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arXiv 2025年

作者： Othmane, Amine Flaßkamp, Kathrin Systems Modeling and Simulation Saarland University Germany

Numerous applications necessitate the computation of numerical solutions to differential equations across a wide range of initial conditions and system parameters, which feeds the demand for efficient yet accurate numerical integration methods. This study proposes a neural network (NN) enhancement of classical numerical integrators. NNs are trained to learn integration errors, which are then used as additive correction terms in numerical schemes. The performance of these enhanced integrators is compared with well-established methods through numerical studies, with a particular emphasis on computational efficiency. Analytical properties are examined in terms of local errors and backward error analysis. Embedded Runge-Kutta schemes are then employed to develop enhanced integrators that mitigate generalization risk, ensuring that the neural network’s evaluation in previously unseen regions of the state space does not destabilize the integrator. It is guaranteed that the enhanced integrators perform at least as well as the desired classical Runge-Kutta schemes. The effectiveness of the proposed approaches is demonstrated through extensive numerical studies using a realistic model of a wind turbine, with parameters derived from the established simulation framework OpenFast. © 2025, CC BY.

关键词： Ordinary differential equations

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Design of large ring-column antenna satellite attitude-orbit integrated füll propulsion system

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Zhendong yu Chongji/Journal of Vibration and Shock 2025年第11期44卷 1-11页

作者： Wang, Shijie Sun, Minzhe Gao, Feng Ji, Yi Sun, Guanghui Dong, Fuxiang School of Astronautics Harbin Institute of Technology Harbin150001 China Frontier Science Center of Space Environment and Interaction of Matters Harbin150001 China State Key Lab for Modeling and Simulation of Complex Systems Harbin150001 China General Department of Communication and Navigation Satellite China Academy of Space Technology Beijing100094 China

As a key space structure in geostationary orbit, 100-meter level large ring-column antenna satellite is tasked with providing high-speed information transmission and stable communication Service in this orbit. This requires satellites having precise orbit maintenance and attitude control capabilities. Due to such satellites typically having larger masses and special structural designs, here, the high-precision orbit prediction algorithm was used to finely model and analyze effects of key space environmental factors of solar pressure, non-spherical perturbations and 3-body gravity on space Operation of such spacecrafts. Furthermore, referring to the design concept of formation-flying gravity field measurement satellites, a distributed füll thrust control strategy based on thruster layout optimization for such spacecrafts was proposed. Then, an improved explicit model predictive control algorithm was proposed based on Laguerre function to convert device capability limitations and Performance requirements into input-output constraints, combine multi-parameter programming, and realize attitude-orbit integrated control of such satellites. The simulation results showed that during stable Operation, position maintenance errors of such satellites can be controlled within a meter-level ränge, and attitude angle errors are better than 0. 03°;the computational efficiency of the proposed control algorithm is significantly higher than that of the Standard model predictive control algorithm and Laguerre function-based model predictive control algorithm;the hardware memory occupied is much lower than that of the explicit model predictive control *** theoretical analysis and numerical verification, it was shown that this study can provide an effective solving scheme for füll thruster integrated control of large ring-column antenna satellites in oomplex space environment using onboard Computers with limited Computing power. © 2025 Chinese Vibration Engineering Society.

关键词： Geostationary satellites

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HRSTORY: Historical News Review Based Online Story Discovery 25

HRSTORY: Historical News Review Based Online Story Discovery

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Renjie Zhou Haoran Ye Jian Wan Yong Liao Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education Hangzhou Dianzi University Hangzhou Zhejiang China Zhejiang University of Science and Technology Hangzhou Zhejiang China University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9798400712456

Story discovery on news streams can help people quickly find story from vast amounts of news, improving the efficiency of information acquisition. Recent online story discovery methods encode text topics and then cluster articles into stories based on similarity. However, the results obtained by these methods are one-time, and clustered news cannot adaptively update in a continuous news stream. Additionally, the inadequate quality of article encoding and the presence of noise data deteriorate the performance of story discovery. To this end, we propose HRSTORY for online story discovery on news streams, which employs a historical news review method to enable news to continuously adapt to the latest environment in the stream data and make corrections and updates. Furthermore, HRSTORY captures better article embeddings through modeling multi-layer relational dependencies within the text. By using sentence-level noise masking, HRSTORY improves the relevance of news article representation to core topics and reduces the interference of noise data. Experiments on real news datasets show that HRSTORY outperforms the state-of-the-art algorithms in unsupervised online story discovery performance.

关键词： historical news

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Investigating the Impact of Pandemic on the Perioperative Healthcare Workers Availability: An Agent-Based Approach 24

Investigating the Impact of Pandemic on the Perioperative He...

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Proceedings of the Winter simulation Conference

作者： Shweta Prasad Vishnunarayan Girishan Prabhu William Hand Industrial and Systems Engineering Department University of North Carolina at Charlotte NC USA School of Modeling Simulation Training University of Central Florida Orlando FL USA Department of Anesthesiology PRISMA Health - Upstate Greenville SC USA

ISBN: (纸本)9798331534202

Protecting healthcare workers (HCWs) during a pandemic is critical to provide timely medical care for patients. Although prior studies have investigated HCW unavailability during the COVID-19 pandemic, the studies have not investigated the impact of parameters such as patient census, vaccination rates, transmission rates, and multiple hospital locations on HCW availability. This research considers a high-risk HCW group of perioperative staff to investigate the impact of segregating and rotating staffing policies on HCW unavailability during a pandemic in a health system with multiple locations. An agent-based model with a SEIR compartmental model was developed to simulate various scenarios. Simulated findings indicate that segregating and rotating policies significantly (p-value <0.01) reduced the peak weekly unavailability of HCWs and the total percentage of HCWs getting infected by as much as 25% and 60% when vaccination rates were lower (<75%). However, these benefits diminished when the vaccination rates increased to 75%.

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Multi-scale Dilated Attention-based Generative Adversarial Network for Infrared and Visible Image Fusion

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Current Optics and Photonics 2025年第2期9卷 120-129页

作者： Yang, Shunyuan Shi, Xiaoping Huo, Ju Sun, Kangjian Wang, Yu School of Astronautics Harbin Institute of Technology Harbin150001 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150001 China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin150001 China Shanghai Space Propulsion Technology Research Institute Shanghai201109 China

In the field of optics, infrared and visible images are often required for use in different situations. In particular, in the field of optical sensors, infrared and visible sensors are mainly used to obtain different band images, which are applied to improve the comprehensive information content of images and to improve the quality and availability of images. In existing infrared and visible image fusion methods, the focus is often placed on retaining the background information from visible images and the salient targets from infrared images. To address this issue, we introduce a multi-scale dilated attention module into the encoder-decoder structure of the generator. By applying dilated convolution and selfattention mechanisms, this module improves the perceptual capability of the model, thereby improving performance without increasing network complexity. This design emphasizes gradient information and detailed features in visible images. Experimental results on the public TNO dataset demonstrate that our method achieves superior visual quality and preserves the most abundant image information. Moreover, experiments on spacecraft images validate the robustness and applicability of our approach. Simultaneously, our method also provides significant technical support for the optical field. © 2025 Current Optics and Photonics.

关键词： Image coding

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Imp: Highly Capable Large Multimodal Models for Mobile Devices

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IEEE Transactions on Multimedia 2025年

作者： Shao, Zhenwei Yu, Zhou Yu, Jun Ouyang, Xuecheng Zheng, Lihao Gai, Zhenbiao Wang, Mingyang Kuang, Zhenzhong Ding, Jiajun Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science Hangzhou310005 China Harbin Institute of Technology Shenzhen Department of Computer Science and Technology Shenzhen China Hangzhou Dianzi University HDU-ITMO Joint Institute Hangzhou310005 China

By harnessing the capabilities of large language models (LLMs), recent large multimodal models (LMMs) have shown remarkable versatility in open-world multimodal understanding. Nevertheless, they are usually parameter-heavy and computation-intensive, thus hindering their applicability in resource-constrained scenarios. To this end, several lightweight LMMs have been proposed successively to maximize the capabilities under constrained scale (e.g., 3B). Despite the encouraging results achieved by these methods, most of them only focus on one or two aspects of the design space, and the key design choices that influence model capability have not yet been thoroughly investigated. In this paper, we conduct a systematic study for lightweight LMMs from the aspects of model architecture, training strategy, and training data. Based on our findings, we obtain Imp - a family of highly capable LMMs at the 2B$\sim$4B scales. Notably, our Imp-3B model steadily outperforms all the existing lightweight LMMs of similar size, and even surpasses the state-of-the-art LMMs at the 13B scale. With low-bit quantization and resolution reduction techniques, our Imp model can be deployed on a Qualcomm Snapdragon 8Gen3 mobile chip with a high inference speed of about 13 tokens/s. © 2025 IEEE.

关键词： Visual languages

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Editorial: Soil modeling and simulation for terramechanics applications of manned and unmanned autonomous vehicles

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Journal of Terramechanics 2025年 117卷

作者： Tekeste, Mehari Way, Thomas R. Alsaleh, Mustafa Physical Systems Modeling and Simulation of Agricultural and Off-Road Machinery Systems Department of Agricultural and Biosystems Engineering Iowa State University AmesIA United States USDA-ARS National Soil Dynamics Lab AuburnAL United States Information Technology Manager Data Warehouse and Business Intelligence GIS Caterpillar Inc. PeoriaIL United States

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An integrated machine learning approach for identifying emergency rescue messages on social media during natural disasters

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Social Network Analysis and Mining 2025年第1期15卷 1-19页

作者： Khallouli, Wael Li, Jiang Huang, Jingwei Rabadi, Ghaith Kovacic, Samuel Department Engineering Management and Systems Engineering Old Dominion University NorfolkVA United States Department Electrical and Computer Engineering Old Dominion University NorfolkVA United States University of Texas Southwestern Medical Center DallasTX United States School of Modeling Simulation and Training University of Central Florida OrlandoFL United States

During large-scale disasters, emergency call centers are often overwhelmed by the large volume of rescue requests and calls for help. Consequently, people are turning to social media platforms to seek assistance. Rescue information posted on these platforms is extremely valuable for first responders to make informed rescue decisions. Therefore, the automatic identification of these requests from the vast amount of data posted on social media during crises is critical yet challenging. This work presents our ongoing research on applying deep learning techniques to extract actionable rescue information from social media during crises. We proposed a novel deep learning model that integrates a fine-tuned BERT to extract low-level statistical features and rule-based Regex filters to extract problem-specific features for emergency tweet identification. The proposed model was evaluated on labeled tweets collected from three hurricane events (Harvey, Ian, and Ida). Experimental results showed that our model performed better than several machine learning and deep learning methods in terms of the Area Under the Precision-Recall Curve (AUC-PR) metric for all events. This study contributed to the crisis informatics literature by introducing a novel deep learning approach for automatically identifying actionable information in social media, which can be adapted for similar natural language processing (NLP) tasks. © The Author(s) 2025.

关键词： Tweets

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UML-based combat effectiveness simulation system modeling within MDE

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Journal of systems Engineering and Electronics 2018年第6期29卷 1180-1196页

作者： ZHU Zhi LEI Yonglin SARJOUGHIAN Hessam LI Xiaobo ZHU Yifan Department of Military Modeling and Simulation School of Systems Engineering National University of Defense Technology Arizona Center for Integrated Modeling and Simulation School of Computing Informatics and Decision Systems EngineeringArizona State University

To reduce complexity, the combat effectiveness simulation system(CESS) is often decomposed into static structure,physical behavior, and cognitive behavior, and model abstraction is layered onto domain invariant knowledge(DIK) and application variant knowledge(AVK) levels. This study concentrates on the specification of CESS’s physical behaviors at the DIK level of abstraction, and proposes a model driven framework for efficiently developing simulation models within model-driven engineering(MDE). Technically, this framework integrates the four-layer metamodeling architecture and a set of model transformation techniques with the objective of reducing model heterogeneity and enhancing model continuity. As a proof of concept, a torpedo example is illustrated to explain how physical models are developed following the proposed framework. Finally, a combat scenario is constructed to demonstrate the availability, and a further verification is shown by a reasonable agreement between simulation results and field observations.

关键词： domain specific modeling model-driven development system engineering effectiveness simulation

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A credibility allocation approach of complex simulation systems 35

A credibility allocation approach of complex simulation syst...

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35th European modeling and simulation Symposium, EMSS 2023

作者： Zhang, Huan Li, Wei Geng, HuaPin Ma, Ping Yang, Ming Control and Simulation Center Harbin Institute of Technology Harbin150080 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150080 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9788885741881

Complex simulation systems usually need to satisfy the credibility requirements and the credibility of complex simulation systems is inextricably linked to the credibility of simulation sub-systems. Hence, in order to guarantee the credibility of complex simulation systems meets requirements, it is necessary to allocate the credibility of simulation sub-systems. Complex simulation systems are composed of several correlative simulation sub-systems and their credibility has correlation relationships, which makes it difficult to obtain the credibility of simulation sub-systems quickly. The existing works mostly ignore the relationships between the sub-systems and are difficult to generate the credibility allocation results of simulation sub-systems. In this paper, we model the credibility allocation problem as a probabilistic inference problem based on the pairwise Markov random field (PMRF). In addition, we apply several inference algorithms to perform the inference exactly and approximately in the credibility PMRF. Finally, experiments are conduted on the air defense combat complex simulation system, and the experiment results demonstrate that the proposed method can obtain the credibility allocation results quickly and accurately. © 2023 The Authors.

关键词： Inference engines

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