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Simulation has been applied to diverse domains such as urban growth modeling and market dynamics modeling. Some of these applications may require validations, based on some real-world observations modeled in the simulation. This validation can be conducted as either qualitative face-validation or quantitative empirical validation; however, as the importance and accumulation of data grows, the importance of quantitative validation has been highlighted in recent studies. The key component of quantitative validation is finding a calibrated set of parameters to regenerate the real-world observations in the simulation models. While the parameter of interest to be calibrated has hitherto been fixed throughout simulation executions, we expand the static parameter calibration in two dimensions in this study, dynamically and heterogeneously. The dynamic calibration changes the parameter values over the simulation period by reflecting the simulation output trend, and the heterogeneous calibration changes the parameter values per simulated entity clusters by considering the similarities of the entity states. We experimented with the proposed calibrations on a hypothetical case and a real-world case. For the hypothetical scenario, we used the wealth distribution model to illustrate how our calibration works. For the real-world scenario, we selected the real estate market model. The models were selected, because of two reasons. First, they have heterogeneous entities, being agent-based models. Second, they are agent-based models exhibiting real-world trends over time.

This study presents a DSDEVS-based method to accelerate simulation execution for AI training in USV (Unmanned Surface vehicle) naval combat scenarios. The proposed approach introduces an event filtering technique that selectively suppresses low-importance sensing events based on the distance to enemy targets. By dynamically adjusting structural couplings and modifying sensing frequency through domain-specific thresholds, the method reduces execution time while maintaining a balance between speed and fidelity. Two key parameters—Event Filtering Distance (EFD) and Sensor Acceleration Time Advance (SATA)—enable conditional event filtering and time advance adjustments within the sensor model. Experimental results demonstrate a 3.03 improvement in runtime, highlighting the effectiveness of the method and the trade-off between simulation speedup and fidelity.

Recently, the population of Seoul has been affected by particulate matter in the atmosphere. This problem can be addressed by developing an elaborate forecasting model to estimate the concentration of fine dust in the metropolitan area. We present a forecasting model of the fine dust concentration with an extended range of input variables, compared to existing models. The model takes inputs from holistic perspectives such as topographical features on the surface, chemical sources of the fine dusts, traffic and the human activities in sub-areas, and meteorological data such as wind, temperature, and humidity, of fine dust. Our model was evaluated by the index-of-agreement (IOA) and the root mean-squared error (RMSE) in predicting PM2.5 and PM10 over three subsequent days. Our model variations consist of linear regressions, ARIMA, and Gaussian process regressions (GPR). The GPR showed the best performance in terms of IOA that is over 0.6 in the three-day predictions.

Background The role of visiting health services has been proven to be effective in promoting the health of older populations. Hence, developing a web system for nurses may help improve the quality of visiting health services for community-dwelling frail older adults. This study was conducted to develop a web application that reflects the needs of visiting nurses. Methods Visiting nurses of public health centers and community centers in South Korea participated in the design and evaluation process. Six nurses took part in the focus group interviews, and 21 visiting nurses and community center managers participated in the satisfaction evaluation. Focus group interviews were conducted to identify the needs of visiting nurses with respect to system function. Based on the findings, a web application that can support the effective delivery of home visiting services in the community was developed. An artificial intelligence (AI) algorithm was also developed to recommend health and welfare services according to each patient’s health status. After development, a structured survey was conducted to evaluate user satisfaction with system features using Kano’s model. Results The new system can be used with mobile devices to increase the mobility of visiting nurses. The system includes 13 features that support the management of patient data and enhance the efficiency of visiting services (e.g., map, navigation, scheduler, protocol archives, professional advice, and online case conferencing). The user satisfaction survey revealed that nurses showed high satisfaction with the system. Among all features, the nurses were most satisfied with the care plan, which included AI-based recommendations for community referral. Conclusions The system developed from the study has attractive features for visiting nurses and supports their essential tasks. The system can help with effective case management for older adults requiring in-home care and reduce nurses' workload. It can also improve communication and networking between healthcare and long-term care institutions.

Multi-resolution modeling (MRM) has been considered as an ideal form of simulation to acquire low-resolution scalability as well as high-resolution modeled details. Although both practical and theoretical interests exist in MRM, actual implementations were quite different in terms of cases and methods. Specifically, MRM implementations range from parameter-based interoperation to model exchanges with different resolutions, yet it is difficult to observe a method that focuses on both of these aspects. To this end, this paper introduces a formalism or multi-resolution translational Discrete Event System Specification (MRT-DEVS). Focusing on the practical perspective, MRT-DEVS intends to ease the implementation’s difficulty and reduce the simulation’s execution costs. Specifically, MRT-DEVS embeds state and event translation functions into the model’s specifications so that it enables MRM with less complex mechanisms in terms of operations. Using the provided case study and a reduction to other MRM methods, the theoretical soundness of the proposed method is supported. Moreover, we discussed the pros and the cons of the proposed method from various MRM perspectives. We expect that with all the provided information, MRMS users would consider the proposed method as a practical option to implement their models.

This paper proposes a new method of designing a data-driven controller for aircraft maneuver. Assuming that we do not have knowledge of the controller and the controlled aircraft, we propose a controller design with explorations of the control inputs and their responses from the aircraft. Specifically, we utilize Bayesian optimization (BO) with Gaussian process (GP) regression for black-box modeling of the aircraft responses from the explored controls, which are selected as samples to experiment with BO. We tested the proposed controller with a rigid six degrees of freedom (6DoF) nonlinear aircraft model by varying the kernel structures of the GP regressions. Our proposed method shows shorter flight times and smaller deviations navigating fixed waypoints compared to the tuned Proportional Integral Derivatives (PID) controller. The proposed controller can be an alternative to PID control, particularly when both controller structure and controlled plant model information are unknown.

The success of military operations depends on soldiers’ execution of the operation as well as resources used for the operation. However, this does not mean that more men and firepower will ensure victory. Military units, just like any other organization, are collections of distributed elements, and improving the organization or command and control (C2) structure of such elements will ultimately show the true power of more men and resources. This paper presents a case study comparing two C2 structures in a counterfire operation, which is a very realistic scenario in some parts of the world. We modeled each structure with meta-networks and agent-based simulations, and then determined why one structure has a better outcome in the simulation. In particular, we jointly analyze the virtual experiment and network metrics, i.e., centralities, to identify the important resources and human factors. This research provides critical insight and suggestions to reform the C2 structure based on quantitative findings. In terms of the C2 structure, assigning detection units to the decentralized echelon brings about the reduction of the time for the targeting process, while the strengthened gun power for multiple targets is proved to have strong influence from the operational perspective.

This study analyzes the Korean housing market using an agent-based simulation from financial and societal perspectives. We initialize heterogeneous household agents using microlevel household survey data from Korea, and we model housing decisions and interactions through a simulated market. First, we validate and calibrate the model to reproduce real-world observations and several stylized facts about the Korean housing market. Then, we conduct a policy experiment to determine the impact of changes in the quantity and cost of housing finance on households’ living conditions and wealth inequality. As proxies for the quantity of housing finance, we adopt the loan-to-value and debt-to-income regulation ratios, classified as macroprudential policy, which are the leverage measures used by the Korean government. The interest rate, one of the levers of monetary policy, is also used as a proxy for housing finance costs. The results of the policy experiment confirm that the quantitative expansion of housing finance provides more benefits to high-income households, thereby worsening wealth inequality. In addition, we find that an increase in housing finance costs lowers the incentive to speculate on housing, but this speculation does not improve the housing status of middle-income households. Finally, we demonstrate that macroprudential policies mitigate the exacerbation of wealth inequality in a relaxed monetary policy state.

This paper proposes a new method of predicting the future state of a ballistic target trajectory. There have been a number of estimation methods that utilize the variations of Kalman filters, and the prediction of the future states followed the simple propagations of the target dynamic equations. However, these simple propagations suffered from no observation of the future state, so this propagation could not estimate a key parameter of the dynamics equation, such as the ballistic coefficient. We resolved this limitation by applying a data-driven approach to predict the ballistic coefficient. From this learning of the ballistic coefficient, we calculated the future state with the future ballistic parameter that differs over time. Our proposed model shows the better performance than the traditional simple propagation method in this state prediction task. The value of this research could be recognized as an application of machine learning techniques to the aerodynamics domains. Our framework suggests how to maximize the synergy by linking the traditional filtering aproaches and diverse machine learning techniques, i.e., Gaussian process regression, support vector regression and regularized linear regression.

During a military operation, the nature of command and control (C2) depends primarily on the intuition of a human commander. If the quality and timing of decision making leads to decisions, good commanders should be able to distinguish between decisions that require urgent responses and those to which responses can be delayed. However, current Republic of Korea military doctrine emphasizes only the speed of decision making and operations, regardless of command type. Hence, we investigate the design parameters for command timing such as decision-making time and ordering/reporting cycle during the offensive operation of a mechanized infantry brigade. We build a simulation model of the brigade's operations that comprises detailed C2 process models. Next, we use the Taguchi method to identify command timing that both optimizes performance and is robust to combat-related noise factors. The results demonstrate that rapid command times do not always correlate with high combat effectiveness. The results are also compared with those using the Latin hypercube design, which also support the insight we found using the Taguchi method although the Taguchi method was better at finding optimal parameter setting in this case. The findings of simulation-based virtual experiments and optimization processes offer guidelines for the detailed design of C2 activities.