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Multi-Targets Visiting Orbit Design Based on Numerical Method

Guidance,Navigation and Control 2025年第1期05卷 139-148页

作者： Zenan Zhong Zhengxiang Sun Songyan Wang Tao Chao Ming Yang Control and Simulation Center Harbin Institute of Technology National Key Laboratory of Modeling and Simulation for Complex Systems Harbin Institute of Technology Beijing Institute of Space Long March Vehicle

In this paper, for the two/three targets in the circle orbit with the same altitude, a numerical method is presented to design an orbit for a spacecraft to visit multiple targets without maneuvering. First,for the two targets' case, an equation is established by the relationship of the two angles that both are the function of the time, one of the angles is the angle between two targets' geocentric position vectors at two times, and another one is the spacecraft flight angle during the two times. The visit time can be solved by the assumption that one of the visit times is known in the equation, and the orbit element can be transferred from the position and velocity vector determined by the solved visit time. Then, for the three targets' case, the problem is decoupled into two cases of two targets, and there is a public target in each case. Based on the method for two targets, two different orbits can be obtained by determining the visit time of the public target. The difference between two orbits can be expressed by the difference in velocity vectors of the public target at different times. A sampling method is utilized to select two velocity vectors with relatively small differences as initial values, the Newton iteration method is used to obtain visit times with higher accuracy. simulation results illustrate the effectiveness of the orbit design methods.

关键词： Orbit design method multiple targets visit circle orbit non-maneuvering

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Optimal Deployment of Target Localization With Distance-Dependent Noise

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IEEE Internet of Things Journal 2025年

作者： Tian, Junxi Yang, Ming Chao, Tao Harbin Institute of Technology Control and Simulation Center National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150001 China

The cooperative localization of radio-based distance measurement is one of the methods to effectively suppress the nonline of sight (NLOS) ranging errors and as a complement to visual/global navigation satellite system (GNSS) localization for unmanned aerial vehicle (UAV) in GNSS challenging or denied environments due to its motility. Target localization can be regarded as a special case of cooperative localization. Meanwhile, the UAV can be considered as a ranging node when the ranging terminal is rigidly mounted on the UAV. To improve the target localization accuracy, the optimal deployment of anchor UAVs is one of the factors we consider when the noise characteristics are different. In this paper, the range-dependent noise in a real flight environment is considered and modeled separately according to the difference in noise characteristics. Subsequently, based on this correlation model, the Fisher information matrix (FIM) and a cost function derived from the A-optimal criterion are provided. Finally, the optimal deployment conditions and the minimum mean square error (MMSE) can be derived by minimizing the cost function as the objective. Through simulation and localization experiments, the validity of the optimal deployment conditions and the corresponding MMSE are verified. Meanwhile, it is concluded that when the noise is range-dependent, it will not affect the optimal deployment result but will significantly change the value of MMSE. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Dynamic-Aware and Static Context Network for large-scale 3D place recognition

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Knowledge-Based Systems 2025年 319卷

作者： Liao, Ming Di, Xiaoguang Liu, Maozhen Lv, Teng Zhang, Xiaofei Zhu, Runwen Control and Simulation Center Harbin Institute of Technology Harbin150080 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150080 China

3D point cloud-based place recognition enables robots to obtain precise global positions without GPS, correct trajectory drift in SLAM, and recover from the kidnapped robot problem. However, in outdoor environments, the presence of moving objects can cause occlusions in point clouds and introduce noise into the data, leading to localization failures. To address this issue, we propose a Dynamic-Aware and Static Context Network (DASC-Net) for large-scale 3D place recognition. Our approach leverages the spatio-temporal consistency of point cloud sequences to accurately segment dynamic objects while incorporating static point cloud context to compensate for feature loss caused by noise interference or occlusions from dynamic objects, thereby enhancing robustness and generalization. Specifically, DASC-Net adopts a two-stage strategy: first, it introduces a coarse-to-fine moving object segmentation method to effectively eliminate dynamic noise;second, it utilizes spatial context association and multi-scale feature aggregation to improve static feature representation and matching. Extensive experimental results demonstrate that DASC-Net outperforms existing place recognition approaches, particularly in dynamic scenes. © 2025

关键词： Image segmentation

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Torus doubling route to chaos and chaos eradication in delayed discrete-time predator–prey models

Torus doubling route to chaos and chaos eradication in delay...

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作者： Ghosh, Bapan Sarda, Suruchi Sahu, Shuchi Differential Equations Modeling and Simulation Group Department of Mathematics Indian Institute of Technology Indore Madhya Pradesh Indore India Center for Basic Sciences Pt. Ravishankar Shukla University Chhattisgarh Raipur India

This paper analyses a class of discrete-time delayed predator–prey models where time delay is incorporated into the prey's density-dependent growth term. We explore how time delay, carrying capacity, and harvesting can generate or suppress chaotic motion. Analytical conditions for local stability of the fixed point are derived by using the Routh–Hurwitz criterion. Increasing the time delay has the great potential to change the coexisting stable fixed point to unstable via a Neimark–Sacker bifurcation. Thus, the systems may experience quasi-periodic and chaotic motion when the coexisting fixed point is unstable. The existence of a quasi-periodic loop, which generates a 2-quasi-periodic loop, and then 4, 8, and 16 quasi-periodic loops are successively detected as the carrying capacity increases. Further increase of carrying capacity induces chaos. Thus, a torus doubling route to chaos is explored in our article, which has been relatively less reported in the existing literature. Nevertheless, we show that harvesting either prey or predator is more likely to suppress chaos and stabilize the coexisting steady state. We draw all the bifurcation diagrams when delay, carrying capacity, and harvesting strength are varied. The qualitative nature of the phase portraits is determined by computing all the Lyapunov exponents. The analysis of these delayed models in a discrete-time framework might attract a wide range of researchers to work in this direction. In the conclusion section, we also disclose some remaining questions and future perspectives. © 2022 John Wiley & Sons, Ltd.

关键词： Difference equations

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Enhancing GPT-3.5's Proficiency in Netlogo through Few-Shot Prompting and Retrieval-Augmented Generation 24

Enhancing GPT-3.5's Proficiency in Netlogo through Few-Shot ...

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Proceedings of the Winter simulation Conference

作者： Joseph Martínez Brian Llinas Jhon G. Botello Jose J. Padilla Erika Frydenlund Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA

ISBN: (纸本)9798331534202

Recognizing the limited research on Large Language Models (LLMs) capabilities with low-resource languages, this study evaluates and increases the proficiency of the LLM GPT-3.5 in generating interface and procedural code elements for NetLogo, a multi-agent programming language and modeling environment. To achieve this, we employed "few-shot" prompting and Retrieval-Augmented Generation (RAG) methodologies using two manually created datasets, NetLogoEvalCode and NetLogoEvalInterface. The results demonstrate that GPT-3.5 can generate NetLogo elements and code procedures more effectively when provided with additional examples to learn from, highlighting the potential of LLMs in aiding the development of agent-based models (ABMs). On the other hand, the RAG model obtained a poor performance. We listed possible reasons for this result, which were aligned with RAG's common challenges identified by the state-of-the-art. We propose future research directions for leveraging LLMs for simulation development and instructional purposes in the context of ABMs.

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Generating TCN Models from Parallel DEVS Models: Semiconductor Manufacturing Systems 24

Generating TCN Models from Parallel DEVS Models: Semiconduct...

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Proceedings of the Winter simulation Conference

作者： Vamsi Krishna Pendyala Hessam S. Sarjoughian Edward J. Yellig Arizona Center for Integrative Modeling & Simulation Arizona State University Tempe AZ USA Intel Corporation Chandler AZ USA

ISBN: (纸本)9798331534202

Machine learning models have the potential to augment the simulation of discrete-event dynamical systems, which is of considerable interest. Such models should serve the central purpose of capturing the temporal dynamics of event-based systems. In this paper, we use simulations of Parallel DEVS (PDEVS) models of a benchmark semiconductor fabrication manufacturing system to generate ARIMA, RNN, LSTM, and TCN models of the same. Single/multi-stage manufacturing statistical and deep learning models are developed and evaluated for different experimental scenarios. We generate Temporal Convolutional Neural (TCN) network models and evaluate their uni/multivariate throughput and turnaround time series by varying wafer lot configurations and sizes. The results show the predicted time series generated by TCN models can approximate the accuracies of simulated PDEVS models while achieving many-fold execution speedup.

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Enhancing realism in LiDAR scene generation with CSPA-DFN and linear cross-attention via Diffusion Transformer model

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Neural Networks 2025年 189卷 107503页

作者： Ye, Shaoxun Di, Xiaoguang Liao, Ming Li, Ximing Control and Simulation Center Harbin Institute of Technology Harbin150080 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin150080 China

Point cloud diffusion models have found extensive applications in autonomous driving and robotics. However, there is still a big gap between their generated LiDAR scene samples and real-world data in terms of visual quality. This discrepancy primarily arises from the loss of detailed information during the decoding process from latent space and the lack of guidance from global 3D structural information in the point cloud generation process, leading to distortions and artifacts in LiDAR scene samples. In this paper, we propose a novel LiDAR Diffusion Transformer Model that integrates Channel-Spatial Parallel Attention and Dilation Fusion Network (CSPA-DFN) with a linear cross-attention post-processing module to refine the generated LiDAR scene samples. Specifically, CSPA-DFN is designed to simultaneously emphasize detailed features across different channels and spatial locations in parallel, leveraging multi-scale dilated convolutions and channel grouping to preserve and enhance these detailed features. In order to provide global 3D structural information and balance performance and efficiency, we design a post-processing module that fuses voxelized features and range images using a linear ReLU cross-attention mechanism. Our approach is evaluated on the unconditional generation task using the KITTI-360 and nuScenes datasets, achieving the state-of-the-art results in LiDAR scene's generation quality. Furthermore, by incorporating semantic labels and camera views into the latent space, in addition to enhancing the model's semantic understanding capability for LiDAR scenes, our method also demonstrates additional performance improvements compared to previous works in terms of LiDAR scene's visual quality. The code implementation has been released on https://***/HITysx/LiDAR-Scene-Generation. © 2025 Elsevier Ltd

关键词： Photointerpretation

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No Time to Lose: Enabling Real-Time Fluorescence Lifetime Imaging on Resource-constrained FPGAs Through Efficient Scheduling 25

No Time to Lose: Enabling Real-Time Fluorescence Lifetime Im...

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Proceedings of the 2025 ACM/SIGDA International Symposium on Field Programmable Gate Arrays

作者： Ismail Erbas Aporva Amarnath Vikas Pandey Karthik Swaminathan Naigang Wang Xavier Intes Center for Modeling Simulation & Imaging in Medicine Rensselaer Polytechnic Institute Troy NY USA IBM T.J. Watson Research Center Yorktown Heights USA

ISBN: (纸本)9798400713965

Fluorescence lifetime imaging (FLI) is a widely used technique in the biomedical field for measuring the decay times of fluorescent molecules, providing insights into metabolic states, protein interactions, and ligand-receptor bindings [1, 5]. However, its broader application in fast biological processes, such as dynamic activity monitoring, and clinical use, such as in guided surgery, is limited by long data acquisition times and computationally demanding data processing. While deep learning has reduced post-processing times, time-resolved data acquisition remains a bottleneck for real-time applications. To address this,we propose a method to achieve near realtime FLI using an FPGA-based hardware accelerator. Specifically,we implemented a GRU-based sequence-to-sequence (Seq2Seq) model [3] on an FPGA board compatible with time-resolved cameras. The GRU model balances accurate processing with the resource constraints of FPGAs, which have limited DSP units and BRAM. The limited memory and computational resources on the FPGA require efficient scheduling of operations and memory allocation to deploy deep learning models for low-latency *** address these challenges by using STOMP, a queue-based discrete-event simulator that automates and optimizes task scheduling and memory management on hardware [4]. By integrating a GRU-based Seq2Seq model and its compressed version, called Seq2SeqLite [2], generated through knowledge distillation (KD), we were able to process multiple pixels in parallel, reducing latency compared to sequential processing. We explore various levels of parallelism to achieve an optimal balance between performance and resource utilization. Our results indicate that the proposed techniques achieved a 17.7x and 52.0x speedup over manual scheduling for the Seq2Seq model and the Seq2SeqLite model, respectively. Furthermore, the Seq2SeqLite model achieved a 98% reduction in parameters compared to the Seq2Seq model.

关键词： computer simulation

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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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A multi-strategy enhanced moth-flame optimization algorithm for complex inverse kinematics problems in series robots

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Cluster Computing 2025年第5期28卷 1-37页

作者： Liu, Jianlin Huang, Haisong Fan, Qingsong Ma, Chi Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guizhou Guiyang550025 China Guizhou Equipment Manufacturing Digital Workshop Modeling and Simulation Engineering Research Center Guizhou Guiyang550025 China State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China School of Institute of Automotive Engineers Hubei University of Automotive Technology Shiyan442002 China

In this study of optimizing the dynamic characteristics of the manipulator, with the increase of motion redundancy, the complexity of solving the inverse motion problem increases significantly, making it extremely challenging to develop a general analytical solution algorithm. To address this challenge, this paper introduces a multi-strategy enhanced moth-flame optimization algorithm, DMFO. Firstly, the uneven initialization of MFO limits its comprehensive initial search capability and leads to slow convergence speed. Consequently, it lacks competitiveness in specific engineering problems. The mutation chaos strategy, quantum evolution strategy and precise elimination strategy are used to improve the formation of DMFO. Secondly, DMFO and eight meta-heuristic algorithms (GA, PSO, ABC, MFO, DE, WO, BKA, COA) are tested on CEC2017, demonstrating its superior local and global exploitation and exploration capabilities. Then these kinematics models of 4,6,7-DOF manipulators is established based on DH method, and the objective function is derived from the forward motion equation. The effectiveness of DMFO in solving the inverse kinematics of robot arms is validated through its application. Finally, the engineering verification is carried out on the manipulator UR16e, which highlights the success of DMFO in addressing the inverse motion problem of multi-DOF serial robots. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Inverse kinematics

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