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Machine Learning and Natural Language Processing (MLNLP), International Conference on

作者： Liu Xue Liu Jie Zhu Peipei Xiang Tao National Key Laboratory of Complex Aviation System Simulation Southwest China Institute of Electronic Technology Chengdu China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

To offer the military personnel a good service of assisting them to get information about enemy's equipment outside Internet, we build a system named MilChat which includes a large language model that has the ability to generate texts and an application which is easy to use. The model of MilChat is fine-tuned by the method of LoRA with military datasets from the basic model, Qwen-7B-Chat which is opensource and has good performance. After fine-tuning, the new model has higher scores than the basic one. Besides, we use the method of Prompt Learning to limit the answers into a standard format. Therefore, it is more accurate and professional than the basic model. This study has also a value of industrial application.

关键词： Accuracy Large language models Machine learning Natural language processing Libraries Internet Personnel Standards Military equipment

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Infrared small target detection algorithm with complex background based on YOLO-NWD 22

Infrared small target detection algorithm with complex backg...

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4th International Conference on Image Processing and Machine Vision, IPMV 2022

作者： Zhou, Xiao Jiang, Lang Guan, Xujun Mou, Xingang Wuhan University Of Technology China Science And Technology On Complex System Control And Intelligent Agent Cooperation Laboratory China

ISBN: (纸本)9781450395823

Because of small number of occupied pixels, lacking shape and texture information, the reliability of infrared remote target detection has always been a difficult research topic. To improve the accuracy and precision of detection of infrared small targets under complex background conditions, a deep learning-based infrared small target detection algorithm YOLO-NWD is proposed. According to the characteristics of small and medium targets in infrared images, multi-channel feature fusion image was used as the input of YOLO detection framework combined with image preprocessing method. Combined with SE module and ASPP module, feature weights are explored to improve feature utilization efficiency. Finally, the normalized Wasserstein distance (NWD) loss is used to replace the original IoU calculation loss to reduce the sensitivity of small target position deviation. The experimental results show that the algorithm proposed in this paper improves the accuracy by 2.5% and the recall rate by 4%. © 2022 ACM.

关键词： Deep learning

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Design of UHF Low-profile Monopole Antenna for Ground Penetrating Radar 15

Design of UHF Low-profile Monopole Antenna for Ground Penetr...

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15th International Conference on Microwave and Millimeter Wave technology, ICMMT 2023

作者： Huang, Jia-Jia Ma, Jing Li, Zhi-Hao Lu, Yong-Han Guo, Li-Wei Peng, Lin Guangxi Guilin541004 China Guilin University of Electronic Technology Key Laboratory of Microwave and Optical Wave Application Technology Education Department of Guangxi Zhuang Autonomous Region Guangxi Guilin541004 China Beijing Simulation Center Science and Technology on Special System Simulation Laboratory Beijing100854 China

ISBN: (纸本)9798350338874

A low-profile and ultra-wideband unidirectional monopole antenna with absorbing material loading is proposed for ground-penetrating radar (GPR) applications in the UHF band. The overall dimensions of the antenna is 0.472λ0 × 0.152λ0×0.006λ0, where λ0 is the free-space wavelength at the lowest frequency of 0.38GHz. The monopole antenna with low profile operates in the 0.38GHz-3GHz band with standing-wave ratio © 2023 IEEE.

关键词： Monopole antennas

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Parameterized Modelling of Stage Separation of Launch Vehicles with Modelica 2nd

Parameterized Modelling of Stage Separation of Launch Vehicl...

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2nd International Conference of Mechanical system Dynamics, ICMSD 2023

作者： Zhang, Wenfeng Cai, Xiaoshuang Liu, Zhendong Liu, Xiong Tong, Yifan Shanghai Aerospace Systems Engineering Institute Shanghai China School of Science Nanjing University of Science and Technology Nanjing China Institute of Launch Dynamics Nanjing University of Science and Technology Nanjing China National Key Laboratory of Complex Multibody System Dynamics Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9789819980475

In order to improve launching capability and performance, almost all launch vehicles in active service contain two or more stages. During the launch process, it is of vital importance to gradually reduce the weight of the launch vehicles by separating and jettisoning spent stages which have burned out its propellant. The separation process between the stages plays a crucial role in guaranteeing the safety of the launch vehicles and ensuring a successful launch. Consequently, it is essential for launch vehicle designers to well understand the stage-separation dynamics which is quite complex and affected by many system parameters. To evaluate the impact of the system parameters on the stage-separation mission, a parameterized model is established for a two-stage launch vehicle using the Modelica language along with the software MWorks. As representative parameters to evaluate, the mass-center positions of the first and second stages, both the positions and directions of the active forces, the residual thrusts, the positive thrusts, the retro thrusts, the masses and moments of inertia of the two stages, are all taken into account for dynamic analysis. The parameterized Modelica model is built by exploiting the idea of the equation-based causal modelling to numerically simulate the stage-separation process and evaluate the influence of the parameters on the system responses. simulation results based on representative case studies indicate that the separation process of the first and second stages can be well described by the Modelica model. Besides, a comparison with Adams software demonstrates the results of the Modelica model agree well with those obtained using Adams. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Launch vehicles

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Rapid Modeling and Model Order Reduction of Launch Vehicle Based on Digital Twin 2nd

Rapid Modeling and Model Order Reduction of Launch Vehicle B...

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2nd International Conference of Mechanical system Dynamics, ICMSD 2023

作者： Huang, Tianyou Wang, Qingtao Liu, Xiong Liu, Zhendong Wang, Qixiao School of Science Nanjing University of Science and Technology Nanjing China Institute of Launch Dynamics Nanjing University of Science and Technology Nanjing China National Key Laboratory of Complex Multibody System Dynamics Nanjing University of Science and Technology Nanjing China Shanghai Aerospace Systems Engineering Institute Shanghai China

ISBN: (纸本)9789819980475

In this paper, a dynamic analysis technique for complex launch vehicle models using digital twin technology is introduced. A parameterized model of the launch vehicle is established and simulations are conducted on a multi-physics simulation platform. The resulting analysis file is then integrated into a system-level multi-physical domain digital twin platform. To simplify the model, singular value decomposition (SVD) method is employed to reduce its complexity. Numerical comparison and error analysis are performed between the reduced model and the original model. By comparing and analyzing the reduced order models with different levels of error, the effectiveness of the digital twin approach is demonstrated. Furthermore, the reduced order model within the digital twin framework is encapsulated. This encapsulation enables the realization of comprehensive life cycle configuration management for the launch vehicle equipment. Overall, this technique offers a promising approach for analyzing and managing complex models of the context of launch vehicles. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Launch vehicles

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MBSE-Based Guidance system simulation of Launch Vehicle 2nd

MBSE-Based Guidance System Simulation of Launch Vehicle

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2nd International Conference of Mechanical system Dynamics, ICMSD 2023

作者： Ma, Guowei Wang, Qingtao Zhang, Wenfeng Yu, Rendong Liu, Xiong School of Science Nanjing University of Science and Technology Nanjing China Institute of Launch Dynamics Nanjing University of Science and Technology Nanjing China National Key Laboratory of Complex Multibody System Dynamics Nanjing University of Science and Technology Nanjing China Shanghai Aerospace Systems Engineering Institute Shanghai China

ISBN: (纸本)9789819980475

In the traditional development process of the launch vehicle guidance system, the system is designed repeatedly in the earlier stage and the verification of the system is conducted in the later stage. This development mode may cause some problems, such as low design efficiency and high cost. In order to improve the shortcomings of the traditional development process, in this paper the MBSE (Model Based system Engineering) method is introduced in the design phase of the launch vehicle guidance system, and the MBSE-based design model is used to carry out system verification in real time. The application of this method greatly improves the design efficiency of the launch vehicle guidance system. The MBSE-based simulation model of the launch vehicle guidance system is established, the system perturbation guidance simulation is carried out, and the result is compared with the standard design trajectory to verify the correctness of the guidance system designed based on MBSE. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Launch vehicles

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Modeling and simulation of Stage Separation system of Launch Vehicles Based on MWorks 2nd

Modeling and Simulation of Stage Separation System of Launch...

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2nd International Conference of Mechanical system Dynamics, ICMSD 2023

作者： Pu, Jinrui Wang, Qingtao Liu, Zhendong Cai, Xiaoshuang Liu, Xiong School of Science Nanjing University of Science and Technology Nanjing China Institute of Launch Dynamics Nanjing University of Science and Technology Nanjing China National Key Laboratory of Complex Multibody System Dynamics Nanjing University of Science and Technology Nanjing China Shanghai Aerospace Systems Engineering Institute Shanghai China

ISBN: (纸本)9789819980475

The current methods for modeling, simulating, and verifying the stage separation system of a launch vehicle are limited to specific models and cannot be universally validated. Moreover, it is a time-consuming and labor-intensive task to remodel a launch vehicle with different topologies. To address this issue, this paper proposes a solution using the software MWorks. The approach involves establishing basic component models for launch vehicles, including the first stage, second stage, thrust, and explosive bolts. These component models are then utilized to create a model library for the stage separation of a launch vehicle. By employing this library, the stage separation process of a specific launch vehicle model can be simulated and analyzed to ensure the safety of the separation under harsh conditions. This methodology offers a more efficient and flexible approach to validate the stage separation system of launch vehicles. By utilizing the established model library, the need for extensive remodeling is eliminated, saving both time and effort. The simulation results provide valuable insights into the safety and effectiveness of the stage separation process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Launch vehicles

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Performance comparison of lithium fractionation from magnesium via continuous selective nanofiltration/electrodialysis

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Chinese Journal of Chemical Engineering 2023年第7期59卷 42-50页

作者： Yong Xu Qingbai Chen Yang Gao Jianyou Wang Huiqing Fan Fei Zhao Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution College of Environmental Science and EngineeringNankai UniversityTianjin 300350China College of Chemical Engineering and Material Quanzhou Normal UniversityQuanzhou 362000China Center for Water and Ecology State Key Joint Laboratory of Environment Simulation and Pollution ControlSchool of EnvironmentTsinghua UniversityBeijing 100084China

Although selective nanofiltration(SNF)and selective electrodialysis(SED)have been widely adopted in the field of Mg^(2+)/Li^(+)separation,their differences have not been illustrated *** this study,for the first time,SNF and SED processes in continuous mode were studied for Li+fractionation from the same brine with high Mg/Li ratios and their differences were discussed in *** a fair analysis of the two processes,typical factors were ***,the optimal operating pressure and feed flow rate for SNF were 2.4 MPa and 140 L·h^(-1),respectively,while the optimal cell-pair voltage and replenishment flow rate for SED were 1.0 V and 14 L·h^(-1),*** the Li^(+)fractionation capacity of the two processes were similar,the selectivity coefficient of SNF was 24.7% higher than that of SED and,thus,the Mg/Li ratio in purified stream of the former was 19.0% lower than that of the *** to higher ion driving force,SED had clear advantages in recovery ratio and concentration ***,the specific energy consumption of SED was 20.1% lower than that of *** study provided a better understanding and guidance for the application and improvement of the two technologies.

关键词： High Mg/Li brine Purification Membrane Nanofiltration Electrodialysis Lithium recovery

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Learning to Mine Context Information for Remote Sensing Small Object Detection

Learning to Mine Context Information for Remote Sensing Sm...

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International Conference on Guidance, Navigation and Control, ICGNC 2024

作者： Li, Lang Tang, Jie Chi, Zhiqiang Niu, Yunqiang Ren, Jun Li, Lei Yao, Xiwen Cheng, Gong Han, Junwei School of Automation Northwestern Polytechnical University Xi’an China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9789819622351

Object detection in remote sensing images has advanced significantly, nevertheless, it still faces the dilemma of low performance in detecting small objects. Due to extremely limited area, small objects can easily be submerged in complex backgrounds and suffer from insufficient context semantic information. To alleviate the aforementioned issues, we design a lightweight plug-and-play module named Cascade Local-Global Context Module (CLGCM) to extract context information. The module contains one cascade operation and two novel Sparse Context Blocks. The cascade operation can obtain long-range context semantic information at a small computational cost. And Sparse Context Block focuses on the most relevant semantic information through a sparsification operation. When integrated with Faster-RCNN and Cascade R-CNN, our module further boosts detection performance on the small object detection benchmark, AI-TOD, which significantly outperforms other mainstream algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Context information Remote sensing images Small object detection

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NWD-YOLOv5: A YOLOv5 Model for Small Target Detection Based on NWD Loss 6

NWD-YOLOv5: A YOLOv5 Model for Small Target Detection Based ...

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6th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Zhou, Liliang Ran, Huaming Xiong, Rongling Tan, Ruijie National Key Laboratory of Complex Aviation System Simulation Chengdu610036 China 10th Research Institute China Electronics Technology Group Corporation Chengdu610036 China School of Mathematics Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9798331541699

Target detection technology is vital in modern social management and urban security, with small target detection—accurately identifying tiny objects in large scenes—posing a key challenge for improving surveillance system accuracy and real-time performance. The YOLO series is still the mainstream target detection model, for the YOLOv5 model, as the IoU metric it adopts still uses the geometrical overlap of the detection frame as the basis of the loss calculation, while the experiments have proved that this metric is unable to effectively utilize the small-scale characteristics of small targets. Therefore, improving the loss metric in the existing framework can help the model to pay more attention to the spatial semantic features of small targets in the image in the training phase of the network, respectively, and thus improve the detection effect. In this paper, we propose the NWDYOLOv5 model, i.e., we introduce the NWD loss that measures the difference in the distribution of the detection frames for the existing target detection framework YOLOv5, which ensures that the loss of the small targets is not neglected in the calculation of the forward propagation loss of the model, and we purposefully modify the structure of the feature extraction network in the original framework by adding a feature extraction layer for the detection of the small targets, which enhances the ability of the model to capture the subtle features thus improving the accuracy of small target detection. Through comparative experiments, we evaluated the detection performance of the original YOLOv5 model, the YOLOv5 model using only the NWD loss, the YOLOv5 model with only the additional small-target detection layer, and the full NWD-YOLOv5 model for small-targeted human bodies on the screened COCO2017 public dataset, respectively. The experimental results show that compared to the original YOLOv5 model, the model with only the NWD loss improves the mAP@.5 metrics on small-target detection by 16.5

关键词： Digital elevation model

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