Simulation Modelling Practice and Theory最新文献

The modeling of driving behavior is pivotal for the accurate simulation of traffic scenarios and for providing human-like decision-making of autonomous driving systems. Car-following (CF) and lane-changing (LC) behaviors are continuous maneuvers within traffic flow, generally modeled separately in the literature. The coherence between these two behaviors may be ignored, leading to unrealistic behavioral simulations. Therefore, this paper establishes a risk field-based driving behavior model for two-dimensional motion, ensuring coherent modeling of CF and LC behaviors under a unified framework. First, a risk quantification method is developed to calculate the risk in two-dimensional scenarios, accounting for risk over the preview time. A cubic polynomial is applied to generate path curves that align with vehicle dynamics. Second, the enhanced behavior model primarily comprises two integral components: path and trajectory planning. These two components aim to identify the path or trajectory that maximizes the benefit while meeting the desired risk. Third, the maximum acceptable risk, representing a higher risk than the desired risk, is defined to facilitate path adjustment and avoid frequent path adjustment. Finally, the proposed model is proved through comparisons with existing models using driving data. Several cases are employed for further analysis to show the model's rationality and potential in various aspects. This study develops the previous risk field-based behavior model from one-dimensional to two-dimensional scenarios, furnishes a unified framework for elucidating driving behavior in various scenarios, and contributes to the progress of behavior modeling.

As a device characterized by multiple degrees of freedom in one driving unit, analytical electromagnetic torque modeling is needed for the rotor position tracking control of a Permanent Magnet Spherical Motor (PMSpM). In this paper, Extreme Gradient Boosting (XGBoost) was proposed to be employed for establishing the output relationship between the rotor position and the electromagnetic torque of PMSpM. The Finite Element Method (FEM) was applied to obtain train data and test data concerning the rotor position and electromagnetic torque of PMSpM. Particle Swarm Optimization (PSO) was applied to optimize partial parameters of XGBoost, which serves to enhance the modeling accuracy of electromagnetic torque via XGBoost. The predictive results of algorithms, including Random Forest (RF), Gradient Boosting Regression Tree (GBRT), Multi-task Gaussian Process (MTGP), and XGBoost, were compared with FEM results and experimental results over multiple indicators. The capability of XGBoost has been validated not only to perform modeling tasks within an abbreviated time span but also to generate models that display amplified accuracy and efficiency.

We present a novel blockchain-based Federated Learning (FL) system that introduces a Byzantine-resilient consensus protocol that performs well in the presence of adversarial participants. Unlike existing state-of-the-art, this system can be deployed in a fully decentralized manner, meaning it does not rely on any single actor to function correctly. Using a Smart Contract-driven workflow coupled with a commitment scheme and a differential privacy-based solution, we ensure training integrity, prevent plagiarism, and protect against leakage of sensitive data while performing effective federated training. We demonstrate the system’s effectiveness by performing simulation and implementation of an end-to-end proof of concept. Our practical implementation showcases the system’s efficiency on a single computer with multiple trainers, revealing low memory demands and manageable network and block I/O, which suggest scalability to larger, more complex networks. The paper concludes by exploring future enhancements, including advanced cryptographic methods for enhanced privacy and potential applications extending the system’s utility to broader domains within FL. Our work lays the groundwork for a new generation of decentralized learning systems, promising increased adoption in real-world scenarios where data privacy and security are of paramount concern.

Cycling as a mode of transport is on an upward trend as a low-emission alternative to driving in urbanized areas nowadays. With the increasing number of cyclists, it is of great importance to assess the capacity of cycling infrastructure in practice. Simulation models are useful tools to investigate bicycle flow performance considering cyclists’ distinct moving behaviors. However, existing bicycle simulation models are restricted by either space discretization, lane-based setup, adaptation from models for car traffic, or complicated calibration requirement in a force-based environment. In addition, cyclists’ decision-making ability in the operational-level cycling behavior are not well-captured in these models. This paper proposes a comprehensible microscopic bicycle simulation model which includes a detailed decision-making process and the ability to simulate continuous-space lateral movement. The model consists of three levels, maneuver decision, movement planning, and physical acceleration. It is able to simulate bicycle flow dynamics in undersaturated traffic conditions on an exclusive bike path. As we do not intend to show the empirical validity of the proposed model, the simulation experiment aims at verifying the model and exploring bicycle flow performance in various scenarios by estimating the fundamental diagrams (FDs). The effect of different path widths on bicycle flow capacity is first explored. Other behavioral factors, including desired speed heterogeneity, overtaking incentive, and safety region size perceived by cyclists, which can potentially influence the shape of the FD are also tested. The model can be further extended to simulate relatively complex cycling behavior with cooperative and anticipative strategies and investigate bicycle flow characteristics in congested traffic conditions.

Wireless Sensor Network (WSN) is a set of several sensor nodes that are used for monitoring heterogeneous physical objects. In WSNs, irregular and bursty traffic Leads to the congestion problem, which incites a decrease in Packet Delivery Ratio (PDR) and increases packet loss as well as end-to-end delay. In the recent era, manifold efforts have been carried out to reduce network congestion however, these solutions have slow and premature optimization. To address optimization issues, this paper presents a self-adaptive source-sending rate optimization algorithm, which is a hybrid version of Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Bifold-objective Proportional Integral Derivative (BPID) called N3-BPID. These techniques play a significant role in optimizing source rates to reduce network congestion. NSGA-III is a reference-based evolutionary approach, which dynamically configures the PID coefficients to get an optimal response. Furthermore, a novel bifold-objective fitness function is designed that balances the trade-offs between two PIDs performance indexes such as the Integral of Absolute Error and the Integral of Square Error. Due to simplicity and efficiency, an identically weighted aggregation mechanism is applied to ensemble both objectives into a single one. The proposed work is implemented to demonstrate a smart border surveillance application using Network Simulator v3 and compared with the state-of-the-art congestion control model Cuckoo Fuzzy PID (CFPID). The experimental result reveals that the proposed algorithm has significantly outperformed existing schemes in terms of PDR by 6.82%, packet loss by 24.52%, end-to-end delay by 15.31%, and queue length deviation by 8.93%.

This article explores the application of simulation-driven decision support systems in the context of healthcare processes. The findings obtained from the review of related research suggest that simulation models have been widely used in the healthcare sector as valuable tools to aid decision-making aimed at the improvement of healthcare process management. However, the analyzed works lack sufficient evidence regarding the utilization of systematic approaches in the development of their proposals. We have conducted a research effort in this field, resulting in the design of a conceptual framework that guides in the systematic development of decision support systems centered around simulation models. The framework includes a specific methodology for developing simulation models, with simulation modeling experimentation serving as a valuable technique for assessing the impact of potential changes on the performance of the process. To illustrate the applicability and practical value of the proposed framework, we have developed an application case within the context of the Urgent Healthcare for Women's (UHW) process in a public hospital in Andalusia, Spain.

Mixed elastohydrodynamic lubrication is the main lubrication style of planetary gear mechanism. In this paper, the dynamic characteristics of planetary gear mechanism under mixed elastohydrodynamic lubrication are investigated using a computational methodology. First, the mathematic model of mixed elastohydrodynamic lubrication is proposed, in which the scaling factors are introduced to depict the balance between the asperities and the oil film. A stiffness formula of the oil film is presented to describe the time varying stiffness of the teeth pair. Then, the stiffness of the oil film and the general stiffness are obtained. Finally, dynamic model of the planetary gear mechanisms is established and the dynamic responses of planetary gear mechanism are analyzed. The influences of operational parameters on stiffnesses and dynamic characteristics of the planetary gear mechanisms are investigated. The simulation results indicate that the nonlinear dynamics characteristics are significantly influenced by the lubrication conditions.

This study investigates the effects of twin rail-mounted gantry crane (RMG) scheduling strategies and handshake area designs on container yard efficiency and energy consumption. A handshake area is set for temporary storage of containers within each block, enabling the cooperation and interference avoidance of twin RMGs. Considering the complexity and dynamics, we establish a simulation model for the twin RMG operations with agent-based and discrete event simulation modeling. Container relocation, microscopic movements of RMGs, and stochastic arrivals of automated guided vehicles (AGVs) and external trucks (ETs) are simulated, and the corresponding efficiency and energy consumption are quantitatively estimated. By testing the six RMG scheduling strategies and thirteen handshake area designs, the results show that the 2-OPT RMG scheduling strategy (an optimization heuristic that attempts to find the schedule with the minimum makespan) outperforms the other strategies in terms of efficiency. However, the optimal RMG strategy for minimal energy consumption depends on the ratio of landside and seaside requests and handshake area settings. Furthermore, the location of the handshake area significantly affects the efficiency and power consumption of AGVs and RMGs. This study can provide decision support to improve the efficiency and reduce energy consumption of the twin RMGs by selecting a specified combination of the RMG scheduling strategy and the handshake area design.