Cloning Agent-based Simulation on GPU

Terrain visualization information integration in agent-based military industrial logistics simulation

The complexity of systems and their multi-faceted relationships mean that the combined application of simulation methods, or hybrid simulation, will enable synergies across techniques and will provide greater insights to problem solving. In this paper we present a hybrid simulation case study in which two models have been developed; an Agent-Based Simulation (ABS) and a Discrete-Event Simulation (DES). The models simulate the operations of offshore wind turbines, with the ABS modelling turbine failure (through, among other things, simulating a degradation model of turbine components) and the DES modelling Maintenance, Repair and Operations (MRO) strategies. The models have been implemented in NetLogo and Simul8 respectively. In this paper we review strategies for the interoperation of our hybrid ABS-DES model and discuss the advantages of using the IEEE 1516 HLA standard for distributed simulation for dynamic information exchange and synchronized model execution.

Advancement in intelligent transportation systems with complex operations requires autonomous planning and management to avoid collisions in day-to-day traffic. As failures and/or inadequacy in traffic safety systems are life-critical, such collisions must be detected and resolved in an efficient way to manage continuously rising traffic. In this paper, we address different types of collision scenarios along with their early detection and resolution techniques in a complex railway system. In order to handle collisions dynamically, a novel agent-based solution approach is proposed using the idea of max-sum algorithm, where each agent communicates and cooperates with others to generate a good feasible solution that keeps the system safe, i.e. collision free. We implement the proposed mechanism in Java Agent DEvelopment Framework (JADE). The simulation results are evaluated with exhaustive experiments and compared with different existing methods to show the efficiency of our proposed approach.

Subway systems are highly vulnerable to disasters because of their confined underground structures and limited evacuation routes, making accurate evacuation analysis essential for reducing casualties. Most existing studies overlook panic behavior, leading to unrealistic assessments of evacuation efficiency. This study develops a modeling framework that integrates panic behavior into agent-based subway evacuation simulations. The framework incorporates three behavioral factors: reaction delays to emergency cues, hesitation at decision points, and irrational route choices. Simulation experiments were conducted under different occupancy conditions, and results from panic-integrated and non-panic scenarios were compared. Findings show that the inclusion of panic significantly prolongs evacuation time, with delays of up to 30% in full-scale scenarios due to congestion and route errors. These outcomes demonstrate that panic behavior exerts a decisive influence on evacuation dynamics and should not be neglected in simulation studies. Incorporating panic into evacuation modeling provides a more realistic basis for designing safer subway systems and developing effective emergency response strategies.

Efficient evacuation is an important task, since it can save people’s lives. In Hungary, to design evacuation, mostly standardized calculations are used, which take into account, for example, the number and age of the people, but not the human behaviour. Human behaviour can be included in agent-based simulations. In this paper, the evacuation simulation of a classroom taking into account cognitive behaviour like panic and reaction time is presented. Moreover, based on the cognitive aspects of agent-based simulations, a new architecture is proposed, which can be used to further develop the presented simulations.

This paper proposes a framework to evaluate the network vulnerability of cities to wildfires. Three cities are selected from the California Public Utilities Commission (CPUC), U.S., fire-threat regions: Orinda, Paradise, and Atascadero. For each city, four different network connectivity measures are calculated, and agent-based evacuation simulations are performed by the Monte Carlo method. In the simulations, the number of isolated vehicles and evacuation time estimates are measured for the following scenarios: (i) no wildfire case with original network; and (ii) wildfire cases with randomly damaged networks that are reduced by 1%, 3%, 5%, 7%, and 10% from the original network. A city-to-city comparison is conducted in relation to network connectivity measures and evacuation simulation results. It is shown that Paradise has the worst network connectivity, and the simulation results reveal that Paradise also has the most sensitive network in relation to random roadway closures caused by wildfire propagation. Thus, among the three cities, Paradise has the most vulnerable network to wildfires as determined through the two analysis results concerning the worst network measures and the simulation results. It is expected that the proposed analysis framework can be generally applied to any city located in a fire-threat region.

Field monitoring serves as an important supervision tool in a variety of engineering domains. An efficient monitoring would trigger an alarm timely once it detects an out-of-control event by learning the state change from the collected sensor data. However, in practice, multiple sensor data may not be gathered appropriately into a database for some unexpected reasons, such as sensor aging, wireless communication failures, and data reading errors, which leads to a large number of missing data as well as inaccurate or delayed detection, and poses a great challenge for field monitoring in sensor networks. This fact motivates us to develop a multitask-learning based field monitoring method in order to achieve an efficient detection when considerable missing data exist during data acquisition. Specifically, we adopt a log likelihood ratio (LR)-based multivariate cumulative sum (MCUSUM) control chart given spatial correlation among neighboring regions within the monitored field. To deal with the missing data problem, we integrate a multitask learning model into the LR-based MCUSUM control chart in the sensor network. Both simulation and real case studies are conducted to validate our proposed approach and the results show that our approach can achieve an accurate and timely detection for an out-of-control state when a large number of missing data exist in the sensor database. Our model provides an effective field monitoring strategy for engineering applications to accurately and timely detect the products with abnormal quality during production and reduce product losses.

Agent-Based Wartime Medical Evacuation Modeling and Simulation

With the growing popularity of Internet of Things (IoT) and sensing technologies, a large number of data streams are being generated at a very rapid pace. To explore the potentials of the integration of IoT and semantic technologies, a few RDF Stream Processing (RSP) query engines are made available which are capable of processing, analyzing and reasoning over semantic data streams in real-time. This way, RSP mitigates data interoperability issues and promotes knowledge discovery and smart decision making for time-sensitive applications. However, a major hurdle in the wide adoption of RSP systems is their query performance. Particularly, the ability of RSP engines to handle a large number of concurrent queries is very limited which refrains large scale stream processing applications (e.g. smart city applications) to adopt RSP. In this paper, we propose a shared-join based approach to improve the performance of an RSP engine for concurrent queries. We also leverage query federation mechanisms to allow distributed query processing over multiple RSP engine instances in order to gain performance for concurrent and distributed queries. We apply load balancing strategies to distribute queries and further optimize the concurrent query performance. We provide a proof of concept implementation by extending CQELS RSP engine and evaluate our approach using existing benchmark datasets for RSP. We also compare the performance of our proposed approach with the state of the art implementation of CQELS RSP engine.

Digital equivalence is the main objective of Digital Twin (DT)s, and simulation is an integral part. DTs reach beyond traditional simulation with the help of real-time synchronization through Industrial Internet of Things (IIoT) technologies. Simulation supports off-line experimentations and planning, while DTs offer synchronous execution and modification. DTs help to understand “what may happen”. Also, they present “what is happening” and its management methodologies. In this paper, we present building aspects and an integration approach for a Digital Twin based Discrete-Event Simulation model. Our approach utilizes a data-driven agent-based simulation within a DT framework. It presents an integration layer that provides two essential features: reconfiguration and state initialization. It gives simulation models configurability and integrity that are required for operating within a DT. Our proposed approach is presented through a use case of a semiconductor manufacturing system. The proposed integration layer extends the usability of current Discrete-Event Simulation (DES) for a DT within a Cyber-Physical Production System.

Agent-based radiological emergency evacuation simulation modeling considering mitigation infrastructures

Predicting the states of the surrounding traffic is one of the major problems in automated driving. Maneuvers such as lane change, merge, and exit management could pose challenges in the absence of intervehicular communications and can benefit from driver behavior prediction. Predicting the motion of surrounding vehicles and trajectory planning need to be computationally efficient for real-time implementation. The main goal of this paper is to develop a fast algorithm that predicts the future states of the neighboring vehicles. The proposed workflow employs Monte Carlo Tree Search (MCTS) along with an on-policy learning technique for fast trajectory planning in multi-lane highway traffic scenarios. Also, for the inclusion of behavioral aspects, cognitive hierarchy and level-K game theories are utilized to predict the reaction and decision of the surrounding drivers. Simulation case studies demonstrate that our proposed approach is real-time implementable and can often avoid collision in difficult simulated confrontations.

Recent research has demonstrated the effectiveness of deep subspace learning networks, including the principal component analysis network (PCANet) and linear discriminant analysis network (LDANet), since they can extract high-level features and better represent abstract semantics of given data. However, their representation does not consider the nonlinear relationship of data and limits the use of features with nonlinear metrics. In this paper, we propose a novel architecture combining the kernel collaboration representation with deep subspace learning based on the PCANet and LDANet for facial expression recognition. First, the PCANet and LDANet are employed to learn abstract features. These features are then mapped to the kernel space to effectively capture their nonlinear similarities. Finally, we develop a simple yet effective classification method with squared [Formula: see text]-regularization, which improves the recognition accuracy and reduces time complexity. Comprehensive experimental results based on the JAFFE, CK[Formula: see text], KDEF and CMU Multi-PIE datasets confirm that our proposed approach has superior performance not just in terms of accuracy, but it is also robust against block occlusion and varying parameter configurations.

Emergency signage systems provide effective route guidance and evacuation information for pedestrians in case of emergencies such as fire, blackout, and explosion. This paper proposes a modified social force (SF) model to investigate crowd evacuation dynamics taking into account the influence of emergency signs. The perceiving probability model is formulated for the quantitative description of the probability that pedestrians can successfully notice the sign and clearly perceive the guidance information. Simulation experiments and controlled experiments are designed to calibrate the parameters of the proposed model. The effectiveness of the modified SF model is preliminarily verified by comparing the simulation results with experimental data and/or empirical results such as fundamental diagrams and self-organization phenomena. A case study of crowd evacuation simulations at a typical Beijing subway station is conducted to evaluate evacuation performance of three signage distribution schemes, i.e., Maximal Covering (MaxCover), Uniform, and Random, which are proposed by the maximal covering location and empirical approaches, as well as contrasted scheme without emergency signs. The effects of the quantity and distribution of emergency signs on crowd evacuation efficiency are studied quantitatively by simulations. The results show that installing emergency signs can improve evacuation efficiency no matter what distribution scheme is adopted. By choosing an appropriate distribution scheme i.e., MaxCover, the evacuation performance can be further improved and the evacuation time can be significantly reduced.

A data assimilation approach is proposed to enhance the dynamic range of the Vortex-In-Cell (VIC) method by simulating future- and past- instances. The VIC method mainly considers a vorticity field from which velocity and acceleration fields are calculated through Poisson equations, respectively bounded by prescribed conditions. In addition, a vorticity time derivative is also available by the vorticity transport equation. The proposed approach focuses on such already available data, i.e., the vorticity and its time derivative fields, for simulating additional instances and getting feedbacks from the corresponding measurement instances, e.g., particle image velocimetry (PTV). However, the self-simulated flow field can be depleted due to a lack of incoming information, which is out of the reconstruction domain at the source instance. To supply that kind of information and thus sustain the simulation, boundary conditions of the simulated instances are required and considered. As a result, the proposed approach can gather corrections from multiple PTV instances while optimizing a single vorticity volume and time-resolved boundary conditions. Since the boundary grid points are much smaller in number than that of the whole volume, one can expect an increased dynamic range. A former work, VIC# (Jeon et al. 2018), which supplements additional constraints and coarse-grid approximation to VIC+ (Schneiders and Scarano 2016), is selected as a 3D method to which the proposed 4D approach is applied. Two explicit Eulerian time-marching methods are tested as a simulation scheme: the forward Euler and the Runge-Kutta methods. A numerical assessment is conducted using the synthetic PTV data, whose ground truth is known, and returns reconstruction qualities based on the velocity and the identified vortical structures. Other practical features regarding convergence and computation complexity are also reported. To visually verify an improvement by the proposed approach, two kinds of time-resolved Shake-the-Box (STB) measurements, which were acquired in high-speed systems, are processed and discussed.