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A high-resolution numerical investigation of unsteady wake vortices for coaxial rotors in hover

Chinese Journal of Aeronautics 2024年第11期37卷 278-299页

作者： Shaoqiang HAN Wenping SONG Zhonghua HAN Jianhua XU Tianfu Engineering-oriented Numerical&Software Innovation Center College of Computer ScienceSichuan UniversityChengdu 610065China National Key Laboratory of Science and Technology on Aerodynamic Design and Research School of AeronauticsNorthwestern Polytechnical UniversityXi’an 710072China

High-resolution numerical simulations for wake vortical flows have long been a challenge in rotor aerodynamics.A novel spectrum-optimized sixth-order Weighted Essentially NonOscillatory(WENO)scheme is proposed to discretize inviscid fluxes on moving overset grids,and the Improved Delayed Detached Eddy Simulation(IDDES)is employed to resolve turbulent *** integration of these methods facilitates a comprehensive numerical investigation into the unsteady vortical flows over coaxial rotors in *** results highlight the substantial improvement in numerical resolution,in terms of both spatial structure and temporal evolution of unsteady multiscale wake *** rotors in hover manifest three primary scales of wake vortex structures:(A)the helical evolution of primary blade tip vortices and the periodic occurrence of strong Blade-Vortex Interactions(BVI);(B)the continuous shedding of small-scale horseshoeshaped vortices from the trailing edges of rotor blades,forming the vortex sheets;(C)the emergence of small-scale secondary vortex braids induced by interactions between rotor tip vortices and the vortex *** vortex structures and their interactions cause high-frequency oscillations in rotor disk loads and induce unsteady perturbations in the local flow *** among these primary vortices,coupled with the generation of secondary vortices,result in the dissipation,distortion,and breakup of the rotor tip vortices,ultimately forming a vortex ***,a substantial quantity of seemingly weak small-scale secondary vortex braids significantly contribute to energy dissipation during the evolution of wake vortices for coaxial rotors in hover.ó2024 Chinese Society of Aeronautics and *** and hosting by Elsevier *** is an open access article under the CC BY-NC-ND license(http://***/licenses/by-nc-nd/4.0/).

关键词： Coaxial rotor Blade tip vortex Secondary vortex High-order scheme IDDES Moving overset grid

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An Investigation of Blockchain-Based Sharding 7th

An Investigation of Blockchain-Based Sharding

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Xiao, Jiahong Liang, Wei Cai, Jiahong Zhu, Hangyu Li, Xiong Xie, Songyou School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Guangdong Financial High-Tech Zone "Blockchain +" Fintech Research Institute Foshan528253 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

ISBN: (纸本)9783031281235

Nowadays, blockchain distributed ledger technology is becoming more and more prominent, and its decentralization, anonymization, and tampering obvious features have been widely recognized. These excellent technical features of blockchain have also made it a hot issue for global research. With the wide application of blockchain technology in various industries, some defects are gradually exposed, and more prominently, the blockchain system is unable to meet the current demand of explosive growth of data volume and frequent data interaction. As one of the key technologies to solve this problem, sharding technology is gaining attention. This article introduces common blockchain scaling schemes and focuses on an overview of blockchain sharding. Sharding technology is introduced from two perspectives of intra-slice consensus and inter-slice consensus. The current mainstream slicing technology is summarized according to three different slicing methods: network sharding, transaction sharding, and state sharding. Finally, the challenges faced by current blockchain sharding technology are analyzed and the full text is summarized. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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EBCFL: An Efficient BlockChain-based Federated Learning Framework for Internet of Vehicles

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IEEE Network 2025年

作者： Cao, Dun Liao, Yonghui Alqahtani, Fayez Wang, Jin School of Computer and Communication Engineering Changsha University of Science and Technology China Changsha University of Science and Technology China Software Engineering Department College of Computer and Information Sciences King Saud University Saudi Arabia Hunan University of Science and Technology China

Federated Learning (FL) can train a shared model while protecting vehicle data privacy, which has significant advantages in improving driving safety and efficiency. However, there are still many challenges in applying FL to Internet of Vehicles (IoV). First, the varying computing capabilities of moving vehicles result in slow convergence speed due to underperforming vehicles. Second, malicious vehicles may tamper with model parameters before uploading them to the central server, which is itself vulnerable to single-point failure. To solve the above challenges, we design an efficient and decentralized blockchain FL framework (Efficient BlockChain-based Federated Learning Framework, EBCFL). This framework effectively prevents possible malicious behaviors in the vehicular network nodes and improves the efficiency of FL through a well-designed security verification mechanism. The experimental results show that EBCFL outperforms other schemes in terms of average test accuracy and average total time cost, indicating that EBCFL significantly improves the learning efficiency while bearing an acceptable additional time cost for blockchain communication, demonstrating its strong ability to guard against malicious behaviours and ensure robustness. © 1986-2012 IEEE.

关键词： Blockchain Federated learning IoV Security verification

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Robust Psoriasis Severity Classification by using Data Augmentation

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Transactions of the Korean Institute of Electrical Engineers 2022年第12期71卷 1841-1847页

作者： Moon, Cho-I Baek, Yoo Sang Choi, Min Hyung Lee, Onseok Dept. of Software Convergence Graduate School Soonchunhyang University Korea Republic of Dept. of Dermatology Guro Hospital Korea University College of Medicine Korea Republic of Dept. of Computer Science and Engineering College of Engineering Design and Computing University of Colorado Denver United States

Psoriasis is a chronic recurrent disease formed by lesions such as erythema and scale. To evaluate the severity of psoriasis, the psoriasis area and severity index (PASI) score have been used in clinical trials and studies. This clinical indicator is subjective, so to overcome these shortcoming, various automatic psoriasis analysis methods based on deep learning have been studied. However, the limited number of data and psoriasis characteristic such as ambiguity of severity deteriorate model performance. One of the simple and powerful methods to overcome these problem is data augmentation. Data augmentation should be used according to data characteristics. Therefore, we analyzed and compared the classification results applied with five data augmentation methods, Geometric transformation, CutMix, Visual Corruptions, AutoAugment, RandAugment, and explored data augmentation method suitable for psoriasis severity classification. We used the EfficientNet B2 for psoriasis severity classification. As a result, when RandAugment or the combination of Geometric transform and Visual Corruptions were used, it showed the best classification performance with an accuracy of 87.5%. In addition, we confirmed the effect of data augmentation for improving model performance and the difference in performance according to single or multiple applications of the data augmentation methods. Through these results, our study can be applied to various studies as a data augmentation method suitable for psoriasis disease image. Copyright © The Korean Institute of Electrical Engineers.

关键词： Deep learning

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Reconfigurable Intelligent Surface Assisted Semantic Communication Systems

Reconfigurable Intelligent Surface Assisted Semantic Communi...

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2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

作者： Shi, Jiajia Chan, Tse-Tin Pan, Haoyuan Lok, Tat-Ming The Chinese University of Hong Kong Department of Information Engineering Hong Kong The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350316728

Semantic communication, which focuses on conveying the meaning of information rather than exact bit reconstruction, has gained considerable attention in recent years. Meanwhile, reconfigurable intelligent surface (RIS) is a promising technology that can achieve high spectral and energy efficiency by dynamically reflecting incident signals through programmable passive components. In this paper, we put forth a semantic communication scheme aided by RIS. Using text transmission as an example, experimental results demonstrate that the RIS-Assisted semantic communication system outperforms the point-To-point semantic communication system in terms of bilingual evaluation understudy (BLEU) scores in Rayleigh fading channels, especially at low signal-To-noise ratio (SNR) regimes. In addition, the RIS-Assisted semantic communication system exhibits superior robustness against channel estimation errors compared to its point-To-point counterpart. RIS can improve performance as it provides extra line-of-sight (LoS) paths and enhances signal propagation conditions compared to point-To-point systems. © 2023 IEEE.

关键词： Fading channels

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Structural Balance Computation of Signed Hypergraphs via Memetic Algorithm 5

Structural Balance Computation of Signed Hypergraphs via Mem...

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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Han, Xinchu Xu, Ruijie Ma, Lijia Shao, Zengyang Bai, Yuan Wang, Shanfeng College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Laboratory of Data Discovery for Health Limited Hong Kong999077 Hong Kong School of Electronic Engineering Xidian University Xi'an710071 China

ISBN: (纸本)9798350393521

The potential imbalances among entities and connections within complex systems can pose significant threats to their overall functionality. In order to enhance the functional security of these complex systems, it becomes imperative to address and alleviate these underlying imbalances. Structural balance computation has gained attention because of its ability to balance relationships among entities within systems. Traditional methods in structural balance computation aim to minimize imbalances within signed networkss. However, signed networkss have limitations in effectively representing complex data relationships in multi-modal or multi-type data. In order to address this constraint, we propose a novel framework called SHMA (Structural Balance of Signed Hypergraphs based on Memetic Algorithm) which aims to identify and minimize imbalances within signed hypergraphs. More specifically, we formulate the degree of imbalance in signed hypergraphs as an extended energy function, thereby transforming the computation of structural balance of the hypergraphs into an optimization problem. Then, we utilize a multi-level memetic algorithm to optimize the energy function and obtain a solution with the least imbalances. To assess the performance of SHMA, we performed experiments on various real-world datasets and conducted a comparative analysis. The experimental results illustrate that SHMA outperforms the alternative algorithms in terms of structural balance computation of signed hypergraphs. In addition, SHMA exhibits fast convergence, enabling it to reach a balanced state more efficiently. © 2023 IEEE.

关键词： Large scale systems

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Recognition and Detection of Diabetic Retinopathy Using Densenet-65 Based Faster-RCNN

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computers, Materials & Continua 2021年第5期67卷 1333-1351页

作者： Saleh Albahli Tahira Nazir Aun Irtaza Ali Javed Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan

Diabetes is a metabolic disorder that results in a retinal complication called diabetic retinopathy(DR)which is one of the four main reasons for sightlessness all over the *** usually has no clear symptoms before the onset,thus making disease identication a challenging *** healthcare industry may face unfavorable consequences if the gap in identifying DR is not lled with effective ***,our objective is to develop an automatic and cost-effective method for classifying DR *** this work,we present a custom Faster-RCNN technique for the recognition and classication of DR lesions from retinal *** pre-processing,we generate the annotations of the dataset which is required for model ***,introduce DenseNet-65 at the feature extraction level of Faster-RCNN to compute the representative set of key ***,the Faster-RCNN localizes and classies the input sample into ve *** experiments performed on a Kaggle dataset comprising of 88,704 images show that the introduced methodology outperforms with an accuracy of 97.2%.We have compared our technique with state-of-the-art approaches to show its robustness in term of DR localization and ***,we performed cross-dataset validation on the Kaggle and APTOS datasets and achieved remarkable results on both training and testing phases.

关键词： Deep learning medical informatics diabetic retinopathy healthcare computer vision

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LLM-Based Digital Twin Water Conservancy Knowledge Graph Construction

LLM-Based Digital Twin Water Conservancy Knowledge Graph Con...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yan Yang Feng Ye Dong Xu Xuejie Zhang Jin Xu College of Computer Science and Software Engineering Hohai University Nanjing China College of Water Conservancy and Hydropower Engineering Hohai University Nanjing China

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

In the study, a novel method for extracting domain-specific knowledge in digital twin water conservancy construction is proposed, addressing the challenges of interdisciplinary complexity and the limitations in existing knowledge extraction models' understanding capabilities of domain-specific knowledge. Utilizing a large language model, the novel method incorporates the domain knowledge of digital twin water conservancy into a local model framework. It employs prompt-based fine-tuning and leverages the semantic understanding and generative capabilities of the model to enhance knowledge extraction precision. To optimize entity extraction, innovative heterogeneous entity alignment strategies are introduced. The efficacy of the proposed method is validated through comparative and ablation studies conducted on a water domain corpus. Results indicate a significant improvement over conventional models, with F1 scores for entity and relationship extraction at 88.63% and 84.46%, respectively, and an entity extraction precision rate of 90.11%. Ablation experiments further demonstrate that our method outperforms the baseline large language model, enhancing the F1 scores for entity and relation extraction by 5.5 and 3.2 percentage points, respectively. By adjusting the amount of domain knowledge injected, the influence of the amount of domain text on the knowledge extraction effect is explored.

关键词： Large language models Semantics Knowledge graphs Water conservation Digital twins Complexity theory Personnel

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SRGAN Optimization Through Enhanced Network Structure and Multi-Loss Functions

SRGAN Optimization Through Enhanced Network Structure and Mu...

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Data science and computer Application (ICDSCA), IEEE International Conference on

作者： Zexin Chen Xiaoxin Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Medical Information Engineering Guangdong Pharmaceutical University Guangzhou China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

An enhanced SRGAN image super-resolution algorithm is proposed to address SRGAN's training instability and limited global information capture. This enhancement optimizes both the network structure and loss function. In network structure optimization, group normalization is introduced as an alternative to batch normalization, addressing its dependency on batch size and improving stability during small-batch training. Additionally, Global Context Block (GCBlock) is integrated into the residual block, enhancing the network's global information capture and detail representation capabilities. In regard to the loss function, this paper introduces L1 loss and MS-SSIM loss as alternatives to the conventional MSE loss. This approach aims to enhance the quality of the generated image in both pixel space and structural information while reducing the smoothing effect. In the experimental section, the ablation experiments evaluate the performance of models with and without GCBlock on five test sets, including U rban1 00 and Manga109. The results demonstrate that GCBlock improves both detail expressiveness and global information capture, with an average PSNR gain of 0.1388 and SSIM improvement of 0.008. In comparison experiments, the improved algorithm achieved an average PSNR increase of 0.270 and an average SSIM increase of 0.018 on the aforementioned test set, indicating a significant improvement in detail expression and visual quality. Finally, the limitations of the enhanced algorithm are discussed, and potential future research directions are proposed.

关键词： Training Visualization Smoothing methods Computational modeling Superresolution Stability analysis Data models Optimization Image reconstruction Thermal stability

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Evolutionary Reinforcement Learning with Parameterized Action Primitives for Diverse Manipulation Tasks 39

Evolutionary Reinforcement Learning with Parameterized Actio...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Qiu, Xianxu Huang, Haiming Chen, Weiwei Lin, Qiuzhen Chen, Wei-Neng Sun, Fuchun College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Electronics and Information Engineering Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)157735897X

Reinforcement learning (RL) has shown promising performance in tackling robotic manipulation tasks (RMTs), which require learning a prolonged sequence of manipulation actions to control robots efficiently. However, most RL algorithms often suffer from two problems when solving RMTs: inefficient exploration due to the extremely large action space and catastrophic forgetting due to the poor sampling efficiency. To alleviate these problems, this paper introduces an Evolutionary Reinforcement Learning algorithm with parameterized Action Primitives, called ERLAP, which combines the advantages of an evolutionary algorithm (EA) and hierarchical RL (HRL) to solve diverse RMTs. A library of heterogeneous action primitives is constructed in HRL to enhance the exploration efficiency of robots and dual populations with new evolutionary operators are run in EA to optimize these primitive sequences, which can diversify the distribution of replay buffer and avoid catastrophic forgetting. The experiments show that ERLAP outperforms four state-of-the-art RL algorithms in simulated RMTs with dense rewards and can effectively avoid catastrophic forgetting in a set of more challenging simulated RMTs with sparse rewards. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Reinforcement learning

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