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A physics-informed neural network-based aerodynamic parameter identification method for aircraft

PHYSICS OF FLUIDS 2025年第2期37卷

作者： Lin, Jie Chen, Shu-sheng Yang, Hua Jiang, Quan-feng Liu, Jie Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha 410073 Hunan Peoples R China China Aerodynam Res & Dev Ctr Computat Aerodynam Inst Mianyang 621000 Sichuan Peoples R China Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China Natl Key Lab Aircraft Configurat Design Xian 710072 Shaanxi Peoples R China

The precise identification of aerodynamic parameters for aircraft is increasingly critical in aircraft design endeavors, particularly for furnishing accurate dynamic models for ground and flight simulation. To mitigate the dependence on traditional identification methods that rely heavily on aircraft models, this work introduces an aerodynamic parameters identification methodology based on Physics-informed Neural Networks (PINNs). By utilizing the six degrees of freedom motion equation as the physical constraint with the neural network, and the aerodynamic parameters to be identified as the neural network variables, the neural network model is trained to serve as a surrogate for the aircraft model. As a case study, the longitudinal motion of the aircraft is employed to identify and analyze the aerodynamic parameters. A comprehensive comparison is conducted among a method based on the genetic algorithm, a conventional neural network-based approach, and the proposed PINN-based methodology. The results obtained demonstrate that the proposed method can effectively mitigate system and data errors, exhibiting high precision in parameter identification and anti-interference capabilities with regard to noise in data. This innovative approach holds the potential to substantially decrease the reliance on flight test data for parameter identification purposes.

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SiamDF: Tracking training data-free siamese tracker

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NEURAL NETWORKS 2023年第1期165卷 705-720页

作者： Cai, Huayue Lan, Long Zhang, Jing Zhang, Xiang Luo, Zhigang Natl Univ Def Technol Inst Quantum Changsha Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Changsha Peoples R China Univ Sydney Sch Comp Sci Sydney Australia Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Lab Changsha Peoples R China

Much progress has been made in siamese tracking, primarily benefiting from increasing huge training data. However, very little attention has been really paid to the role of huge training data in learning an effective siamese tracker. In this study, we undertake an in-depth analysis of this issue from a novel optimization perspective, and observe that training data is particularly adept at background suppression, thereby refining target representation. Inspired by this insight, we present a data-free siamese tracking algorithm named SiamDF, which requires only a pre-trained backbone and no further fine-tuning on additional training data. Particularly, to suppress background distractors, we separately improve two branches of siamese tracking by retaining the pure target region as target input with the removal of template background, and by exploring an efficient inverse transformation to maintain the constant aspect ratio of target state in search region. Besides, we further promote the center displacement prediction of the entire backbone by eliminating its spatial stride deviations caused by convolution-like quantification operations. Our experimental results on several popular benchmarks demonstrate that SiamDF, free from both offline fine-tuning and online update, achieves impressive performance compared to well-established unsupervised and supervised tracking methods.& COPY;2023 Elsevier Ltd. All rights reserved.

关键词： Siamese tracking Tracking training data-free Pre-training Scale estimation Sharing computation

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Feature Fusion Distillation 1

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31st International Conference on Artificial Neural Networks (ICANN)

作者： Tan, Chao Liu, Jie Natl Univ Def Technol Sci & Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Peoples R China

ISBN: (数字)9783031159374

ISBN: (纸本)9783031159374;9783031159367

Most recent efforts made in knowledge distillation (KD) can be credited to filling the representation gap between the cumbersome teacher and its light student. In general, the soft targets, the intermediate feature representation in hidden layers, or a couple of them from the teacher serve as the supervisory signal to educate the student. However, previous works aligned hidden layers one on one and cannot make full use of rich context knowledge. To this end, we propose a Feature Fusion Module (FFM) to concatenate diverse feature maps from different layers to aggregate knowledge as the to-be-distilled dark knowledge. Moreover, to hedge the adverse effects of the fused feature maps, we devise an Asymmetric Switch Function (ASF) to make the transfer process more reliable. The combination of FFM and ASF is termed Feature Fusion Distillation (FFD). Experiments of image classification, object detection, and semantic segmentation on individual benchmarks show FFD jointly assist the student in achieving encouraging performance. It is worth mentioning that when the teacher is ResNet34, the ultimately educated student ResNet18 achieves 71.40% top-1 accuracy on ImageNet-1K.

关键词： Neural network compression Knowledge distillation Knowledge transfer

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Evaluating mesh quality with graph neural networks

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ENGINEERING WITH COMPUTERS 2022年第5期38卷 4663-4673页

作者： Wang, Zhichao Chen, Xinhai Li, Tieju Gong, Chunye Pang, Yufei Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Peoples R China China Aerodynam Res & Dev Ctr Mianyang 621000 Sichuan Peoples R China

The quality of the finite element mesh has a considerable effect on the efficiency and accuracy of computational fluid dynamics (CFD) simulations. To ensure the generated mesh is of good quality, many quality metrics have been proposed to assess the generated mesh, such as aspect ratio, skewness, Jacobian ratio, etc. Such metrics, however, are primarily employed to detect locally distorted mesh elements. There are still no justifiable thresholds for determining whether the generated mesh is of sufficient quality for simulation. Consequently, it is necessary for the professionals to assess the generated mesh afterward which is time-consuming and labor-intensive work. With the ability to learn features on the graph, the graph neural networks have been successfully applied in many application areas to reduce human-computer interaction. In this paper, we define mesh quality evaluation as a graph classification problem. We first propose a novel and sparse-implemented algorithm to transform the mesh data into graph data. We then introduce a deep graph neural network, GMeshNet, to evaluate the mesh quality. Experimental results on the NACA-Market and NACA6510 mesh datasets demonstrate the effectiveness of our proposed network.

关键词： Computational fluid dynamics Mesh quality evaluation Graph neural network Deep learning application

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Three-Dimensional Tradeoffs for Consensus Algorithms: A Review

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022年第2期19卷 1216-1228页

作者： Li, Linhui Shi, Peichang Fu, Xiang Chen, Peng Zhong, Tao Kong, Jinzhu Natl Univ Def Technol Coll Comp Sci Natl Key Lab Parallel & Distributed Proc Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Sci Key Lab Software Engn Complex Syst Changsha 410073 Peoples R China Zhejiang Lab Hangzhou 311100 Peoples R China Kylin Software Co Ltd Changsha 410073 Peoples R China

Blockchain has been applied in many fields to solve the problems of trust, security, efficiency benefiting from its tamper-proof and traceability of data. However, it is still necessary to consider the technical constraints that limit the large-scale application of blockchain: scalability, security, and decentralization cannot be achieved altogether. Consensus algorithm is the core of blockchain, which determines the performance of blockchain system to a certain extent. The existing reviews or surveys mainly focus on processes of consensus algorithms, but fall short in covering the current trends and scenarios, thereby lacking intrinsic understanding of their design philosophy. In this paper, we propose a multi-dimensional tradeoff model and unearth various indicators of different dimensions to guide the construction of consensus algorithms. To summarize the existing efforts, we compare and analyze various classical consensus algorithms, and focus on the design principles of these algorithms under the multi-dimensional tradeoff model. According to different requirements, each algorithm has different tradeoffs. Furthermore, we provide different solutions for blockchain in different dimensions. Finally, we summarize the development trend of consensus design and the key technology prospects of blockchain. This is, to the best of our knowledge, the first survey that accomplishes such goals.

关键词： Blockchains Security Consensus algorithm Scalability Throughput Bitcoin Technological innovation Scalability decentralization security consensus

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Faster and Stronger: Unleashing Data Processing Potential Through Hardware Heterogeneity

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IEEE INTERNET OF THINGS JOURNAL 2025年第10期12卷 14559-14576页

作者： Wang, Cong Luo, Yang Du, Wenzhuo Wang, Ke Gu, Naijie Yu, Jun Huawei Technol Co Ltd Distributed & Parallel Software Lab 2012 Labs Shenzhen 518100 Peoples R China Univ Sci & Technol China Dept Comp Sci & Technol Hefei 230052 Anhui Peoples R China Univ Sci & Technol China Inst Adv Technol Hefei 230052 Anhui Peoples R China Univ Sci & Technol China Dept Automat Hefei 230052 Anhui Peoples R China

With the rapid advancement of AI technology, there has been a substantial surge in the need for computational resources. Particularly in deep learning, machine learning, and large-scale data analysis, the processing of extensive datasets necessitates exceptionally high levels of computational efficacy and speed. Conventional homogeneous computing platforms, predominantly reliant on central processing units (CPUs), have encountered challenges in meeting the escalating demands for high-performance computing. Consequently, this study advocates for heterogeneous hardware acceleration technology, strategically migrating data operations from CPU to varied hardware components [e.g., graphics processing unit (GPU), neural processing unit (NPU)] to enhance processing efficiency and computational performance during the data preprocessing phase. We conducted experiments to evaluate the impact of utilizing hardware heterogeneous acceleration technologies on data processing speed under various workloads and system hardware configurations. By adjusting parameters like batch size and CPU utilization rates, we compared the performance of frameworks that support hardware heterogeneity with popular deep learning frameworks (e.g., PyTorch and TensorFlow) across various hardware configurations and neural network models. Empirical findings demonstrate that the system framework optimized through heterogeneous hardware acceleration technology (the preprocessing speed is improved in all the given experimental environment tests) exhibits commendable universality and superiority in performance. Codes are available at https://***/mindspore-ai/mindspore.

关键词： Data processing Training Artificial intelligence Data preprocessing Computational modeling Hardware acceleration Data models Graphics processing units Central Processing Unit Tensors hardware heterogeneous acceleration load balancing machine learning performance optimization

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RF-Siamese: Approaching Accurate RFID Gesture Recognition With One Sample

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第1期23卷 797-811页

作者： Ma, Zijing Zhang, Shigeng Liu, Jia Liu, Xuan Wang, Weiping Wang, Jianxin Guo, Song Cent South Univ Sch Comp Sci & Engn Changsha 410017 Peoples R China Cent South Univ Sch Comp Sci & Engn Sci & Technol Parallel & Distributed Proc Lab PDL Changsha 410017 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Performing accurate sensing in diverse environments is a challenging issue in wireless sensing technologies. Existing solutions usually require collecting a large number of samples to train a classifier for every environment, or further assume similar sample distribution between different environments such that a model trained in one environment can be transferred to another. In this paper, we propose RF-Siamese, an RFID-based gesture sensing approach that achieves comparable accuracy to existing solutions but requires only a few samples in each eivironment. RF-Siamese leverages Siamese networks to distinguish different gestures with only a small number of samples and is enhanced by several novel designs to achieve high accuracy in diverse environments. First, the network structure and parameters (e.g., loss function and distance metric) are carefully designed to be suitable for RFID gesture recognition. Second, a permutation-based dataset generation strategy is proposed to make full use of the collected samples to enhance the recognition accuracy. Third, a template matching method is proposed to extend the Siamese network to classify multiple gestures. Extensive experiments on commercial RFID devices demonstrate that RF-Siamese achieves a high accuracy of 0.93 with only one sample of each gesture when recognizing 18 different gestures, while state-of-the-art approaches based on transfer learning and meta learning achieve an accuracy of only 0.59 and 0.70, respectively.

关键词： Sensors Gesture recognition Radiofrequency identification Wireless sensor networks Wireless communication Transfer learning Mobile computing RFID few-shot learning siamese network

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DeepEnhancer: Temporally Consistent Focal Transformer for Comprehensive Video Enhancement 24

DeepEnhancer: Temporally Consistent Focal Transformer for Co...

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4th Annual International Conference on Multimedia Retrieval (ICMR)

作者： Jiang, Qin Wang, Qinglin Chi, Lihua Ma, Wentao Li, Feng Liu, Jie Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China Hunan GuoKe Computil Technol Co Ltd Changsha Peoples R China Anhui Agr Univ Sch Informat & Artificial Intelligence Hefei Peoples R China Hefei Univ Technol Hefei Peoples R China

ISBN: (纸本)9798400706028

Restoring and colorizing old films is a comprehensive video enhancement task, marked by the presence of heterogeneous and structured degradations. Our DeepEnhancer addresses this challenge through a unified pipeline that combines restoration and colorization. In this workflow, we implement a bidirectional propagation strategy. Specifically, we incorporate second-order feature alignment to reduce the accumulation of inaccuracies in optical flow estimation. Simultaneously, we utilize cross-scale long-term attention mechanisms to model correlations within hidden states, thereby ensuring spatial and temporal consistency. To address the notable content loss in aging films, we introduce a temporally consistent focal transformer guided by global information. This transformer utilizes various window levels with distinct sub-window sizes to seamlessly integrate fine-grained and coarse-grained features. Comprehensive experimental results conclusively demonstrate the superiority of our model in both quantitative and qualitative comparisons when compared to existing approaches.

关键词： video restoration colorization Focal Transformer temporal consistency

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CSR&RV: An Efficient Value Compression Format for Sparse Matrix-Vector Multiplication 19th

CSR&RV: An Efficient Value Compression Format for Sparse Mat...

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19th International Conference on Network and parallel Computing (NPC)

作者： Yan, Junjun Chen, Xinhai Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha 410073 Peoples R China

ISBN: (纸本)9783031213946;9783031213953

Sparse Matrix-Vector Multiplication (SpMV) plays a critical role in many areas of science and engineering applications. The storage space of value array in general real sparse matrices accounts for costly. However, the existing compressed formats cannot balance the compressed rate and computational speed. To address this issue, we propose an efficient value compression format implemented by AVX512 instructions called Compressed Sparse Row and Repetition Value (CSR&RV). This format stores each different value once and uses the indexes array to store the position of values, which reduces the storage space by compressing the value array. We conduct a series of experiments on an Intel Xeon processor and compare it with five other formats in 30 real-world matrices. Experimental results show that CSR&RV can achieve a speedup up to 3.86x (1.66x on average) and a speedup up to 12.42x (3.12x on average) for single-core and multi-core throughput, respectively. Meanwhile, our format can reduce the memory space by 48.57% on average.

关键词： Sparse matrix-vector multiplication Value compression Storage format AVX512

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Subtraction of Hyperledger Fabric: A blockchain-based lightweight storage mechanism for digital evidences

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JOURNAL OF SYSTEMS ARCHITECTURE 2024年 153卷

作者： Fu, Xiang Ma, Haoliang Ding, Bo Wang, Huaimin Shi, Peichang Natl Univ Def Technol Key Lab Parallel & Distributed Comp Changsha 410073 Hunan Province Peoples R China State Key Lab Complex & Crit Software Environm Changsha 410073 Hunan Province Peoples R China

After more than ten years of development, the application of blockchain has expanded beyond the financial field to include other sectors such as E-government and the medical field. One of the most renowned blockchain platforms is Hyperledger Fabric, which is widely adopted by existing works for managing digital evidences (DEs). However, these works overlook the distinct differences between DEs and token-transfer transactions and often adopt the full implementation of Hyperledger Fabric, leveraging chaincode to manage the hash of DEs while keeping the original data off-chain to conserve storage resources. This approach poses challenges such as off-chain data loss, reduced data verification efficiency, and increased resource consumption. In this paper, we introduce a lightweight storage mechanism called Blockchain Zhengju Keeper (BZK) that addresses these issues. In BZK, we subtract Hyperledger Fabric by (1) modifying the endorsement policy to reduce the risk of off-chain data loss, (2) simplifying the DE upload-onchain process while keeping the parallel execution, and (3) abandoning chaincode for DE verification to reduce latency. Comprehensive simulation experiments demonstrate the practicality of the proposed mechanism. Furthermore, compared to Hyperledger Fabric, BZK can reduce storage resource consumption by 68% and lower verification latency by 97%.

关键词： Blockchain Hyperledger fabric Lightweight storage Digital evidence

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