Simulation Modelling Practice and Theory最新文献_第9页

Simulating optimal flood evacuation using heuristic algorithms and path-choice behaviors 利用启发式算法和路径选择行为模拟最优洪水疏散

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-06-23 DOI: 10.1016/j.simpat.2025.103167

Housseyn Chebika , Guoqiang Shen , Haoying Han , Mahmoud Mabrouk , Brahim Nouibat

Effective path planning in flooding emergency rescue scenarios is essential for ensuring timely evacuation while minimizing safety risks. Conventional path-planning algorithms often prioritize the shortest or most cost-efficient routes, potentially neglecting safety considerations. To address this limitation, this study introduces an improved path-planning method using a behavior-based A-star (A*) algorithm designed for evacuation scenarios. A cellular automata (CA) environment is applied to address common challenges associated with traditional A* algorithms, including path inefficiencies, longer distances, and difficulties in handling dynamic flood environments. The key innovation of this study is the optimization of a heuristic function by integrating depth sensitivity perception (DSP), which directly influences evacuation behavior by prioritizing safer paths based on real-time water depth assessments during path selection. Experimental results across diverse flood scenarios demonstrate that the optimized A* algorithm significantly outperforms traditional A-star and Dijkstra’s algorithms, achieving reductions in explored nodes by 90.06 % and 93.13 %, lowering safety risks, and shortening computational times by 87.65 % and 88.06 %, respectively. These findings validate the efficacy of the depth-sensitive heuristic in enhancing evacuation pathfinding within complex flood environments.

在洪水紧急救援场景中，有效的路径规划对于确保及时疏散和最大限度地降低安全风险至关重要。传统的路径规划算法通常优先考虑最短或最具成本效益的路线，潜在地忽略了安全考虑。为了解决这一限制，本研究引入了一种改进的路径规划方法，该方法使用基于行为的a -star （a *）算法，该算法专为疏散场景设计。应用元胞自动机（CA）环境来解决与传统A*算法相关的常见挑战，包括路径效率低下、距离较长以及处理动态洪水环境的困难。本研究的关键创新点是通过集成深度敏感感知（DSP）优化启发式函数，该函数在路径选择过程中基于实时水深评估来优先考虑更安全的路径，直接影响疏散行为。不同洪水场景下的实验结果表明，优化后的A*算法显著优于传统的A-star和Dijkstra算法，探测节点减少了90.06%和93.13%，降低了安全风险，计算时间缩短了87.65%和88.06%。这些发现验证了深度敏感启发式算法在复杂洪水环境中增强疏散寻路的有效性。

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引用次数: 0

Efficient prior specification in procedural 3D modelling 有效的事先规范程序三维建模

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-06-21 DOI: 10.1016/j.simpat.2025.103165

Ioannis Kleitsiotis , George Tsirogiannis , Spiridon Likothanassis

Procedural modelling programs can be used to generate 3D scenes of infinite variety, alleviating the need for manual repetitive tasks in 3D modelling. We utilize a probabilistic programming interpretation of controlled procedural modelling programs, and address the issue of prior misspecification, which can hinder the accurate representation of 3D models. We are interested in cases where prior knowledge is available as probabilistic tail bounds on global, high-level features of the 3D scene. In general, specifying the prior parameters satisfying the aforementioned high-level prior knowledge requires a parameter space search. However, programs with a large number of random variables, 3D scenes described by multiple procedural modelling programs and the need for repeated prior predictive checks might necessitate a prolonged prior parameter search. We reduce the time complexity of prior parameter search, and thus improve the process of modelling 3D scenes, by replacing computationally expensive computations of tail bounds constraints with the lower bounds provided by Selberg’s inequality. We present the theoretical underpinnings of our method and a detailed feasibility problem formulation that can be solved numerically. We compare our method to related approaches in the literature, and finally, we demonstrate its application in the procedural generation of 3D scenes in the agricultural domain.

程序化建模程序可用于生成无限种类的3D场景，减轻了3D建模中手动重复任务的需要。我们利用控制程序建模程序的概率编程解释，并解决了先前错误说明的问题，这可能会阻碍3D模型的准确表示。我们感兴趣的是先验知识作为3D场景全局高级特征的概率尾界可用的情况。一般来说，指定满足上述高级先验知识的先验参数需要进行参数空间搜索。然而，具有大量随机变量的程序，由多个程序建模程序描述的3D场景以及需要重复的先验预测检查可能需要长时间的先验参数搜索。我们降低了先验参数搜索的时间复杂度，从而改进了三维场景建模的过程，方法是用Selberg不等式提供的下界取代计算代价高昂的尾界约束计算。我们提出了我们的方法的理论基础和一个详细的可行性问题的表述，可以解决数值。我们将我们的方法与文献中的相关方法进行了比较，最后，我们展示了它在农业领域3D场景程序生成中的应用。

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引用次数: 0

MBMRF: A modified bidirectional IPv6 multicast protocol with mixed upward and downward forwarding for TSCH-enabled WSANs MBMRF：一种改进的双向IPv6组播协议，用于使能tsch的wwsans，具有混合向上和向下转发

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-06-20 DOI: 10.1016/j.simpat.2025.103172

Eden Teshome Hunde , Shereen Ismail

Wireless Sensor and Actuator Networks (WSANs) consist of numerous embedded devices that collaborate to perform complex tasks, surpassing the capabilities of traditional wired networks. This collaboration is efficiently enabled through multicast protocols. While multicast protocols offer significant advantages for WSANs, many fail to meet certain performance requirements. To address these challenges, we propose the Modified Bidirectional Multicast RPL Forwarding (MBMRF) protocol.

This study tackles limitations in existing Internet Protocol version 6 (IPv6) multicast protocols, including the Routing Protocol for Low Power and Lossy Networks (RPL) and Bidirectional Multicast RPL Forwarding (BMRF). The proposed MBMRF protocol introduces a novel mixed upward and downward multicast packet forwarding mechanism optimized for multi-channel Time Slotted Channel Hopping (TSCH) networks. Furthermore, to ensure sufficient timeslot allocation for scheduling mixed up-and-down packet transmissions, the protocol incorporates a modified version of the Orchestra scheduling algorithm.

The proposed MBMRF protocol was implemented and simulated on Zolertia (Z1) motes using the Contiki operating system and evaluated against existing IPv6 multicast protocols, including Stateless Multicast RPL Forwarding (SMRF), Enhanced Stateless Multicast RPL Forwarding (ESMRF), and BMRF. Results show that MBMRF significantly reduces buffer overflow and network-wide energy consumption compared to SMRF, ESMRF, and BMRF, with only a marginal increase in memory usage.

无线传感器和执行器网络（wsan）由许多嵌入式设备组成，这些设备协作执行复杂任务，超越了传统有线网络的能力。这种协作通过多播协议有效地实现。虽然多播协议为无线局域网提供了显著的优势，但许多协议无法满足某些性能要求。为了解决这些挑战，我们提出了改进的双向多播RPL转发（MBMRF）协议。本研究解决了现有互联网协议版本6 （IPv6）多播协议的局限性，包括低功耗和有损网络路由协议（RPL）和双向多播RPL转发（BMRF）。提出的MBMRF协议引入了一种针对多通道时隙跳频（TSCH）网络进行优化的新型混合向上和向下多播分组转发机制。此外，为了确保有足够的时隙分配用于调度混合上下包传输，该协议包含了Orchestra调度算法的修改版本。提出的MBMRF协议在Zolertia （Z1）上使用Contiki操作系统进行了实现和仿真，并与现有的IPv6组播协议进行了比较，包括无状态组播RPL转发（SMRF）、增强无状态组播RPL转发（ESMRF）和BMRF。结果表明，与SMRF、ESMRF和BMRF相比，MBMRF显著减少了缓冲区溢出和网络范围的能量消耗，而内存使用仅略有增加。

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引用次数: 0

Analysis and optimizations of PMI and rank selection algorithms for 5G NR 5G NR的PMI和rank选择算法分析与优化

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-06-19 DOI: 10.1016/j.simpat.2025.103162

Gabriel Carvalho, Sandra Lagén

Multiple-Input Multiple-Output (MIMO) is crucial for enhancing spectral efficiency, channel capacity, coverage, and robustness. However, it requires significant computations to determine a precoding matrix for transmitted data streams. In closed-loop MIMO, as adopted in 3GPP 5G NR, these computations occur on the user side. To avoid transmitting large matrices, 3GPP defined codebooks with pre-defined precoding matrices indexed by the Precoding Matrix Indicator (PMI). The User Equipment (UE) selects a PMI and a Rank Indicator (RI) to report to the Next Generation Node Base (gNB) as part of the Channel State Information (CSI) feedback. PMI/RI selection can be done via exhaustive search or more efficient techniques, which are crucial for real UE implementations due to their impact on computational complexity and energy consumption. This paper analyzes various PMI/RI selection techniques using the open-source ns-3 5G-LENA simulator. We have implemented state-of-the-art techniques in the system-level simulator and carried out extensive simulation campaigns. Also, we propose new PMI/RI selection methods by focusing on performance versus computational complexity trade-offs. Our proposed techniques show a superior simulation speedup (3.71x to 1.119x) with minimal throughput degradation (3% to 3.3%) compared to exhaustive search, depending on sub-band downsampling settings. Other state-of-the-art techniques implemented exhibit greater throughput losses (up to 8.3%) for a lower speedup (up to 3.54x) or similar losses with smaller speedups and potential slowdowns.

多输入多输出（MIMO）对于提高频谱效率、信道容量、覆盖范围和鲁棒性至关重要。然而，它需要大量的计算来确定传输数据流的预编码矩阵。在3GPP 5G NR采用的闭环MIMO中，这些计算发生在用户端。为了避免传输大矩阵，3GPP定义了带有预定义的预编码矩阵的码本，这些预编码矩阵由预编码矩阵指示器（PMI）索引。用户设备（UE）选择一个PMI和一个Rank Indicator （RI）作为通道状态信息（CSI）反馈的一部分报告给下一代节点基础（gNB）。PMI/RI选择可以通过穷举搜索或更有效的技术来完成，由于它们对计算复杂性和能耗的影响，这对于真正的UE实现至关重要。本文使用开源的ns-3 5G-LENA模拟器分析了各种PMI/RI选择技术。我们在系统级模拟器中实施了最先进的技术，并进行了广泛的模拟活动。此外，我们通过关注性能与计算复杂性的权衡，提出了新的PMI/RI选择方法。与穷举搜索相比，我们提出的技术显示出卓越的模拟加速（3.71倍至1.119倍），吞吐量下降最小（3%至3.3%），具体取决于子带下采样设置。实现的其他最先进的技术在较低的加速（高达3.54倍）下表现出更大的吞吐量损失（高达8.3%），或者在较小的加速和潜在的减速下表现出类似的损失。

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引用次数: 0

RCPFH: Reliable controller placement in software-defined networks using fuzzy systems and a modified walrus optimization algorithm RCPFH：在软件定义网络中使用模糊系统和改进的海象优化算法的可靠控制器布局

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-06-17 DOI: 10.1016/j.simpat.2025.103171

Maryam Shamsoddini, Ali Ghaffari, Masoud Kargar, Nahideh Derakhshanfard

Software-Defined Networking (SDN) is a novel network architecture that separates the control plane from the data plane, enabling centralized and programmable management of network resources. One of the key challenges in SDN is determining the optimal number and locations of controllers, called the Controller Placement Problem (CPP), to ensure balanced load distribution, minimal latency, and high network reliability. This paper introduces a novel three-phase approach called Reliable Controller Placement using Fuzzy Logic and Metaheuristic Algorithms (RCPFH), which efficiently optimizes controller placement in SDN environments. In the first phase, the approach employs a fuzzy logic system guided by Levy Flight parameters to estimate the optimal number of controllers by evaluating critical factors such as energy consumption, congestion levels, and load variance across the network. The second phase utilizes a Modified Walrus Optimization Algorithm to identify the most suitable controller positions, considering path reliability, processing capacity, and propagation delay. Finally, in the third phase, backup controllers are selected to enhance system reliability in the event of controller failure. The proposed RCPFH framework is evaluated using four real-world network topologies from the ZOO Topology dataset. Comparative experiments with state-of-the-art approaches demonstrate significant performance improvements: up to a 38 % reduction in energy consumption, an 11 % decrease in load variance, a 36 % increase in network availability, a 17 % reduction in average latency, and a 15 % decrease in link failure rate. These results validate the effectiveness of RCPFH in optimizing SDN performance while maintaining robustness and operational efficiency.

软件定义网络（SDN）是一种新颖的网络架构，它将控制平面和数据平面分离开来，实现对网络资源的集中和可编程管理。SDN的关键挑战之一是确定控制器的最佳数量和位置，称为控制器放置问题（CPP），以确保均衡的负载分配，最小的延迟和高网络可靠性。本文介绍了一种使用模糊逻辑和元启发式算法（RCPFH）的新型三相方法，称为可靠控制器放置，该方法有效地优化了SDN环境中的控制器放置。在第一阶段，该方法采用以Levy Flight参数为指导的模糊逻辑系统，通过评估整个网络的能耗、拥塞水平和负载变化等关键因素来估计控制器的最优数量。第二阶段采用改进的海象优化算法，考虑路径可靠性、处理能力和传播延迟，确定最合适的控制器位置。最后，在第三阶段，选择备用控制器，以提高系统在控制器失效时的可靠性。使用来自ZOO Topology数据集的四种真实网络拓扑来评估所提出的RCPFH框架。与最先进的方法进行的比较实验显示了显著的性能改进：能耗减少38%，负载变化减少11%，网络可用性增加36%，平均延迟减少17%，链路故障率减少15%。这些结果验证了RCPFH在优化SDN性能的同时保持鲁棒性和运行效率的有效性。

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引用次数: 0

JaGW: A hybrid meta-heuristic algorithm for IoT workflow placement in fog computing environment JaGW：用于雾计算环境下物联网工作流放置的混合元启发式算法

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-06-17 DOI: 10.1016/j.simpat.2025.103163

Hemant Kumar Apat , Bibhudatta Sahoo

In recent years, applications of the Internet of Things (IoT) have experienced rapid growth, driven by the widespread adoption of IoT devices in various sectors. However, these devices are typically resource-constrained in terms of computing power and storage capacity. As a result, they often offload the generated data and tasks to nearby edge devices or fog computing layers for further processing and execution. The fog computing layer is located in close vicinity of the IoT devices and comprises a set of heterogeneous fog computing nodes to supplement the capacities of resource-constrained IoT devices. The fog computing nodes often pose computational challenges for various computation-intensive tasks such as image processing application, comprises various machine learning and artificial intelligence enabled tasks. In such a scenario, finding the effective task placement for dynamic and heterogeneous applications is computationally hard. In this work, we formulate the IoT application workflow placement problem as a multi-objective optimization problem formulated as Integer Linear Programming (ILP) model with the objective of minimizing the makespan, cost of execution, and energy consumption. A hybrid metaheuristic approach is proposed that combines the strengths of the Jaya algorithm (JA) and Grey Wolf Optimization (GWO) named as JaGW to derive a sub-optimal solution. The proposed JaGW is compared with conventional GWO and other state of the art algorithms such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) using the Montage scientific workflow dataset. The simulation results demonstrate that the proposed algorithm achieves an average reduction in energy consumption of 24.84% compared to JAYA, 14.67% compared to ACO, 14.65% compared to PSO, and 8.78% compared to GWO, thereby exemplifying its superior performance over other metaheuristic algorithms.

近年来，由于物联网设备在各个领域的广泛采用，物联网（IoT）的应用经历了快速增长。然而，这些设备在计算能力和存储容量方面通常受到资源限制。因此，他们经常将生成的数据和任务卸载到附近的边缘设备或雾计算层，以便进一步处理和执行。雾计算层位于物联网设备附近，由一组异构雾计算节点组成，以补充资源受限的物联网设备的能力。雾计算节点通常为各种计算密集型任务（如图像处理应用）带来计算挑战，包括各种机器学习和人工智能支持的任务。在这种情况下，为动态和异构应用程序找到有效的任务布局在计算上是困难的。在这项工作中，我们将物联网应用工作流放置问题制定为一个多目标优化问题，该问题制定为整数线性规划（ILP）模型，目标是最小化完工时间、执行成本和能耗。提出了一种混合元启发式方法，将Jaya算法（JA）和灰狼优化（GWO）的优点结合起来，称为JaGW，以获得次优解。使用蒙太奇科学工作流数据集，将提出的JaGW与传统的GWO和其他最先进的算法（如蚁群优化（ACO）和粒子群优化（PSO））进行比较。仿真结果表明，该算法与JAYA相比平均能耗降低24.84%，与ACO相比平均能耗降低14.67%，与PSO相比平均能耗降低14.65%，与GWO相比平均能耗降低8.78%，证明了其优于其他元启发式算法的性能。

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引用次数: 0

Multi-intersection platoon ecological speed planning strategy and method for autonomous driving simulation testing 自动驾驶仿真测试中多交叉口排生态速度规划策略与方法

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-06-10 DOI: 10.1016/j.simpat.2025.103166

Chuanxiang Ren , Li Lu , Xiang Liu , Fangfang Fu , Lin Cheng

With the rapid development of Internet of Vehicles (IoV) technology, ecological speed planning has become a critical challenge in eco-driving, particularly in reducing energy consumption and improving the efficiency of autonomous vehicles. A key research focus is how to achieve energy savings and emission reductions by optimizing driving speed under various complex conditions, while simultaneously ensuring driving comfort and traffic efficiency. In view of this, a multi-intersection ecological speed planning strategy and method for autonomous platoon is proposed, aiming to reduce speed fluctuations and energy consumption of autonomous platoon in multiple driving scenarios. Firstly, the scenarios of platoon passing through the current intersection and its downstream intersection are analyzed, and then, the strategies for the platoon to pass through the current and its downstream intersections are proposed, including constant speed strategy (CSS) and segmented speed strategy (SSS). Moreover, the platoon ecological speed planning method is presented, which includes the calculation of the passage period, the capacity in the passage period of the intersections, and the platoon ecological speed. Finally, different simulation situations are designed in view of different ecological speed strategies, and compared with the single intersection platoon speed strategy (SIPSS) and the no speed strategy (NSS). The results indicate that the CSS and the SSS can mitigate the speed fluctuations of the platoon through intersections, reduce the fuel consumption and delay time, and outperform the SIPSS and NSS. Especially in the current intersection with a queuing platoon, the proposed strategy reduces fuel consumption and delay time by up to 67.21 % and 2.74 %, respectively.

随着车联网技术的快速发展，生态速度规划已成为生态驾驶的关键挑战，特别是在降低能源消耗和提高自动驾驶汽车效率方面。如何在各种复杂条件下通过优化行驶速度实现节能减排，同时保证驾驶舒适性和交通效率是一个关键的研究重点。鉴于此，提出了一种多交叉口的自主队列生态速度规划策略和方法，旨在减少自主队列在多驾驶场景下的速度波动和能量消耗。首先分析了车队通过当前交叉口及其下游交叉口的场景，然后提出了车队通过当前交叉口及其下游交叉口的策略，包括恒速策略（CSS）和分段速度策略（SSS）。在此基础上，提出了队列生态速度规划方法，包括交叉口通行周期、通行周期通行能力和队列生态速度的计算。最后，针对不同的生态速度策略设计了不同的仿真场景，并与单交叉口排速策略（SIPSS）和无速度策略（NSS）进行了比较。结果表明，CSS和SSS能有效缓解车辆通过交叉口时的速度波动，降低车辆的油耗和延迟时间，优于sips和NSS。特别是在当前存在排队队列的交叉口，该策略可将燃油消耗和延误时间分别降低67.21%和2.74%。

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引用次数: 0

Optimizing IoMT network performance using gateway placement 使用网关布局优化IoMT网络性能

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-06-07 DOI: 10.1016/j.simpat.2025.103164

R. Mahalakshmi, N. Lalithamani

Gateway Device placement for processing patient data, plays a pivotal role in providing accurate and critical care for patient monitoring and assistance in a connected healthcare system. Long Range Wide Area Network (LoRaWAN) is a wide area network protocol used in providing low-power, seamless communication between the patient health sensors, monitors and the health assistance services in a healthcare network using gateway devices. Objective of the research work is to achieve optimization in the gateway by an adaptive device placement strategy using LoRaWAN thereby improving the network performance. The strategy is a multi-objective optimization, which includes Coverage Maximization, connectivity Optimization, and Energy Minimization for improving the network performance. The gateway placement strategy is verified with the simulation results obtained from Omnet++ Framework for LoRa (FLoRa). The network performance improvement is verified with key metric results observed based on the gateway placement strategy, resulted in Average Energy Consumption being 4.44 mJ, Packet Delivery Ratio (PDR) at 69 %, and Throughput reaching to 481 bps.

用于处理患者数据的网关设备在为连接的医疗保健系统中的患者监测和协助提供准确和关键的护理方面发挥着关键作用。远程广域网（LoRaWAN）是一种广域网协议，用于在使用网关设备的医疗保健网络中的患者健康传感器、监视器和健康辅助服务之间提供低功耗、无缝通信。研究工作的目的是通过采用LoRaWAN的自适应设备放置策略来实现网关的优化，从而提高网络性能。该策略是一个多目标优化，包括覆盖最大化、连通性优化和能量最小化，以提高网络性能。利用omnet++框架对LoRa （FLoRa）的仿真结果验证了网关放置策略。通过基于网关放置策略观察到的关键指标结果验证了网络性能的改进，导致平均能耗为4.44 mJ，分组传递率（PDR）为69%，吞吐量达到481 bps。

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引用次数: 0

A novel open-source framework for performing TSN schedules 一个用于执行TSN调度的新颖的开源框架

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-06-06 DOI: 10.1016/j.simpat.2025.103147

Aellison C.T. Santos , Renan M. Silva , Ben Schneider , Malte Wilhelm , Iguatemi E. Fonseca , Vivek Nigam

Due to the complexity of deployed networks, as well as its NP-complete traffic scheduling problem (Craciunas et al., 2016b), Time Sensitive Networking (TSN) configuration is error-prone and challenging when done manually. We present TSNsched, an open-source framework for TSN configuration. The proposed framework has workflows that enable the generation, validation, and deployment of TSN schedules. TSNsched takes as input the network logical topology, expressed as flows, its latency and jitter requirements, generating schedules for TSN switches by reducing different variations of traffic scheduling problems to logical theories that can be automatically solved using Satisfiability Modulo Theories (SMT) solvers. TSNsched provides customized network simulators for validation of the generated schedules. We describe by example how these tool workflows can be used to analyze, validate, and deploy TSN configurations.

由于部署网络的复杂性，以及其NP-complete流量调度问题（Craciunas et al., 2016b），时间敏感网络（TSN）配置在手动完成时容易出错且具有挑战性。我们提出TSNsched，一个用于TSN配置的开源框架。建议的框架具有支持TSN计划的生成、验证和部署的工作流。TSNsched将以流表示的网络逻辑拓扑、时延和抖动需求作为输入，通过将流量调度问题的不同变化形式简化为可以使用可满足模理论（Satisfiability Modulo theories， SMT）求解器自动求解的逻辑理论，生成TSN交换机的调度。TSNsched提供定制的网络模拟器来验证生成的时间表。我们通过示例描述如何使用这些工具工作流来分析、验证和部署TSN配置。

引用次数: 0

Enhanced flame detection in virtual tunnels using DEV-YOLOv8 and digital twin systems 使用DEV-YOLOv8和数字孪生系统增强虚拟隧道中的火焰探测

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-06-02 DOI: 10.1016/j.simpat.2025.103143

Yongjiang Li , Xingguo Song , Faming Lin , Xu Fang

Highway tunnel fires pose significant safety challenges requiring reliable detection and response strategies. Traditional testing methods face limitations due to insufficient sample diversity and high costs, thus making virtual environments a promising alternative for algorithm development. This study proposes DEV-YOLOv8, an enhanced YOLOv8 model integrated with digital twin technology for flame detection in simulated tunnel environments. The model incorporates three key enhancements: deformable convolutions for adaptive feature extraction, an efficient multi-scale attention mechanism for stable parameter updates, and a cross-stage partial connections module for efficient multi-scale fusion. A Unity-ROS2-based digital twin system enables cost-effective simulation and dataset generation. Experiments demonstrate DEV-YOLOv8’s superiority with 2.2%/1.5%/1.2% improvements in accuracy/recall/[email protected] respectively, while reducing computational cost by 0.8 GFLOPs. The integrated system achieves 98.21% simulation accuracy and 96.19% physical deployment accuracy, with latency differences

<

3.15 ms and RRT delays below 9.91 ms. This work establishes an efficient framework combining deep learning and digital twins for adaptive fire detection in tunnel environments.

公路隧道火灾带来了重大的安全挑战，需要可靠的探测和响应策略。传统的测试方法由于样本多样性不足和成本高而受到限制，因此虚拟环境成为算法开发的一个有希望的替代方案。本研究提出了DEV-YOLOv8，一种集成了数字孪生技术的增强型YOLOv8模型，用于模拟隧道环境中的火焰探测。该模型包含三个关键的改进：用于自适应特征提取的可变形卷积，用于稳定参数更新的高效多尺度注意机制，以及用于高效多尺度融合的跨阶段部分连接模块。基于unity - ros2的数字孪生系统可实现经济高效的模拟和数据集生成。实验证明了DEV-YOLOv8的优势，在准确率/召回率/[email protected]方面分别提高了2.2%/1.5%/1.2%，同时降低了0.8 GFLOPs的计算成本。集成系统的仿真精度为98.21%，物理部署精度为96.19%，时延差异为<；3.15 ms和RRT延迟低于9.91 ms。这项工作建立了一个结合深度学习和数字孪生的有效框架，用于隧道环境中的自适应火灾探测。

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引用次数: 0