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International Journal of Minerals,Metallurgy and Materials 2024年第3期31卷 508-517页

作者： Runhao Zhang Jian Yang Han Sun Wenkui Yang State key {1. of Advanced Special Steel School of Materials Science and EngineeringShanghai UniversityShanghai 200444China

The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking *** ELM model exhibites the best performance compared with the models of MLR and ***-ELM and FOS-ELM are applied for sequential learning and model *** optimal number of samples in validity term of the FOS-ELM model is determined to be 1.00,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the *** variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization *** lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one *** hit ratios of the predicted lime utilization ratio in the error ranges of±1.,±3%,and±5%are 61.1.%,90.63%,and 94.1.%,*** coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,*** system exhibits desirable performance for applications in actual industrial pro-duction.

关键词： basic oxygen furnace steelmaking machine learning lime utilization ratio dephosphorization online sequential extreme learning machine forgetting mechanism

A graph neural network approach to the inverse design for thermal transparency with periodic interparticle system

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Chinese Physics B 2024年第8期33卷 295-303页

作者：刘斌王译浠 Department of Electronic Information and Artificial Intelligence LeShan Normal UniversityLeShan 614099China Department of Physics State Key Laboratory of Surface Physicsand Key Laboratory of Micro and Nano Photonic Structures(MOE)Fudan UniversityShanghai 200438China

Recent years have witnessed significant advances in utilizing machine learning-based techniques for thermal metamaterial-based structures and devices to attain favorable thermal transport *** the various thermal transport behaviors,achieving thermal transparency stands out as particularly desirable and *** earlier work demonstrated the use of a thermal metamaterial-based periodic interparticle system as the underlying structure for manipulating thermal transport behavior and achieving thermal *** this paper,we introduce an approach based on graph neural network to address the complex inverse design problem of determining the design parameters for a thermal metamaterial-based periodic interparticle system with the desired thermal transport *** work demonstrates that combining graph neural network modeling and inference is an effective approach for solving inverse design problems associated with attaining desirable thermal transport behaviors using thermal metamaterials.

关键词： thermal metamaterial thermal transparency inverse design machine learning graph neural net-work

Multi-locomotion transition of tensegrity mobile robot under different terrains

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Science China(Technological Sciences) 2024年第2期67卷 536-557页

作者： YANG Qi LIU XinYu WANG PanFeng SONG YiMin SUN Tao key {1. of Mechanism Theory and Equipment Design(Tianjin University) Ministry of EducationTianjin UniversityTianjin 300350China

Knowing how to make a multi-locomotion robot achieve locomotion transition under different terrains is a challenging problem,especially for tensegrity robots with multi-locomotion *** this study,a motion planning method for the transition of a multi-locomotion tensegrity robot(hereafter TJUBot)under different terrains is *** robot can achieve four locomotion modes:earthworm-like,inchworm-like,tumbling,and sliding locomotion with only two *** models of the four locomotion modes under five typical terrains,including flat ground,confined space,obstacle,gap,and descending slope,are established using the energy ***,the kinematic characteristics(driving law and initial position)of the robot under these terrains are *** this basis,motion planning for the locomotion transition of TJUBot is conducted,which includes a perception strategy based on three laser sensors and a transition strategy under different *** driving laws of the two motors that can ensure the effective locomotion transition of TJUBot under different terrains are naturally ***,experiments are *** demonstrate that the robot can achieve effective locomotion transition when the motion planning method is used.

关键词： multi-locomotion robot tensegrity kinematic modeling locomotion transition

machine learning-assisted smart epitaxy ofⅢ-Ⅴsemiconductors

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Science China Materials 2024年第9期67卷 3041-3042页

作者： Yue Hao key {1. of Wide Band Gap Semiconductor Technology School of MicroelectronicsXidian UniversityXi’an710071China

Semiconductor epitaxial growth is the front-end process for manufacturing microelectronic and optoelectronic *** beam epitaxy(MBE),capable of precisely controlling atomic layer deposition,has emerged as a crucial technique in chip atomic-level manufacturing[1..However,in semiconductor epitaxy,the heterogeneous nature of materials is complicated,and the growth condition fluctuations poses significant challenges in defect suppression[2],substantially impacting chip performance and yield.

关键词： optoelectronic smart semiconductors

Deep Video Harmonization by Improving Spatial-temporal Consistency

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machine Intelligence Research 2024年第1期21卷 46-54页

作者： Xiuwen Chen Li Fang Long Ye Qin Zhang key {1. of Media Audio and Video Ministry of Education Communication University of ChinaBeijing 100024China

Video harmonization is an important step in video editing to achieve visual consistency by adjusting foreground appear-ances in both spatial and temporal *** methods always only harmonize on a single scale or ignore the inaccuracy of flow estimation,which leads to limited harmonization *** this work,we propose a novel architecture for video harmoniza-tion by making full use of spatiotemporal features and yield temporally consistent harmonized *** introduce multiscale harmon-ization by using nonlocal similarity on each scale to make the foreground more consistent with the *** also propose a fore-ground temporal aggregator to dynamically aggregate neighboring frames at the feature level to alleviate the effect of inaccurate estim-ated flow and ensure temporal *** experimental results demonstrate the superiority of our method over other state-of-the-art methods in both quantitative and visual comparisons.

关键词： Harmonization temporal consistency video editing video composition nonlocal similarity

machine learning for parameters diagnosis of spark discharge by electro-acoustic signal

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Plasma Science and Technology 2024年第8期26卷 64-72页

作者：熊俊卢诗宇刘晓明周文俊查晓明裴学凯 State key {1. of Power Grid Environmental Protection School of Electrical Engineering and AutomationWuhan UniversityWuhan 430072People’s Republic of China State key {1. of Reliability and Intelligence of Electrical Equipment Hebei University of TechnologyTianjin 300131People’s Republic of China

Discharge plasma parameter measurement is a key focus in low-temperature plasma *** diagnostics often require costly equipment,whereas electro-acoustic signals provide a rich,non-invasive,and less complex source of discharge *** study harnesses {1. learning to decode these *** establishes links between electro-acoustic signals and gas discharge parameters,such as power and distance,thus streamlining the prediction *** building a spark discharge platform to collect electro-acoustic signals and implementing a series of acoustic signal processing techniques,the Mel-Frequency Cepstral Coefficients(MFCCs)of the acoustic signals are extracted to construct the *** {1. learning models(Linear Regression,k-Nearest Neighbors,and Random Forest)are introduced and applied to the predictors to achieve real-time rapid diagnostic measurement of typical spark discharge power and discharge *** models display impressive performance in prediction precision and fitting *** them,the k-Nearest Neighbors model shows the best performance on discharge power prediction with the lowest mean square error(MSE=0.00571.and the highest R-squared value(R^(2)=0.93877).The experimental results show that the relationship between the electro-acoustic signal and the gas discharge power and distance can be effectively constructed based on the {1. learning algorithm,which provides a new idea and basis for the online monitoring and real-time diagnosis of plasma parameters.

关键词： discharge plasma plasma real-time diagnosis electro-acoustic signal machine learning acoustic signature

Simulation of optimal work extraction for quantum systems with work storage

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Chinese Physics B 2024年第2期33卷 195-203页

作者：宋鹏飞张旦波 key {1. of Atomic and Subatomic Structure and Quantum Control(Ministry of Education) and School of PhysicsSouth China Normal UniversityGuangzhou 510006China Guangdong Provincial key {1. of Quantum Engineering and Quantum Materials Guangdong–Hong Kong Joint Laboratory of Quantum Matterand Frontier Research Institute for PhysicsSouth China Normal UniversityGuangzhou 510006China

The capacity to extract work from a quantum heat machine is not only of practical value but also lies at the heart of understanding quantum *** this paper,we investigate optimal work extraction for quantum systems with work storage,where extracting work is completed by a unitary evolution on the composite *** consider the physical requirement of energy conservation both strictly and on *** both,we construct their corresponding unitaries and propose variational quantum algorithms for optimal work *** show that maximal work extraction in general can be feasible when energy conservation is satisfied on *** demonstrate with numeral simulations using a continuous-variable work *** work show an implementation of a variational quantum computing approach for simulating work extraction in quantum systems.

关键词： quantum algorithm quantum thermodyanmics

Stackelberg Game for Wireless Powered and Backscattering Enabled Sensor Networks

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China Communications 2024年第3期21卷 189-204页

作者： Lyu Bin Cao Yi Wang Shuai Guo Haiyan Hao Chengyao key {1. of Ministry of Education in Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network Suzhou 215006China Guangdong-Hong Kong-Macao Joint laboratory of Human-{1. Intelligence-Synergy Systems Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

This paper investigates a wireless powered and backscattering enabled sensor network based on the non-linear energy harvesting model, where the power beacon(PB) delivers energy signals to wireless sensors to enable their passive backscattering and active transmission to the access point(AP). We propose an efficient time scheduling scheme for network performance enhancement, based on which each sensor can always harvest energy from the PB over the entire block except its time slots allocated for passive and active information delivery. Considering the PB and wireless sensors are from two selfish service providers, we use the Stackelberg game to model the energy interaction among them. To address the non-convexity of the leader-level problem, we propose to decompose the original problem into two subproblems and solve them iteratively in an alternating manner. Specifically, the successive convex approximation, semi-definite relaxation(SDR) and variable substitution techniques are applied to find a nearoptimal solution. To evaluate the performance loss caused by the interaction between two providers, we further investigate the social welfare maximization problem. Numerical results demonstrate that compared to the benchmark schemes, the proposed scheme can achieve up to 35.4% and 38.7% utility gain for the leader and the follower, respectively.

关键词： backscatter communication energy interaction stackelberg game wireless powered sensor network

DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 879-893页

作者： Shuaiqi Liu Siqi Wang Chaolei Sun Bing Li Shuihua Wang Fei Li College of Electronic and Information Engineering Hebei UniversityBaodingChina machine Vision Technology Innovation Center of Hebei Province BaodingChina The State key {1. of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science and Technology Henan Polytechnic UniversityJiaozuoChina

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample *** tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD ***,the functional connectivity matrix was constructed to extract primary ***,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel ***,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between *** extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD *** proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

关键词： machine learning medical image processing medical signal processing

Comprehensive Relation Modelling for Image Paragraph Generation

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machine Intelligence Research 2024年第2期21卷 369-382页

作者： Xianglu Zhu Zhang Zhang Wei Wang Zilei Wang Automation Department University of Science and Technology of ChinaHefei 230027China Center for Research on Intelligent perception and Computing National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100864China

Image paragraph generation aims to generate a long description composed of multiple sentences,which is different from traditional image captioning containing only one *** of previous methods are dedicated to extracting rich features from image regions,and ignore modelling the visual *** this paper,we propose a novel method to generate a paragraph by modelling visual relationships ***,we parse an image into a scene graph,where each node represents a specific object and each edge denotes the relationship between two ***,we enrich the object features by implicitly encoding visual relationships through a graph convolutional network(GCN).We further explore high-order relations between different relation features using another graph convolutional *** addition,we obtain the linguistic features by projecting the predicted object labels and their relationships into a semantic embedding *** these features,we present an attention-based topic generation network to select relevant features and produce a set of topic vectors,which are then utilized to generate multiple *** evaluate the proposed method on the Stanford image-paragraph dataset which is currently the only available dataset for image paragraph generation,and our method achieves competitive performance in comparison with other state-of-the-art(SOTA)methods.

关键词： Image paragraph generation visual relationship scene graph graph convolutional network(GCN) long short-term memory