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Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition

作者： Lingzhi Liu Baohua Qiang Yuanchun Wang Xianyi Yang Jubo Tian Shihao Zhang Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology China Hebei Key Laboratory of Intelligent Information Perception and Processing The 54th Research Institute of CETC China

ISBN: (纸本)9781450397056

In recent years, object detection has been widely used in various fields such as face detection, remote sensing image detection and pedestrian detection. Due to the complex environment in the actual scene, we need to fully obtain the feature information in the image to improve the accuracy of object detection. This paper proposes an object detection algorithm based on coordinate attention and contextual feature enhancement. We design a multi-scale attention feature pyramid network, which first uses multi-branch atrous convolution to capture multi-scale context information, and then fuses the coordinate attention mechanism to embed location information into channel attention, and finally uses a bidirectional feature pyramid structure to effectively fuse high-level features and low-level features. We also adopt the GIoU loss function to further improve the accuracy of object detection. The experimental results show that the proposed method has certain advantages compared with other detection algorithms in the PASCAL VOC datasets.

关键词： Feature pyramid network Object detection Coordinate attention Atrous convolution

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Optimization Deployment of Temperature Measurement Points for CZ Silicon Single Crystal Growth Equipment Based on Improved Fuzzy C-Means Clustering

Optimization Deployment of Temperature Measurement Points fo...

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第43届中国控制会议

作者： Weichao Huang Siyuan Cao Ding Lui School of Automation and Information Engineering Xi'an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The inverse heat transfer method is an effective inversion algorithm for solving the heat transfer at non-measurable places of the study object. However, the current inverse heat transfer inversion method has the problems of low accuracy and difficult selection of temperature measurement point locations. To solve this problem, this paper proposes an optimization algorithm for measurement point locations based on improved fuzzy c-means clustering(IFCM). The method first generates the initial clustering center through a specific density function and introduces the Mahalanobis distance to solve the problem that the traditional fuzzy c-means clustering results depend on the initial clustering center and the results are unstable. Then, an optimized deployment algorithm based on IFCM temperature measurement points is proposed and compared with other mean value clustering algorithms. Finally, the optimal temperature measurement position on the crystal surface is selected by the IFCM temperature measurement point optimization deployment algorithm on the CZ(Czochraski)silicon single crystal growth equipment, and the inversion results of the randomly selected temperature measurement point scheme and the optimized temperature measurement point scheme are compared by the real-time inversion algorithm based on the dynamic matrix control multi-boundary heat flux, and the results of the optimized temperature measurement point scheme are obtained to be better than that of the randomly selected temperature measurement point scheme.

关键词： Fuzzy c-means clustering IFCM Temperature measurement point optimization Inverse heat transfer

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A Prediction-Based Multi-Objective VM Consolidation Approach for Cloud Data Centers

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Computers, Materials & Continua 2024年第7期80卷 1601-1631页

作者： Xialin Liu Junsheng Wu Lijun Chen Jiyuan Hu School of Computer Science Northwestern Polytechnical UniversityXi’an710005China School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’an710021China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and TelecommunicationsXi’an710021China School of Software and Microelectronics Northwestern Polytechnical UniversityXi’an710005China School of Automation Xi’an University of Posts and TelecommunicationsXi’an710021China

Virtual machine(VM)consolidation aims to run VMs on the least number of physical machines(PMs).The optimal consolidation significantly reduces energy consumption(EC),quality of service(QoS)in applications,and resource *** paper proposes a prediction-basedmulti-objective VMconsolidation approach to search for the best mapping between VMs and PMs with good timeliness and practical *** use a hybrid model based on Auto-Regressive Integrated Moving Average(ARIMA)and Support Vector Regression(SVR)(HPAS)as a prediction model and consolidate VMs to PMs based on prediction results by HPAS,aiming at minimizing the total EC,performance degradation(PD),migration cost(MC)and resource wastage(RW)*** results usingMicrosoft Azure trace show the proposed approach has better prediction accuracy and overcomes the multi-objective consolidation approach without prediction(i.e.,Non-dominated sorting genetic algorithm 2,Nsga2)and the renowned Overload Host Detection(OHD)approaches without prediction,such as Linear Regression(LR),Median Absolute Deviation(MAD)and Inter-Quartile Range(IQR).

关键词： VM consolidation prediction multi-objective optimization machine learning

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Multi-scale fusion residual encoder-decoder approach for low illumination image enhancement

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The Journal of China Universities of Posts and Telecommunications 2022年第2期29卷 63-72页

作者： Pan Xiaoying Wei Miao Wang Hao Jia Fengzhu School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsXi'an 710121China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and TelecommunicationsXi'an 710121China School of Software Northwestern Polytechnical UniversityXi'an 710072China

The sensing light source of the line scan camera cannot be fully exposed in a low light environment due to the extremely small number of photons and high noise,which leads to a reduction in image quality.A multi-scale fusion residual encoder-decoder(FRED)was proposed to solve the *** directly learning the end-to-end mapping between light and dark images,FRED can enhance the image’s brightness with the details and colors of the original image fully restored.A residual block(RB)was added to the network structure to increase feature diversity and speed up network ***,the addition of a dense context feature aggregation module(DCFAM)made up for the deficiency of spatial information in the deep network by aggregating the context’s global multi-scale *** experimental results show that the FRED is superior to most other algorithms in visual effect and quantitative evaluation of peak signal-to-noise ratio(PSNR)and structural similarity index measure(SSIM).For the factor that FRED can restore the brightness of images while representing the edge and color of the image effectively,a satisfactory visual quality is obtained under the enhancement of low-light.

关键词： image enhancement low illumination feature fusion residual network

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Semantic Maximum Relevance and Modal Alignment for Cross-Modal Retrieval 22

Semantic Maximum Relevance and Modal Alignment for Cross-Mod...

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Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition

作者： Pingping Sun Baohua Qiang Zhiguang Liu Xianyi Yang Guangyong Xi Weigang Liu Ruidong Chen Shihao Zhang Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology China Hebei Key Laboratory of Intelligent Information Perception and Processing The 54th Research Institute of CETC China

ISBN: (纸本)9781450397056

With the increasing abundance of multimedia data resources, researches on mining the relationship between different modalities to achieve refined cross-modal retrieval are gradually emerging. In this paper, we propose a novel Semantic Maximum Relevance and Modal Alignment (SMR-MA) for Cross-Modal Retrieval, which utilizes the pre-trained model with abundant image text information to extract the features of each image text, and further promotes the modal information interaction between the same semantic categories through the modal alignment module and the multi-layer perceptron with shared weights. In addition, multi-modal embedding is distributed to the normalized hypersphere, and angular edge penalty is applied between feature embedding and weight in angular space to maximize the classification boundary, thus increasing both intra-class similarity and inter-class difference. Comprehensive analysis experiments on three benchmark datasets demonstrate that the proposed method has superior performance in cross-modal retrieval tasks and is significantly superior to the state-of-the-art cross-modal retrieval methods.

关键词： Semantic Relevance CLIP Cross-Modal Retrieval Modal Alignment

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Adaptive Neural Finite-Time Deployment of Heterogeneous Multi-agent Systems via a Cross-Species Bionic PDE-ODE Approach

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Artificial Intelligence Science and Engineering 2025年第1期1卷 52-63页

作者： Jingtao MAN Zhigang ZENG School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074China

For large-scale heterogeneous multi-agent systems(MASs)with characteristics of dense-sparse mixed distribution,this paper investigates the practical finite-time deployment problem by establishing a novel crossspecies bionic analytical framework based on the partial differential equation-ordinary differential equation(PDE-ODE)***,by designing a specialized network communication protocol and employing the spatial continuum method for densely distributed agents,this paper models the tracking errors of densely distributed agents as a PDE equivalent to a human disease transmission model,and that of sparsely distributed agents as several ODEs equivalent to the predator population *** coupling relationship between the PDE and ODE models is established through boundary conditions of the PDE,thereby forming a PDE-ODE-based tracking error model for the considered ***,by integrating adaptive neural control scheme with the aforementioned biological models,a“Flexible Neural Network”endowed with adaptive and self-stabilized capabilities is constructed,which acts upon the considered MASs,enabling their practical finite-time ***,effectiveness of the developed approach is illustrated through a numerical example.

关键词： large-scale heterogeneous MASs cross-species bionic framework practical finite-time deployment PDEODE approach adaptive neural control

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Lookup table meets local laplacian filter: pyramid reconstruction network for tone mapping 23

Lookup table meets local laplacian filter: pyramid reconstru...

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Proceedings of the 37th International Conference on Neural Information processing Systems

作者： Feng Zhang Ming Tian Zhiqiang Li Bin Xu Qingbo Lu Changxin Gao Nong Sang National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology DJI Technology Co. Ltd National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology and Hubei Key Laboratory of Brain-inspired Intelligent Systems

Tone mapping aims to convert high dynamic range (HDR) images to low dynamic range (LDR) representations, a critical task in the camera imaging pipeline. In recent years, 3-Dimensional Look-Up Table (3D LUT) based methods have gained attention due to their ability to strike a favorable balance between enhancement performance and computational efficiency. However, these methods often fail to deliver satisfactory results in local areas since the look-up table is a global operator for tone mapping, which works based on pixel values and fails to incorporate crucial local information. To this end, this paper aims to address this issue by exploring a novel strategy that integrates global and local operators by utilizing closed-form Laplacian pyramid decomposition and reconstruction. Specifically, we employ image-adaptive 3D LUTs to manipulate the tone in the low-frequency image by leveraging the specific characteristics of the frequency information. Furthermore, we utilize local Laplacian filters to refine the edge details in the high-frequency components in an adaptive manner. Local Laplacian filters are widely used to preserve edge details in photographs, but their conventional usage involves manual tuning and fixed implementation within camera imaging pipelines or photo editing tools. We propose to learn parameter value maps progressively for local Laplacian filters from annotated data using a lightweight network. Our model achieves simultaneous global tone manipulation and local edge detail preservation in an end-to-end manner. Extensive experimental results on two benchmark datasets demonstrate that the proposed method performs favorably against state-of-the-art methods.

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Text Matching application for integration between industry and education 14

Text Matching application for integration between industry a...

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14th International Conference on Measuring technology and Mechatronics Automation, ICMTMA 2022

作者： Luo, Yuxuan Li, Xiaoge School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9781665499781

A new talent training mode of collaboration between industry and education was proposed, which aims to reduce the gaps of talent definition between enterprise demand and college education. We formulates scenario as a text matching task of similarity between courses details and job position information, developed a ERNIE+DPCNN based model for the task. The model has been benchmarked on LCQMC dataset, the results show that it achieves the art-of state performance of Acc 0.8809, Recall 0.8745, and FX score 0.8739, We conducted many experiments about the text similarity calculation between domains of industry and educations. The experiment results show our approach has much potential to optimize the curriculum system while applying in this field. © 2022 IEEE

关键词： Industries Training Mechatronics Automation Collaboration Benchmark testing Task analysis

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Directional fabrication of an ultra-light and porous bacterial nanocellulose/MXene composite aerogel for efficient thermal management

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International Journal of Biological Macromolecules 2025年第Pt 4期311卷 144054页

作者： Li, Siqi Luo, Lei Hasan, Md. Zahid Shi, Naman Liu, Qimin Li, Daiqi Yue, Chengfei Zhang, Ruquan Sha, Sha Zhu, Jiadeng State Key Laboratory of New Textile Materials and Advanced Processing Technology School of Textile Science and Engineering Wuhan Textile University Wuhan430200 China Institute of Design Innovation and Fiber Science Wuhan Textile University Wuhan430073 China Smart Devices Brewer Science Inc. SpringfieldMO65806 United States

Global warming has intensified extreme weather events, necessitating advanced thermal management systems to protect human health. Due to its unique properties of increasing phonon scattering at interlayer interfaces and wide spectral absorption, MXene has great potential in thermal management. However, the self-stacking tendency of MXene caused by hydrogen bonding and van der Waals force limits its further application. Herein, we have successfully fabricated an ultra-light (0.011 g cm−3) three-dimensional (3D) bacterial nanocellulose (BNC)/MXene aerogel (BM) through directional freezing. The BNC serves as scaffolds, offering a porous structure with ultra-low thermal conductivity (0.038 W m−1 K−1), high porosity (99.33 %), and excellent insulation (ΔT of 164 at 200 °C). Additionally, the BM aerogel shows remarkable photo-thermal conversion efficiency (98 % solar absorption, 0.25–2.5 μm), quick thermal response (88 °C in 10 s), and thermal stability (89 °C sustained after 120 min). These multifunctional properties make the BM aerogel a potential candidate for advanced thermal management in response to global climate challenges. © 2025 Elsevier B.V.

关键词： Van der Waals forces

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Two Birds with One Stone: A Link Prediction Model for Knowledge Hypergraph Based on Fully-Connected Tensor Decomposition 9th

Two Birds with One Stone: A Link Prediction Model for Know...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Pang, Jun Qin, Hong-Chao Liu, Yan Liu, Xiao-Qi Wuhan University of Science and Technology Hubei430065 China Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial System Hubei430065 China Beijing Institute of Technology Beijing100081 China Wuhan Jiangang Middle School Hubei430050 China

ISBN: (纸本)9783031466632

Knowledge hypergraph link prediction aims to predict missing relationships in knowledge hypergraphs and is one of the effective methods for graph completion. The existing optimal knowledge hypergraph link method based on tensor decomposition, i.e., GETD (Generalized Model based on Tucker Decomposition and Tensor Ring Decomposition), has achieved good performance by extending Tucker decomposition, but there are still two main problems: (1)GETD does not establish operations or connections between any two tensor factors, resulting in limited representation of tensor correlation (referred to as finiteness);(2)The tensor decomposed by GETD is highly sensitive to the arrangement of tensor patterns (referred to as sensitivity). In response to the above issues, we propose a knowledge hypergraph link prediction model, called GETD+, based on fully-connected tensor decomposition(FCTN). By combining Tucker decomposition and FCTN, a multi-linear operation/connection is established for any two factor tensors obtained from tensor decomposition. This not only enhances the representation ability of tensors, but also eliminates sensitivity to tensor pattern arrangement. Finally, the superiority of the GETD+ model was verified through a large number of experiments on real knowledge hypergraph datasets and knowledge graph datasets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tensors

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