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High-Fidelity modeling & Co-simulation with HyFlow

High-Fidelity Modeling & Co-simulation with HyFlow

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Workshops on AI4EA, F-IDE, CoSim-CPS, CIFMA 2022, Collocated with the 20th International conference on Software Engineering and Formal Methods, SEFM 2022

作者： Barros, Fernando J. Department of Informatics Engineering University of Coimbra Coimbra Portugal

ISBN: (纸本)9783031262357

Developing high-fidelity representations of cyber-physical systems (CPSs) requires an accurate description of continuous signals, a problem that remains a challenge for many modeling approaches. this paper presents HyFlow, a modeling and simulation formalism that provides both dense outputs and sampling constructs to support an accurate representation of continuous systems. HyFlow can describe hierarchical and modular models that facilitate the representation of complex systems. HyFlow extends the original HyFlow formalism by introducing supporting for the process interaction worldview. For demonstrating formalism’s ability to represent high-fidelity hybrid systems, the paper describes a HyFlow model of the exponential time differential (ETD) integrator, a numerical method able to produce very accurate/exact solutions for some types of systems. As an application, paper describes the model of a simplified microwave oven (MWO) grill element driven by an on-off digital controller. a C++ implementation of, is also presented. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cyber Physical system

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Hull deformation angle measurement algorithm based on improved particle swarm optimization inertia matching 20

Hull deformation angle measurement algorithm based on improv...

引用

20th IEEE International conference on Mechatronics and Automation, ICMA 2023

作者： Wei, Xiaofeng Yu, Fei Fan, Shiwei Zhang, Ya Ning, Zixuan Xing, Wen Shen, Feng Harbin Institute of Technology School of Instrumentation Science and Engineering No. 92 Xidazhi Street Heilongjiang Province Harbin China

ISBN: (纸本)9798350320831

the high precision measurement of hull deformation angle is very important for building uniform space datum of ship. the inertial quantity matching hull deformation measurement method based on the strapdown inertial navigation system is widely used in the hull deformation measurement. Aiming at the difficulties in obtaining model parameters and low accuracy of hull deformation measurement method with inertia matching under dynamic sea conditions, an improved particle swarm optimization (IPSO) algorithm for hull deformation angle measurement with inertia matching was proposed. By introducing dynamic adjustment factor of inertia weight and learning factor of real-time adjustment in particle swarm, the optimization algorithm can improve the convergence of searching parameters of hull deformation angle. then, the identified deformation angle parameters are used to construct the Kalman filter model of hull deformation angle based on angular rate matching. Finally, the high-precision hull deformation angle is estimated in real time. the simulation results show that the accuracy of hull deformation angle estimation based on the IPSO algorithm is 10% higher than that of the traditional inertia matching method. © 2023 IEEE.

关键词： Angle measurement

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Identification of Deformable Linear Object Dynamics from Input-output Measurements in 3D Space 20

Identification of Deformable Linear Object Dynamics from Inp...

引用

20th IFAC Symposium on system Identification (SYSID)

作者： Floren, Merijn Mamedov, Shamil Noel, Jean-Philippe Swevers, Jan Katholieke Univ Leuven MECO Res Team Leuven Belgium Flanders Make KU Leuven Leuven Belgium

Controlling deformable linear objects requires reliable models that capture their complex and high-dimensional dynamics. this paper aims to obtain accurate state-space models of such objects from input-output measurements only, tailored for the use in a model predictive control setting. the proposed identification approach is initialized by a physics-inspired baseline model which approximates a deformable linear object by a serial chain of rigid bodies connected via elastic joints. the nonlinear residual dynamics of the baseline model are then modeled by an artificial neural network, where a key element is the so-called guided residual search that first infers the latent residual and state trajectories in the time domain, in this way facilitating supervised learning of the neural network. the efficacy of the modeling approach is demonstrated on experimental data of the 3D end-point motion of a flexible aluminum rod, excited by a 7-axis Franka Panda robot arm. Compared to a physics-inspired baseline model, the proposed method achieves a 14% prediction performance increase, and compared to a fully neural network-based approach with a similar number of parameters, it achieves a 58% improvement, while also reporting a training time that is more than 5 times faster. Copyright (C) 2024 the Authors. this is an open access article under the CC BY-NC-ND license (https://***/licneses/by-nc-nd/4.0/)

关键词： Deformable linear objects Nonlinear system identification State-space models Neural networks optimization Optimal control theory

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Performance optimization and Analysis of the Unstructured Discontinuous Galerkin Solver on Multi-Core and Many-Core Architectures 24

Performance Optimization and Analysis of the Unstructured Di...

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24th IEEE International conference on High Performance Computing and Communications, 8th IEEE International conference on Data Science and systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Dai, Zhe Deng, Liang Wang, Yueqing Wang, Fang Li, Ming Zhang, Jian Computational Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang China State Key Laboratory of Aerodynamics China Aerodynamics Research and Development Center Mianyang China

ISBN: (纸本)9798350319934

the discontinuous Galerkin (DG) algorithm is a representative high order method in Computational Fluid Dynamics (CFD) area which possesses considerable mathematical advantages such as high resolution, low dissipation, and dispersion. However, DG is rather computationally intensive to demonstrate practical engineering problems. this paper discusses the implementation of our in-house practical DG application in three different programming models, as well as some optimization techniques, including grid renumbering and mixed precision to maximize the performance improvements in a single node system. the experiment on CPU and GPU shows that our CUDA, OpenACC, and OpenMP-based code obtains a maximum speedup of 42.9x, 35.3x, and 8.1x compared with serial execution by the original application, respectively. Besides, we systematically compare the programming models in two aspects: performance and productivity. Our empirical conclusions facilitate the programmers to select the right platform with a suitable programming model according to their target applications. © 2022 IEEE.

关键词： Graphics processing unit

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A Parallel Intelligent Search and Rescue system for Swarm Robots Based on Digital Twin 20th

A Parallel Intelligent Search and Rescue System for Swarm Ro...

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20th International conference on Intelligent Computing (ICIC)

作者： Guo, Luosong Cai, Gengyuan Zhu, Kun Qiu, Houming Liu, Chengyong Nanjing Univ Aeronaut & Astronaut Sch Comp Sci & Technol Nanjing 211106 Jiangsu Peoples R China

ISBN: (纸本)9789819756742;9789819756759

In recent years, urban search and rescue (USAR) tasks in unknown environments have arisen widespread attention in the domain of Smart City. However, the complexity and unpredictability of unknown environments pose great difficulties for decision-making in search and rescue phases, ultimately resulting in serious consequences. To address the decision-making problems, this paper proposes a hierarchical swarm robots digital twin framework, which includes a physical layer, virtual layer, twin data layer, parallel decision layer, and service layer, to provide efficient decision support for USAR tasks in large static unknown indoor environments. Besides, considering the requirements of reality, we specifically design two modules: (1) real-time twin model modeling module, solving the issue of poor real-time performance in the offline construction of twin models. (2) parallel decision path planning module based on deep reinforcement learning, solving the difficulty users face in obtaining direct decision support. In our experiment, we implement the proposed framework using Unity3D and the Robot Operating system (ROS). the results show that the framework is capable of improving the decision efficiency of USAR tasks. Additionally, the update frequency of the proposed framework is kept in less than 1 s while providing optimal path planning.

关键词： Urban search and rescue Digital twin Swarm robots

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Data Management Perspectives on Prescriptive Analytics 28

Data Management Perspectives on Prescriptive Analytics

引用

28th International conference on Database theory, ICDT 2025

作者： Meliou, Alexandra Abouzied, Azza Haas, Peter J. Haque, Riddho R. Mai, Anh Vittis, Vasileios University of Massachusetts AmherstMA United States New York University Abu Dhabi United Arab Emirates

ISBN: (纸本)9783959773645

Decision makers in a broad range of domains, such as finance, transportation, manufacturing, and healthcare, often need to derive optimal decisions given a set of constraints and objectives. Traditional solutions to such constrained optimization problems are typically application-specific, complex, and do not generalize. Further, the usual workflow requires slow, cumbersome, and error-prone data movement between a database, and predictive-modeling and optimization packages. All of these problems are exacerbated by the unprecedented size of modern data-intensive optimization problems. the emerging research area of in-database prescriptive analytics aims to provide seamless domain-independent, declarative, and scalable approaches powered by the system where the data typically resides: the database. Integrating optimization with database technology opens up prescriptive analytics to a much broader community, amplifying its benefits. We discuss how deep integration between the DBMS, predictive models, and optimization software creates opportunities for rich prescriptive-query functionality with good scalability and performance. Summarizing some of our main results and ongoing work in this area, we highlight challenges related to usability, scalability, data uncertainty, and dynamic environments, and argue that perspectives from data management research can drive novel strategies and solutions. © Alexandra Meliou, Azza Abouzied, Peter J. Haas, Riddho R. Haque, Anh Mai, and Vasileios Vittis.

关键词： Constrained optimization

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Multi-resolution optimization methodology of voltage and reactive power in new distribution systems

Multi-resolution optimization methodology of voltage and rea...

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20th International conference on AC and DC Power Transmission 2024 (ACDC 2024)

作者： Shanshan Zhao Xiangjun Duan Fei Gao Yajie Li Jianfang Li Yu Zhang China Electric Power Research Institute (CEPRI) State Grid Corporation of China (SGCC) Beijing People's Republic of China

ISBN: (数字)9781837241552

Due to carbon peaking and carbon neutrality, the new distribution system for energy connection will become the development direction of the AC and DC distribution system in the future. With the high penetration of intermittent distributed energy resources and flexible load, the problems of voltage violation and fluctuation have been becoming more and more serious. thus the urgent need arises to solve the problem of voltage and reactive power optimization in the new distribution system. In this paper, the multi-resolution model definition and its background are presented at first. Secondly, a multi-resolution optimization model of voltage and reactive power is proposed to analyse dynamic operation characteristics of the new distribution system caused by the stochastic nature of distributed energy resources and load. And then based on the above model, the corresponding optimization methodology is proposed. the simulation results show that the proposed model and methodology can not only provide systematic descriptions of dynamic operation characteristics, but also greatly reduce the computational burden and make the analysis less time-consuming. therefore, it is significantly meaningful to make safe and stable operation of the new distribution system.

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Design and optimization of mini MIMO antenna 20

Design and optimization of mini MIMO antenna

引用

20th International conference on Optical Communications and Networks, ICOCN 2022

作者： Zheng, Xuemei Ma, Long Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Jilin China School of Electrical Engineering Northeast Electric Power University Jilin China

ISBN: (数字)9781665458986

ISBN: (纸本)9781665458986

this article designs three MIMO antennas for WLAN and WIMAX. the center resonance frequencies are 2.4 GHz, 3.5 GHz, and 5.2 GHz, respectively. the electromagnetic simulation software HFSS is used to design and optimize the MIMO antenna. the MIMO antenna is designed with dielectric substrates with different thicknesses and sizes and antenna element patches with different shapes. Use the antenna layout in each frequency band to obtain good isolation. Make the center working frequency reach more than 15dB. the antenna has the characteristics of omnidirectional radiation in the working frequency band, and the isolation and gain are calculated through the S parameter sweep frequency setting, and then the optimization function is used to optimize and modify the antenna. © 2022 IEEE.

关键词： Electromagnetic simulation

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optimization Design of Trigger Timing for Multi-Stage Coil Launcher

Optimization Design of Trigger Timing for Multi-Stage Coil L...

引用

Pulsed Power conference, International conference on High-Power Particle Beams and International Symposium on Electromagnetic Launch Technology (EAPPC/BEAMS/EML), Euro-Asian

作者： Yadong Zhang Jianping Cao Ao Zhou School of Electrical Engineering and Automation Wuhan University Wuhan China

ISBN: (数字)9789038661353

ISBN: (纸本)9798350352733

To reasonably set the triggering time of each stage coil of the energy recovery type reluctance launcher and improve efficiency, this paper proposes a method based on Bayesian Random Forest Model (BO-RF) and Northern Goshawk optimization algorithm (NGO) to predict and optimize the on-off timing of IGBT switches in each stage coil. the method was validated by building a simulation model of a 5-stage energy recovery reluctance launcher. the results showed that after optimization, the armature muzzle velocity increased from 71.2m/s to 89.89m/s, and the launcher efficiency increased from 26.78% to 32.86%.

关键词： Coils Insulated gate bipolar transistors Forestry Switches Predictive models Prediction algorithms Timing Bayes methods Integrated circuit modeling optimization

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Intelligent system for Distributed Deep Learning Training Traffic optimization 24

Intelligent System for Distributed Deep Learning Training Tr...

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作者： Lu, Weipeng Wu, Shuang Ou, Junhui Niu, Bing He, Jin Song, Kevin Zheng, Xinwei Ustc Hefei China Vmware Beijing China

ISBN: (纸本)9798350319934

Distributed training crossing multiple computing nodes and accelerators has been the mainstream solution for large model training. Precedent work on distributed deep learning (DDL) training acceleration has focused on improving the efficiency of communication and GPU resource scheduling, with little attention given to network bandwidth usage. However, due to cluster GPU fragmentation, more than one training job is frequently assigned to the same node to reduce waiting time. the competition for network bandwidth among various distributed training jobs becomes a problem that cannot be overlooked, and this competition results in a considerable reduction in training speed. We propose a system deployed in cluster nodes that includes traffic monitoring, classification, and prediction of DDL training status and implement a priority-based traffic scheduling policy that considers DDL training features. We demonstrate that this policy is beneficial compared to Linux's default policy, which can increase training speed by approximately 18%. Additionally, we increase the training speed by approximately 20% in the case of continuous background traffic by increasing the bandwidth priority during DDL training. © 2022 IEEE.

关键词： Deep learning

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