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Efficient blocked symmetric compressed sparse column method for finite element analysis

Frontiers of Mechanical Engineering 2025年第1期20卷 87-104页

作者： Yingjun WANG Shijie LUO Jinyu GU Yuanfang ZHANG National Engineering Research Center of Novel Equipment for Polymer Processing The Key Laboratory of Polymer Processing Engineering of the Ministry of EducationGuangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced ManufacturingSouth China University of TechnologyGuangzhou 510641China Shien-Ming Wu School of Intelligent Engineering South China University of TechnologyGuangzhou 511442China

In finite element analysis(FEA),optimizing the storage requirements of the global stiffness matrix and enhancing the computational efficiency of solving finite element equations are pivotal *** address these goals,we present a novel method for compressing the storage of the global stiffness matrix,aimed at minimizing memory consumption and enhancing FEA *** method leverages the block symmetry of the global stiffness matrix,hence named the blocked symmetric compressed sparse column(BSCSC)*** also detail the implementation scheme of the BSCSC method and the corresponding finite element equation solution *** approach optimizes only the global stiffness matrix index,thereby reducing memory requirements without compromising FEA computational *** then demonstrate the efficiency and memory savings of the BSCSC method in FEA using 2D and 3D cantilever beams as *** addition,we employ the BSCSC method to an engine connecting rod model to showcase its superiority in solving complex engineering ***,we extend the BSCSC method to isogeometric analysis and validate its scalability through two examples,achieving up to 66.13%memory reduction and up to 72.06%decrease in total computation time compared to the traditional compressed sparse column method.

关键词： finite element analysis global stiffness matrix blocked symmetric property memory reduction isogeometric analysis

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Recent advances in iron-based sulfides electrocatalysts for oxygen and hydrogen evolution reaction

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Chinese Chemical Letters 2024年第1期35卷 181-195页

作者： Jing Mei Yuqing Deng Xiaohong Cheng Xing Wang Qi Wu State Key Laboratory of New Textile Materials and Advanced Processing Technologies Wuhan Textile UniversityWuhan 430200China Hubei Key Laboratory of Pollutant Analysis&Reuse Technology College of Chemistry and Chemical EngineeringHubei Normal UniversityHuangshi 435002China Hubei Key Laboratory of Low Dimensional Optoelectronic Materials and Devices Hubei University of Arts and ScienceXiangyang 441053China

Increasing environmental pollution and shortage of conventional fossil fuels have made it urgent to develop renewable and clean energy sources. Electrocatalytic water splitting, with its abundant raw materials, simple process, and zero carbon emission, is considered one of the most promising processes for producing carbon-neutral hydrogen which has excellent energy conversion efficiency and high gravimetric energy density. Among them, oxygen evolution reaction (OER) electrocatalysts and hydrogen evolution reaction (HER) electrocatalysts are critical to decreasing the intrinsic reaction energy barrier and boosting the hydrogen evolution efficiency. Therefore, it is imperative to develop and design low-cost, highly active, and stable OER and HER electrocatalysts to lower the overpotential and drive the electrocatalytic reactions. Transition metal sulfides, especially iron-based sulfides, have attracted extensive exploration by researchers as a result of its high abundance in the Earth's crust and near-metallic conductivity. Consequently, in this review, we systematically and comprehensively summarize the progress in the application of iron-based sulfides and their composites as OER and HER electrocatalysts in electrocatalysis. Detailed descriptions and illustrations of the special relationships among their composition, structure, and electrocatalytic performance are presented. Finally, this review points out the challenges and future prospects of iron-based sulfides in practical applications for designing and fabricating more promising iron-based sulfide OER and HER electrocatalysts. We believe that iron-based sulfide materials will have a wide range of application prospects as OER and HER electrocatalysts in the future.

关键词： Iron-based sulfides Electrocatalysts Doping Composites Water splitting

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A self-powered system to electrochemically generate ammonia driven by palladium single atomelectrocatalyst

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SusMat 2024年第5期4卷 178-188页

作者： Hao Hu Shuyuan Pan Zhiyong Ma Kaiyi Liu Yi Li Haifeng Bao Chengwei Deng Fang Luo Zehui Yang State Key Laboratory of New Textile Materials&Advanced Processing Technology College of Materials Science and EngineeringWuhan Textile UniversityWuhanChina Faculty of Materials Science and Chemistry China University of GeosciencesWuhanChina Shaanxi Coal Chemical Industry Technology Research Institute Co. Ltd.Xi’anChina State Key Laboratory of Space Power-Sources Technology Shanghai Institute of Space Power SourcesShanghaiChina Hubei Key Laboratory for New Textile Materials and Applications Wuhan Textile UniversityWuhanChina

The utilization of single atoms(SAs)as trifunctional electrocatalyst for nitro-gen reduction,oxygen reduction,and oxygen evolution reactions(NRR,ORR,and OER)is still a formidable ***,we devise one-pot synthesized palladium SAs stabilized on nitrogen-doped carbon palladium SA electrocat-alyst(Pd-SA/NC)as efficient trifunctional electrocatalyst for NRR,ORR,and ***-SA/NC performs a robust catalytic activity toward NRR with faradaic efficiency of 22.5%at-0.25 V versus reversible hydrogen electrode(RHE),and the relative Pd utilization efficiency is enhanced by 17-fold than Pd-NP/*** addition,the half-wave potential reaches 0.876 V versus RHE,amounting to a 58-time higher mass activity than commercial Pt/***,the overpotential at 10 mA cm-2 is as low as 287 mV for Pd-SA/NC,outperforming the commer-cial IrO2 by 360 times in turnover frequency at 1.6 V versus ***,the assembled rechargeable zinc-air battery(ZAB)achieves a maximum power den-sity of 170 mW cm-2,boosted by 2.3 times than Pt/C–*** constructed ZABs efficiently power the NRR-OER system to electrochemically generate ammonia implying its superior trifunctionality.

关键词： nitrogen reduction reaction oxygen evolution reaction oxygen reduction reaction single atom

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High‑Speed Sirospun Conductive Yarn for Stretchable Embedded Knitted Circuit and Self‑Powered Wearable Device

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Advanced Fiber Materials 2023年第1期5卷 154-167页

作者： Li Niu Jin Wang Kai Wang Heng Pan Gaoming Jiang Chaoyu Chen Pibo Ma Engineering Research Center of Knitting Technology Ministry of EducationCollege of Textile Science and EngineeringJiangnan UniversityWuxi 214122China State Key Laboratory of New Textile Materials and Advanced Processing Technologies Wuhan Textile UniversityWuhan 430200China

In the intelligent era,the textile technique is a high efficiency,mature and simple manufacturing solution capable of fabricat-ing fully flexible wearable ***,the external circuit with its integration and comfort limitations cannot satisfy the requirements of intelligent wearable and portable *** study presents an industrialized production method to fabricate core–shell structure conductive yarn for direct textile use,prepared by the high-speed sirospun *** integration and flexibility are significantly improved over previous *** sirospun conductive yarn(SSCY)and the intarsia technique can provide the SSCY seamless and convenient embedded knitted circuit(SSCY-EKC)to form a full textile electrical element as the channel of power and signals transmission,allowing for a stable resistance change and wide strain range for meeting practical *** based on the triboelectric nanogenerator(SSCY-TENG)can be designed as a caution carpet with attractive design and good washability for a self-powered sensor that recognizes human ***,intrinsic textile properties such as washability,softness,and comfort *** benefits such as excellent extension,fitting,and stretchability,the SSCY-EKC used herein can realize a fully flexible electrical textile with a high potential for physical detection,body gesture recognition,apparel fashion,and decoration.

关键词： Sirospun technique Embedded knitted circuit Intarsia technique Stretchable Wearable devices

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Integrating the dynamic optimization strategies of Adam and AdaGrad 13

Integrating the dynamic optimization strategies of Adam and ...

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13th International Conference on intelligent Control and Information processing, ICICIP 2025

作者： Huang, Zhixuan Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

Artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the rise of big data and deep learning, AI has advanced in applications like recommendation systems, image recognition, and machine translation, primarily through optimizing loss functions in deep neural networks to improve accuracy and reduce training *** descent is the core optimization method but faces challenges like slow convergence and local minima. To overcome these, algorithms like Momentum, AdaGrad, RMSProp, Adadelta, Adam, and Nadam have been developed, introducing momentum and adaptive learning rates to accelerate convergence. This paper presents a new optimization algorithm that combines the strengths of Adam and AdaGrad, offering better adaptability to different learning rates. © 2025 IEEE.

关键词： Contrastive Learning

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Attitude Detection Model Based on Improved High Resolution Network 13

Attitude Detection Model Based on Improved High Resolution N...

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13th International Conference on intelligent Control and Information processing, ICICIP 2025

作者： Shi, Dongliang Li, Yuhan Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

2D human posture detection, a challenging task in computer vision, aims to identify human body key points and is widely used. The HRNet model performs well in human key point detection. To enhance its effect in human posture detection and address suboptimal prediction accuracy in interactive occlusion, this paper combines HRNet and Transformer. The Transformer's attention mechanism handles complex image dependencies, and HRNet obtains image features. By embedding the Transformer's attention mechanism module into HRNet's backbone network and integrating a self-designed double-branch structure and convolutional attention module, an improved HRNet model is constructed. The DConvAttention module processes extracted features, and the convolutional attention mechanism balances image frequencies, enhancing processing and discrimination of complex backgrounds and occlusions and improving detection performance. In tests of complex and occluded images, the improved HRNet Local model has a 4.2M parameter increase, 0.5G FLOPs increase, and 1% mAP increase compared to HRNet, with the same recall rate. The improved HRNet Global model has 11M more parameters, 1.1G more FLOPs, a similar or slightly higher recall rate, and a 2% higher mAP value. © 2025 IEEE.

关键词： Face recognition

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Plasma-assisted fabrication of multiscale materials for electrochemical energy conversion and storage

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Carbon Energy 2025年第2期7卷 144-171页

作者： Chen Li Tengfei Zhang Zhong Qiu Beirong Ye Xinqi Liang Xin Liu Minghua Chen Xinhui Xia Chen Wang Wangjun Wan Yongqi Zhang Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengduChina Key Laboratory of Engineering Dielectric and Applications(Ministry of Education) School of Electrical and Electronic EngineeringHarbin University of Science and TechnologyHarbinChina State Key Laboratory of New Textile Materials and Advanced Processing Technology Wuhan Textile UniversityWuhanChina College of Materials Science and Engineering Zhejiang University of TechnologyHangzhouChina School of Materials Science and Engineering Zhejiang UniversityHangzhouChina Zhejiang Academy of Science and Technology for Inspection and Quarantine HangzhouChina

Plasma,the fourth state of matter,is characterized by the presence of charged particles,including ions and *** has been shown to induce unique physical and chemical ***,there have been increased applications of plasma technology in the field of multiscale functional materials'preparation,with a number of interesting *** review will begin by introducing the basic knowledge of plasma,including the definition,typical parameters,and classification of plasma *** this,we will provide a comprehensive review and summary of the applications(phase conversion,doping,deposition,etching,exfoliation,and surface treatment)of plasma in common energy conversion and storage systems,such as electrocatalytic conversion of small molecules,batteries,fuel cells,and *** article summarizes the structure-performance relationships of electrochemical energy conversion and storage materials(ECSMs)that have been prepared or modified by *** also provides an overview of the challenges and perspectives of plasma technology,which could lead to a new approach for designing and modifying electrode materials in ECSMs.

关键词： electrocatalysts energy conversion and storage nanomaterials plasma

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A Novel Self-Learning Network Integrating Contrastive Learning, Perceptual Learning and Masked Image Modelling 15

A Novel Self-Learning Network Integrating Contrastive Learni...

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15th International Conference on Graphics and Image processing, ICGIP 2023

作者： Chen, Yingxian Yang, Rui Lan, Rushi Guangxi Key Laboratory Image and Graphic Intelligent Processing Guilin University of Electronic Technology China

ISBN: (纸本)9781510675018

Unsupervised learning methods in computer vision have achieved remarkable success, exceeding the performance of supervised learning methods. It is noteworthy that current unsupervised learning methods share certain similarities, particularly in their data augmentation techniques. Masking, a type of data augmentation, can be utilized for both contrastive learning and masked image modelling. This paper presents a novel deep learning approach on visual unsupervised learning. It integrates previous methods such as contrastive learning, perceptual learning, self-distillation and masked image modelling. In our method, we treat the network that handles the original images as the teacher network, and the network that handles the masked images as the student network. The student network employs the representations extracted by the projection head for contrastive learning, while the features generated by the decoder are employed for masked image modeling. The process of self-knowledge distillation is facilitated by perceptual learning between the teacher and student networks. This model aligns with the main idea of contrastive learning, which aims to pull similar images closer while pushing dissimilar images further apart. Simultaneously, it reflects the main idea of masked image modelling, which enables the extraction of semantic information from large scale masked pixel reconstruction tasks. Additionally, we compare the effect of self-supervised methods to the performance of the model. Our results show that with only 75 epochs of fine-tuning, our 29M-parameter model achieves 78.5% top-1 accuracy on the ImageNet-1k dataset. © 2024 SPIE. All rights reserved.

关键词： Unsupervised learning

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Micro-Expression Recognition Based on Spatio-Temporal Feature Extraction of key Regions

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Computers, Materials & Continua 2023年第10期77卷 1373-1392页

作者： Wenqiu Zhu Yongsheng Li Qiang Liu Zhigao Zeng College of Computer Science Hunan University of TechnologyZhuzhou412007China Intelligent Information Perception and Processing Technology Hunan Province Key LaboratoryZhuzhou412007China

Aiming at the problems of short duration,low intensity,and difficult detection of micro-expressions(MEs),the global and local features of ME video frames are extracted by combining spatial feature extraction and temporal feature *** on traditional convolution neural network(CNN)and long short-term memory(LSTM),a recognition method combining global identification attention network(GIA),block identification attention network(BIA)and bi-directional long short-term memory(Bi-LSTM)is *** the BIA,the ME video frame will be cropped,and the training will be carried out by cropping into 24 identification blocks(IBs),10 IBs and uncropped *** alleviate the overfitting problem in training,we first extract the basic features of the preprocessed sequence through the transfer learning layer,and then extract the global and local spatial features of the output data through the GIA layer and the BIA layer,*** the BIA layer,the input data will be cropped into local feature vectors with attention weights to extract the local features of the ME frames;in the GIA layer,the global features of the ME frames will be ***,after fusing the global and local feature vectors,the ME time-series information is extracted by *** experimental results show that using IBs can significantly improve the model’s ability to extract subtle facial features,and the model works best when 10 IBs are used.

关键词： Micro-expression recognition attention mechanism long and short-term memory network transfer learning identification block

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Combined UAMP and MF Message Passing Algorithm for Multi-Target Wideband DOA Estimation with Dirichlet Process Prior

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Tsinghua Science and technology 2024年第4期29卷 1069-1081页

作者： Shanwen Guan Xinhua Lu Ji Li Rushi Lan Xiaonan Luo Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic TechnologyGuilin 541004China Guilin Huigu Institute of AI Industrial Technology Guilin 541004China School of Information Engineering Nanyang Institute of TechnologyNanyang 473000China

When estimating the direction of arrival (DOA) of wideband signals from multiple sources, the performance of sparse Bayesian methods is influenced by the frequency bands occupied by signals in different directions. This is particularly true when multiple signal frequency bands overlap. Message passing algorithms (MPA) with Dirichlet process (DP) prior can be employed in a sparse Bayesian learning (SBL) framework with high precision. However, existing methods suffer from either high complexity or low precision. To address this, we propose a low-complexity DOA estimation algorithm based on a factor graph. This approach introduces two strong constraints via a stretching transformation of the factor graph. The first constraint separates the observation from the DP prior, enabling the application of the unitary approximate message passing (UAMP) algorithm for simplified inference and mitigation of divergence issues. The second constraint compensates for the deviation in estimation angle caused by the grid mismatch problem. Compared to state-of-the-art algorithms, our proposed method offers higher estimation accuracy and lower complexity.

关键词： wideband direction of arrival(DOA)estimation sparse Bayesian learning(SBL) unitary approximate message passing(UAMP)algorithm Dirichlet process(DP)

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