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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Meng, Lei Ye, Zhonglin Zhao, Haixing Yang, Yanlin Chen, Yang Wang, Zhaoyang College of Computer Qinghai Normal University Tibetan Information Processing Engineering Technology and Research Center of Qinghai Province The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810008 China

ISBN: (纸本)9798350319934

At present, as an emerging direction of artificial intelligence, graph neural networks (GNNs) have developed rapidly and can solve many graph data problems efficiently. The real world is composed of a large number of networks. Compared with the widely studied single-relationship feature information networks, the real-world networks are mostly composed of multi-relationship feature information networks in which different types of objects are interconnected through complex relationships. Complex structural information and rich semantic information are included by multi-relationship feature information networks, thereby the real network can be restored more realistically. However, one type of relational feature of the network is merely considered by most of the existing GNNs, and the information of multi-relationship features and the hidden information between multi-relationship features are ignored in the network. In order to deal with these restrictions, a novel graph convolutional network (TCGCN) based on an attention mechanism is proposed, which aims to extract different relational features in multi-relationship feature networks and the hidden information between them more comprehensively, thereby improving the performance of GNNs in downstream tasks. In this paper, an improved attention mechanism is used to aggregate different types of single relational features, and the aggregated multiple single relational feature network information is fused with the mixed relational feature network information into the final node representation. The proposed TCGCN outperforms most other methods in extensive experiments conducted on three real-world datasets. © 2022 IEEE.

关键词： Graph neural networks

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MSCA-UNet: Multi-Scale Channel Adaptive Sensing Module for Medical Image Segmentation

MSCA-UNet: Multi-Scale Channel Adaptive Sensing Module for M...

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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Diao, Liangjin Shen, Longfeng Chen, Jiacong Ge, Fangzhen Li, Mengwen College of Computer Science and Technology Huaibei Normal University Anhui Huaibei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui Hefei China

ISBN: (纸本)9798331517021

Recent advancements in CNNs for medical image segmentation have focused on the combination of CNN and Transformer architectures to capture both local and global features. However, challenges remain, such as effectively utilizing fused features at the encoder-decoder bottleneck and addressing blurred object edges in datasets. To tackle these issues, we introduce a multi-scale channel adaptive sensing module, enhancing the model's representational and perceptual capabilities by integrating multi-scale and channel-specific information. Additionally, we propose a cross-perception module and multi-scale segmentation head to improve global coordination and multi-scale information gathering. Experiments on the ACDC and Synapse datasets demonstrate our model's superiority. © 2024 IEEE.

关键词： Image segmentation

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Dual-Band HRRP Recognition via Wavelet Packet Decomposition and Redundancy Reduction Model 2

Dual-Band HRRP Recognition via Wavelet Packet Decomposition ...

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2nd IEEE International Conference on Signal, Information and Data processing, ICSIDP 2024

作者： Yang, Wei Qi, Zhuchang Wu, Heke Li, Yang Zhang, Liang Wang, Yanhua Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou450000 China Xi'an Institute of Modern Control Technology Xi'an710065 China Beijing Key Laboratory of Embedded Real-time Information: Processing Technology Beijing100081 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401120 China

ISBN: (纸本)9798331515669

High resolution range profiles (HRRP) is widely used in radar automatic target recognition. Due to aspect sensitivity, HRRP of different targets can be similar under certain aspects, making it difficult for recognition via traditional methods. One way to mitigate it is to increase the frequency dimension, utilizing the differences of target frequency response to extract inter-class discriminative information. Therefore, we propose a HRRP recognition model based on redundancy reduction criterion and wavelet packet decomposition. Specifically, we use deep wavelet packet decomposition to perform multi-level decomposition of dual-band HRRP and adaptively extract discriminative features of sub-wavelets;we use redundancy reduction layer to cluster intra-class features and expand inter-class features, thereby reducing feature heterogeneity. Furthermore, we propose a deep discriminative correlation analysis for joint analysis and transformation of dual-band features to fuse complementary information. Experimental results show that proposed method has excellent recognition performance. Additionally, analysis study also verify the effectiveness of each submodule. © 2024 IEEE.

关键词： Wavelet decomposition

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Dual-Band HRRP Fusion Recognition via Wavelet Decomposition Embedded Autoencoder 2

Dual-Band HRRP Fusion Recognition via Wavelet Decomposition ...

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2nd IEEE International Conference on Signal, Information and Data processing, ICSIDP 2024

作者： Wang, Weijia Qi, Zhuchang Wang, Lue Yang, Wei Zhang, Liang Wang, Yanhua Xi'an Institute of Modern Control Technology Xi'an710065 China Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798331515669

High-resolution range profile (HRRP) is a crucial technique for radar target recognition. Due to the coupling of azimuthal scattering information and aspect sensitivity, fine target recognition using HRRP remains challenging. One way to mitigate HRRP aspect sensitivity is through enhancing the dimensionality of radar information acquisition. Therefore, this paper proposes a Dual-Band HRRP Fusion Recognition Model via Wavelet Decomposition Embedded Autoencoder (DF-WD-AENet). The method we proposed mainly comprises two modules. Firstly, the autoencoder model based on wavelet decomposition (WD-AEM) achieves constraint on feature information, making it better suited for dual-band fusion. Next is the dual-band information fusion module (DFM), in which we achieved the interaction and fusion of feature information through convolutional block attention module (CBAM) and omni-dimensional dynamic convolution (ODConv), and used a fusion loss function to train the model. Experiment shows that the recognition rate of our method is 7.73% higher than that of single-band, proving the effectiveness of the proposed method. © 2024 IEEE.

关键词： Wavelet decomposition

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Microstructure and mechanical properties of a cast heat-resistant rare-earth magnesium alloy

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China Foundry 2023年第4期20卷 289-298页

作者： Xiao-ping Zhu Jun-qing Yao Hai-long Wu Xin-wang Liu Hua Liu Zi-tian Fan Shu-lin Lü Kai Wang Zi-dong Wang School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083China State Key Laboratory of Materials Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China Dekai Intelligent Casting Co. Ltd.Zhuozhou 072750HebeiChina China Iron and Steel Research Institute Group Beijing 100081China Guangdong Hongtu Wuhan Die-casting Co. Ltd.Wuhan 430207China

Microstructure,mechanical properties and phase transformation of a heat-resistant rare-earth(RE)Mg-16.1Gd-3.5Nd-0.38Zn-0.26Zr-0.15Y(wt.%)alloy were *** as-cast alloy is composed of equiaxedα-Mg matrix,net-shaped Mg5RE and Zr-rich *** to aging hardening curves and tensile properties variation,the optimized condition of solution treatment at 520℃for 8 h and subsequent aging at 204℃for 12 h was *** continuous secondary Mg5RE phase predominantly formed at grain boundaries during solidification transforms to residual discontinuousβ-Mg5RE phase and fine cuboid REH2particles after heat *** annealed alloy exhibits good comprehensive tensile property at 350℃,with ultimate tensile strength of 153 MPa and elongation to fracture of 6.9%.Segregation of RE elements and eventually RE-rich precipitation at grain boundaries are responsible for the high strength at elevated temperature.

关键词： heat-resistant magnesium alloy rare earth microstructure phase transformation tensile property strengthening

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Inspection Area Allocation for Forest Surveillance Based on Different Starting Points of Multi-UAVs 7th

Inspection Area Allocation for Forest Surveillance Based on ...

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7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023

作者： Zhou, Zhengjun Mu, Lingxia Ning, Kaikai Zhang, Youmin Xue, Xianghong Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Department of Mechanical Industrial and Aerospace Engineering Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9789819733392

An inspection allocation algorithm based on different starting points and endurance capabilities of unmanned aerial vehicles (UAVs) is proposed for the multi-UAVs forest inspection process. Firstly, the area map to be inspected is rasterized, and the distances between the starting points of each UAV and individual grid cells are computed, which will be used for area decomposition. Subsequently, an evaluation function is employed to allocate individual grid cells reasonably to each UAV. Then, the boundary lines of the allocated sub-regions are generated, by which the raster map is converted into an inspection map with continuous boundaries. This ultimately yields the sub-regions for inspection by each UAV. Simulation results demonstrate that this method effectively allocates the forest inspection area to the various UAVs in a rational manner. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Inspection

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Low Viscosity Polyurethane Modified Epoxy Resin Grouting for Steel Ring Reinforcement of Subway Tunnel 2

Low Viscosity Polyurethane Modified Epoxy Resin Grouting for...

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2022 2nd International Conference on Fluid and Chemical Engineering, ICFCE 2022

作者： Huang, Liang Xiao, Ming Li, Weilong Bi, Shuguang Tang, Jiagong Luo, Xianmeng Ran, Jianhua Hubei Key Laboratory of Biomass Fibers and Eco-Dyeing & Finishing State Key Laboratory of New Textile Materials and Advanced Processing Technologies College of Chemistry and Chemical Engineering Wuhan Textile University Wuhan430200 China Wuhan Rongshengjimei Technology Co. Ltd. Wuhan430050 China

Steel ring reinforcement technology is an effective method to eliminate the damage of tunnel structure caused by ground load, surrounding foundation pit and underground water source. The grouting material used to fill the gap between the tunnel segment and the steel ring should have the characteristics of low viscosity, high strength, rapid curing at room temperature and anti-subway vibration. In this work, a special polyurethane modified epoxy resin grouting was fabricated to overcome the inherent brittleness of epoxy resin, and was combined with the reactive diluent and the amine curing agent to suit the above applications. The results showed that both tensile and impact strength of epoxy resin grouting modified by polyurethane were improved obviously compared with those without modification, presenting an obvious toughening effect. Although the viscosity increased slightly, it can still meet the perfusion requirements of steel ring reinforcement of subway tunnels. © Published under licence by IOP Publishing Ltd.

关键词： Epoxy resins

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NPIPVis:A visualization system involving NBA visual analysis and integrated learning model prediction

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Virtual Reality & intelligent Hardware 2022年第5期4卷 444-458页

作者： Zhuo SHI Mingrui LI Meng WANG Jing SHEN Wei CHEN Xiaonan LUO Department of Art and Design University of Guilin Electronic TechnologyGuilin 541004China Department of Computer and Information Security University of Guilin Electronic TechnologyGuilin 541004China Department of Guangxi Key Laboratory of Image and Graphic Intelligent Processing University of Guilin Electronic TechnologyGuilin 541004China Department of State Key Laboratory of CAD&CG University of ZhejiangHangzhou 310058China

Background Data-driven event analysis has gradually become the backbone of modern competitive sports analysis. Competitive sports data analysis tasks increasingly use computer vision and machine-learning models for intelligent data analysis. Existing sports visualization systems focus on the player–team data visualization, which is not intuitive enough for team season win–loss data and game time-series data visualization and neglects the prediction of all-star players. Methods This study used an interactive visualization system designed with parallel aggregated ordered hypergraph dynamic hypergraphs, Calliope visualization data story technology,and i Storyline narrative visualization technology to visualize the regular statistics and game time data of players and teams. NPIPVis includes dynamic hypergraphs of a team’s wins and losses and game plot narrative visualization components. In addition, an integrated learning-based all-star player prediction model, SRR-voting, which starts from the existing minority and majority samples, was proposed using the synthetic minority oversampling technique and Random Under Sampler methods to generate and eliminate samples of a certain size to balance the number of allstar and average players in the datasets. Next, a random forest algorithm was introduced to extract and construct the features of players and combined with the voting integrated model to predict the all-star players, using GridSearch CV, to optimize the hyperparameters of each model in integrated learning and then combined with five-fold cross-validation to improve the generalization ability of the model. Finally, the SHapley Additive ex Planations(SHAP) model was introduced to enhance the interpretability of the model. Results The experimental results of comparing the SRR-voting model with six common models show that the accuracy, F1-score, and recall metrics are significantly improved, which verifies the effectiveness and practicality of the SRR-voting model. Conc

关键词： Sports visualization Parallel aggregated ordered hypergraph Calliope IStoryline Integrated learning SHAP model

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Leveraging Slot-Aware Enhancements for Improved Zero-Shot Dialogue State Tracking

Leveraging Slot-Aware Enhancements for Improved Zero-Shot Di...

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Frontiers technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Tianhang Song Haolei Hu Xinxin Chen Kangli Zi Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Beijing China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Task-oriented dialogue systems (TOD) aim to help users complete specific tasks through multiple rounds of dialogue, in which Dialogue State Tracking (DST) is a key component. The training of DST models typically necessitates the availability of a substantial corpus of dialogue data with turn-level annotations. However, the collection of such data is both costly and inefficient. Despite the impressive performance of systems based on advanced large language models (LLMs), their closed-source nature restricts the potential for local deployment. To address this challenge, this paper proposes a new zero-shot slot-aware enhanced model (SAE), which introduces a novel coded prompting framework to enhance the DST capability of small parameter models in new domains and task scenarios. In particular, we have devised a prompting framework that employs code to refine task instructions. Furthermore, we have proposed a slot-aware question generation mechanism that generates slot-aware questions based on contextual details in accordance with the dialogue context, thereby enhancing the model's perception and matching capabilities for slots. In order to verify the effectiveness of the model, we conducted experiments on the MultiWOZ dataset. The results show that the SAE model has achieved significant performance improvements in zero-shot DST tasks.

关键词： Training Adaptation models Codes Large language models Computational modeling Termination of employment Predictive models Question generation Data models Context modeling

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Unpaired Thick Cloud Removal with Multiscale SAR-optical Data Fusion

Unpaired Thick Cloud Removal with Multiscale SAR-optical Dat...

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Computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Xuanyu Xiang Yihua Tan National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Cloud removal is vital for the analysis of optical satellite images. To alleviate the impact of thick clouds, recent advances integrate deep learning withmultimodal data. To relax the requirements of paired training samples, existing methods utilized cycle-consistent architecture to learn the relation between cloudy and cloud-free images based on unpaired training samples. When considering thick cloud removal based on unpaired training data, the relevant studies are insufficient and they face two challenges: 1) The information in non-cloudy areas is not preserved well after cloud removal; 2) The recovered information lacks consistency with the global textures and structures. Based on optical-SAR fusion and cycle-consistent training, this paper proposes Multiscale Cycle-consistent Fusion (MCF) model. MCF designs preservation loss to overcome the first challenge, and proposes global-local discriminator combined with Parallel Dilated Channel-weighted Module (PDCM) for the second challenge. MCF is evaluated both on a simulated dataset and a real dataset, which demonstrates its effectiveness.

关键词： Training Deep learning Optical losses Clouds Training data Optical imaging Data models

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