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Designing High-Porosity Porous Structures with Complex Geometries for Enhanced Thermal Conductivity Using Selective Laser Melting and Heat Treatment

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Acta Metallurgica Sinica(English Letters) 2024年第5期37卷 808-824页

作者： Hulin Tang Xiang zhang Chenping zhang Tian Zhou shiyue Guo Gaopeng xu Rusheng Zhao Boyoung Hur xuezheng Yue School of Materials Science and engineering University of Shanghai for Science and TechnologyShanghai 200082China National Center for Stomatology National Clinical Research Center for Oral DiseasesShanghai Key Laboratory of StomatologyDepartment of Oral Maxillofacial-Head and Neck OncologyShanghai Ninth People’s HospitalShanghai Jiao Tong University School of MedicineCollege of StomatologyShanghai Jiao Tong UniversityShanghai 200011China Longzhong Lab Wuhan of Science and TechnologyYancheng 224000China Tokyo Metropolitan university TokyoJapan Gyeongsang National university JinjuKorea

Rapid advancements in the aerospace industry necessitate the development of unified,lightweight and thermally conductive *** complex geometries,including bionic and porous structures,is paramount in thermally conductive structures to attain improved thermal *** design of two high-porosity porous lattice structures was inspired by pomelo peel structure,using Voronoi parametric *** combining characteristic elements of two high-porostructuressity porous lattice structures designed,a novel high-porosity porous gradient structure is *** structure is based on gradient *** selective laser melting(SLM),fabrication comprises ***-state thermal characteristics are evaluated via finite element analysis(FEA).The experimental thermal conductivity measurements correlate well with simulation results,validating the sequence of K_L as the highest,followed by D_K_L and then D_*** treatment significantly improves thermal conductivity,enhancing the base material by about 45.6%and porous structured samples by approximately 43.7%.

关键词： Selective laser melting Porous lattice structure Thermal conductivity Heat treatment

IR-YOLO: Real-Time Infrared Vehicle and Pedestrian Detection

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Computers, Materials & Continua 2024年第2期78卷 2667-2687页

作者： Xiao Luo Hao zhu Zhenli zhang department of Electrical engineering and automation Jiangxi University of Science and TechnologyGanzhou341000China Jiangxi Provincial Key Laboratory of Maglev Technology Ganzhou341000China Graduate School of Information Production and SystemsWaseda UniversityKitakyushu-shiFukuoka808-0135Japan

Road traffic safety can decrease when drivers drive in a low-visibility *** application of visual perception technology to detect vehicles and pedestrians in infrared images proves to be an effective means of reducing the risk of *** tackle the challenges posed by the low recognition accuracy and the substan-tial computational burden associated with current infrared pedestrian-vehicle detection methods,an infrared pedestrian-vehicle detection method A proposal is presented,based on an enhanced version of You Only Look Once version 5(YOLOv5).First,A head specifically designed for detecting small targets has been integrated into the model to make full use of shallow feature information to enhance the accuracy in detecting small ***,the Focal Generalized Intersection over Union(GIoU)is employed as an alternative to the original loss function to address issues related to target overlap and category ***,the distribution shift convolution optimization feature extraction operator is used to alleviate the computational burden of the model without significantly compromising detection *** test results of the improved algorithm show that its average accuracy(mAP)reaches 90.1%.Specifically,the Giga Floating Point Operations Per second(GFLOPs)of the improved algorithm is only *** contrast,the improved algorithms outperformed the other algorithms on similar GFLOPs,such as YOLOv6n(11.9),YOLOv8n(8.7),YOLOv7t(13.2)and YOLOv5s(16.0).The mAPs that are 4.4%,3%,3.5%,and 1.7%greater than those of these algorithms show that the improved algorithm achieves higher accuracy in target detection tasks under similar computational resource *** the other hand,compared with other algorithms such as YOLOv8l(91.1%),YOLOv6l(89.5%),YOLOv7(90.8%),and YOLOv3(90.1%),the improved algorithm needs only 5.5%,2.3%,8.6%,and 2.3%,respectively,of the *** improved algorithm has shown significant advancements in balancing accuracy and computati

关键词： Traffic safety infrared image pedestrians and vehicles focal GIoU distributed shift convolution

Equivalent Impedance Parameter Calculation of Three-phase Symmetrical Loads for Harmonic Source Location

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 251-260页

作者： Yi zhang Bijie Liu Caihua Lin Zhenguo Shao Yuncong xu the department of Electrical engineering and automation Fuzhou UniversityFuzhouChina State Grid Fujian Electric Power Research Institute FuzhouChina

The equivalent impedance parameters of loads have been widely used to identify and locate the harmonic ***,the existing calculation methods suffer from outliers caused by the zero-crossing of the *** outliers can result in inaccuracy and unreliability of harmonic source *** address this issue,this paper proposes an innovative method of equivalent impedance parameter calculation of three-phase symmetrical loads that avoid *** correctness and effectiveness of the proposed method are verified by simulations on Simulink using actual monitoring *** results show that the proposed method is not only simple and easy to implement but also highly accurate.

关键词： abc-αβtransformation equivalent impedance parameters of load harmonic source location nonlinear load power quality

A multi-feature-based intelligent redundancy elimination scheme for cloud-assisted health systems

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 491-510页

作者： Ling Xiao Beiji Zou Xiaoyan Kui Chengzhang zhu wensheng zhang xuebing Yang Bob zhang School of Computer Science and engineering Central South UniversityChangshaChina Hunan engineering Research Center of Machine Vision and Intelligent Medicine Central South UniversityChangshaChina The College of Literature and Journalism Central South UniversityChangshaChina Institute of automation Chinese Academy of SciencesBeijingChina department of Computer and Information Science University of MacaoMacaoChina

Redundancy elimination techniques are extensively investigated to reduce storage overheads for cloud-assisted health *** eliminates the redundancy of duplicate blocks by storing one physical instance referenced by multiple *** compression is usually regarded as a complementary technique to deduplication to further remove the redundancy of similar blocks,but our observations indicate that this is disobedient when data have sparse duplicate *** addition,there are many overlapped deltas in the resemblance detection process of post-deduplication delta compression,which hinders the efficiency of delta compression and the index phase of resemblance detection inquires abundant non-similar blocks,resulting in inefficient system ***,a multi-feature-based redundancy elimination scheme,called MFRE,is proposed to solve these *** similarity feature and temporal locality feature are excavated to assist redundancy elimination where the similarity feature well expresses the duplicate ***,similarity-based dynamic post-deduplication delta compression and temporal locality-based dynamic delta compression discover more similar base blocks to minimise overlapped deltas and improve compression ***,the clustering method based on block-relationship and the feature index strategy based on bloom filters reduce IO overheads and improve system *** demonstrate that the proposed method,compared to the state-of-the-art method,improves the compression ratio and system throughput by 9.68%and 50%,respectively.

关键词： big data cloud computing compression data compression medical applications performance evaluation

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1342-1357页

作者： Sheng Qi Rui Wang Tao zhang xu Yang Ruiqing Sun Ling Wang College of Systems engineering National University of Defense TechnologyChangsha 410073China Xiangjiang Laboratory Changsha 410205China College of Computer National University of Defense TechnologyChangsha 410073China department of automation Tsinghua UniversityBeijing 100084China IEEE

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are *** a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec ***,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables ***,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is *** data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between *** by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these *** mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast ***-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

关键词： Evolutionary algorithms learning swarm optimiza-tion sparse large-scale optimization sparse large-scale multi-objec-tive problems two-layer encoding.

Geometric prior guided hybrid deep neural network for facial beauty analysis

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 467-480页

作者： Tianhao Peng Mu Li Fangmei Chen Yong xu David zhang The School of Computer Science and Technology Guizhou UniversityGuiyangChina department of automation Moutai InstituteRenhuaiGuizhouChina The School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhenChina The Information and Communication engineering department Dalian Minzu UniversityDalianChina The School of Data Science The Chinese University of Hong KongShenzhenShenzhenChina

Facial beauty analysis is an important topic in human *** may be used as a guidance for face beautification applications such as cosmetic *** neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable ***,most existing DNN-based models regard facial beauty analysis as a normal classification *** ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty *** be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the *** by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial ***,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two *** model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric *** performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid *** the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

关键词： deep neural networks face analysis face biometrics image analysis

Segment differential aggregation representation and supervised compensation learning of ConvNets for human action recognition

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Science china(Technological Sciences) 2024年第1期67卷 197-208页

作者： REN ZiLiang zhang QieShi CHENG Qin xu ZhenYu YUAN Shuai LUO DeLin School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China School of Electronic engineering and automation Guilin University of Electronic TechnologyGuilin 541004China department of the mechanical engineering Erlangen-Nuremberg UniversityErlangen 91508Germany School of Aerospace engineering Xiamen UniversityXiamen 361102China

With more multi-modal data available for visual classification tasks,human action recognition has become an increasingly attractive ***,one of the main challenges is to effectively extract complementary features from different modalities for action *** this work,a novel multimodal supervised learning framework based on convolution neural networks(Conv Nets)is proposed to facilitate extracting the compensation features from different modalities for human action *** on information aggregation mechanism and deep Conv Nets,our recognition framework represents spatial-temporal information from the base modalities by a designed frame difference aggregation spatial-temporal module(FDA-STM),that the networks bridges information from skeleton data through a multimodal supervised compensation block(SCB)to supervise the extraction of compensation *** evaluate the proposed recognition framework on three human action datasets,including NTU RGB+D 60,NTU RGB+D 120,and *** results demonstrate that our model with FDA-STM and SCB achieves the state-of-the-art recognition performance on three benchmark datasets.

关键词： action recognition segment frame difference aggregation supervised compensation learning ConvNets

Standardized Volume Power Density Boost in Frequency-Up Converted Contact-Separation Mode Triboelectric Nanogenerators

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Research 2024年第2期2023卷 691-702页

作者： Zhongjie Li Chao Yang Qin zhang Geng Chen Jingyuan xu Yan Peng Hengyu Guo School of Mechatronic engineering and automation Shanghai UniversityShanghai 200444P.R.China Institute of Artificial Intelligence Shanghai UniversityShanghai 200444P.R.China National engineering Research Center of Turbo-Generator Vibration School of Energy and EnvironmentSoutheast UniversityNanjing 210096P.R.China institute of Microstructure Technology Karlsruhe Institute of TechnologyKarlsruhe 76344Germany Shanghai Artificial Intelligence Laboratory Shanghai 200444P.R.China department of Applied Physics Chongqing UniversityChongqing 400044P.R.China

The influence of a mechanical structure's volume increment on the volume power density (VPD) of triboelectric nanogenerators (TENGs) is often neglected when considering surface charge density and surface power density. This paper aims to address this gap by introducing a standardized VPD metric for a more comprehensive evaluation of TENG performance. The study specifically focuses on 2 frequency-up mechanisms, namely, the integration of planetary gears (PG-TENG) and the implementation of a double-cantilever structure (DC-TENG), to investigate their impact on VPD. The study reveals that the PG-TENG achieves the highest volume average power density, measuring at 0.92 W/m^(3). This value surpasses the DC-TENG by 1.26 times and the counterpart TENG by a magnitude of 69.9 times. Additionally, the PG-TENG demonstrates superior average power output. These findings introduce a new approach for enhancing TENGs by incorporating frequency-up mechanisms, and highlight the importance of VPD as a key performance metric for evaluating TENGs.

关键词： increment neglected considering

Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 157-169页

作者： Rui Wang zhiming Zhou Kaiwen Li Tao zhang Ling Wang Xin xu Xiangke Liao IEEE the Xiangjiang Laboratory and the College of Systems engineering National University of Defense TechnologyChangsha 410073China the Institute of automation Chinese Academy of SciencesBeijing 100190China the College of Systems engineering National University of Defense TechnologyChangsha 410073 the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha 410073China the department of automation Tsinghua UniversityBeijing 100084China the College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China the College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex *** the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

关键词： Branch-and-bound(B&B) combinatorial optimization deep learning graph neural network imitation learning