In this paper, the previously described Use-Case-Dependent modeling Approach is evaluated and its usefulness for grid simulation is explored. One of the main merits of the approach is its application in automated syst...
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ISBN:
(纸本)9798350363647;9798350363630
In this paper, the previously described Use-Case-Dependent modeling Approach is evaluated and its usefulness for grid simulation is explored. One of the main merits of the approach is its application in automated system analysis. Previous results also suggest a reduced usage of computing resources. This however has not yet been systematically tested and properly verified. Therefore, to inspect the validity of this approach, the methodology is explained and simulation results are compared to measurements of the modeled DC system. It is shown that the grid models adequately represent the system's behavior and the simulation results can therefore be used for system analysis. Discrepancies can be explained by an insufficient modeling of the measured system's damping elements. These differences are however small enough to be mitigated by usually applied error margins, especially for system stability analysis. Although more work is required for full verification, the validity and versatility of the approach is demonstrated.
The omnidirectional mobile robotic arm system is a highly coupled, multivariate, and highly nonlinear system. The traditional modeling approach uses Lagrange method to establish dynamic model, which suffers from issue...
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ISBN:
(纸本)9798350388084;9798350388077
The omnidirectional mobile robotic arm system is a highly coupled, multivariate, and highly nonlinear system. The traditional modeling approach uses Lagrange method to establish dynamic model, which suffers from issues of complex modeling, unknown model parameters, and insufficient accuracy in modeling. In this paper, a modeling method based on Koopman operator theory is proposed for this system, which is essentially a data-driven modeling method. Specifically, this paper collects data on system dynamic characteristics and transforms complex nonlinear models into high-dimensional linear models. This paper preprocesses the voltage data in the dataset based on platform characteristics and obtains the Koopman high-dimensional linear model, avoiding the complex modeling problems using Lagrange methods. simulation has been performed, which confirms the accuracy and effectiveness of using Koopman operator theory for modeling omnidirectional mobile manipulators.
Power analysis is a key step in hardware (HW) development, particularly at the Register Transfer Level (RTL) stage where significant design modifications can still be made. As designs scale, power analysis requires in...
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ISBN:
(纸本)9798350380415;9798350380408
Power analysis is a key step in hardware (HW) development, particularly at the Register Transfer Level (RTL) stage where significant design modifications can still be made. As designs scale, power analysis requires increasingly larger simulation traces, sometimes reaching terabytes. This can take weeks or months to process, often making the analysis impractical. Power models are used to speed up this stage, but building them also requires large datasets, making the process slow. This work proposes a methodology to accelerate ML-based power modeling by training on a subset of the dataset, focusing on representative windows through clustering. Applied to a RISC-V Rocket core and masked AES, our approach achieves up to 49x speedup with minimal loss of accuracy, predicting power consumption with less than 5% error.
Brushless DC (BLDC) electric motors are used worldwide for their efficiency, controllability, reduced power operability, high durability, and low noise. Replicating the real application set up in the simulation world,...
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High-resolution flood modeling is enabled by utilizing high-resolution input derived by remote sensing technologies such as Light Detection and Ranging (LiDAR) systems. However, there is a long-standing trade-off betw...
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ISBN:
(纸本)9798350344868;9798350344851
High-resolution flood modeling is enabled by utilizing high-resolution input derived by remote sensing technologies such as Light Detection and Ranging (LiDAR) systems. However, there is a long-standing trade-off between the computational time and spatial resolution for a flood simulation. In this paper, we propose a novel deep learning-based geospatial encoder-decoder for flood modeling consisting of (i) accuracy-preserving coarse-graining of the input topography, (ii) simulating flood with the coarser model, and (iii) downscaling the simulated flood to super-resolution. Our experiments show that our approach accelerates flood simulation up to 50 times faster with 1/16 scale while MSE of 0.0179, which is 10.3% less than the baseline with bilinear interpolation. Especially, we observe 20.5% reduction of MSE on average for the 5% worst cases.
By analyzing traditional distributed energy resource (DER) modeling methods, this paper proposes a feasible method of DER modeling based on static generator in the simulation software - DIgSILENT. behavioral model is ...
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By analyzing traditional distributed energy resource (DER) modeling methods, this paper proposes a feasible method of DER modeling based on static generator in the simulation software - DIgSILENT. behavioral model is proposed in this paper to simplify the DER model, in which simple electronic circuits or mathematical functions are used to reflect the physical characteristics of a certain model. With the application of the modeling method, the battery energy storage system (BESS) are implemented in DIgSILENT simulation environment. In each simulation case, the behavioral model as well as the control strategy is illustrated and the simulation result is analyzed in the end. (C) 2022 The Author(s). Published by Elsevier Ltd.
Envelope modeling is an efficient way to obtain large-signal amplitude and phase dynamics of fast-varying sinusoidal signals, required for e.g. resonant frequency tracking of power converters. In addition, the method ...
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ISBN:
(纸本)9798350318272;9798350318265
Envelope modeling is an efficient way to obtain large-signal amplitude and phase dynamics of fast-varying sinusoidal signals, required for e.g. resonant frequency tracking of power converters. In addition, the method eliminates fast-varying parameters from the model, resulting in reduced simulation time and memory requirements. The paper focuses on envelope modeling capacitor-powered resonant inverter feeding a time-varying series RLC load in burst mode so that DC-link capacitor voltage variation is considered as state variable and must then be combined with the dynamics of AC-side variables. simulation results are presented to validate the suggested analysis method.
The evaluation of operational effectiveness for Unmanned Aerial Vehicle (UAV) swarm precision strike missions is critical in the contemporary unmanned warfare. Nonetheless, current evaluation methods often overlook th...
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ISBN:
(纸本)9798350354638;9798350354621
The evaluation of operational effectiveness for Unmanned Aerial Vehicle (UAV) swarm precision strike missions is critical in the contemporary unmanned warfare. Nonetheless, current evaluation methods often overlook the emergence and dynamic variability inherent in UAV swarm precision strikes, resulting in suboptimal evaluations. This study introduces an innovative evaluation framework with Agent-Based modeling and simulation (ABMS) to rectify these deficiencies. In alignment with established warfighting knowledge and principles, we have meticulously crafted the mission scenario and simulation environment to mirror the intricacies of UAV swarm precision strikes. Subsequently, agent models for the UAVs, radar systems, and surface-to-air missile systems have been elaborately constructed, encompassing their behavioral dynamics, functional attributes, and interactive relationships. We then conducted numerous combat simulations and collected relevant data. Finally, based on the extensive simulation data, we have conducted a multi-faceted analysis of operational effectiveness, focusing on such critical factors as maneuverability, firepower, estimation, and communication. The findings of our experimental analyses elucidate the impact of these various factors on the rates of target destruction and UAV attrition, providing a reference for decision-making processes associated with UAV swarm precision strike operations.
In light of the challenges and costs of real-world testing, autonomous vehicle developers often rely on testing in simulation for the creation of reliable systems. A key element of effective simulation is the incorpor...
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ISBN:
(纸本)9798350384581;9798350384574
In light of the challenges and costs of real-world testing, autonomous vehicle developers often rely on testing in simulation for the creation of reliable systems. A key element of effective simulation is the incorporation of realistic traffic models that align with human knowledge, an aspect that has proven challenging due to the need to balance realism and diversity. Towards this end, in this work we develop a framework that employs reinforcement learning from human feedback (RLHF) to enhance the realism of existing traffic models. This work also identifies two main challenges: capturing the nuances of human preferences on realism and unifying diverse traffic simulation models. To tackle these issues, we propose using human feedback for alignment and employ RLHF due to its sample efficiency. We also introduce the first dataset for realism alignment in traffic modeling to support such research. Our framework, named TrafficRLHF, demonstrates its proficiency in generating realistic traffic scenarios that are well-aligned with human preferences through comprehensive evaluations on the nuScenes dataset.
The early stages of manufacturing innovation are characterized by ambiguity and uncertainty, often named as the fuzzy-front end of innovation. The practice of model-based systems engineering (MBSE) in manufacturing ca...
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ISBN:
(纸本)9798350358810;9798350358803
The early stages of manufacturing innovation are characterized by ambiguity and uncertainty, often named as the fuzzy-front end of innovation. The practice of model-based systems engineering (MBSE) in manufacturing can aid the reduction of ambiguity, by capturing technical requirements and system specification in a centralized modeling environment, sharable by all relevant stakeholders. However, challenges often arise when attempting to achieve this type of collaborative work environment between the various distinct technical and less/non-technical teams, with particular knowledge sets, professional backgrounds, and motivations. Furthermore, manufacturing system design can greatly benefit from early and extensive experimentation, which can be achieved through the building of digital models and performance of simulation runs, to access different considerations and compare alternatives relating to system architecture, material flow and production planning. Despite simulation technologies' high maturity level and the large number of tools available, their effective implementation in the manufacturing sector continues to be very limited, with low adoption levels being often attributed to a persisting lack of the necessary competencies, along with difficulties in the integration of modeling practices within the enterprises' workflows. This paper exemplifies the use of discrete event simulation as a centralized tool for supporting the early stages of manufacturing system design for an alkaline water electrolyser system, laying the foundations for future work on the development of a centralized engineering model and utilization of MBSE to achieve domain interoperability.
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