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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271-280 订阅

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Control of Chaos in Fractional computer Virus Model

Control of Chaos in Fractional Computer Virus Model

引用

Computational Intelligence Approaches and Applications (ICCIAA), international conference on

作者： Jamal Oudetallah Imane Zouak Wasim Audeh Adel Ouannas Amina-Aicha Khennaoui Iqbal M. Batiha Shaher Momani Department of Mathematics University of Petra Amman Jordan Department of Mathematics and Informatics University of Larbi Ben M'hidi Oum El Bouaghi Algeria NTIC Faculty University of Constantine 2 Constantine Algeria Department of Mathematics Al Zaytoonah University of Jordan Amman Jordan Nonlinear Dynamics Research Center (NDRC) Ajman University Ajman UAE Department of Mathematics University of Jordan Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper investigates the control of fractional discrete computer virus model. By employing fractional-order difference equations, we capture the complex dynamics and long- term memory effects inherent in virus propagation. A novel control strategy is proposed to stabilize the system and suppress virus outbreaks. The stability analysis of the controlled system is conducted. Numerical simulations are presented to validate the effectiveness of the proposed control approach. The results underscore the importance of control in managing fractional-order systems and highlight its potential applications in cyber security and other fields requiring precise regulation of complex dynamics.

关键词： computer viruses Computational modeling Difference equations Numerical simulation Stability analysis Regulation Mathematical models Numerical models Control theory Viruses (medical)

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simulation-Based Metrological Temperature Ensuring and Control of the Temperature Field in an Induction Furnace Based on Various Parameters

Simulation-Based Metrological Temperature Ensuring and Contr...

引用

international conference on computer and Automation Engineering, ICCAE

作者： Jan Rybář Samuel Emebu Sayed Yousuf Sayed Sohaibullah Zarghoon Lukáš Bartalský Peter Onderčo Andrej Smetánka Radek Matušů Faculty of Mechanical Engineering Institute of Automation Informatization and Measurement Slovak University of Technology Bratislava Slovakia Department of Separation Science LUT School of Engineering Science Lappeenranta-Lahti University of Technology Lappeenranta Finland Department of Auto mechanical Engineering Faculty of Electro-Mechanic Kabul Polytechnic University Kabul Afghanistan Department of Automation and Control Engineering Faculty of Applied Informatics Tomas Bata University in Zlín Zlín Czech Republic

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Induction heating is widely used in industrial furnaces because of its rapid response time and high energy efficiency. To design and optimize these furnaces, computer-aided simulations are essential. This study aims to develop an accurate temperature field model for a square cross-section steel billet in an induction furnace by leveraging various parameters in COMSOL Multiphysics software. The simulation analysis incorporated temperature dynamics, considering skin depth, temperaturedependent, and constant parameters of the steel billet. The datadriven model was converted into a state-space representation utilizing MATLAB's System Identification Toolbox. This conversion facilitated the design of a Linear Quadratic Regulator (LQR) with integral gain and pole placement. Further, these controllers' performance was analyzed, and controllers were successfully implemented to regulate the temperature field of the billet from initial temperature to reference temperature, utilizing the distributed parameter system control circuit.

关键词： Billets Analytical models Temperature Accuracy Computational modeling Furnaces Mathematical models Temperature control Steel Induction heating

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Surrogate modeling: Review and Opportunities for Expert Knowledge Integration 16

Surrogate Modeling: Review and Opportunities for Expert Know...

引用

16th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th international conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Šturek, Dušan Lazarova-Molnar, Sanja Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Kaiserstr. 89 Karlsruhe 76133 Germany Mærsk Mc-Kinney Møller Institute University of Southern Denmark Campusvej 55 Odense 5230 Denmark

Digital Twins (DTs), essential for digitalization and Industry 4.0, demand high-fidelity yet computationally efficient models for real-time replication of physical systems. However, simulating continuous dynamical systems, often stiff and highly granular, poses significant computational challenges. Driven by industry's need for more efficient simulations, this study explores Surrogate modeling (SMing) as a solution to reduce computational costs while preserving accuracy, enabling real-time performance, iterative design optimization and enhancing feasibility of DT simulations. We review existing traditional and machine learning SMing approaches, analyzing their limitations, accuracy and efficiency. We, furthermore, emphasize the critical role of domain expertise in SMing workflows and explore systematic strategies for incorporating expert knowledge to improve model reliability and applicability. Finally, we identify and discuss challenges and opportunities that emerge by the fusion of expert knowledge with machine learning techniques, highlighting their potential to advance next-generation SMing techniques. © 2025 Elsevier B.V.. All rights reserved.

关键词： High-fidelity simulation Knowledge Integration Surrogate modeling

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Sampling-Based Density Peaks Clustering for Energy-Aware Industrial IoT Networks

Sampling-Based Density Peaks Clustering for Energy-Aware Ind...

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IEEE Students' conference on Electrical, Electronics and computer Science (SCEECS)

作者： S Rajkumar R Gopalakrishnan T S Shreeraksha S Dhanaraj V Kabiyashwanth Computer Science and Engineering K. S. Rangasamy College of Technology India Electrical and Electronics Engineering K. S. Rangasamy College of Technology India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

In Industrial Internet of Things (IIoT) settings, effective data processing and energy management are essential due to the resource-limited characteristics of the linked equipment. This research introduces a unique Sampling-Based Density Peaks Clustering (SDPC) algorithm designed for energy-efficient clustering in IIoT networks. The suggested method improves classic Density Peak Clustering by incorporating a sampling mechanism that decreases computational burden while preserving excellent clustering precision. SDPC efficiently clusters devices by determining cluster centers through local density and distance parameters, hence reducing communication costs and prolonging the network’s operational lifespan. The energy consumption model is integrated into the clustering process, prioritizing devices with greater energy reserves as cluster heads, hence enhancing energy load distribution throughout the network. simulation results indicate that SDPC realizes substantial enhancements, featuring a 30% decrease in energy usage and a 25% augmentation in network durability relative to leading clustering methods. This method has significant potential for enhancing energy management in IIoT networks, facilitating more sustainable and scalable industrial processes.

关键词： computer science Energy consumption Electric potential Costs simulation Clustering algorithms Data processing Energy efficiency Industrial Internet of Things Load modeling

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simulation-Based Synthetic Data Generation for Automated Training of Bin-Picking Segmentation Models

Simulation-Based Synthetic Data Generation for Automated Tra...

引用

international conference on Autonomous Robots and Agents, ICARA

作者： Ander Iriondo Andoni Rivera Aitor Madrazo Aitor Gutierrez Ander Ansuategi Iñaki Maurtua Autonomous and Intelligent Systems Tekniker - BRTA Eibar Basque Country Spain

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Object segmentation is critical for addressing binpicking challenges in robotics, where scenes often involve overlapping, partially occluded objects. Accurate segmentation facilitates precise object localisation and identification. In this work, we introduce a Unity-based tool with domain randomisation functionalities for generating synthetic cluttered bin-picking scenes with automatic pixel-level annotations. We validated the usefulness of the system in two real use cases: the manipulation of vegetables and mechanical parts. By allowing models to be trained entirely on synthetic data, this solution eliminates the need for costly and time-consuming real-world data collection and annotation.

关键词： Training computer vision Annotations Computational modeling Object segmentation Grasping Data collection Data models Object recognition Synthetic data

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CertiFail at the ICST 2025 Tool Competition – Self-Driving Car Testing Track

CertiFail at the ICST 2025 Tool Competition – Self-Driving ...

引用

international conference on Software Testing, Verification, and Validation, ICST

作者： Fasih Munir Malik Sajad Mazraeh Khatiri Dept of Computer Science University of Bern Bern Switzerland

ISBN: (数字)9798331508142

ISBN: (纸本)9798331508159

In the context of Cyber Physical Systems like Self Driving Cars the transition from field operational testing to simulation based testing offer the advantage of lower cost, higher efficiency and the possibility of recording and repeating exact failing conditions. Yet traditional simulation based testing require execution of a long list of test cases, which means high computational costs and longer computational time. To combat this drawback, one can use different techniques to select and run test cases that are more likely to fail. In this work, we propose CertiFail a test case selection model that is an ensemble of several Machine learning models to more accurately predict if a test case is likely to fail. With CertiFail we achieve an accuracy of 73.7 percent in selection of test cases that fail. In addition CertiFail surpasses baseline models in terms of accurately predicting test cases that fail.

关键词： Software testing Costs Computational modeling Machine learning Predictive models Cyber-physical systems Autonomous automobiles Recording Computational efficiency Automobiles

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Learning Robust Observable to Address Noise in Quantum Machine Learning 20th

Learning Robust Observable to Address Noise in Quantum Mac...

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20th international conference on Grid, Cloud, and Cluster Computing, GCC 2024, 3rd international conference on Quantum Technologies, ICEQT 2024, and 21st international conference on modeling, simulation and Visualization Methods, MSV 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

作者： Khanal, Bikram Rivas, Pablo Department of Computer Science Baylor University WacoTX United States

ISBN: (纸本)9783031858833

Quantum Machine Learning (QML) has emerged as a promising field that combines the power of quantum computing with the principles of machine learning. One of the significant challenges in QML is dealing with noise in quantum systems, especially in the Noisy Intermediate-Scale Quantum (NISQ) era. Noise in quantum systems can introduce errors in quantum computations and degrade the performance of quantum algorithms. In this paper, we propose a framework for learning observables that are robust against noisy channels in quantum systems. We demonstrate that it is possible to learn observables that remain invariant under the effects of noise and show that this can be achieved through a machine-learning approach. We present a toy example using a Bell state under a depolarization channel to illustrate the concept of robust observables. We then describe a machine-learning framework for learning such observables across six two-qubit quantum circuits and five noisy channels. Our results show that it is possible to learn observables that are more robust to noise than conventional observables. We discuss the implications of this finding for quantum machine learning, including potential applications in enhancing the stability of QML models in noisy environments. By developing techniques for learning robust observables, we can improve the performance and reliability of quantum machine learning models in the presence of noise, contributing to the advancement of practical QML applications in the NISQ era. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Quantum noise

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Fuzzy Lateral Control for Unmanned Vehicles: Addressing Probabilistic Packet Loss Issue Under Random Sampling 8th

Fuzzy Lateral Control for Unmanned Vehicles: Addressing Pro...

引用

8th international conference on Life System modeling and simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Hu, Aogui Cao, Zhiru Peng, Chen Wu, Jiancun School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9789819602933

In this paper, the fuzzy control problem of unmanned vehicles is studied. In the unmanned vehicle systems, the lateral offset, yaw rate and heading offset are sampled and sent to the control center, and may unfortunately be dropped due to transmission conflicts. To solve the packet loss issues, a fuzzy model is first established to approximate the nonlinear lateral dynamics of the unmanned vehicles. The measured signals are assumed to be sampled in a random way, induced by which, the phenomenon of probabilistic packet loss is considered. Then, a method of input delay considering double stochasticity delay is used to model a probability distribution of the packet loss problem under stochastic sampling. Furthermore, relaxed sufficient conditions are given to ensure the robust mean square exponential stability and H∞ performance of the closed-loop system. Finally, the effectiveness of the lateral controller design is verified via a simulation example. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Packet loss

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Generate MC/DC test case based on black box SysML state machine

Generate MC/DC test case based on black box SysML state mach...

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2024 international conference on computer Application and Information Security, ICCAIS 2024

作者： Meng, Chenhao Jia, Zhangtao Li, Haoyu Zhang, Xiaoxiao Wang, Jing Nankai University Tianjin China Beijing Institute of Computer Applications and Simulation Technology Beijing China

ISBN: (纸本)9781510689312

With the increasing complexity of safety-critical systems, the significance of testing these systems has grown correspondingly. Generating test cases based on the SysML models of systems offers an effective solution to address the challenges posed by system complexity, thereby improving the efficiency and thoroughness of testing. However, traditional symbolic execution techniques face limitations when dealing with black-box behaviors in SysML models. To overcome this, we propose an extension to SysML that allows modelers to explicitly identify black-box behaviors and indicate the parameters that may vary within these behaviors. Based on the extended SysML state machine model, we introduce an automatic test case generation algorithm grounded in symbolic execution. The generated test case suite achieves high Modified Condition/Decision Coverage (MC/DC). Our contributions include: 1) we propose an extension to SysML that supports the modeling of black-box behaviors, facilitating the automatic generation of test cases using symbolic execution methods;2) we develop a symbolic execution-based test case generation algorithm that produces test case suites with high MC/DC;and 3) we validate the effectiveness of the proposed approach through experiments on the state machine of an automotive collision-avoidance system. © The Authors.

关键词： black-box test MBSE state machine symbolic execution SysML test case generation

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Multiphysics modeling of a Novel MEMS Accelerometer Based on Electromagnetic Induction

Multiphysics Modeling of a Novel MEMS Accelerometer Based on...

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Thermal, Mechanical and Multiphysics simulation and Experiments in Micro-Electronics and Micro-Systems EuroSimE

作者： Ozberk Ozturk Fares Tounsi Laurent Francis Denis Flandre Murat Kaya Yapici Faculty of Engineering and Natural Sciences Sabanci University Istanbul Türkiye ICTEAM Institute UCLouvain Louvain-la-Neuve Belgium Department of Electrical and Computer Engineering University of Washington Seattle USA

ISBN: (数字)9798350393002

ISBN: (纸本)9798350393019

This work reports on the development of a micromachined monolithic single-axis electromagnetic accelerometer based on the relative motion between the windings of a concentric planar transformer, whose internal inductor is suspended. The mechanical and electromagnetic modelling of the sensor is presented, as well as the implementation of a virtual read-out circuit. Electromagnetic simulations were used to extract the electrical performance parameters in the form of a lumped-element model. The results demonstrate that rotation between the two windings leads to an approximately linear variation in the mutual inductance. Furthermore, measurement circuit simulations revealed that the sensor could achieve an in-plane acceleration sensitivity of 32.9 mV/g for a matched network when excited with 3V pp , 100 MHz excitation voltage.

关键词： Accelerometers Micromechanical devices Analytical models Inductance Sensitivity Voltage measurement Windings Electromagnetic induction Transformers Integrated circuit modeling

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