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

共 35208 条记录，以下是151-160 订阅

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Web3D-Based Lightweight simulation for Mass Evacuation at Transportation Hubs 41st

Web3D-Based Lightweight Simulation for Mass Evacuation at ...

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41st computer Graphics international conference, CGI 2024

作者： Li, John Liu, Zhicheng Tian, Feng Jia, Jinyuan Tongji University Shanghai201804 China Duke Kunshan University Jiangsu Suzhou215316 China School of Game Jilin Animation Institute Jilin Changchun132000 China

ISBN: (纸本)9783031818059

With urban populations soaring, subway systems have become vital arteries of transportation, necessitating robust safety measures. In light of this, the need for effective fire evacuation strategies in these complex infrastructures is both urgent and critical. Current simulation tools often struggle with the demands of large-scale modeling, particularly in real-time settings, due to computational and rendering constraints. This paper introduces an innovative approach that leverages the agility and accessibility of Web3D technology to propose a lightweight, large-scale multi-person evacuation planning algorithm specifically designed for subway stations. Our approach optimizes smoke dispersion processes and integrating advanced algorithms that account for psychological factors and environmental familiarity of individuals during emergencies. The use of Web3D technology for dynamic rendering facilitates real-time, interactive simulations of smoke and crowd dynamics, enhancing the practicality and effectiveness of fire evacuation procedures. Our experiments on subway stations have demonstrated the effectiveness of the proposed approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Subway stations

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Multi Source DOA Estimation Based on Complex-valued Neural Networks 24

Multi Source DOA Estimation Based on Complex-valued Neural N...

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2024 international conference on Virtual Reality, Image and Signal Processing, ICVISP 2024

作者： Zhang, Ziyan Wang, Chengyi Yi, Shichao Department of Computer Jiangsu University of Science and Technology Zhenjiang China Department of Science Jiangsu University of Science and Technology Zhenjiang China

ISBN: (纸本)9798400710926

In recent years, the combination of deep learning and direction of arrival estimation has been used in array signal processing. Standard neural networks use the complex covariance matrix of array signals as features, splitting the real and imaginary parts into dual channels or calculating complex radians as a single channel. Also, combine the first two as a three-channel input. The former only applies linear transformation to the real and imaginary parts, ignoring the geometric significance of complex numbers, and the latter's linear mapping of complex radians is not completely equivalent to the linear transformation of complex planes. Therefore, we propose a DOA estimation model based on a Complex-valued Convolutional Neural Network (CVCNN). After modeling and simulation, compared with traditional Real-valued Convolutional Neural Networks (RVCNN), CVCNN has higher accuracy in various snapshot numbers and signal-to-noise ratio (SNR) environments. The method proposed in this article can effectively identify the incident wave direction of multiple unknown signals. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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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...

引用

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

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

引用

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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Trend-aligned feature correlation: Holistic feature relevance metric on time series data 2

Trend-aligned feature correlation: Holistic feature relevanc...

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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： He, Runhai Song, Qingqing Zhou, Quanhua Zhang, Zhenxing Zhang, Shoujing Faculty of Applied Mathematics and Informatics Belarusian State University Minsk Belarus

ISBN: (纸本)9781510689053

Existing time series correlation research methods often rely too much on overall statistical characteristics and ignore the dynamic changes of data in the time dimension, especially in the identification of trend turning points and complex correlations. This study proposes an innovative trend-aligned feature correlation matching method (TAFC), which breaks through the limitations of traditional correlation analysis. TAFC adopts a sophisticated data preprocessing process and introduces two unique metrics: trend overlap index metric (Q) and numerical correlation metric (P). The P metric effectively captures the numerical correlation between data by combining the normalized difference measurement (NDM) and the Pearson correlation coefficient. In addition, TAFC also introduces a custom granular smoothing mechanism to balance trend feature preservation and noise suppression, aiming to comprehensively evaluate the multi-dimensional and multi-granular correlation of time series data. This study uses simulated scenario data based on chi-square distribution and multimodal distribution for experimental verification, and the verification range includes 36 sets of simulation data. The results show that this method can significantly distinguish data sets with different trend characteristics. The introduction of granularity has been verified to help make trend features more explicit. At the same time, when 5% random noise is introduced, the algorithm error is controlled within 5.60%-12.24%. This study provides a novel and accurate method for computer-aided time series data analysis, which has important application value. © 2025 SPIE.

关键词： Correlation coefficients computer simulations Analytical research Data modeling Time series analysis Data analysis Histograms Data processing Multifractal analysis Statistical analysis

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Sequence Generator Network for Neuromechanical Control of Rat Hindlimbs 13th

Sequence Generator Network for Neuromechanical Control of Ra...

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13th international conference on Biomimetic and Biohybrid Systems, Living Machines 2024

作者： Jackson, Clayton B. Nourse, William R. P. Quinn, Roger D. Department of Mechanical and Aerospace Engineering Case Western Reserve University ClevelandOH44106-7222 United States Department of Electrical Computer and Systems Engineering Case Western Reserve University ClevelandOH44106-7222 United States

ISBN: (纸本)9783031725968

Mammalian locomotion is a complex behavior arising from interaction between neural and biomechanical systems, driven by rhythmic activity originating in the spinal cord. Although it has been extensively studied, the structure of the circuits that produce this behavior remains unknown. One approach to modeling the rhythmic activity is with half-center models, in which there are alternating periods of flexion and extension to coordinate muscle activity. While this approach is sufficient for simple antagonistic muscle pairs, it can be difficult to expand the controller for more complex models with muscle synergies. This work introduces a method of modeling the activity in the spinal cord with a population of neurons exhibiting a continuous cycle of activity, rather than the push-pull of half-centers. To evaluate the effectiveness of this neural model for locomotive behavior, we integrate it with a biomechanical simulation to control the muscle activity in a pair of rat hindlimbs. With this controller, a pair of simulated rat hindlimbs is able to walk on the ground with joint trajectories exhibiting similar features to the animal during locomotion. This model of the spinal cord activity shows promising results on a simple model and demonstrates the ability to be adapted to control more complex biomechanical models with muscle synergies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Rats

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