Turin Department of Computer Science Turin Italy

ISBN: (纸本)9798331524937

The increasing energy demand of data centers poses a significant challenge for universities and public and private organizations striving to optimize energy resources and reduce environmental impact. Developing a digital twin for the Department of computer Science data center at the University of Turin represents a strategic approach to advanced monitoring and management. Specifically, the digital twin allows for the integration of real-time data with dynamic simulations, providing an accurate virtual representation of the data center. This model enables real-time energy load analysis, temperature distribution monitoring, and the simulation of corrective actions to improve HVAC system efficiency, optimize rack layout, and reduce cooling and ventilation energy consumption. The organization of the energy model requires a precise and complex structure, beginning with the modeling of the physical components of the data center and extending to the detailed simulation of thermal flows and zone interactions. The model configuration includes creating distinct thermal zones for hot and cold aisles, racks, and plenums to accurately reflect internal thermal exchange patterns. Integration with dynamic simulation tools enables precise parameter calibration to yield accurate performance and consumption predictions and provides a safe environment to test innovative technologies without impacting physical infrastructure. A digital twin also offers substantial opportunities for predictive maintenance, allowing timely interventions to prevent failures, and reduces operational costs through the simulation of optimization scenarios. In terms of sustainability, it facilitates life cycle analysis and carbon footprint reduction. Consequently, developing a digital twin for the University of Turin data center not only enhances system reliability and sustainability but also serves as a replicable example for other similar-sized facilities. © 2025 IEEE.

关键词： Energy efficiency

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Dynamic Crowd modeling and Anomalous Behavior Prediction Using GMM and Time Series Analysis in Real-Time Smart City Environment 4

Dynamic Crowd Modeling and Anomalous Behavior Prediction Usi...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kanchana, R. Fernandez, F. Mary Harin Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331523923

The Research Concentrates on simulating and investigating dynamic crowd using Gaussian Mixture Models (GMM) and synthetic time-series data in real time smart city environments. A novel approach generates and designs various crowd behaviors, incorporating normal flow, high congestions, and erratic patterns, leveling to detect and predict anomalies that pose safety issues. Mock datasets, imitating real-world situations, are generated using spatial-temporal features such as crowd density, velocity, and movement direction. To cluster these behaviors into distinct operational states, the GMM is employed, while irregularities are recognized as outliers to the learned distributions. The simulation framework introduces temporal tendencies and real-time capabilities, aiding dynamic variance detection and proactive crowd organization. Investigational results prove the efficiency of the proposed approach in clustering behavioral states and detecting anomalies, providing a scalable and computationally efficient result for inner-city protection and resource optimization. The study implements a foundation for deploying real-time crowd monitoring systems in smart cities, addressing challenges in occasion management, transportation, and disaster awareness. © 2025 IEEE.

关键词： Gaussian distribution

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IMC Based PID Controller for Spherical Tank Liquid Level Control 1

IMC Based PID Controller for Spherical Tank Liquid Level Con...

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1st international conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Kadu, C.B. Bhosale, V.P. Vikhe, P.S. Mandhare, V.V. Pravara Rural Engineering College Department of Instrumentation & Control Engineering Loni India Pravara Rural Engineering College Department of Computer Engineering Loni India

ISBN: (纸本)9798350355611

In the process industries, it's hard to control a non-linear process. Nonlinear behavior is frequently seen in real processes. The challenging problem of controlling a spherical tank is result of its nonlinearity caused by the continuously varying cross section. An IMC-based PID controller is employed to improve efficiency of controlling the level of spherical tanks which exhibit nonlinear dynamics. For processes up to second-order with dead time and first-order numerator dynamics, a single tuning parameter will be offered. The process model is determined employing mathematical modeling and standard step response-based system identification. The laboratory model of an interacting and non-interacting nonlinear system is introduced, which is capable of performing more complex tasks. To verify the effectiveness of the suggested IMC based PID controller, results of real-time experiments and a simulation study on spherical tank level control are incorporated. © 2025 IEEE.

关键词： Three term control systems

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Bio-Inspired Home Localization Using Event-Based Vision and Spiking Neural Networks in Simulated Environment

Bio-Inspired Home Localization Using Event-Based Vision and ...

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2025 IEEE international conference on simulation, modeling, and Programming for Autonomous Robots, SIMPAR 2025

作者： Kausar, Rizwana Zayer, Fakhreddine Sudevan, Vidya Viegas, Jaime Dias, Jorge Khalifa University Department of Electrical Engineering United Arab Emirates Khalifa University Center for Autonomous Robotic Systems United Arab Emirates Khalifa University Department of Computer and Information Engineering United Arab Emirates

ISBN: (纸本)9798331516857

In this work, we present a bio-inspired approach for home localization using event-based visual data and spiking convolutional neural networks (S-CNNs) in a simulated environment within NVIDIA Omniverse. Drawing inspiration from the navigational strategies of insects, which utilize visual cues and homing vectors to return to their nests, our approach leverages event cameras that mimic the asynchronous change-driven visual processing of insect eyes. To process this sparse, event-driven input, we employ spiking neural networks (SNNs), which mirror the spike-based transmission of information in biological neural systems. The proposed system utilizes a quadrotor equipped with an event camera to capture dynamic, asynchronous visual data and processes it using an S-CNN trained to estimate relative home vectors. This vector encoding method is inspired by the unit-circle representation of gaze directions observed in insect homing behavior, as outlined in recent studies. By integrating event-based vision and SNNs, our approach ensures energy-efficient computation, robustness to lighting variations, and adaptability to dynamic scenes. We validate the framework in a 3D environment modeled within ISAAC Sim, where the quadrotor autonomously navigates back to a designated "nest"location. Comparative analysis with conventional frame-based neural networks demonstrates the superiority of the proposed system in terms of efficiency, robustness, and accuracy. This work establishes a novel bio-inspired framework for integrating event-based data and spiking neural networks, paving the way for energy-efficient localization in robotics and smart home environments. Future work will explore the deployment of the system in a multi-quadrotor environment to coordinate collaborative tasks including the integration of multi-modal sensory inputs such as Camera, IMU, Gas sensors and GPS data, and the extension of the framework to real-world settings for further validation and scalability. © 2025 IE

关键词： Convolutional neural networks

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AI-Assisted Data modeling and High-Resolution Image Synthesis: An Interactive Framework with Latent Diffusion Model Visualization

AI-Assisted Data Modeling and High-Resolution Image Synthesi...

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2025 international conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Borah, Asha Rani Archana Rani, K. Pushpa Ziara, Sajjad Lakhanpal, Sorabh Vidyadhari, Chalasanvi New Horizon College of Engineering Department of Computer Science Engineering Bangalore India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Lovely Professional University Punjab Phagwara India Griet Department of Information Technology Bachupally Telangana Hyderabad India

ISBN: (纸本)9798331527495

In this research paper, we present the design and implementation of an AI assisted interactive framework for data modeling and high resolution image synthesis that leverages both state of the art latent diffusion models and visualisation of the real-time algorithm. The system proposed enables user to be able to better understand complex data modeling processes and creates high resolution images in the latent space using diffusion. The framework is both interactive, allowing modifications in parameters and real time visualization of change, making it a useful tool both for educational and for advanced AI research. In this paper, we also discuss the underpinning AI-assisted data modeling algorithms, and why they enable efficient data manipulation as well as high quality image synthesis then provide simulation results and practical applications to illustrate the capabilities of the framework of visualizing the latent diffusion process and generating high resolution images. © 2025 IEEE.

关键词： Flow visualization

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MSCVP: Multiscale Network Emulation Based on the Integration of modeling, simulation, Container, Virtualization, and Physical Networks 3rd

MSCVP: Multiscale Network Emulation Based on the Integration...

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3rd international conference on Cyberspace simulation and Evaluation, CSE 2024

作者： Wang, Xiaofeng Zhang, Guoxiu Jia, Yan He, Xinyi Hu, Ning School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Pengcheng Laboratory Shenzhen China Harbin Institute of Technology (Shenzhen) Shenzhen China

ISBN: (纸本)9789819645022

A cyber range, a special type of computer network emulation environment, allows users to evaluate network security technologies. Traditional network emulation methods, such as packet-level discrete event simulation, have difficulty meeting the realistic network security evaluation requirements of a cyber range. Thus, a series of new technologies, such as container-based network emulation and virtualization-based network emulation, have emerged. The key problem in network emulation is the contradiction between fidelity and cost. To solve this problem, considering the respective advantages of mathematical modeling, packet-level discrete event simulation, container, virtualization and physical networks, this paper introduces a multiscale network emulation method based on the above five methods, named MSCVP, to achieve a tradeoff between fidelity and cost. To improve the emulation performance, on the basis of cloud computing platforms, MSCVP focuses on the seamless integration of packet-level discrete event simulation, container, virtualization and physical networks. Given the dynamics of network topologies, MSCVP further integrates mathematical modeling. The experiments show that MSCVP can achieve seamless integration of the above five methods, enabling worm propagation at the million-node level and dynamic network emulation of space–ground integrated information networks. Compared with the other methods, MSCVP enhances the efficiency of network emulation deployment, achieving an implemented of 56.87%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber Range Multiscale Network Emulation Network Emulation Network Security Evaluation Network Topology Emulation

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Lightweight design and deployment of object detection model for low-light scenes 4

Lightweight design and deployment of object detection model ...

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4th international conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Sun, Kangjian Huo, Ju Jiang, Xiaona Gao, Yichao Cai, Chen School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin China China FAW Corporation Limited Changchun China College of Design and Engineering National University of Singapore Singapore Signal and Communication Research Institute China Academy of Railway Sciences Beijing China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Low-light scenes are common in fields such as autonomous driving, which tests the robustness of intelligent systems. This paper tests the performance of object detection in low-light scenes based on the YOLOv8n model. In order to solve the constraint of limited computing power of embedded devices, the YOLOv8n model is adjusted through model pruning and knowledge distillation. The complexity of the model is reduced through these lightweight operations. While ensuring the detection performance as much as possible, the multiply-accumulate operations (MACs) and parameters of the model decreased by about 40% and 27% respectively. The proposed model is evaluated on the Exclusively Dark (ExDARK) dataset and achieves a mean average precision (mAP) value of 0.672. In addition, the proposed model is migrated to the embedded platform NVIDIA Jetson Xavier NX. The deployment process is optimized by multi-process scheduling and multi-thread scheduling. Experiments are also conducted on real data, and the results show that the proposed model can provide a feasible solution for vision-based nighttime autonomous driving. © 2025 SPIE.

关键词： Object detection Deep learning Image processing

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