Simulation Modelling Practice and Theory最新文献

With the increasing integration of Artificial Intelligence (AI) in microgrid control systems, there is a risk that malicious actors may exploit vulnerabilities in machine learning algorithms to disrupt power generation and distribution. In this work, we study the potential impacts of adversarial attacks on Vehicle-to-Microgrid (V2M), and discuss potential defensive countermeasures to prevent these risks. Our analysis shows that the decentralized and adaptive nature of microgrids makes them particularly vulnerable to adversarial attacks, and highlights the need for robust security measures to protect against such threats. We propose a framework to detect and prevent adversarial attacks on V2M services using Generative Adversarial Network (GAN) model and a Machine Learning (ML) classifier. We focus on two adversarial attacks, namely inference and evasion attacks. We test our proposed framework under three attack scenarios to ensure the robustness of our solution. As the adversary’s knowledge of a system determines the success of the executed attacks, we study four gray-box cases where the adversary has access to different percentages of the victim’s training dataset. Moreover, we compare our proposed detection method against four benchmark detectors. Furthermore, we evaluate the effectiveness of our proposed method to detect three benchmark evasion attack. Through simulations, we show that all benchmark detectors fail to successfully detect adversarial attacks, particularly when the attacks are intelligently augmented, obtaining an Adversarial Detection Rate (ADR) of up to 60.4%. On the other hand, our proposed framework outperforms the other detectors and achieves an ADR of 92.5%.

Traffic simulation techniques play a crucial role in transportation engineering, offering a sophisticated framework for analysing the intricate dynamics within transportation systems. This paper thoroughly reviews the latest developments in traffic simulation techniques and their applications in improving road safety. Drawing from a comprehensive analysis of recent literature from the Scopus database spanning 2014 to 2024, this review highlights the various analytic methods employed in traffic simulation and their practical applications. Focusing mainly on microsimulation techniques, the study underscores their ability to provide proactive and reactive surrogate safety measures, offering stakeholders valuable insights into traffic safety dynamics. Leveraging methodologies such as microsimulation modelling, surrogate safety measures, statistical model creation, simulation-based conflict prediction, and sensitivity analysis, contemporary research aims to address safety concerns comprehensively. However, the absence of comprehensive crash simulation models presents a significant challenge, raising doubts about the efficacy of traffic simulation in road safety assessment. To overcome this challenge, interdisciplinary research is essential to develop practical solutions that harness technological advancements and foster collaboration across domains. By overcoming existing limitations and refining methodologies, researchers can pave the way for more robust and comprehensive approaches to traffic safety evaluation, contributing significantly to the global goal of enhancing road safety.

With developments in geotechnical engineering, directional rock-breaking technology has been applied in large quantities. As a novel non-explosive rock-breaking technology, Instantaneous Expansion with a Single Crack (IESC) has been studied and applied to some extent in the past few years. IESC uses expansion gas to fracture rock mass in the predetermined direction by a special energy-gathering tube, which has the advantages of high safety, strong directional ability, and easy to operate. At present, there is a lack of in-depth investigation on the directional fracture mechanism of rock under the action of IESC. According to damage mechanics, the fundamental reason for rock fracture is due to the initiation, expansion, and penetration of internal cracks. In this study, a 3-D numerical model based on the theory of progressive failure is established to study the directional rock fracture mechanism of IESC, while a Conventional Expansion (CE) model without energy-gathering tube is established for comparative research. The maximum tensile stress criterion and unified strength criterion are used to identify damage failure of the element. The evolution processes of four key parameters are simulated, the types and degrees of tensile/compressive damage of the unit are analyzed, which aims to decipher the model's directional fracture mechanism under IESC loading. The established 3-D numerical models are validated by comparing with experimental results. The research results can contribute to further understanding the directional rock fracture mechanism of IESC and provide a theoretical basis for the application of IESC in the field.

Cloud computing provides users and programs with scalable resources and on-demand services virtually in real time, making it a fundamental paradigm in modern computing. The concept for using remote computing resources is novel. Cloud computing relies on task scheduling to boost system performance, reduce execution time, and optimize resource use. Due to exponential task increase and problem complexity, the search space is huge. Optimization tasks like this are NP-hard. This work aims to find a near-optimal solution for a multi-objective task scheduling problem in the cloud while lowering search time. Using the Genetic Algorithm (GA) and Gravitational Search Algorithms (GSA) benefits while avoiding their drawbacks, we offer a standard cloud computing task scheduling method to improve system performance and optimize the Quality of service (QoS) parameters like energy, makespan, resource utilization and throughput. We use CloudSim to test standard functions, real-time, and synthetic workloads. The obtained results are compared to other similar, metaheuristic-based techniques that were evaluated under the same conditions. The designed technique outperforms Gravitational Search Algorithms (GSA), Ant Colony Optimization(ACO), and Particle Swarm optimization(PSO) in Degree Of Imbalance (12%), resource utilization (9%), Mean Response Time (7%) and energy consumption (6%).

The linear cutting process in rock poses challenges for verification in field experiments, laboratory investigations, or numerical simulations. This study aims to analyze the rock cutting process and disc cutter force estimation when using linear cutting mode. Three-dimensional numerical simulations using the explicit dynamic finite element method (LS-DYNA software) are conducted to characterize the cutting process. In this regard, two computational algorithms (Lagrangian and Smoothed Particle Hydrodynamics (SPH)) and two material models (Johnson-Holmquist Concrete (JHC) and Riedel-Hiermaier-Thoma (RHT)) are compared, with SPH and RHT identified as more suitable for rock cutting simulation. The results of comparative analyses show that the Lagrangian computational algorithm is highly dependent on the erosion value, hence this method is not suitable for the simulation of the rock-cutting process. Comparing to the RHT material constitutive model, the Johnson-Holmquist model does not well model the post-failure softening strain behavior, which leads to a reduction in the width of the failure area. The comparative analyses also show that the normal and rolling forces predicted by the JHC model are well over 30% higher than the actual experimental results, while the RHT model shows a good agreement between the predictions and the actual results. Overall, the RHT material model with the use of the SPH computational algorithm shows a very good combination in rock cutting process simulation.